varundus 2026-09-13 08:00

Zotero/001_artiklid/1106v1003.md

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-category: literaturenote
-citekey: 1106v1003
-title: 1106v1-003
-authors: ""
-zotero_storage: Y63IV4B8
-collections: imporditud
-folder: 001_artiklid
-status: converted
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-# **Evaluating Alternative Maintenance Strategies for Low-Volume Roads in Sub-Saharan Africa**
-
-C.C.COOK
-
-Recent applications ofthe World Bank's Highway Design and Maintenance (HDM) model in sub-Saharan Africa indicate that periodic maintenance is generally justified by vehicle operating cost savings only on paved roads with traffic levels of more than 100 vehicles per day (vpd) and on unpaved roads with traffic levels of more than 50 vehicles per day. If traffic levels are below 50 vpd on paved roads and 20 vpd on unpaved roads, the HD M indicates that periodic maintenance should be postponed indefinitely. Road rehabilitation in potentially productive areas often appears economically justified at lower traffic volumes, based on the increase in agricultural production that occurs when road access is provided. These benefits may be lost if a rehabilitated road is allowed to deteriorate because of lack of maintenance. The potential loss of agricultural production is not considered in the HDM evaluation of an optimal road maintenance strategy. The relationship between road condition and agricultural productivity to develop an appropriate maintenance strategy for low-volume roads in sub-Saharan Africa is examined in this paper. Timely periodic maintenance is generally justified ifthe initial road construction investment was justified. If the expected increases in agricultural production or the related generated traffic is slow to materialize, the preferred economic strategy would be to defer periodic maintenance for 2 to 4 years. It is critical that roads be maintained routinely to preserve road access, which is the key to the effects of road investments on development.
-
-The countries of sub-Saharan Africa share several characteristics that lead to low traffic volumes on most of their rural roads. Much of sub-Saharan Africa is still sparsely populated. This population is largely engaged in subsistence agriculture and often employs shifting cultivation, combined with or complemented by transhumant or nomadic herding. The modern transport needs associated with these traditional patterns of economic activity are minimal.
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-The region is characterized by extremes of temperature and rainfall, which result in wide variations in vegetative cover and soil quality. Generally speaking, soils that are suitable for road building have relatively low agricultural potential, whereas soils with high agricultural potential are not well adapted for road construction and maintenance. The variations in climate also affect the choice of appropriate road maintenance strategies because, at low traffic volumes, climate is much more important than traffic in determining the rate of road deterioration.
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-The traditional approach to development in sub-Saharan Africa has been to encourage the rural population to produce cash crops for export to generate cash income that can then be exchanged for manufactured goods. Extensive road networks have been built, at considerable cost, in pursuit of this objective. In many cases, these road building programs have been associated with complementary investments to promote crop production or with rural development projects to promote rural welfare. However, growth in agricultural production has not kept pace with the growth of the population, which has resulted in declining levels of economic activity and low traffic volumes on much of the present road network.
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-Sub-Saharan African countries today are faced with critical decisions regarding the use of their scarce public resources. In the past, many countries have invested heavily in roads without allocating the necessary funds in their recurrent budgets to maintain these assets. Consequently, the benefits expected from these investments have not been fully realized. Roads that have been allowed to deteriorate due to lack of maintenance now require expensive rehabilitation or even complete reconstruction. Yet it is difficult to justify maintenance expenditures when traffic volumes remain low.
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-The World Bank called attention to the road maintenance problem in 1980 and has since been giving continuous attention to the development of analytic tools for planning and programming maintenance expenditures in the most cost-effective manner (1, 2). More recently, it has focused attention on the resource allocation problems raised by road deterioration in the absence of adequate maintenance (3). Although the problem is worldwide, the Bank has been giving particular attention to sub-Saharan Africa, where development efforts have been least successful and where resource constraints are most severe.
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-Recent applications of the Bank's analytic tools, the Highway Design and Maintenance (HOM) model and its companion Expenditure Budgeting Model, have been made using the road networks of several sub-Saharan African countries. This work has shown that periodic maintenance is generally justified by user cost savings at traffic levels approaching 100 vehicles per day (vpd) for paved roads and 50 vpd for earth roads. There is a gray area in which periodic maintenance may or may not be justified, depending on the unit costs of the maintenance activity and on the vehicle mix using the road. This gray area lies roughly between 50 and 100 vpd for paved roads and between 20 and· 50 vpd for gravel roads. The HOM analysis indicates that below these traffic levels periodic maintenance should be postponed indefinitely.
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-At the same time, however, road rehabilitation and construction studies often show that larger infrastructure investments would be viable-at lower initial traffic levels. The reason for this apparent inco~sistency is that construction and rehabilitation studies take into ac~ount the effect of road improvements in generating- or regenerating-economic activity in the surrounding area, apart from the normal growth of traffic that is
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-considered in maintenance analysis. The purpose of this paper is to examine the reiaiionship between road condition and agricultural productivity in greater detail to determine the most appropriate maintenance strategy for low-volume roads.
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-## METHODOLOGICAL APPROACHES
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-Tin: classic methodology for estimating the benefits of road investment is based on user cost savings (4). This methodology underlies the HOM model, which estimates user costs as a function of changing road conditions for a given vehicle mix, traffic growth pattern, and time period. The application of different maintenance and improvement strategies, with their associated cost streams, produces different road conditions over time, with associated user cost streams. The HOM model defines the optimal strategy as the one that has the lowest total net present value. The Expenditure Budgeting Model will select the next best program of maintenance activity under budget constraints, when sufficient funds are not available to implement the optimal strategy.
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-It has long been recognized that user cost savings reflect only a rough measurement of the benefits accruing to society from road investments (5). In particular, the user cost approach does not adequately describe what happens when road access is provided in rural areas. Rural road construction is not merely an incremental change in an existing situation. Roads fundamentally transform the rural way of life by providing opportunities for contact and communication with a wider world. In particular, rural roads provide access to markets, which enables specialization of production and exchange between producers and consumers. Road access is a prerequisite for development programs that are based on the production of cash crops, for which imported inputs and information are needed, as well as markets in which surplus production can be sold. In short, rural roads facilitate the transformation of the rural economy from a static system based on subsistence to a dynamic system based on trade with the outside world.
-
-A methodology to justify rural road investments in terms of the value added in agriculture as a result of the expected change in production has been developed by Carnemark et al. (6). This approach is commonly used in evaluating rural road investments that are part of a rural development package. Beenhakker has shown that the value-added approach is conceptually equivalent to the user cost savings approach (7). If rural markets were perfect and if all transactions were reflected in transport activity, the benefits of rural road investments would indeed correspond to measurable changes in traffic.
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-The value-added approach assumes that, by reducing transport costs, a rural road improvement will provide an adequate incentive for traders to purchase cash crops at a price that makes it rewarding for the farmer to produce them. It further assumes that these reduced transport costs will be in effect over the life of the project-in other words, that the road will be maintained so that its condition will not deteriorate. Maintenance costs are included in calculating the costs of the improvement. Consequently, if the initial investment is justified, subsequent maintenance should also be justified.
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-## THE PROBLEM
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-A maintenance planner could be faced with a problem 5 to JO years after the initial investment was made, that traffic on the road does not appear to justify the planned investment in pefiodic inaintenance. Should the road be abandoned? Should it be allowed to deteriorate to the point where rehabilitation will become necessary? Or should it be maintained, regardless of present traffic levels, in order to preserve the benefits of the rural development program?
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-The answers to these questions are not obvious. *A* number of other questions need to be answered before a truly optimal maintenance policy can be determined.
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-## Has the Rural Development Program Failed?
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-It is possible that the failure of traffic to materialize reflects a genuine failure of the development process. This may be due to physical constraints that were not adequately taken into account in planning investments, such as limited area of suitable soils or inadequate water supply. It may be due to socioeconomic factors such as inappropriate land tenure systems, labor force constraints, or lack of marketing and credit facilities. It may be due to incorrect sector policies on matters such as pricing and taxation, or to the promotion of unsuitable technical packages. Finally, it may be due to unfavorable changes in international commodity markets. All of these are factors over which the maintenance planner has no control. There is no reason to expect that additional investment in road maintenance will reverse the situation, and periodic maintenance in this case is not justified.
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-#### Is the Lag Time Longer Than Expected?
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-Rural development prograJ.ll.S are frequently opt1m1st1c in forecasting the speed with which new technologies will be adopted and new production will take place. The expected change process often does take place but at a somewhat slower rate than expected. This delay reduces the rate of return on the original investment package, but this is not the concern of the maintenance planner. From his point of view, the original investment is a sunken cost. What he must be concerned with is the return on a periodic maintenance investment that will keep the road in service for a longer period. Because periodic maintenance represents only a fraction of the cost of the original investment, periodic maintenance is likely to be justified in this case.
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-# Is the Development Process Taking a Different Form?
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-Although rural development programs often fail to achieve their intended objectives, particularly if those objectives are defined in terms of the increased production of cash crops, they may well achieve other results that could be seen as contributing to rural welfare. Many of these results depend directly on road access, even though they do not generate great amounts of traffic. Farmers may apply extension advice and inputs such as fertilizer and pesticides to the production of food crops for on-farm consumption and local exchange. Women may be able to diversify family diets through small-scale production of vegetables, fruits, and poultry products. Health and education services may reach out to rural areas, and human factors of production may improve. A wider range of commercial goods may appear in local markets, thereby providing incentives for
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-more productive use-of leisure time in cash-generating activities. Underutilized labor may respond to a wider range of employment opportunities. A growing network of social relations may begin to link the rural community into regional and national systems.
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-In this situation, a real cost is associated with road deterioration, especially when a road reaches the point where it is no longer trafficable. Rising road user costs due to deterioration create negative incentives for road use to service providers, traders, and transporters from outside the area, as well as to those rural residents who can afford to use vehicles. At some point, the costs will become too great, and the critical actors in the rural development process will simply cease to use the roads. In an extreme case, the countryside will revert to a subsistence economy, but with this difference-rural residents, the "beneficiaries" of the original investment, will have become deeply disillusioned about development and will be more reluctant to take the risks associated with change and growth in the future.
-
-## **A MODEL FOR EVALUATING MAINTENANCE INVESTMENT**
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-A proposed model for evaluating alternative strategies for the maintenance of improved low-volume rural roads in potentially productive agricultural areas is described in the following paragraphs. The model is based on experience in sub-Saharan Africa, where road deterioration followed by reversion to a subsistence economy can frequently be observed. The principles of the model, however, can be applied to any developing area. In cases where the development process is less problematic than it is in sub-Saharan Africa, the priority of periodic maintenance should be more readily demonstrated.
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-Consider the case of an unpaved road, the initial construction cost of which is C, and the expected lifetime of which is t years with annual routine maintenance costs of r(C) and periodic maintenance costs every n years of p(C). It is assumed at the time of road construction that agricultural production will increase by x percent per year for y years as a result of the road. For the sake of simplicity, it can be assumed that no complementary investments are needed to generate this change in production.
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-The benefit of the investment is the value added due to the increase in agricultural productivity, or the difference between the farmgate value of production and the cost of inputs (including farm labor) multiplied by the increment of production. Again for the sake of simplicity, let us assume that the crop mix, the farmgate price of crops, and the cost of inputs do not change over the analysis period. (This assumption is commonly made to ease the computation of expected benefits, but it is not essential to the analysis. What matters is what will happen to the net value added in agriculture, taking into account all of these factors.) Total benefits are represented by the discounted stream of incremental producti~n benefits and total costs by the discounted stream of construction and maintenance costs.
-
-To illustrate this model, let us assume that the lifetime of the road is 25 years, annual routine maintenance costs are 5 percent of construction costs, and periodic maintenance costs are 20 percent of construction costs every 8 years. Let us further assume that the value of agricultural production (V) will increase by 5 percent per year for IO years as a result of road construction. (This factor could also represent the share of the growth in value added that is attributable to roads that are part of integrated rural development projects.)
-
-In this case, the present value of benefits (discounted at 12 percent) is 2.555(V) and the present value of costs (discounted at 12 percent) is l.3 I 7(C). The investment is justified when the net present value (benefits minus costs) is greater than zero or, in other words, the rate of return is greater than 12 percent. The limitingcaseisgivenby2.555(V) = l.317(C),orC = I.94(V).ln other words, under the assumptions outlined above, the investment is justified when construction costs are approximately twice the value added in agriculture prior to construction. The rate of return on the case where C = 2V is 11.6 percent.
-
-Let us now consider what will happen if the roads are not periodically maintained. If routine road maintenance is regularly performed, unit road user costs will remain approximately constant for the first 8 years. After that, the road will begin to deteriorate and user costs will gradually increase. Eventually, the condition of the road will reach a point where user costs will become a major deterrent to traffic and the road will be, for all practical purposes, abandoned. If routine maintenance is not regularly performed, the road will deteriorate much more rapidly and will eventually become impassable and therefore will require complete rehabilitation. However, for this example, let us assume that routine maintenance continues and that the road gradually deteriorates until the surface is completely lost (Figure I).
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-A critical assumption concerns the rate of growth of user costs with deferred maintenance. Let us assume that after the first 8 years, user costs will increase by about I 0 percent per year in the absence of timely periodic maintenance. If the benefits of value added in agriculture decrease in proportion to increased user costs, this would mean a loss of 9 percent in the benefits expected in year 9 and of about 18 percent in the benefits expected in year 10. In the following years, expected agricultural benefits will not increase, and losses due to road deterioration will continue to mount. If no periodic maintenance is done, these losses could amount to 80 percent of incremental annual production by the end of the analysis period.
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-In actual fact, however, user costs do not increase indefinitely. At some point, the surface of the road will become completely worn away but the structure, which is preserved through routine maintenance, will still remain intact. Let us assume that this occurs after about 6 years of deterioration, when the road reaches its worst condition consistent with continued routine maintenance. At this point, losses would amount to about 40 percent of potential benefits. Under these assumptions, with an initial investment the rate of return of which is 11.6 percent, the rate of return for a timely investment in periodic maintenance, calculated over a 14-yr period, treating previous investments as sunk costs, and assuming that routine maintenance is regularly provided, is 12.4 percent.
-
-#### **SENSITIVITY TO ASSUMPTIONS**
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-Before using this model to explore the effects of deferred maintenance, let us examine the effects of possible changes in some of the basic assumptions. For example, if the pattern of increasing agricultural production originally attributed to the road had been expected to extend over 15 years, the periodic maintenance investment would have a rate of return of 27.3 percent (Figure 2). In this case, failure to maintain the road in year 8 would cause agricultural production to level off at about
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-![](_page_3_Figure_0.jpeg)
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-FIGURE 1 Benefits or timely periodk maintenance; benefit growth = 10 years and maintenance interval= 8 years.
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-![](_page_3_Figure_2.jpeg)
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-Fl G URE 2 Benefits or timely periodic maintenance; benefit growth = 15 years and maintenance interval = 8 years.
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-50 percent of the maximum potential production. If periodic maintenance is done in year 8, agricultural production will reach its expected maximum in year 15, as planned. Failure to execute periodic maintenance a second time in year 16 would cause this maximum production to decline, but it would level off at the rate corresponding to the maximum road deterioration, which in this case would be higher than the maximum benefits gained with no periodic maintenance.
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-If periodic maintenance is needed at more frequent intervals, a smaller proportion of the benefits depends on each maintenance expenditure and the expenditure becomes more difficult to justify. For example, if periodic maintenance needs to be performed after 5 years, and benefits are expected to grow over IO years, the rate of return on the periodic maintenance investment alone would be -5 .9 percent (Figure 3). In this case, benefits in the absence of timely road maintenance would reach a level just slightly higher than those corresponding to the maximum road deterioration. If benefits were expected to extend over 15 years, a failure to perform periodic maintenance in year 5 would cause the benefits to plateau at less than 40 percent of their potential (Figure 4). Periodic maintenance would be needed again in year 10 and if it were not performed, the benefits would peak at about 60 percent of the potential and then decline to the level corresponding to maximum road deterioration.
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-The model is also somewhat sensitive to the proportion of the original construction costs required for periodic maintenance, which is here assumed to be 20 percent. If, in our example, periodic maintenance costs 25 percent of construction costs, the rate of return on an 8-yr cycle with a I 0-yr benefit growth period would fall to 8.3 percent, and the expenditure would no longer be justified. With benefits growing over 15 years, the rate of return would be 23.1 percent instead of27.3 percent. If periodic maintenance would be necessary after 5 years, the rate of return would be -9.2 percent with a JO-yr benefit period and 5.6 percent with a 15-yr period.
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-Finally, our example considers an original investment, the rate of return of which is close to 12 percent, which is generally accepted as the minimum rate needed to justify an investment package. An initial investment with a higher rate of return would clearly justify a greater expenditure on periodic maintenance, all other things being equal.
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-## EFFECTS OF ALTERNATIVE MAINTENANCE STRATEGIES
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-Consider the alternative strategy of deferring periodic maintenance (Figures 5 and 6). One consequence of deferral is a higher cost when the maintenance does take place, which will be calculated by spreading the assumed cost over the time period considered. Thus, if periodic maintenance is required.every 8 years and costs 20 percent of construction costs, it is assumed that each year of deferral would add 2.5 percent to the cost factor. If periodic maintenance has to be done every 5 years and costs 20 percent of construction costs, each year of deferral would add 4 percent to the cost factor.
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-A major consequence of deferral is that benefits of the original investment are reduced for the years when periodic maintenance should have taken place but did not. These reductions represent the losses that would have been avoided if periodic maintenance had been performed in a timely manner. We will assume that deferred periodic maintenance restores the
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-![](_page_4_Figure_10.jpeg)
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-Fl GU RE 3 Benefits of timely periodic maintenance; benefit growth = 10 years and maintenance interval = 5 years.
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-![](_page_5_Figure_1.jpeg)
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-FIGURE 4 Benefits of timely periodic maintenance; benefit growth = 15 years and maintenance interval = 5 years.
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-![](_page_5_Figure_3.jpeg)
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-FIGURES Benefits of deferred maintenance (10 years); benefit growth= 10 years and maintenance interval = 8 years.
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-![](_page_6_Figure_2.jpeg)
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-FIGURE 6 Benefits of deferred maintenance (8 years); benefit growth = 10 years and maintenance interval= 5 years.
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-road surface to its original good condition, and that routine maintenance retains this condition until periodic maintenance is due once again.
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-In the case of maintenance, which should, from a technical point of view, be done every 8 years, rates of return can be calculated for maintenance deferred to years 10, 12, 14, or 16 (Table 1). This comparison shows that deferred maintenance would give the highest rate of return between year 12 and year 14. To determine the optimal year for deferred maintenance, the net present values of these alternative strategies are compared for the year of timely periodic maintenance, which is the year in which the decision has to be made. This analysis suggests that year 12 is the optimal year in both cases (Figures 7 and 8).
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-A similar analysis for the situation in which periodic maintenance is needed every *5* years is shown in Table 2. In this
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-TABLE I MAINTENANCE INTERVAL OF 8 YEARS
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-| Benefit growth over JO years    | NPVin<br>Year 8 | IRR(%), |
-|---------------------------------|-----------------|---------|
-|                                 |                 |         |
-|                                 |                 |         |
-| Timely maintenance (Year 8)     | 0.004           | 12.4    |
-| Deferred maintenance (Year I 0) | 0.020           | I6.3    |
-| Deferred mllintenance (Year 12) | 0.054           | 19.8    |
-| Deferred maintenance (Year 14)  | 0.039           | I6.9    |
-| Deferred maintenance (Year I6)  | 0.000           | 12.0    |
-| Benefit growth over 15 years ·  |                 |         |
-| Timely maintenance (Year 8)     | 0.235           | 27.3    |
-| Deferred maintenance (Year IO)  | 0.296           | 38.0    |
-| Deferred maintenance (Year I 2) | 0.306           | 47.1    |
-| Deferred maintenance (Year I4)  | 0.248           | 45.8    |
-| Deferred maintenance (Year 16)  | 0.180           | ~9.5    |
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-case, deferring periodic maintenance to years 8, IO, 12, or 15 was considered. As shown in Table 2, periodic maintenance is never justified in this case, but timely maintenance is the optimal strategy if benefits extend over lO years, and deferring periodic maintenance to year IO is the optimal strategy if benefits are expected to extend over 15 years.
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-TABLE 2 MAINTENANCE INTERVAL OF 5 YEARS
-
-|                                | NPVin  |        |
-|--------------------------------|--------|--------|
-|                                | Year 5 | IRR(%) |
-| Bene/ii growlh over 10 years   |        |        |
-| Timely maintenance (Year 5)    | -0.124 | -5 .9  |
-| Deferred maintenance (Year 8)  | -0.136 | -8.8   |
-| Deferred maintenance (Year IO) | -O.I47 | -I2.4  |
-| Deferred maintenance (Year 12) | -0.156 | -20.3  |
-| Deferred maintenance (Year 15) | -0.163 | -24.1  |
-| Benefit growth over 15 years   |        |        |
-| Timely maintenance (Year 5)    | -0.047 | 8.2    |
-| Deferred maintenance (Year 8)  | -0.063 | 4.8    |
-| Deferred maintenance (Year IO) | -0.050 | 6.0    |
-| Deferred maintenance (Year 12) | -0.053 | 4.8    |
-| Deferred maintenance (Year 15) | -0.068 | 0.8    |
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-# CONCLUSIONS
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-The conditions under which periodic maintenance investment in unpaved rural roads can be justified by losses avoided in agriculture alone are relatively restrictive. In the case of a barely feasible initial investment, the benefits of which extend over 10 years, timely periodic maintenance is likely to be justified only if
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-![](_page_7_Figure_1.jpeg)
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-FIGURE 7 Benefits of deferred maintenance (12 years); benefit growth = 10 years and maintenance interval = 8 years.
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-![](_page_7_Figure_3.jpeg)
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-FIGURE 8 Benefits of deferred maintenance (12 years); benefit growth= 15 years and maintenance interval= 8 years.
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-maintenance costs are less than 20 percent of construction costs and the maintenance interval is 8 years or more. Furthermore, even in this case, a more efficient use of resources will be achieved by deferring maintenance for 2 to 4 years. Should b~nefit growth extend over a longer period, timely maintenance would become more feasible, but the economically optimal year of maintenance would still be 2 to 4 years later than the year that would be selected according to technical criteria.
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-Roads that require periodic maintenance at frequent, 5-yr intervals are difficult to justify with agricultural benefits.Unless the costs of such maintenance are considerably less in relation to construction costs than we have assumed here (or construction costs are less in relation to agricultural value added), it might be worthwhile to consider an alternative design standard with higher initial costs that can support traffic for a longer time before periodic maintenance would be needed.
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-Two caveats should be noted here. The first is a reminder that the model assumes th!lt routine maintenance is regularly performed, even in the absence of periodic maintenance, thereby ensuring that the road does not suffer serious structural deterioration. Expenditures on routine maintenance are almost al ways justified, even at extremely low traffic levels. However, it is often the case that routine maintenance is not done or is not correctly done. In this situation, the road will deteriorate faster and the benefits and costs of periodic maintenance will be higher than shown here.
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-A second caveat is that the benefits here calculated in terms of losses avoided in agriculture are at least partly reflected in agricultural traffic. Therefore, they cannot simply be added to the user cost savings for existing and projected traffic. Theoretically, user cost savings for non-agricultural traffic could be counted in addition to losses avoided in agriculture. However, in practice it is often difficult to distinguish between agricultural and non-agricultural traffic in rural areas. Therefore, the most prudent approach would be to analyze alternative maintenance strategies either in terms of user cost savings (for traffic volumes above 50 vpd for paved ro&ds or 20 vpd for earth roads) or of value added in agriculture (for roads with lower traffic volumes).
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-### **OTHER** CONSIDERATIONS
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-Non-economic considerations often play a major role in government decision-making on the maintenance of lowvolume roads in areas of strategic or political significance. A minimal priority network is needed in each country to ensure social cohesion and to enable governments to fulfill their obligations in times of crisis, such as war or famine. In such cases, the appropriate maintenance strategy would be the least costly alternative to ensure minimum access. In countries where traffic levels are extremely low, it may be more sensible for governments to seek alternative solutions to meet basic access needs instead of maintaining an extensive road network.
-
-#### **REFERENCES**
-
-- l. *The Road Maintenance Problem and International Assistance.*  World Bank, Washington, D.C., 1981.
-- 2. *Highway Design and Maintenance Model.* Vol. 3. World Bank, Washington, D.C., 1985.
-- 3. *The Road Deterioration Problem in Developing Counlries.*  World Bank, Washington, forthcoming. Some initial findings were presented in Asif Faiz and Clell Harral, "The Road Deterioration Problem in Developing Countries: The State of the Road Networks," and related papers presented at the Transportation Research Board 66th Annual Meeting, January 1987.
-- 4. R. Winfrey. *Economic Analysis for Highways.* International Textbook Company, 1968; also, H. G. VanderTakand Anandarup Ray, *The Economic Benefits of Road Transpor/ Projects,* World Bank Staff Occasional Paper No. 13, 1971.
-- *5.* A. A. Walters. *The Economics of Road User Charges.* World Bank Staff Occasional Paper No. 5, 1968.
-- 6. C. Carnemark, J. Biderman, and D. Bovet. *The Economic Analysis of Rural Road Projects.* World Bank Staff Working Paper 241. Washington, D.C., 1976.
-- 7. **H.** L. Beenhakker. *lden1ification and Appraisal of Rural Road Projects.* World Bank Staff Working Paper 362. Washington, D.C., 1979.
-
-*The views and interprelations presenled in !his paper are those of the author and should not be attribUled to the World Bank, to its affiliated organizations, or to any individual acting in their behalf.*

Zotero/001_artiklid/2016aastariigieelarveseadus.md

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-category: literaturenote
-citekey: 2016aastariigieelarveseadus
-title: 2016. aasta riigieelarve seadus – Riigi Teataja
-authors: ""
-url: "https://www.riigiteataja.ee/akt/123122015006"
-zotero_key: PBUSF6K2
-zotero_storage: VZMEB93S
-collections: magistritöö / seadused
-folder: 001_artiklid
-status: converted
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-Väljaandja: Riigikogu Akti liik: seadus
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-Teksti liik: algtekst-terviktekst Redaktsiooni jõustumise kp: 01.01.2016 Redaktsiooni kehtivuse lõpp: 23.11.2016 Avaldamismärge: RT I, 23.12.2015, 6
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-> Välja kuulutanud Vabariigi President 18.12.2015 otsus nr 717
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-# **2016. aasta riigieelarve seadus**
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-Vastu võetud 09.12.2015
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-**§ 1. Riigieelarve tulud, kulud, investeeringud ja finantseerimistehingud**
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-
-# **§ 2. Seadustest tulenevate määrade ja piirsummade kehtestamine**
-
-- (1) Õppetoetuste ja õppelaenu seaduse alusel kehtestatavad määrad on:
-- 1) üliõpilase vajaduspõhise õppetoetuse saamiseks arvestatava keskmise sissetuleku ülemmäär 358 eurot kuus vastavalt seaduse § 5 lõike 2<sup>1</sup> punktile 3;
-- 2) kutseõppe õppekava õpilase põhitoetuse suurus 60 eurot õppekuus vastavalt seaduse § 12 lõikele 1;
-- 3) doktoranditoetuse suurus 422 eurot kalendrikuus vastavalt seaduse § 12 lõikele 1;
-- 4) üliõpilase vajaduspõhise õppetoetuse suurus taotleja ühe kuu sissetuleku korral kuni 89,50 eurot 220 eurot õppekuus, sissetuleku korral vahemikus 89,51 kuni 179 eurot 135 eurot õppekuus ning sissetuleku korral vahemikus 179,01 kuni 358 eurot 75 eurot õppekuus vastavalt seaduse § 12 lõikele 2;
-- 5) üliõpilase vajaduspõhise eritoetuse suurus 135 eurot õppekuus vastavalt seaduse § 12 lõikele 1;
-- 6) üliõpilase põhitoetuse suurus 55,93 eurot ja täiendava toetuse suurus 28,13 eurot õppekuus vastavalt seaduse § 42 lõikele 5.
-- (2) Põhikooli- ja gümnaasiumiseaduse § 77 lõike 3 alusel kehtestatav õpetaja lähtetoetuse suurus on 12 783 eurot.
-- (3) Tartu Ülikooli seaduse § 7<sup>3</sup> lõike 1 alusel kehtestatav maksimaalne arst-residendi koha maksumus on 2542,7 eurot kuus.
-- (4) Riiklike peretoetuste seaduse § 4 lõigete 2 ja 3 alusel kehtestatav lapsetoetuse määr on 9,59 eurot kuus ja lapsehooldustasu määr 76,70 eurot kuus.
-- (5) Vanemahüvitise seaduse § 3 lõike 6 alusel kehtestatav vanemahüvitise määr on 390 eurot kuus.
-- (6) Puuetega inimeste sotsiaaltoetuste seaduse § 5 lõike 1 alusel kehtestatav puuetega inimeste sotsiaaltoetuste määr on 25,57 eurot kuus.
-- (7) Tööturuteenuste ja -toetuste seaduse alusel kehtestatavad määrad on:
-- 1) ettevõtluse alustamise toetuse ülemmäär 4474 eurot vastavalt seaduse § 19 lõikele 9;
-- 2) tugiisikuga töötamise teenuse osutamise eest makstav tunnitasu määr 2,56 eurot vastavalt seaduse § 23 lõikele 6;
-- 3) töötutoetuse päevamäär 4,41 eurot vastavalt seaduse § 31 lõikele 1;
-- 4) stipendiumi päevamäär 3,84 eurot vastavalt seaduse § 35 lõikele 6;
-- 5) sõidu- ja majutustoetuse määr ühe kilomeetri kohta 0,10 eurot ja ülemmäär 26 eurot päevas vastavalt seaduse § 37 lõikele 5;
-- 6) madala sissetulekuga töötavale isikule tehtava tagasimakse suuruse arvutamise aluseks olev määr 228 eurot vastavalt seaduse § 37<sup>3</sup> lõikele 4;
-- 7) tööalase rehabilitatsiooni teenuse osutamise ülemmäär 1800 eurot koos käibemaksuga kalendriaastas vastavalt seaduse § 23<sup>1</sup> lõikele 4.
-- (8) Sotsiaalhoolekande seaduse alusel kehtestatavad määrad on:
-- 1) üksi elava isiku või perekonna esimese liikme toimetulekupiir 130 eurot kuus;
-- 2) psüühiliste erivajadustega inimeste erihoolekande kogukonnas elamise teenusele suunatud isiku maksimaalne omaosalus 230 eurot kuus ning ööpäevaringsele erihooldusteenusele suunatud isiku maksimaalne omaosalus 230 eurot kuus, kui ööpäevaringset erihooldusteenust osutatakse hoolekandeasutuse ühes või igas eraldiseisvas hoones korraga rohkem kui kaheteistkümnele isikule, ja ööpäevaringsele erihooldusteenusele suunatud isiku maksimaalne omaosalus 270 eurot kuus, kui ööpäevaringset erihooldusteenust osutatakse hoolekandeasutuse ühes või igas eraldiseisvas hoones kuni kaheteistkümnele isikule;
-- 3) ööpäevaringse erihooldusteenuse maksimaalne maksumus kohtumäärusega hoolekandeasutusse paigutatud isiku kohta 909 eurot kuus ja alaealise isiku kohta 1945 eurot kuus;
-- 4) vajaduspõhise peretoetuse sissetulekupiir perekonna esimese liikme kohta 358 eurot kuus.
-- (9) Riikliku matusetoetuse seaduse § 6 lõike 1 alusel kehtestatav matusetoetuse suurus on 250 eurot.
-- (10) Sotsiaalmaksuseaduse § 2<sup>1</sup> alusel kehtestatav sotsiaalmaksu maksmise aluseks olev kuumäär on 390 eurot.
-- (11) Spordiseaduse § 13 lõike 2 alusel kehtestatav olümpiavõitja toetuse määr on 650 eurot kuus.
-- (12) Riigieelarve seaduse alusel kehtestatavad määrad on:
-- 1) riigi võlakohustuste suurim lubatud jääk 1 840 000 000 eurot vastavalt seaduse § 69 lõikele 2;
-- 2) Vabariigi Valitsuse antavate laenude ja riigigarantiide suurim lubatud jääk 577 000 000 eurot vastavalt seaduse § 61 lõikele 5.
-- (13) Kooskõlas välissuhtlemisseaduse § 20 punktiga 5 ei ole vaja Riigikogus ratifitseerida välislepinguid, millest ühelgi eesseisval eelarveaastal Eesti Vabariigile tulenevate varaliste kohustuste või saamata jääva tulu maht ei ületa 3 protsenti riigiasutusele, kelle algatusel välisleping sõlmitakse, jooksvaks eelarveaastaks ettenähtud kulude summast.
-- (14) Muuseumiseaduse alusel on:
-
-- 1) 2016. aasta jooksul eksponeeritavate näituste riigi tagatava näituse kahjuhüvitise kogusumma piirmäär 42 000 000 eurot vastavalt seaduse § 28 lõike 3 punktile 1;
-- 2) omavastutuse suurus 2000 eurot ühe näituse kohta vastavalt seaduse § 28 lõike 3 punktile 2;
-- 3) kümmet miljonit eurot ületava väärtusega näituse "Fotorealism: 50 aastat hüperrealistlikku maali", mille omanik on Tübingeni *Institut für Kulturaustausch* Saksamaalt, kavandatav vedamise periood 28. veebruarist 30. juunini 2016, eksponeerimise periood 17. märtsist 12. juunini 2016 vastavalt seaduse § 28 lõike 3 punktile 3.
-
-# **§ 3. Kohaliku omavalitsuse üksustele tasandus- ja toetusfondi jaotamise põhimõtted**
-
-(1) Tasandusfondi toetus *T* arvutatakse järgmiselt:
-
-$$T = (AK - AT) * k$$
-, kus
-
-$$AK = \sum_{n=1}^{6} C_n * P_n$$
-
-$$AT = (TM_{2015} + RM_{2015}) * 0,5 + (TM_{2014} + RM_{2014}) * 0,3 + (TM_{2013} + RM_{2013}) * 0,2 + MM_{40155}$$
-
-$$\sum_{n=0}^{6} C_n * P_n$$
-
-- *T* tasandusfondi suurus konkreetses kohaliku omavalitsuse üksuses;
-- *AK* konkreetse kohaliku omavalitsuse üksuse arvestuslik keskmine tegevuskulu;
-- *AT* konkreetse kohaliku omavalitsuse üksuse arvestuslikud tulud;
-- k toetustaseme koefitsient väärtusega 0,9;
-- *C<sup>n</sup>* konkreetse kohaliku omavalitsuse üksuse laste (0–6 eluaastat) arv, kooliealiste (7–18 eluaastat) arv, tööealiste (19–64 eluaastat) arv, vanurite (65+ eluaastat) arv rahvastikuregistri andmetel, kohalikest teedest maanteede ja tänavate arvestuslik pikkus (kõvakattega maanteed koefitsiendiga 0,26; tänavad 0,74; mittekõvakattega maanteed 0,047) kilomeetrites riikliku teeregistri andmetel ning hooldatavate ja hooldajateenust saavate puudega isikute kaalutud keskmine arv aastatel 2006–2008 hooldajatoetuse aruandele vastavalt;
-- *P<sup>n</sup>* kohaliku omavalitsuse üksuste arvestuslik keskmine tegevuskulu ühe lapse, kooliealise, tööealise, vanuri, hooldatava ja hooldajateenust saava puudega isiku kohta ning kohalikest teedest maanteede ja tänavate arvestusliku pikkuse ühe kilomeetri kohta eurodes;
-  - konkreetse kohaliku omavalitsuse üksuse laste arvu, kooliealiste arvu, tööealiste arvu, vanurite arvu, hooldatavate ja hooldajateenust saavate puudega isikute kaalutud keskmise arvu ning kohalikest teedest maanteede ja tänavate arvestuslike koefitsientidega korrigeeritud pikkuse kilomeetrites ja iga vastava näitaja osas ühe ühiku kohta arvutatud arvestusliku keskmise tegevuskulu korrutiste kogusumma eurodes;
-- *TM* üksikisiku tulumaksu laekumine konkreetses kohaliku omavalitsuse üksuses vastavalt 2013., 2014. ning 2015. aastal korrutatuna 11,6-ga ning jagatuna vastaval aastal kehtinud tulumaksuseaduse redaktsiooni § 5 lõike 1 punktis 1 toodud määraga;
-  - konkreetse kohaliku omavalitsuse üksuse arvestuslik maamaks (1,25 protsenti üldise maa ja 0,6 protsenti põllumajandusmaa maksustamise hinnast 2015. aastal), millest on maha arvatud looduskaitsealade soodustused, sealhulgas alates 2016. aastast kehtima hakkavad looduskaitsealade soodustused, mille mõju on suurem kui 1000 eurot kohaliku omavalitsuse kohta, ning kodualuse ja ühiskondlike ehitiste aluse maa maksuvabastuse mõju;
-
-*RM* – konkreetse kohaliku omavalitsuse üksuse kohaliku tähtsusega maardlate maavarade kaevandamisõiguse tasu laekumine vastavalt 2013., 2014. ning 2015. aastal.
-
- (2) Toetusfondi vahendite jaotamise arvnäitajad, arvnäitajate väärtused ja arvnäitajatega arvestamise alused kehtestab Vabariigi Valitsus riigieelarve seaduse § 48 lõikes 4 nimetatud määrusega.
-
-#### **§ 4. Valitsemisalade tegevuskulude liigendamine**
-
-- (1) Minister ja riigisekretär liigendavad oma valitsemis- või haldusala eelarve TK-objektikoodiga kavandatud vahendid majandusliku sisu järgi ja administratiivselt, lähtudes kulude täpsustumisest erinevate toetuse saajate vahel.
-- (2) Käesoleva seaduse § 1 6. osa 9. jao *Rahandusministeeriumi valitsemisala* konto 70 *Saadud siirded riigiasutustelt* arvel tehtavate maantee-, vee- ja õhutranspordi korraldamise kulude jaotuse maavalitsuste vahel kinnitab majandus- ja taristuminister.
-- (3) Käesoleva seaduse § 1 6. osa 9. jao *Rahandusministeeriumi valitsemisala* konto 70 *Saadud siirded riigiasutustelt* arvel tehtavate sotsiaalse kaitse kulude jaotuse maavalitsuste vahel kinnitab sotsiaalkaitseminister ning ennetustegevuste ja teenuste jaotuse maavalitsuste vahel kinnitab tervise- ja tööminister.
-- (4) Käesoleva seaduse § 1 6. osa 9. jao *Rahandusministeeriumi valitsemisala* konto 70 *Saadud siirded riigiasutustelt* arvel rahvastikuregistriga seotud ülesannete täitmiseks tehtavate kulude jaotuse maavalitsuste vahel kinnitab siseminister.
-
-## **§ 5. Tegevusala alusel määratud kulude tegemine**
-
-- (1) Vabariigi Valitsusel on õigus teha muudatusi käesoleva seadusega määratud kulude tegevusala klassifikaatori COFOG liigenduses ministeeriumide valitsemisalade ja Riigikantselei haldusala ning põhiseaduslike institutsioonide vahel administratiivselt ja majandusliku sisu järgi.
-- (2) Ministeeriumidel oma valitsemisala ja Riigikantseleil oma haldusala piires ning põhiseaduslikel institutsioonidel on õigus teha muudatusi käesoleva seadusega määratud kulude tegevusala klassifikaatori COFOG liigenduses administratiivselt ja majandusliku sisu järgi.
-
-### **§ 6. Riigi Kinnisvara Aktsiaseltsile riigieelarves planeeritud rendikulu mahtude muutmine**
-
- Vabariigi Valitsusel on õigus põhjendatud juhtudel muuta ministeeriumile, Riigikantseleile või põhiseaduslikule institutsioonile käesoleva seaduse §-s 1 eelarve kontodel 5, 450, 650 ja 206 objektikoodiga SE000028 *Vahendid Riigi Kinnisvara ASile* ettenähtud vahendeid riigieelarves vastavale ministeeriumile, Riigikantseleile või põhiseaduslikule institutsioonile samal eelarve kontol ettenähtud vahendite ulatuses.
-
-# **§ 7. Haridus- ja Teadusministeeriumi valitsemisala eelarve kasutamine ja täiendav liigendamine**
-
- Haridus- ja teadusministril on õigus muuta käesoleva seaduse § 1 6. osa 2. jao *Haridus- ja Teadusministeeriumi valitsemisala* programmidesisest vahendite liigendust tegevuskulude ja investeeringute vahel, muutmata seejuures riigieelarve seaduse § 31 lõike 1 alusel Vabariigi Valitsuse kehtestatud investeeringute kinnisasjadesse liigendust objektide kaupa. Haridus- ja teadusministri tehtavad muudatused kooskõlastatakse eelnevalt Rahandusministeeriumiga riigieelarve seaduse § 35 lõike 4 alusel sätestatud korra kohaselt.
-
-#### **§ 8. Eesti Haigekassa eelarvepositsioon**
-
- Eesti Haigekassa seaduse § 36 lõike 1<sup>2</sup> alusel kinnitatav Eesti Haigekassa põhitegevuse tulem on järgmine:
-
-- 1) 2016. aastal –9,1 miljonit eurot;
-- 2) 2017. aastal –1,3 miljonit eurot;
-- 3) 2018. aastal –0,9 miljonit eurot;
-- 4) 2019. aastal 0 eurot.
-
-Eiki Nestor Riigikogu esimees

Zotero/001_artiklid/63284820080110112047.md

@@ -1,258 +0,0 @@
----
-category: literaturenote
-citekey: 63284820080110112047
-title: 63_2848_20080110112047
-authors: ""
-zotero_storage: L5YBXS5F
-collections: imporditud
-folder: 001_artiklid
-status: converted
----
-# **LOCAL TESTS OF THE OPERATING SPEED MODELS FOR CURVES**
-
-*Piras C.* 
-
-*PhD Student – University of Cagliari* – *[cpiras@unica.it](mailto:cpiras@unica.it)* 
-
-*Pinna F.* 
-
-*Researcher – University of Cagliari – [fpinna@unica.it](mailto:fpinna@unica.it)*
-
-### **ABSTRACT**
-
-The paper aims to test operating speed models for curve sections on local two lane rural roads.
-
-In the beginning, the most important national and international models, which enable to calculate operating speed, are summed up. The form of the prediction models and number of variables used in each of them vary considerably. The most common operating speed models, proposed by many researchers of different countries, provide V85 as a function of the Degree of Curvature (DC) or of the Curvature Change Rate of the single curve (CCRs). All operating speed models, considered in this paper, are developed through regression analysis of collected speeds of free passenger cars.
-
-Afterward speed data are recorded on local curves. Different constraints have been imposed to select curve sites: rural area, relatively low traffic volume, marked and paved roadways with constant lane width, no stop control or intersections near to curve, the design speed ranges from 60 km/h to 100 km/h and the general speed limit is 90 km/h (in accordance with the "Italian Road Code"), longitudinal grades ≤ 5%.
-
-Subsequently, the most important national and international models, which have been gathered preceding, are applied on chosen curve sections, in order to calculate V85.
-
-Speed surveys are compared with the results of the models application.
-
-The article includes also a summary of the data collected and the results of a statistical analysis showing speed trends.
-
-The target of the paper is to verify on local roads the applicability of the existing speed models in order to implement them in the following step of the research work.
-
-*Keywords: two-lane rural highway, speed prediction, operating speed, design consistency* 
-
-### **1. INTRODUCTION**
-
-In the last years, in order to increase road safety, the importance given to the respect of the driver expectancy is improved. Several studies show that geometric design consistency enhances road safety conditions. The complex relationships between road features and driver behaviour play a very important role in collision occurrence. In fact, accident probability is higher where alignment consistency lacks. For this reason, different studies show that parameters, which are still present in some guidelines in force in many countries, are outdated.
-
-The most common criteria for design consistency evaluation are based on the operating speed concept. AASHTO defines operating speed as "the speed at which drivers are observed operating their vehicles during free-flow conditions, the 85th percentile of the distribution of observed speeds is the most frequently used measure of the operating speed associated with a particular location or geometric feature"[1]. Besides, the distribution of operating speed on alignment is a quantitative measure of the general character of the road and the relationships between driver behaviour and highway. In fact, the distribution of operating speed may identify a geometric inconsistency when there is a high rate of change for successive roadway sections. Several design standards use already operating speed concept to select design speed values and/or alignment element admissible values.
-
-The concept of geometric design consistency is also used to correlate accident risk with geometric alignment. Along horizontal curves accident rate is up to 4 times higher than tangents. For this reason, the majority of operating speed studies focus on horizontal curves of two-lane rural highways, even if there are also many tangent models.
-
-The principal target of this paper is testing curve speed models, for two lane rural roads, on local highways using data collected on different horizontal curve sections.
-
-Before, the most important national and international models, that permit to calculate operating speed, are reviewed. Subsequently, these models are tested on three sections of two lane rural roads. Next speed surveys are carried out. Only passenger vehicles are included in this study.
-
-The research project, which is carried out by the Department of Land Engineering of the University of Cagliari, aims to develop a speed model between road features (e.g. CCR, available sight distance) and operating speed.
-
-## **2. SPEED MODELS**
-
-Several studies show that the car speed plays an important role in accident occurrence, in particular when considerable high speed reductions are required. Therefore, the design standards of different countries require to compare design speeds of adjacent sections, in order to limit the difference between their values.
-
-Many countries still use the design speed as base parameter to calculate the limit values of the alignment elements. On the one hand the standard of some countries are based on the operating speed concept, on the other hand the most of guidelines of the other refer to this concept only in the post-road design. Finally, several countries use the expected operating speed as base to select design speed or/and specific geometric element values and to reduce design inconsistencies.
-
-For example, the Australian standards are based on McLean research that defines 85th percentile speed as a function of the curve radius and the desired speed, which is "the speed at which driver chooses to travel under free-flow conditions, when they are not constrained by alignment features" [2].
-
-The form of the operating speed prediction models and number of variables used in each of them vary considerably. The most common operating speed models, proposed by many researchers of different countries, provide V85 as a function of the Degree of Curvature (DC) or of the Curvature Change Rate of the single curve (CCRs) or of the Radius (R). These parameters are given by the following equations:
-
-$$DC = 100 \times \left(360 / 2\pi R\right) \tag{Eq. 1}$$
-
-$$CCRs = [63700 \times (\frac{L_{c1}}{2R} + \frac{L_{cr}}{R} + \frac{L_{c2}}{2R})]/L$$
- (Eq. 2)
-
-(where: DC [degree/100 m], R = radius of circular curve [m], CCRs [gon/km], Lc1 and Lc2 = lengths of clothoids (preceding and succeeding circular curve) [m], Lcr = length of circular curve [m], L = overall length of curve section [km], 63700 = 200/π × 103 ).
-
-In this paragraph several models that permit to calculate operating speed as a function of CCRs [Table 1] and curve radius are reviewed [Table 2].
-
-| Model                             | Equation                              | R2   |  |  |
-|-----------------------------------|---------------------------------------|------|--|--|
-| McLean - Australia [2]            | V85 = 101.2 – 0.043 CCRs              | 0.87 |  |  |
-| Lamm et al. – Germany [2]         | V85 = 106<br>/ (8270 + 8.01 CCRs)     | 0.73 |  |  |
-| Lamm et al. [2]                   | V85 = 95.6 – 0.0438 CCRs              | 0.82 |  |  |
-| Psarianos et al. – Greece [2]     | V85 = 106<br>/ (10150.1 + 8.529 CCRs) | 0.81 |  |  |
-| Lamm et al – United States [2]    | V85 = 93.85 – 0.05 CCRs               | 0.79 |  |  |
-| Krammes and Ottesen [2]           | V85 = 103.04 – 0.053 CCRs             | 0.80 |  |  |
-| where: V85 [km/h]; CCRs [gon/km]. |                                       |      |  |  |
-
-**Table 1 Operating speed prediction models using CCRs value** 
-
-![](_page_3_Figure_1.jpeg)
-
-**Figure 1 Graph representing analytical models where V85 is expressed as a function of CCRs** 
-
-All operating speed models considered in this paper are developed through regression analysis of adopted speed on two lane rural roads of free passenger cars (headway of at least 5 seconds). Speed data are recorded with dry and wet pavement conditions, on longitudinal grades ≤ 6% (even if its effect is generally ignored).
-
-In the tables, the international models that better represent local situations have been summarized.
-
-| Model                     | Equation                            | R2   |  |  |
-|---------------------------|-------------------------------------|------|--|--|
-| Ottesen and Krammes [3]   | V85 = 103.66 – 1.95 · (1746.38 / R) | 0.80 |  |  |
-| Kannelaidis et al. [4]    | V85 = 129.88 – (623.1 / √R)         | 0.78 |  |  |
-| Lamm et al. [2]           | V85 = 94.398 – (3188.656/R)         | 0.79 |  |  |
-| where: V85 [km/h]; R [m]. |                                     |      |  |  |
-
-**Table 2 Operating speed prediction models using radius value** 
-
-![](_page_4_Figure_1.jpeg)
-
-**Figure 2 Graph representing analytical models where V85 is expressed as a function of horizontal radius, R** 
-
-### **2.1 Italy**
-
-A study, carried out by the Department of Civil Engineering of the University of Trieste, proposes an operating speed prediction model for curves and tangents. The researchers define also environmental speed: as the maximum speed that can be reached on tangents or very large radius curves belonging to a homogeneous road section identified by an analysis of the curvature change rate, CCR [5]. They provide a prediction model to obtain the operating speed as a function of the horizontal radius and the environmental speed, which is obtained as a function of the road section geometric features. Starting with Australian study, speed environment represents the speed which drivers go at when they are not conditioned by traffic or by features of the single road elements [5]. The environmental speed value depends on the road section geometric characteristics, available sight distance, frequency of junctions and accesses, terrain type, carriageway width, and so on.
-
-$$V_{env} = 200.97 \times CCR - 0.16 \tag{Eq. 3}$$
-
-(where: R2 = 0.87, Venv [km/h], CCR [gon/km])
-
-All data are collected during daylight hours under dry pavement condition and in the presence of low traffic. The researchers consider roads with the usual cross sections of Italian two-lane rural roads: with two lanes (3,25 ≤ lane width ≤ 3,75 m), with two paved shoulders (0 ≤ shoulder width ≤ 1,50 m), with the longitudinal grade ≤ 4.00% (even if prediction models are not influenced by grades).
-
-They test the influence of the length of the curve, the radius of the preceding curve, the length of the preceding tangent and the super-elevation. In the prediction of speed value on curve the most significant are the radii of the curves (R) and the environmental speed (Venv) of the homogenous section.
-
-$$V_{85} = V_{env}/(1 + 4.75/R \times 0.58)$$
- (Eq. 4)
-
- (where: R2 = 0.88, R = the curve radius [m], Venv and V85 [km/h]).
-
-Most speed prediction models estimate the operating speed on horizontal curves using two-dimensional alignment variables. A few models consider the effects of vertical grades on speed; some specify a range of grades in which it is valid.
-
-### **3. LOCAL TESTS**
-
-The preliminary aim of this research is to collect vehicle speeds on a particular site, in order to test V85 models that have been explained above. Several two-lane rural highway segments are chosen for data collection. The following constraints have been imposed for the selection of curve sites: rural area, relatively low traffic volume, marked and paved roadways with constant lane width, no stop control or intersections near to curves, maximum longitudinal grade 4,00 %. All data are collected during daylight hours, in good weather conditions and with a dry pavement. The data collecting excludes non-passenger cars and vehicles with less than 5 second headway from the previous vehicle (free flow). The speed surveys are carried out using a radar speedometer. Road alignments is made up of tangent and circular curves, there are not clothoids. In fact, highways chosen are designed with preceding guidelines within them clothoids was not mentioned.
-
-### **3.1 Site n. 1 – S.S. n. 547**
-
-The curve section is located along the S.S. n. 547. The cross-section is made up of two lanes that are 3,50 m wide, no paved shoulders which are 1,00 m wide, Longitudinal grade is 3,75 %.
-
-![](_page_5_Figure_9.jpeg)
-
-**Figure 3 Plan of site n. 1 – S.S. n. 547** 
-
-The speed data are collected at a middle point of circular curve (R = 70 m). A total of 253 individual speed observations are collected on both directions. The operating speed registered is 61 km/h.
-
-![](_page_6_Figure_1.jpeg)
-
-**Figure 4 Speed distribution on site n. 1** 
-
-### **3.2 Site n. 2 – S.S. n. 125**
-
-The curve section is located along the S.S. n. 125. The cross-section is made up of two lanes that are 3,25 m wide, no paved shoulders which are 0,50 m wide. Longitudinal grade is 2,70 %.
-
-![](_page_6_Figure_5.jpeg)
-
-**Figure 5 Plan of site n. 2 – S.S. n. 125** 
-
-The speed data are collected at middle point of circular curve (R = 55 m). A total of 168 individual speed observations are collected on both directions. The operating speed registered is 49 km/h.
-
-![](_page_7_Figure_1.jpeg)
-
-**Figure 6 Speed distribution on site n. 2** 
-
-### **3.3 Site n. 3 – S.S. n. 125 (new section)**
-
-The cross section of the roads are equal to those recommended by the new Italian guidelines for two-lane rural roads (type C1) [6]: it is 10.5 m width (two lanes width 3.75 m and two shoulders width 1.50 m). All the geometric elements of the horizontal and vertical alignments are designed according to the Italian design guidelines [6]. The general speed limit is 90 km/h (in accordance with the "Italian Road Code").
-
-The speed data are collected on a middle point of the circular curve (R = 1500 m). A total of 208 individual speed observations are collected on both directions. The horizontal curve is preceded and succeeded by clothoids.
-
-The operating speed registered is 129 km/ h.
-
-![](_page_7_Figure_7.jpeg)
-
-**Figure 7 Plan of site n. 3 – S.S. n. 125** 
-
-![](_page_8_Figure_1.jpeg)
-
-**Figure 8 Speed distribution on site n. 3** 
-
-All results are summarized in the Table 3
-
-**Table 3 Operating speed registered on several sites** 
-
-|          | R    | L   | CCRs | i    | V85 |
-|----------|------|-----|------|------|-----|
-| Site n.1 | 70   | 60  | 910  | 3,75 | 61  |
-| Site n.2 | 55   | 80  | 1158 | 2,70 | 49  |
-| Site n.3 | 1500 | 285 | 30   | 3,00 | 129 |
-
-where: L = length of horizontal curve [m], V85 = operating speed [km/h]; CCRs = Curvature Change Rate of the single curve [gon/km]; R = curve radius[m]; i = longitudinal grade [%].
-
-For the preliminary estimation of operating speed, the predicting model, which have been found in literature, are used. The results are summarized in the Table 4.
-
-The paper wants to test, on some significant sections, several national and international models in order to implement the most significant with another variables, for example available sight distance.
-
-The comparison between speeds observed on curve sections shows that the V85 collected is not perfectly in accordance with the results of the different relationships that are analysed above, even if they are similar.
-
-It is most important to underline that although two sites have a relatively small radius, the operating speed models results are similar to speed collected.
-
-Instead it is significant that on the site located in the road designed in accordance with new Italian design standards [6] operating speed gathered is bigger than ones calculated using analytical models.
-
-**Table 4 Operating speed values** 
-
-|                                                     |                                          | Site n.1          | Site n.2          | Site n.3          |  |
-|-----------------------------------------------------|------------------------------------------|-------------------|-------------------|-------------------|--|
-| Model                                               | Equation                                 | V85<br>(surveyed) | V85<br>(surveyed) | V85<br>(surveyed) |  |
-|                                                     |                                          | 61                | 49                | 129               |  |
-| McLean<br>Australia                                 | V85 = 101.2 – 0.043 CCRs                 | 62                | 51                | 100               |  |
-| Lamm et<br>al.<br>Germany                           | V85 = 106<br>/ (8270 + 8.01 CCRs)        | 64                | 57                | 118               |  |
-| Psarianos<br>et al.                                 | V85 = 106<br>/ (10150.1 + 8.529<br>CCRs) | 56                | 50                | 96                |  |
-| Lamm et<br>al. United<br>States                     | V85 = 93.85 – 0.05 CCRs                  | 48                | 36                | 92                |  |
-| Krammes<br>and<br>Ottesen                           | V85 = 103.04 – 0.053 CCRs                | 55                | 42                | 101               |  |
-| Ottesen<br>and<br>Krammes                           | V85 = 103.66 – 1.95 · (1746.38 /<br>R)   | 55                | 42                | 101               |  |
-| Kannelaidis<br>et al.                               | V85 = 129.88 – (623.1 / √R)              | 55                | 46                | 114               |  |
-| University<br>of Trieste                            | Venv = 200.97 · CCR –0.16                | 68                | 65                | 116               |  |
-|                                                     | V85c = Venv / (1 + 4.75 / R 0.58)        | 48                | 44                | 109               |  |
-| where: V85 [km/h]; Venv [km/h] CCRs [gon/km]; R [m] |                                          |                   |                   |                   |  |
-
-The table underlines like the models considered are linked with local road environment. In fact there are high differences among several results. For this reason, drawing up a model, locally valid, could be necessary. The target of this first step of project research is to know local validity of some operating speed models.
-
-### **4. CONCLUSION**
-
-Finally the aim is improving these prediction models or drawing up a new model. Therefore it will be necessary to enlarge sample data and to consider some other independent variables, as for instance available sight distance. An experimental survey is actually ongoing.
-
-10
-
-Once such variables as category of the road, characteristics of the traffic, and so on, have been identified, we can determine some relationships, which are valid not only locally. As a consequence, it is opportune to widen the sample of roads studied.
-
-Further surveys will be carried out in order to complete the test of the model explained above. The aim of the analysis was to investigate the relationship between the operating speed and the geometric features, into the studies of several countries, in order to create an operating speed model, where the V85 is expressed as a function of CCR and available sight distance.
-
-This paper represents a first step, of our research project, that, starting from the existing models, wants to create a model locally valid. The following step will be select other sites to know the models, which represent better the driver behavior on rural roads, which are designed in accordance with the new Italian Road Guidelines.
-
-### **ENDNOTES**
-
-- [1] AASTHO American Association of State Highway and Transportation Officials (2001) - *A Policy on Geometric Design of Highways and Streets* - AASTHO, Washington D.C.
-- [2] LAMM, R. PSARIANOS, B. MAILAENDER, T. (1999) *Highway Design and Traffic Safety Engineering Handbook -* McGraw-Hill Handbooks, New York, United States.
-- [3] OTTESEN, J.L. KRAMMES, R. (2000) "Speed-Profile Model for a Design-Consistency Evaluation Procedure in the United States" - *Transportation Research Record 1701 National Research Council*, Washington D.C., pp. 76 – 85.
-- [4] GIBREEL, G.M. EASA, S.M. EL-DIMEERY, I.A. (2001) "Prediction of Operating Speed on Three-Dimensional Highway Alignments" - *Journal of Transportation Engineering 127*, RE No. 1127, pp. 21 – 30.
-- [5] CRISMAN, B. MARCHIONNA, A. PERCO, P. ROBBA, A. ROBERTI, R. (2005) "Operating Speed Prediction Model for Two-Lane Rural Roads" - *3rd International Symposium on Highway Geometric Design*, Chicago, USA.
-- [6] MINISTERO DELLE INFRASTRUTTURE E DEI TRASPORTI, (2001) *Norme funzionali e geometriche per la costruzione delle strade D.M. 05/11/2001* - Roma, Italy.
-
-### **REFERENCES**
-
-AASTHO American Association of State Highway and Transportation Officials (2001) - *A Policy on Geometric Design of Highways and Streets*, AASTHO, Washington D.C. BIRD, R. N. HASHIM, I.H. (2005) - "Operating Speed and Geometry Relationships for Rural Single Carriageways in the UK" - *3rd International Symposium on Highway Geometric Design*, Chicago, USA.
-
-BEVILACQUA, A. DI MINO, G. NIGRELLI, J. (2005) - *An Experimental Investigation on the Relationship Between Speed and Road Geometry* - 3rd International SIIV Congress, Bari, Itlay.
-
-CRISMAN, B. MARCHIONNA, A. PERCO, P. ROBBA, A. ROBERTI, R. (2005) - "Operating Speed Prediction Model for Two-Lane Rural Roads" - *3rd International Symposium on Highway Geometric Design*, Chicago, USA.
-
-CUNNINGHAM, J. (2005) - "Recent Developments in Geometric Design in Australia" - *3rd International Symposium on Highway Geometric Design*, Chicago, USA.
-
-FIGUEROA, A. M. TARKO, A.P. (2005) - "Free-flow Speed Changes in the Vicinity of Horizontal Curves" - *3rd International Symposium on Highway Geometric Design*, Chicago, USA.
-
-HASSAN, Y. MISAGHI, P. ADATTA, M. (2005) - "Speed-Based Measures for Evaluation of Design Consistency on Canadian Roads" - *3rd International Symposium on Highway Geometric Design*, Chicago, USA.
-
-GIBREEL, G.M. EASA, S.M. EL-DIMEERY, I.A. (2001) - "Prediction of Operating Speed on Three-Dimensional Highway Alignments" - *Journal of Transportation Engineering 127*, RE No. 1127, pp. 21 – 30.
-
-LAMM, R. PSARIANOS, B. MAILAENDER, T. (1999) - *Highway Design and Traffic Safety Engineering Handbook -* McGraw-Hill Handbooks, New York, United States.
-
-MINISTERO DELLE INFRASTRUTTURE E DEI TRASPORTI, (2001) - *Norme funzionali e geometriche per la costruzione delle strade D.M. 05/11/2001* - Roma, Italy. OTTESEN, J.L. KRAMMES, R. (2000) – "Speed-Profile Model for a Design-Consistency Evaluation Procedure in the United States" - *Transportation Research* 
-
-*Record 1701 National Research Council*, Washington D.C., pp. 76 – 85. RICHL, L. SAYED, T. (2005) - "Effect of Speed Prediction Models on Design
-
-Consistency" - *3rd International Symposium on Highway Geometric Design*, Chicago, USA.
-
-ZIMMERMANN, M. (2005) - "Increased Safety Resulting from Quantitative Evaluation of Sight Distances and Visibility Conditions of Two-Lane Rural Roads", *3rd International Symposium on Highway Geometric Design*, Chicago, USA.

Zotero/001_artiklid/Road Safety Indicators/abdellapenalizedconwaymaxwellpoisson2019.md

@@ -1,373 +0,0 @@
----
-category: literaturenote
-citekey: abdellapenalizedconwaymaxwellpoisson2019
-title: "Penalized Conway-Maxwell-Poisson regression for modelling dispersed discrete data: The case study of motor vehicle crash frequency"
-authors: "Abdella, Galal M.; Kim, Jinho; Al-Khalifa, Khalifa N.; Hamouda, Abdel Magid"
-year: 2019
-date: 2019-00-00 2019
-doi: 10.1016/j.ssci.2019.06.036
-publication: Safety Science
-url: "https://doi.org/10.1016/j.ssci.2019.06.036"
-zotero_key: DEHVSRAU
-zotero_storage: 8IUMGZV6
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Abdella, Galal M."
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-### Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/09257535)
-
-## Safety Science
-
-journal homepage: [www.elsevier.com/locate/safety](https://www.elsevier.com/locate/safety)
-
-![](_page_0_Picture_5.jpeg)
-
-# Penalized Conway-Maxwell-Poisson regression for modelling dispersed discrete data: The case study of motor vehicle crash frequency
-
-![](_page_0_Picture_7.jpeg)
-
-Galal M. Abdella[a,](#page-0-0)[⁎](#page-0-1) , Jinho Kim[b](#page-0-2) , Khalifa N. Al-Khalifa[a](#page-0-0) , Abdel Magid Hamoud[aa](#page-0-0),[c](#page-0-3)
-
-- <span id="page-0-0"></span><sup>a</sup> *Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar*
-- <span id="page-0-2"></span><sup>b</sup> *Rutgers Business School, Rutgers University, NJ, USA*
-- <span id="page-0-3"></span><sup>c</sup> *Qatar Transportation and Traffic Safety Center, Qatar University, Doha, Qatar*
-
-#### ARTICLE INFO
-
-#### *Keywords:* COM-Poisson regression Motor vehicle crash rate Poisson regression Generalized linear models
-
-ABSTRACT
-
-Statistical modelling of road crashes has been of extreme interest to researchers over the last decades. Such models are necessary for the investigation of the opportunities for road safety improvement. The motor vehicle crash frequency (MVC-F) is probably the most important count of road crashes. In practice, like many of other discrete variables, this count is often diagnosed with over- or underdispersion, i.e. the variance is greater or less than the mean. The traditional regression models, especially those based on the Poisson distribution, are inefficient in modelling dispersed count data. On the contrary, the Conway-Maxwell-Poisson (COM-Poisson) distribution has been proven powerful in modelling count data with a wide range of dispersion. In crash data modelling, many situations may give rise to collinearity between contributory crash factors. Under this situation, the maximum likelihood estimates of the coefficients of the COM-Poisson GLM become increasingly unreliable as the collinearity among the model predictors increases. This paper addresses this issue and proposes a penalized likelihood scheme to be used with the COM-Poisson GLM regression for improving its prediction performance. For better GLM regression output, we suggest implementing the penalized COM-Poisson GLM regression under a K- fold cross-validation framework. A real-world crash example is provided, showing the performance of the penalized COM-Poisson GLM regression compared to the Poisson and the classical COM-Poisson GLM regressions.
-
-### **1. Introduction**
-
-Statistical models for road crash data have become of extreme importance for all aspects of transport and traffic systems (TTS). These models, in particular, are helpful in establishing an evidence-based framework through which transportation authority can develop innovative road safety strategies and interventions. Over the past years, there has been a growing interest in literature on the development of statistical models for road safety studies ([Lord and Persaud, 2000; Lord](#page-6-0) [et al., 2005; Lord and Mannering, 2010; Kokonendji, 2014; Frank and](#page-6-0) [Samia, 2015; Paraskevi et al., 2015; Abdella et al., 2017; Abdur Rouf](#page-6-0) [et al., 2018](#page-6-0)).
-
-The Poisson distributions are the most popular for road safety management and analysis. The motor vehicle crash frequency (MVC-F) is probably the most common crash count variable ([Nazha et al., 2018](#page-6-1)). Since the MVC-F is usually characterized by over- and underdispersion, the use of the traditional Poisson-based models becomes inappropriate. The over-dispersion phenomenon usually occurs when the variance of the crash count exceeds the expected mean, i.e. *V Y E Y* ( ) ( ) > . On the contrary, the under-dispersion occurs when the variance is less than the expected mean, i.e. *V Y E Y* ( ) ( ) < . The over-dispersion in the MVC-F count usually occurs when the dataset on some road segments or timeintervals exhibits a large number of zero values ([Vangala et al., 2015](#page-6-2))*.* Several significant contributions have been conducted over the last years on the applications of the Conway-Maxwell-Poisson (COM-Poisson) in modeling over- and underdispersion count variables. [Guikema and Coffelt \(2008\)](#page-6-3) developed a COM-Poisson GLM formulation using two different links (dual-link) functions for modeling discrete counts. These two functions are = + <sup>=</sup> *ln µ*( ) *x <sup>i</sup> <sup>i</sup>* <sup>0</sup> *<sup>i</sup>* <sup>1</sup> and = + <sup>=</sup> *ln*( ) *z <sup>j</sup> q <sup>i</sup>* <sup>0</sup> *<sup>i</sup>* <sup>1</sup> , where *p* and *q* are the covariates used in the location and the shape link functions. [Lord et al. \(2008\)](#page-6-4) modified the dual-link formulation by replacing the second link function by the value of the shape parameter . The authors used log( ) <sup>1</sup> to link the response variable with the model predictors and then suggested using the
-
-*E-mail addresses:* [gmg5005@qu.edu.qa](mailto:gmg5005@qu.edu.qa) (G.M. Abdella), [jinho.kim@rutgers.edu](mailto:jinho.kim@rutgers.edu) (J. Kim), [Alkhalifa@qu.edu.qa](mailto:Alkhalifa@qu.edu.qa) (K.N. Al-Khalifa), [Hamouda@qu.edu.qa](mailto:Hamouda@qu.edu.qa) (A.M. Hamouda).
-
-<span id="page-0-1"></span><sup>⁎</sup> Corresponding author.
-
-Bayesian method for estimating the coefficients of the resulted model. The selection of the Bayesian estimation here is mainly attributed to its favourable mathematical and practical properties. The study has shown that the COM-Poisson GLMs perform the same as the NB models. One advantage of the COM-Poisson GLM over the NB model is that it can handle both the over- and underdispersed count data [\(Lord et al.,](#page-6-4) [2008\)](#page-6-4). This feature would, for sure, expands the domain of usage of the COM-Poisson-based models to include a wide range of industrial and service sectors. [Sellers and Shmueli, \(2010\)](#page-6-5) considered the log linear form of the Poisson regression by [McCullagh and Nelder, \(1997\)](#page-6-6) and proposed a COM-Poisson GLM formulation using a ( ( )) ( ) *E Y log* = as a link function. This function is often preferable over others for it would lead to a direct estimation of the coefficients of the GLM regressors. Both of the above studies are mainly based on the assumption of having a fixed value of the shape parameter, i.e. the shape parameter is independent of the covariates of the model. [Sellers and Andrew \(2016\)](#page-6-7) developed another format of the COM-Poisson, named as a zero-inflated COM–Poisson (ZICMP), having the capability of modelling both overand under-dispersion dataset. The ZICMP has revealed a performance relatively close to the zero-inflated negative binomial (ZINB) with respect to the Akaike Information Criterion (AIC).
-
-In the practice of road crash modeling, there are many situations may give rise to multicollinearity –or simply collinearity between contributory crash factors. In statistics, the collinearity refers to the existence of a significant linear relationship between the model factors. The collinearity has a severe impact on the reliability of the GLM estimates as well as the interpretation of the results. Despite the attractive prediction performance of the COM-Poisson-based regression models, we believe that there is still a need for developing COM-Poisson regression models that are capable of not only accommodating the dispersion issue but also being insensitive to the potential collinearity between road crash factors.
-
-This work develops a penalized likelihood formulation to be used in the COM-Poisson GLM regression to stabilizing the estimates of the regression parameters. We develop the penalized likelihood function by adding an *L*<sup>2</sup> norm to the squared norm of the GLM coefficients in the likelihood function by [Sellers and Shmueli \(2010\)](#page-6-5). Considering the effectiveness of the COM-Poisson distribution in modelling dispersed count data, our penalized COM-Poisson GLM regression would provide a practitioner with a powerful tool for simultaneously overcoming both the "dispersion" and "collinearity" problems. In order to achieve a better predictive performance of our model, we implement the penalized COM-Poisson GLM regression under a K *fold*-cross-validation framework.
-
-We organize the rest of this research paper as follows: [Section 2](#page-1-0) introduces the COM-Poisson distribution. [Section 3](#page-1-1) describes the penalized COM-Poisson GLM regression model. [Section 4](#page-2-0) describes the cross-validation approach. [Section 5](#page-3-0) illustrates the implementation of the penalized COM-Poisson GLM for modeling a real-world example. [Section 6](#page-5-0) presents a summary of the findings and outlines extensions for future research work.
-
-#### <span id="page-1-0"></span>**2. The COM-Poisson distribution**
-
-The COM-Poisson is usually considered as a general form of the Poisson distribution. [Conway and Maxwell, \(1962\)](#page-6-8) firstly develop the COM-Poisson for analyzing queuing systems. Later, its statistical properties are studied and reported by [Shmueli et al., \(2005\)](#page-6-9). The probability mass function (pmf) of the COM-Poisson distribution is as follows:
-
-$$Pr(Y_i = y_i \mid \gamma_i, \theta) = f(y_i; \gamma_i, \theta) = \frac{\gamma_i^{y_i}}{Q(\gamma_i, \theta)(y_i!)^{\theta}};$$
-  
-
-$$y_i = 0, 1, \dots : i = 1, 2, \dots n$$
-(1)
-
-where *yi <sup>n</sup>* is the *i* th discrete response value, *<sup>i</sup>* is the mean value of the observations ( >0) *<sup>i</sup>* , and is the shape parameter. When < 1 , the problem is called over-dispersion and under-dispersion when > 1 . The function *Q*( , ) *<sup>i</sup>* is defined as the normalization constant and found as follow [\(Shmueli et al., 2005; Sellers and Shmueli, 2010](#page-6-9))
-
-$$Q(\gamma, \theta) = \sum_{j=0}^{\infty} \frac{\gamma^{j}}{(j!)^{\theta}}$$
-(2)
-
-and,
-
-$$Pr(Y_i = y_i \mid Y_i, \ \theta) = \frac{Y_i^{y_i}}{\sum_{j=0}^{m} \frac{y_j^{j}}{(j)^{\beta}}(y_i!)^{\theta}}; \quad y_i = 0, \ 1, \ \cdots; i = 1, \ 2, \ \cdots n$$
-(3)
-
-The sum of COM-Poisson independent variables may reduce to three different probability distributions. These are (1) Poisson ( = 1) ( , *Q* ) = *e <sup>i</sup> <sup>i</sup>*, (2) Geometric ( *<sup>i</sup>*< = 1, 0) ( , *Q <sup>i</sup>* ) 1 (1 ) = *<sup>i</sup>* , and (3) Bernoulli ( [\(Shmueli et al., 2005; Sellers and Shmueli,](#page-6-9) [2010\)](#page-6-9). The moments of the COM-Poisson distribution are estimated as derivations of *Q*( , ) *<sup>i</sup>* ; these are:
-
-<span id="page-1-2"></span>
-$$E(Y_i) = \frac{\partial logQ(\gamma_i, \vartheta)}{\partial log\gamma_i} \approx \gamma_i^{\frac{1}{9}} - \frac{\vartheta - 1}{2\vartheta}; \tag{4}$$
-
-<span id="page-1-3"></span>
-$$V(Y_i) = \frac{\partial E(Y_i)}{\partial \log \gamma_i} = \frac{\partial \log^2 Q(\gamma_i, \, \theta)}{\partial \log^2 \gamma_i};$$
- (5)
-
-The approximations in Eqs. [\(4\)](#page-1-2) and [\(5\)](#page-1-3) are accurate when 1 or > 10 *<sup>i</sup>* ([Minka et al., 2003\)](#page-6-10).
-
-## <span id="page-1-1"></span>**3. Methodology: the penalized COM-Poisson GLM**
-
-This section introduces the COM-Poisson GLM reported by [Sellers &](#page-6-5) [Shmueli, \(2010\)](#page-6-5) and shows the steps followed in this work to develop its penalized version. The GLM using ( ( )) ( ) *E Y log* = is denoted as follows:
-
-<span id="page-1-4"></span>
-$$logE(Y_i) = log(\gamma) = X_i \beta = \beta_0 + \beta_1 X_{i1} + \beta_2 X_{i2} + \dots + \beta_p X_{ip},$$
- (6)
-
-where *Xij <sup>n</sup>* is the *i th* observation of the *j th*model predictor. The values are the coefficients of the model predictor in the GLM. There are two well-known techniques for estimating the coefficients of the GLM shown in Eq. [\(6\).](#page-1-4) These are the weighted least squares and the maximum likelihood. As the COM-Poisson is usually classified as one of the exponential family distributions, the maximum likelihood method is more preferable as a way to achieve an accurate estimation. The loglikelihood function for a set of *n* independent readings with constant is as follows ([Sellers and Shmueli, 2010\)](#page-6-5):
-
-$$\log \mathcal{L}(\gamma_{h} \vartheta | y_{1}, y_{2}, \dots, y_{n}) = \Lambda_{\gamma_{l}} = \sum_{i=1}^{n} y_{i} \mathbf{X}_{i} \boldsymbol{\beta} - \sum_{i=1}^{n} \vartheta log y_{i}! - \sum_{i=1}^{n} log Q(\mathbf{X}_{i} \boldsymbol{\beta}, \vartheta).$$
-
-$$\tag{7}$$
-
-The *L*<sup>2</sup> penalized regression shrinks the coefficient estimates of the COM-Poisson GLM towards zeros, but will never become exactly zeros. The purpose of this shrinkage is mainly to prevent overfitting arising due to the collinearity between the model predictors. The penalized scheme of the maximum likelihood is shown in Eq. [\(8\)](#page-1-5) and reformatted in Eq. [\(9\):](#page-1-6)
-
-<span id="page-1-5"></span>
-$$\lambda_{\lambda}^{Ridge} = \sum_{i=1}^{n} y_{i} \mathbf{X}_{i} \dot{\boldsymbol{\beta}} - \sum_{i=1}^{n} \theta \log y_{i}! - \sum_{i=1}^{n} \log Q(\mathbf{X}_{i} \dot{\boldsymbol{\beta}}, \theta) - \lambda \sum_{j=1}^{p} \beta_{j}^{2}.$$
-(8)
-
-<span id="page-1-6"></span>Or;
-
-$$\Lambda_{\lambda}^{Ridge} = \sum_{i=1}^{n} y_{i} X_{i} \beta - \sum_{i=1}^{n} \theta \log y_{i}! - \sum_{i=1}^{n} \log Q(X_{i} \beta, \theta) - \lambda ||\beta||_{2}^{2}.$$
-(9)
-
-Here, 0 is a pre-chosen penalization (shrinkage) parameter that can be chosen to achieve certain penalty strength and . <sup>2</sup> <sup>2</sup> is the squared Euclidean norm. Note that, larger means more shrinkage – and vice versa. The *L*<sup>2</sup> penalization performs particularly well when the
-
-true values of are small or even zero. Let ' and ' be the solution of the penalized log-maximum likelihood in Eq. [\(9\),](#page-1-6) then the following two properties are true:
-
-- Property 1: As <sup>0</sup>, ' , where is the solution of the COM-Poisson GLM model by [Sellers and Shmueli \(2010\).](#page-6-5)
-- Property 2: As , <sup>0</sup> '
-
-Our regression model and the classical COM-Poisson GLM regression by [Sellers & Shmueli \(2010\)](#page-6-5) are similar in that both use the same link function, but they are dissimilar in that they use different maximum likelihood formulations. The classical COM-Poisson GLM regression using a constant dispersion parameter is a special case of our regression model when no shrinkage on the coefficients of the GLM estimates is applied, i.e. =0. In the next section, we refer to our model as the penalized COM-Poisson GLM.
-
-## <span id="page-2-0"></span>**4. Penalized likelihood estimation based on the cross-validation**
-
-The *L*<sup>2</sup> penalization may cause some bias. The proper selection of the penalization parameter ( ) reduces the negative effect of large bias and results in a better mean squared error. [Hoerl and Kennard, \(1970\)](#page-6-11) firstly proposed this parameter when the ridge regression is introduced as a solution for the collinearity problem. Since then, several methods have been developed to find the most efficient estimate of this parameter ([Kibria, 2003; Khalaf and Shukur, 2005; Alkhamisi et al., 2006;](#page-6-12) [Alkhamisi and Shukur, 2008; Muniz and Kibria 2009\)](#page-6-12).
-
-The ridge trace plotted by [Hoerl and Kennard, \(1970\)](#page-6-11) is a graphical method to determine an appropriate value of . This method suggests visualizing the changes in the GLM coefficients over a wide range of values and to select the value of when the changes in these coefficients are insignificant. This method is a kind of trade-off between the variance and the bias of the coefficient estimates. To maintain a small bias, the minimum value of the corresponding range of would be an appropriate choice. This method is applied in this paper to specify the practical range of . Later, K values of are selected from this range, including extremes, and arbitrarily assigned to the K cross-validation cycles. The proposed approach for solving the penalized likelihood estimation under the cross-validation is described below:
-
-- **Step 1**: Split the original dataset into K equally sized sub-datasets.
-- **Step 2**: Define *w n* = K as the sub-dataset size, where *n* is the size of the original dataset.
-- **Step 3:** Let // the subset containing indices of K sub-datasets.
-- **Step 4**: Let *j* // the order index of a sub-dataset, where *j* = 1, 2, , K
-- **Step 5:** Let be a subset of size K containing all possible values of . That is, = { <sup>1</sup>, 2, , *<sup>K</sup>*}.
-- **Step 6**: For *j* = 1 to K
-  - (i) Name the *j* th sub-dataset in as a "training" sub-dataset and the (K 1) remaining sub-datasets as the "validating" sub-datasets.
-  - (ii) Solve Equation [\(9\)](#page-1-6) and find ' *<sup>j</sup>* and ' . Note that, is the tuning parameter applied when the *j th* sub-dataset is selected as the "training" sub-dataset.
-  - (iii) Calculate the Mean Absolute Error (MAE), which is one of the methods available in the literature to measure the prediction accuracy. The Mean Absolute Error for any sub-dataset in is calculated as follows:
-
-$$MAE_j = \frac{1}{w} \sum_{i=1}^{w} |y_i - \hat{y}_i| = \frac{1}{w} \sum_{i=1}^{w} |e_i|,$$
- (10)
-
-where the fitted value *yi* can be calculated by applying Eq. [\(4\)](#page-1-2) after
-
-setting 
-$$\hat{y}_i = exp\left(x_i^T \hat{\beta}_{\lambda_j}\right)$$
-. The error,  $e_i = y_i - \hat{y}_i$ , is calculated as follows:  
- $e_i = y_i - \hat{\beta}_0 - \hat{\beta}_1 X_{i1} - \hat{\beta}_2 X_{i2} - \cdots - \hat{\beta}_p X_{ip}$  (11)
-
-(iv) Compare the Median Absolute Deviation (MAD) for each cycle as follows:
-
-$$MAD_j = \text{median } |MAE_t - \text{median}(\mathbf{M}_j)|, t = 1, 2, \dots, (K - 1),$$
-(12)
-
-where **M***<sup>j</sup>* is a list of MAE values defined as
-
-$$\mathbf{M}_{j} = [\mathrm{MAE}_{1}, \, \mathrm{MAE}_{2}, \cdots, \mathrm{MAE}_{j}] \tag{13}$$
-
-- **Step 7**: End of the "For" loop
-- **Step 8**: Let **D** the matrix contains the MAD*<sup>j</sup>* values of all validation cycles.
-- **Step 9**: Find MADmin to specify the optimal penalization parameter as follows:
-
-$$MAD_{min} = argmin\{\mathbf{D}\} = arg \min_{j=1,...,K} \{MAD_j\}$$
-(14)
-
-- **Step 10**: Let *opt* be the value of used in the cross-validation cycle having the minimum MAD. This cycle is already defined in **Step 9**.
-- **Step 11**: By using *opt* , fit the penalized COM-Poisson GLM model to the whole dataset and find *opt* and *opt* .
-- **Step 12**: Let and *opt* be the optimal solution of the penalized maximum likelihood (PML) problem
-
-Eq. [\(9\)](#page-1-6) could be directly solved by using the constrained nonlinear optimization tool (nlminb) available in R software. In this paper, the R Software available at [https://cran.r-project.org/web/packages/](https://cran.r-project.org/web/packages/COMPoissonReg) [COMPoissonReg](https://cran.r-project.org/web/packages/COMPoissonReg) was modified to capture the structure of our penalized likelihood estimation. Further methods for solving such problem are available in [Sellers and Shmueli, \(2010\).](#page-6-5) Another way to find y*<sup>i</sup>* is by obtaining the inverse-CDF of y*<sup>i</sup>* and [\(Sellers and Shmueli, 2010\)](#page-6-5).
-
-In order to specify the optimal penalization parameters, we calculate the MAD for each cycle using the MAE values, which are used to measure the prediction accuracy, and select the parameters for the minimum value of MAD, that is, MADmin, as the optimal penalization parameters. This mechanism for the optimal parameters is described in the steps 6, 9 and 10.
-
-The penalized likelihood estimation is widely used to reduce overfitting in estimating the regression parameters, including linear, logistic, semi-parametric and proportional hazards regression [\(Verweij](#page-6-13) [and Van Houwelingen, 1994; Ciuperca et al., 2003; Rondeau et al.,](#page-6-13) [2003; Goeman, 2010; Göbl et al., 2015\)](#page-6-13). The overfitting problem may arise due to the collinearity among predictors or in the condition of the high dimensionality. [Goeman \(2010\)](#page-6-14) provided several GLMs in R software, named as "*penalized*" package, with *L*<sup>1</sup> – LASSO and/or *L*<sup>2</sup> – ridge penalty terms, using likelihood-cross validations. The regression models supported by the "*penalized*" package are the linear, logistic, Poisson regression, and the Cox Proportional Hazards model.
-
-In this paper, we are specifically interested in tackling the over- and under-dispersion issues arising during the crash count regression for
-
-<span id="page-2-1"></span>**Table 1** Summary statistics for the *K* sub-datasets.
-
-| Statistic | Sub-dataset |       |       |       |  |  |  |  |  |
-|-----------|-------------|-------|-------|-------|--|--|--|--|--|
-|           | 1           | 2     | 3     | 4     |  |  |  |  |  |
-| Min       | 0           | 0     | 0     | 0     |  |  |  |  |  |
-| Max       | 54          | 51    | 53    | 48    |  |  |  |  |  |
-| Mean      | 13.76       | 11.25 | 10.95 | 10.25 |  |  |  |  |  |
-| Median    | 11          | 7     | 7     | 8     |  |  |  |  |  |
-| AIC       | 1401        | 1335  | 1503  | 1332  |  |  |  |  |  |
-| R2        | 0.30        | 0.21  | 0.20  | 0.17  |  |  |  |  |  |
-
-<span id="page-3-1"></span>**Table 2** The VIF and Tolerance values of X1 and X2 for the 4-tested sub-datasets.
-
-|                      | Regressors   |              |              |              |              |              |              |              |  |  |  |  |
-|----------------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--|--|--|--|
-|                      | X1           |              |              |              | X2           |              |              |              |  |  |  |  |
-|                      | Sample 1     | Sample 2     | Sample 3     | Sample 4     | Sample 1     | Sample 2     | Sample 3     | Sample 4     |  |  |  |  |
-| VIF<br>Tolerance (T) | 1.15<br>0.86 | 1.14<br>0.88 | 1.11<br>0.90 | 1.08<br>0.92 | 1.15<br>0.86 | 1.14<br>0.88 | 1.11<br>0.90 | 1.08<br>0.92 |  |  |  |  |
-
-<span id="page-3-2"></span>**Table 3** Comparison of estimated coefficients in the first cycle.
-
-| Parameter | Regression Model |                 |                           |  |  |  |  |  |  |  |
-|-----------|------------------|-----------------|---------------------------|--|--|--|--|--|--|--|
-|           | Poisson          | COM-Poisson GLM | Penalized COM-Poisson GLM |  |  |  |  |  |  |  |
-|           | –                | 0.39360         | 0.37940                   |  |  |  |  |  |  |  |
-| 0         | −9.9467          | −10.988         | −10.761                   |  |  |  |  |  |  |  |
-| 1         | 0.5515           | 0.5819          | 0.5561                    |  |  |  |  |  |  |  |
-| 2         | 0.7361           | 0.8051          | 0.8086                    |  |  |  |  |  |  |  |
-
-which we use the ridge COM-Poisson GLM. Note that the ridge Poisson GLM is a special case of our ridge COM-Poisson GLM when *Q*( , ) = *e <sup>i</sup> <sup>i</sup>*. Even under this special case of similarity, the estimated regression parameters resulted by using the ridge COM-Poisson GLM are expected to be different from [Goeman, \(2010\),](#page-6-14) because the outcomes of the cross-validation vary according to the run-up settings and the criteria of a decision.
-
-### <span id="page-3-0"></span>**5. An illustrative example: the MVC-F in Toronto, Ontario**
-
-In this section, we compare the prediction accuracy of the penalized COM-Poisson GLM with both the Poisson and the classical COM-Poisson GLM. For a fair comparison, this paper uses the overdispersed crash dataset collected in 1995 at 868 signalized intersections located in Toronto, Ontario. Several researchers have used this dataset [\(Lord,](#page-6-15) [2000; Miaou and Lord, 2003; Miranda-Moreno and Fu 2007; Lord et al.,](#page-6-15) [2008; Sellers and Shmueli, 2010](#page-6-15)). For a further detailed description of this dataset, see [Lord et al. \(2008\)](#page-6-4).
-
-There are 868 observations in the dataset which consist of the MVC-F, as a response variable ( Y ), and two log-transformed traffic flow, as predictors ( X1 and X2). In this example, the original dataset is divided into four sub-datasets for applying the systematic K -fold CV approach. Each of them contains 217 observations. According to this division, the parameters K and *w* are set at 4 and 217, respectively. [Table 1](#page-2-1) summarizes the statistics of the sub-datasets of one cycle solution.
-
-Many diagnostics have been proposed in the previous literature to assess the degree of collinearity[\(Abdella et al. 2019\)](#page-5-1). The Pearson correlation coefficient ( ) **R**<sup>2</sup> is the typical among them. The **R**<sup>2</sup> takes a value between −1 and +1, where a negative value indicates a negative correlation, 0 means no correlation between the two variables and a positive value means positive correlation. The **R**<sup>2</sup> in the Toronto crash dataset is 0.31. For each sub-dataset, the **R**<sup>2</sup> of the two traffic flow regressors is calculated and reported in [Table 1](#page-2-1). The collinearity, can also be indicated by using the Variance Inflation Factor (VIF) of regression coefficients, *<sup>i</sup>* . The VIF shows the degree of influence of the multicollinearity on the variance of regression estimates, which is defined as
-
-$$VIF_{i} = \frac{1}{(1 - r_{i}^{2})}; \qquad i = 1, 2, \dots, p,$$
-(15)
-
-where *p* is the number of model variables and *ri* <sup>2</sup> is the **R**<sup>2</sup> value when the regressor X*<sup>i</sup>* is regressed on the other factors. An equivalent indicator to the VIF is the Tolerance (T = VIF−1), which measures the amount of information whether the regressor X*<sup>i</sup>* can deliver under a particular level of collinearity. The Tolerance takes a maximum value of 1. If the regressor X*<sup>i</sup>* has no correlation (*ri* <sup>2</sup> = 0) with the other ( *p* 1 regressors, then the T is equal to 1. The VIF and T values of all subdatasets for the classical Poisson regression are calculated in [Table 2](#page-3-1).
-
-When VIF >10 , the model predictors are considered to be highly correlated with each other. The multicollinearity level in the Toronto crash dataset is relatively low; see [Table 2.](#page-3-1) The effective range of λ of the Toronto crash dataset is found to be from 0.1 to 0.4. Four values of , including extremes, are selected. These are = { <sup>1</sup>, 2 3 4 , , } = { 0.1, 0.2, 0.3, 0.4 }. [Table 3](#page-3-2) displays the results of fitting the penalized COM-Poisson GLM to the first sample of the crash data. Two more regression models are applied in this cycle and compared with the proposed model. These are the Poisson model and the classical COM-Poisson GLM by [Sellers and Shmueli, \(2010\)](#page-6-5).
-
-Note that, the dispersion parameters ( of both the penalized and classical COM-Poisson GLM regression models are assumed constant and unknown across observations. [Table 3](#page-3-2) indicates that the first "training" sub-dataset is characterized with over-dispersion ( = < 0. 3794 1) . This finding is used here to justify the usage of the COM-Poisson instead of the ordinary Poisson model. In order to create a scale consistency of the estimated coefficients of the three compared models, we follow [Sellers](#page-6-5) [and Shmueli, \(2010\)](#page-6-5) and report the estimated coefficients of the penalized and classical COM-Poisson models in terms of . The comparison in [Table 3](#page-3-2) is only to show the extent to which the compared models are close to each other. When developing prediction models, it is essential to test how well the data-set meets the assumptions of the model. To check the assumption that the residuals are uncorrelated with each other, we
-
-<span id="page-3-3"></span>![](_page_3_Figure_15.jpeg)
-
-![](_page_3_Figure_16.jpeg)
-
-![](_page_3_Figure_17.jpeg)
-
-**Fig. 1.** Plotting the deviance residuals produced by the fitted penalized COM-Poisson GLM at *j*=1 versus the observations order.
-
-<span id="page-4-0"></span>![](_page_4_Figure_0.jpeg)
-
-![](_page_4_Figure_1.jpeg)
-
-**Fig. 2.** The Histogram plot for the deviance residuals of the three "validating" sub-datasets of the first cycle.
-
-<span id="page-4-1"></span>![](_page_4_Figure_3.jpeg)
-
-**Fig. 3.** Plotting the Mean Absolute Error (MAE) under different levels of shrinkage in the coefficients of the penalized COM-Poisson GLM regression.
-
-<span id="page-4-2"></span>**Table 4** Calculating the MAD of the first cycle of validation.
-
-| Sample | MAE  | M )1<br>Median( | MAE<br>median(M )  1 | MAD1   |
-|--------|------|-----------------|----------------------|--------|
-| 2      | 3.96 | 3.96            | 0.0000               | 0.1887 |
-| 3      | 4.74 |                 | 0.7843               |        |
-| 4      | 3.77 |                 | 0.1887               |        |
-
-<span id="page-4-3"></span>**Table 5** Summary of the last three cycles of cross-validation.
-
-| Cycle,<br>j | j   |        | Penalized COM-Poisson GLM Coefficients | MADj   |        |        |
-|-------------|-----|--------|----------------------------------------|--------|--------|--------|
-|             |     |        | 0                                      | 1      | 2      |        |
-| 2           | 0.2 | 0.3590 | −12.502                                | 0.7269 | 0.7970 | 0.1866 |
-| 3           | 0.3 | 0.2614 | −11.902                                | 0.6501 | 0.8099 | 0.2136 |
-| 4           | 0.4 | 0.3825 | −10.313                                | 0.5886 | 0.7146 | 0.0289 |
-
-<span id="page-4-4"></span>**Table 6** Estimated GLM coefficients of the Toronto, Ontario crash dataset.
-
-| Parameter | Regression Model |                 |                           |  |  |  |  |
-|-----------|------------------|-----------------|---------------------------|--|--|--|--|
-|           | Poisson          | COM-Poisson GLM | Penalized COM-Poisson GLM |  |  |  |  |
-|           | –                | 0.34917         | 0.33714                   |  |  |  |  |
-| 0         | −10.234          | −11.702         | −11.507                   |  |  |  |  |
-| 1         | 0.6029           | 0.6558          | 0.6333                    |  |  |  |  |
-| 2         | 0.7037           | 0.7911          | 0.7945                    |  |  |  |  |
-| MSE       | 23.60            | 32.57           | 32.54                     |  |  |  |  |
-
-calculated the residual values of the three "validating" sub-datasets and plotted these values versus the order of the observations ([Fig. 1](#page-3-3)). The residual plots are supposed to show random patterns. Non-random patterns may violate the assumption that the residuals are independent of each other. Fortunately, here, the independence assumption of residuals
-
-<span id="page-4-5"></span>![](_page_4_Figure_12.jpeg)
-
-**Fig. 4.** The scatter plot of MSE of fitting the penalized COM-Poisson GLM, classical COM-Poisson GLM and Poisson models for the crash dataset of Toronto.
-
-is confirmed as the three "validating" sub-dataset reveal a random behaviour of residuals. Numerous techniques are available to test the normality assumption of residuals, i.e. *e N µ <sup>i</sup>* ( , )2 . The simplest one is the Histogram, which is also an efficient to present whether the residuals are skewed or whether the dataset includes outliers. [Fig. 2](#page-4-0) shows an approximately normal distribution of the residuals produced by the three "validating" sub-datasets. To build the **M**<sup>1</sup> matrix**,** the first cycle solution and 0.1 is used to compute the MAE of each "validating" sub-dataset. The **M**<sup>1</sup> matrix is found to equal [3.96 4.74 3.77] *<sup>T</sup>***.** In our cross-validation approach, a K -number of values are selected and arbitrarily assigned to the cycles of cross-validation. The best results of our approach should be reached when the value MAE from each "validating" sub-datasets at a cycle *j* is as low as possible which can be achieved over the range of . To investigate the convenience of choosing <sup>1</sup> = 0.1 for the first cycle, we plot the MAE values of the three "validating" sub-datasets versus several values of ; see [Fig. 3](#page-4-1).
-
-Graphically, for all "validating" sub-datasets, setting <sup>1</sup>=0.1 is the best choice among others. Note that, the importance of performing the "suitability check" step decreases as the slope of the practical range of decreases – and vice versa. The MAD1 value is calculated as shown in
-
-<span id="page-5-2"></span>**Table 7** Summary statistics for the *K* = {2,6,8} sub-datasets.
-
-| Statistic | K = 2 |      | K = 6 |      |      |      |      |      | K = 8 |      |      |      |     |      |      |      |
-|-----------|-------|------|-------|------|------|------|------|------|-------|------|------|------|-----|------|------|------|
-|           | 1     | 2    | 1     | 2    | 3    | 4    | 5    | 6    | 1     | 2    | 3    | 4    | 5   | 6    | 7    | 8    |
-| Min       | 0     | 0    | 0     | 1    | 0    | 0    | 0    | 0    | 0     | 1    | 0    | 0    | 0   | 0    | 0    | 0    |
-| Max       | 54    | 53   | 51    | 53   | 47   | 46   | 54   | 50   | 51    | 53   | 47   | 46   | 51  | 50   | 54   | 50   |
-| Mean      | 10.6  | 12.4 | 10.4  | 13.9 | 10.4 | 11.3 | 10.9 | 12.0 | 10.5  | 14.7 | 10.6 | 12.1 | 9.4 | 10.9 | 11.8 | 12.7 |
-| Median    | 8     | 18   | 7     | 10.5 | 8    | 7    | 9    | 10   | 7     | 11   | 8    | 8    | 7.5 | 9    | 10   | 9    |
-
-<span id="page-5-3"></span>**Table 8** The coefficients of estimation using K = {2,6,8}
-
-| Parameter |         | Penalized COM-Poisson GLM |         |  |  |  |  |  |
-|-----------|---------|---------------------------|---------|--|--|--|--|--|
-|           | K = 2   | K = 6                     | K = 8   |  |  |  |  |  |
-| 0         | −3.6828 | −4.737                    | −4.9727 |  |  |  |  |  |
-| 1         | 0.2034  | 0.3168                    | 0.3377  |  |  |  |  |  |
-| 2         | 0.2533  | 0.248                     | 0.2429  |  |  |  |  |  |
-| MSE       | 32.95   | 31.57                     | 29.49   |  |  |  |  |  |
-
-[Table 4.](#page-4-2) Having the MAD1 value is calculated, we repeat the above procedures with *j* = 2, 3 and 4. [Table 5](#page-4-3) summarizes the results of the last three cycles of the cross-validation approach.
-
-Comparing the MAD*<sup>j</sup>* values in [Table 5,](#page-4-3) the fourth cycle in which <sup>4</sup>=0.4 has the lower MAD. Hence, we set *opt* = 0.4 as the optimal shrinkage parameter and fit the penalized COM-Poisson GLM model to the whole crash dataset; see [Table 6](#page-4-4).
-
-The estimated GLM coefficients of both COM-Poisson GLM model are very close ([Table 6\)](#page-4-4). The results of fitting the classical COM-Poisson GLM are identical to those reported by [Sellers and Shmueli \(2010\)](#page-6-5). How well the fitted model predicts the response variable is necessary to assess the model adequacy. In this example, we use the mean squared error (MSE) as a measure of adequacy. Since the well-fitted model leads to the minimum MSE value, the penalized GLM COM-Poisson is the best to fit the Toronto crash dataset (MSE = 32.54) shown in [Table 6](#page-4-4). The last column in [Table 6](#page-4-4) represents the optimal solution of Eq. [\(9\).](#page-1-6) For further understanding of the performance of the penalized COM-Poisson GLM regression model, one can compare its performance with the Negative Binomial (NB) and the restricted generalized Poisson regression (RGPR) models available in [Sellers and Shmueli \(2010\).](#page-6-5)
-
-To draw inference about the sensitivity of the penalized COM-Poisson GLM to changes in , we proposed different levels of *λ* and computed the MSE at each level; see [Fig. 4.](#page-4-5) The MSE values of both the Poisson and the classical COM-Poisson GLM models were plotted for the purpose of comparison. Since the last two models are not penalizationbased models, their MSE values over the proposed range of *λ* are set fixed at their magnitudes reported in [Table 6.](#page-4-4) These are 32.60 and 32.57, respectively.
-
-[Fig. 4](#page-4-5) shows that the penalized COM-Poisson GLM provides the lowest MSE value when it is compared with other models. The one exception here is the case when *λ* = 1.2. In this case, the classical COM Poisson GLM outperforms our model. Moreover, as the range of *λ*, in this example, is set from 0.1 to 0.4, the penalized COM Poisson GLM would deliver the lowest MSE regardless of how the values are assigned to the cycles of the cross-validation.
-
-## <span id="page-5-0"></span>**6. Optimizing the K-parameter**
-
-The selection of K is critical to the quality of the cross-validation. In [Section 5](#page-3-0), we used K = 4. The results have shown the advantage of the penalized-COM Poisson over its counterparts the classical COM-Poisson and the Poisson regression. In this section, we conduct an analytical study to investigate the performance of the Penalized-COM-Poisson under several values of K.
-
-Three different values of K = {2,6,8} were suggested. [Table 7](#page-5-2) shows the descriptive analysis of the sub-dataset under each value of K. [Table 8](#page-5-3) reports the fitting results using the three values of K. The comparison between the MSE values of the three models shows a significant advantage of the third model using K = 8. The case of K = 2 has the worst MSE values comparing with all K values considered in this study. The three proposed models, similar to the case of K = 4, have a negative intercept.
-
-## **7. Conclusion**
-
-This paper integrates the penalized likelihood estimation, more specifically the ridge penalty function, with the COM-Poisson GLM regression to enhance its prediction accuracy under the condition of the collinearity. Using the penalized likelihood estimation makes the COM-Poisson GLM regression more rigorous to inflation in the standard errors of GLM estimates. The real-world example illustrated the excellent performance of the penalized COM-Poisson GLM regression in simultaneously accommodating both the dispersion and collinearity problems.
-
-To specify the value of the penalization parameter , we applied the the penalized likelihood estimation under the K-*fold* cross-validation. A limitation associated with this approach is the case of a small dataset in which the estimate of the penalization parameter *opt* is unreliable. To handle this situation, we recommend to select K = 1 or K n = . The second option ( K n = ) is known in literatures as leave-one-out (LOO) cross-validation.
-
-The preference of the penalized COM-Poisson GLM should significantly arise in high-dimensional count data regression in which the collinearity is more likely to occur. Comparing the COM-Poisson GLM with those in the "*penalized*" package, in modeling high-dimensional crash data, could be one of our future works. Under the high-dimensional road safety, collecting large-scale observations is an expensive task in terms of time and effort.
-
-According to [Sellers and Shmueli, \(2010\),](#page-6-5) the COM-Poisson has the flexibility to reduce to a logistics regression and to produce similar estimates. Motivated by the importance of the logistic regression in several real-world disciplines, this finding would also be interesting for future investigations.
-
-#### **Acknowledgment**
-
-Authors are grateful to Dominique Lord (Texas A&M University) and Srinivas Geedipally (Texas A&M Transportation Institute) for providing us the dataset of the real-example of Toronto, Ontario.
-
-#### **References**
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Zotero/001_artiklid/Road Safety Indicators/abuzwidahcrashriskanalysisdifferent2018.md

@@ -1,373 +0,0 @@
----
-category: literaturenote
-citekey: abuzwidahcrashriskanalysisdifferent2018
-title: Crash risk analysis of different designs of toll plazas
-authors: "Abuzwidah, Muamer; Abdel-Aty, Mohamed"
-year: 2018
-date: 2018-00-00 2018
-doi: 10.1016/j.ssci.2018.02.024
-publication: Safety Science
-url: "https://doi.org/10.1016/j.ssci.2018.02.024"
-zotero_key: U6P6IF8Z
-zotero_storage: 39EXXHR4
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Abuzwidah, Muamer"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-#### Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/09257535)
-
-## Safety Science
-
-journal homepage: [www.elsevier.com/locate/safety](https://www.elsevier.com/locate/safety)
-
-![](_page_0_Picture_5.jpeg)
-
-# Crash risk analysis of different designs of toll plazas
-
-Muamer Abuzwidah[a,](#page-0-0)[⁎](#page-0-1) , Mohamed Abdel-Aty[b](#page-0-2)
-
-- <span id="page-0-0"></span><sup>a</sup>Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates
-- <span id="page-0-2"></span><sup>b</sup>Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States
-
-![](_page_0_Picture_10.jpeg)
-
-ARTICLE INFO
-
-Keywords: Traditional toll plaza Hybrid toll plaza All-electronic toll collection And toll by plate Cashless toll Open road tolling Log linear
-
-#### ABSTRACT
-
-The Expressways (toll roads) offer a high level of service, and the Global Electronic Toll Collection market has exceeded the expectation. However, finding optimal designs for toll plazas is an issue with world-wide relevance, and this study makes a significant contribution towards this end. The main goal of this study is to examine for the first time the traffic safety impact of using different designs of the Hybrid Toll Plaza (HTP). In addition to that, this study helps understand the relationship between the crash frequency and several important crash-related factors and circumstances of these facilities. Crash data from a seven-year period was investigated, and a hundred mainline toll plazas in Florida were evaluated. The results of this study proved that there is a significant difference between the different designs of the HTP. And there is an indication that the majority of crashes occurred at diverge-and-merge areas before and after the plaza. Moreover, the results indicated significant relationships between the crash frequency and toll plaza types, annual average daily traffic, and driver-age. This study has also proved that the HTP and the All-Electronic Toll Collection (AETC) were associated with less number of crashes than the Traditional Toll Plazas by 44.7 and 72.6 percent, respectively. Also, one of the findings with interesting safety implications is the interaction between design type and percentage of prepaid transponder vs. manual payment users regarding crash rate. Overall, for those agencies who cannot adopt the AETC system, improving traffic safety at toll plazas should take a priority.
-
-#### 1. Introduction
-
-The use of toll roads has risen dramatically in many countries around the world; and The Global Electronic Toll Collection market is valued at 5.42 billion U.S Dollar in 2014 and it is expected to reach 10.57 to 15.62 billion by 2022 growing at a CAGR of 9.16 to 12.71percent, ([WISE GUY, 2017; MRR, 2017](#page-7-0)). In fact, the factors such as reduction in traffic congestion, low travel time, cashless travel facility and free flow of traffic are driving the Electronic Toll Collection (ETC) market growth ([AB Newswire, 2017](#page-7-1)).
-
-In the United States, there are many tolled road facilities; these facilities vary in type, size, ownership, and tolling systems deployed. Some of these facilities are private along with those owned and operated by various public agencies around the States ([Yang et al., 2014](#page-7-2)). And even though toll roads offer high mobility benefits, traditional toll facilities may pose high traffic safety risk. Moreover, only a limited number of studies have explored the factors that affect safety at toll plazas.
-
-The Transport Department of the Hong Kong SAR government conducted a trial traffic guidance scheme at the toll plaza of a busy road tunnel to improve traffic flow and traffic safety at this facility. A study by [Wong et al. \(2006\)](#page-7-3) conducted an observational before-and-after study to assess the effects of the scheme. The results indicate that the trial scheme provided good guidance to drivers. Traffic safety was improved, and the travel time of auto-toll traffic improved in certain periods. Another study by [Prasetijo et al. \(2016\)](#page-7-4) investigated the accidents at the toll plaza gates due to impacts of the vehicles with concrete crash barriers with results in damage and fatalities. The study conducted a simulation test for crash barrier Tensile Wire Fracture toughness system. The results show that the material for a new alternative crash barrier is likely to reduce the rate of death due to accident and damage with regards to road furniture and vehicles.
-
-[Valdés et al. \(2017\)](#page-7-5) evaluated the effectiveness of different electronic toll collection lane types, and how signage and queues affect safety and driver behavior and operations in toll plazas using driving simulator. The results showed that overhead signage configuration associated with the corresponding speed limits per lane was found to be significant in reducing the acceleration noise, thus reducing the potential for lane change related crash patterns. Also, female drivers tend to have a faster deceleration rate than male drivers in the toll booth approach zone. Moreover, no significant difference between genders on average running speed was found in basic tollway segment at the
-
-E-mail addresses: [mabuzwidah@sharjah.ac.ae](mailto:mabuzwidah@sharjah.ac.ae) (M. Abuzwidah), [m.aty@ucf.edu](mailto:m.aty@ucf.edu) (M. Abdel-Aty).
-
-<span id="page-0-1"></span><sup>⁎</sup> Corresponding author.
-
-beginning of the experiment.
-
-[Saad et al. \(2017\)](#page-7-6) explored some of the factors that affect driving behavior and safety at toll plaza. This study assessed driving behavior using a driving simulator. They found that drivers experiencing the open road tolling (ORT) have safer driving behavior than those who use the tollbooth. [Van Dijk et al. \(1999\)](#page-7-7) studied toll plaza in twofold: to configure the types of toll booths with multiple payment functionalities (cash, credit cards, and electronic payment); and to determine the number of toll booths for each type. The model was also used to validate the spacing, safety, and accessibility of the toll plaza. A hybrid approach of simulation and queueing theory were used in this study. The study showed that fewer toll booths were needed when different payment systems were separated, as a combination of different payment systems at one toll booth would substantially enlarge the variability of service times. This variability appeared to dominate the 'inefficiency' of separate toll booths which may seem counterintuitive. Consequently, the initial design had to be completely redesigned. Overall, none of these studies evaluate or simulate the impacts of using different designs of the toll plazas on traffic safety.
-
-The literature also showed that different toll collection systems have been adopted by different toll agencies around the world with no uniform standard ([Mohamed et al., 2000; Schau](#page-7-8)fler, 1997). The most common toll collection systems can be summarized as follows:
-
-Traditional Toll Plaza (TTP); this design requires vehicles to rapidly decelerate, navigate through different fare transaction options, and then accelerate and merge with traffic [\(Miami-Dade Expressway, 2017;](#page-7-9) [McDonald and Stammer, 2001; Abuzwidah and Abdel-Aty, 2015](#page-7-9)). These confusing maneuvers constitute safety challenges and form high risk locations on toll roads.
-
-Hybrid Toll Plaza (HTP); this system retrofits existing tollbooths with express open Electronic Toll Collection (ETC) lanes. This design is allowing more than 81 percent of the vehicles in Florida to travel at full speeds using electronic transponders or license plate recognition technology in an open road environment with fewer diverge and merge maneuvers before and after the toll plaza. The HTP design combines either express Open Road Tolling (ORT) lanes on the mainline and separate traditional toll collection to the side, or traditional toll collection on the mainline and separate ORT lanes to the side [\(FTE, 2017](#page-7-10)). However, the HTP is widely deployed by many toll authorities such as in Florida, Illinois, New Jersey, and many other states.
-
-All-Electronic Toll Gantry or All-Electronic Toll Collection (AETC); this system is completely barrier-free that replaces all tollbooths with regular express ETC lanes to change the toll plaza to be similar to regular segments. The AETC system allows driving straight through an open road without needing to change lanes, stop the vehicle, or even slow down to pay a toll. The payment will be done automatically, instantly and accurately by using the automatic toll collection transponder known as prepaid transponder [\(FTE, 2017\)](#page-7-10).
-
-Past studies and the current data have indicated that certain locations at the TTP and HTP are more likely to experience traffic crashes than the regular segments on the expressway. In April 2006 in Washington, D.C., investigators for the U.S. National Traffic Safety Board (NTSB) revealed that the most dangerous locations on the highways are toll plazas. In the same year, the NTSB reported that 49 percent of all crashes on expressways in Illinois occurred at toll plazas, and three times as many people died in them as in crashes on the rest of the same roadways. Also, 30 percent of all crashes on the Pennsylvania Turnpike happened at toll plazas and 38 percent of all collisions on New Jersey toll highways are toll plaza incidents [\(NTSB, 2017](#page-7-11)). An older study [\(Mohamed et al., 2000\)](#page-7-8) found that about 32 percent of the total crashes that occurred on the Orlando expressway system were located at the Traditional Toll Plazas.
-
-However, there are some obstacles to the use of the AETC systems in many countries around the world, and that because these systems require good arrangements between the tolling agencies and the department of motor vehicles, as well as the law enforcement and the regulations. These regulations and arrangements are needed to identify and bill drivers who do not have the prepaid transponder.
-
-Although the use of the HTP and AETC systems has demonstrated measured improvements in traffic operations and environmental issues, the question is: What if we cannot apply the AETC? What are the alternative solutions? The answer was either adopting the HTP design or improving the current situations of the TTPs and it was proved that some simple and quick treatments could improve safety at TTP ([Sze](#page-7-12) [et al., 2008](#page-7-12)).
-
-For example, [Wong et al. \(2006\)](#page-7-3) evaluated the effects of a traffic guidance scheme for auto-toll lanes on traffic safety at TTP and they found that the overall lane-changing rate decreased significantly by 23 percent and the pooled conflict count decreased sharply by 44 percent, also the crash count decreased sharply by 38 percent. A better option however is to apply the HTP design and that also has some safety issues and there is a lack of research that compares and evaluates the different designs of the HTP, especially nowadays many agencies are adopting the HTP design without a uniform design standard.
-
-Overall, to the best of our knowledge there were no studies evaluated the safety impacts of using different design of Hybrid Toll plazas ([Fig. 1\)](#page-2-0). Therefore, the main goals are the following:
-
-- 1. Cover the urgent need of assessing the traffic safety impacts of using different designs of the HTP. It is worth mention that many agencies want to adopt Design 2 ([Fig. 1\)](#page-2-0) and that because it just requires adding separate ORT lanes to the side without touching the existing main-line toll plazas.
-- 2. Investigate the relationship between crash frequency and the crash related variables such as geometric characteristics, toll plaza types, traffic volumes, and driver-age.
-
-#### 2. Methodology
-
-## 2.1. Safety Performance Functions (SPFs)
-
-Data from the reference group are used to estimate a safety performance function (SPF) that relates crash frequency of the sites to their traffic and geometrical characteristics. Generally, a safety performance function (SPF) is a crash prediction model, which relates the frequency of crashes to traffic (e.g., annual average daily traffic) and the roadway characteristics (e.g., number of lanes, width of lanes, width of shoulder, etc.). There are two main types of SPFs in the literature: (1) full SPFs and (2) simple SPFs. Full SPF is a mathematical relationship that relates both traffic parameters and geometric parameters as explanatory variables, whereas simple SPF includes annual average daily traffic (AADT) as the sole explanatory variable in predicting crash frequency on a roadway entity. It is worth mentioning that the calibrated Crash Modification Factors in the Highway Safety Manual are based only on the simple 'SPF' [\(Abdel-Aty et al., 2014](#page-7-13)). Therefore, a series of SPFs were developed in the first part of this study using Negative Binomial Regression Models.
-
-## 2.2. Negative binomial regression model
-
-Crash data have a gamma-distributed mean for a population of systems, allowing the variance of the crash data to be more than its mean [\(Shen, 2007\)](#page-7-14). Suppose that the count of crashes on a roadway section is Poisson distributed with a mean λ, which itself is a random variable and is gamma distributed, and then the distribution of frequency of crashes in a population of roadway sections follows a negative binomial probability distribution ([Hauer, 1997\)](#page-7-15). Count data is usually modeled using a Poisson distribution; the main characteristic of the Poisson distribution is that its mean is equal to its variance. Several studies found that a negative binomial distribution fits crash frequency data better [\(Brown et al., 2006](#page-7-16)) [\(Abdel-Aty and Radwan, 2000\)](#page-7-17). The Negative Binomial (NB) is similar to a Poisson distribution, though its
-
-<span id="page-2-0"></span>![](_page_2_Figure_1.jpeg)
-
-Fig. 1. Different designs of the Hybrid Toll Plazas (Source: (Google Earth)).
-
-variance is greater than its mean [\(Agresti, 2002\)](#page-7-18). The NB model can be used to estimate crashes at locations, such as toll plaza segments ([Chang, 2005\)](#page-7-19).
-
-Eq. (1) presents the NB probability distribution function.P (yi) ∼ Negbin (λi, k):
-
-$$\frac{\Gamma(1/k+y_i)}{y_i!\Gamma(1/k)} \left(\frac{k\lambda_i}{1+k\lambda_i}\right)^{y_i} \left(\frac{1}{1+k\lambda_i}\right)^{1/k}$$
-(1)
-
-Where,
-
-y = number of crashes on a certain location at a certain period, λ= Expected number of crashes on a certain location at a certain period
-
-k = over-dispersion parameter.
-
-The NB estimation equation is given as follows;
-
-$$Log\lambda_i = log(EV_i) + (\beta_0 + \beta X_i + \varepsilon_i).$$
-(2)
-
-where λ<sup>i</sup> is the estimated number of crashes at location i; β is a vector of explanatory variables, and EV<sup>i</sup> is the exposure variable which is included in models as an offset term to account for unequal exposure to crashes along toll plazas. Count models such as NB need a mechanism to deal with different exposure scenarios. For example the Annual Average Daily Traffic (AADT), or expected number of crashes, can be included as an exposure offset term in NB models ([Chen, 1997](#page-7-20)). By offsetting the exposure, the response variable becomes the log crash frequency at a certain location. ε<sup>i</sup> is the term adjusting for over dispersion. It is an error term due to a combination of variables omitted from the model and pure randomness. Maximum Likelihood Estimation (MLE) is used to estimate the coefficients of the explanatory variables and the over dispersion parameter. The goodness of fit of the NB model is measured by analyzing the over dispersion parameter.
-
-## 2.3. Log-Linear model
-
-Past studies and current data have indicated that certain locations at toll plazas are more likely to be more risky than regular segments on the expressway. However, to the best of our knowledge there were no studies that investigated the association between traffic crashes and toll plaza types, AADT or driving maneuver variables among various age groups.
-
-A log-linear model is a generalized linear model for Poisson-distributed data; it specifies how the size of a cell count depends on the levels of the categorical variables for that cell. The nature of this specification relates to the association and interaction structure among the variables [\(Christensen, 1990; Abdel-Aty et al., 1998\)](#page-7-21). The log-linear model describes the association and interaction patterns among a set of categorical variables [\(Knoke, and Burke, 1980\)](#page-7-22). The estimates of parameters resulting from the model can be converted to estimate the odds ratio between variables. The formulation of a log-linear model with three variables and two-way interactions is as follows:
-
-$$log m_{ijk} = \nu + \lambda_i^x + \lambda_j^y + \lambda_k^z + \lambda_{ij}^{xy} + \lambda_{jk}^{yz} + \lambda_{ik}^{xz}$$
-(3)
-
-Where:
-
-Log mijk = log the expected frequency of cell in which:
-
-x = i, y = j, z = k;
-
-υ overall effect = .
-
-λ = effect due to the i level of x; <sup>i</sup> x th
-
-λ = effect due to the j level of y; <sup>j</sup> y th
-
-λ = effect due to the k level of z; <sup>k</sup> z th
-
-λ = interaction of x at the i level and y at the j level; ij xy th th
-
-λ = interaction of x at the i level and z at the k level; ik xz th th
-
-λ = interaction of y at the j level and z at the k level. jk yz th th
-
-For instance, when the model contains the term λij xy, which is the effect due to the interaction of x being at level j, it also contains λi , the <sup>e</sup>ffect due to the ith level of x, and λj , the effect due to the jth level of y. A reason for including lower-order terms is that the statistical significance and practicable interpretation of a higher order term depends on how the variables are coded. Since this model contains an X–Y twofactor term, it permits association between X and Y, controlling for Z. It also permits an X–Z association, controlling for Y, and a Y–Z association, controlling for X [\(Abdel-Aty et al., 1998](#page-7-23)). By using Eq. (1) for two
-
-cells, the log odds logit can be determined. And it helps understand how the independent variables affect the response variables (especially the toll plaza types); the logit models are constructed according to the response variables as follows:
-
-$$\begin{split} \log(m_{ijk}/m_{i1k}) &= [\nu + \lambda_{i}^{x} + \lambda_{j}^{y} + \lambda_{k}^{z} + \lambda_{ij}^{xy} + \lambda_{jk}^{yz} + \lambda_{ik}^{xz}] \\ &- [\nu + \lambda_{i}^{x} + \lambda_{j}^{y} + \lambda_{k}^{z} + \lambda_{ij}^{xy} + \lambda_{jk}^{yz} + \lambda_{ik}^{xz}] \\ &= [[\lambda_{j}^{y} - \lambda_{i}^{x}] + [\lambda_{ij}^{yy} - \lambda_{ij}^{xy}] + [\lambda_{jk}^{yz} - \lambda_{ik}^{yz}]] \end{split} \tag{4}$$
-
-Thus, for instance, in Eq. (4), we are modeling the log of the odds that y = j instead of y = 1, when x = I and z = k.
-
-#### 3. Data description
-
-Different tolling systems are expanding on the Florida expressways. In the past decade, many toll agencies converted their Traditional Toll Plaza s to either Hybrid Toll plaza or all-electronic toll collection system.
-
-Multiple sets of data maintained by Florida Department of Transportation ([FDOT, 2017](#page-7-24)) were used in this study including: Crash Analysis Reporting (CAR) system, Roadway Characteristics Inventory (RCI) system, TranStat IView aerial mapping system, and Financial Project Search Database. Moreover, Google Earth and the publication reports of Florida Turnpike and Central Florida Expressway Authority ([CFX, 2017\)](#page-7-25) were used to verify the toll plazas' characteristics. A hundred mainline toll plazas located on approximately 750 miles of toll roads in Florida were selected. Crash data from seven-year period (2007–2013) were investigated. Crashes that occurred within the influence areas (1 mile before and 0.5 mile after the centerline of the mainline toll plaza ([MUTCD, 2017](#page-7-26))) were extracted from the crash database and data in the period when toll plazas were being upgraded in addition to six months after were excluded from the analysis.
-
-## 4. Analysis and results
-
-#### 4.1. Part I: Comparison between different designs of the Hybrid toll plaza
-
-Data from 60 Hybrid Toll Plazas (HTP) were used in this approach and crash data from a seven-year period was investigated. [Fig. 1](#page-2-0) shows different designs of the Hybrid Toll Plazas; Design 1 (D1) combines express Open Road Tolling (ORT) lanes on the mainline and separate traditional toll collection to the side; while Design 2 (D2) combines traditional toll collection on the mainline and separate ORT lanes to side.
-
-A series of Safety Performance Functions (SPFs) using Negative Binomial (NB) models were fitted to establish the relationships between traffic and roadway characteristics, and crash frequency at the vicinity of HTPs. Exploratory modeling indicated that the crash frequency is not significantly associated with traffic direction, number of entry and exit ramps, and distance to entry and exit ramps.
-
-[Table 1](#page-3-0) shows the parameter estimates of the final model. Only log Annual Average Daily Traffic (AADT) and the type of design came out to be significant with the expected signs. The coefficient of AADT has a
-
-<span id="page-3-0"></span>Table 1 Estimates of Coefficients for Different designs of the HTP.
-
-| Negative Binomial                                  |  |
-|----------------------------------------------------|--|
-| Analysis of Maximum Likelihood Parameter Estimates |  |
-
-| Crash type | Injury level | Parameter                                           | Estimates                          | Pr > ChiSq                     | AIC     |
-|------------|--------------|-----------------------------------------------------|------------------------------------|--------------------------------|---------|
-| All        | All          | Intercept<br>Log AADT<br>Design type*<br>Dispersion | −16.755<br>1.883<br>0.169<br>0.113 | < 0.0001<br>< 0.0001<br>0.0433 | 168.653 |
-
-Design type\* = (D1-and-D2) dummy variable (i.e. D1 = 0 and D2 = 1).
-
-positive sign, which indicates that as AADT increases, the frequency of HTP-related crashes increases.
-
-This finding could be attributed to the fact that the AADT is considered as the exposure factor; more traffic at the HTP would lead to higher chances of crash occurrence. This is expected, since higher AADT results in higher exposure of vehicles to weaving maneuvers (change lanes) before and after the toll plaza, which in turn would result in higher crash frequencies.
-
-The design type was found to be significant; the effect of design 2 (D2) of the HTP was compared to the design 1 (D1) (the base case). The parameter estimates given in [Table 1](#page-3-0) can be used to estimate the Incident Rate Ratios (IRR) by exponentiation of the regression coefficients Exp[β]. IRR value shows that the risk of crashes at D2 was approximately 19 percent higher than at the D1, given that all other variables are constant.
-
-The increased crash risk at the D2 may be explained by the fact that more than 81 percent of the vehicles in Florida are equipped with prepaid toll transponders [\(FTE, 2017](#page-7-10)). Thus, the use of D2 will cause more than 81 percent of the traffic to diverge and merge before and after the toll plaza and that could be a very confusing to the drivers; while in D1 only 19 percent or less of the traffic (vehicles without transponders) will need to diverge and merge before and after the toll plaza.
-
-The good news is that Florida Turnpike has scheduled all of the HTPs that have D2 to be converted to All-Electronic Toll Collection. However, D2 could be a good temporary design in the places that have low prepaid transponders users. In the other words, it is dependent upon the percentage of the automatic tolling users. So, as this percentage increases, more traffic will need to diverge and merge; thus, this design becomes riskier.
-
-## 4.2. Part II: Comparison between diverge-and-merge areas
-
-Similar to part I, data from 60 Hybrid Toll Plazas was used in this approach. Crash data from a seven-year period was investigated. [Table 2](#page-3-1) shows the parameter estimates of the final model resulting from 120 observations. Sixty Hybrid Toll Plazas, each one has two locations, one before (Diverge) and one after (Merge) the Toll plaza (60 ∗ 2 = 120 observations).
-
-It should be noted that the data used in this part included only the mainline crashes, i.e. crashes that occurred at the toll booths were excluded. [Fig. 2](#page-4-0) shows the diverge-and-merge areas that were investigated in this part of the analysis.
-
-The log Annual Average Daily Traffic (AADT) and the location came out to be significant with the expected signs in the final SPF model. The coefficient of AADT has a positive sign, which indicates that as AADT increases, the frequency of diverge-and-merge related crashes increases. This is expected, since higher AADT results in higher exposure of vehicles to weaving maneuvers (change lanes) before and after the toll plaza, which in turn would result in higher crash frequencies.
-
-The location (diverge-and-merge) dummy variable (i.e. diverge = 1 and merge = 0) was found to be significant. Since the lengths are
-
-<span id="page-3-1"></span>Table 2 Estimates of Coefficients of diverge and merge areas of the HTP.
-
-| Negative binomial<br>Analysis of maximum likelihood parameter estimates |              |                                                  |                                        |                                |         |
-|-------------------------------------------------------------------------|--------------|--------------------------------------------------|----------------------------------------|--------------------------------|---------|
-| Crash type                                                              | Injury level | Parameter                                        | Estimates                              | Pr > ChiSq                     | AIC     |
-| All                                                                     | All          | Intercept<br>Log AADT<br>Location*<br>Dispersion | −20.6811<br>2.2327<br>0.2103<br>2.2667 | < 0.0001<br>< 0.0001<br>0.0317 | 365.607 |
-
-Location\* = (diverge-and-merge) dummy variable (i.e. diverge = 1 and merge = 0).
-
-<span id="page-4-0"></span>![](_page_4_Picture_0.jpeg)
-
-![](_page_4_Figure_1.jpeg)
-
-Fig. 2. Diverge-and-merge areas before and after the HTP (). Source: [MUTCD, 2017](#page-7-26)
-
-different between the (diverge-and-merge) areas, the frequency of crashes were controlled by the segments' lengths. The effect of diverge was compared to the merge (the base case), and the coefficient of the location was positive indicating that diverge section is associated with more crashes than the merge section. The Incident Rate Ratios (IRR) value shows that the risk of crashes at diverge area before the HTP was approximately 23 percent higher than at the merge area after the plaza, given that all other variables are constant.
-
-The increased crash risk at diverge area may be explained by the fact that diverge section of the Hybrid Toll Plaza is associated with potential sudden lane change. Moreover, drivers may be distracted when they prepare to pay, search for cash or cards, and selecting either the cash or coin lane. While in the merge area, drivers just accelerate and focus on merging with traffic, especially if the acceleration lane has a good design and signage.
-
-However, choosing a good toll plaza location and finding ways to increase the percentage of prepaid transponder users are potential means of reducing lane changes at toll plazas. Adequate warning of no sudden lane change and good signage should be given on the approach to the diverge area. Offering toll discounts for prepaid transponder users and educating drivers can incentivize drivers to enroll in this system.
-
-#### 4.3. Part III: Log-Linear models
-
-Data including all types of toll plazas in Florida were used in this approach. A hundred mainline toll plazas, each one has two locations, one before and one after the toll plaza (100 ∗ 2 = 200 segments). Several Log-linear models with different variables in each model were developed, and only the significant variables and best fit model was presented. The effects of AADT and crash related factors were examined, and interactions among them were considered. Crash data from a seven-year period was used, and only crashes that occurred within the influence areas of the toll plazas were investigated. The final model was developed to investigate the association between toll plaza types and both driver-age and the AADT. The three main effects and all three possible two-way interactions were included in this model. [Fig. 3](#page-5-0) shows different types of mainline toll plazas.
-
-It should be noted that in order for the model to fit the data in a proper way, the variables should be categorized. Thus, toll plaza types categorized to three categories, driver-age categorized to two categories, and the AADT categorized to two categories. These categories were as follows:
-
-(1) " X = Toll Plaza Types: They were categorized into three levels and coded as follows:
-
-i = level:
-
-"1" = Hybrid Toll Plaza (HTP)
-
-"2" = All-Electronic Toll Collection (AETC)
-
-"0" = Traditional Toll Plaza (TTP) - (the base case).
-
-(2) Y = driver-age: It was categorized into two levels and coded as follows:
-
-j = level:
-
-"1" = Young (15–30 years old)
-
-"0" = Not young (Older than 30 years old) - (the base case).
-
-The 30 years old was selected as the cutoff point for age based on the literature ([FDOT, 2017](#page-7-24)) [\(Kim et al., 1995\)](#page-7-27), and criteria similar to those used by car insurance companies for when drivers start to drive more carefully due changes in life style and their life cycle ([FDOT,](#page-7-24) [2017\)](#page-7-24).
-
-(3) Z = Annual Average Daily Traffic (AADT): It was categorized into two levels based on the 50th percentile and coded as follows:
-
-k = level:
-
-"1" = Low
-
-"0" = High (the base case).
-
-<span id="page-5-0"></span>![](_page_5_Picture_1.jpeg)
-
-**Traditional Toll Plaza (TTP)** (Source: FTE, 2017)
-
-![](_page_5_Picture_3.jpeg)
-
-**Hybrid Toll Plaza (HTP)** (Source: CFX, 2017)
-
-![](_page_5_Picture_5.jpeg)
-
-**All-Electronic Toll Collection (AETC)** (Source: FTE, 2017)
-
-Fig. 3. Different types of the Mainline Toll Plazas.
-
-This is a three-variable model with variables x = (toll plaza types), y = (driver-age), and z = (AADT).
-
-Normally, the G2 goodness-of-fit statistic and p-value are used to determine the rejection or acceptance of the model. The larger value of G2, the more evidence there is against the null hypothesis (Ho), where Ho = model fits the relationship and Ha = model does not fit the relationship. Hence, the smaller G2 is better, but it depends on the degrees of freedom. The larger p-value (> 0.05) indicates that the estimated model fits the relationship. The model contains all three main effects and all three possible two-way interactions. [Table 3](#page-6-0) shows the parameter estimates and odds ratios.
-
-The results showed that the values of the G2 = 1.27, Pvalue = 0.5311, and the DF = 2 indicated that the model significantly fits the data. So, it can describe the associations between the variables by computing the odds ratios. With the logit model, the parameters provide a measure of the magnitude and direction of effects of the independent variables on the response variable ([Kim et al., 1995\)](#page-7-27). The parameter estimates for individual and interaction terms and their odds ratios are presented in [Table 3.](#page-6-0) Conceding the total number of crashes, the odds ratio is as follows:
-
-The odds ratio of Hybrid Toll Plaza (level 1) is 0.553 (Safety Effectiveness = 100–55.3 = 44.7 percent) from the base case Traditional Toll Plaza (level 0). Also, the odds ratio of the level 2 = allelectronic toll collection is 0.274 (Safety Effectiveness = 100–27.4 = 72.6 percent) from the base case Traditional Toll Plaza, given that all other variables are constant. These results proved that the HTP and the AETC are much safer than the Traditional Toll Plaza, and this is consistent with the previous finding even with different statistical models [\(Abuzwidah et al., 2014; Yang](#page-7-28) [et al., 2014; Abuzwidah, 2011; Zhang et al., 2009](#page-7-28)).
-
-The estimate of driver-age is negative, so it gives odds of less than one. The decrease of the crash risk of this age group for total crashes at all types of toll plazas may be explained by the fact that the proportion of the young drivers is less than the middle age and older (age > 30). This is same for the interaction of (AETC (level 2) ∗ young age).
-
-The odds of (HTP ∗ young age) is 3.2 percent higher than the base cases. This may be explained by the literature that very young and young drivers probably have less driving experience, and with low AADT they tend to speed and commit driving violations [\(Kim et al.,](#page-7-27) [1995\)](#page-7-27); especially with the risk of diverge-and-merge before and after the Hybrid Toll Plaza. This fact was clear for the total crashes at all types of toll plazas, the odds ratio of the interaction (young age ∗ Low AADT) is 9.5 percent more than the base case (older than 30∗ high AADT), so this might explain their involvement in crashes.
-
-<span id="page-6-0"></span>Table 3 The estimate of log-linear model.
-
-| Response          |    | TPT × Driver-age × AADT |            |  |  |
-|-------------------|----|-------------------------|------------|--|--|
-| Source            | DF | Chi-Square              | Pr > ChiSq |  |  |
-| TPT*              | 2  | 6.36                    | 0.0257     |  |  |
-| Driver-age        | 1  | 2.23                    | 0.1124     |  |  |
-| TPT × driver-age  | 2  | 4.69                    | 0.0581     |  |  |
-| AADT              | 1  | 1.22                    | 0.3153     |  |  |
-| TPT × AADT        | 2  | 0.33                    | 0.8368     |  |  |
-| Driver-age × AADT | 1  | 1.77                    | 0.1664     |  |  |
-| Likelihood Ratio  | 2  | 1.27                    | 0.5311     |  |  |
-
-| Parameter         |           | Estimate& (Odds ratio) | Standard Error | Chi-Square | Pr > ChiSq |
-|-------------------|-----------|------------------------|----------------|------------|------------|
-| TPT*              | 1         | −0.5922                | 0.1335         | 5.79       | 0.0192     |
-|                   |           | (0.553)                |                |            |            |
-|                   | 2         | −1.2935                | 0.2112         | 0.22       | 0.5442     |
-|                   |           | (0.274)                |                |            |            |
-| Driver-age        | Young     | −0.07543               | 0.1324         | 2.23       | 0.1245     |
-|                   |           | (0.927)                |                |            |            |
-| TPT × Driver-age  | 1 Young   | 0.0321                 | 0.1415         | 0.06       | 0.6523     |
-|                   |           | (1.032)                |                |            |            |
-|                   | 2 Young   | −0.3822                | 0.2505         | 4.12       | 0.0352     |
-|                   |           | (0.682)                |                |            |            |
-| AADT              | Low       | 0.0223                 | 0.1107         | 1.22       | 0.2532     |
-|                   |           | (1.022)                |                |            |            |
-| TPT × AADT        | 1 Low     | −0.5229                | 0.1532         | 0.00       | 0.8851     |
-|                   |           | (0.593)                |                |            |            |
-|                   | 2 Low     | −0.9795                | 0.1907         | 0.28       | 0.4211     |
-|                   |           | (0.375)                |                |            |            |
-| Driver-age × AADT | Young Low | 0.0915                 | 0.1292         | 1.77       | 0.1237     |
-|                   |           | (1.095)                |                |            |            |
-|                   |           |                        |                |            |            |
-
-TPT\* = Toll Plaza Types (i.e. TTP (0), HTP (1), and AETC (2)).
-
-The low AADT has odds 2.2 percent higher than the high AADT. This is because at low AADT, the speed is usually high. So, drivers are more likely to make a sideswipe, hit the tollbooths and/or lost control.
-
-## 5. Conclusions
-
-Expressways (toll roads) are considered as an important part of any successful transportation system, because they are carrying a significant number of the daily trips and offering a high level of service. however, traditional toll plazas still experience many crashes, many of which are severe and the literature showed a very limited studies that addressed toll plaza safety, and non of them were evaluated the safety impacts of using different design of Hybrid Toll Plazas (HTP). Therefore, this study examines for the first time the traffic safety impact of using different designs of HTP. And to the best of our knowledge, there is an urgent need for this evaluation since many agencies want to adopt Design 2 of the HTP ([Fig. 1\)](#page-2-0) because it just requires adding separate ORT lanes to the side without touching the existing main-line toll plazas. Also, this study helps understand the relationship between the crash frequency and several important crash-related factors and circumstances such as toll plaza types, traffic volume, and driver-age. Crash data from a sevenyear period was investigated, and a hundred mainline toll plazas were evaluated using multiple analytical techniques. The current data has indicated that certain designs and locations at toll plazas are more likely to experience traffic crashes than regular segments of the expressways.
-
-The Incident Rate Ratios (IRR) value shows that the risk of crashes at design 2 (D2) of the Hybrid Toll Plaza was approximately 19 percent higher than at the design 1 (D1), given that all other variables are constant. The increased crash risk at D2 may be explained by the fact that more than 81 percent of the vehicles in Florida are equipped with prepaid toll transponders. Thus, the use of D2 will cause more than 81 percent of the traffic to diverge and merge before and after the toll plaza. While in D1, only 19 percent or less of the traffic (vehicles without transponders) will need to diverge and merge before and after the toll plaza. However, D2 could be a good temporary design in the places that have low prepaid transponder users. In other words, it is dependent upon the percentage of the ETC users; as this percentage increases, more traffic will need to diverge and merge; thus, this design becomes riskier.
-
-Another finding is there is an indication that the majority of crashes occurred at diverge and merge areas before and after the HTP. The IRR value shows that the risk of crashes at diverge areas were approximately 23 percent higher than at the merge areas, given that all other variables are constant. The increased crash risk at diverge areas may be explained by the fact that diverge sections before the Hybrid Toll Plazas are associated with potential sudden lane change. Moreover, drivers may be distracted when they prepare to pay, search for cash or cards, and selecting either the cash or coin lane, while in the merge area they just accelerate and focus on merging with traffic, especially if the acceleration lane has a good design and signage.
-
-The results also indicated significant relationships between the crash frequency and toll plaza types, annual average daily traffic, and driver-age. This means all of these three variables significantly affect the frequency of toll plazas-related crashes. Moreover, it was found that the HTP and the All-Electronic Toll Collection (AETC) were associated with less number of crashes than at the Traditional Toll Plaza by 44.7 and 72.6 percent, respectively. Therefore, HTPs and AETCs are much safer than the Traditional Toll Plazas. For those agencies that cannot adopt the AETC systems, improving traffic safety at HTP and traditional toll plazas should take a priority.
-
-#### Acknowledgements
-
-The authors would like to thank SAFER-SIM for partially funding this research. SAFER-SIM is funded by a University Transportation Center lead by the University of Iowa.
-
-#### References
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Zotero/001_artiklid/Road Safety Indicators/aguerovalverdespatialanalysisfatalinjury2006.md

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----
-category: literaturenote
-citekey: aguerovalverdespatialanalysisfatalinjury2006
-title: Spatial analysis of fatal and injury crashes in Pennsylvania
-authors: "Aguero-Valverde, Jonathan; Jovanis, Paul P."
-year: 2006
-date: 2006-00-00 2006
-doi: 10.1016/j.aap.2005.12.006
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2005.12.006"
-zotero_key: 2UJNIEQT
-zotero_storage: HXKQNNQZ
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Aguero-Valverde, Jonathan"
-status: converted
----
-![](_page_0_Picture_0.jpeg)
-
-Accident Analysis and Prevention 38 (2006) 618–625
-
-![](_page_0_Picture_2.jpeg)
-
-# Spatial analysis of fatal and injury crashes in Pennsylvania
-
-Jonathan Aguero-Valverde, Paul P. Jovanis ∗
-
-*Department of Civil and Environmental Engineering, Pennsylvania Transportation Institute, University Park, PA 16802-1408, USA* Received 15 July 2005; received in revised form 1 December 2005; accepted 12 December 2005
-
-## **Abstract**
-
-Using injury and fatal crash data for Pennsylvania for 1996–2000, full Bayes (FB) hierarchical models (with spatial and temporal effects and space–time interactions) are compared to traditional negative binomial (NB) estimates of annual county-level crash frequency. Covariates include socio-demographics, weather conditions, transportation infrastructure and amount of travel. FB hierarchical models are generally consistent with the NB estimates.
-
-Counties with a higher percentage of the population under poverty level, higher percentage of their population in age groups 0–14, 15–24, and over 64 and those with increased road mileage and road density have significantly increased crash risk. Total precipitation is significant and positive in the NB models, but not significant with FB. Spatial correlation, time trend, and space–time interactions are significant in the FB injury crash models.
-
-County-level FB models reveal the existence of spatial correlation in crash data and provide a mechanism to quantify, and reduce the effect of, this correlation. Addressing spatial correlation is likely to be even more important in road segment and intersection-level crash models, where spatial correlation is likely to be even more pronounced.
-
-© 2006 Elsevier Ltd. All rights reserved.
-
-*Keywords:* Full Bayes hierarchical model; Spatial correlation; Negative binomial model; Crash risk; Weather conditions and crash risk
-
-# **1. Introduction**
-
-Many factors affecting crashes operate at a spatial scale (e.g. land-use policy, demographic characteristics and highway infrastructure functional class). It is therefore reasonable to explore the use of spatial models of crash occurrence to better understand the implications of these policies.
-
-In most roadway accident studies, crashes are grouped in spatial units that range from intersection or road section level to zip code or county level (e.g. [Amoros et al., 2003; Miaou](#page-7-0) [et al., 2003; Noland and Oh, 2004; Noland and Quddus, 2004;](#page-7-0) [MacNab, 2004\).](#page-7-0) One concern with these studies is the effect of spatial correlation (i.e. the spatial dependence among observations), which produces higher variance of the estimates and therefore, underestimated standard errors.
-
-Recent developments in spatial modeling techniques have enabled researchers to investigate important issues related to risk estimation, unmeasured confounding variables, and spatial dependence [\(Richardson, 1992\).](#page-7-0) An important advantage of spatial models is that spatial effects may reflect unmeasured confounding variables. This is particularly useful for unmeasured confounders that vary in space like weather, population, and others. More important yet, "the methods also facilitate spatial smoothing and data pooling when regions under investigation involve small-population areas", [MacNab \(2004\). H](#page-7-0)ere the term 'small-population areas' refers to areas that present very few events, given a rare-event phenomenon, for example roadway crashes.
-
-Previous research has dealt with the spatial component of road crashes in different ways. Crashes have been modeled as point events (e.g. [Levine et al., 1995; Jones et al., 1996\),](#page-7-0) while others have modeled road crashes at different area levels, ranging from road sections to local census tracks or counties (e.g. [Shankar et al., 1995; Amoros et al., 2003; Miaou et al., 2003;](#page-7-0) [Noland and Oh, 2004; MacNab, 2004\).](#page-7-0)
-
-Honolulu census tract data have been used [\(Levine et al.,](#page-7-0) [1995b\) i](#page-7-0)n a continuous model for predicting crashes. Analysis at the "ward" (census track) level has been conducted [\(Noland and](#page-7-0) [Quddus, 2004\)](#page-7-0) for fatalities, serious injuries, and slight injuries using four different categories of predictor variables: land-use indicator variables (employment and population density), road characteristics, demographic characteristics (age cohorts), and
-
-<sup>∗</sup> Corresponding author. Tel. +1 814 865 9431. *E-mail address:* ppj2@engr.psu.edu (P.P. Jovanis).
-
-traffic flow proxies (proximate and total employment). Countrylevel data for Illinois ([Noland and Oh, 2004\)](#page-7-0) were used to estimate the expected number of crashes using infrastructure characteristics and demographic indicators as independent variables in a negative binomial (NB) model. Limitations of these studies are the use of proxy variables for traffic flow estimation and the lack of spatial correlation analysis. An additional paper ([Amoros et al., 2003\)](#page-7-0) developed NB models at county level in France that included interactions between road type and county.
-
-Poisson-based full Bayes (FB) hierarchical models of countylevel fatal (K), incapacitating (A), and non-incapacitating (B) injuries were estimated using both frequency and rate for the state of Texas ([Miaou et al., 2003\).](#page-7-0) Conditional auto-regressive model (CAR) was used to model spatial correlation and Markov Chain Monte Carlo (MCMC) was used to sample the posterior probability distribution. The main limitation of this paper is the use of the surrogate variables: percent of time that the road is wet, sharp horizontal curves, and roadside hazards. These predictor variables were estimated by proportions of crashes. For example, for percent of time that the road is wet, the variable was estimated by dividing the number of crashes that occurred under wet pavement by the total number of crashes. These estimators are clearly biased in the direction of the effect. Given the poor definition of contributing factors in the model, it is likely that the spatial correlation is overestimated. In a recent paper, [Miaou and Song \(2005\)](#page-7-0) used the same approach and data in the ranking of sites for engineering safety improvements.
-
-The adoption of the FB hierarchical approach by Miaou is an important advance in model estimation and is a departure point for this paper. The purpose of this research is to develop spatial models of road crash frequency for the State of Pennsylvania at the county level while controlling for socioeconomic, transportation-related, and environmental factors. The results from FB hierarchical spatial models are compared with the more traditional approach using an NB distribution to model crash frequency. Particular attention is paid to the inclusion of weather as a predictor and the search for spatial correlation among neighboring counties.
-
-#### **2. Methodology**
-
-# *2.1. The Poisson and negative binomial distributions*
-
-When data arise as counts, the Poisson distribution is typically used to model them. Traffic crashes are a clear example of count data, therefore, a Poisson distribution is a useful stating point (see for example [Jovanis and Chang, 1986; Shankar et al., 1995\).](#page-7-0) An important characteristic of the Poisson distribution is that its variance is equal to its mean. Several authors (e.g. [Shankar et](#page-7-0) [al., 1995; Noland and Quddus, 2004\)](#page-7-0) have argued that vehicle crashes are better represented by an NB distribution, which is a count distribution generated by a Poisson process with variance greater that the mean (see for example, [Hamed et al., 1998;](#page-7-0) [Hamed,1999\).](#page-7-0)
-
-Several goodness-of-fit measures have been proposed for this kind of model including the Poisson *R*2, R<sup>2</sup> <sup>P</sup>, and the Freeman–Tukey *R*2, R<sup>2</sup> PFT ([Fristrøm et al., 1995\).](#page-7-0) Another measure of goodness-of-fit uses the overdispersion parameter α of the NB model [\(Miaou, 1996\).](#page-7-0) NB models are estimated using R statistical Software [\(R Development Core Team, 2004\).](#page-7-0)
-
-## *2.2. Spatial modeling using full Bayes hierarchical approach*
-
-Many spatial modeling techniques may be developed within a Bayesian approach because of its flexibility in structuring complicated models, inferential goals, and analysis [\(Miaou et](#page-7-0) [al., 2003\).](#page-7-0) Bayesian inference has been used in the past in disease mapping and ecological analysis and just recently, it has been applied to crash modeling (e.g. [Miaou et al., 2003;](#page-7-0) [MacNab, 2004; Miaou and Song, 2005\).](#page-7-0) For a detail description of Bayesian inference, see [Gelman et al. \(2003\).](#page-7-0)
-
-The problem of group estimation, namely estimating the parameters of a common distribution thought to underlay a collection of outcomes for similar types of units, has motivated much research in Bayesian statistics. One seeks to make conditional estimates of the true outcome rate in each unit of observation (e.g. fatal crashes rate by county), given the parameters of the common density. Such estimation for sets of similar units is known as 'hierarchical modeling' because of its conditioning on higher stage densities ([Congdon, 2003\).](#page-7-0) At the first stage, the observed counts are modeled as a function of area-level summaries such as risk or relative risk. At the second stage, a joint distribution is specified for the collection of these risks as a function of explanatory variables. The second stage distribution depends on unknown parameters and these are assigned a (hyper) prior distribution at the third stage [\(Wakefield et al.,](#page-7-0) [2000\).](#page-7-0)
-
-In the case of this study, the model developed by [Besag et al.](#page-7-0) [\(1991\)](#page-7-0) is the base of the formulation used, as shown in Eqs. (1) and (2):
-
-$$y_i \sim \text{Poisson}(e_i \theta_i)$$
- (1)
-
-where *yi* is the number of fatal crashes in county *i*, θ*<sup>i</sup>* the risk in county *i*, and *ei* the exposure in county *i*; in this case the exposure is the total daily vehicle-miles traveled (DVMT) by county. DVMT was also included as explanatory variable to account for possible non-linearity between crash frequency and DVMT. This is the first stage in the model. The log risk is modeled as:
-
-$$\log(\theta_i) = \alpha + x_i' \beta + v_i + u_i \tag{2}$$
-
-where *xi* represents a vector of explanatory variables, or covariates, *β* a vector of fixed effect parameters, vi the uncorrelated heterogeneity or unstructured error and *ui* the correlated heterogeneity or spatial correlation. The last two variables are known as random effects, therefore, this kind of model is commonly known as a mixture model where fixed and random effects are combined.
-
-In the third stage, a uniform prior distribution is assigned to α and a highly non-informative normal distribution is assigned to the *β*'s with mean 0 and variance 1000 corresponding to vague prior beliefs, given the scale of the covariates. On the other hand <span id="page-2-0"></span>the prior distribution for the uncorrelated heterogeneity (vi) is N(0, τ<sup>2</sup> <sup>v</sup> ), where τ<sup>2</sup> <sup>v</sup> is the precision (precision = 1/variance) with a prior gamma distribution Ga (0.5,0.0005) as suggested by [Wakefield et al. \(2000\).](#page-7-0) This prior distribution, as in case of the *β*'s, represents a highly non-informative distribution. The definition of the precision corresponds to the fourth stage of the model hierarchy.
-
-#### *2.3. Conditional auto-regressive model*
-
-A pair of areas are considered neighbors if they are adjacent; *ui* is the correlated heterogeneity or spatial correlation term reflecting a shared border. For the spatial correlation term, the CAR proposed by [Besag \(1974\)](#page-7-0) is used:
-
-$$[u_i|u_j, i \neq j, \tau_u^2] \sim N(\bar{u}_i, \tau_i^2)$$
-(3)
-
-where
-
-$$\bar{u}_i = \frac{1}{\sum_j w_{ij}} \sum_i u_j w_{ij} \tag{4}$$
-
-and
-
-$$\tau_i^2 = \frac{\tau_u^2}{\sum_j w_{ij}} \tag{5}$$
-
-*wij* = 1 if *i*, *j* are adjacent or 0 otherwise.
-
-As in the unstructured case, the τ<sup>2</sup> <sup>u</sup> controls the variability of *u* and its prior was selected Ga (0.5, 0.0005) as suggested by [Wakefield et al. \(2000\).](#page-7-0)
-
-#### *2.4. Space–time models*
-
-Several authors (e.g. [Miaou et al., 2003; Knorr-Held](#page-7-0) [and Besag, 1998; Waller et al., 1997; Knorr-Held, 2000;](#page-7-0) [Bernardinelli et al., 1995\)](#page-7-0) have studied the issue of changes in disease risk over time and space; however, only the work from Bernardinelli et al. presents a clear specification of the time–space interaction term. Extending the model of Bernardinelli to include the effect of covariates:
-
-$$y_{ij} \sim \text{Poisson}(e_{ij}\theta_{ij})$$
- (6)
-
-$$\log(\theta_{ij}) = \alpha + \sum_{k} \beta_k x_{ijk} + v_i + u_i + (\varphi + \delta_i) t_j$$
- (7)
-
-where *yij* is the observed number of crashes for the *i*th area, *i* = 1, ..., *N*, and the *j*th time interval, *j* = 1, ..., *T*, *α* the constant term, *xijk* the *k*th covariate for the *i*th area, in the *j*th time interval, *β<sup>k</sup>* the regression coefficient, vi the uncorrelated heterogeneity, *ui* the spatial correlation, *ϕ* the mean linear time trend over all areas, *tj* the time interval *j*, and *δ<sup>i</sup>* the interaction between time and area effect.
-
-This model specification allows for spatiotemporal interactions where temporal trend in crash risk may be different for different spatial locations and may even have spatial structure. However, all temporal trends, including the main temporal trend ϕ, are assumed to be linear, which may be seen as a restrictive assumption [\(Lawson et al., 2003\).](#page-7-0)
-
-For comparison of the different Bayesian hierarchical models the deviance information criterion (DIC), proposed by [Spiegelhalter et al. \(2002\)](#page-7-0) is used. It compares the fit and complexity of hierarchical models in which the number of parameters are not clearly defined. DIC is based on the posterior distribution of the deviance statistic and can be seen as a generalization of the Akaike information criterion (AIC).
-
-DIC is defined as an estimate of fit plus twice the effective number of parameters as in Eq. (8):
-
-$$DIC = \mathbf{D}(\bar{\boldsymbol{\theta}}) + 2\mathbf{p_D} = \bar{\mathbf{D}} + \mathbf{p_D}$$
- (8)
-
-where *D*(¯ *θ*) is the deviance evaluated at ¯ *θ*, the posterior means of the parameters of interest, *p***<sup>D</sup>** the effective number of parameters in the model, and *D*¯ the posterior mean of the deviance statistic *D*(*θ*). As with AIC, models with lower DIC values are preferred.
-
-For each Bayesian hierarchical model two MCMC from different starting points were estimated in order to assure convergence. Depending on the complexity of the models, the first 3000–5000 iterations in each chain were removed as burn-ins. Finally, other 5000 iterations were performed for each chain resulting in a sample distribution of 10,000 for each parameter. Convergence of the models was monitored by visual examination of MCMC chains, autocorrelation plots, and Gelman–Rubin statistic plots. FB hierarchical models are estimated using Win-BUGS software ([Spiegelhalter et al., 2004\).](#page-7-0)
-
-## **3. Data description**
-
-Fatal and injury crash data are obtained from PennDOT ([Bureau of Highway Safety and Traffic Engineering,](#page-7-0) [1997–2001\).](#page-7-0) Two different sets of crash models were estimated: those using fatal crashes only as the dependent variable and those using just injury crashes. The spatial distribution of crashes has a very particular form. [Fig. 1](#page-3-0) shows the number of fatal crashes by county in year 2000 (similar distributions were obtained for other years). It is clear that the highest values are clustered around the cities of Philadelphia in the southeast portion of the state and Pittsburg in the southwest. The fatal crash rate map [\(Fig. 2\)](#page-3-0) presents a very different pattern as high crash rates occur mainly in counties with low to medium number of crashes. This may be due to the fact that fatal crashes are rare events, the counts are very low and therefore, a small increase of say, two or three crashes will significantly increase the rate especially for those counties with low levels of travel (i.e. DVMT).
-
-Risk factors, all measured at the county level and obtained for the years 1996–2000, are divided into three main categories: socioeconomic, transportation infrastructure-related, and environmental factors. [Table 1](#page-4-0) summarizes the definitions of each of the variables used to characterize the factors and contains descriptive statistics. Several different variable definitions were considered in exploring alternative model specifications.
-
-Socioeconomic factors include county-level summaries of age, sex, level of poverty (all from [US Census Bureau, 2004a,](#page-7-0) [2004b, 2004c\) a](#page-7-0)nd level of drunk driving [\(Pennsylvania Uniform](#page-7-0) [Crime Reporting System, 2004\).](#page-7-0) [Shinar \(1978\),](#page-7-0) [Evans \(1991,](#page-7-0)
-
-<span id="page-3-0"></span>![](_page_3_Figure_2.jpeg)
-
-Fig. 1. Number of fatal crashes by county for the year 2000.
-
-[2004\),](#page-7-0) and [Kam \(2003\)](#page-7-0) have suggested factors including age, sex, and personality to explain crash risk. Age is characterized as the percentage of persons younger than 15 (proxy for young pedestrians), between 15 and 24 (proxy for young drivers), and percent of persons over 65 (proxy for senior drivers) with the percentage of population between 25 and 65 used as a baseline. Note, these variables correspond to the total population rather than the population of drivers, but these are the only data publicly available. Area deprivation has been found to be positively related to crash occurrence (e.g. [Chichester et al., 1998; Abdalla](#page-7-0) [et al., 1997; Noland and Quddus, 2004\)](#page-7-0) and the percentage of persons under poverty is used as a surrogate of area deprivation in this paper. In the case of drunk driving, authors like [Evans](#page-7-0) [\(1991\)](#page-7-0) and [Gary et al. \(2003\)](#page-7-0) have found association between alcohol consumption and crash risk. The number of DUI arrests in a county is used to relate alcohol consumption to driving and crash frequency [\(Pennsylvania Uniform Crime Reporting](#page-7-0) [System, 2004\).](#page-7-0)
-
-Transportation-related factors included daily vehicle-miles traveled and amount of infrastructure [\(PennDOT, 2004\).](#page-7-0)
-
-![](_page_3_Figure_7.jpeg)
-
-Fig. 2. Fatal crash rate by county for the year 2000 (crashes by million DVMT).
-
-<span id="page-4-0"></span>Table 1 Summary statistics of variables included in the models.
-
-| Type                                                                                                 | Variables                                     | Minimum                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | Median | Mean    | Maximum                                                                               | Standard deviation |
-|------------------------------------------------------------------------------------------------------|-----------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|---------|---------------------------------------------------------------------------------------|--------------------|
-|                                                                                                      | Fatal crash frequency                         | 0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | 14     | 20.59   | 137                                                                                   | 19.94              |
-|                                                                                                      | Injury crash frequency                        | 33<br>600<br>1328.85<br>12370<br>35<br>615<br>1349.44<br>12489<br>0<br>5.5<br>5.84<br>136.64<br>254.75<br>270.83<br>142.06<br>261.45<br>276.67<br>14.87<br>19.69<br>19.51<br>9.36<br>12.2<br>13.04<br>9.86<br>15.97<br>16.2<br>46.46<br>48.74<br>48.95<br>4.4<br>10.8<br>10.66<br>18<br>314<br>581.29<br>4231<br>0.87<br>3.38<br>3.55<br>0.16<br>2.33<br>4.09<br>42.65<br>81.93<br>79.34<br>0.31<br>1.68<br>1.78<br>13.78<br>21.98<br>22.59<br>0.78<br>2.45<br>2.96<br>32.14<br>42.11<br>44.57<br>20.44<br>27.69<br>29.4<br>2.44<br>35.76<br>40.5<br>2.49<br>36.16<br>41.96 |        | 1951.17 |                                                                                       |                    |
-|                                                                                                      | Fatal and injury crash frequency              |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         |                                                                                       | 1969.85            |
-|                                                                                                      | Fatal crash rate (by million DVMT)            |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         | 16.51                                                                                 | 2.39               |
-|                                                                                                      | Injury crash rate (by million DVMT)           |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         | 813.46                                                                                | 90.74              |
-|                                                                                                      | Fatal and injury crash rate (by million DVMT) |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         | 821.28                                                                                | 90.84              |
-|                                                                                                      | Population under 15 years (%)                 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         | 22.64                                                                                 | 1.62               |
-| Crashes<br>Socioeconomic<br>factors<br>Transportation-related factors<br>Environment-related factors | Population between 15 and 24 (%)              |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         | 30.04                                                                                 | 2.87               |
-|                                                                                                      | Population 65 years and older (%)             |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         | 21.97                                                                                 | 2.29               |
-|                                                                                                      | Males (%)                                     |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         | 55.71                                                                                 | 1.3                |
-|                                                                                                      | Population under poverty (%)                  |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         | 23.8                                                                                  | 3.11               |
-|                                                                                                      | DUI arrests frequency                         |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         |                                                                                       | 750.89             |
-|                                                                                                      | DUI rate (by 1000 population)                 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         | 12.7<br>25.63<br>89.12<br>5.67<br>36.66<br>16.83<br>68.19<br>43.32<br>127.09<br>114.4 | 1.48               |
-|                                                                                                      | DVMT (millions)                               |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         |                                                                                       | 4.46               |
-|                                                                                                      | Federal aid DVMT (%)                          |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         |                                                                                       | 8.37               |
-|                                                                                                      | Miles of road (Thousand)                      |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         |                                                                                       | 0.95               |
-|                                                                                                      | Federal aid miles of road (%)                 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         |                                                                                       | 4.18               |
-|                                                                                                      | Miles of road density (mi/mi2)                |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         |                                                                                       | 2.28               |
-|                                                                                                      | Mean total precipitation (in)                 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         |                                                                                       | 7.64               |
-|                                                                                                      | Mean number of rainy days                     |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         |                                                                                       | 5.07               |
-|                                                                                                      | Mean total snowfall (in)                      |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         |                                                                                       | 24.42              |
-|                                                                                                      | Mean number of days with snow                 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |        |         |                                                                                       | 26.15              |
-
-Vehicle-miles traveled is often used as indication of exposure to crash risk [\(Miaou et al., 2003; Fristrøm et al., 1995;](#page-7-0) [Jovanis and Chang, 1986\)](#page-7-0) along with the number of miles of different functional classes per county ([Noland and Quddus,](#page-7-0) [2004\).](#page-7-0) The number of miles of infrastructure in each county was included in the model to estimate the effect of different levels of transportation infrastructure supply in the expected crash rate. The functional categories were aggregated in two groups: federal aid (i.e. interstate, other freeways or expressways, other principal arterials, minor arterials and major collectors), and non-federal aid roads (i.e. minor collectors and local roads). Even though the amount of travel (DVMT) was included into the model as an offset term, the proportion of travel occurring on federal aid roads was included in the models as a separate variable to account for the possible differences in design and operation levels between federal aid and nonfederal aid roads where the former are expected to have higher levels.
-
-Environmental factors included descriptors for level of rain and snow ([National Climatic Data Center, 2004\).](#page-7-0) Environmental factors have been investigated in the past by [Jovanis and](#page-7-0) [Chang \(1986\),](#page-7-0) [Shankar et al. \(1995\),](#page-7-0) [Fristrøm et al. \(1995\), a](#page-7-0)nd [Edwards \(1996\), a](#page-7-0)mong others. The studies found positive correlation between weather and crash frequency. The amount of precipitation, snowfall, and number of rainy days and days with snow are analyzed in this study. Hundreds of weather stations are used to generate estimated surfaces for each variable and then the variables are summarized at county level for inclusion in the database. Spatial trend and correlation of weather variables with elevation are included into the models to improve their predictive power and fit. For more details on estimation of weather variables see [Aguero-Valverde \(2005\).](#page-7-0)
-
-#### **4. Results**
-
-A series of NB regression models is used in initial data analysis and to provide a comparison set for FB models to follow. In each model, all variables listed in Table 1 are the starting point; variables are removed if they have significance levels above 0.10. Table 2 presents the NB model of fatal crashes. For this model three transportation-related variables are significant: DVMT, infrastructure mileage, and percentage of travel on federal aid roads. The coefficient for DVMT is negative which implies that the fatal crash rate decrease with DVMT or, in
-
-Table 2 Negative binomial model of fatal crashes
-
-| Variable                         | Estimate | Standard error | z value | Pr(> z ) |
-|----------------------------------|----------|----------------|---------|----------|
-| DVMT                             | −0.033   | 0.005          | −6.46   | 1.08E−10 |
-| Infrastructure<br>mileage        | 0.060    | 0.026          | 2.26    | 0.024    |
-| DVMT on federal<br>aid roads (%) | −0.006   | 0.003          | −2.30   | 0.021    |
-| Persons under<br>poverty (%)     | 0.039    | 0.004          | 9.22    | 2.0E−16  |
-| Persons 0–14 (%)                 | 0.051    | 0.009          | 5.52    | 3.4E−08  |
-| Total precipitation              | 0.014    | 0.004          | 3.74    | 0.000    |
-| 1997                             | 0.290    | 0.083          | 3.49    | 0.000    |
-| 1998                             | 0.238    | 0.077          | 3.11    | 0.002    |
-| 1999                             | 0.236    | 0.070          | 3.38    | 0.001    |
-| 2000                             | 0.274    | 0.071          | 3.86    | 0.000    |
-| Dispersion<br>parameter α        | 0.021    | 0.006          | 3.67    | 0.000    |
-
-Residual deviance, 351.71 on 325 d.f.; AIC, 1980.6; 2 × log-likelihood, <sup>−</sup>1958.611; <sup>R</sup><sup>2</sup> <sup>P</sup>, 0.962; <sup>R</sup><sup>2</sup> FT, 0.967; and <sup>R</sup><sup>2</sup> K, 0.968.
-
-other words, the fatal crash frequency increases with DVMT at a decreasing rate. Total mileage is significant and positively correlated with fatal crash frequency while the percentage of travel in federal aid roads is negatively correlated. Higher design standards for federal aid roads may be responsible for this reduction on fatal crash risk.
-
-Several socioeconomic variables are significant in the model. The variable representing area deprivation, percent of population under poverty, as well as persons less than 15 years are highly significant and positive. These findings are consistent with the expectation of increases in crash risk with increases in area deprivation and number of young persons (under 15).
-
-Within the environmental variables the only one that is significant is total precipitation. Several facts may contribute to this: among them that total precipitation and rainy days as well as total snowfall and snowy days are highly correlated (0.93 and 0.88, respectively).
-
-Fixed time effects are also significant and positive, which means that the expected number of fatal crashes increased for years 1997–2000 compared to the base year of 1996. Overall goodness-of-fit for the model is excellent for all utilized fit statistics. The dispersion parameter is positive and highly significant indicating the presence of overdispersion, which is common with models estimating the expected number of crashes.
-
-For the NB injury crash models (Table 3) the original set of independent variables form the starting point. Fatal and injury models differ in the number of significant predictors, six for the fatal crash model and eight for the injury model without including the fixed time effects that are significant in both models.
-
-For the injury model, four transportation-related variables are significant: DVMT, infrastructure mileage, mileage density, and percentage of federal aid roads. As in the fatal crash model, DVMT is negative suggesting an increasing risk of injury crashes
-
-Table 3 Negative binomial model of injury crashes
-
-| Variable                            | Estimate | Standard error | z value | Pr(> z ) |
-|-------------------------------------|----------|----------------|---------|----------|
-| Intercept                           | 2.257    | 0.498          | 4.53    | 5.96E−06 |
-| DVMT                                | −0.031   | 0.007          | −4.36   | 1.29E−05 |
-| Infrastructure<br>mileage           | 0.147    | 0.024          | 6.14    | 8.08E−10 |
-| Mileage density                     | 0.089    | 0.008          | 11.66   | 2.00E−16 |
-| Mileage of federal<br>aid roads (%) | 0.016    | 0.003          | 5.38    | 7.35E−08 |
-| Persons 0–14 (%)                    | 0.060    | 0.013          | 4.61    | 4.02E−06 |
-| Persons 15–24 (%)                   | 0.024    | 0.007          | 3.55    | 0.000    |
-| Persons 65 and<br>over (%)          | 0.030    | 0.009          | 3.40    | 0.001    |
-| Total precipitation                 | 0.010    | 0.003          | 3.80    | 0.000    |
-| 1997                                | 0.204    | 0.060          | 3.41    | 0.001    |
-| 1998                                | 0.136    | 0.055          | 2.46    | 0.014    |
-| 1999                                | 0.107    | 0.053          | 2.00    | 0.046    |
-| 2000                                | 0.111    | 0.054          | 2.05    | 0.040    |
-| Dispersion<br>parameter α           | 0.030    | 0.003          | 11.93   | 0.000    |
-
-Residual deviance, 338.78 on 322 d.f.; AIC, 4191.1; 2 × log-likelihood, <sup>−</sup>4163.099; <sup>R</sup><sup>2</sup> <sup>P</sup>, 0.981; <sup>R</sup><sup>2</sup> FT, 0.983; and <sup>R</sup><sup>2</sup> <sup>K</sup>, 0.974.
-
-at a decreasing rate. The next two variables represent the amount of infrastructure in absolute and relative terms, respectively, and are both positively correlated with injury crashes, as expected. The third variable measures the percentage of roads under federal aid, and the positive coefficient indicates that increases in the variable value increase the injury crash risk. Federal aid roads in our study have generally higher speeds; non-federal aid roads have low operational speeds, which may generally reduce the probability of injury or fatality.
-
-In the case of socioeconomic variables, percentage of persons under 15 is significant and with the same sign as for fatal crashes. In addition, the surrogates for young drivers and pedestrians (persons between 15 and 24) and elderly drivers and pedestrians (persons 65 and over) are significant, while in the fatal crash model those variables are not found to be significant.
-
-The total precipitation effect is also positive in the injury model. The fixed time effects are positive and significant for injury crash models. Their coefficient values, however, decrease from 0.20 in 1997 to 0.11 in 2000.
-
-# *4.1. Full Bayes hierarchical models*
-
-Several FB fatal crash models are tested. The basic FB fatal crash model includes the variables found to be significant in the NB models as well as fixed time effects and uncorrelated heterogeneity. Fixed time effects are found not significant and as a result they are excluded from further models. When a model including unstructured heterogeneity and spatial correlation is estimated, the overdispersion of the model is due to unstructured heterogeneity, and therefore, fatal crashes do not indicate significant spatial correlation (for more see [Aguero-Valverde,](#page-7-0) [2005\).](#page-7-0)
-
-Finally, an FB model with uncorrelated heterogeneity and without fixed time effects is estimated (see Table 4). DVMT, poverty, and persons under 15 are significant and very similar to the coefficients from the NB model. This FB model presents the lowest value of DIC, which means that, among the models estimated in this work, this is the model that best fits the data. Given the fact that there is no evidence of spatial correlation or time dependence in the fatal crash model, no further models accounting for space–time interactions are estimated.
-
-Table 4 Full Bayes model of fatal crashes with uncorrelated heterogeneity (*vi*)
-
-| Variable                      | Estimate |        | Credible set 95% |         |
-|-------------------------------|----------|--------|------------------|---------|
-|                               | Mean     | S.D.   | 2.50%            | 97.50%  |
-| DVMT                          | −0.0397  | 0.0104 | −0.0600          | −0.0197 |
-| Infrastructure mileage        | 0.0466   | 0.0496 | −0.0504          | 0.1439  |
-| DVMT on federal aid roads (%) | 0.0030   | 0.0033 | −0.0034          | 0.0096  |
-| Persons under poverty (%)     | 0.0273   | 0.0076 | 0.0119           | 0.0420  |
-| Persons 0–14 (%)              | 0.0662   | 0.0128 | 0.0414           | 0.0913  |
-| Total precipitation           | −0.0006  | 0.0016 | −0.0037          | 0.0026  |
-| σ2<br>v                       | 0.0337   | 0.0091 | 0.0190           | 0.0545  |
-
-D¯ , 1842.0; D(¯ θ), 1794.0; DIC, 1890.0; and *p*D, 48.32.
-
-Table 5 Full Bayes model of injury crashes with spatial correlation (*ui*), time trend (ϕ), and space × time interactions (δ*i*)
-
-| Variable                            | Estimate |          | Credible set 95%   |          |  |
-|-------------------------------------|----------|----------|--------------------|----------|--|
-|                                     | Mean     | S.D.     | 2.50%              | 97.50%   |  |
-| Intercept                           | 3.110    | 0.764    | 1.593              | 4.582    |  |
-| DVMT                                | −0.092   | 0.013    | −0.118             | −0.067   |  |
-| Infrastructure mileage              | 0.323    | 0.051    | 0.227              | 0.425    |  |
-| Mileage density                     | 0.131    | 0.022    | 0.086              | 0.174    |  |
-| Mileage of federal aid<br>roads (%) | 0.026    | 0.007    | 0.013              | 0.040    |  |
-| Persons 0–14 (%)                    | 0.047    | 0.020    | 0.008              | 0.088    |  |
-| Persons 15–24 (%)                   | 0.015    | 0.012    | −0.008             | 0.039    |  |
-| Persons 65 and over (%)             | 0.013    | 0.016    | −0.018             | 0.044    |  |
-| Total precipitation                 | 5.86E−05 |          | 2.27E−04 −3.85E−04 | 5.01E−04 |  |
-| σ2<br>u                             | 0.176    | 0.038    | 0.115              | 0.263    |  |
-| ϕ                                   | −0.015   | 0.004    | −0.022             | −0.008   |  |
-| σ2<br>δ                             | 6.66E−04 | 2.15E−04 | 3.31E−04           | 0.001164 |  |
-
-D¯ , 3261.0; D(¯ θ), 3159.0; DIC, 3363.0; and *p*D, 102.1.
-
-An FB injury crash model including fixed time effects and uncorrelated heterogeneity or random error was tested next, then a model that replaces the unstructured error term in each country with a CAR error term, or spatial correlation. From these models it was evident that temporal and spatial effects were significant and therefore, the space–time model shown in Eq. [\(7\)](#page-2-0) is estimated (without including the unstructured error term). The model includes a spatially correlated term (*ui*), a time trend effect (ϕ), and an interaction term between time and area effect (δ*i*) as presented in Table 5.
-
-From the estimates of the coefficients in Table 5, there are several important points to highlight. First, all transportationrelated coefficients are significant and fairly close to the values in the NB model. In the case of age cohorts, persons-under-15 is significant, positive, and very similar to the NB estimate. The coefficients of the other two cohorts are not significant. As in the FB fatal model, the precipitation effect is non-statistically significant for the injury model.
-
-The time trend effect is significant and negative for the FB injury crash model. This means that controlling for all the other variables in the model, the number of injury crashes decreases over time. This is consistent with previous FB models for injury crashes (not presented here), but more important yet, if one examines the NB model for injury crashes, it is evident that the coefficients for the fixed time effects generally decrease with time after a positive 'jump' from 1996 (base year) to1997. Note that the coefficient estimate for the time trend may be biased if the underlying time trend for the population is not linear; however, with only 5 years of data this is very difficult to assess.
-
-The Poisson extra-variation due to spatial correlation (*ui*) is significantly greater than the variation due to the space–time interaction. The variance of *u* (σ<sup>2</sup> <sup>u</sup>) is equal to 0.176, while the variance of δ (σ<sup>2</sup> <sup>u</sup>) is equal to 0.00066. However, the fact that the variance of δ is different from zero indicates that the space–time interaction term is significant.
-
-## **5. Conclusions**
-
-There is no evidence of spatial correlation in fatal crashes; however, spatial correlation was found to be significant in injury crashes. The variance of the spatially correlated term (σ<sup>2</sup> <sup>u</sup>) is significant in the FB injury crash model, which implies that some Poisson extra-variation in the data can be explained by spatial correlation.
-
-Results concerning the effects of the covariates on fatal and injury crash risk are mostly consistent in the direction and magnitude for NB and FB models. In general, highly significant variables in the NB models are also significant in the FB models. On the other hand, variables just marginally significant in the NB models are generally non-significant in the FB models. Because the FB models address spatial correlation and take into consideration all sources of uncertainty, the authors believe the FB models more accurately associate covariates with crash risk and are better suited for this type of data.
-
-Fatal crash models have fewer significant variables compared to injury models. The main reason for this is the small number of events for fatal crashes, roughly a fatal crash for every thousand injury crashes. Given this difference of three orders of magnitude, it is expected that the random variation of the fatal crash data to be small compared to the variation explained by the covariates in the injury models. This may also be a sample size issue; with a larger sample size the noise or random variation is likely to be reduced and therefore, a higher portion of the variation can be explained by the covariates.
-
-## **6. Recommendations for future research**
-
-Crash models at the county level have several advantages over other types of crash models; one of the most important ones is the availability of transportation and socioeconomic data at county level. With the increase in quality and quantity of geographic information systems (GIS) data available in the Internet, public organizations may be able to incorporate additional land-use covariates in crash models, exploring land-use transportation interactions and their effects on crash risk.
-
-Now that data on poverty and other socioeconomic variables are available for the years 2001 and 2002 the database can be increased by 40%. This will have two important benefits: first, more data will improve the explanatory power of the models, especially in the fatal crash models; and secondly, two more years of data will help to estimate more precisely the time effect for both fatal and injury models.
-
-Given the existence of spatial correlation, at least for injury models, it is expected that spatial correlation plays a more important role at smaller spatial scales. FB models with spatial correlation may be even more useful at road section level where units are smaller and closer and therefore, the probability of spatial correlation is higher. Using FB models with spatial correlation is a natural next step in research work.
-
-Area deprivation (in the form of poverty percent in the case of this work) is consistently correlated with crash risk. More research on the causes of this correlation and on the causaleffect relationship between these phenomena is needed. The <span id="page-7-0"></span>relationship likely requires some level of spatial aggregation (e.g. county or census track), so analyses at the segment level are unlikely. This limitation implies that more studies at county or census track level are necessary in order to shed some light on the issue.
-
-From the methodological standpoint, FB hierarchical models are more effectively developed by starting with simple specifications, building to more complex models. The output from the simpler models can be used as initial values in the MCMC. Two or more MCMCs with different initial values for all the parameters are recommended in order to assure convergence to a consistent value. This method allows the modeling to verify output from each model and generally be in greater control of the FB model building process.
-
-# **References**
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-- Fristrøm, L., Ifver, J., Ingebrigtsen, S., Kulmala, R., Thomsen, L.K., 1995. Measuring the contribution of randomness, exposure, weather, and daylight to the variation in road accident counts. Accid. Anal. Prev. 27, 1–20.
-- Gary, S.L.S., Aultman-Hall, L., McCourt, M., Stamatiadis, N., 2003. Consideration of driver home county prohibition and alcohol-related vehicle crashes. Accid. Anal. Prev. 35, 641–648.
-- Gelman, A., Carlin, J., Stern, H.S., Rubin, D.B., 2003. Bayesian Data Analysis, second ed. Chapman & Hall/CRC.
-- Hamed, M., 1999. Analysis of pedestrians' behavior at pedestrian crossings. Saf. Sci. 38 (1), 63–82.
-- Hamed, M., Jaradat, Easa, 1998. Analysis of commercial mini-bus accidents. Accid. Anal. Prev. 30 (5), 555–567.
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-- Jones, A.P., Langford, I.H., Bentham, G., 1996. The application of K-function analysis to the geographical distribution of road traffic accident outcomes in Norfolk, England. Soc. Sci. Med. 42 (6), 879–885.
-- Jovanis, P., Chang, H.L., 1986. Modeling the relationship of accidents to miles traveled. Transportation Res. Rec. 1068, 42–51.
-- Kam, B.H., 2003. A disaggregate approach to crash rate analysis. Accid. Anal. Prev. 35, 693–709.
-- Knorr-Held, L., 2000. Bayesian modelling of inseparable space–time variation in disease risk. Stat. Med. 19, 2555–2567.
-- Knorr-Held, L., Besag, J., 1998. Modelling risk from a disease in time and space. Stat. Med. 17, 2045–2060.
-- Lawson, A.B., Browne, W.J., Vidal Rodeiro, C.L., 2003. Disease Mapping with WinBUGS and MLwiN. John Wiley and Sons. Levine, N., Kim, K.E., Nitz, L.H., 1995a. Spatial analysis of Honolulu motor
-- vehicle crashes. I. Spatial patterns. Accid. Anal. Prev. 27 (5), 663–674.
-- Levine, N., Kim, K.E., Nitz, L.H., 1995b. Spatial Analysis of Honolulu motor vehicle crashes. II. Zonal generators. Accid. Anal. Prev. 27 (5), 675–685.
-- MacNab, Y.C., 2004. Bayesian spatial and ecological models for small-area accident and injury analysis. Accid. Anal. Prev. 36 (6), 1028–1091.
-- Miaou, S.P., 1996. Measuring the Goodness-of-Fit of Accident Prediction Models. FHWA-RD-96-040. Federal Highway Administration, Washington, DC.
-- Miaou, S., Song, J.J., Mallick, B.K., 2003. Roadway traffic crash mapping: a space–time modeling approach. J. Transportation Stat. 6 (1), 33–57. Miaou, S., Song, J., 2005. Accid. Anal. Prev. 37 (4), 699–720.
-- National Climatic Data Center, 2004. NNDC Climate Data Online. National Oceanic and Atmospheric Administration. Web page: [http://www.ncdc.](http://www.ncdc.noaa.gov/oa/climate/stationlocator.html) [noaa.gov/oa/climate/stationlocator.html,](http://www.ncdc.noaa.gov/oa/climate/stationlocator.html) visited 07/15/2004.
-- Noland, R.B., Oh, L., 2004. The effect of infrastructure and demographic change on traffic-related fatalities and crashes: a case study of Illinois country-level data. Accid. Anal. Prev. 36, 525–532.
-- Noland, R.B., Quddus, M.A., 2004. A spatially disaggregate analysis of road casualties in England. Accid. Anal. Prev. 36 (6), 973–984.
-- Pennsylvania Department of Transportation, 2004. Highway Statistics. Web page: [http://www.dot.state.pa.us](http://www.dot.state.pa.us/)/, visited 7/13/2004.
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-- R Development Core Team, 2004. R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. URL <http://www.r-project.org/>.
-- Richardson, S., 1992. Statistical methods for geographical correlation studies. In: Elliott, P., Cuzick, J., English, D., Stern, R. (Eds.), Geographical and Environmental Epidemiology: Methods for Small-Area Studies. Oxford University Press, London, pp. 181–204.
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Zotero/001_artiklid/Road Safety Indicators/ahmedroadtrafficaccidentalinjuries2023.md

@@ -1,185 +0,0 @@
----
-category: literaturenote
-citekey: ahmedroadtrafficaccidentalinjuries2023
-title: "Road traffic accidental injuries and deaths: A neglected global health issue"
-authors: "Ahmed, Sirwan K.; Mohammed, Mona G.; Abdulqadir, Salar O.; El‐Kader, Rabab G. Abd; El‐Shall, Nahed A.; Chandran, Deepak; Rehman, Mohammad E. Ur; Dhama, Kuldeep"
-year: 2023
-date: 2023-05-02 2023-5-02
-doi: 10.1002/hsr2.1240
-publication: Health Science Reports
-url: "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154805/"
-zotero_key: NGMCCUST
-zotero_storage: XJNEMQED
-collections: doktoritöö / HLO
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Ahmed, Sirwan K."
-status: converted
----
-#### PERSPECTIVE
-
-![](_page_0_Picture_5.jpeg)
-
-# Road traffic accidental injuries and deaths: A neglected global health issue
-
-Sirwan K. Ahmed1,[2](https://orcid.org/0000-0002-8361-0546) | Mona G. Mohammed[3](https://orcid.org/0000-0002-7041-444X) | Salar O. Abdulqadir[2](https://orcid.org/0000-0002-4831-9577) | Rabab G. Abd El‐Kader3,4 | Nahed A. El‐Shall[5](http://orcid.org/0000-0002-2013-487X) | Deepak Chandran[6](https://orcid.org/0000-0002-9873-6969) | Mohammad E. Ur Rehman<sup>7</sup> | Kuldeep Dhama8
-
-#### Correspondence
-
-Sirwan K. Ahmed, Department of Pediatrics, Rania Pediatric & Maternity Teaching Hospital, Rania, Sulaymaniyah, Kurdistan Region 46012, Iraq; Department of Nursing, University of Raparin, Rania, Sulaymaniyah, Kurdistan Region 46012, Iraq.
-
-Email: [sirwan.ahmed1989@gmail.com](mailto:sirwan.ahmed1989@gmail.com) and [sirwan.k.ahmed@uor.edu.krd](mailto:sirwan.k.ahmed@uor.edu.krd)
-
-# Abstract
-
-Across the world, traffic accidents cause major health problems and are of concern to health institutions; nearly 1.35 million people are killed or disabled in traffic accidents every year. In 2019, 93% of road traffic injury‐related mortality occurred in low‐ and middle‐income countries with an estimated burden of 1.3 million deaths. This issue is growing; by 2030, road traffic injuries will be the seventh leading cause of death globally. The present report highlights an overview of road traffic accidents, accidental injuries, and deaths, associated risk factors, important precautions, safety rules, and counteracting management strategies. In modern cultures, road accidents are a major source of death and serious injuries. Road traffic injuries are a substantial yet underserved public health issue around the world that requires immediate attention. To prevent accidents in the long term, it is essential to adopt conservative preventive measures that can minimize collisions and promote a safe road environment.
-
-# KEYWORDS
-
-autonomous vehicles, death, global health, management, prevention, risk factors, road traffic accidents
-
-<sup>1</sup> Department of Pediatrics, Rania Pediatric & Maternity Teaching Hospital, Rania, Iraq
-
-<sup>2</sup> Department of Nursing, University of Raparin, Rania, Iraq
-
-RAK College of Nursing, RAK Medical and Health Sciences University, Ras Al Khaimah, UAE
-
-<sup>4</sup> Faculty of Nursing, Mansoura University, Mansoura, Egypt
-
-<sup>5</sup> Department of Poultry and Fish Diseases, Faculty of Veterinary Medicine, Alexandria University, Edfina, Egypt
-
-<sup>6</sup> Department of Veterinary Sciences and Animal Husbandry, Amrita School of Agricultural Sciences, Amrita Vishwa Vidyapeetham University, Coimbatore, Tamil Nadu, India
-
-<sup>7</sup> Department of Medicine, Rawalpindi Medical University, Rawalpindi, Pakistan
-
-<sup>8</sup> Division of Pathology, ICAR‐Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
-
-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
-
-<sup>© 2023</sup> The Authors. Health Science Reports published by Wiley Periodicals LLC.
-
-# 1 | INTRODUCTION
-
-Across the world, traffic accidents cause majorhealth problems and are of concern to health institutions, nearly 1.35 million people are killed or disabled in traffic accidents every year, about 3700 people die every day in fatal accidents alone, half of which are cyclists, motorcyclists, or pedestrians, as vulnerable road users.[1](#page-4-0) For example, in the United States, car accidents are considered one of the main causes of death. In 2020 alone, about 40,000 people died in a traffic accident and about 2.1 million people visited emergency units due to traffic accidents. That is estimated at \$430 billion in medical costs and quality of life and lives lost.[2](#page-4-1)
-
-Traffic injuries are the leading cause of death among children and youth aged 5–29, with 20 to 50 million injuries that do not cause death but cause disability and serious economic damage to individuals, families, and countries; the loss is due to the cost of medical care and lost wages of the infected or deceased person. Their families are often forced to leave work or school to care for their injured in traffic accidents. According to the WHO, the cost of road traffic crashes in most countries is estimated to be around 3% of their GDP.[3](#page-4-2) The present report highlights an overview of road traffic accidents, accidental injuries, and deaths, associated risk factors, important precautions, safety rules, and counteracting management strategies.
-
-# 2 | CONCEPT DEFINITIONS
-
-An accident is defined as an unexpected and uncontrollable incident in which the action and reaction of an item or person causes personal injury or property damage. A traffic accident is defined as the failure of the road vehicle driver system to perform one or more activities required for the trip to be completed without harm or loss. Traffic accidents are mostly caused by poor maintenance of the road network and a lack of efficient and systematic enforcement.[4](#page-4-3)
-
-The main contributors to traffic accidents include poor road conditions, reckless passing, drowsy driving, sleepwalking, intoxication, illness, use of mobile phones, eating and drinking in the car, inattention in the event of a street accident, and the inability of other drivers to react quickly enough to the situation.[5,6](#page-4-4) Regardless of the vehicle type, traumatic injuries after traffic accidents can affect any part of the body. Vulnerable parts of the body that lead to fatal consequences include the head, chest, abdominal pelvis, and spine.[7](#page-4-5)
-
-The general health and well‐being of citizens in the United States depend on safe transportation. It is a crucial component of the country's transportation and travel infrastructure.[8](#page-4-6) Safety breaches occur through harms caused by inadvertent behaviors or events. The main objective of transportation safety planning is to increase safety by supporting initiatives to create policies, programs, and projects related to all transportation infrastructure, and it also aims to reduce the number of injuries and deaths caused by traffic accidents on public roads.[9](#page-4-7) Establishing a strong transportation policy will have a direct impact on individual health, such as reducing exposure to air pollution and the problems caused by air pollution. Furthermore, it is the right of everyone to have access to safe, healthy, and affordable transportation. The government then prioritizes supporting a healthy society with innovative and modern designs and providing appropriate and safe opportunities by building a safe and an appropriate transportation infrastructure that can reduce the rate of injuries and deaths from transportation accidents is a priority of the government.[10](#page-4-8)
-
-# 3 | CAUSES AND RISK FACTORS
-
-Regarding risk factors for traffic accidents, socioeconomic status is considered the main cause of traffic accidents. According to WHO data, road traffic accidents disproportionately affect low‐ and middle‐income countries, where 90% of all road traffic deaths occur despite these countries having only 60% of the world's vehicles, African countries have higher rates of traffic accidents than European countries, which have lower rates of traffic accidents, even in high‐income countries, people with poor economic conditions are more likely to be involved in traffic accidents.[3](#page-4-2) Traffic and road accidents are considered one of the leading human causes of death among children and adolescents aged 5–29 years. Another reason is sex; men are more likely to be involved in traffic accidents than women, especially young men under the age of 25, hey accounting for 3 out of 4 deaths in traffic accidents (73%), which is three times more likely than their peer girls. However, the greatest causes of death in traffic accidents are not wearing seat belts, restraining children, and not wearing helmets, which certainly reduces the risk of brain injury and death in motorcycle accidents, the risk of injury and death to the back seat and front passengers is also related to the use of seatbelts.[3](#page-4-2)
-
-In general, the approach to the road safety system approach is more based on the risk‐based method, considering human mistakes and trying to ensure the safety of road users. In general, the approach to the road safety system approach is more based on the risk‐based method, considering human errors, and trying to ensure the safety of road users. The system can be designed to be forgiving of human errors and consider human sensitivity toward traffic victims, including; safety roads, roadside safety, safety speed, safety vehicles and safe users, and all these principles must be considered to reduce the number of victims in traffic accidents.[3](#page-4-2)
-
-There is a direct relationship between speeds and the possibility occurring of an accident, as well as severity of the events; with a 1% increase in mean speeds increases the probability occurring of the fatal crash by 4% and an increase in the risk of serious injuries by 3%. The use of alcohol and other substances while driving increases the risk of fatal accidents, resulting in serious deaths or injuries. If a driver's test of alcohol is more than 0.04 grams per deciliter (G/DL), the risk of traffic accidents is very high. Then the type of drug used by drivers will change the result of traffic accidents, for example, those who have used a stimulant drug such as amphetamines are five times more likely to have a fatal accident than those who did not take the drug.[3](#page-4-2)
-
-Another cause of traffic accidents is being busy while driving, which can cause serious damage. Mobile operation is a crucial issue for driving safety that requires intervention. For example, when people are busy with their mobile phones while driving, the risk of traffic accidents increases about four times more than those who do not use their mobile phones while driving. The reason is that the use of mobile phones while driving disrupts the person's reaction to the brakes and traffic signs and makes it unable to stay on the line and follow the appropriate distance. What is expected to increase day by day increases the risk of crash with mobile phone use.[3](#page-4-2)
-
-Furthermore, road design has a significant impact on road safety. This covers the safety of all road users, for example, pedestrians, cyclists, and motorcyclists. It is very important to consider the safety of all road users when designing roads. To reduce the risk of accidents for road users, streets, cycle lanes, safe crossings, and other traffic calming measures are very important. Then, unsafe vehicles that are not of good quality or do not meet fundamental norms of vehicle safety, because, of course, vehicle safety is very important to prevent traffic accidents and reduce the risk of accidents. According to available data, the risk of traffic accidents for vehicle occupants and pedestrians increases significantly in the absence of fundamental norms of vehicle safety requirements[.3](#page-4-2)
-
-#### 4 | MANAGEMENT
-
-Medical management it is very important to familiarize drivers and people that in the event of a road accident, the first step is to call an ambulance and road health centers immediately and go to a nearby hospital. Medication can aid in healing if the injury is not too severe. The administration of tetanus shots and pain relievers by injection is an example of common safety precautions[.11](#page-4-9) In the event of severe blood loss, a blood transfusion may be necessary. In case of bone fractures, doctors do very well and scientifically place the bones in place, and the replacement of a bone that has been dislocated, but does not need surgery is called a closed reduction. In extreme circumstances, surgical intervention is required to operate on the victims of fatal accidents in emergency to save their lives. The surgeon also should be aware of the hidden injuries beneath a seemingly healthy abdomen and other parts of the body. Blunt organ trauma, as well as penetrating spine and back trauma, are all possibilities[.12,13](#page-4-10)
-
-However, delays in identifying and treating people injured in a road traffic accident worsen their conditions. In addition, secure the treatment of pre‐hospital for the injured in traffic accidents as soon as possible and then receive the necessary treatment in the hospital is to increase the standard of health care for postcrash care and is considered one of the necessary tasks.
-
-The implementation of traffic laws by drivers, including the law on drinking during driving, wearing safety belts, complying with speed limits, wearing helmets, and stabilizing children strictly in private places, reducing the death rate and injuries related to these special behaviors[.3](#page-4-2)
-
-Injuries and fatalities from motor vehicle collisions can be avoided by following appropriate precautions and safety rules. Understanding the dangers and taking precautions to ensure health and safety while driving, at home or abroad, can help prevent these accidents and deaths. Always buckle up, regardless of how short the trip of your trip/travel is a car passenger whether sit in front or back seat of the car, should be sure he is tied with belt seats, and children should be attached in the back of the car on a special seat that is suitable for their age, height, and weight. When operating a motorcycle, motorbike, or bicycle, always wear a helmet. Avoid traveling with a drunk driver and never drive while drunk or high. Speed limits must be respected and drive without interruptions or distractions. For example, refrain from texting, emailing, or accessing social networks while driving. Crossing the street should always be done with caution, especially in countries where the steering wheel is on the left side of the car. Only use cabs that are clearly identified and try to choose seatbelt‐equipped taxis. Avoid traveling in large, heavy, or packed minivans or buses that are top‐heavy, overweight, or packed.[14,15](#page-5-0)
-
-It is the duty of governments to work to provide the conditions of road safety, which is in coordination with different sectors such as transportation, health, law, education, and civilian organizations that work to raise awareness of society. Then design a safe traffic infrastructure, stabilize equipment and signs roads, improve the quality of vehicles and other means of transportation, monitor traffic accidents for victims and repair roads, implement and establish traffic laws related to traffic dangers, expand transportation trails, conduct scientific research, raise awareness of society with traffic laws and using the means of transportation.[3,10](#page-4-2)
-
-Existing interventions in high‐income countries aimed at reducing road traffic accidents and injuries have shown to be effective.[15](#page-5-1) Speed cameras, seatbelt laws, and campaigns have all been successful in reducing the number of accidents and injuries on the roads.[15](#page-5-1)
-
-Collecting and monitoring data on road traffic accidents and injuries is crucial for understanding trends and identifying areas where improvements can be made. Data can provide insight into the causes of accidents and injuries, which can help inform interventions and policies. Types of data needed include the number and severity of accidents, the demographics of those involved, and the types of vehicles and infrastructure involved.[16](#page-5-2) Challenges in collecting and analyzing data include inconsistencies in reporting, limited resources for data collection and analysis, and privacy concerns.[17](#page-5-3)
-
-Government agencies have a significant role to play in setting road safety regulations and standards, as well as providing funding for infrastructure improvements and education and training programs. Vehicle manufacturers can also contribute by designing and producing safer vehicles with advanced safety features. Road safety organizations can raise awareness and promote education and training programs for drivers, pedestrians, and cyclists.
-
-Emerging technologies, such as autonomous vehicles, have the potential to greatly reduce the number of accidents caused by human error. However, this technology is still in its early stages, and there are many challenges that need to be addressed before it can be fully implemented. Implications for road safety policies and programs include creating new safety regulations, providing training for law enforcement officers and emergency responders on how to deal with autonomous vehicles, and ensuring that infrastructure is designed to accommodate these new technologies.
-
-An overview of accident and death injuries and deaths in road traffic, associated risk factors, salient precautions, safety rules, and counteracting management strategies to be adopted is shown in Figure [1](#page-3-0).
-
-To reduce traffic accidents and the damage caused by traffic accidents, we can rely on raising awareness among people in general
-
-<span id="page-3-0"></span>![](_page_3_Figure_7.jpeg)
-
-FIGURE 1 A pictorial representation on road traffic accidental injuries and deaths, risk factors, salient precautions, safety rules, and management strategies. Figure was designed by [Biorender.com](http://Biorender.com) program.
-
-<span id="page-3-1"></span>TABLE 1 Strategies to reduce traffic accidents.
-
-| 1 | Education and<br>Training     | One of the most effective ways to raise public awareness about road safety is through education and training.<br>Governments, NGOs, and other organizations can conduct training sessions for drivers, pedestrians, and other road<br>users to teach them about safe driving practices, traffic rules, and pedestrian safety. |
-|---|-------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| 2 | Campaigns                     | Awareness campaigns can be launched through various mediums, such as billboards, radio, TV, social media, and other<br>platforms to reach a wider audience. These campaigns can highlight the importance of road safety and the<br>consequences of reckless driving.                                                          |
-| 3 | Infrastructure<br>Development | Improving infrastructure such as road signage, pedestrian crossings, and street lighting can help in reducing accidents.<br>Governments can also construct speed bumps, roundabouts, and other traffic‐calming measures to reduce speeding<br>and improve road safety.                                                        |
-| 4 | Law Enforcement               | Effective law enforcement can deter reckless driving and encourage adherence to traffic rules. Governments can increase<br>the number of traffic police and use technology such as speed cameras to catch violators and enforce penalties.                                                                                    |
-| 5 | Partnerships                  | Partnerships between governments, NGOs, and other organizations can help to overcome barriers in low‐income<br>countries. For example, NGOs can provide funding and expertise to governments for road safety programs.                                                                                                        |
-| 6 | Community<br>Engagement       | Community engagement is important to create a sense of ownership and responsibility towards road safety. Local<br>organizations can conduct awareness campaigns and engage with communities to encourage safe driving practices<br>and promote pedestrian safety.                                                             |
-
-and drivers in particular. Also, orient them about the risks, safety measures, and necessary preventive measures during driving. In general, it can be said that public awareness, road safety infrastructure, and traffic rules are the most effective reasons for reducing traffic accidents. Transportation is not only important in contemporary life, but has also become crucial and resourceful.
-
-Raising public awareness and overcoming barriers in low‐income countries to reduce traffic accidents, injuries, and deaths can be achieved through several strategies (Table [1](#page-3-1)).
-
-# 5 | CONCLUSION
-
-Increased traffic input is unavoidable; however, the outcome must be taken carefully in terms of producing accidents. It should be noted that the increase in accidents requires safety. In modern cultures, road accidents are a major source of death and serious injuries. Road traffic injuries are a substantial yet underserved public health issue around the world that requires immediate attention. Conservative preventive approaches to successful long‐term prevention are needed. Road transportation is the most complex and deadly system that people must face on a daily basis. Road safety management aims to preserve and improve the current safety of a road network by minimizing collisions and creating a safe road environment for its users, allowing it to be used in an effective and safe way in the future. It is concerned with the execution of road safety regulations, administration, and organization in the authorities in charge of reducing road collisions and fatalities.
-
-## AUTHOR CONTRIBUTIONS
-
-Sirwan K. Ahmed: Conceptualization; data curation; formal analysis; investigation; methodology; project administration; resources; software; supervision; validation; visualization; writing—original draft; writing—review and editing. Mona G. Mohammed: Data curation; methodology; resources; software; validation; visualization; writing original draft; writing—review and editing. Salar O. Abdulqadir: Data curation; formal analysis; investigation; methodology; resources; validation; visualization; writing—review and editing. Rabab G. Abd El‐Kader: Data curation; investigation; resources; validation; writing review and editing. Nahed A. El‐Shall: Resources; writing—review and editing. Deepak Chandran: Data curation; formal analysis; resources; software; validation; visualization; writing—review and editing. Mohammad E. Ur Rehman: Resources; writing—review and editing. Kuldeep Dhama: Data curation; formal analysis; investigation; methodology; resources; software; supervision; validation; visualization; writing—review and editing.
-
-#### ACKNOWLEDGMENTS
-
-The authors thank their respective institutes and universities.
-
-#### CONFLICT OF INTEREST STATEMENT
-
-The authors declare no conflict of interest.
-
-### DATA AVAILABILITY STATEMENT
-
-All data presented in the present review is available online and can be accessed from the appropriate reference in the reference list.
-
-#### ETHICS STATEMENT
-
-Not required.
-
-#### TRANSPARENCY STATEMENT
-
-The lead author Sirwan Khalid Ahmed affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
-
-#### ORCID
-
-Sirwan K. Ahmed <https://orcid.org/0000-0002-8361-0546> Mona G. Mohammed <https://orcid.org/0000-0002-7041-444X> Salar O. Abdulqadir <https://orcid.org/0000-0002-4831-9577> Nahed A. El‐Shall <http://orcid.org/0000-0002-2013-487X> Deepak Chandran <https://orcid.org/0000-0002-9873-6969> Mohammad E. Ur Rehman [https://orcid.org/0000-0001-](https://orcid.org/0000-0001-8118-4849) [8118-4849](https://orcid.org/0000-0001-8118-4849)
-
-Kuldeep Dhama <https://orcid.org/0000-0001-7469-4752>
-
-## REFERENCES
-
-- <span id="page-4-0"></span>1. World Health Organization (WHO). Global Status Report on Road Safety 2018; 2018. [https://www.who.int/publications/i/item/978](https://www.who.int/publications/i/item/9789241565684) [9241565684](https://www.who.int/publications/i/item/9789241565684)
-- <span id="page-4-1"></span>2. Centers for Disease Control and Prevention (CDC). Transportation Safety; 2022. <https://www.cdc.gov/transportationsafety/index.html>
-- <span id="page-4-2"></span>3. World Health Organization (WHO). Road Traffic Injuries; 2022. [https://](https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries) [www.who.int/news-room/fact-sheets/detail/road-traffic-injuries](https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries)
-- <span id="page-4-3"></span>4. Eid HO, Abu‐Zidan FM. Distraction‐related road traffic collisions. Afr Health Sci. 2017;17:491‐499.
-- <span id="page-4-4"></span>5. Ashraf I, Hur S, Shafiq M, Park Y. Catastrophic factors involved in road accidents: underlying causes and descriptive analysis. PLoS One. 2019;14:e0223473.
-- 6. Sun L‐L, Liu D, Chen T, He M‐T. Road traffic safety: an analysis of the cross‐effects of economic, road and population factors. Chin J Traumatol. 2019;22:290‐295.
-- <span id="page-4-5"></span>7. Zaloshnja E, Miller TR, Hendrie D. Effectiveness of child safety seats vs safety belts for children aged 2 to 3 years. Arch Pediatr Adolesc Med. 2007;161:65‐68.
-- <span id="page-4-6"></span>8. Hull R, Herbel S, Gaines D, Waldheim N. Building Links to Improve Safety: How Safety and Transportation Planning Practitioners Work Together. United States. Federal Highway Administration. Office of Safety; 2016.
-- <span id="page-4-7"></span>9. Centers for Disease Control and Prevention (CDC). CDC Transportation Recommendations; 2018. [https://www.cdc.gov/](https://www.cdc.gov/transportation/) [transportation/](https://www.cdc.gov/transportation/)
-- <span id="page-4-8"></span>10. Ang BH, Chen WS, Lee SWH. Global burden of road traffic accidents in older adults: a systematic review and meta‐regression analysis. Arch Gerontol Geriat. 2017;72:32‐38.
-- <span id="page-4-9"></span>11. Salvagioni DAJ, Mesas AE, Melanda FN, et al. Prospective association between burnout and road traffic accidents in teachers. Stress Health. 2020;36:629‐638.
-- <span id="page-4-10"></span>12. Deresse E, Komicha MA, Lema T, Abdulkadir S, Roba KT. Road traffic accident and management outcome among in Adama
-
-- Hospital Medical College. Central Ethiopia Pan Afr Med J. 2021;38:190.
-- 13. Centers for Disease Control and Prevention (CDC). Road Traffic Injuries and Deaths—A Global Problem; 2020. [https://www.cdc.gov/](https://www.cdc.gov/injury/features/global-road-safety/index.html) [injury/features/global-road-safety/index.html](https://www.cdc.gov/injury/features/global-road-safety/index.html)
-- <span id="page-5-0"></span>14. Sauber‐Schatz EK BM, Parker EM, Sleet DA. CDC Health Information for International Travel (Yellow Book). Chapter 8 – Travel by Air, Land & Sea – Road & Traffic Safety; 2020. [https://](https://wwwnc.cdc.gov/travel/yellowbook/2020/travel-by-air-land-sea/road-and-traffic-safety) [wwwnc.cdc.gov/travel/yellowbook/2020/travel-by-air-land](https://wwwnc.cdc.gov/travel/yellowbook/2020/travel-by-air-land-sea/road-and-traffic-safety)[sea/road-and-traffic-safety](https://wwwnc.cdc.gov/travel/yellowbook/2020/travel-by-air-land-sea/road-and-traffic-safety)
-- <span id="page-5-1"></span>15. Ernstberger A, Joeris A, Daigl M, et al. Decrease of morbidity in road traffic accidents in a high income country—an analysis of 24,405 accidents in a 21 year period. Injury. 2015;46: S135‐S143.
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-- <span id="page-5-2"></span>16. Imprialou M, Quddus M. Crash data quality for road safety research: current state and future directions. Accident Anal Preven. 2019;130: 84‐90.
-- <span id="page-5-3"></span>17. Chang F‐R, Huang H‐L, Schwebel DC, Chan AHS, Hu G‐Q. Global road traffic injury statistics: challenges, mechanisms and solutions. Chin J Traumatol. 2020;23:216‐218.
-
-How to cite this article: Ahmed SK, Mohammed MG, Abdulqadir SO, et al. Road traffic accidental injuries and deaths: A neglected global health issue. Health Sci Rep. 2023;6:e1240. [doi:10.1002/hsr2.1240](https://doi.org/10.1002/hsr2.1240)

Zotero/001_artiklid/Road Safety Indicators/ayatiinvestigationroletrafficvolume2011.md

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----
-category: literaturenote
-citekey: ayatiinvestigationroletrafficvolume2011
-title: Investigation on the role of traffic volume in accidents on urban highways
-authors: "Ayati, Esmaeel; Abbasi, Ehsan"
-year: 2011
-date: 2011-00-00 2011
-doi: 10.1016/j.jsr.2011.03.006
-publication: Journal of Safety Research
-url: "https://doi.org/10.1016/j.jsr.2011.03.006"
-zotero_key: VEX26R2A
-zotero_storage: 9Z5XVDJV
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Ayati, Esmaeel"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-# Journal of Safety Research
-
-j o u r n a l h om e p a g e : www. e l s ev i e r. c om / l o c a t e / j s r
-
-![](_page_0_Picture_5.jpeg)
-
-# Investigation on the role of traffic volume in accidents on urban highways
-
-Esmaeel Ayati ⁎, Ehsan Abbasi <sup>1</sup>
-
-Department of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
-
-#### article info abstract
-
-Article history: Received 31 March 2010 Received in revised form 4 January 2011 Accepted 1 March 2011 Available online 27 May 2011
-
-Keywords: No injury accidents More severe accidents Urban highways Light non-passenger car vehicles Negative binomial (NB) regression model
-
-Introduction: Roadway safety is a major concern for the general public and public agencies, as roadway crashes claim many lives and cause substantial economic loss each year. In Iran, a large number of vehicles are involved in road accidents each year, which cause many deaths and extensive property damage; such accidents are among the major causes of death and disability in the country. Method: To reduce roadway accidents, the factors that affect the occurrence and severity of accidents should be scrutinized to prevent or reduce their effect. The method that many researchers have adopted to determine the effective parameters surrounding road accidents in recent years is through statistical modeling of accidents. In this article, the role of different kinds of vehicles in traffic flow are investigated separately in terms of the likelihood of crashes on urban highways, and the vehicles are divided into three groups: passenger cars, heavy vehicles, and light nonpassenger car vehicles. Poisson and negative binomial (NB) regression models were applied to model the accidents in this research, which were categorized into two groups: no injury (property damage only) accidents and more severe (injury and fatal) accidents. Results: Ultimately, we conclude that light nonpassenger car vehicles (i.e., taxis and motorcycles) play the largest role in the occurrence of crashes on urban highways for both types of accidents.
-
-© 2011 National Safety Council and Elsevier Ltd. All rights reserved.
-
-#### 1. Introduction
-
-Roadway safety is a major concern for the general public and public agencies. Indeed, roadway crashes claim many lives and cause substantial economic loss each year [\(Ma, Kockelman, & Damien,](#page-5-0) [2008\)](#page-5-0). In Iran, a large number of vehicles are involved in road accidents each year, causing significant death, disability, and property damage. Recent investigations in Iran indicate that 25% of casualties due to unnatural deaths are caused by traffic accidents ([Attaran,](#page-4-0) [1996\)](#page-4-0). Based on the results of research done on the cost of traffic accidents in Iran in 2001, 19,727 and 117,600 people have been killed and injured, respectively, in about 450,000 accidents [\(Legal Medicine](#page-4-0) [Organization of Iran, 2002\)](#page-4-0), which is estimated to have cost more than 3% of the gross national product ([Ayati, 2008](#page-4-0)). Developing sound or reliable statistical models for analyzing motor-vehicle crashes is important in highway safety studies. The models can be used for identifying hazardous sites, predicting motor-vehicle collisions, and developing accident modification factors ([Lord & Park, 2008\)](#page-4-0).
-
-In this paper, the role of vehicle type volume in traffic flow in the occurrence of accidents on urban highways is investigated through modeling no injury and more severe accidents by Poisson and NB regression models. The vehicle types are divided into three groups, including: passenger cars; heavy vehicles including truck trailers, trucks, buses, and minibuses; and light non-passenger car vehicles including taxis, pickups, and motorcycles. The purpose of the study was to scrutinize the role of each group of vehicles in the likelihood of accidents. The role of heavy vehicles in accident occurrence is often discussed, but it must be determined whether heavy vehicles result in more accidents or whether passenger cars or light non-passenger car vehicles, like taxis or motorcycles, play a key role in accident occurrence or frequency.
-
-# 2. Literature review
-
-Numerous studies have been conducted in recent years to investigate the frequency and severity of accidents on different parts of rural and urban freeways and highways, two-lane roads and arterials, or intersections using various methods of modeling accidents [\(Anastasopoulos & Mannering, 2009; Chang, 2005; Delen, Shadra, &](#page-4-0) [Bessonov, 2006; Lord & Park, 2008; Lord, Washington, & Ivan, 2007; Ma](#page-4-0) [et al., 2008; Milton, Shankar, & Mannering, 2008; Wang, Quddus, & Ison,](#page-4-0) [2009](#page-4-0)). Poisson and NB regression models have been used widely for predicting the occurrence of accidents on highways ([Lord, 2006; Lord &](#page-4-0) [Park, 2008; Savolainen & Tarko, 2005\)](#page-4-0). In addition, more advanced regression models such as zero-inflated negative binomials and Conway–Maxwell–Poisson generalized linear models are applied to modeling crashes [\(Lord, 2006; Lord et al., 2007; Lord & Park, 2008](#page-4-0)). In previous studies, the average daily traffic (ADT) or annual average daily traffic (AADT) were used as volume-related independent variables for
-
-<sup>⁎</sup> Corresponding author. Tel.: +98 0915 310 7483; fax: +98 0511 8676769. E-mail addresses: [esmaeel@ayati.co.uk](mailto:esmaeel@ayati.co.uk) (E. Ayati), [ehsan.abbasi395@gmail.com](mailto:ehsan.abbasi395@gmail.com)
-
-<sup>(</sup>E. Abbasi). <sup>1</sup> Tel.: +98 0915 314 7887.
-
-modeling ([Ma et al., 2008; Wang et al., 2009\)](#page-5-0) or, considering the number of lanes on each side, the average daily traffic per lane was applied ([Chang, 2005; Lee & Mannering, 2002; Milton et al., 2008](#page-4-0)). Researchers also applied variables such as the average daily percentage of trucks in the traffic stream or the average daily truck traffic (ADTT) ([Anastasopoulos & Mannering, 2009; Milton et al., 2008](#page-4-0)) to consider the responsibility of heavy vehicles in the frequency and severity of highway accidents.
-
-In their studies on the frequency and severity of highway accidents using a mixed logit model, [Milton et al. \(2008\)](#page-5-0) concluded that the average daily traffic per lane had an increasing role in the likelihood of no injury accidents on highways [\(Milton et al., 2008](#page-5-0)). [Chang \(2005\)](#page-4-0) concluded, in his investigation on the frequency of freeway accidents with a comparison between NB regression and artificial neural network models using variables such as ADT per lane, the percentage of trucks in the traffic flow and peak hour factor (PHF); [Chang \(2005\)](#page-4-0) concluded that, given the traffic volume in the peak hour, volume-related variables that led to conflicts between vehicles and increased exposure to potential risks of accidents correlated with an increasing number of vehicles and trucks.
-
-#### 3. Methodology
-
-In this research, the accidents on urban highways in Mashhad were modeled by Poisson and NB regression models, two models for no injury accidents and two for more severe accidents (injury or fatal). The specific contribution of this research is the separation of the total traffic volume into the passenger car volume, the heavy vehicle volume, including truck trailers, trucks, buses, and minibuses, and the light non-passenger car vehicle volume, including taxis, pickups, and motorcycles. The role of volume in the occurrence of accidents with property damage and injury or fatalities was investigated thoroughly to determine what kind of traffic has a significant role in accident occurrence. Thus, the independent variables of both no injury and more severe accident models are as follows:
-
-- 1. passenger car volume (Veh/hr)
-- 2. heavy vehicle volume (passenger car equivalent)
-- 3. light non-passenger car vehicle volume (passenger car equivalent)
-
-Accident data are usually two-level data; the first and main level is often road segments (i.e., the highway is divided into several parts or segments). The base of segmentation can differ, and this segmentation was based on total traffic volume in this study (i.e., the total traffic volume [passenger car equivalent] is constant for each part of the highway). The second level is the hours of the day, that is, the traffic peak hours are considered as the first sub-level, the day non-peak hours the second, and the night non-peak hours the third. The accident data for Mashhad urban highways were collected by the Transportation and Traffic Organization of Mashhad. After developing the models, the Poisson and NB models for no injury and more severe accidents were compared, and the better model was then validated to prove the efficiency of that model in modeling urban highway accidents.
-
-# 3.1. Poisson regression model
-
-In the Poisson regression model, the i-th observation of the dependent variable yi is modeled as a random Poisson variable with mean λi:
-
-$$P(y_i) = \frac{e^{-\lambda_i} \lambda_i^{y_i}}{y_i 1} \tag{1}$$
-
-The most important property of the Poisson regression model is the equality of the mean and variance of the distribution, which is the main shortcoming of this model and blocks the "overdispersion" of accident data. In the Poisson model, the conditional variance is equal to the conditional mean:
-
-$$V(y_i|\mathbf{x}_i) = E(y_i|\mathbf{x}_i) = \lambda_i = e^{\mathbf{X}_i'\beta}$$
-(2)
-
-The log-likelihood of the Poisson regression model is given by
-
-$$L = \sum_{i=1}^{n} \ln \frac{e^{-\lambda_i} \lambda_i^{y_i}}{y_i 1} = \sum_{i=1}^{n} \left[ -\lambda_i + y_i \ln \lambda_i - \ln y_i \right]$$
- (3)
-
-To estimate the regression coefficients by the maximum likelihood method, the derivative of the log-likelihood relative to the vector of coefficients, β, is set equal to zero:
-
-$$\frac{\partial L}{\partial \beta} = \sum_{i=1}^{n} \left( y_i - e^{x_i \beta} \right) \mathbf{x}_i = 0 \tag{4}$$
-
-The estimation of the regression coefficients in the Poisson regression model is not obtained with a direct equation, but the Newton-Raphson iteration procedure is used for estimating the unknown parameters of the model. To perform the estimation process, the corresponding iteration algorithm is analyzed by SAS statistical software to obtain the calculated coefficients. The predicted value of ŷ<sup>i</sup> is the conditional mean or average number of accidents given xi, which is also denoted as λi, that is, the mean of the random variable yi, which follows a Poisson distribution. This value is typically not an integer number, whereas the observed value yi is a count ([Anselin,](#page-4-0) [2002\)](#page-4-0).
-
-#### 3.2. Negative binomial regression model
-
-In the negative binomial (NB) model yi, the i-th observation of the dependent variable has the following probability distribution function:
-
-$$p(y_i) = \frac{\Gamma(y_i + r)}{y_i!\Gamma(r)} \left[ \frac{\mu_i}{\mu_i + r} \right]^{y_i} \left[ \frac{r}{\mu_i + r} \right]^r$$
- (5)
-
-The conditional mean of yi, for the vector of observed independent variables, xi, is given by
-
-$$E(y_i|\mathbf{x}_i) = \mu_i = e^{\mathbf{x}_i^i \beta} \tag{6}$$
-
-The NB distribution does not have the disadvantage of the Poisson distribution because there is no equality of the mean and variance of the distribution. The relationship between the mean and variance of an NB distribution is
-
-$$V(Y) = \mu + \frac{1}{r}\mu^2 \tag{7}$$
-
-The variance of the NB distribution is always greater than the mean. Thus, it fits the data with a variance greater than the mean. α= 1/r is the dispersion parameter. For accident data with low dispersion, the Poisson distribution gives plausible results, but, if overdispersion exists in the data, the variance of data will be greater than the mean. In such cases, the NB distribution is preferred. In the NB regression model, the dispersion parameter should be estimated in addition to regression parameters. r must be positive, so r\*=lnr is estimated instead, which can take on any value. The log-likelihood function of the NB model for the i-th observation is obtained from the following equation:
-
-$$L_{i} = \sum_{i=0}^{y_{i}-1} \ln(e^{r*} + j) = \ln y_{i}! + e^{r*} \ln(e^{r*}) + y_{i} \ln(\mu_{i}) - (e^{r*} + y_{i}) \ln(\mu_{i} + e^{r*})$$
-
-ð8Þ
-
-<span id="page-2-0"></span>To estimate β and r\*, as in the Poisson model, the iteration procedure of Newton-Raphson is applied ([Agresti, 2002\)](#page-4-0). The related iteration algorithm is written in SAS to obtain the unknown parameters. In the NB regression model, the predicted value of ŷ<sup>i</sup> is the conditional mean or average number of accidents given xi. This is also denoted as μi, that is, the mean of the random variable yi, which follows the NB distribution.
-
-#### 4. Data
-
-The accident data for Mashhad urban highways in the years 2006 and 2007 were used for modeling accidents on urban highways. This information was collected by Traffic and Transportation Organization of Mashhad with the help of GIS and police reports. In this study, the accidents were divided into two groups: accidents with property damage only (no injury) and more severe accidents (injury or fatal). Therefore, accident statistics were collected in two groups: no injury and more severe. Accident data are usually two-level data, the first and main level is often the road segment (i.e., the highway is divided into several parts). In this study, highway segmentation was performed based on total traffic volume. The second level is the hour, that is, traffic peak hours are considered the first sub-level, the day non-peak hours the second, and the night non-peak hours the third. To scrutinize the roles of the different vehicle types in crash occurrence or severity, the traffic was separated into passenger cars, heavy vehicles, and light non-passenger car vehicles. The volume data were not readily available separately from the Traffic and Transportation Organization of Mashhad, which was one of the study limitations. The organization conducts a comprehensive survey once every few years in November and obtains the passing volume of different kinds of vehicles through the main urban roads in terms of daily hours. In November, the traffic conditions in Mashhad are normal, and the models made in this research do not consider the seasonal changes of traffic; this could be another limitation of the research because the statistics of traffic volume in different seasons were not available, and the volume data are related to different hours of the day. According to this statistics, one can perceive the traffic combination and percentage of each vehicle type in traffic and can trust this combination in the future; however, the total (equivalent) traffic volume is updated every year.
-
-The Traffic and Transportation Organization of Mashhad gives the total traffic volume on different urban roads, particularly highways in peak hours, for future years. According to this total equivalent volume, the passenger car equivalent factors of vehicle types, the percentage of each type in traffic combinations, and the ratio of the total volume in non-peak hours to that in peak hours, the volumes of each vehicle type in peak hours and 2 hours representing the day and night nonpeak hours were obtained. The volume of passenger cars and equivalent volume of heavy vehicles and light non-passenger car vehicles at peak hours and 2 representative hours of non-peak day and night hours were also obtained. These calculations were performed with Excel. The passenger car equivalent factors of vehicle types are given in Table 1 ([Transportation and Traf](#page-5-0)fic Organization of [Mashhad, 2008](#page-5-0)).
-
-The total volume (passenger car equivalent) can be obtained with the following equation:
-
-$$V_{te} = e_1 N_1 + \dots + e_n N_n (9)$$
-
-Table 1 The passenger car equivalent factors of vehicle types.
-
-| Heavy<br>vehicles | Bicycle and<br>Motorcycle | Intracity<br>bus | Intercity<br>bus | Minibus | Pickup | Taxi | Passenger<br>car |
-|-------------------|---------------------------|------------------|------------------|---------|--------|------|------------------|
-| 2.5               | 0.5                       | 5                | 2.5              | 2       | 1      | 2    | 1                |
-
-where Vte is the total equivalent volume and ei and Ni are the equivalent factor and number of vehicles of type i, respectively. The number of each type of vehicle is given by
-
-$$N_i = \frac{P_i V_{te}}{e_1 P_1 + \dots + e_n P_n} \tag{10}$$
-
-where Pi is the percentage of vehicles of type i.
-
-Because the number of vehicle types is counted hourly, these data are represented in terms of vehicle per hour, Veh/hr. Eq. (10) was used in Excel to calculate the number of each vehicle type according to the total equivalent volume with the percentage of each vehicle type on any road segment at any hour. The summary statistics of crashes and independent variables of the models in the years 2006 and 2007 for 156 sections of Mashhad urban highways in 936 sub-sections are presented in Table 2.
-
-## 5. Statistical modeling
-
-After collecting the data, the models were developed for accidents with property damage only (no injury) and more severe (injury or fatal) accidents to identify the factors influencing the accident occurrence of urban highways. Independent variables applied in the models included xij,1, xij,<sup>2</sup> and xij,3, the volume of passenger cars, heavy vehicles, and light non-passenger car vehicles on the i-th section in the j-th period.
-
-#### 5.1. Poisson regression model
-
-To estimate the unknown parameters of the model, the iteration method of Newton-Raphson is applied. First, a vector of regression parameters is considered as a primary estimate, β̂ 0, and the second parameter estimate is obtained from iteration relation (11) and so on until the difference between two successive estimates becomes less than the tolerance level. The estimate before the last is the desired estimated coefficient. The iteration relation is as follows:
-
-$$\hat{\beta}_{k+1} = \hat{\beta}_k - \hat{H}_k^{-1} \hat{g}_k \tag{11}$$
-
-where ĝ<sup>k</sup> is the gradient evaluated at β̂ <sup>k</sup>, the first partial derivative of the log likelihood in the gradient, and Ĥ<sup>k</sup> is the Hessian evaluated at β̂ <sup>k</sup>, the second partial derivative of log likelihood, as follows:
-
-$$g = \sum_{i=1}^{n} (y_i - \hat{\lambda}_i) x_i, H = -\sum_{i=1}^{n} \hat{\lambda}_i x_i' x_i$$
- (12)
-
-For evaluating the significance of independent variables, the inverse Hessian obtained at the last iteration will be the asymptotic variance matrix. The variance of the estimates is the diagonal elements and the standard errors their square roots. The t statistic for each parameter is also constructed as the ratio of the parameter estimate over its standard error. If the p-value of a parameter is less than the required level of significance (0.05 or less), the corresponding variable is significant and will stay in the model. Otherwise, it is
-
-Table 2 Summary statistics of accidents and independent variables.
-
-| Variable                                                                                                        | Mean               | S.D.              | Min            | Max                  |
-|-----------------------------------------------------------------------------------------------------------------|--------------------|-------------------|----------------|----------------------|
-| Dependent variables<br>number of accidents with property loss only<br>number of more severe accidents           | 7.07<br>0.97       | 8.86<br>1.43      | 0<br>0         | 80<br>12             |
-| Independent variables<br>Passenger car volume<br>Heavy vehicle volume<br>Light non-passenger car vehicle volume | 1046<br>562<br>551 | 807<br>427<br>419 | 33<br>29<br>42 | 4593<br>2425<br>2225 |
-
-<span id="page-3-0"></span>neglected in the model [\(Anselin, 2002](#page-4-0)). Such calculations were proceeding by SAS, estimating the parameters and evaluating their significance for no injury and more severe accidents, which is presented in Table 3. Thus, the evaluated number of no injury accidents on section i in period j is obtained from the following equation:
-
-$$\lambda_{ij} = exp(x'_{ij}\beta) = exp(0.0003x_{ij,1} + 0.0005x_{ij,3})$$
- (13)
-
-The evaluated number of more severe accidents on section i in period j is also as follows:
-
-$$\lambda_{ij} = exp(-0.6134 + 0.00084x_{ij,3})$$
- (14)
-
-Knowing the evaluated value of λij, the Poisson model developed for estimating the likelihood of yij no injury and more severe accidents is obtained as follows:
-
-$$P(y_{ij}) = \frac{e^{-\lambda_{ij}} \lambda_{ij}^{y_{ij}}}{y_{ij}!} \tag{15}$$
-
-# 5.2. Negative binomial regression model
-
-For estimating the regression coefficients and dispersion parameters, the Newton-Raphson iteration procedure is applied as in the Poisson model. Using r\*= 0(r= 1) in the first (Poisson model), the regression parameters β are estimated with the following iteration:
-
-$$\hat{\beta}^{(t+1)} = \hat{\beta}^{(t)} - \left[\hat{H}_{\beta}^{(t)}\right]^{-1} \hat{g}_{\beta}^{(t)} \tag{16}$$
-
-where g<sup>β</sup> = − ∑ i= 1 ∂Li <sup>∂</sup><sup>β</sup> ; <sup>H</sup><sup>β</sup> <sup>=</sup> − ∑ i= 1 ∂<sup>2</sup>Li ∂β∂β′ . Then, r\* is estimated iteratively as
-
-$$\hat{r}^{*(t+1)} = \hat{r}^{*(t)} - \left[\hat{H}_r^{(t)}\right]^{-1} \hat{g}_r^{(t)}$$
-(17)
-
-where gr <sup>=</sup> −∑ <sup>∂</sup>Li <sup>∂</sup><sup>r</sup> ; Hr <sup>=</sup> −∑ <sup>∂</sup><sup>2</sup> Li <sup>∂</sup><sup>r</sup> <sup>2</sup>. Now, r\* and the estimated regressors are applied as the initial estimate θ in the following iteration to estimate the updated values of r\* and β:
-
-$$\hat{\theta}^{(t+1)} = \hat{\theta}^{(t)} - \left[\hat{H}_{\beta r}^{(t)}\right]^{-1} \hat{g}_{\beta r}^{(t)} \tag{18}$$
-
-Table 3 Estimation results for Poisson and negative binomial (NB) regression models.
-
-| Variable                                  | No injury accidents |                | Injury or fatal<br>accidents |                |  |
-|-------------------------------------------|---------------------|----------------|------------------------------|----------------|--|
-|                                           | Parameter           | t<br>statistic | Parameter                    | t<br>statistic |  |
-| Poisson model                             |                     |                |                              |                |  |
-| Constant                                  | −0.1611             | −1.77          | −0.6134⁎                     | −2.56          |  |
-| Passenger car volume                      | 0.0003              | 15.78          | −0.00006                     | −0.95          |  |
-| Heavy vehicle volume                      | 0.00002             | 0.63           | −0.00024                     | −1.92          |  |
-| Light non-passenger car vehicle<br>volume | 0.0005              | 12.80          | 0.00084                      | 7.31           |  |
-| Negative binomial model                   |                     |                |                              |                |  |
-| Constant                                  | −0.8112             | −2.91          | −0.9231                      | −2.77          |  |
-| Passenger car volume                      | 0.00042             | 5.29           | −0.00012                     | −1.33          |  |
-| Heavy vehicle volume                      | −0.00001            | −0.10          | −0.00014                     | −1.17          |  |
-| Light non-passenger car vehicle<br>volume | 0.00061             | 4.73           | 0.00102                      | 6.33           |  |
-| Dispersion parameter                      | 1.0874              | 17.04          | 0.7832                       | 7.39           |  |
-
-<sup>⁎</sup> Highlighted coefficients are significant at 5%.
-
-where
-
-$$\theta = \begin{bmatrix} \beta \\ r^* \end{bmatrix}, g_{\beta r} = \begin{bmatrix} g_{\beta} \\ g_r \end{bmatrix}, H_{\beta r} = \begin{bmatrix} H_{\beta} & -\sum \frac{\partial^2 L_i}{\partial r^* \partial \beta} \\ \left(-\sum \frac{\partial^2 L_i}{\partial r^* \partial \beta}\right)' & H_r \end{bmatrix}$$
-(19)
-
-The estimated values of r\* and β in the iteration before the last, θ̂ are of interest.r= exp(r\*) is the inverse dispersion parameter [\(Agresti,](#page-4-0) [2002\)](#page-4-0). The significance evaluation of the independent variables of the NB regression model is similar to that of the Poisson model. The results of developing the NB model for no injury and more severe accidents are presented in Table 3. Therefore, the evaluated number of no injury accidents on section i in period j is obtained from following equation:
-
-$$\mu_{ij} = exp(x_{ij}\beta) = exp(-0.8112 + 0.00042x_{ij,1} + 0.00061x_{ij,3})$$
-(20)
-
-The evaluated number of more severe accidents on section i in period j is also obtained as
-
-$$\mu_{ij} = exp(-0.9231 + 0.00102x_{ij,3})$$
- (21)
-
-Knowing the evaluated value of μij, r= 1/α= 0.92 and r= 1.277 for no injury and more severe accidents, respectively, the NB model for estimating the likelihood of yij no injury and more severe accidents is obtained as
-
-$$p(y_{ij}) = \frac{\Gamma(y_{ij} + r)}{y_{ij}\Gamma(r)} \left[ \frac{\mu_{ij}}{\mu_{ij} + r} \right]^{y_{ij}} \left[ \frac{r}{\mu_{ij} + r} \right]^{r}$$
-(22)
-
-# 6. Goodness-of-fit evaluation and comparison of models
-
-After developing the models and identifying the effective factors in accident occurrence, the evaluation and comparison between models is discussed. To evaluate the goodness-of-fit of the models, the Akaike Information Criterion (AIC) was used. The lower the AIC, the better the model fit, and the model with the least AIC is the best one. The goodness-of-fit of the applied models cannot be evaluated independently, but this fit is evaluated by comparing the value of AIC for each model. For comparing models, first the Poisson and NB models are compared together with respect to the data dispersion. To do this, the significance of the dispersion parameter in the NB model and the likelihood ratio (LR) test were used. The statistic of the LR test is obtained as
-
-$$LR = -2\left(LL_p - LL_{NB}\right) \tag{23}$$
-
-where LLp and LLNB are the log-likelihood of the Poisson and NB models, respectively. This statistic has a Chi-squared distribution with k−1 degrees of freedom, in which k is the number of regression coefficients. If this statistic is greater than the value of χ0.05 <sup>2</sup> (k−1), then the second model is better than the first. The consequence of this test for comparing models is usually equivalent to evaluating the significance of the dispersion parameter. The second stage of comparison is the goodnessof-fit evaluation of models and their fit comparison. The AIC is calculated using the following equation [\(Liu & Cela, 2008](#page-4-0)):
-
-$$AIC = -2LL + 2k (24)$$
-
-where LL is log-likelihood and k is the number of parameters. The results of the second stage often approve the conclusion of the first stage. The <span id="page-4-0"></span>results of the goodness-of-fit evaluation of models and their comparison are presented in Table 4.
-
-The results for no injury accidents show that the dispersion parameter is significant in the NB model, and the negative value of two times the log likelihood is much greater for the Poisson model than the NB model. The value of the LR statistic is LR=−2(LLp−LLNB)= 3377.3, which is much greater than χ0.05 <sup>2</sup> (3) equal to 7.81. Therefore, it is concluded that the NB regression model is better than the Poisson model for no injury accidents. The comparison of the AIC value between models demonstrates that the NB model with lower AIC is better than the Poisson model for no injury accidents.
-
-For more severe accidents, the dispersion parameter is also significant, and the LR statistic is equal to LR=−2(LLp−LLNB)= 148.4, which indicates that the NB model is better than the Poisson model. Because the AIC value for the NB model is less than that of the Poisson model, it is evident that this model is better than the Poisson model for modeling injury or fatal accidents on urban highways. Therefore, the NB regression model is an efficient and worthy model for modeling no injury and more severe accidents on urban highways. This claim can be evaluated by examining the validity of this model in predicting crashes on urban highways.
-
-## 7. Validating the NB regression model
-
-To validate the NB regression model for modeling accidents, this model was used to predict the number and likelihood of no injury and more severe accidents on other urban highways to examine the validity of the model by comparing the observed and predicted numbers of accidents on each section of highway over a specified period. The statistics for the accidents and traffic flow on the highway were provided as before. The Pearson residuals test was used for validation, which is described as follows (Agresti, 2002):
-
-$$S = \sum_{i=1}^{n} \sum_{j=1}^{3} s_{ij} = \sum_{i=1}^{n} \sum_{j=1}^{3} \left( \frac{y_{ij} - M(y_{ij})}{\sqrt{V(y_{ij})}} \right)^{2} = \sum_{i=1}^{n} \sum_{j=1}^{3} \frac{(y_{ij} - \mu_{ij})^{2}}{\mu_{ij} + \alpha \mu_{ij}^{2}}$$
-(25)
-
-where yij is the observed and μij the predicted number of accidents on i-th section of the highway in the j-th period of the day, α is the dispersion parameter and n is the number of sections of the highway. S follows a Chi-squared distribution with N-k degrees of freedom, where N is the number of observations and k is the number of regression coefficients. The closer together the observed and predicted numbers of accidents are, the more valid the model is. The critical value of S is equal to χ0.05 <sup>2</sup> (N−k), and a value of S less than it implies the validity of the model. Referring to the development of the NB model, the predicted numbers of no injury and more severe accidents on the i-th section of the highway in the j-th period of the
-
-Table 4 Results of goodness of fit evaluation and comparison between the Poisson and NB models.
-
-|                           | Poisson | Negative binomial |
-|---------------------------|---------|-------------------|
-| No injury accidents       |         |                   |
-| alpha                     |         | 1.0874 (17.04)    |
-| -2LL                      | 9054.8  | 5377.6            |
-| AIC                       | 9062.8  | 5387.6            |
-| Injury or fatal accidents |         |                   |
-| alpha                     |         | 0.7832 (7.39)     |
-| -2LL                      | 2485.3  | 2336.9            |
-| AIC                       | 2493.3  | 2346.9            |
-
-day were obtained from Eqs. [\(20\) and \(21\),](#page-3-0) respectively. The highway was divided into 40 sections and three periods of the day, leading to N= 120. For accidents with property loss only, S was equal to 41.32, which is less than χ0.05 <sup>2</sup> (120−3)= 143.21 and implies that the model is valid. However, for more severe accidents, S was equal to 39.72, which is less than χ0.05 <sup>2</sup> (120−2)= 144.62 and implies the validity of NB regression model for modeling more severe accidents.
-
-### 8. Conclusions
-
-The results of this research indicate that the volume of passenger cars and light non-passenger car vehicles plays a significant role in the likelihood of no injury accidents, but the volume of heavy vehicles does not have much effect on the likelihood of no injury accidents on urban highways. The volume of light non-passenger car vehicles increases the likelihood of more severe accidents, but the volume of passenger cars and heavy vehicles do not have much influence on the likelihood of more severe accidents. Therefore, it is concluded that light non-passenger car vehicles, including taxis and motorcycles, play a considerable part in the occurrence of accidents on urban highways, including both no injury and more severe accidents, but heavy vehicles do not. These conclusions will be helpful for future projects involving the management of traffic on urban highways. In these projects, planning to control the role of taxis and motorcycles in accident occurrence should be adopted. This could include the separation of these vehicles types from normal flow on urban highways. The results also indicate that the negative binomial regression model is a valid and efficient model for modeling and predicting the accidents on urban highways. The study had some limitations, two of which are related to the research data, as mentioned in [Section 4:](#page-2-0) the volume data for each kind of vehicle were not readily available, and the models did not consider the seasonal changes of traffic because the statistics for traffic volume in different seasons were not available. The other limitation of the research is related to the models used for modeling accidents. Poisson and NB regression models have their own limitations.
-
-#### References
-
-Agresti, A. (2002). Categorial Data Analysis: Case Study - Negative Binomial Regression model. Nascar Lead Changes, 1975–1979. Hoboken, NJ: Wiley [www.nascar.com](http://www.nascar.com)
-
-Anastasopoulos, C., & Mannering, F. L. (2009). A note on modeling vehicle accident frequencies with random-parameters count models. Accident Analysis and Prevention, 41, 153–159.
-
-Anselin, L. (2002). Appendix C: Ordinary Least Square and Poisson Regression Models. Champaign-Urbana, IL: University of Illinois.
-
-Attaran, H. (1996). : Iran Journal of Legal Medicine.
-
-Ayati, E. (2008). The cost of traffic accidents in Iran (Publication No. 345). : Ferdowsi University of Mashhad.
-
-Chang, L. -Y. (2005). Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network. Journal of Safety Science, 43, 541–557.
-
-Delen, D., Shadra, R., & Bessonov, M. (2006). Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accident Analysis and Prevention, 38, 434–444.
-
-Lee, J., & Mannering, F. (2002). Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. Accident Analysis and Prevention, 41, 798–808.
-
-Legal Medicine Organization of Iran. (2002).
-
-Liu, W., & Cela, J. (2008). Count Data Models in SAS (Paper 371–2008). SAS Global Forum 2008: Statistics and Data Analysis.
-
-Lord, D. (2006). Modeling motor vehicle crashes using Poisson-gamma models: examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter. Accident Analysis and Prevention, 38 (4), 751–766.
-
-Lord, D., & Park, Y. -J. (2008). Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates. Accident Analysis and Prevention, 40(4), 1441–1457.
-
-Lord, D., Washington, S. P., & Ivan, J. N. (2007). Further notes on the application of Zero-inflated models in highway safety. Accident Analysis and Prevention, 39,
-
-- <span id="page-5-0"></span>Ma, J., Kockelman, K. M., & Damien, P. (2008). A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods. Accident Analysis and Prevention, 40, 964–975.
-- Milton, J. C., Shankar, V. N., & Mannering, F. L. (2008). Highway accident severities and the mixed logit model: An exploratory empirical analysis. Accident Analysis and Prevention, 40, 260–266.
-- Savolainen, P. T., & Tarko, A. P. (2005). Safety impacts at intersections on curved segments. Transportation Research Record, 1908, 130–140.
-- Transportation and Traffic Organization of Mashhad, Iran (2008). Comprehensive studies of transportation office. Fourth statistical paper of transportation of Mashhad.
-- Wang, C., Quddus, M. A., & Ison, S. G. (2009). Impact of traffic congestion on road accidents: A spatial analysis of the M25 motorway in England. Accident Analysis and Prevention, 41, 798–808.
-
-Ehsan Abbasi is a M.Sc. He received his M.D. in the Road and Transportation Engineering from the Ferdowsi University of Mashhad, Iran. His areas of expertise include safety of traffic and modeling accidents. His recent work has been on modeling accidents on urban highways in terms of traffic flow and road geometry characteristics, using Poisson, NB and ZI regression models.
-
-Dr. Esmaeel Ayati is a Full Professor in the Department of Engineering at Ferdowsi University of Mashhad, Iran. He received his M.D. in Road and Civil Engineering from Tehran University and his Ph.D. in Road and Transportation Engineering from Aston University. His area of expertise is safety of traffic and his researches on evaluating the cost of traffic accidents is unique in Iran and his book titled "The Cost of Traffic Accidents in Iran" is the only valid reference in this area in Iran.

Zotero/001_artiklid/Road Safety Indicators/carterroadsafetyfundamentalsconcepts2017.md

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----
-category: literaturenote
-citekey: carterroadsafetyfundamentalsconcepts2017
-title: "Road Safety Fundamentals: Concepts, Strategies, and Practices that Reduce Fatalities and Injuries on the Road"
-authors: "Carter, Daniel; Gelinne, Dan; Kirley, Bevan; Sundstrom, Carl; Srinivasan, Raghavan; Palcher-Silliman, Jennifer; United States. Department of Transportation. Federal Highway Administration. Office of Safety"
-year: 2017
-date: 2017-11-01 2017-11-01
-url: /view/dot/49570
-zotero_key: CQRDSY86
-zotero_storage: DJC4MAUZ
-collections: doktoritöö / HLO
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Carter, Daniel"
-status: converted
----
-# **Road Safety Fundamentals**
-
-![](_page_0_Picture_1.jpeg)
-
-**Concepts, Strategies, and Practices that Reduce Fatalities and Injuries on the Road**
-
-![](_page_0_Picture_3.jpeg)
-
-## **Notice**
-
-This document is disseminated under the sponsorship of the U.S. Department of Transportation (USDOT) in the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained in this document.
-
-The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers' names appear in this report only because they are considered essential to the objective of the document.
-
-## **Quality Assurance Statement**
-
-The Federal Highway Administration (FHWA) provides high-quality information to serve Government, industry, and the public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement.
-
-This document can be downloaded for free in full or by individual unit at: [https://rspcb.safety.fhwa.dot.gov/r](https://rspcb.safety.fhwa.dot.gov/training.aspx)sf/
-
-| 1. Report No.<br>FHWA-SA-18-003                                                                                                                                                                                                                      | 2. Government Accession No.                                                                                                                                                                                                                                                                   |                                 | 3. Recipient's Catalog No.                                                                         |
-|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------|----------------------------------------------------------------------------------------------------|
-| 4. Title and Subtitle<br>Road Safety Fundamentals:<br>on the Road                                                                                                                                                                                    | Concepts, Strategies, and Practices that Reduce Fatalities and Injuries                                                                                                                                                                                                                       | 5. Report Date<br>November 2017 |                                                                                                    |
-| 7. Author(s)                                                                                                                                                                                                                                         |                                                                                                                                                                                                                                                                                               |                                 | 6. Performing Organization Code                                                                    |
-| Lead Editor: Daniel Carter, P.E., Senior Research Associate                                                                                                                                                                                          |                                                                                                                                                                                                                                                                                               |                                 |                                                                                                    |
-| Unit Authors:                                                                                                                                                                                                                                        | Unit 1: Dan Gelinne, Program Coordinator, UNC Highway Safety                                                                                                                                                                                                                                  |                                 | 8. Performing Organization Report No.                                                              |
-| Research Center<br>Center<br>Research Center                                                                                                                                                                                                         | Unit 2: Bevan Kirley, Research Associate, UNC Highway Safety Research<br>Unit 3: Carl Sundstrom, P.E., Research Associate, UNC Highway Safety<br>Unit 4: Raghavan Srinivasan, Ph.D., Senior Transportation Research                                                                           | and Address                     | 9. Performing Organization Name<br>University of North Carolina,<br>Highway Safety Research Center |
-| Engineer; Daniel Carter<br>Manager, UNC Highway Safety Research Center                                                                                                                                                                               | Unit 5: Daniel Carter; Jennifer Palcher-Silliman, Communications                                                                                                                                                                                                                              |                                 | 10. Work No. (TRAIS)                                                                               |
-| Safety Research Center                                                                                                                                                                                                                               | Layout and Graphics: Graham Russell, Graphic Designer, UNC Highway                                                                                                                                                                                                                            |                                 | 11. Contract or Grant No.                                                                          |
-| Technical Editors: Jennifer Palcher-Silliman; Patty Harrison,<br>Communications Specialist, UNC Highway Safety Research Center;<br>Caroline Mozingo, Senior Manager of Communications, Education and<br>Outreach, UNC Highway Safety Research Center |                                                                                                                                                                                                                                                                                               | Text book                       | 13. Type of Report and Period Covered                                                              |
-| 12. Sponsoring Agency Name and Address<br>Federal Highway Administration Office of Safety<br>1200 New Jersey Ave., SE<br>Washington, DC 20590                                                                                                        |                                                                                                                                                                                                                                                                                               |                                 | 14. Sponsoring Agency Code                                                                         |
-| 15. Supplementary Notes                                                                                                                                                                                                                              | The project manager for the textbook was Felix H. Delgado, P.E., FHWA Office of Safety                                                                                                                                                                                                        |                                 |                                                                                                    |
-| 16. Abstract                                                                                                                                                                                                                                         | This book provides an introduction to the fundamental concepts of road safety. The book's goal is to equip<br>the reader with a broad base of knowledge about road safety. Thus, the focus is in communicating concepts<br>rather than providing instruction in detailed analysis procedures. |                                 |                                                                                                    |
-| students in a university setting.                                                                                                                                                                                                                    | The audience for this book is two-fold. First, this is intended for those whose job addresses some aspect of<br>road safety, particularly in a public agency setting. Second, this book is intended for professors and                                                                        |                                 |                                                                                                    |
-| disciplines will benefit from the concepts presented here.                                                                                                                                                                                           | This book seeks to lay the foundation of road safety knowledge regardless of a particular discipline.<br>Professionals with a background in engineering, planning, public health, law enforcement, and other                                                                                  |                                 |                                                                                                    |
-| 17. Key Words                                                                                                                                                                                                                                        |                                                                                                                                                                                                                                                                                               |                                 | 18. Distribution Statement                                                                         |
-
-**No restrictions**
-
-22. Price **FREE**
-
-**data, multidisciplinary approaches**
-
-19. Security Classif. (of this report) **Unclassified**
-
-**road safety, road safety management process, site-level management, system level management, human behavior, safety performance measures, road safety**
-
-> 20. Security Classif. (of this report) **Unclassified**
-
-21. No. of Pages
-
-**188**
-
-![](_page_3_Picture_0.jpeg)
-
-## **TABLE OF CONTENTS**
-
-## **UNIT <sup>1</sup> Foundations of Road Safety**
-
- **Chapter 1: Context of Road Safety**
-
- **Chapter 2: Road Safety Through the Years**
-
- **Chapter 3: Multidisciplinary Approaches**
-
- **Chapter 4: Road Users**
-
-# **UNIT <sup>2</sup> Human Behavior and Road Safety Chapter 5: Understanding Human Behavior**
-
- **Chapter 6: Changing Human Behavior**
-
-# **UNIT <sup>3</sup> Measuring Safety Chapter 7: Importance of Safety Data**
-
- **Chapter 8: Types of Safety Data**
-
- **Chapter 9: Improving Safety Data Quality**
-
-## **UNIT <sup>4</sup> Solving Safety Problems**
-
- **Chapter 10: Road Safety Management Process**
-
- **Chapter 11: Site-Level Safety Management**
-
- **Chapter 12: System-Level Safety Management**
-
-## **UNIT <sup>5</sup> Implementing Road Safety Efforts**
-
- **Chapter 13: Who Does What**
-
- **Chapter 14: Road Safety Research**
-
- **Chapter 15: Strategic Communications**
-
- **Chapter 16: Advancing Road Safety**
-
-## **INTRODUCTION**
-
-This book provides an introduction to many of the fundamental concepts of road safety. These concepts cover areas such as the nature of road safety issues, human behavior in the road environment, and identifying and solving road safety problems. The goal of this book is to equip the reader with a broad base of knowledge about road safety. Thus, the focus of the text is in communicating concepts rather than providing instruction in detailed analysis procedures.
-
-The audience for this book is two-fold. First, this is intended for those whose job addresses some aspect of road safety, particularly in a public agency setting. This is especially relevant for individuals who have been tasked with managing road safety but who do not have formal training in road safety management. In order to show practical applications of each road safety concept, this book contains many examples that demonstrate the concepts in real-world settings. Second, this book is intended for professors and students in a university setting who can use individual units or this entire book to add an emphasis on road safety as part of graduatelevel work. Each unit provides learning objectives and sample exercises to assist professors as they incorporate content into their courses.
-
-As a final note, this book is intended to lay the foundation of road safety knowledge regardless of a particular discipline. Professionals with a background in engineering, planning, public health, law enforcement, and other disciplines will benefit from the concepts presented here.
-
-## **ABOUT THIS BOOK**
-
-This book is divided into five units according to major topics of road safety knowledge. Each unit is divided into multiple chapters that address the primary concepts of the unit. The beginning of each unit provides a list of learning objectives that indicate what the reader will be able to understand, describe, identify, or otherwise do by the end of the unit.
-
-Each chapter presents call-out boxes, glossary definitions, and references as shown below.
-
-**Call-out boxes** are provided throughout the book to provide examples of concepts presented in the chapter.
-
-conference for March 1926. During the interim between the two drew up a model "**Uniform Vehicle Code**" covering registration and titling of vehicles, licensing of drivers, and operation of vehicles on the highways. The code incorporated the best features of the numerous and varied State laws then on the statute books. The second conference approved this code and recommended it to the State legislatures as the basis for uniform motor vehicle legislation.
-
-Studies following this 1926 conference concluded that determining the causes of crashes was far more difficult than they had presumed. The problem warranted a sustained program of research by a national organization. The Conference agreed, and the Highway Research Board (HRB) organized the Committee on Causes and Prevention of Highway Accidents
-
-**Balanced Design for Safety** In the 1920s and 1930s, it was good
-
-engineering practice to design new highways as much as possible in long straight lines or "tangents." When it became necessary to change direction, the engineer laid out a circular curve, the radius of which he selected to fit
-
-the ground with the least construction policy. In practice, engineers made the it was cheaper to do so, but with little consistency. Engineers expected motorists driving these roads to adjust their speeds curves safe design speed might be considerably lower than the posted
-
-and safe design speed on curves. In 1935, highway engineer Joseph Barnett of the BPR an "assumed design speed," a comfortable top speed for drivers outside of urban areas.
-
-highway engineers to worry about this inconsistency between posted speed limits
-
-the country, and between 1921 and 1939, the distance of paved roads
-
-of State Highway Officials in 1938, Barnett's "balanced design" concept became a permanent feature of U.S. roadway design. Today, standards for designing curves, such as design speed, curve radius and superelevation (the tilt of the road through Geometric Design of Highways and Streets, State Highway Transportation Officials.
-
-ROAD SAFETY FUNDAMENTALS UNIT 1: FOUNDATIONS OF ROAD SAFETY **1-13**
-
-nationwide. The HRB played a major part in subsequent efforts to reduce
-
-#### **Federal Government Role in Highway Development**
-
-The growing use of motor vehicles during the 1920s was mirrored by the expansion of the Federal building roads. In its early form, the Office of Public Roads was organized under the U.S. Department of Agriculture, playing a large role in
-
-Following the Federal Aid Road Act of 1916, this office would become the Bureau of Public Roads (BPR), with State highway departments on road projects. Work continued on the expansion of highways across Federal Highway Printing Office,
-
-**Uniform Vehicle Code** A code covering registration and licensing of
-
-**C**
-
-the highways.
-
-**Glossary definitions** are provided along the side of the page. These correspond to words in bold face in the page content.
-
-#### **References C**
-
-to source material are provided along the side of the page. These are numbered consecutively through the unit and correspond to numbers in the page content.
-
-![](_page_7_Picture_0.jpeg)
-
-## **Foundations of Road Safety UNIT 1**
-
-#### **LEARNING OBJECTIVES**
-
-After reading the chapters and completing exercises in Unit 1, the reader will be able to:
-
-- J **DESCRIBE** the importance of road safety and how it relates to public health, economic, environmental and demographic trends
-- J **RECOGNIZE** roles and responsibilities of various disciplines and approaches to improving road safety
-- J **DISTINGUISH** between nominal and substantive safety
-- J **IDENTIFY** key points in the history of road safety in the U.S., including key legislation and agency formation, and understand how these decisions have shaped today's roadways
-- J **IDENTIFY** different groups of road users and challenges unique to each group
-
-## **Context of Road Safety**
-
-Road safety is an important part of everyday life. Across the nation, people use roads and sidewalks to get to work, school, stores, and home. Public agencies work to ensure that people arrive at their destination without incident.
-
-However, not every trip is without incident. Deaths and injuries resulting from motor vehicle crashes represent a significant public health concern. The World Health Organization (WHO) estimates that motor vehicle crashes kill more than 1 million people around the world each year, and seriously injure as many as 20 to 50 million.**<sup>1</sup>** These crashes affect all road users, from vehicle drivers and passengers to pedestrians, bicyclists, and transit users.
-
-Though road safety in the U.S. has steadily improved over time, it remains a priority for transportation agencies, legislators, and advocacy organizations. Over the past 10 years in the U.S., an average of approximately 37,000
-
-![](_page_8_Figure_6.jpeg)
-
-**FIGURE 1-2**: Traffic Fatalities in the U.S. by Person Type, 2013 (Source: NHTSA FARS)
-
-people were killed each year and an estimated 2.3 million were injured in motor vehicle crashes.**<sup>2</sup>** While many of these deaths and injuries are sustained by motor vehicle passengers and drivers,
-
-![](_page_8_Figure_9.jpeg)
-
-[http://www.who.int/](http://www.who.int/features/factfiles/roadsafety/en) [features/factfiles/](http://www.who.int/features/factfiles/roadsafety/en) [roadsafety/en](http://www.who.int/features/factfiles/roadsafety/en)
-
-### **2**
-
-National Highway Traffic Safety Administration (NHTSA). Fatality Analysis Reporting System (FARS). [http://www.nhtsa.](https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars) [gov/FARS](https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars)
-
-![](_page_8_Figure_13.jpeg)
-
-**FIGURE 1-1**: Traffic Fatalities in the U.S. by Year, 1983-2013 (Source: NHTSA FARS)
-
-![](_page_9_Figure_0.jpeg)
-
-**FIGURE 1-3**: Fatality Rate and Vehicle Miles Traveled, 1966-2013 (Source: NHTSA FARS)
-
-#### **Crash frequency**
-
-The number of crashes occurring per year or other unit of time.
-
-#### **Crash rate**
-
-The number of crashes normalized by a particular population or metric of exposure.
-
-#### **Crash outcome**
-
-Measured by the types of injuries sustained to the people involved in the crash.
-
-they also impact motorcyclists, pedestrians, bicyclists, and users of transit vehicles. This challenge requires a comprehensive approach to improving safety, involving numerous stakeholders and decision makers from a variety of perspectives and disciplines.
-
-## **Defining Safety**
-
-In the simplest terms, safety can be defined as the absence of risk or danger. Focusing this term to address transportation, road safety can be characterized by the ability of a person to travel freely without injury or death. A perfectly safe transportation system would not experience crashes between various road users. Though absence of all crashes is an optimal condition, and many transportation agencies have a goal of zero deaths on the road, the reality is that people continue to get injured or killed on streets and highways across the nation. The challenge posed to the road safety field is to minimize the frequency of crashes and the resulting deaths and injuries using all currently available tools, knowledge, and technology. This challenge is made
-
-more complex due to the multitude of factors influencing safety, from infrastructure to vehicle design to human behavior.
-
-Road safety professionals typically measure safety by the number and rate of crashes and by the severity of those crashes. **Crash frequency**, or the number of crashes occurring per year or other unit of time, is another commonly used metric. **Crash rates** are numbers of crashes normalized by a particular population or metric of exposure. Commonly cited crash rates include crashes per 100,000 people living in a particular State, city or country. Some crash rates present crash numbers per miles traveled or licensed drivers. **Crash outcomes** can be measured by the types of injuries sustained to the people involved in the crash, typically categorized by fatalities and injury severity. Focusing on crashes that result in severe injuries and fatalities is one strategy that agencies use to prioritize their safety activities.
-
-In addition to the measures described above, safety professionals can use surrogate measures, such as
-
-conflicts (near misses), avoidance maneuvers, and the time to collision if no evasive action is taken, to determine the level of safety risk and identify specific problems. Safety problems may exist even in locations that do not have a demonstrated history of crashes, just as someone who smokes is at higher risk for lung cancer even if no cancer has yet been detected. This can be especially true for non-motorized road user safety, such as pedestrians and bicyclists, since crashes involving these road users may be infrequent and appear random at first sight. In such locations where crashes are sparse or distributed across the system, safety professionals can use surrogate measures to fill the gaps and assess the road's level of risk. Observing traffic at an intersection, for example, may reveal a pattern of near misses and other conflicts between vehicles and pedestrians. This pattern may not appear in crash data, but can be a valuable source of information to highlight the potential for safety risk.
-
-Safety perception is also an important consideration for travel choices. There are a number of reasons why someone may or may not choose a particular route to drive, walk or bike. Pedestrians who perceive an intersection to be unsafe may cross in a midblock location, where they are more easily able to find a gap in traffic. Motorists may feel uneasy about making a left turn across multiple lanes of traffic, so they may choose to turn right and travel out of their way to perform a U-turn instead. Safety perception impacts road user decisions but is not easily understood by looking at crash data. Safety professionals
-
-can use surveys, driving simulators, and other modern technologies to understand the safety perception of road users.
-
-Evaluating the safety of a particular network, corridor or intersection requires an understanding of both nominal and substantive safety. Originally introduced by Dr. Ezra Hauer,**<sup>3</sup>** these terms offer a helpful framework for assessing the safety of a particular location. Decades of research and evaluation in the field of road safety have revealed a wealth of knowledge concerning the proper designs and policies that contribute to the safety of a particular location. Roadways constructed according to the best and latest recommended research and design standards are said to be nominally safe. **Nominal safety** is an absolute statement about the safety of a location based only on its adherence to a particular set of design standards and related criteria. A road that was nominally safe when it was first opened to traffic may become nominally unsafe when the roadway design standards change, even though the road's crash performance has not changed.
-
-While nominal safety considers the design of a road, it does not incorporate any information about the frequency, type and severity of crashes occurring on the facility. The historical and long-term objective safety of a location based on crash data is known as **substantive safety**. A particular intersection that has experienced fewer than expected crashes over an extended period will be referred to as a substantively safe location, while a corridor with a higher than expected number of crashes is substantively unsafe.
-
-**3**
-
-Hauer, E. *Observational Before/After Studies in Road Safety. Estimating the Effect of Highway and Traffic Engineering Measures on Road Safety.* Pergamon Press. 1997.
-
-### **Nominal safety**
-
-An absolute statement about the safety of a location based only on its adherence to a particular set of design standards and related criteria.
-
-#### **Substantive safety**
-
-Historical and long-term objective safety of a location based on crash data.
-
-Unlike nominal safety, substantive safety operates on a continuum and allows for a range of explanations as to why a particular safety problem exists.
-
-Another key distinction is the fact that a location can be nominally safe – adhering to all standards and design criteria – while experiencing high rates of crashes, making it substantively unsafe. Similarly, a substantively safe location (one that has a lower than expected crash rate) may be nominally unsafe if it does not meet the applicable design standards.
-
-Agencies and safety professionals should strive to prioritize the substantive safety of a facility. Simply building a road that meets all the current design standards will not ensure that the road is substantively safe. Using professional judgement to prioritize safety improvements and select appropriate designs within a range of options, based on observations of road user behavior and other available data, will increase the chance that all factors are considered. The end result will be a road that moves a step closer to the ultimate goal of having a transportation system free of injuries and deaths.
-
-## **Road Safety Decisions and Trade-offs**
-
-The goal of improving safety exists alongside other goals of the transportation system, such as mobility, efficient movement of people and goods, environmental concerns, public health, and economic goals. In this way, transportation professionals and policy makers often refer to
-
-![](_page_11_Figure_5.jpeg)
-
-**FIGURE 1-4**: Comparison of nominal and substantive concepts of [safety http://](https://safety.fhwa.dot.gov/geometric/pubs/mitigationstrategies/chapter1/1_comparnominal.cfm) [safety.fhwa.dot.gov/geometric/pubs/miti](https://safety.fhwa.dot.gov/geometric/pubs/mitigationstrategies/chapter1/1_comparnominal.cfm) [gationstrategies/chapter1/1\\_comparnom](https://safety.fhwa.dot.gov/geometric/pubs/mitigationstrategies/chapter1/1_comparnominal.cfm) [inal.cfm](https://safety.fhwa.dot.gov/geometric/pubs/mitigationstrategies/chapter1/1_comparnominal.cfm) (Source: NCHRP Report 480, Transportation Research Board, 2002)
-
-trade-offs – making a decision to favor one goal at the expense of another. While those in the field of road safety continually look for new designs and technologies to advance all goals, there continue to be many instances where public agencies must weigh competing goals for a location or portion of the road network and decide what trade-offs should be made for the goal of increasing road safety.
-
-Below are several examples:
-
-J **Roundabouts:** A city may decide to install a roundabout at an intersection to decrease the potential conflicts between various movements at the intersection. Safety is improved, especially related to left-turns, since all turns are now part of the circle. However, a roundabout does require traffic on the main road to slow their speeds and navigate through the roundabout. During heavy traffic, especially
-
-if it is unbalanced among the intersection legs, this may cause a decrease in the overall throughput of the intersection. However, this is a trade-off to produce fewer crashes.
-
-- J **Bicycle Helmet Requirements:**  In order to improve bicyclist safety, some jurisdictions have adopted ordinances that require bicyclists to wear helmets. In practice, this can reduce the risk of head injuries among cyclists, but it may also reduce the number of people who choose to ride a bicycle. Adopting such ordinances would prioritize safety while potentially reducing bicycle ridership.
-- J **Red Light Cameras:** Red light camera enforcement monitors signalized intersections and
-
-records information about those who violate red light laws, typically resulting in citations through the mail. These cameras have been shown to improve safety by decreasing the types of crashes that result in serious injury,**<sup>4</sup>** but installing the cameras can be met with significant public opposition.
-
-J **Protected Left Turns:** To minimize the risk of severe left-turn crashes at signalized intersections, engineers may choose to provide left turning drivers an exclusive protected left turn phase (green arrow). While this minimizes crash risk by separating the left turning vehicles from other movements, it also requires that extra time be added specifically for left
-
-Council, et al. Safety Evaluation of Red-Light Cameras. Federal Highway Administration. April 2005. [https://](https://www.fhwa.dot.gov/publications/research/safety/05048/05048.pdf) [www.fhwa.dot.](https://www.fhwa.dot.gov/publications/research/safety/05048/05048.pdf) [gov/publications/](https://www.fhwa.dot.gov/publications/research/safety/05048/05048.pdf) [research/safety/](https://www.fhwa.dot.gov/publications/research/safety/05048/05048.pdf) [05048/05048.pdf](https://www.fhwa.dot.gov/publications/research/safety/05048/05048.pdf) 
-
-**4**
-
-![](_page_12_Picture_6.jpeg)
-
-![](_page_13_Picture_0.jpeg)
-
-turns, which can increase delay for the rest of the traffic at the intersection.
-
-- J **Rumble Strips:** In rural locations, rumble strips can be installed as a measure to alert drivers when they are running off the road. However, these rumble strips are usually installed on the edge of the road or the paved shoulder where bicyclists can safely and comfortably ride separated from traffic. This may result in bicyclists riding in the road where they are more vulnerable to crashes with motor vehicles.
-- J **Trees and Landscaping:** Street trees, shrubs, and other vegetation can serve a valuable purpose in roadside environments – particularly creating shade for the sidewalk, serving as a buffer between the road and sidewalk area, and even creating "visual friction" that can keep vehicle speeds down. However, trees can also pose a safety risk for vehicles that run off the road and collide with them. Vegetation that is
-
-- too close to an intersection can restrict sight distance, contributing to crashes. Selected tree and vegetation removal is an excellent example of a trade-off between safety and other beneficial features of trees.
-- J **Traffic Signal Installation:** A high-speed, high-volume road with multiple traffic lanes may separate housing developments from an elementary school. In order for children living in the housing development to safely travel to and from school, a traffic signal and crosswalk may be installed along the busy road. Motorists will be delayed since they are required to stop for a period of time while the students cross, but the crossing is safer for those students.
-- J **Access Management:** Left turns in and out of shopping centers, especially along multilane roads, can result in severe injuries to motorists when crashes occur. Eliminating these left turns by building raised median islands and consolidating driveways
-
-can eliminate these risky movements; however, this prevents direct access to the stores by potential customers.
-
-Sometimes improving safety for one group of road users may negatively impact the safety of another group. It can also be the case that improving mobility for a group of road users may negatively affect the safety of that same group. There is no absolutely correct answer to many of these trade-offs, as they are all context-specific. Transportation professionals need to discuss the various trade-offs in the context of a particular community's transportation goals. These types of trade-offs are made every day, and require the cooperation of numerous agencies and stakeholders, all of whom have a role to play in transportation decision-making. Despite the temptation to study road safety as a self-contained system, there are a multitude of factors influencing and being influenced by road safety and travel behavior. In order to make informed decisions
-
-![](_page_14_Picture_2.jpeg)
-
-about the transportation system, transportation professionals must understand the impacts – both positive and negative – that design, operations, and policy decisions have on the safety of the transportation network as well as the impacts on other areas such as public health, mobility, environmental quality, and economic growth.
-
-#### **EXERCISES**
-
-- J **LIST** various ways to measure the safety of a road and describe the advantages and disadvantages of each. Consider factors such as the type of information each safety measure provides as well as other issues such as how it can be collected.
-- J **DESCRIBE** a change that could be made to a road or intersection that would improve one transportation goal (e.g., traffic operations, public health, mobility and access, environmental quality, or economic growth) at the expense of the safety of road users.
-- J **DESCRIBE** a change that could be made to a road or intersection that would improve road safety at the expense of another transportation goal (e.g., traffic operations, public health, mobility and access, environmental quality, or economic growth).
-- J **DESCRIBE** a change that could be made to a road or intersection that would improve safety for a road user but not at the expense of other users, or other goals.
-
-## **Road Safety Through the Years**
-
-When examining current efforts to address road safety, it is useful to view them in the context of American transportation history. Recent decades have witnessed numerous advances in the field of road safety. This growing national consciousness about the need for safer roads provides a stark contrast to the first half of the twentieth century when the focus was on highway expansion. The following chapter will provide an overview of the major milestones and achievements that led to the transportation system we have today, as well as the policies and practices that were implemented to address a growing safety problem.
-
-## **Late Nineteenth Century and the Popularity of Bicycling**
-
-An exploration of the history of road safety in the U.S. can begin at many different points – some of our roads were developed as precolonial routes and others were trails blazed by Native Americans. In terms of lasting influence on the modern transportation network,
-
-however, it is most useful to begin the discussion in the late nineteenth century.
-
-In the 1880s and 1890s, bicycles were the dominant vehicle on our nation's roads. With the introduction of the "safety" bicycle, with two wheels of the same size, and the pneumatic tire in the late 1880s, the bicycling craze became an economic, political, and social force in the U.S. By 1890, the U.S. was manufacturing more than 1 million bicycles each year.
-
-At that time, bicyclist behavior particularly careless or risk-taking behavior—was a contributing factor to bicycle crashes. However, the biggest contributor to crashes existed outside the cities; the poor condition of the nation's roads made cycling a laborious and dangerous process. Bicycle groups worked at the Federal, State, and local levels to secure road improvement legislation. The work of these advocacy groups became known as the Good Roads Movement.
-
-![](_page_15_Picture_9.jpeg)
-
-Three men with bicycles on bridge near Pierce Mill, Washington, D.C., 1885. *(Source: Brady-Handy Collection, U.S. Library of Congress)*
-
-#### **Consequences of Speeding**
-
-As in modern times, in the early days of the automobile, posted speed limits were set far below the speed of which most motor vehicles were capable.
-
-With faster and heavier traffic, it became dangerous to drive in the middle of the road and the States began painting centerlines on the pavements to channel traffic in lanes. At 40 miles per hour, these lanes appeared uncomfortably narrow to most motorists, especially when passing trucks. The lane lines also caused trucks to run closer to the shoulder, causing the slab edges and corners of the road to break. To provide greater safety and reduce edge damage, State highway departments built wider pavements and made new roads straighter.
-
-To build support, advocates tailored their message to farmers with the argument that bad roads, by increasing transportation expenditures, cost more than good roads. While engineers, writers, and politicians joined the movement, bicyclists dominated the Good Roads Movement until cars arrived in the early twentieth century.**<sup>5</sup>**
-
-By the close of century, automobiles had slowly begun to share the roads with bicyclists and pedestrians, benefitting from many of the road improvement efforts spearheaded by cycling groups. In 1899, a motor vehicle struck and killed a New York City pedestrian. This event marked the first time in the U.S. that a person was killed in a crash involving a motor vehicle.**<sup>6</sup>**
-
-## **Rise of Motor Vehicles in the Early Twentieth Century**
-
-In 1905, only 78,000 automobiles, most of which were confined to the cities, traveled the U.S. Ten years
-
-These improvements along with mechanical advances in vehicles, such as more powerful engines and four-wheel brakes, in turn encouraged even faster speeds.
-
-Thus, after 1918, highway design followed a spiral of cause and effect, resulting in faster and faster speeds and wider and wider pavements. The motivating force behind this spiral was the driving speed preferences of the great mass of vehicle operators. The public authorities were never able to impose or enforce speed limits for very long if the majority of drivers considered the limits unreasonably low. Now, many current engineering practices use the 85th percentile speed – or the speed at which the majority of drivers travel – as the method of setting speed limits.
-
-later, 2.33 million automobiles were traveling the country's roads, and by 1918, this number had increased to 5.55 million. Mass production made this increase possible as it lowered vehicle manufacturing costs, putting vehicles within the reach of the middle class. As more vehicles became available at a lower price, the pattern of daily travel in the U.S. began to shift. Longer vehicle trips replaced shorter trips by foot or bicycle, and development patterns began to follow suit. The motor age had arrived, and with it a new kind of highway would evolve, designed specifically for motor vehicles.
-
-Expansion of automobile use had immediate positive effects on the national economy and quality of life around the country. Yet proliferation of motor vehicles also had a negative side. As millions of new drivers took to the roads, traffic crashes increased rapidly—tripling from 10,723 in 1918 to 31,215 in 1929.**<sup>7</sup>**
-
-**5**
-
-Source: Richard F. Weingroff, "A Peaceful Campaign of Progress and Reform: The Federal Highway Administration at 100," *Public Roads 57*, no. 2 (Autumn, 1993), [http://www.](https://www.fhwa.dot.gov/publications/publicroads/93fall/p93au1.cfm) [fhwa.dot.gov/](https://www.fhwa.dot.gov/publications/publicroads/93fall/p93au1.cfm) [publications/](https://www.fhwa.dot.gov/publications/publicroads/93fall/p93au1.cfm) [publicroads/93fall/](https://www.fhwa.dot.gov/publications/publicroads/93fall/p93au1.cfm) [p93au1.cfm](https://www.fhwa.dot.gov/publications/publicroads/93fall/p93au1.cfm)
-
-**6**
-
-Soniak, Matt. When and Where Was the First Car Accident? Mental Floss. 2 December 2012. [http://mentalfloss.](http://mentalfloss.com/article/31807/when-and-where-was-first-car-accident) [com/article/31807/](http://mentalfloss.com/article/31807/when-and-where-was-first-car-accident) [when-and-where](http://mentalfloss.com/article/31807/when-and-where-was-first-car-accident)[was-first-car](http://mentalfloss.com/article/31807/when-and-where-was-first-car-accident)[accident](http://mentalfloss.com/article/31807/when-and-where-was-first-car-accident)
-
-**7**
-
-Source: *America's Highways, 1776-1976: A History of the Federal-Aid Program*. Federal Highway Administration (U.S. Government Printing Office, Washington D.C., 1976).
-
-![](_page_17_Picture_0.jpeg)
-
-Secretary of Commerce Herbert Hoover, center, with President Calvin Coolidge, right, in February 1924. *(Source: Harris & Ewing Collection, U.S. Library of Congress)*
-
-## **Shifting Attention to Safety**
-
-Recognizing the rise in crashes and resulting injuries and fatalities, Secretary of Commerce Herbert Hoover convened the First National Conference on Street and Highway Safety in Washington, D.C., in December 1924. Here, for the first time, representatives of State highway and motor vehicle commissions, law enforcement, insurance companies, automobile associations and a multitude of other stakeholders and interest groups met in one place to discuss how to address the growing number of fatalities and serious injuries.
-
-Prior to the conference, committees were established to perform research into areas such as planning and zoning, traffic control, motor vehicles, statistics, and other areas related to road safety. These committees reported wide differences in traffic regulations from State to State and city to city. For instance, twenty States did not attempt to collect crash statistics, only eight States required reporting crashes that resulted in personal
-
-injury, and 38 required railroads and common carriers to report highway crashes. Other committees devoted their attention to issues like traffic control and vehicle speeds, infrastructure and maintenance concerns, and issues impacting vehicles and their drivers.
-
-Conference participants supported a wide range of measures to reduce the rate of crashes and recommended that legislative, administrative, technical, and educational bodies adopt them. Conference participants also recommended that the States take the lead by passing adequate motor vehicle laws and setting up suitable agencies for administering the laws, policing the highways, registering vehicles, and licensing drivers.
-
-To the Federal Government, the conference assigned the role of encouragement, assembly and distribution of information, and the development of recommended practices. Adoption and implementation of these recommended practices would be left to the individual States.
-
-Secretary Hoover called a second conference for March 1926. During the interim between the two conferences, a special committee drew up a model "**Uniform Vehicle Code**" covering registration and titling of vehicles, licensing of drivers, and operation of vehicles on the highways. The code incorporated the best features of the numerous and varied State laws then on the statute books. The second conference approved this code and recommended it to the State legislatures as the basis for uniform motor vehicle legislation.
-
-Studies following this 1926 conference concluded that determining the causes of crashes was far more difficult than they had presumed. The problem warranted a sustained program of research by a national organization. The Conference agreed, and the Highway Research Board (HRB) organized the Committee on Causes and Prevention of Highway Accidents
-
-to coordinate crash research nationwide. The HRB played a major part in subsequent efforts to reduce the consequences of crashes.**<sup>8</sup>**
-
-## **Federal Government Role in Highway Development**
-
-The growing use of motor vehicles during the 1920s was mirrored by the expansion of the Federal Government's role in funding and building roads. In its early form, the Office of Public Roads was organized under the U.S. Department of Agriculture, playing a large role in funding roadways within national parks and forests.
-
-Following the Federal Aid Road Act of 1916, this office would become the Bureau of Public Roads (BPR), charged with working cooperatively with State highway departments on road projects. Work continued on the expansion of highways across the country, and between 1921 and 1939, the distance of paved roads
-
-### **Uniform Vehicle Code**
-
-A code covering registration and titling of vehicles, licensing of drivers, and operation of vehicles on the highways.
-
-**8**
-
-Source: *America's Highways, 1776-1976: A History of the Federal-Aid Program*. Federal Highway Administration (U.S. Government Printing Office, Washington D.C., 1976).
-
-### **Balanced Design for Safety**
-
-In the 1920s and 1930s, it was good engineering practice to design new highways as much as possible in long straight lines or "tangents." When it became necessary to change direction, the engineer laid out a circular curve, the radius of which he selected to fit the ground with the least construction cost, but which could not be less than a certain minimum fixed by department policy. In practice, engineers made the curves sharper than this minimum when it was cheaper to do so, but with little consistency. Engineers expected motorists driving these roads to adjust their speeds to the varying radii, and on the sharper curves safe design speed might be considerably lower than the posted speed limit.
-
-Increasing concern for road safety led many highway engineers to worry about this inconsistency between posted speed limits and safe design speed on curves. In 1935, highway engineer Joseph Barnett of the BPR proposed that all new rural roads conform to an "assumed design speed," a comfortable top speed for drivers outside of urban areas.
-
-With its adoption by American Association of State Highway Officials in 1938, Barnett's "balanced design" concept became a permanent feature of U.S. roadway design. Today, standards for designing curves, such as design speed, curve radius and superelevation (the tilt of the road through a curve) are provided in A Policy on Geometric Design of Highways and Streets, produced by the American Association for State Highway Transportation Officials.
-
-#### **Safety Signs**
-
-Before World War I, most States were using signs to warn road users of danger ahead, particularly at railroad crossings; railroad companies themselves were required to post warning signs at all public road crossings. However, there was little agreement between States about the specific design of these warning devices, and the signs were a variety of shapes, sizes, and colors.
-
-In 1929, the American Engineering Council surveyed sign practices in all U.S. cities with a population of more than 50,000
-
-and created a document that was, in effect, a manual of the best practices of the time. Recognizing the need for standard practices for signs in rural and urban areas, the American Association of State Highway Officials and the National Conference on Street and Highway Safety organized a Joint Committee on Uniform Traffic Control Devices in 1931 and introduced a new manual for national use in 1935. The manual of best practices changed over time to become the Manual on Uniform Traffic Control Devices.
-
-**9**
-
-Source: Weingroff, Richard. *A Peaceful Campaign Of Progress And Reform: The Federal Highway Administration at 100*. Public Roads Magazine. Vol. 57 No. 2. July 1993. [http://www.](https://www.fhwa.dot.gov/publications/publicroads/93fall/p93au1.cfm) [fhwa.dot.gov/](https://www.fhwa.dot.gov/publications/publicroads/93fall/p93au1.cfm) [publications/](https://www.fhwa.dot.gov/publications/publicroads/93fall/p93au1.cfm) [publicroads/93fall/](https://www.fhwa.dot.gov/publications/publicroads/93fall/p93au1.cfm) [p93au1.cfm](https://www.fhwa.dot.gov/publications/publicroads/93fall/p93au1.cfm)
-
-increased from 387,000 miles to nearly 1.4 million miles.**<sup>9</sup>** The BPR recognized that the antiquated highway system was one of the contributing causes of the high crash toll, but did not go so far as to identify primary crash causes or recommend potential solutions.
-
-During this time, an emphasis was placed on expanding the Federal role in the process of highway design and development. This effort culminated in 1944 when Congress approved the development of a National System of Interstate Highways along with that year's Federal Aid Highway Act. Though expansive in scope, calling for a 40,000 mile network, the legislation was not accompanied by any funds to support the development of these highways. Without funding, the legislation did not significantly expand the highway system.
-
-Road safety continued to present a national concern. In May 1946, President Harry S. Truman spoke at the Highway Safety Conference to rally public support to improve State motor vehicle laws, driver licensing, and education. After summarizing
-
-![](_page_19_Picture_9.jpeg)
-
-President Harry S. Truman, 1945. *(Source: U.S. Library of Congress)*
-
-his unsuccessful efforts as a U.S. senator to enact Federal legislation on motor vehicle registration and driver licensing, the President said Congress was not yet ready to interfere with what many perceived as State prerogatives. However, he noted that the Federal Government would not stand aside if the rates of highway fatalities continued to rise.**<sup>10</sup>**
-
-#### **10**
-
-*D. Eisenhower and the Federal Role in Highway Safety*, accessed May 23, 2013, [http://www.](http://www.fhwa.dot.gov/infrastructure/safetyin.cfm) [fhwa.dot.gov/](http://www.fhwa.dot.gov/infrastructure/safetyin.cfm) [infrastructure/](http://www.fhwa.dot.gov/infrastructure/safetyin.cfm)
-
-[safetyin.cfm](http://www.fhwa.dot.gov/infrastructure/safetyin.cfm)
-
-Source: Richard F. Weingroff and the assistance of Sonquela Seabron, *President Dwight* 
-
-![](_page_20_Picture_0.jpeg)
-
-President Dwight D. Eisenhower speaks to the White House Conference on Highway Safety, 1954. *(Source: Eisenhower Presidential Library)*
-
-## **Post War Development and Growth**
-
-Economic conditions following World War II led to even higher levels of driving and automobile ownership. Personal savings of almost \$44 billion created a market for housing and other types of goods, chief among them new automobiles. Automobile production jumped from a nearly 70,000 in 1945 to 3.9 million in 1948.
-
-Because of this increase in vehicle production, motor vehicle registrations spiked and the number of drivers on the nation's roads and highways reached unprecedented levels. Under wartime rationing of rubber, and specifically tires, States had implemented speed controls to reduce wear and tear and improve tire longevity. With the end of rationing and emergency speed controls at the conclusion of the war, highway travel returned to pre-war levels and began a steady climb of about 6 percent per year, which would continue for nearly three decades.
-
-While the increasing popularity of low density housing development (i.e., the suburbs) and the availability of motor vehicles created perfect conditions for more driving,
-
-the nation's roads and highways were unprepared for the increase in traffic. Under wartime restrictions, States were unable to adequately maintain their highways. With widespread operation of overloaded trucks and reduced maintenance, the State highway systems were in worse structural shape post-war than before the war.
-
-## **Development of the Interstate Highway System**
-
-Though the National System of Interstate Highways had been established by legislation in 1944, little progress was made over the next decade. Without funding, established routes were slow to develop. That changed in 1956, when President Dwight D. Eisenhower signed the Federal-Aid Highway Act of 1956. This legislation linked the development of the interstate highway system to the interest of national defense and assigned funding that would rapidly expand the highway network.**<sup>11</sup>** The act established a dedicated funding stream and a plan for highway development that launched the nation into an unprecedented era of expansion in which new interstate corridors linked cities and towns to one another.
-
-**11**
-
-[http://www.](https://www.fhwa.dot.gov/publications/publicroads/06jan/01.cfm) [fhwa.dot.gov/](https://www.fhwa.dot.gov/publications/publicroads/06jan/01.cfm) [publications/](https://www.fhwa.dot.gov/publications/publicroads/06jan/01.cfm) [publicroads/](https://www.fhwa.dot.gov/publications/publicroads/06jan/01.cfm) [06jan/01.cfm](https://www.fhwa.dot.gov/publications/publicroads/06jan/01.cfm)
-
-**12 13**
-
-Source: Richard F. Weingroff and the assistance of Sonquela Seabron, *President Dwight D. Eisenhower and the Federal Role in Highway Safety*, accessed May 23, 2013, [http://www.](https://www.fhwa.dot.gov/infrastructure/safetyin.cfm) [fhwa.dot.gov/](https://www.fhwa.dot.gov/infrastructure/safetyin.cfm) [infrastructure/](https://www.fhwa.dot.gov/infrastructure/safetyin.cfm) [safetyin.cfm](https://www.fhwa.dot.gov/infrastructure/safetyin.cfm)
-
-#### **14**
-
-[http://www.](https://www.fhwa.dot.gov/infrastructure/50interstate.cfm) [fhwa.dot.gov/](https://www.fhwa.dot.gov/infrastructure/50interstate.cfm) [infrastructure/](https://www.fhwa.dot.gov/infrastructure/50interstate.cfm) [50interstate.cfm](https://www.fhwa.dot.gov/infrastructure/50interstate.cfm)
-
-#### **15**
-
-Title 49 of the United States Code, Chapter 301, Motor Vehicle Safety, [https://www.gpo.](https://www.gpo.gov/fdsys/pkg/USCODE-2009-title49/html/USCODE-2009-title49-subtitleVI.htm) [gov/fdsys/pkg/](https://www.gpo.gov/fdsys/pkg/USCODE-2009-title49/html/USCODE-2009-title49-subtitleVI.htm) [USCODE-2009](https://www.gpo.gov/fdsys/pkg/USCODE-2009-title49/html/USCODE-2009-title49-subtitleVI.htm) [title49/html/](https://www.gpo.gov/fdsys/pkg/USCODE-2009-title49/html/USCODE-2009-title49-subtitleVI.htm) [USCODE-2009](https://www.gpo.gov/fdsys/pkg/USCODE-2009-title49/html/USCODE-2009-title49-subtitleVI.htm) [title49-subtitleVI.](https://www.gpo.gov/fdsys/pkg/USCODE-2009-title49/html/USCODE-2009-title49-subtitleVI.htm) [htm](https://www.gpo.gov/fdsys/pkg/USCODE-2009-title49/html/USCODE-2009-title49-subtitleVI.htm)
-
-#### **16**
-
-Federal Motor Vehicle Safety Standard (FMVSS) No. 218, [https://www.federal](https://www.federalregister.gov/documents/2015/05/21/2015-11756/federal-motor-vehicle-safety-standards-motorcycle-helmets) [register.gov/](https://www.federalregister.gov/documents/2015/05/21/2015-11756/federal-motor-vehicle-safety-standards-motorcycle-helmets) [documents/2015/05/](https://www.federalregister.gov/documents/2015/05/21/2015-11756/federal-motor-vehicle-safety-standards-motorcycle-helmets) [21/2015-11756/](https://www.federalregister.gov/documents/2015/05/21/2015-11756/federal-motor-vehicle-safety-standards-motorcycle-helmets) [federal-motor](https://www.federalregister.gov/documents/2015/05/21/2015-11756/federal-motor-vehicle-safety-standards-motorcycle-helmets)[vehicle-safety](https://www.federalregister.gov/documents/2015/05/21/2015-11756/federal-motor-vehicle-safety-standards-motorcycle-helmets)[standards](https://www.federalregister.gov/documents/2015/05/21/2015-11756/federal-motor-vehicle-safety-standards-motorcycle-helmets)[motorcycle-helmets](https://www.federalregister.gov/documents/2015/05/21/2015-11756/federal-motor-vehicle-safety-standards-motorcycle-helmets)
-
-Despite the enthusiasm of political and business leaders, the growth of this system was not without its critics. These critics primarily denounced the destruction of homes and separation of communities that sometimes resulted from new highways bisecting established neighborhoods. Though this opposition halted projects in some locations, it did not stop the expansion of the interstate highway system.
-
-## **Highway Safety Act of 1966**
-
-In 1964, the U.S. faced a sharp rise in the number of traffic fatalities. An increased number of vehicles on the roadways combined with a public culture that did not prioritize roadway safety consciousness led to 47,700 deaths on the nation's highways, an increase of 10 percent over the number of fatalities that occurred in 1963. These deaths prompted the nation to take a hard look at road safety efforts and resulted in Congressional hearings in March 1965 to raise public awareness of the growing national crisis.**<sup>12</sup>**
-
-To respond to these trends, the nation needed a change of direction in the design and operation of its roads and vehicles. This change began with reviewing safety standards in these areas and conducting research to identify effective measures to improve safety. The 1960s was a pivotal decade for road safety due to the passage of laws that provided funding and new policies. On September 9, 1966, President Lyndon B. Johnson signed the National Traffic and Motor Vehicle Safety Act of 1966 and the Highway Safety Act of 1966. The signing ceremony in the Rose Garden
-
-of the White House marked a transformation in the role of the Federal Government in road safety. This role had been growing during the Eisenhower administration, but became a larger area of emphasis as fatalities on the nation's highways climbed toward 50,000. Those in the federal government observed that the steps taken during the previous two decades to reverse the climbing number of fatalities had failed, and they believed that road safety should no longer be left solely to the responsibility of the States, the automobile industry, and the individual drivers.**<sup>13</sup>**
-
-This legislation established the U.S. Department of Transportation (USDOT) and transformed the Bureau of Public Roads into the Federal Highway Administration (FHWA). New bureaus were added to address safety in areas of growing concern, such as the Bureau of Motor Carrier Safety and National Highway Safety Bureau (these would later become the Federal Motor Carrier Safety Administration and the National Highway Traffic Safety Administration, respectively). The USDOT proceeded to develop programs and initiatives and pave the way for activities still in place today.**<sup>14</sup>**
-
-Advances in vehicle design and policy were also an area of emphasis during the 1960s. In 1968, federal legislation required vehicles to provide seat belts. **<sup>15</sup>** Federal law also required States to begin implementing motorcycle helmet laws in order to qualify for particular sources of funding.**<sup>16</sup>** These requirements led to more widespread implementation of safety policies through the late
-
-![](_page_22_Picture_0.jpeg)
-
-President Lyndon B. Johnson signs the National Traffic and Motor Vehicle Safety Act of 1966 and the Highway Safety Act of 1966. *(Source: LBJ Presidential Library)*
-
-1960s and 1970s. Section 402 of the Highway Safety Act established a revenue stream for funding to directly support State programs aimed at improving road safety. Known as the State and Community Highway Safety Grant Program, the funds originally supported a variety of program areas, including many many behavioral safety programs that are still in existence today. **<sup>17</sup>**
-
-## **Energy Crises and Safety Legislation in the 1970s and 1980s**
-
-The 1970s and 1980s were characterized by energy crises in 1973 and 1979 that had immediate and lasting impacts on travel trends. Vehicle Miles Traveled (VMT) decreased following each of these events, as Americans drove less due to rising fuel costs. Strategic legislative action by Congress, such as the National Maximum Speed Law of 1974 which prohibited speeds higher than 55 miles per hour also
-
-helped by decreasing fuel costs. The law would later be repealed in 1995, allowing States to set their own maximum speed limits.
-
-Between 1970 and 2007, there were two periods of time when VMT decreased from the previous year. These years include 1974 and 1979, each of which saw a roughly 18 billion mile decrease in VMT from the previous year.**<sup>18</sup>** As driving decreased, so did traffic fatalities. From 1973 to 1974, for example, traffic fatalities went down 16 percent – the largest single year decline since 1941-1942.**<sup>19</sup>** Driving levels began to increase again once fuel costs normalized, so the reductions were not sustained beyond the period of economic stagnation.
-
-The Highway Safety Act of 1973 established a specific methodology for improving roadway safety from an engineering perspective. It required the States to first survey all hazardous locations and examine **17**
-
-Governors Highway Safety Association. Section 402 State and Community Highway Safety Grant Program. [http://www.ghsa.](http://www.ghsa.org/about/federal-grant-programs/402) [org/about/federal](http://www.ghsa.org/about/federal-grant-programs/402)[grant-programs/402](http://www.ghsa.org/about/federal-grant-programs/402)
-
-**18**
-
-[http://www.](https://www.fhwa.dot.gov/policyinformation/travel_monitoring/tvt.cfm) [fhwa.dot.gov/](https://www.fhwa.dot.gov/policyinformation/travel_monitoring/tvt.cfm) [policyinformation/](https://www.fhwa.dot.gov/policyinformation/travel_monitoring/tvt.cfm) [travel\\_monitoring/](https://www.fhwa.dot.gov/policyinformation/travel_monitoring/tvt.cfm) [tvt.cfm](https://www.fhwa.dot.gov/policyinformation/travel_monitoring/tvt.cfm)
-
-**19**
-
-[https://crashstats.](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811346) [nhtsa.dot.gov/Api/](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811346) [Public/ViewPub](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811346) [lication/811346](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811346)
-
-#### **20**
-
-Source: "HSIP History," accessed October 22, 2013, [http://safety.fhwa.](https://safety.fhwa.dot.gov/hsip/gen_info/hsip_history.cfm) [dot.gov/hsip/gen\\_](https://safety.fhwa.dot.gov/hsip/gen_info/hsip_history.cfm) [info/hsip\\_history.cfm](https://safety.fhwa.dot.gov/hsip/gen_info/hsip_history.cfm) and "Subchapter J— Highway Safety: Part 924—Highway Safety Improvement Program," accessed October 21, 2013, [http://www.](https://www.gpo.gov/fdsys/pkg/CFR-2003-title23-vol1/pdf/CFR-2003-title23-vol1-chapI-subchapJ.pdf) [gpo.gov/fdsys/pkg/CFR-](https://www.gpo.gov/fdsys/pkg/CFR-2003-title23-vol1/pdf/CFR-2003-title23-vol1-chapI-subchapJ.pdf)[2003-title23-vol1/pdf/](https://www.gpo.gov/fdsys/pkg/CFR-2003-title23-vol1/pdf/CFR-2003-title23-vol1-chapI-subchapJ.pdf) [CFR-2003-title23-vol1](https://www.gpo.gov/fdsys/pkg/CFR-2003-title23-vol1/pdf/CFR-2003-title23-vol1-chapI-subchapJ.pdf) [chapI-subchapJ.pdf.](https://www.gpo.gov/fdsys/pkg/CFR-2003-title23-vol1/pdf/CFR-2003-title23-vol1-chapI-subchapJ.pdf)
-
-#### **21**
-
-Source: "What is the Motor Carrier Safety Assistance Program (MCSAP)?" accessed May 23, 2013, [https://www.](https://www.federalregister.gov/documents/2000/03/21/00-6819/motor-carrier-safety-assistance-program) [federalregister.gov/](https://www.federalregister.gov/documents/2000/03/21/00-6819/motor-carrier-safety-assistance-program) [documents/2000/03/](https://www.federalregister.gov/documents/2000/03/21/00-6819/motor-carrier-safety-assistance-program) [21/00-6819/motor](https://www.federalregister.gov/documents/2000/03/21/00-6819/motor-carrier-safety-assistance-program)[carrier-safety](https://www.federalregister.gov/documents/2000/03/21/00-6819/motor-carrier-safety-assistance-program)[assistance-program](https://www.federalregister.gov/documents/2000/03/21/00-6819/motor-carrier-safety-assistance-program)
-
-### **22**
-
-[http://www.iihs.org/](http://www.iihs.org/iihs/topics/laws/safetybeltuse) [iihs/topics/laws/](http://www.iihs.org/iihs/topics/laws/safetybeltuse) [safetybeltuse](http://www.iihs.org/iihs/topics/laws/safetybeltuse)
-
-#### **23**
-
-Source: "Intermodal Surface Transportation Efficiency Act of 1991 Information," last updated May 16, 2013, accessed July 05, 2013, [http://www.fhwa.](https://www.fhwa.dot.gov/planning/public_involvement/archive/legislation/istea.cfm) [dot.gov/planning/](https://www.fhwa.dot.gov/planning/public_involvement/archive/legislation/istea.cfm) [public\\_involvement/](https://www.fhwa.dot.gov/planning/public_involvement/archive/legislation/istea.cfm) [archive/legislation/](https://www.fhwa.dot.gov/planning/public_involvement/archive/legislation/istea.cfm) [istea.cfm.](https://www.fhwa.dot.gov/planning/public_involvement/archive/legislation/istea.cfm)
-
-#### **24**
-
-See next page.
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-![](_page_23_Figure_10.jpeg)
-
-the causes of crashes at these sites. A benefit/cost analysis was then performed to prioritize needed improvements. This process set the stage for the current safety management processes and would be refined and improved over the years.
-
-The Highway Safety Act of 1973 also clarified the relationship between the Federal Government and the States. The Federal Government was to direct policy and program components, while the States were responsible for implementing those policies and programs.**<sup>20</sup>**
-
-During the 1970s, Congress also established the Motor Carrier Safety Assistance Program (MCSAP). This program provides financial assistance to States to reduce the number and severity of crashes and hazardous materials incidents involving commercial motor vehicles (CMV) through inspection and enforcement programs focused on trucks, carriers, and driver regulations. **<sup>21</sup>**
-
-Vehicle safety continued to be a priority in the 1970s and 1980s, as more States began to implement laws requiring the use of seat belts and motorcycle helmets. New York became the first State to adopt a mandatory seat belt law in 1984, and other States soon followed suit.**<sup>22</sup>**
-
-The Federal Motor Vehicle Safety Standard 213 brought attention to child passenger safety. This standard was the first to outline specific requirements for restraint systems designed for children.
-
-## **Multimodal Shift in the 1990s**
-
-The 1990s saw a shift from transportation policies that focused on motor vehicle safety and efficiency to an acknowledgement of alternate modes of transportation, such as bicycling, walking and use of public transit. The Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) added a multimodal perspective to the Federal-aid highway program.
-
-While ISTEA was not specifically focused on transportation safety, it created some programs to promote safer travel. For example, ISTEA enhanced road safety with new programs that encouraged the use of safety belts and motorcycle helmets.**<sup>23</sup>** The legislation also required the installation of airbags for drivers and front passengers in all cars and trucks.**<sup>24</sup>**
-
-In 1998, the Transportation Equity Act for the 21st Century (TEA-21) provided more focus for roadway safety planning by establishing safety and security as planning
-
-![](_page_24_Figure_0.jpeg)
-
-Officers use specialized devices to measure drivers' blood alcohol content.
-
-priorities. Prior to TEA-21, a State or Metropolitan Planning Organization (MPO) may have incorporated safety in its goals or long-range transportation plan, but specific strategies to increase safety were seldom included in statewide and metropolitan planning processes or documents.
-
-TEA-21 established the Highway Safety Infrastructure program (not to be confused with the Highway Safety Improvement Program, which would be developed several years later), which funded safety improvement projects to eliminate safety problems.
-
-The TEA-21 legislation also encouraged States to adopt and implement effective programs to improve the quality (e.g. timeliness, accuracy, completeness, uniformity and accessibility) of State data needed to identify safety priorities for national, State and local road safety programs.**<sup>25</sup>**
-
-Motor Vehicle Safety Acts are signed, creating
-
-Not to be lost among the TEA-21 legislation, another pivotal moment in transportation legislation came in 2000 when an important provision related to alcohol was included in the USDOT appropriation act. The appropriation carried a requirement that all States must enact laws to limit the legal **blood alcohol content** (BAC) of drivers to 0.08 percent.**<sup>26</sup>** This limit was in line with similar limits imposed on drivers in other countries, though some European countries limit the legal BAC to 0.05 percent.
-
-While 19 States and Washington, D.C., had already enacted this law, the Federal mandate provided a further incentive for other States to do so: States that did not pass the law by 2004 would forego a portion of their transportation funding. Though specific laws vary, each State now recognizes the legal limit of 0.08 percent blood alcohol content.**<sup>27</sup>**
-
-**24**
-
-[http://www.history.](http://www.history.com/this-day-in-history/federal-legislation-makes-airbags-mandatory) [com/this-day-in](http://www.history.com/this-day-in-history/federal-legislation-makes-airbags-mandatory)[history/federal](http://www.history.com/this-day-in-history/federal-legislation-makes-airbags-mandatory)[legislation-makes](http://www.history.com/this-day-in-history/federal-legislation-makes-airbags-mandatory)[airbags-mandatory](http://www.history.com/this-day-in-history/federal-legislation-makes-airbags-mandatory)
-
-**25**
-
-Source: "TEA-21 – Transportation Equity Act for the 21st Century Fact Sheets," last modified April 5, 2011, accessed June 2, 2013, [http://www.](https://www.fhwa.dot.gov/tea21/factsheets/index.htm) [fhwa.dot.gov/tea21/](https://www.fhwa.dot.gov/tea21/factsheets/index.htm) [factsheets/index.htm](https://www.fhwa.dot.gov/tea21/factsheets/index.htm)
-
-### **Blood alcohol content**
-
-The percentage of alcohol in a person's blood, used to measure driver intoxication.
-
-**26**
-
-Rodriguez-Iglesias, C.; Wiliszowski, ClH.; Lacey, J.H. Legislative History of .08 Per Se Laws, National Highway Traffic Safety Administration, Report No. DOT HS 809 286, June 2001
-
-**27**
-
-[http://www.ghsa.org/](http://www.ghsa.org/state-laws/issues/alcohol%20impaired%20driving) [html/stateinfo/laws/](http://www.ghsa.org/state-laws/issues/alcohol%20impaired%20driving) [impaired\\_laws.html](http://www.ghsa.org/state-laws/issues/alcohol%20impaired%20driving)
-
-![](_page_25_Figure_0.jpeg)
-
-## **Legislation in the 21st Century**
-
-Twenty-first century legislation continued to move Federal transportation funding and policy in the direction of focusing on multimodal, data-driven approaches to improving the transportation system. One specific area of focus was a move toward safety planning. Transportation safety planning shifts the focus of traditional planning efforts to a more comprehensive process that integrates safety into transportation decision-making. Safety planning encompasses corridors and entire transportation networks at the local, regional, and State levels, as well as specific sites.**<sup>28</sup>**
-
-In 2005, Congress passed the Safe, Accountable, Flexible, and Efficient Transportation Equity Act—A Legacy for Users (SAFETEA-LU). SAFETEA-LU raised the stature of Federal road safety programs by establishing the Highway Safety Improvement Program (HSIP) as a core Federal-aid program tied to strategic safety planning and performance. HSIP is one of six core Federal-aid programs under which funds are apportioned directly to the States. One of the major elements of the HSIP was
-
-the requirement for each State to develop and implement a Strategic Highway Safety Plan (SHSP).**<sup>29</sup>** The plans sought to establish data-driven approaches that were coordinated with a broad range of stakeholders and utilized a diverse set of disciplines (e.g., engineering, enforcement, education and emergency response). These datadriven plans had to include clear methods for measuring progress toward safety goals.
-
-The Moving Ahead for Progress in the 21st Century Act (MAP-21) was signed into law in 2012. The 2012 legislation transformed the policy and programmatic framework for investments in the country's transportation infrastructure, enhancing the programs and policies established in 1991.
-
-MAP-21 doubled funding for road safety improvement projects, strengthened the linkage among modal safety programs and created a positive agenda to make significant progress in reducing highway fatalities and serious injuries. It provided increased focus on the importance of high quality data, transportation infrastructure and the safety of local streets.
-
-**28**
-
-Source: "Transportation Safety Planning (TSP)," accessed August 13, 2013, [http://safety.fhwa.](https://safety.fhwa.dot.gov/tsp/) [dot.gov/hsip/tsp/](https://safety.fhwa.dot.gov/tsp/) and "Transportation Safety Planning Fact Sheet," accessed August 13, 2013, [http://safety.fhwa.](https://safety.fhwa.dot.gov/tsp/fact_sheet.cfm) [dot.gov/hsip/tsp/](https://safety.fhwa.dot.gov/tsp/fact_sheet.cfm) [fact\\_sheet.cfm](https://safety.fhwa.dot.gov/tsp/fact_sheet.cfm).
-
-**29**
-
-Title 23 United States Code § 148
-
-## **Conclusion**
-
-Exploring the history of travel trends and safety in the U.S. helps illustrate how past decisions have led to the transportation system seen today. Safety has not always been a deciding factor in how roads are built. However, today, safety is a top priority of the USDOT.**<sup>30</sup>** Most State and local transportation agencies share USDOT's goal; some have even set goals to reduce total traffic fatalities to zero. These "vision zero" and "toward zero deaths" goals are guiding transportation projects by requiring safety to be incorporated into every step of project planning, design, construction and operation.
-
-Future safety issues will certainly arise as technological advancements lead to changes in the vehicle fleet. Autonomous and potentially driverless vehicles are being developed and tested across the world. Though safety improvements are touted as a benefit of these advanced vehicles, safety will continue to be a priority as they
-
-![](_page_26_Picture_3.jpeg)
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-begin to share the roads with older vehicles, bicyclists, and pedestrians. As can be learned from the history of road safety in the U.S., complex problems must be met with safety advancements, legislative action, and collaboration.
-
-#### **30**
-
-U.S. Department of Transportation. Strategic Plans. November 2015. [https://www.](https://www.transportation.gov/mission/budget/dot-budget-and-performance-documents#StrategicPlans) [transportation.gov/](https://www.transportation.gov/mission/budget/dot-budget-and-performance-documents#StrategicPlans) [mission/budget/](https://www.transportation.gov/mission/budget/dot-budget-and-performance-documents#StrategicPlans) [dot-budget-and](https://www.transportation.gov/mission/budget/dot-budget-and-performance-documents#StrategicPlans)[performance](https://www.transportation.gov/mission/budget/dot-budget-and-performance-documents#StrategicPlans)[documents#](https://www.transportation.gov/mission/budget/dot-budget-and-performance-documents#StrategicPlans) [StrategicPlans](https://www.transportation.gov/mission/budget/dot-budget-and-performance-documents#StrategicPlans)
-
-#### **EXERCISES**
-
-- J **RESEARCH** a federal transportation law addressed in this chapter and write a summary about the law, emphasizing the safety aspects.
-- J **FIND** a recent news article that involves road safety (more than just a local news article on a recent crash) and write a summary describing the effort undertaken by the public agency, how it was received by the public, and whether it was shown to be effective in increasing road safety.
-- J **USE** https://www.govtrack.us to find a transportation bill currently proposed or under review by Congress. Describe how the legislation would be expected to affect road safety.
-- J **RESEARCH** the legal driving Blood Alcohol Content (BAC) by state in the U.S. and create a table showing the comparison. Select one state where the legal BAC is lower than the federal requirement, locate a paper or news article describing how that BAC level was decided, and write a summary.
-
-## **Multidisciplinary Approaches**
-
-![](_page_27_Picture_3.jpeg)
-
-Road safety is a complex issue, and any efforts to improve safety must address not only the roadway but also road user behavior, vehicle design, interaction between road users, and the effect of the roadway on all road users. Road safety partners include anyone who influences road user safety, including those in infrastructure safety, behavioral roadway safety, transportation planning, public health, public safety and many other disciplines. Each of these disciplines is able to provide a unique perspective and each has specific methods for addressing road safety. It is becoming increasingly common for these various disciplines to work in collaboration with one another to address road safety through comprehensive programs. Instead of focusing on traditional "silos" of activity, agencies hope that this interaction and collaboration among various disciplines will lead to continued safety improvements.
-
-#### **The E's**
-
-A popular multidisciplinary approach to road safety is sometimes referred to as the "four E's": Engineering, Education, Enforcement, and Emergency response. These E's broadly represent the various disciplines that bring together stakeholders who care about making the road safe for all users. Sometimes a fifth "E" for evaluation is added to this list to represent the important role of evaluating what works and what doesn't. This emphasizes the fact that good data is crucial to the improvement of road safety.
-
-This chapter will discuss road safety efforts from the disciplines of roadway design and engineering, public education, and enforcement campaigns. Working in collaboration with one another, as described above, these groups can share the burden of road safety responsibilities and create comprehensive programs to address the various factors that may contribute to crashes.
-
-## **Roadway Design and Engineering**
-
-Several types of transportation professionals are responsible for roadway safety engineering. Broadly speaking, the roadway safety engineering community includes transportation planners and engineers.
-
-Transportation planning plays a critical role in determining the shape of the transportation system and provides an early opportunity for professionals to address safety needs. Before a road project is designed or built, it is influenced by any number of comprehensive and strategic transportation plans that are coordinated to ensure that the system being developed is one that matches the vision of the local community. Planners work with stakeholders such as the general public, business owners, policy makers, and advocates to establish plans for how the transportation system can best serve every group's needs.
-
-In the past, the traditional planning process focused on economic development, environmental quality, and mobility as the three primary concerns. Most States consider infrastructure safety improvements as part of preservation or improvements projects or within operational changes undertaken by traffic offices. States are now able to use the Highway Safety Improvement Program (HSIP) to fund safety projects in at high priority locations. This program allows development of targeted solutions and approaches that address the contributing factors to collisions, thereby seeking to achieve a higher return on safety investments.
-
-Roadway engineers work on the design, construction and system preservation of the roadways. In particular, engineers are charged with designing roads that minimize the chance that crashes will occur while balancing the needs for efficiency and mobility. Engineers also work to design roads and intersection in such a way that minimizes crash severity and injury risk when crashes do occur. Engineers affect the safety of the built environment by incorporating safety in to the planning process at the beginning of a project; selecting design alternatives that prioritize safety considerations; using design elements that maximize the safety of each part of the road or intersection; ensuring quality and safe construction, operation, and maintenance of the roads; and addressing safety problems at existing locations.
-
-Infrastructure improvements such as paved shoulders, rumble strips, and improved nighttime visibility may prevent drivers from veering off the roadway, and still other opportunities exist for improving the roadside and road user behavior. For example, when a driver veers off the roadway, it is important to provide a roadside environment that reduces the potential for crashes and injury. Roadside slopes and objects such as drainage structures, trees, and utility poles are examples of roadside elements that engineers can target for improvements to road safety performance. One engineering method to increase roadside safety is to create a clear zone—an unobstructed, traversable roadside area that allows a driver to stop safely or regain control of the vehicle that has left the roadway.
-
-Countermeasures that Work: A Highway safety Countermeasures Guide for State Highway Safety Offices, 7th edition, 2013 DOT HS 811 727
-
-#### **Countermeasures That Work**
-
-Countermeasures That Work**<sup>31</sup>** is a comprehensive guidance document providing details of different programs and interventions that are effective in improving safety. The guide is published regularly by NHTSA.
-
-Engineering solutions must incorporate the different needs and preferences of a variety of user groups. As mentioned previously, this often means that tough decisions and trade-offs must be made to arrive at infrastructure solutions that balance the needs of different users. This trade-off can be illustrated with an example of a signalized intersection. Improving intersection safety for pedestrians may involve adding pedestrian crossing time to the signal or separating turn movements to eliminate high risk conflicts. Protected left-turn phases can also improve safety for vehicles, as shown previously. But these new or longer signal phases either add time to the cycle length or keep the same length while reducing time for the through movements. Regardless, the result is more delay to both pedestrians and motorists. In such situations, it is necessary to consider all of these needs and select the appropriate signal timing that meets the needs of all users. Adhering to design standards – creating nominally safe conditions – is only one aspect of the complex roadway design and engineering field. Addressing substantive safety through design strategies requires an understanding of multiple perspectives, trade-offs and user needs.
-
-## **Public Education and Enforcement Campaigns**
-
-Public education and communications campaigns are commonly used to improve road user attitudes and awareness. The structure and delivery methods of these campaigns can take many forms. However, they generally involve materials (media advertisements, informational brochures, posters, presentations, etc.) to inform people of a desired behavior and the benefits of such behavior (or conversely, the risks of an unwanted behavior).
-
-While standalone informational or educational campaigns can improve awareness or perceptions about road safety issues, they are unlikely to change road user behavior. Rather, campaigns that educate the public about increased law enforcement efforts aimed at a particular behavior have been shown to be effective. Generally referred to as "high-visibility enforcement" these campaigns increase the perceived enforcement of a particular law. When people believe there is a high probability of being caught, they are more likely to follow the law. The Click it or Ticket campaign is one of the most widely known examples of high-visibility enforcement. In this case, simply enforcing the seatbelt law was not sufficient. The key to this program's success was the media coverage and other informational campaigns telling the public that law enforcement officers are looking for people who are not wearing a seatbelt. In other words, for those people who do not typically wear a seatbelt, the law itself was not sufficient motivation to change. The motivation came from a
-
-![](_page_30_Picture_0.jpeg)
-
-perceived threat of being caught and ticketed.
-
-When safety professionals analyze possible educational campaigns, they must consider the factors that affect people's behavior and the probability that the campaign will change such behavior. Simply communicating safety messages and enforcing laws may not lead to a change in behavior if a road is designed in a way that allows (or unintentionally encourages) unsafe behaviors. For example, to address a speeding problem on a wide multilane arterial where the posted speed is 35 miles per hour, enforcement and education may not be the only solution. Narrowing the roadway and creating more "visual friction" along the roadside may be needed to alter the desired design speed of
-
-#### **Targeted Enforcement**
-
-To reinforce pedestrian safety laws, police departments can initiate targeted enforcement operations at crosswalks. Under this approach, a law enforcement officer in plain clothes will attempt to cross the street at an uncontrolled crosswalk. Drivers who do not yield to the officer will be pulled over and either cited or warned by patrol vehicles waiting beyond the crosswalk. More info: [http://www.nhtsa.gov/staticfiles/](http://www.nhtsa.gov/staticfiles/nti/pdf/812059-PedestrianSafetyEnforceOperaHowToGuide.pdf) [nti/pdf/812059-PedestrianSafetyEnforce](http://www.nhtsa.gov/staticfiles/nti/pdf/812059-PedestrianSafetyEnforceOperaHowToGuide.pdf) [OperaHowToGuide.pdf](http://www.nhtsa.gov/staticfiles/nti/pdf/812059-PedestrianSafetyEnforceOperaHowToGuide.pdf)
-
-the road. Supplemental education and enforcement campaigns can then help reinforce the proper behavior. This emphasizes the need for cooperation and coordination between disciplines to accomplish
-
-meaningful improvements to road safety.
-
-Zegeer, C. V., Blomberg, R. D., Henderson, D., Masten, S. V., Marchetti, L., Levy, M. M., Fan, Y., Sandt, L. S., Brown, A., Stutts, J., & Thomas, L. J. (2008b). Evaluation of Miami–Dade pedestrian safety demonstration project. Transportation Research Record 2073, 1-10.
-
-While there is evidence to suggest some success for well-designed and executed safety education campaigns when they are targeted at children,**<sup>32</sup>** the same results have not been shown for teens and adults when an educational campaign stands alone. Though well-intentioned, these approaches generally assume that people are not performing the desired behavior simply because they lack the appropriate information. However, this idea fails to take into account the fact that, in general, most human behavior is not the result of conscious, rational deliberation. People are largely influenced by emotions, values, social context, and culture, among many other factors. Thus, simply being presented with information or facts alone is unlikely to lead to any lasting behavior change. In the context of transportation safety, most people do not engage in risky or undesirable behaviors due to a lack of knowledge about the desired behavior. Instead, people act based on a variety of contributing factors.
-
-For example, consider the behavior of a pedestrian on a multi-lane undivided arterial. The goal of the pedestrian is to get to a bus stop located directly across the street from his current location. The pedestrian almost certainly knows that the desired behavior is to walk a quarter mile to the signalized intersection, wait and cross with the crossing signal, and then to backtrack a quarter mile to the bus station. However, instead the pedestrian chooses to cross in the middle of the block. The fact is that there are many factors that
-
-![](_page_31_Picture_5.jpeg)
-
-#### **Bicycle Safer Journey**
-
-Bicycle Safer Journey is an educational program intended to provide bicycle safety skills and education to children. The program uses interactive video lessons to teach children safe bicycling skills and provides resources for parents and teachers. The program can be accessed online at [http://www.](http://www.pedbikeinfo.org/bicyclesaferjourney/) [pedbikeinfo.org/bicyclesaferjourney.](http://www.pedbikeinfo.org/bicyclesaferjourney/)
-
-influence the pedestrian's decision to cross mid-block (time, ability, weather, etc.), but likely the most important factor is that doing so just makes sense. People are wired to choose the option that makes the most intuitive sense. Efforts to change this behavior only through signs, posters or other educational campaigns will likely have only minimal effect.
-
-Similar examples can be found throughout the transportation safety field. Most people already know they should wear their seatbelt, obey posted speed limit signs, and limit distractions while driving. Yet some people refuse to wear a seatbelt,
-
-![](_page_32_Picture_0.jpeg)
-
-#### **Click It or Ticket**
-
-Click It or Ticket is a successful seat belt enforcement campaign that has helped to increase the national seat belt usage rate. The program uses public education to communicate the law and risks of not using seat belts in a variety of settings. The campaigns provide waves of education and enforcement along with high visibility media coverage to publicize and sustain the campaign. NHTSA manages this campaign annually with assistance from the State Highway Safety offices, law enforcement agencies, and national- and local-paid advertising.
-
-some people speed, and some people text while driving. Knowledge alone is not enough.
-
-Successful education and enforcement campaigns recognize the reality of people's behaviors and apply this knowledge to the safety efforts. For example, social norms and cultural influences can provide some explanation for why certain behaviors are common - even those behaviors known to be unsafe. Marketing interventions based on social norms have been applied
-
-### **Media Campaign Effectiveness**
-
-Well planned and executed media campaigns centered on reducing alcohol-impaired driving can be effective in reducing the occurrence of alcohol related crashes. A study in 2004 pointed to a 13 percent decrease in alcohol related crashes following these types of campaigns.**<sup>33</sup>**
-
-to the areas of distracted driving and driving under the influence of alcohol. Such methods provide a way to examine safety problems and **33**
-
-Elder, R.W., et al. Effectiveness of mass media campaigns for reducing drinking and driving and alcohol-involved crashes: a systematic review. July 2004. [http://](http://www.sciencedirect.com/science/article/pii/S0749379704000467) [www.sciencedirect.](http://www.sciencedirect.com/science/article/pii/S0749379704000467) [com/science/](http://www.sciencedirect.com/science/article/pii/S0749379704000467) [article/pii/](http://www.sciencedirect.com/science/article/pii/S0749379704000467) [S0749379704000467](http://www.sciencedirect.com/science/article/pii/S0749379704000467)
-
-![](_page_33_Picture_0.jpeg)
-
-#### **Strategic Highway Safety Plan**
-
-Provides a framework for developing a coordinated and comprehensive approach to addressing road safety across a State.
-
-**34**
-
-[https://safety.](https://safety.fhwa.dot.gov/shsp/guidebook/) [fhwa.dot.gov/shsp/](https://safety.fhwa.dot.gov/shsp/guidebook/) [guidebook/](https://safety.fhwa.dot.gov/shsp/guidebook/)
-
-**35**
-
-[http://safety.fhwa.](https://safety.fhwa.dot.gov/systemic/) [dot.gov/systemic](https://safety.fhwa.dot.gov/systemic/)
-
-what might be done to address them through education and enforcement.
-
-## **Comprehensive Safety Programs**
-
-While each discipline has its own strengths, significant improvements in roadway safety are more likely when a program encompasses many disciplines rather than just one. Interdisciplinary team efforts can take on safety problems using multiple approaches and are therefore greater in scope than individual disciplines working in isolation. The need for this "multiple approach" solution requires collaboration among many parties. This type of collaboration is most clearly seen when agencies seek to create a comprehensive safety plan. Creating a comprehensive safety plan for a city, county, or state must be a data driven process. In doing so, agencies first begin by analyzing their safety data to identify emphasis areas where concentrated efforts are likely to yield the largest
-
-reduction in fatalities and serious injuries.
-
-A State's **Strategic Highway Safety Plan** (SHSP) is an example of a comprehensive safety plan, and one of the best examples of a multidisciplinary, data driven planning effort. A State SHSP provides a framework for developing a coordinated and comprehensive approach to addressing road safety across a State. In the development of a State's SHSP, safety stakeholders from across the State and across disciplines will consider all the data available (i.e., crash, injury surveillance, roadway and traffic, vehicle, enforcement, and driver data) that will help an agency understand where more safety emphasis is needed.**<sup>34</sup>** Beyond crash records, an agency may choose to rely on alternate data sources like roadway characteristics and its own knowledge of crash risk to pursue systemic safety strategies. A systemic approach proactively identifies locations that may have a high risk of crashes but where the risk has not yet resulted in actual crashes.**<sup>35</sup>**
-
-Demographic data showing where population growth has occurred, or where it is expected, can also influence an agency's safety plans. One of the most critical components of the SHSP is an evaluation of past efforts, so that the agency can know what strategies are working and so that progress toward goals can be measured and tracked over time.
-
-Road safety planning, like the field of safety itself, is multidisciplinary in nature and relies upon the expertise and involvement of numerous perspectives. Once developed, these safety plans influence activities ranging from roadway design and engineering to law enforcement and safety education.
-
-Each of the agencies and organizations involved in transportation safety brings a unique and valuable perspective to bear on the roadway safety problem. Their competing philosophies, worldviews and problem solving approaches, however, can make collaboration difficult. Creating a foundation
-
-for effective collaboration and establishing a process to support collaborative efforts are two ways to overcome these barriers. One way to create a foundation for collaboration is to ensure that each agency understands the impact that its actions have on road safety and that each makes safety its top priority. The example of a State Strategic Highway Safety Plan shows this type of collaboration. The SHSP process brings together all potential areas of safety emphasis, including intersections, non-motorized users, rural crashes, and others, and uses a data driven approach to identify priorities and areas of need. This foundation can be further strengthened by identifying which agencies or organizations are responsible for implementing each of the strategies identified in the SHSP.
-
-In the U.S., no single player manages all programs and disciplines that impact road safety. Therefore, collaboration among all players is fundamental to consistently reduce serious injuries and fatalities.
-
-#### **EXERCISES**
-
-- J **FIND** the website for your State or local road safety program. Identify initiatives that your State or local agency is implementing in the areas of planning, engineering, education, and enforcement.
-- J **CONSIDER** a hypothetical situation where it is your job to convene a team of professionals to visit a high crash intersection and explore possible solutions to the safety problem. Create a list of the people who should be included on that team and briefly describe each person's role. Be sure to
-- consider the many different types of programs and strategies that can be used to improve road safety.
-- J **SELECT** an area of concern, either a specific type of road user or an unsafe behavior, and discuss how road safety in this topic area could be addressed or improved through multiple disciplines. Possible topics include:
-  - J Older drivers
-  - J Underage drinking
-  - J Fatigued or drowsy driving
-  - J Pedestrians
-
-## **Road Users**
-
-Drivers of motor vehicles are far from the only users of the road, despite accounting for the majority of trips taken in the U.S. The public right-of-way on most roads is usually shared by a number of different users, traveling by a variety of modes for any number of different reasons. Transportation professionals must understand the mobility and safety needs of different user groups and how they interact with one another to gain a better understanding of safety problems and their potential solutions.
-
-Road user groups include:
-
-- J Passenger vehicle drivers and occupants
-- J Drivers of trucks and other large vehicles
-- J Motorcyclists
-- J Pedestrians
-- J Bicyclists
-
-## **Passenger Vehicle Drivers and Occupants**
-
-Passenger vehicles are typically defined as sedans, pickup trucks, minivans, and sport utility vehicles and represent the primary mode of transportation for the majority of Americans. Since these vehicles account for the vast majority of registered vehicles and vehicle miles traveled, it is not surprising that much of the transportation infrastructure prioritizes the needs of these drivers.
-
-However, despite the priority given to drivers of passenger vehicle, there remain many unresolved safety issues for these drivers. At the core of most of these issues are the driver's actions while navigating the road network. Engineers may work to make a road nominally safe by ensuring it follows the latest recommendations and design standards. However, drivers do not always interact with the road system as road designers expect them to. Thus, a nominally safe road may be much less safe in a substantive sense. While the common reaction has been to assume that some fault or "driver error" led to the crash, this approach fails to take into account a common behavioral principle known as behavioral adaptation. Simply put, behavioral adaptation refers to the unconscious process by which people react to their environment -- people cannot be considered to be a constant in the system.
-
-Consider a town that wants to resurface and widen a two-lane collector roadway through an older neighborhood with mature street trees. The existing road has 9.5 foot wide lanes, a 30 mi/h (48 ki/h) speed limit, and street trees between the roadway and sidewalk. Design guidance may suggest a typical lane width of 12 feet and a wider roadside clear zone. It is easy to assume that the safest choice would be to design a road with the widest lanes possible and removal
-
-![](_page_36_Picture_0.jpeg)
-
-The intended speed of this road is 35 miles per hour, but the wide design of the road and the number of lanes leads drivers to drive much faster.
-
-of the roadside hazards. However, after this resurfacing and widening project was completed, both traffic speeds and crash severity along this roadway may increase considerably. On the surface, this may seem counterintuitive.
-
-In essence, most people drive at a speed that *feels* safe to them. To reach this "safe speed," people unconsciously assess the roadway and its characteristics. Navigating a narrow, curvy road with significant roadside hazards is more challenging than navigating a straight, wide road with large clear zones, so people unconsciously drive slower and more cautiously on the narrow road. When the driving task is made easier by widening the lanes and removing roadside
-
-hazards, people will not maintain their original behavior. In fact, the assumption should be that people will adapt to this change and unconsciously change their behavior accordingly, in this case by increasing their speed.
-
-Behavioral adaptation is not specific to passenger vehicles. When designing the transportation infrastructure, engineers must consider how human behavior plays affects all roadway users. Roadway designers must design roads not for the way in which they would like users to behave, but for the way in which users actually behave. Behavior of drivers and other road users will be covered in a greater detail in Unit 2.
-
-## **Drivers of Trucks and Other Large Vehicles**
-
-Much of the transportation network across the country serves an important commercial need. Truck drivers, in particular, play a significant role in the national economy and are responsible for moving goods between and within cities and States. Large trucks account for only 4 percent of registered vehicles in the U.S., but they make up 9 percent of total vehicle miles traveled and accounted for 12 percent of total traffic fatalities in 2013.**<sup>36</sup>** These large trucks share space on the roads with passenger vehicles, and have their own safety needs. Nationally in 2013, there were just under 4,000 people killed in crashes involving large trucks, and 71 percent of them were occupants of the other vehicle involved in the crash. However, large truck safety has improved over time. Between 2004 and 2013, the miles covered by large trucks increased by roughly 25 percent, while fatalities involving large trucks decreased by about 20 percent (from 4,902 to 3,906).**<sup>37</sup>**
-
-Commercial trucks are not the only large vehicles on the roads. Transit vehicles occupy space on our roadways as well, though they typically serve pedestrians and bicyclists. Transit vehicles that share space with passenger vehicles also have unique needs and challenges. Many of the safety issues associated with transit vehicles are similar to those of large trucks. Bus operators have to consider how stopping in traffic impacts the flow and operation of the transportation system, and must also consider the safety of their passengers boarding and disembarking the vehicle.
-
-Road designs that accommodate large vehicles can sometimes be directly at odds with designs that favor pedestrians and bicyclists. For example, a pedestrian is more comfortable crossing an intersection if the turns are very tight, where the distance between corners in minimized to shorten the walking distance and decrease the time in the roadway. Large trucks and buses, however, require a larger turning radius (when compared to passenger vehicles) in order to turn safely. When designing intersections for large trucks, designers are tempted to increase the amount of space in an intersection and widen the corners. This change will make the turn easier, but it will also be more uncomfortable (and possibly less safe) for pedestrians. As described previously, these trade-offs need to be assessed and discussed when planning road projects.
-
-## **Motorcyclists**
-
-In recent years, motorcycling has become increasingly popular throughout the U.S. Since 2000 the number of registered motorcycles in the U.S. has nearly doubled. **<sup>38</sup> <sup>39</sup>** The result was a 71% increase in the number of motorcyclist fatalities (from 2,897 in 2000 to 4,957 in 2012). Motorcyclists represented 15 percent of all traffic fatalities in 2012, compared to just 7 percent of fatalities in 2000. **<sup>40</sup>** Motorcyclists are significantly overrepresented in traffic fatalities since they account for only 3 percent of registered vehicles and 0.7 percent of total vehicle miles traveled in 2012.**<sup>41</sup>**
-
-**36**
-
-[http://www-nrd.](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812150) [nhtsa.dot.gov/](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812150) [Pubs/812150.pdf](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812150)
-
-**37**
-
-**38**
-
-[http://www.](https://www.fhwa.dot.gov/policyinformation/statistics/2013/pdf/mv1.pdf) [fhwa.dot.gov/](https://www.fhwa.dot.gov/policyinformation/statistics/2013/pdf/mv1.pdf) [policyinformation/](https://www.fhwa.dot.gov/policyinformation/statistics/2013/pdf/mv1.pdf) [statistics/2013/pdf/](https://www.fhwa.dot.gov/policyinformation/statistics/2013/pdf/mv1.pdf) [mv1.pdf](https://www.fhwa.dot.gov/policyinformation/statistics/2013/pdf/mv1.pdf)
-
-**39**
-
-[https://www.fhwa.](https://www.fhwa.dot.gov/ohim/hs00/pdf/mv1.pdf) [dot.gov/ohim/hs00/](https://www.fhwa.dot.gov/ohim/hs00/pdf/mv1.pdf) [pdf/mv1.pdf](https://www.fhwa.dot.gov/ohim/hs00/pdf/mv1.pdf)
-
-**40**
-
-[http://www-fars.](https://www-fars.nhtsa.dot.gov/Main/index.aspx) [nhtsa.dot.gov/](https://www-fars.nhtsa.dot.gov/Main/index.aspx) [Main/index.aspx](https://www-fars.nhtsa.dot.gov/Main/index.aspx)
-
-**41**
-
-[http://www-nrd.](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812035) [nhtsa.dot.gov/](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812035) [Pubs/812035.pdf](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812035)
-
-![](_page_38_Picture_0.jpeg)
-
-In general, many of the roadway modifications done to improve safety for passenger vehicles can pose a challenge for motorcyclists. Rumble strips can be difficult to traverse, especially at low speeds. Guard rails, in particular cable barriers, can present a serious hazard to a motorcyclist impacting one at a high speed. Within the driving environment, motorcyclists are small compared to larger vehicles and can be difficult to see, especially early or late in the day when lighting levels are lower.
-
-## **Pedestrians**
-
-Walking is the most basic form of transportation. At some point during a typical day, nearly every person is a pedestrian. People walk to get to a bus station, to go from home to school, or to get from a
-
-parked vehicle to the front door of a business. Some walking trips are taken out of necessity – not all households own a vehicle, **<sup>42</sup>** and children and individuals with disabilities may not have the option to drive. Many more walking trips are taken by choice, especially for exercise or health. A 2012 survey found that 39 percent of trips taken by foot are done for exercise or personal health purposes. **<sup>43</sup>** Walking is also more common in densely populated urban areas, due to the close proximity of destinations and other services like transit stations.
-
-Regardless of the reasons for walking, this mode accounts for nearly 11 percent of all trips taken in the U.S., according to the 2009 National Household Travel Survey (NHTS). **<sup>44</sup>** The NHTS shows that about a third of all trips taken in the U.S. are shorter than one mile, and 35 percent of these trips are taken by foot. In the 2005 Traveler Opinion and Perception Survey (TOP), conducted by FHWA, data showed that about 107.4 million Americans (51 percent of the traveling public) use walking as a regular mode of travel. **<sup>45</sup>**
-
-Pedestrians (along with bicyclists) are among the most vulnerable road users, and this is reflected in crash data. The 4,743 pedestrians killed in 2012 represented 14.1 percent of total traffic fatalities in the U.S. that year. Between 2008 and 2012, motor vehicle fatalities decreased 13 percent, while pedestrian fatalities increased 8 percent. Within the population of pedestrians, there are certain groups which are especially vulnerable. These include young children, older adults, and individuals with disabilities.
-
-**42**
-
-[https://info.ornl.gov/](https://info.ornl.gov/sites/publications/Files/Pub50854.pdf) [sites/publications/](https://info.ornl.gov/sites/publications/Files/Pub50854.pdf) [Files/Pub50854.pdf](https://info.ornl.gov/sites/publications/Files/Pub50854.pdf)
-
-**43**
-
-[http://www.](http://www.pedbikeinfo.org/data/factsheet_general.cfm) [pedbikeinfo.org/](http://www.pedbikeinfo.org/data/factsheet_general.cfm) [data/factsheet\\_](http://www.pedbikeinfo.org/data/factsheet_general.cfm) [general.cfm](http://www.pedbikeinfo.org/data/factsheet_general.cfm)
-
-**44**
-
-[http://www.](http://www.pedbikeinfo.org/cms/downloads/15-year_report.pdf) [pedbikeinfo.org/](http://www.pedbikeinfo.org/cms/downloads/15-year_report.pdf) [cms/downloads/15](http://www.pedbikeinfo.org/cms/downloads/15-year_report.pdf) [year\\_report.pdf](http://www.pedbikeinfo.org/cms/downloads/15-year_report.pdf)
-
-**45**
-
-[http://www.fhwa.](https://www.fhwa.dot.gov/reports/traveleropinions/1.htm) [dot.gov/reports/](https://www.fhwa.dot.gov/reports/traveleropinions/1.htm) [traveleropinions/](https://www.fhwa.dot.gov/reports/traveleropinions/1.htm) [1.htm](https://www.fhwa.dot.gov/reports/traveleropinions/1.htm)
-
-![](_page_39_Picture_0.jpeg)
-
-Before (left) and after (right) pictures of Stone Way North. *(Source: Seattle DOT)*
-
-#### **Road Diet**
-
-In 2008, Seattle Department of Transportation implemented a road diet on a 1.2-mile (1.9-kilometer) section of Stone Way North from N 34th Street to N 50th Street. In addition to serving motor vehicles, this segment of Stone Way North helps connect a bicycle path with a park. Within five blocks are eight schools, two libraries, and five parks.
-
-The modified segment was originally a four-lane roadway carrying 13,000 vehicles per day. For this corridor, the city's 2007 bicycle master plan recommended climbing lanes and shared lane markings (previously known as
-
-80 percent following the project. *Summarized from a 2011 Public Roads article: [http://www.fhwa.dot.gov/publications/](https://www.fhwa.dot.gov/publications/publicroads/11septoct/05.cfm)*
-
-Young children are a vulnerable road user group, and may be more likely than adults to rely on walking as a primary transportation mode – especially before they are old enough to drive. One area of concern is creating a safe environment for young children when they walk to school. Safety professionals need to ensure that sidewalks and street crossings have the appropriate measures to assist children in traveling safely, and educate children about safe walking.
-
-Another vulnerable portion of the pedestrian population includes those who are blind or visually
-
-*[publicroads/11septoct/05.cfm](https://www.fhwa.dot.gov/publications/publicroads/11septoct/05.cfm)* impaired. These pedestrians have increased challenges in navigating the road safely, particularly at street crossings. Challenges faced by a blind or visually impaired pedestrian include finding the appropriate crossing point at an intersection corner or midblock location, determining the appropriate time to cross, and crossing quickly and accurately. Both crossing and traversing a sloped sidewalk can be equally difficult for an individual in a wheelchair, where even slight cracks or bumps in the sidewalk can present
-
-major obstacles. The difficulties of these challenges increase at locations
-
-"sharrows"). The cross section reduced the number of travel lanes to add bicycle lanes and parking on both sides. The resulting corridor saw a decrease in the 85th percentile speed, while the overall capacity remained relatively unchanged despite the reduction in the number of lanes. The number of bicyclists on the corridor increased by 35 percent, but crashes involving bicyclists did not increase. Pedestrian crashes declined by
-
-with unusual geometry, irregularly timed signals, or non-stop vehicle flow such as roundabouts and channelized turn lanes.
-
-Older adults face many challenges as well. There are a number of age-related changes that affect the functional ability of older adults to safely walk and cross the street. These changes include diminished physical capability, sensory perception, cognitive skills and lag in reflexive responses. Eyesight deterioration can diminish an older person's ability to see and read guide signs, slow their reaction time and decrease their ability to gauge a vehicle's approaching speed or proximity. **<sup>46</sup>**
-
-Drivers and pedestrians share responsibility for many pedestrian fatalities, as both parties attempt to navigate through the same space at the same time. Though we know that certain factors are likely to result in more severe pedestrian crashes, such as speed **<sup>47</sup>**, no single cause stands out as the major contributor to pedestrian crashes. For this reason, no single countermeasure alone would likely make a substantial impact on the number of pedestrian crashes. A successful countermeasure program should use a mix of engineering, environmental, educational and enforcement measures to improve pedestrian safety. **<sup>48</sup>**
-
-![](_page_40_Picture_3.jpeg)
-
-Source: "Identifying Countermeasure Strategies to Increase Safety to Older Pedestrians," National Highway Traffic Safety Administration, 1.
-
-![](_page_40_Picture_5.jpeg)
-
-[https://www.](https://www.aaafoundation.org/sites/default/files/2011PedestrianRiskVsSpeed.pdf) [aaafoundation.org/](https://www.aaafoundation.org/sites/default/files/2011PedestrianRiskVsSpeed.pdf) [sites/default/files/](https://www.aaafoundation.org/sites/default/files/2011PedestrianRiskVsSpeed.pdf) [2011Pedestrian](https://www.aaafoundation.org/sites/default/files/2011PedestrianRiskVsSpeed.pdf) [RiskVsSpeed.pdf](https://www.aaafoundation.org/sites/default/files/2011PedestrianRiskVsSpeed.pdf)
-
-#### **48**
-
-Source: "Identifying Countermeasure Strategies to Increase Safety to Older Pedestrians," National Highway Traffic Safety Administration, 36
-
-![](_page_40_Picture_9.jpeg)
-
-## **Bicyclists**
-
-Bicyclists were some of the first users of U.S. roads, and the group that made the earliest push to improve road conditions. In recent years, bicycling has seen a rise in popularity for both recreation and transportation. Data from the 2009 NHTS showed that while only 1 percent of all trips are taken by bicycle, the number of bicycle trips doubled between 1990 and 2009.**<sup>49</sup>**
-
-While bicyclists account for only 1 percent of all trips, the 726 bicyclist fatalities in 2012 represented 2 percent of all traffic fatalities that year.**<sup>50</sup>** While the number of bicyclists killed has risen only slightly since 2008, the decline in motor vehicle deaths means that bicyclists account for an increasing share of total traffic fatalities.
-
-Bicyclists face unique challenges as road users. More often than not, bicyclists share space with motor vehicles and are considered legal users of the road in most locations. Many potential bicycle riders are not comfortable sharing the road with heavy vehicular traffic and may be deterred from riding their
-
-bicycles. Intersections can also pose a challenge to bicycle riders when they include high volumes of turning traffic and a large number of lanes. These barriers to bicycling, busy street segments and intersections, often discourage potential riders even when the rest of a bicycle network is comfortable. Many bicyclists are willing to go out of their way to use a route that has lower vehicle volumes and speeds, or bicycle facilities that are separated from traffic. Safe bicycle facilities can also improve connections to shopping, transit, jobs, schools, and essential services.
-
-## **Conclusion**
-
-Successful road safety programs will consider the needs of all users when planning and developing transportation projects. Each user group plays an important role in the transportation system, and each has unique safety needs that safety professionals must consider. Road user decisions are influenced by a variety of factors, and the combinations of factors that result in particular travel behavior cannot easily be categorized or understood in simple terms.
-
-## **49**
-
-[http://www.](http://www.pedbikeinfo.org/cms/downloads/15-year_report.pdf) [pedbikeinfo.org/](http://www.pedbikeinfo.org/cms/downloads/15-year_report.pdf) [cms/downloads/](http://www.pedbikeinfo.org/cms/downloads/15-year_report.pdf) [15-year\\_report.pdf](http://www.pedbikeinfo.org/cms/downloads/15-year_report.pdf)
-
-**50**
-
-[http://www-nrd.](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812018) [nhtsa.dot.gov/](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812018) [Pubs/812018.pdf](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812018)
-
-#### **EXERCISES**
-
-- J **PROVIDE** an example of a road project where the changes resulted in improvements for one user group, but negatively impacted another group. This example could be hypothetical or based on a real world experience.
-- J **VISIT** the Fatality Analysis Reporting System (FARS) Encyclopedia home page (http://www-fars.nhtsa.dot.gov/Main/
-
-index.aspx). Use the data available for the most recent year to document fatality numbers for the different road user groups discussed in this chapter (e.g. motorists, pedestrians, bicyclists). What other information on road user safety can you find with the tools available in FARS, and what data is not included?
-
-THIS PAGE INTENTIONALLY LEFT BLANK
-
-![](_page_43_Picture_0.jpeg)
-
-## **Human Behavior and Road Safety UNIT 2**
-
-#### **LEARNING OBJECTIVES**
-
-After reading the chapters and completing exercises in Unit 1, the reader will be able to:
-
-- J **EXPLAIN** the systems that drive human behavior and give examples of each
-- J **EXPLAIN** why it is important to consider the nature of human behavior when designing and implementing systems or programs
-
-## **Understanding Human Behavior**
-
-## **Introduction**
-
-Guinea worm disease is a parasitic infection that occurs in remote parts of Africa. Symptoms of Guinea worm disease can be debilitating and lead to secondary infections, both of which can affect an infected person's ability to perform everyday tasks including working, harvesting food and caring for children. The disease is caused by drinking water contaminated with Guinea worm larvae. When a worm is mature, it creates a painful blister on the infected person's skin. If the person immerses the affected body part in water, it can temporarily relieve the pain from the blister. However, this also allows the worm to release eggs into the water, continuing the infection cycle by spreading the disease to others**<sup>1</sup>** .
-
-There were 3.5 million cases of Guinea worm disease throughout the world in 1986. By 2015, there were only 22 cases. In 30 years the disease has been nearly eradicated — the only human disease to be eradicated besides smallpox**2,3**. How were such large advances made in only 30 years, and what does this have to do with road safety?
-
-Unlike smallpox, there are no known medicines or vaccines that prevent Guinea worm disease. Eradication, therefore, required a different approach: changing human behavior.
-
-## **The Human Factor**
-
-A report from the National Cooperative Highway Research Program (NCHRP) defines "human factors" as follows:
-
-*Human factors is an applied, scientific discipline that tries to enhance the relationship between devices and systems, and the people who are meant to use them. As a discipline, human factors approaches system design with the "user" as its focal point. Human factors practitioners bring expert* 
-
-Cairncross, S., Muller, R., and Zagaria, N. (2002). Dracunculiasis (Guinea Worm Disease) and the Eradication Initiative. Clinical Microbiology Reviews, 223-246.
-
-World Health Organization and the Carter Center, Eradication of Guinea Worm Disease: Case Statement, 2016. Available at: [https://](https://www.cartercenter.org/resources/pdfs/health/guinea_worm/2016-gw-case-statement.pdf) [www.cartercenter.](https://www.cartercenter.org/resources/pdfs/health/guinea_worm/2016-gw-case-statement.pdf) [org/resources/pdfs/](https://www.cartercenter.org/resources/pdfs/health/guinea_worm/2016-gw-case-statement.pdf) [health/guinea\\_](https://www.cartercenter.org/resources/pdfs/health/guinea_worm/2016-gw-case-statement.pdf) [worm/2016-gw-case](https://www.cartercenter.org/resources/pdfs/health/guinea_worm/2016-gw-case-statement.pdf)[statement.pdf](https://www.cartercenter.org/resources/pdfs/health/guinea_worm/2016-gw-case-statement.pdf)
-
-![](_page_44_Picture_15.jpeg)
-
-WHO, Dracunculiasis Fact Sheet. May 2016. Available at: [http://www.who.](http://www.who.int/mediacentre/factsheets/fs359/en/) [int/mediacentre/](http://www.who.int/mediacentre/factsheets/fs359/en/) [factsheets/fs359/en/](http://www.who.int/mediacentre/factsheets/fs359/en/)
-
-![](_page_44_Picture_17.jpeg)
-
-4
-
-John L. Campbell, Monica G. Lichty; et al. (2012). National Cooperative Highway Research Program Report 600: Human Factors Guidelines for Road Systems (Second Edition). Washington, D.C.: Transportation Research Board.
-
-Kahneman, D. (2011) Thinking, Fast and Slow. Farrar, Straus and Giroux
-
-*knowledge concerning the capabilities and limitations of human beings that are important for the design of devices and systems of many kinds.***<sup>4</sup>**
-
-In road safety, the term human factors is typically used to describe how people respond to the roadway environment. However, people are not simply users of the transportation system. Humans also design, engineer, build and maintain the roadway environment, the vehicles using it and the laws governing behavior of roadway users and vehicle manufacturers. In that sense, the entire transportation system is a product of human factors. The term human factors conveys an oversimplified notion of the role of human behavior in transportation safety. The human part of the equation is more complex than simply a list of discrete factors.
-
-## **Key Principles of Human Behavior**
-
-To understand human behavior, it is important to bear in mind four key principles.
-
-- J Human behavior is guided by two different systems (deliberative and intuitive).
-- J Humans are not exclusively logical, rational beings.
-- J Human behavior is heavily influenced by the environment.
-- J Humans make mistakes.
-
-Let's discuss these concepts as they relate to road safety.
-
-## **Human behavior is guided by two different systems**
-
-Human behavior is largely guided by
-
-two different systems – a deliberate, rational system (deliberative) and an implicit, unconscious system (intuitive)**<sup>5</sup>** .
-
-The deliberative system is a conscious system wherein a person considers information using rational thought, logic and reasoning in deciding on an action.
-
-#### **For example:**
-
-When driving home after work, a driver decides to change routes to avoid an area that is usually congested at this time of day.
-
-In this example, the driver considered the available information (time of day and previous experience with that location) and made a conscious decision to take another route.
-
-The intuitive system is an implicit, unconscious process by which a person makes nearly instantaneous decisions and takes a resulting action.
-
-#### **For example:**
-
-As a driver approaches a signalized intersection, the light turns yellow. Without conscious thought, the driver either proceeds through the intersection or comes to a stop.
-
-In this example, the driver receives information from the environment (the yellow light), combines it with an understanding of the specific circumstance based on the driver's previous experiences, and takes an action almost immediately. It is important to realize the intuitive system acts nearly instantaneously without the driver's awareness of the process.
-
-We often incorrectly assume that human behavior is largely controlled by the deliberative system when, in fact, most behaviors are a result of the intuitive system. In other words, most human behavior is not the result of conscious, rational deliberation. For most actions, we don't have enough time or available information to do a logical analysis before acting. The intuitive system allows us to act without this timeconsuming conscious decisionmaking process.
-
-To think of this another way, consider the deliberative system as similar to the decision making process of a computer. Computers function exclusively using a deliberative system. They take in information, process it using explicit algorithms and deliver a result. However, humans do not function this way. They generally make decisions that appear to reflect instinctual processes rather than systematic rational considerations.
-
-## **Humans are not exclusively logical, rational beings**
-
-Although people take in and interpret information, they do so in the context of a number of factors, such as prior experience, emotions, cultural norms, moral beliefs, social pressures, convenience, habits and financial considerations, among many others. Rational calculations based on objective evidence are often not even possible, and when they are, they must compete with these other influences. This is why people often make decisions that are not necessarily the most appropriate choice for their health and wellbeing.
-
-We know that cooking dinner at home may be healthier, but sometimes it is easier and more convenient to have a pizza delivered instead. We know we should exercise more and get the recommended amount of sleep each night, but work, family and other obligations
-
-### **Flossing and human behavior**
-
-At some point in your life, you have probably been told that regular flossing is good for your dental health. Through the years, you've likely had conversations about flossing with your dentist or dental hygienist who encouraged you to floss more. Perhaps they showed you the proper way to floss and sent you home with your own floss in an effort to encourage you to start. For a few days or weeks after the appointment, you may have deliberately flossed more regularly. But if you're like most people, you soon reverted to old habits, and the floss sat unused in a cabinet. Why do we do this?
-
-Typically, programs aimed at influencing human behavior take an educational or informational approach on the assumption that people act a certain way because
-
-they lack knowledge about the potential consequences, benefits or alternatives. Surely, if people simply were aware of the benefits of flossing or the consequences of not flossing, they would change their behavior and become regular flossers. This approach appeals to the belief that human behavior is rational.
-
-Although commonly used, this approach fails to take into account the complexities of human behavior. Other factors influence our behaviors – flossing is inconvenient, takes time and can be uncomfortable. The negative consequences of not flossing (e.g., gum disease) are not immediately visible. Although people repeatedly hear messages explaining why flossing is important, rates of flossing among the general population remain low.
-
-can make this difficult. We know we should get a flu shot, but a fear of needles may keep us away.
-
-Humans are largely intuitive beings which means their actions are rarely the result of a systematic, rational decision making process. For years, many people did not brush their teeth daily, even though the benefits of brushing were widely known. It was not until mint flavoring was added to toothpaste that daily brushing became the norm. That is, information about the benefits of brushing did little to change behavior; what convinced people to brush was a desire for the clean feeling they associated with the mint.**<sup>6</sup>** Because the factors that affect human behavior are complex and interrelated, behavior is not easily changed. When attempting to change the behavior of others, we tend to assume that people are entirely logical and rational beings. However, experience repeatedly shows this is not the case.
-
-This situation is not unique to dental hygiene. We regularly do things that we generally realize are not in our best interest. We don't eat the recommended amounts of fruits and vegetables. We don't get enough sleep. We don't exercise as much as we should. Think about examples from your own life.
-
-## **Human behavior is heavily influenced by the environment**
-
-Often the environment has a much stronger influence on a person's behavior than internal conditions (e.g., attitudes and personality) commonly assumed to be influential. The environment includes the physical environment, as well as
-
-#### **Optical Speed Bars**
-
-Optical Speed Bars (OSBs) have shown promise in reducing vehicle speeds in advance of hazardous locations**<sup>7</sup>** . OSBs are a series of white rectangular markings, placed just inside both edges of the travel lane and spaced progressively closer, to create the illusion of increasing speed when traveling at a constant rate as well as the impression of a narrower lane**<sup>8</sup>** . A compelling characteristic of optical speed bars is that they operate on intuitive, rather than conscious, decisions made by drivers/riders. By creating a sense of increasing speed as riders approach a dangerous curve, they should induce riders to slow down – as an instinctive reaction rather than a conscious decision.
-
-social and organizational contexts, such as policies and social norms.
-
-Given that most behavior is intuitive, people generally do not know the true reasons for their actions, nor can they validly articulate what might influence their actions. People can usually provide explanations for their behavior after the fact, but research shows people are often not aware of the strong influence of environmental factors on their behavior. An example in Unit 1 of this book describes a town that aims to resurface and widen a
-
-6
-
-Duhigg, Charles. The Power of Habit: Why We Do What We Do in Life and Business
-
-Gates TJ, Qin X, Noyce DA. Effectiveness of Experimental Transverse-Bar Pavement Marking as Speed-Reduction Treatment on Freeway Curves. In Transportation Research Record: Journal of the Transportation Research Board, No. 2056, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 95–103.
-
-Federal Highway Administration. Engineering Countermeasures for Reducing Speeds: A Desktop Reference of Potential Effectiveness. [http://safety.fhwa.](https://safety.fhwa.dot.gov/roadway_dept/horicurves/fhwasa07002/ch7.cfm) [dot.gov/roadway\\_](https://safety.fhwa.dot.gov/roadway_dept/horicurves/fhwasa07002/ch7.cfm) [dept/horicurves/](https://safety.fhwa.dot.gov/roadway_dept/horicurves/fhwasa07002/ch7.cfm) [fhwasa07002/ch7.](https://safety.fhwa.dot.gov/roadway_dept/horicurves/fhwasa07002/ch7.cfm) [cfm](https://safety.fhwa.dot.gov/roadway_dept/horicurves/fhwasa07002/ch7.cfm).
-
-#### **Environment affects behavior**
-
-Research has shown that people are more likely to help another person (in a nonemergency situation) if they see someone else helping first. Social psychologist Robert Cialdini demonstrated this by counting donations given to a street musician with and without a colleague first modeling the behavior by donating money. He found that many more people gave the musician money when the behavior was modeled than in the control condition with no behavior modeling (eight donations in the modeled condition versus one donation in the control condition). Further, when people in the modeled condition were asked why they donated, no one realized that they had been influenced by the behavior of another person. Instead, they attributed their donations to something else, such as enjoyment of the song or how they felt about the person playing the music**9,10,11**.
-
-two-lane collector roadway through a neighborhood with mature trees. However, after completing the project, both speeds and crash severity increased. Behavioral adaptation refers to the unconscious process by which people react to their environment. While driving, people unconsciously assess the roadway and its characteristics and modify their behaviors accordingly.
-
-This may seem counterintuitive, but as discussed, human behavior is an intuitive process that is heavily influenced by the environment. A wider road with limited roadside hazards feels safer and people unconsciously adapt their behavior accordingly. You should assume people would not maintain their original behavior when the driving environment is changed.
-
-Roadway designers must design roads not for the way in which they would like users to behave, but for the way in which users actually will behave. In general, people don't just do what they are told to do — by a sign, a law or another person. Instead, they integrate information from many parts of their environment along with their own historical experience as they determine (usually non-consciously) what they should do in a given situation**<sup>12</sup>**.
-
-## **Humans make mistakes**
-
-Both our deliberative and intuitive systems can lead us to make mistakes. Actions reached by a deliberative process can be mistaken if we fail to consider all relevant information or if we process it incorrectly. Similarly, our intuitive system can lead to errors in situations with which we have little or no experience. Experience helps to refine the intuitive processes, so the likelihood of mistakes declines with exposure to situations. However, mistakes are inevitable and the transportation infrastructure needs to be designed with the recognition that road users will make mistakes and that they will often make them in predictable ways.
-
-Have you ever looked down at your speedometer and realized that you were driving substantially over the speed limit? You probably didn't make a conscious (deliberative) decision to exceed the speed limit. Instead, you reached that speed by taking cues about the proper speed from your environment (intuitive). Characteristics of the road, such as wide lanes, multiple travel lanes,
-
-9
-
-Cialdini. R.B. (20005). Basic Social Influence Is Underestimated. Psychological Inquiry, 16: 158-161
-
-Cialdini, R.B., Demaine, L.J., Sagarin, B.J., Barrett, D.W., Rhoads, K., & Winter, P.L. (2006). Managing social norms for persuasive impact. Social Influence, 1: 3-15
-
-Cialdini, R.B. (2007). Descriptive social norms as underappreciated sources of social control. Psychometrika, 72: 263-268
-
-Etzioni, Amitai. Human Beings Are Not Very Easy To Change After All, Saturday Review, June, 1972.
-
-![](_page_49_Picture_0.jpeg)
-
-**FIGURE 2-1**: Example of unconscious clues leading to higher speed
-
-presence of a median and long gentle curves, convey the message that the road can accommodate high speeds. Additionally, the speed of other vehicles is a particularly salient indicator of the right speed.
-
-Recall this example from Unit 1 (Figure 1-1). Although the posted speed limit is 35 mph, many people drive much faster than that. This is not because they all have a blatant disregard for safety. Instead, they are unconsciously taking cues from their environment, which is telling them it is safe to travel at a higher speed. Add to this the fact that modern vehicles have been engineered for occupant comfort, so many of the auditory and haptic cues (e.g., wind noise, bumps, road noise, etc.) that previously gave drivers feedback about their speed have been eliminated. On a road
-
-with very few other vehicles, the only clear clue to one's speed is the speedometer.
-
-Roads constructed according to the recommended design standards may be considered safe, but in reality, they may only be nominally safe. The fact that some road designs encourage higher speeds can make it substantively unsafe. The transportation system is designed, built, maintained, governed and used by humans. It is often cited that human error contributes to more than 90% of traffic crashes, most often referring to a road user error. However, it is important to remember that errors by road users are not the only human errors that can occur.
-
-#### **For example:**
-
-On a rural two-lane road, an SUV
-
-driver over-compensates when a tire slips off the roadway causing the vehicle to roll over and strike a tree on the opposite side of the road.
-
-Where was the human error in this example? Was it in the driver who didn't stay on the road and overcompensated with steering? Was the road maintained improperly or inadequately? Could the crash have been prevented if edgeline rumble strips had been installed, or if the road had a paved shoulder instead of a soft gravel shoulder? Should the tree next to the roadway have been removed? Could SUVs be designed so they are less susceptible to roll over? The answer is that a combination of several of these caused the crash, not simply the driver's error. Just as
-
-in airplane crashes, it is quite rare that any single factor in a motor vehicle crash was the sole reason for the crash or for its severity.
-
-Meeting nominal safety does not guarantee that a crash will never occur, nor does it guarantee that users will behave in the intended way. Those in charge of the road should use professional judgement to prioritize safety improvements and select appropriate designs within a range of options based on consideration of road user behavior. Unit 3 discusses how many kinds of data, such as crash data and behavioral observation, can be used to evaluate the substantive safety of the road.
-
-#### **EXERCISES**
-
-- J **MAKE** a list of your own driving behaviors. What behaviors involve the deliberative system? What behaviors are intuitive?
-- J **WORK** with your state department of transportation to identify a crash cluster in your area. It's highly unlikely that many
-
-drivers have independently made the same mistake at the same location. What characteristics of the roadway as designed or built — may have contributed to this cluster of crashes? What modifications might be made that would not be offset by behavioral adaptation?
-
-![](_page_50_Picture_8.jpeg)
-
-## **Changing Human Behavior**
-
-Let's return to the Guinea worm disease example. You may be wondering why something so seemingly unrelated to improving safety in a modern transportation system was used to introduce this unit. The fact that Guinea worm disease is nearly extinct in only 30 years is a tremendous achievement. The fact that it was done through behavior change alone, without the use of vaccines or medication, is unprecedented.
-
-Road safety professionals would be wise to consider the successful approach to eradicate Guinea worm disease. Although the desired behaviors may be different, the general strategies for influencing human behavior are the same. Even though changing human behavior is exceedingly difficult, it is possible to achieve behavior change. However, this requires that we take into account the nature of human behavior instead of assuming that simply providing information is sufficient.
-
-## **Understanding factors that influence human behavior**
-
-To change human behavior, it is important to identify and understand not only the target behavior but also any other factors that influence the behavior. Attempting to change a behavior without a full understanding of the many contributing factors will almost certainly fail.
-
-![](_page_51_Picture_8.jpeg)
-
-**FIGURE 2-2**: Guinea worm disease hotspots in Africa
-
-![](_page_52_Figure_0.jpeg)
-
-**FIGURE 2-3**: Guinea worm disease factors, behaviors and outcomes
-
-Doctors knew that Guinea worm disease spreads by people drinking contaminated water, and contamination of the water supply occurred when an infected person used the water supply to temporarily relieve the symptoms of infection. Hence, the eradication campaign focused on two main behaviors:
-
-- J Drinking contaminated water
-- J Using drinking water sources to temporarily relieve the pain caused by the infection
-
-Simply identifying these behaviors was not sufficient. In order to be successful, public health officials considered many other factors influencing these behaviors.
-
-**Why were people drinking contaminated water?**
-
-- J **Availability** Uncontaminated drinking water may not have been available in the community.
-- J **Money** People and communities lacked financial resources to obtain clean drinking water.
-- J **Understanding** People did not know that the water was contaminated and/or how the
-
-disease was transmitted.
-
-J **Lack of immediate consequences** – Guinea worm disease symptoms did not appear until one year post-infection.
-
-**Why were people using drinking water to relieve the pain caused by the infection?**
-
-- J **Immediate benefit**  Water submersion resulted in immediate pain relief.
-- J **Limited availability of water** Because water is scarce, most water sources were used for drinking.
-- J **Unavailability of alternative treatments** – The lack of medical infrastructure meant limited access to treatment options.
-- J **Money** People lacked financial resources to obtain medical treatment even when available.
-  - J **Understanding**  People lacked knowledge about how the disease is transmitted.
-
-A thorough understanding of the factors that influence behavior is necessary to develop a plan for behavior change.
-
-look for the cause of the outcome, instead we need to look for the weak links in the causal chain and intervene at these links. In the case of Guinea worm disease, the limited availability of water and lack of understanding about the disease and its transmission were factors influencing both behaviors. Thus, these factors were targeted in the intervention.
-
-To develop a plan, we cannot merely
-
-Public health officials informed the people about the dangers of drinking contaminated water and how the water was becoming contaminated. However, they knew that simply providing this information would not be sufficient.
-
-In order to make the right behavior the easy choice, officials combined education with environmental change – they educated people on guinea worm disease and made clean water more accessible. Water sources known to be contaminated were treated to prevent transmission, and new clean water sources were created. When water sources could not be treated, villagers were given cloth filters to decontaminate their water before drinking. When people had access to clean water, they were less likely to drink contaminated water, thus significantly reducing the chance of infection**<sup>13</sup>**. This change to the environment (i.e., making clean water available) proved to be key in eliminating the disease.
-
-While this is an oversimplified description of the complex and multifaceted approach that occurred over 30 years, it highlights what can be accomplished when principles of behavior change are at the core of a
-
-comprehensive approach.
-
-## **Approaches to Changing Behavior**
-
-We are constantly exposed to attempts to influence our behavior. Consider the following things that you may encounter in everyday life:
-
-- J A brochure in your doctor's office about the benefits of getting a flu shot
-- J A requirement that restaurants include nutritional information in their menu
-- J Stores that charge for plastic shopping bags
-- J Public service announcements (PSAs) about bullying
-- J Cities that provide large recycling bins and small garbage bins
-- J A law requiring that everyone wear seatbelts
-
-Most of these attempts either provide information (e.g., a brochure with information about flu shots or a PSA detailing the negative impact of bullying), or they change the environment in such a way to encourage a different behavior (e.g., stores that charge for plastic shopping bags or cities that provide large recycling bins and small garbage bins). Understanding the nature of the problem is important in determining which approach has the best chance of success.
-
-## **Education, safety messages and raising awareness**
-
-Education and awareness-raising campaigns are often the first and only tools tried when attempting to influence behavior. In general,
-
-13
-
-Cairncross, S., Muller, R., and Zagaria, N. (2002). Dracunculiasis (Guinea Worm Disease) and the Eradication Initiative. Clinical Microbiology Reviews, 223-246.
-
-![](_page_54_Figure_0.jpeg)
-
-This tip card from the early 1900s was likely ineffective in changing the crossing habits of people since it relied solely on providing information.
-
-the goal of such campaigns is to communicate information with the assumption that once the audience is aware of the information, they will then act in the desired manner. In other words, educational campaigns appeal to the deliberative system and assume that human behavior is usually a product of rational thought. Because of this, information alone almost never works. However, information can be helpful as part of a more comprehensive program.
-
-Far too often information-based approaches are used in isolation, without careful consideration of whether the problem can be effectively addressed through raising awareness alone. Is the information new to the audience? Is it likely that knowing this information will produce the desired outcome? To draw from a previous
-
-example: does simply knowing that you should floss convince you to floss regularly?
-
-Consider the effect of an educational campaign on Guinea worm disease. Would an educational or awareness raising campaign be enough to produce lasting and consistent behavior change? On the one hand, there was a lack of knowledge about the disease among those affected, especially about how the disease was transmitted. However, this education cannot influence the additional — and likely more important — factors contributing to the problem. For example, education will not improve the availability of clean drinking water or access to alternative medical resources, nor will it provide the financial resources necessary to increase access to either. Thus, an education campaign, on its own, would not
-
-![](_page_55_Picture_0.jpeg)
-
-have been a successful tactic. Instead, health officials needed a more comprehensive approach.
-
-The same considerations can be applied to road safety problems. Consider the following example:
-
-Your city manager notices an increase in pedestrian crashes following the placement of a new bus stop on Elm Street, a busy multi-lane road without a median. The bus stop is located in the middle of the block across from a large shopping center (Figure 2-4). The nearby intersections on either side are signalized and have street lights, crosswalks and pedestrian signals. The intersection and crosswalks meet all applicable design standards and are therefore nominally safe. Although city engineers intended that people would cross the street at the intersections, observations show that many people are crossing mid-block from the shopping center to the bus stop, resulting in frequent conflicts with vehicles. The city wants to improve safety in this area and has decided to undertake a media campaign encouraging people to cross only at crosswalks.
-
-**What behavior is being targeted?** Crossing Elm Street midblock.
-
-**What are the other factors influencing this behavior?**
-
-- J **Convenience** Crossing midblock provides a more direct route to the bus stop. People coming from the shopping center are likely carrying shopping bags, which could be difficult to carry long distances.
-- J **Previous experience** It is likely that people have successfully crossed similar streets (or even the same street) in this manner many other times, so their limited previous experience suggests this is a safe option. (we say limited experience because people not likely to be aware of the location's crash history).
-- J **Time pressure** Buses run on a schedule, and people may want to cross as quickly as possible to be sure they catch the next bus.
-
-Is an educational or awareness raising campaign targeting this behavior likely to be effective? No. In all likelihood, people who are
-
-crossing the street in this spot know there are crosswalks at the nearby intersections. They are crossing the street here because it is easier and more convenient, and their previous experiences tell them they will be successful. Additional information is unlikely to alter these factors; therefore an informational or awareness-raising campaign alone will not be effective. However, that does not mean that all hope is lost. Improving the safety of pedestrians crossing Elm Street is still possible with the right approach.
-
-## **Changing the environment**
-
-A preferred alternative to education is changing the environment. We know that people act based on information gleaned from the world around them, and that most of our behavior is unconscious and driven by the intuitive system. By changing the environment, people can be moved towards the behavior of interest. In other words – if you
-
-can't change the person, change the world so that the person will follow.
-
-We know that people are crossing Elm Street mid-block because it is quicker, easier and more convenient than using the crosswalks at the nearby intersections. Information or awareness campaigns are unlikely to influence this behavior because the behavior is not due to a lack of awareness or information. Instead, we need to change the environment so that the pedestrians are no longer crossing somewhere other than a marked crosswalk.
-
-Possible changes to the environment include building a wall or putting up a fence to deter people from crossing at the mid-block, or building a pedestrian bridge to keep people out of the flow of traffic. These solutions might be cost prohibitive, and research shows that most pedestrians will still cross a street at ground level even when a pedestrian bridge is available.**14,15**
-
-![](_page_56_Picture_6.jpeg)
-
-Moore, R.L., Older, S.J., Pedestrians and Motors are Compatible in Today's World. Traffic Engineering, Institute of Transportation Engineers, Washington, DC, September, 1965.
-
-![](_page_56_Picture_8.jpeg)
-
-Rasanen, M, T. Lajunen, F. Alticafarbay, and C. Aydin, Pedestrian Self Reports of Factors Influencing the Use of Pedestrian Bridges, Accident Analysis and Prevention, 39, pp. 969-973, 2007.
-
-![](_page_56_Picture_10.jpeg)
-
-**FIGURE 2-5**: Pedestrian hybrid beacon (Source: pedbikeimages.org/Mike Cynecki)
-
-Another approach would be to move the bus stop closer to the existing crosswalks, assuming people will choose to cross at the crosswalk since it is now more convenient. Finally, another alternative would be to install a marked crosswalk with a pedestrian hybrid beacon close to the area where people are crossing (Figure 2-5). This solution recognizes the factors influencing people's behavior and provides an alternative that would improve safety and be acceptable to pedestrians.
-
-16
-
-Tison, J. & Williams, A.F. (2012). Analyzing the First Years of the Click It or Ticket Mobilizations (DOT HS 811 232). Washington, D.C.: National Highway Traffic Safety Administration.
-
-#### 17
-
-Pickrell, T. M., & Li, R. (2016, February). Seat Belt Use in 2015—Overall Results (Traffic Safety Facts Research Note. Report No. DOT HS 812 243). Washington, DC: National Highway Traffic Safety Administration.
-
-We know that people respond in predictable ways to their environments — far more than to internal conditions like attitudes and personality. Environment can include both the physical (built) environment and things like policies, laws and social norms. Let's revisit the speeding example from earlier in the unit (Figure 2-1). Although the posted speed limit is 35 mph, in reality many people drive much faster than that. What might we do to get drivers to slow down on this road? One option is to post additional speed limit signs or run local PSAs about the dangers of speeding. However, consider whether informational signs would result in lower speeds. Are drivers speeding because they are not aware of the speed limit? Are drivers unaware of the potential dangers of high speeds? The answer to both of these questions is "not likely."
-
-One example of environmental change to reduce driver speed is the use of traffic calming measures. Features such as speed humps and mini roundabouts are examples of physical alterations to the driving environment that influence how
-
-![](_page_57_Picture_7.jpeg)
-
-Seat belt use in the United States has a remarkably similar, though opposite trajectory to that of Guinea worm disease. Though seatbelts were required in U.S. vehicles starting in the late 1960s, use of this equipment was low. Observational surveys from the early to mid-1980s found use of 5-14 percent.**<sup>16</sup>** By 2015, however, observed seatbelt use had climbed to 88.5 percent.**<sup>17</sup>**
-
-As with Guinea worm disease, no single effort was responsible for increasing seat belt use in the United States. Instead, efforts that focused on changing the environment (e.g., enactment of seat belt and child passenger safety laws) were coupled with high visibility enforcement (e.g., Click-it-or-Ticket). Education played a role in these efforts, but not in raising awareness for the dangers of not wearing seat belts. Rather, education was needed to inform people that belt use is required and, especially, to create the perception among the driving public that police were actively enforcing seat belt laws.
-
-road users respond. In the previous speeding example, the overall design of the road has already been established, but the lanes could be narrowed or even reduced to one in each direction to communicate, "This is a road where you should drive slower." Traffic calming addresses the intuitive system in that it results in drivers slowing down
-
-without being aware of doing so.**<sup>18</sup>**
-
-## **Consider behavior in addressing travel safety**
-
-Making strides in road safety is possible. However, as with the near eradication of Guinea worm disease, significant achievements will not happen overnight. When implementing a program or intervention aimed at changing behavior, it is important to remember that any road safety issue is likely the result of a combination of factors. Consequently, it is unlikely that any one program or intervention will completely solve the problem. However, combining behavioral science principles with engineering design can help to produce significant advances. See Unit 4 for a discussion of how to identify and address road safety problems.
-
-## **Conclusion**
-
-In short, human behavior is extraordinarily complex. Consequently, it is difficult to influence. Simple, common sense approaches like merely raising awareness or otherwise providing information about an issue virtually never succeed.
-
-**Humans are not exclusively logical, rational beings.**
-
-We often assume human behavior is controlled by a logical deliberative process when, in fact, most behaviors are a result of an intuitive process. Many factors influence behavior including education, emotions, cultural norms, religious beliefs, social pressures, convenience, habits and finances. Understanding human behavior
-
-and the many factors that influence behavior is crucial to solving safety problems.
-
-**People generally don't simply do what they are told to do.**
-
-Information and awareness-raising campaigns are often the first tools used in efforts to influence behavior. However, these approaches are too often adopted without careful consideration of whether the behavior is likely to be changed merely with information (which the public often already has).
-
-The environment heavily influences human behaviors. We are constantly processing information and adjusting our behaviors accordingly. Most of this behavior is unconscious and driven by the intuitive system.
-
-**Changing behavior requires an understanding of all influencing factors.**
-
-Before attempting to influence behavior, it is essential to identify and understand the important factors influencing a behavior. Targeting a behavior without a full understanding of these factors will almost certainly be unsuccessful.
-
-Because so much of what we do is intuitive and heavily influenced by our environment, we sometimes respond to the transportation infrastructure in ways not anticipated by the engineers who designed it. By changing the environment, people can be nudged towards the behavior of interest.**<sup>19</sup>** In other words, if you can't change the person (and you usually can't!), change the world so that the person will follow.
-
-18
-
-Lewis-Evans, B. & Charlton, S.G. (2006), Explicit and implicit processes in behavioural adaptation to road width. Accident Analysis & Prevention, 38, 610-617.
-
-19
-
-Thaler, R.H., and Sunstein, C.R. Nudge: Improving Decisions About Health, Wealth, and Happiness.
-
-![](_page_59_Picture_0.jpeg)
-
-Bus stop with crossing pedestrians in Portland, Ore. (Source: pedbikeimages.org/Laura Sandt)
-
-**The transportation system is designed, built, maintained, governed and used by humans.**
-
-Using the term human factors to refer exclusively to the user perspective (i.e., drivers, pedestrians, etc.) can easily convey an oversimplified notion of the role of humans in the transportation system. From design to use, humans play a role in every step of the transportation system. In that sense, the entire transportation system is a product of human factors. For that reason, safety professionals must consider both the role of the environment (e.g., transportation
-
-infrastructure) and the user when trying to understand behavior and develop solutions to safety problems.
-
-In sum, significant advances in road safety are possible, but changes will not happen overnight. Human behavior is not easy to change. With thoughtful, comprehensive approaches that take into account an understanding of human behavior and the environment in which people live, we can develop programs, policies and countermeasures that have a better chance of significantly improving road safety.
-
-#### **EXERCISES**
-
-- J **CREATE** a causal diagram to model the behavior(s) and environmental factor(s) that contribute to the following.
-  - J The flu
-  - J Unhelmeted motorcyclist fatalities
-
-J Using the causal diagrams from exercise 1, **IDENTIFY** the weak links in the causal chain and describe an intervention aimed at changing the target behavior(s).
-
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-
-![](_page_61_Picture_0.jpeg)
-
-## **Measuring Safety UNIT 3**
-
-#### **LEARNING OBJECTIVES**
-
-After reading the chapters and completing exercises in Unit 3, the reader will be able to:
-
-- J **DESCRIBE** why measuring safety is important
-- J **IDENTIFY** the different types of available data
-- J **UNDERSTAND** the challenges and accuracy of data
-- J **SELECT** data for different road safety objectives
-
-## **Importance of Safety Data**
-
-Good quality safety data are the core of any successful effort to improve road safety. Local, State, and Federal agencies use crash data as well as roadway, vehicle, driver history, emergency response, hospital, and enforcement data to improve road safety. All of these data sources can be used, in isolation or jointly, to produce projects, programs, and guide policies that reduce injuries and save lives. These types of data are collectively categorized as safety data in this book.
-
-Safety professionals in many disciplines – highway design, transportation planning, operations, road maintenance, law enforcement, education, emergency response services, policy makers, infrastructure program management, road safety management, and public health – use safety data to identify problem areas, select countermeasures, and monitor countermeasure impact.
-
-Road safety management and project development has become increasingly data-driven and evidence-based. This approach to road safety emphasizes safety performance (i.e., number of crashes), rather than solely adhering to engineering standards, personal experience, beliefs, and intuition. For example, in the past, road improvements were considered "safe" if the improvements met the standards contained in the Manual on Uniform Traffic Control
-
-Devices (MUTCD) and A Policy on Geometric Design of Highway and Streets, also known as the Green Book**1,2**. However, most of these standards are engineering based (i.e., nominal safety as discussed in Unit 1), and were not necessarily based on an evaluation of actual road safety performance. Presently, transportation professionals use safety data (such as crash data, road characteristics, and traffic volume) to evaluate road safety performance and inform their decisions. This substantive approach challenges professionals to quantify the expected consequences and outcomes of safety strategies in real measurements, such as the expected number of crashes, injuries, and fatalities.
-
-The selection of road safety measures and treatments can benefit from an understanding of the intricacies and limitations of safety data. This unit presents many kinds of safety data, explores the current process used to collect data, and discusses the impact that these processes have on data quality (i.e., accuracy and reliability). The unit also discusses ways to improve data quality and analysis.
-
-## **Relating Nominal and Substantive Safety to Data**
-
-The concepts of nominal and substantive safety were first introduced in Unit 1 of this textbook. Nominal safety refers to whether
-
-Manual on Uniform Traffic Control Devices (MUTCD), Federal Highway Administration, 2009.
-
-A Policy on Geometric Design of Highways and Streets, American Associations of State Highway Transportation Officials, 6th edition, 2011.
-
-or not a design (or design element) meets minimum design criteria based on national or State standards and guidance documents, such as the AASHTO Green Book or the MUTCD. Substantive safety refers to the actual safety performance, such as expected number of collisions by type and severity on a road.
-
-The contrast of these concepts is directly linked to this discussion of safety data. To determine if a road is nominally safe we do not need safety data; we only need to know if all design standards were followed. However, we need high quality safety data and data analysis to determine if a road is substantively safe. Typically, the analysis includes estimating the expected number of crashes and comparing it against the road's actual safety performance. More information on safety analysis is presented in Unit 4, Solving Safety Problems.
-
-## **Use of Safety Data in Road Safety Management**
-
-Data are integral to safety decision making, both in prioritizing investments and in identifying analyzing the most effective techniques and interventions. The more comprehensive and accurate the data, the better the resulting decisions. Understanding contributing factors to crashes and how best to implement potential countermeasures is complex, and it may involve a variety of agencies and historical data challenges. Because of this complexity, both accurate data and high quality data analysis is necessary for road safety management. A great database is only as useful as the analysis and application of that data. Table 3-1 explores the relationship between data quality and data analysis quality and shows why agencies should strive to improve both of these areas.
-
-![](_page_63_Picture_4.jpeg)
-
-#### **HIGH QUALITY ANALYSIS LOW QUALITY ANALYSIS**
-
-#### **BEST CASE**
-
-## **HIGH QUALITY DATA**
-
-The agency is likely to reach the best safety decisions. Analysts are aware of data capabilities and limitations. This is the most expensive to achieve, due to the need for good data and training on how to conduct analyses.
-
-#### **MISSED OPPORTUNITY**
-
-The agency needs to invest in high quality analysis. Otherwise, the agency has wasted money in databases that are not being utilized to their potential. Good data with poor analysis will lead to poor decisions.
-
-#### **PROMISING**
-
-## **LOW QUALITY DATA**
-
-A robust analysis that recognizes the limitations of the data can still produce useful results. The agency should focus on improving data quality.
-
-#### **WORST CASE**
-
-Poor data and poor analysis will lead to bad decisions. The agency may be better off relying on judgment.
-
-**TABLE 3-1**: Data and Analysis Quality Comparison
-
-Crash data analysis using quantifiable metrics and scientifically defensible methods can help decision makers improve road safety by reducing more injuries and saving more lives at a lower cost. Accurate crash data help determine crash and severity trends, such as increases or decreases in certain types of crashes. Data also help safety professionals pinpoint high crash locations and identify highrisk users, such as younger drivers, older drivers, impaired drivers, and motorcyclists. Examining the characteristics of crashes allows road safety professionals to identify contributing crash factors related to roadway environment, design, or behavioral adaptations. This type of analysis will lead to a more effective selection of countermeasures that will reduce future crash occurrences or crash severity. Planners and engineers can use crash data to show quantitative information to decision makers on
-
-how specific planning guidance, design proposals, or engineering countermeasures can save lives.
-
-Safety professionals could seek to improve safety by relying merely on their gut judgment. The results of such an approach, however, would be quite unreliable. As shown in Table 3-1, safety professionals can improve their decision making process by using high quality data together with robust analysis processes. This unit will focus on the data itself. The use of the data in safety management is presented and discussed in Unit 4.
-
-Good quality safety data and analysis are the keys to identifying real safety issues on roads and evaluating the best methods for improving safety. The following chapters provide an overview of different types of safety data and of ways in which agencies can improve the quality of their data.
-
-## **Types of Safety Data**
-
-As highway safety analysis methods continue to evolve, it is equally important to focus on quality data to conduct these safety analyses. Transportation agencies can and should incorporate road characteristics, traffic volume, and enforcement and citation data, and other information into their safety analysis processes. This will enable them to better identify safety problems and prescribe solutions that improve safety and make more efficient use of safety funds.
-
-Single sources of safety data also do not give a complete picture of the safety risks on our roads. For example, using crash data by itself leaves safety practitioners with purely reactive approaches identifying locations where crashes have already happened. By combining crash data with other types of data, more details begin to emerge. For example, by combining crash data and detailed road inventory information, safety practitioners can develop a more in-depth understanding of the road attributes that contribute to crash risk. This will allow them to adopt a proactive approach, seeking out those factors associated with a high risk of crashes and addressing sites that share those "**elements**" before a crash occurs.
-
-Crash, roadway, and traffic data should be integrated or combined using common or "linking" reference systems, such as mileposts
-
-#### **Chicago's Use of Injury Data to Benchmark Safety Goals and Progress**
-
-Chicago DOT completed a comprehensive pedestrian crash analysis in 2011 to inform the citywide Chicago Pedestrian Plan. This analysis evaluated various crash types, contributing environmental factors, and different age groups using the Illinois Department of Transportation crash data files. The findings present crash density citywide, by ward, and around schools. The data also highlighted key crash conditions and served as a benchmark for measuring the City of Chicago's road safety goals.
-
-Reference: City of Chicago 2011 Pedestrian Crash Analysis, Summary Report, Chicago Department of Transportation, Accessed September 2016 at [https://www.](https://www.cityofchicago.org/city/en/depts/cdot/supp_info/2011_pedestrian_crashanalysis.html) [cityofchicago.org/city/en/depts/cdot/supp\\_](https://www.cityofchicago.org/city/en/depts/cdot/supp_info/2011_pedestrian_crashanalysis.html) [info/2011\\_pedestrian\\_crashanalysis.html](https://www.cityofchicago.org/city/en/depts/cdot/supp_info/2011_pedestrian_crashanalysis.html)
-
-or geospatial position. These data should also have the ability to be linked to the State's other road safety databases, including citation data or injury surveillance systems. Additionally, commercial motor vehicle data could also be linked based upon common data elements involved in crashes and inspections.
-
-Not all types of safety data are available or used by all practitioners. Safety data exist in distinct databases that are maintained by different agencies and often are accessible only to those agencies. One role for safety professionals is to bring together safety databases
-
-#### **Roadway elements**
-
-Physical features of the road such as travel lanes, shoulder width, pavement condition, and roadside characteristics
-
-and analyze them using logical and statistically robust processes.
-
-Safety data can be categorized into two groups based on criteria of core data needs for safety evaluations, data availability, accuracy, and usefulness to safety practitioners and researchers. Some safety data are used often and are critical to safety analysis for many agencies. Other safety data are used less often but can be supplemental to specific safety analyses. This chapter provides general information on safety data in these two groups:
-
-#### **Critical data**
-
-- J Crashes
-- J Traffic volume
-- J Road characteristics
-
-#### **Supplemental data**
-
-- J Conflicts and avoidance maneuvers
-- J Injury surveillance and emergency medical systems
-- J Driver history
-- J Vehicle registrations
-- J Citations and enforcement
-- J Naturalistic
-- J Driving simulator
-- J Public opinion
-- J Behavioral observation
-
-## **Crash Data**
-
-#### **Description**
-
-Crash data is the most widely used type of safety data, and it is essential in road safety analysis. Crashes are currently viewed as the most objective and reliable measurements of road safety. However, there are challenges with crash data, such as human error in reporting, unreported crashes, and the length
-
-of time it often takes for crashes to be entered into a database. Crash data is also the primary measure of effectiveness for safety efforts, since the goal is to decrease crash occurrences and lower the severity of crashes that do occur. Crash records typically provide details on events leading to the crash, vehicles, and people involved in crashes, as well as the consequences of crashes, such as fatalities, injuries, property damage, and citations.
-
-#### **Data collection process**
-
-Crash data collection begins when a State highway patrol trooper or local police officer arrives at the crash scene. The officer completes a crash report, documenting the specifics of the crash. While the specifics and level of detail of the crash data vary from State to State, in general, the most basic crash data consist of where and when the crash occurred, what type of crash it was, and who was involved. The specific data collected on crashes is determined by State agencies, local government agencies, and often a coalition of law enforcement agencies. The exact data fields and coding differ from State to State. The level of detail in a crash report may also differ by the severity of a crash. For instance, in some States property damage only crashes (PDO) are self-reported and, thus, often have less information than injury crashes, which are reported by law enforcement officers.
-
-States also differ in the threshold of what is required for a crash to be reported. Reporting of crashes can vary by threshold requirements, such as "only injury crashes" or "PDO crashes over an estimated \$2,000 in damage." These thresholds are
-
-- **Crash date and time**: The date (year, month, and day) and time (00:00-23:59) when the crash occurred. **B**
-- **Crash county**: The county or equivalent specific crash within a State. entity where the crash physically occurred. **C**
-- **Case identifier**: The unique identifier within a given year that identifies a **A**
-
-**Contributing circumstances, road**: Apparent condition of the
-
-**Roadway surface** 
-
-**I**
-
-**conditions**: The
-
-condition at the time and place of a crash.
-
-road that may have contributed to the crash.
-
-**Weather conditions**: The prevailing atmospheric conditions that existed at the time of the crash. **K**
-
-**Contributing circumstances, environment**: Apparent environmental conditions which may have contributed to the crash. **L**
-
-**Light conditions**: The type/level of light that existed at the time of the motor vehicle crash. **M**
-
-![](_page_67_Figure_8.jpeg)
-
-- **Manner of crash/collision impact**: The identification of the manner in which two motor vehicles in transport initially came together without regard to the direction of force. This data element refers only to crashes where the first harmful event involves a collision between two motor vehicles in transport. **N**
-- **First harmful event**: The first injury or damage-producing event that characterizes the crash type. **O**
-  - **Location of first harmful event relative to the trafficway**: The location of the first harmful event as it relates to its position within or outside the trafficway. **P**
-- warning sign. **School bus-related**: Indicates whether a school bus or motor vehicle functioning as a school bus for a school-related purpose is involved in the crash. The school bus, with or without a passenger on board, must be directly involved as a contact motor vehicle or indirectly involved as a non-contact motor vehicle (children struck when boarding or alighting from the school bus, two vehicles colliding as the result of the stopped school bus, etc.). **Q Q R S** Crash sketch/ diagram Crash narrative **N,O,P**
-
-**FIGURE 3-1** (above, left): Data elements on a crash report form. (Source: North Carolina DOT)
-
-**Work zonerelated**: A crash that occurs in or related to a construction, maintenance, or utility work zone, whether or not workers were actually present at the time of the crash. Work zone-related crashes may also include those involving motor vehicles slowed or stopped because of the work zone, even if the first harmful event occurred before the first **R**
-
-**Source of information**: Affiliation of the person completing the crash report. **S**
-
-unrelated to the number of crashes that are actually occurring on a road, but the reported numbers could look quite different. Changes to the crash reporting thresholds can happen abruptly and may significantly affect the crash data. Consider how the safety of a road, based on reported crashes, would appear in the years before and after a crash reporting threshold change from \$1,000 to \$4,000. You would expect to see fewer reported crashes after the change, since crashes with damage below \$4,000 would no longer be reported, even though there may be no real change in the number of crashes occurring.
-
-After the crash investigation is completed by the officer for the investigating agency, it usually undergoes an internal quality review. Passing the internal review, the crash report is sent to the State crash database. In some cases, the data is transmitted electronically, while in other cases the State agency receives a paper copy of the crash report.
-
-The agency that maintains crash data for the State may be the State department of transportation (DOT), the department of motor vehicles (DMV), or a State law enforcement agency. This agency will in turn make the data available to various other agencies. Federal, State, and local governments, as well as metropolitan planning organizations, advocacy groups, auto and insurance industries, and private consultants request crash data to conduct various transportation planning activities and analysis. The agency maintaining the data may provide raw or filtered datasets to local agencies and to national databases, such as the
-
-National Highway Traffic Safety Administration's (NHTSA's) Fatality Analysis Reporting Systems (FARS).
-
-The time between the crash occurrence and the availability of the crash data from the State crash database varies and typically depends on the type of crash reporting system and the State and local government capabilities. This time period between crash occurrence and the report's availability for analysis defines the timeliness of the crash data. While some agencies can provide complete data with a very short turnaround (i.e., less than a month), others take significantly longer (i.e., up to two years) due to backlogs and personnel shortages. Agencies who have the majority of their crashes reported electronically from law enforcement typically have shorter turnarounds on the crash data.
-
-#### **Common data elements**
-
-Common data elements for crash data include information on date, location, injury severity, types of vehicles, and characteristics of persons involved. Crash narratives and diagrams are typically found in the original crash reports, though generally not in the crash database. Narratives and diagrams are most useful when the safety professional desires to know the exact location of the crash, such as the particular approach of an intersection.
-
-NHTSA developed the Model Minimum Uniform Crash Criteria (MMUCC) in 1998 as a model set of data elements that should be collected to enable safety professionals to conduct data-driven analyses. States are encouraged to adopt MMUCC standards,
-
-though they are not required to match these recommendations. MMUCC, currently in its fourth edition, recommends the crash data elements listed below. Chapter 9 presents further information on MMUCC on page 3-30.
-
-#### **Data sources and custodians**
-
-Crash details may be available from different sources or systems. State agencies and institutions typically maintain the State crash database. These include State departments of transportation, departments of motor vehicles, departments of public safety, or in some cases, State universities under contract to a specific department. Local agencies, such as cities or metropolitan planning organizations, may also maintain their own crash databases within local record management systems. These local systems are most frequently housed by the local police, public works, or transportation departments.
-
-#### **Transportation safety applications**
-
-Crash data serve as the primary observable measure of safety (or lack thereof) on the road. Transportation professionals can use crash data to analyze a single crash, a specific site, an entire corridor, or a large area, such as in regional or Statewide planning. Crash data can be used to provide guidance to transportation decision makers and to guide the formation of safety legislation.
-
-#### **Coordination or integration with other data sets**
-
-In transportation departments, other data elements frequently used along with crash data include road characteristics and traffic volume
-
-data. For example, by combining road characteristics with crash data, safety professionals are able to identify road elements that may lead to higher frequency or injury severity of crashes, and therefore develop a systemic approach to reduce that crash risk at many of the locations that have those risk elements. Using traffic volume, agencies can calculate crash rates (e.g., crashes per road vehicle) to better identify locations requiring safety improvements.
-
-#### **Caution on the use of crash rates**
-
-Crash rate calculation (crashes per amount of traffic) is a simplistic measure that may be useful when comparing sites with similar characteristics and traffic volumes. However, the relationship between crashes and volume is not linear and can therefore lead to wrong conclusions if that assumption is made when considering volume increases on a road or comparing roads of different types. Unit 4 discusses how an analyst can use safety performance functions to avoid this error.
-
-#### **Data challenges and gaps**
-
-Some of the most common issues found in crash reporting include incomplete data (for example a driver's blood alcohol content is often missing), delays in entering the data into databases, inaccurate crash locations, and wrongly assigned fault and wrong choice of crash type. Some of these issues can be fixed by training police officers and those who enter the data into the database, as well as by using technology checks in data collection. Agencies should periodically conduct independent quality checks on the accuracy and reliability of their data.
-
-#### **Challenges with the Use of Crash Data to Systemically Identify High Risk Locations**
-
-The Oregon DOT identified pedestrian and bicycle crashes as one of its primary focus areas for infrastructure funding. While pedestrians and bicyclists account for more than 15% of all traffic fatalities statewide, the locations of serious injuries and fatalities appear to be random. Therefore, in 2013, ODOT set out to develop a program that focuses the limited available funding for infrastructure countermeasures on locations with the greatest crash potential. In order to identify these higherrisk locations, ODOT is working to discover behavioral patterns and road conditions that lead to pedestrian and bicycle crashes. While a promising approach, this analysis is constrained by the limited availability
-
-of road information (e.g., bicyclist and pedestrian volumes, the presence of crosswalks, turn lanes, driveway activity, and sight distances). While the lack of these data does not preclude such an analysis, it does reduce the certainty of the findings. An additional benefit from this effort is that it has helped ODOT identify current data deficiencies which ODOT is currently working to fix.
-
-Reference: Pedestrian and Bicycle Safety Implementation Plan, Oregon Department of Transportation, February 2014. Accessed September 2016 at [https://www.oregon.gov/](https://www.oregon.gov/ODOT/HWY/TRAFFIC-ROADWAY/docs/pdf/13452_report_final_partsA+B.pdf) [ODOT/HWY/TRAFFIC-ROADWAY/docs/](https://www.oregon.gov/ODOT/HWY/TRAFFIC-ROADWAY/docs/pdf/13452_report_final_partsA+B.pdf) [pdf/13452\\_report\\_final\\_partsA+B.pdf](https://www.oregon.gov/ODOT/HWY/TRAFFIC-ROADWAY/docs/pdf/13452_report_final_partsA+B.pdf)
-
-## **Traffic Volume Data**
-
-#### **Description**
-
-Traffic volume data indicates how many road users travel on a road or through an intersection. The most prevalent type of volume data is a count of daily use by motorized vehicle traffic. This type of traffic volume data can be measured in many ways depending on the intended use. Volume measurements include:
-
-- J Annual average daily traffic (AADT)
-- J Average daily traffic (ADT)
-- J Total entering vehicles (TEV) for intersections
-- J Turning movement counts
-- J Vehicle miles traveled (VMT)
-- J Pedestrian counts
-- J Bicyclist counts
-- J Percentage of traffic for specific vehicle types (e.g., heavy trucks or motorcycles)
-
-AADT is the average number of vehicles passing through a segment from both directions of the mainline route for all days of a specified year. As AADT requires continuous year-round counting, these data are often unavailable for many road segments. In these cases, ADT is used to estimate AADT by using shorter duration counts of that road and then adjusting those volumes by daily and seasonal factors. Other data used for crash analysis include turning movement counts and TEV at intersections and VMT on a road segment, which is a measure of segment length and traffic volume. VMT are useful for highway planning and management, and a common measure of road use. Along with other data, VMT is often used to estimate congestion, air quality, and expected gas tax revenues, and can serve as a proxy for the level of a region's economic activity. Volume data is also occasionally collected for bicyclists and pedestrians at road segments and crossing locations.
-
-#### **Data collection process**
-
-Volume data can be collected automatically or manually. Vehicle volume data is typically collected using automated counters, such as magnetic induction loops, pneumatic tube counters, microwave, radar, or video detection. These automated counters can also be configured to classify vehicles and produce counts by vehicle type (e.g., trucks, single passenger vehicles, etc.). For shorter durations or occasional counts, transportation agencies use manual traffic counts performed by observers, either in the field or through video cameras. Manual counting is also used often for bicyclist or pedestrian counts, although there are a number of additional technologies, such as infrared beams, that can be used to collect non-motorized volume data. These manual counts can range in length from one-hour counts to full-day counts, depending on the agency's needs and practices. Fitness tracking apps may also provide additional information to jurisdictions regarding where bicyclist and pedestrian activity is occurring. Some care is needed when using these data due to the selfselection bias present from users having to opt-in to the tracking and only using for specific types of activities (e.g., fitness cycling rather than commuting).
-
-Each State has its own traffic data collection needs, priorities, budget, and geographic and organizational constraints. These differences cause agencies to select different equipment for data collection, use different data collection plans, and emphasize different data reporting outputs. The FHWA Traffic Monitoring Guide (TMG) highlights
-
-#### **What's the difference between ADT and AADT?**
-
-Short term traffic counts are typically collected at a location for a 12-, 18-, or 24-hour period. Average Daily Traffic (ADT) is the count of traffic calculated to reflect the 24-hour (daily) volume of the date it was collected. The Annual Average Daily Traffic (AADT) is calculated for an entire year from the ADT by adjusting that simple average traffic volume to take into account the different travel patterns that occur during short duration count periods. For example, a summer traffic count taken in a beach vacation town would need to be adjusted downward to reflect the average traffic volume for the year, since traffic would be much higher in the summertime.
-
-best practices and provides guidance to highway agencies in traffic volume data collection, analysis, and reporting3 . The TMG presents recommendations to improve and advance current programs with a view towards the future of traffic monitoring. Traffic data is used to assess current and past performance and to predict future performance. Some States are utilizing traffic data from intelligent transportation systems (ITS) to support coordination of planning and operations functions at the Federal and State levels.
-
-#### **Common data elements**
-
-Volume data must include the counted volume, location, date, and duration of the count. Depending on the method used, the volume data may also contain information on vehicle classification, speed, or weight; lane position; weather; and directional factors. From these data, transportation professionals can calculate the average number of
-
-Traffic Monitoring Guide, Federal Highway Administration, Office of Highway Policy Information, September 2013.
-
-vehicles that traveled each segment of road and daily vehicle miles traveled for specific groups of facilities, vehicle types, and vehicle speeds.
-
-#### **Data sources and custodians**
-
-State highway agencies collect and maintain traffic volume data for State-controlled roads. These data are shared with the U.S. Department of Transportation in order to monitor road usage and safety trends. Local jurisdictions also collect and maintain traffic volume data; the scope, consistency, and quality of these data varies by jurisdiction.
-
-#### **Transportation safety applications**
-
-Agencies use volume data to support activities in design, maintenance, operations, safety, environmental analysis, finance, engineering, economics, and performance management. For instance, total traffic volume estimates or forecasts on a section of road are used to generate State and nationwide
-
-estimates of total distance traveled. Annual traffic volumes are also essential in network screening, diagnosis, and the selection of countermeasures (see further presentation of these processes in Unit 4). When selecting appropriate crash modification factors (CMFs) to estimate the benefit of potential countermeasures, a safety practitioner must use traffic volumes to confirm that the CMFs are suitable for the site in consideration.
-
-#### **Coordination or integration with other data sets**
-
-Other data elements frequently used with traffic volume data in safety applications include road characteristic inventories and crash data. For example, an agency that uses traffic volume and crashes together can identify sites with highest potential for safety improvements and target specific crash types. This allows them to better identify and prioritize locations for safety improvements.
-
-#### **Spatial Data and Road Safety**
-
-Many of the types of data presented in this chapter can be stored in a spatial format and displayed in a GIS. GIS is a particularly powerful tool designed to store, manipulate, analyze, and visualize data that is linked to a location. This makes it valuable to highway safety practitioners who can use a common referencing system for much of their highway data and link it together in GIS. For example, a single GIS database can contain road attributes, such as number of lanes, pavement condition, and lighting; crash information; and traffic volumes. This information can then be used to analyze crash hotspots and trends, such as multi-vehicle crashes in the vicinity of signalized intersections.
-
-![](_page_73_Picture_10.jpeg)
-
-This GIS map displays signalized intersections as squares and crashes as dots and allows the analyst to easily identify crashes occurring within 150 feet of a signalized intersection (denoted by circular areas around each intersection).
-
-#### **Data challenges and gaps**
-
-One of the biggest challenges in collecting accurate volume data is implementing a quality assurance process to ensure that counts are accurately recorded. Traffic volume for most roads is also based on sampling, which leads to estimates of volume on much of the road. As technology continues to develop and become more prevalent on our roads and in our vehicles, the accuracy will improve considerably. Additionally, pedestrian and bicyclist counts are more susceptible to higher variability due to their lower volumes; thus, longer count durations and additional locations are required for accurate data applications.
-
-## **Road Characteristics Data**
-
-#### **Description**
-
-Road characteristics data is also referred to as road inventory data. The most basic road characteristics data typically includes road name or route number, road classification, location coordinates, number of lanes, lane width, shoulder width, and median type. Intersection characteristics typically include road names, area type, location coordinates, traffic control, and lane configurations. The collection of these data elements supports an enhanced safety analysis and investment decision making when combined with other datasets, such as crash information.
-
-#### **Data collection process**
-
-Road characteristics data can be collected through several methods including photo or video logs, field surveys, aerial surveys, integrated GIS and global positioning systems (GPS) mapping, and vehiclemounted Light Detection and
-
-![](_page_74_Figure_14.jpeg)
-
-**FIGURE 3-2**: This image from the FHWA Model Inventory of Roadway Elements (v. 1.0) illustrates roadway elements.
-
-Ranging (LIDAR) technology. Some States find it more cost effective to purchase these data from third party providers.
-
-#### **Common data elements**
-
-Transportation agencies typically collect those road characteristics that they need or can be collected based on the available funds. Road characteristics are collected for many different purposes, such as road maintenance and improvement projects. Given that States have different priorities and funding structures, the elements of road characteristics data is not the same from State to State or among local agencies.
-
-To provide guidance on road characteristics that are the most needed for safety analysis, the FHWA developed the Model Inventory of Roadway Elements (MIRE). MIRE provides a recommended (but not required) list of road characteristics elements specifically for safety analysis. The elements are divided into the categories shown in Table 3-2. Chapter 9 presents further
-
-information on MIRE on page 3-32.
-
-#### **Data sources and custodians**
-
-Road characteristics data are collected at both the local and Statewide levels. At the local level, having data on details, such as traffic control devices, sidewalks, or the number of travel lanes, can be beneficial for safety evaluations and safety project prioritization. These data are maintained by the city or by a higher level agency such as a MPO.
-
-State road characteristics data include physical road attributes, traffic control devices, rail grade crossings, and structures, such as bridges and tunnels. Each State highway agency, some local transportation and public works departments, and regional planning agencies collect and maintain road characteristics data. In addition, most States also have supplemental inventory data for bridges as part of the National Bridge Inventory and railroad grade crossings as part of the Federal Railroad Administration's Railroad Grade Crossing Inventory. These databases usually can be linked to the Statewide road inventory.
-
-#### **Transportation safety applications**
-
-Road safety professionals can use road characteristics data to access data about the physical characteristics of crash sites or other priority sites. Road characteristics data is essential for network screening, development or calibration of crash prediction models, and related applications. These data are also valuable on the large scale level to estimate where crashes are expected to occur on the system.
-
-#### **Coordination with other data sets**
-
-Road characteristics data can be linked with crash and volume data to improve safety analysis and problem identification. Combining datasets in this way allows safety professionals to identify areas with a high
-
-| CATEGORY          | EXAMPLES OF MIRE DATA ELEMENTS                                                                                                                                                                     |
-|-------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Roadway Segment   | Roadway classification<br>Paved surface characteristics<br>Number and type of travel lanes<br>Shoulder, median, and roadside descriptors<br>Pedestrian and bicyclist facilities<br>Traffic volumes |
-| Roadway Alignment | Curve and grade information                                                                                                                                                                        |
-| Roadway Junction  | Traffic control devices<br>Intersection features<br>Interchange and ramp descriptors                                                                                                               |
-|                   |                                                                                                                                                                                                    |
-
-**TABLE 3-2**: Categories of MIRE Elements
-
-![](_page_76_Figure_0.jpeg)
-
-**FIGURE 3-3:** Using Safety Data Together
-
-potential for safety improvements (by means of a network screening process) and identify appropriate countermeasures. However, the road characteristics data must share a common reference system with the crash and volume data in order to link them together. The most common methods of linking road data with crash or volume data use a linear referencing system, such as routes and mileposts, or a spatial referencing system, where all files share the same coordinate system.
-
-#### **Data challenges and gaps**
-
-Collecting accurate road characteristics data can be a timeconsuming and expensive process. Data collection that is done only for part of a road network results in gaps in inventories of road features such as the location of guardrails, shoulder widths, and rumble strips. Transportation agencies are continually looking for newer technologies to streamline the collection of this detailed data. Also, it is more common for road characteristics data to be fuller and more detailed for State system roads compared to local roads, since local agencies typically have less funding, fewer staff, and less general prioritization for collecting road characteristics data.
-
-![](_page_77_Picture_0.jpeg)
-
-Observing interactions between road users, like these drivers and crossing pedestrians, can be a good way to gain supplemental data about safety effects.
-
-## **Supplemental Safety Data**
-
-In addition to the critical transportation safety datasets (crashes, road characteristics, and traffic volume), there are many other datasets that can be used and combined to conduct additional types of evaluations on the effectiveness of programs, human behaviors and safe decision making, and public opinions.
-
-**Conflicts, Avoidance Maneuvers, and Other Interactions**
-
-Observing conflicts between road users, avoidance maneuvers, such as swerving or hard braking, and other interactions, such as failures to yield can provide valuable information
-
-on road safety. These other measures of safety are referred to as surrogate measures. They occur more frequently than actual crashes and therefore enable agencies to identify safety risks more quickly and in a proactive manner (i.e., before the crash occurs). However, by their nature of being surrogates, there is potential for inaccuracy in determining which types of conflicts are good indicators of crashes.
-
-Surrogate safety data is collected by in-field observers or through recordings that capture the behaviors and interactions of road users. These recordings can be made through stationary cameras or dashboard-mounted video cameras.
-
-Increasingly, researchers are using programs to automatically identify potential events. This eliminates the need to scan visually through the entire video.
-
-Observing interactions between road users can provide valuable information on the safety effect of certain road elements, such as signals or signs, and help identify the probability of crashes under different conditions. If a reliable relationship between the observations and crashes is known, such studies may also provide insights into the potential for safety issues between road users, such as between vehicle drivers and pedestrians.
-
-However, one of the biggest challenges for using observations of road user interactions is that they are surrogate measures of safety. To date, we lack good research that would quantitatively equate surrogate measures of safety to crash data. If such relationships were known, safety professionals could conduct evaluations with a large number of surrogate measures in a relatively short period of time. This contrasts with the need to wait for years for sufficient crash data to support a good analysis.
-
-#### **Injury Surveillance and Emergency Medical Systems Data**
-
-Injury surveillance systems (ISS) typically provide data on emergency medical systems (EMS), hospital emergency departments, hospital admissions/discharges, trauma registry, and long-term rehabilitation. This information is used to track injury causes, severity, costs, and outcomes. Although an injury associated with
-
-#### **Evaluation of Children Involved in Off-Roadway Crashes Using Trauma Center Records**
-
-Many off-roadway crashes are not reported by law enforcement and are thus missed when conducting safety evaluations using police crash reports. One group that is particularly affected by this lack of data is young children injured by passenger vehicles in driveways and parking lots. This lack of information provides safety professionals with little knowledge about crash risk factors and actual incident rates that could be used to allocate resources and promote safety interventions and good design and behaviors. A 2010 study (Rice et al.) in California used records from eight trauma centers to identify the frequency and characteristics of these crashes. This study highlighted the inconsistencies with external causeof-injury codes used by emergency departments, but suggests that there is value to surveillance of off-roadway pedestrian injuries at trauma centers as a way of identifying incidents that are not captured by other data sources.
-
-Reference: Rice TM, Trent RB, Bernacki K, Rice JK, Lovette B, Hoover E, Fennell J, Aistrich, AZ, Wiltsek D, Corman E, Anderson CL, Sherck J. (2012). Trauma center-based surveillance of nontraffic pedestrian injury among California children. Western Journal of Emergency Medicine; 13.2.
-
-a traffic crash is only one type of injury in these medical systems, traffic crash injuries can be a useful source of data in bridging the gap between traditional traffic safety and public health issues. Hospital records are also often the only source of information on bicycle and pedestrian crashes that are not recorded by the police, such as those that occur in non-roadway locations like parking lots and driveways.
-
-Hospitals often use the external cause of injury classifications to code causes of patient injuries, including those from traffic crashes. These data can provide a description of injury severity, type of crash (e.g., motor vehicle passenger, bicyclist), and, in some cases, the location of incident. However, the data is often incomplete or non-specific. In order to provide a more comprehensive understanding of motor vehicle crash outcomes, NHTSA developed the Crash Outcome Data Evaluation System (CODES), which links crash, vehicle, and behavior characteristics to their specific medical and financial outcomes. Hospital injury data most often includes date, injury severity, cause, and demographic information. Personal identifying information is not included.
-
-Hospital data can be used by a variety of governmental and non-governmental agencies to investigate the causes of injuries. Based on this analysis, the agencies can develop a safety campaign to reduce injuries to particular
-
-demographics. It can also be used to identify the full magnitude of crashes for a specific user group or demographic that is not recorded or reported by law enforcement. For example, hospital data can help safety professionals better understand the number of bicyclist crashes, since many bicycle-related crashes are not reported to law enforcement.
-
-Hospital data are often difficult to use for those who administer roads, primarily the State DOT. The data is time consuming to acquire and may not contain complete data. Additionally, since there are no personal identifiers relating hospital injury data to specific crash records, the linkage is difficult and is seldom done. For these reasons, State DOTs rarely use these data; it is most often employed by public health researchers. However, there continues to be efforts at both Federal and State levels to develop better ways to integrate injury surveillance and emergency medical systems data with crash data.
-
-Hospital data can be used to investigate causes of injuries, and is often the only source of information on some bicycle and pedestrian crashes.
-
-![](_page_79_Picture_5.jpeg)
-
-#### **Driver History Data**
-
-Departments of Motor Vehicles (DMVs) maintain driver history data on all licensed drivers in the State. DMVs typically create a driver record when a person enters the State licensing system to obtain a driver's license or when an unlicensed driver commits a violation or is involved in a crash. State driver history databases interact with the National Driver Register (NDR) and the Commercial Driver License Information Systems (CDLIS) to prevent drivers with a history of atfault crashes or inordinate number of citations from obtaining multiple or subsequent licenses.
-
-The driver history data contain information such as:
-
-- J Basic identifiers (e.g., name, address, driver license number)
-- J Demographics (e.g., age, birth date, gender)
-- J Information relevant to license and driver improvement actions (e.g., license issue, expiration and renewal dates, license class, violation dates, suspension periods)
-
-One challenge with using these data is that they are almost never shared outside a DMV. State or local DOTs do not have access to these data while developing their HSIPs (or conducting location specific safety studies). Sharing driver history data nationally is limited and could be improved by creating inter-agency data sharing partnerships that address privacy concerns and allow State DOTs to work with the data.
-
-#### **Vehicle Registration Data**
-
-Vehicle registration data includes
-
-information about registered vehicles in a State and is also typically maintained by the DMV. Vehicle registration systems may also contain information regarding commercial vehicles and carriers registered in a particular State and licensed to travel in other States. These data can provide information on the vehicle population within a State or county to be used in large scale safety analysis. These data can also help identify owners in the event of a crash or traffic violation.
-
-Typical vehicle registration data may include owner information, license plate number, vehicle make, model, and year of manufacture, body type, vehicle identification number, and miles traveled. Common data for commercial vehicles may include U.S. Department of Transportation (DOT) number, carrier information, and inspection or out-of-service information.
-
-#### **Citations and Enforcement**
-
-Citation data refers to data on individual drivers that records any illegal actions that were cited by a law enforcement officer. It includes traffic violations, such as reckless driving, driving under the influence, and not carrying adequate car insurance; traffic crashes; driver's license suspensions, revocations, and cancellations; and failures to appear in court. The data can also include the traffic infractions that have been adjudicated by the courts.
-
-These data are helpful in identifying and tracking those individuals with a higher potential for unsafe driving behaviors. In an attempt to control crash occurrences, States may monitor high-risk drivers by reviewing their driver history
-
-records, paying particular attention to driver citations. Ideally, States track a citation from the time it is issued by a law enforcement officer through its disposition in a court of law. Citation information tracked and linked to driver history files enable States to screen drivers with a history of frequent citations for actions known to increase crash risk. States have found citation tracking systems useful in detecting repeat traffic offenders prior to conviction. It can also be used to track the behavior of particular law enforcement agencies and the courts with respect to dismissals and plea bargains. Many law enforcement agencies use citations as a method of tracking and measuring the effectiveness of enforcement efforts.
-
-Some constraints exist with the use of citation and enforcement data to help prevent crashes. Some States have difficulty in maintaining accurate citation information because local jurisdictions may collect different data elements from varying citation forms. Obtaining and managing judicial information is also a challenge because of the various levels of court administration and jurisdiction. Unfortunately, in some States judges do not have access to the offender's driver history at the time of sentencing, so many offenders escape the stricter penalties sanctioned for repeat offenses. In addition, the traffic safety community often lacks access to adjudication information due to privacy concerns.
-
-#### **Naturalistic Driving Data**
-
-Naturalistic driving data are driver behavior data collected during
-
-actual driving trips through technology placed in the vehicle. This technology typically includes video camera views of the driver, speed and vehicle motion sensors, and location tracking equipment. Data such as video might be collected on a continuous basis, or only after certain events like hard braking. Using data collected by this equipment, researchers are able to gather information on the underlying causes of crashes by observing drivers in a natural driving situation. Frequently collected data include road environment information, such as weather; driver information, such as eye movements; and information on vehicle movement including location on the road, acceleration, deceleration, and speed.
-
-### **Strategic Highway Research Program 2**
-
-The largest naturalistic study in the United States to date is the second Strategic Highway Research Program (SHRP2), which included over 3,400 drivers participating in the study. SHRP2 data includes over 5,400,000 individual trips and over 36,000 crash, near crash, and baseline driving events. FHWA provides more information on SHRP2 at [https://www.fhwa.dot.gov/](https://www.fhwa.dot.gov/goshrp2) [goshrp2.](https://www.fhwa.dot.gov/goshrp2)
-
-These data are used to evaluate how drivers interact with and react to the road, other road users, and other environmental features. Driver observation is used to understand fundamental issues of driver behavior and to develop improved safety countermeasures. The data are primarily used in research studies on a variety of topics. The data from the SHRP2 program have been used to study safety
-
-![](_page_82_Picture_0.jpeg)
-
-Driving simulators like this one are often used to evaluate driver behavior under specific conditions and in a cost-effective way.
-
-issues including prevention of road departures, driver reaction to posted speed limits, and driver response to curves in the road, in addition to many non-safety-related topics.
-
-A challenge with collecting a large amount of naturalistic data is the high cost of recruiting participants, instrumenting vehicles, and reducing and analyzing the data. The process of coding (observing) the behaviors of the driver while driving is time-consuming and is typically conducted on a frame-byframe basis, leading to expensive data collection and lengthy study periods. The data are highly private (i.e., contains videos of driver faces), and therefore are typically difficult to access or distribute. Despite these challenges, naturalistic driving data provides a unique and extremely
-
-insightful look at fundamental issues of road safety.
-
-#### **Driving Simulator Data**
-
-Due to the high cost of naturalistic driving studies and the rarity of traffic crashes, driving simulators are often used to efficiently and safely evaluate driver behavior under different conditions. Researchers are able to study many different conditions and complex environments without exposing drivers to danger through replicating a wide range of road, traffic, and environmental conditions, as well as driver behaviors such as distractions, impairment, and fatigue. New types of road designs can be guided by the use of simulators, particularly complex features, such as urban highway interchanges.
-
-Simulators can also be used for driver education to teach people about the effects of driver distractions or to prepare young drivers for different conditions before they encounter them on the road. Truck simulators are used to replicate the driving environment for different types of commercial trucks and used to safely train new drivers.
-
-#### **Public Opinion Data**
-
-Feedback from the general public can be a useful source of information for safety professionals. Safety professionals can use information on road safety issues and concerns from the public to identify specific locations or types of conditions where people have real or perceived traffic safety concerns.
-
-There are many different ways to collect this information, such as a phone-based survey, web-based tools (pins on maps or online forms), meetings, or intercept surveys. Common data collected are the type of concern, location, and type of mode (i.e., walking, bicycling, transit user, or driving).
-
-These data are typically collected at the local level, frequently as part of a transportation planning process or as a collaborative effort with law enforcement. Bringing residents and police officers to join the road safety audit teams or diagnosis teams during their field visits is also a beneficial way to learn about the experiences of the road users in the study area.
-
-These data may provide valuable insights about what the travelling public perceives as dangerous; however, it may be a biased sample
-
-![](_page_83_Figure_6.jpeg)
-
-**FIGURE 3-4**: Observations of motorcyclists showed how many were wearing DOT-compliant helmets (Source: National Occupant Protection Use Survey)
-
-based on those who self-select to provide the information to the researching agencies. Findings will be subjective as each person perceives a condition based on their individual experiences only. Different persons perceive different issues and recommend different "best" solutions for the same condition. Conclusions based on survey findings should be used with care.
-
-#### **Behavioral Observation**
-
-Observational surveys of road user behaviors are an effective method of data collection on information that may otherwise be inaccurately recorded due to self-reporting bias or are difficult to capture through other means. Several examples of data typically recorded using direct observation are the use of mobile devices (texting or calling), right turn on red, safety belt use, motorcycle or bicycle helmet use, and traffic control violations, such as rolling through stop signs.
-
-These data are collected through observing road users on the road. Large scale surveys collecting a high number of observations will provide the most accurate sample of the road user population in the study area. Additionally, robust observation data will cover differing road types and land use characteristics and contain observations at different times of day, week, and season. An example of a large-scale data collection effort is the National Occupant Protection Use Survey conducted annually by NHTSA. In 2013, over 52,000 occupants were observed in nearly 40,000 vehicles. The data, summaries, and evaluations from this program may be viewed on the NHTSA website**<sup>4</sup>** .
-
-## **Data Users**
-
-While many agencies use safety data, most of them have different goals. For example, a city traffic engineer may have a specific scope for identifying and treating specific high priority sites, whereas a safety analysist with a State may be focused on safety at the system level. Moreover, safety researchers and graduate students may be focused on a whole range of safety evaluations that are not intended to be action plans to improve safety at a specific site or system. Each type of data user may have different levels of access to these various types of safety data.
-
-The following tables provide common uses and data needs for these different types of data users.
-
-| 4 |                                      |
-|---|--------------------------------------|
-|   | https://crashstats.<br>nhtsa.dot.gov |
-
-|                      | DATA TYPE                                                                                | USEFULNESS   | ACCESSIBILITY | OFTEN PAIRED                                    |
-|----------------------|------------------------------------------------------------------------------------------|--------------|---------------|-------------------------------------------------|
-|                      | Crash                                                                                    | Essential    | High          | Road characteristics,<br>Traffic volumes        |
-|                      | Road characteristics                                                                     | Essential    | High          | Crash,<br>Traffic volumes                       |
-|                      | Traffic volumes                                                                          | Essential    | High          | Crash,<br>Road characteristics                  |
-|                      | Naturalistic driving                                                                     | Supplemental | Moderate      |                                                 |
-|                      | Conflicts/avoidance<br>maneuvers                                                         | Supplemental | Low           | Road characteristics,<br>Traffic volumes        |
-| MICS AND RESEARCHERS | Citations                                                                                | Supplemental | Low           | Crash, Traffic volumes,<br>Road characteristics |
-|                      | Driving simulator                                                                        | Supplemental | Low           | Road characteristics                            |
-| ACADE                | Behavior observation                                                                     | Supplemental | Low           | Crash,<br>Road characteristics                  |
-|                      | Injury surveillance                                                                      | Supplemental | Very low      |                                                 |
-|                      | Driver history                                                                           | Supplemental | Very low      |                                                 |
-|                      | Vehicle registration                                                                     | Supplemental | Very low      |                                                 |
-|                      | Public opinion                                                                           | Supplemental | Very low      |                                                 |
-|                      | TABLE 3-3 (above/next page): Data Use by Safety Professionals, Academics and Researchers |              |               |                                                 |
-
-|                  | DATA TYPE                        | USEFULNESS    | ACCESSI<br>BILITY | OFTEN PAIRED                             |
-|------------------|----------------------------------|---------------|-------------------|------------------------------------------|
-|                  | Crash                            | Essential     | High              | Road characteristics,<br>Traffic volumes |
-|                  | Road<br>characteristics          | Essential     | High              | Crash,<br>Traffic volumes                |
-| ONALS            | Traffic volumes                  | Essential     | High              | Crash,<br>Road characteristics           |
-| M LEVEL ANALYSIS | Public opinion                   | Supplemental  | High              | Crash,<br>Road characteristics           |
-| SAFETY PROFESSI  | Conflicts/avoidance<br>maneuvers | Non-essential | Low               | Road characteristics,<br>Traffic volumes |
-|                  | Citations                        | Non-essential | Low               |                                          |
-| FOR SYSTE        | Behavior<br>observation          | Non-essential | Low               |                                          |
-|                  | Injury surveillance              | Non-essential | Very low          |                                          |
-|                  | Driver history                   | Non-essential | Very low          |                                          |
-|                  | Vehicle registration             | Non-essential | Very low          |                                          |
-|                  | Naturalistic driving             | Non-essential | Very low          |                                          |
-|                  | Driving simulator                | Non-essential | Very low          |                                          |
-
-|                                       | DATA TYPE                        | USEFULNESS    | ACCESSI<br>BILITY | OFTEN PAIRED                                    |
-|---------------------------------------|----------------------------------|---------------|-------------------|-------------------------------------------------|
-|                                       | Crash                            | Essential     | High              | Road characteristics,<br>Traffic volumes        |
-|                                       | Road<br>characteristics          | Essential     | High              | Crash,<br>Traffic volumes                       |
-|                                       | Traffic volumes                  | Essential     | High              | Crash,<br>Road characteristics                  |
-| ONALS                                 | Public opinion                   | Supplemental  | High              | Crash,<br>Road characteristics                  |
-| FOR SPECIFIC SITES<br>SAFETY PROFESSI | Conflicts/avoidance<br>maneuvers | Supplemental  | Low               | Road characteristics,<br>Traffic volumes        |
-|                                       | Citations                        | Supplemental  | Low               | Crash, Traffic volumes,<br>Road characteristics |
-|                                       | Behavior<br>observation          | Supplemental  | Low               | Crash,<br>Road characteristics                  |
-|                                       | Injury surveillance              | Non-essential | Very low          |                                                 |
-|                                       | Driver history                   | Non-essential | Very low          |                                                 |
-|                                       | Vehicle registration             | Non-essential | Very low          |                                                 |
-|                                       | Naturalistic driving             | Non-essential | Very low          |                                                 |
-|                                       | Driving simulator                | Non-essential | Very low          |                                                 |
-
-#### **Other Types of Road Safety Data**
-
-Additional types of data can also be useful to road safety professionals. These types of data may include:
-
-#### **Insurance data**
-
-(e.g., carrier, policy number, expiration date, claims cost)
-
-These data can provide insights into associations between insurance status and safety.
-
-#### **Demographic data**
-
-(e.g., population by gender, age, rural/ urban, residence, and ethnicity)
-
-These data can be used for normalizing crash data to a state's general population.
-
-#### **Safety program evaluation data**
-
-(e.g., surveys, assessments, inspections)
-
-These data can provide feedback on the effectiveness of a new safety program.
-
-#### **Maintenance data**
-
-(e.g., guardrail replacement)
-
-These data may indicate where unreported crashes are occurring.
-
-#### **EXERCISES**
-
-- J **IDENTIFY** possible relationships between the safety data presented in this chapter and census data (e.g., traffic safety vs. population density).
-- J **CONSIDER** if, in the future, vehicles store pre-crash data in a "black box" type of event recording device. What types of data would you like it to store and how would you use this data (i.e., what types of analysis would you recommend conducting)?
-- J **EXPLORE** what type of safety analysis could be made possible using communication between vehicles (V2V) and also between vehicles and infrastructure (V2I - i.e., roads, intersections, etc.).
-- J **DETERMINE** how safety professionals can incorporate operational data, such as those from dynamic tolling lanes and speed sensors, into a safety analysis program.
-
-## **Improving Safety Data Quality**
-
-## **Quality Measures Of Data**
-
-The previous chapters in this unit have made the case that data are critical when seeking to improve road safety. However, simply having data is not enough. Good decisions require good data. When collecting, recording, maintaining, and analyzing safety data, road safety professionals must focus on the quality of data. Data-driven analysis tools are continually advancing and can help set priorities and select appropriate safety strategies, but the need for quality data to drive these tools is clear. Professionals commonly recognize that data quality can be measured on six criteria – timeliness, accuracy, completeness, uniformity, integration, and accessibility. Each of these criteria are presented in this chapter.
-
-#### **Timeliness**
-
-Timeliness is a measure of how quickly an event is available within a data system. State and local agencies can use technologies to automate crash data collection and quickly process police crash reports for analytic use. However, some agencies still rely on traditional methods, such as paper form data collection and manual data entry; these data collection methods can result in significant time lags. Many States, however, are moving closer to real-time data collection methods by using electronic reporting to improve the timeliness of data collection and submission.
-
-#### **Accuracy**
-
-Accuracy is a measure of how reliable the data are and whether they correctly represent reality. For example, exact crash location is an important detail for accuracy. A crash occurring at the intersection of First Street and Main Street should be recorded as occurring at that intersection. Accurate data are crucial during the analysis phase to generate road safety statistics and to pinpoint safety problems. Errors may occur at any stage of the data collection process. Common data accuracy errors include:
-
-- J Typographic errors (for data entered manually )
-- J Inaccurate and vague descriptions of the crash location
-- J Incorrect descriptions or entry of road names, road surface, level of accident severity, vehicle types, etc.
-- J Subjectivity on details that rely on the opinion of the reporting officer (e.g., property damage thresholds, excessive speed for conditions)
-
-Technology can and is currently being used to improve accuracy and reduce errors. Automatic internal data quality checks are important for this purpose. These types of checks would determine if two data fields contain possibly conflicting data, and if so, bring it to the attention of the data analyst. An example of
-
-![](_page_88_Picture_0.jpeg)
-
-Many police officers now use in-car computers to complete and submit electronic crash reports, increasing the timeliness of data availability. (Source: Town of Hanover, NH)
-
-conflicting data fields would be a crash type recorded as "rear end" but the crash report says that one car was hit on the "side".
-
-#### **Completeness**
-
-Completeness is a measure of missing information. It may range from missing data on the individual crash forms to missing information due to unreported crashes.
-
-Unreported crashes, particularly non-injury crashes, present a drawback to crash data analysis. Without knowing about these crashes, we cannot recognize the full magnitude of certain types of crashes (e.g., pedestrian involved crashes). Non-injury crashes, or property damage only (PDO) crashes, involve damage less than a specified threshold (e.g., \$1,000); these thresholds vary from State to State. The parties involved in PDO crashes
-
-are typically not required to report the crash and often agree to work out the financial damages personally or through their automobile insurance policies. In some States, even when PDOs are reported, they are not always added into the crash database.
-
-In addition to the limitations from absent data due to unreported crashes, fluctuations in the thresholds (i.e., dollar amounts) can make it difficult to compare data from previous years. Unreported PDO crashes are one of many measures of "completeness" that road safety professionals must consider when collecting and analyzing data. A lack of complete data hinders the ability to measure the effectiveness of safety countermeasures (e.g., safety belts, helmets, and red light cameras) or change in crash severity.
-
-#### **Uniformity**
-
-Uniformity is a measure of how consistent information is coded in the data system or how well it meets accepted data standards. Numerous law enforcement agencies within each State, some of which are not the primary users of the crash data, are responsible for crash data collection. The challenge for States is ensuring there is consistency among the various agencies when collecting and reporting crash data. One example of inconsistent or nonuniform data can be the location of a crash. If one agency, for example the State highway patrol, uses GPS to document a crash at one of several entrances (driveways) to a shopping center, but the city police use a linear reference system (e.g., distance from an intersection), there is a potential for inconsistent crash location data.
-
-The Model Minimum Uniform Crash Criteria (MMUCC) is used by States to ensure uniform crash data. MMUCC is an optional guideline that presents a model minimum set of uniform variables or data elements for describing a motor vehicle crash. This uniformity assists transportation safety professionals and governments in making decisions that lead to safety improvements. Similarly, MIRE provides a recommended list of elements to use when reporting road and traffic characteristics, thereby increasing uniformity of road network data. More information on MMUCC and MIRE is presented at the end of this chapter.
-
-#### **Integration**
-
-Data integration is a measure of whether different databases can
-
-### **Crash Data Improvement Program**
-
-The Federal government established the Crash Data Improvement Program (CDIP) to provide states with a means to measure the quality of the information within their crash database. It is intended to provide the states with metrics that can be used to establish measures of where their crash data stand in terms of its timeliness, the accuracy and completeness of the data, the consistency of all reporting agencies reporting the information in the same way, the ability to integrate crash data with other safety databases, and how the state makes the crash data accessible to users. Additionally, CDIP was established to help familiarize the collectors, processors, maintainers, and users with the concepts of data quality and how quality data help to improve safety decisions. CDIP also included a guide that presents information on each data quality characteristic and how to measure them.
-
-Reference: Crash Data Improvement Program, National Highway Traffic Safety Administration, [https://safety.fhwa.dot.](https://safety.fhwa.dot.gov/cdip/summary.cfm) [gov/cdip/summary.cfm](https://safety.fhwa.dot.gov/cdip/summary.cfm)
-
-be linked together to merge the information in each database into a combined database. Each State maintains its own crash database. However, crash data alone do not typically provide sufficient details on issues like environmental risk factors, driver experience, or medical consequences. Linking crash data to other databases, such as road characteristics, driver licensing, vehicle registration, and hospital outcome data assists analysts and planners in evaluating the relationship of the circumstances of the crash and other factors (e.g., human, road, medical treatment) at the time of the crash. In addition, integrated databases promote collaboration among agencies, which can lead to improvements in the data and the data collection process.
-
-Some data are more challenging to integrate with other data sets. For example, hospital data are difficult to integrate with crash data due to the lack of a common identification system (as well as medical privacy laws). This is different from crash, road characteristics, and volume data, which can share a common referencing system on the road and thus be integrated and linked more easily for analysis.
-
-Spatially-located data in a GIS system can be integrated simply based on spatial position. This geographic integration can assist agencies in bringing together data that were gathered by various departments or agencies that may use different data storage standards and reference systems.
-
-#### **Accessibility**
-
-Accessibility is a measure of how easy it is to retrieve and manipulate safety data in a system, in particular by those entities that are not the data system owners. Complete, accurate, and timely data easily made available to localities, MPOs, and other safety partners can greatly enhance transportation planning and safety investments. Agencies or departments who house safety data, especially crash data, should consider how accessible the data are to external parties and how the process of obtaining data could be streamlined.
-
-## **Data Improvement Strategies**
-
-Local, State, and Federal agencies, as well as non-governmental organizations, require accurate data to be available for analysis and problem solving. Thus, programs to improve data should be in the work programs of all agencies invested in road safety. Data could be improved by changes in policy, technology, assessments, and training.
-
-#### **Policy**
-
-With so many agencies and organizations involved in the data collection process, published policy is a necessity. A standard set of procedures can provide a clear expectation of each agency's roles and responsibilities in data collection. Federal guidance and State legislation or administrative policy and regulations generally form a basis for policy. An example of Federal guidance comes from the provision in the MAP-21 transportation legislation that requires States to collect a comprehensive set of roadway and traffic fundamental data elements (FDEs) on all public roads**<sup>5</sup>** .
-
-#### **Technology**
-
-Technology plays an important role in data collection improvement. Federal legislation provides funds that allow States to improve their data collection systems with the latest technology for quality data collection and integration. Technology is not static and is always changing. Some technology examples that help facilitate data collection include electronic crash reporting systems, GPS location devices, barcode or magnetic strip technologies, wireless communications, error checking, and conflicting fields.
-
-#### **Assessments**
-
-Assessments are official evaluations that government agencies conduct
-
-Moving Ahead for Progress in the 21st Century Act, Section 1112, §148(f)(2)
-
-to determine the effectiveness of a traffic safety process or program. A team of outside experts conducts a comprehensive assessment of the highway safety program using an organized, objective approach and well-defined procedures that:
-
-- J Provide an overview of the program's current status in comparison to pre-established standards
-- J Note the program's strengths and weaknesses
-- J Provide recommendations for improvement
-
-Both FHWA and NHTSA provide these types of assessments, such as the Roadway Data Improvement Program (RDIP), which can improve the quality of an agency's data through expert technical assistance and fresh perspectives. When State agencies request an RDIP assessment, an FHWA team reviews and assesses a State's roadway data system for the content of the data collected; for the ability to use, manage and share the data; and to offer recommendations for improving the road data. The RDIP also examines the State's ability to coordinate and exchange road data with local agencies, such as those in cities, counties, and MPOs**<sup>6</sup>** .
-
-#### **Training**
-
-Education and training of transportation professionals play a vital role in improving data and data collection. For example, law enforcement officers create the crash data that is used by safety professionals to conduct studies and evaluate road safety. Thus, law enforcement need to understand how crash data are
-
-used in policy development and investment decisions, infrastructure improvements, and safety planning. Through proper education and training programs, law enforcement can have a broader perspective of their contribution to reducing crashes through improved data reporting. Other examples include training transportation professionals on the latest data collection tools and technology, advising court officials and adjudicators on important changes to safety legislation and penalties, and training personnel on how to handle crash reports with inaccurate or missing information.
-
-## **Federal Guidance**
-
-The following two sections present examples of Federal guidance that leads State agencies into improving the quality of their safety data.
-
-### **Model Minimum Uniform Crash Criteria**
-
-Statewide motor vehicle traffic crash data systems provide the basic information necessary for effective road safety efforts at any level of government—local, State, or Federal. Unfortunately, the use of State crash data is often hindered by the lack of uniformity between and within States. Data definitions, the number and type of data elements, and the threshold for collecting data varies from jurisdiction to jurisdiction. The Model Minimum Uniform Crash Criteria (MMUCC) was developed to help bring greater uniformity to crash data collection and provide national guidance to data collectors. MMUCC represents a voluntary and collaborative effort to generate uniform, accurate, reliable, and credible crash data to
-
-6
-
-Federal Highway Administration Roadway Safety Data Program, [http://safety.fhwa.](https://safety.fhwa.dot.gov/rsdp/technical.aspx) [dot.gov/rsdp/](https://safety.fhwa.dot.gov/rsdp/technical.aspx) [technical.aspx](https://safety.fhwa.dot.gov/rsdp/technical.aspx)
-
-support data-driven highway safety decisions at a State and a national level. MMUCC serves as a foundation for State crash data systems.
-
-Since MMUCC is a minimum set of recommended crash data, States and localities may choose to collect additional motor vehicle crashrelated data elements if they feel the data are necessary to enhance decision-making. Implementation of MMUCC is a collaborative effort involving the Governors Highway Safety Association, FHWA, NHTSA, and the Federal Motor Carrier Safety Administration (FMCSA).
-
-The MMUCC Guideline is updated every four or five years to address emerging highway safety issues, simplify the list of recommended data elements, and clarify definitions of each data element.
-
-#### **MMUCC Data Elements**
-
-MMUCC consists of data elements recommended to be collected by investigators at the crash scene. From the crash scene information, additional data elements can be derived to assist law enforcement. Additional data elements are available through linkage to driver history, hospital and other health/ injury data, and road inventory data. Each group of data elements has a unique identifier that describes the type of data element and whether it is derived or linked data.
-
-MMUCC data elements are divided into four major groups that describe various aspects of a crash: crash, vehicle, person, and roadway. Each data element includes a definition, a set of specific attributes, and a rationale for the specific attribute.
-
-For the entire list of MMUCC data
-
-#### **MMUCC Example Element**
-
-The following is the MMUCC format for "Person Data Element Derived from Collected Data."
-
-PD1. Age
-
-Definition: The age in years of the person involved in the crash
-
-Source: This data element is derived from Date of Birth (P2) and Crash Date and Time (C3).
-
-#### Attribute:
-
-*•* Age in years
-
-Rationale: Age is necessary to determine the effectiveness of safety countermeasures appropriate for various age groups.
-
-elements, refer to the latest edition of the MMUCC Guideline located at **www.mmucc.us**.
-
-The MMUCC data elements represent a core set of data elements. The fourth edition (2012) of the MMUCC Guideline contains 110 data elements and recommends that States collect all 110 data elements. To reduce the data collection burden, MMUCC recommends that law enforcement at the scene should collect 77 of the 110 data elements. From crash scene information, 10 data elements can be derived, while the remaining 23 data elements should be obtained after linkage to other State data files. States unable to link to other State data to obtain the MMUCC linked data elements should collect, at a minimum, those linked data elements feasible for collecting on the crash report. At the same time, States should work to develop data linkage capabilities so they eventually are able to obtain, via linkage, all of the information to be generated by the MMUCC linked data elements.
-
-Segment location/linkage elements Segment classification Segment cross section Segment roadside descriptors Other segment descriptors Segment traffic flow data Segment traffic operations/control data
-
-Other supplemental segment descriptors
-
-**ROADWAY SEGMENT ROADWAY ALIGNMENT**
-
-Horizontal curve data Vertical curve data
-
-#### **ROADWAY JUNCTION**
-
-At-grade intersection/junctions Interchange and ramp descriptors
-
-**FIGURE 3-5**: MIRE Data Elements Category Descriptors (Source: MIRE version 1.0)
-
-### **Model Inventory of Roadway Elements**
-
-Critical safety data include not only crash data, but also road inventory data, traffic data, and other information. State DOTs need accurate and detailed data on road characteristics as they develop and implement strategic highway safety plan (SHSPs) and look toward making more data driven safety investments.
-
-With the need for and availability of so many types of data, the question becomes "How can transportation agencies be sure that they are collecting the necessary roadway data to make effective road safety decisions?" MIRE is a vitally important resource that defines the data needed to help transportation agencies build a road characteristics database that will lead to good safety analysis. MIRE defines 202 individual characteristics of the road system that should be collected. These characteristics are referred to as data elements. The elements fall into three broad categories:
-
-- J Roadway segment descriptors
-- J Roadway alignment descriptors
-
-#### J Roadway junction descriptors
-
-Most State and local transportation agencies do not have all the data needed to use analysis tools such as SafetyAnalyst, the Interactive Highway Safety Design Model, and other tools and procedures identified in the Highway Safety Manual. MIRE provides a structure for road inventory data that allows State and local transportation agencies to use these analysis tools with their own data rather than relying on default values that may not reflect local conditions.
-
-As the need for road inventory information has increased, new and more efficient technologies to collect road characteristics have emerged. However, the collected data need a framework for common information sharing. Just as MMUCC provides guidance for consistent crash data elements, MIRE provides a structure for roadway inventory data elements using consistent definitions and attributes. It defines each element, provides a list of attributes for coding, and assigns a priority status rating of "critical" or "value added" based on the element's importance for use in analytic tools, such as
-
-SafetyAnalyst.
-
-The latest version of MIRE can be viewed and downloaded from the FHWA Office of Safety site, **http:// safety.fhwa.dot.gov/tools/data\_tools/ mirereport/.**
-
-Figure 3-5 displays a breakdown of the major data element categories and subcategories contained in MIRE. MIRE further breaks down each subcategory into individual data elements. For a complete listing of MIRE data elements, refer to the MIRE publication.
-
-While the complete list of MIRE elements is rather extensive, there are a basic set of elements within MIRE called the Fundamental Data Elements (FDE) that an agency needs to conduct safety analyses regardless of the specific analysis tools used or methods applied. As discussed, the need for improved and more robust safety data is increasing due to the development of a new generation of safety data analysis tools and methods.
-
-## **Linking Data Through A Referencing System**
-
-The types of road safety data presented in this unit are only useful as much as they are capable of being linked through a common geospatial relational location referencing system. States recognize that they must have a common relational location referencing system (i.e., geographic information system or linear referencing system) for all public roads if they are going
-
-to integrate different types of safety data. If all safety data are referenced to the same system, the road characteristics data can be linked with the crash data, which would permit the State to identify locations on all public roads where crash patterns are occurring that can be reduced through known countermeasures.
-
-In most States, development of a common referencing system for all public roads will require significant effort and cooperation with local agencies. The Federal Highway Performance Monitoring System requires GIS-based referencing for all roads in the Federal-aid highway system, interstate highways, and public roads not classified as local roads or rural minor collectors.**<sup>7</sup>** However, significant travel occurs on local roads and rural minor collectors. Some local agencies have or are developing, their own GIS-based referencing systems for roads in their inventory data. Light detection and ranging (LIDAR) systems are often used to accurately survey the road network. The State should work with local agencies to incorporate these referencing systems into the State base map. Once the referencing systems are combined, attribute data for additional mileage can be added when either State or local agencies develop or expand inventories. Moreover, as stated above, this will lead to the ability to link crashes with inventory and traffic data, enabling the State to use the more advanced problem identification methods on more and more miles of public roads.
-
-Memorandum on Geospatial Network for All Public Roads, Office of Highway Policy Information, August 7, 2012. Accessed October 2017 at [https://](https://www.fhwa.dot.gov/policyinformation/hpms/arnold.pdf) [www.fhwa.dot.gov/](https://www.fhwa.dot.gov/policyinformation/hpms/arnold.pdf) [policyinformation/](https://www.fhwa.dot.gov/policyinformation/hpms/arnold.pdf) [hpms/arnold.pdf](https://www.fhwa.dot.gov/policyinformation/hpms/arnold.pdf)
-
-## **Conclusion**
-
-Data are crucial to improving road safety. Safety data consist of various kinds of data that can be used to identify safety problems and priorities so that safety partners in many agencies can address important issues. Data such as crash data, traffic volume data, and road characteristics data are often used and are critical for safety analysis by many agencies. Other data, such
-
-as conflict observations, emergency medical data, and citation data, can be useful in a supplemental role for specific studies. Regardless of the type of safety data, the quality of the data is vitally important. Agencies that collect safety data should strive to improve their timeliness, accuracy, completeness, uniformity, integration, and accessibility to maximize their potential to drive good decisions.
-
-#### **EXERCISES**
-
-- J **SELECT** a scenario below. Assume that you are using crash data as your primary data to inform your decisions. Explain how each of the six quality criteria discussed in this chapter could affect your evaluation of the current safety situation and your recommendations.
-  - J You are prioritizing intersections in a city to be treated with enhanced visibility treatments, such as larger signs, wider markings, and additional signal heads.
-  - J You are developing a public outreach effort to communicate the need to yield to pedestrians at crosswalks. You wish to focus your efforts to the areas of the city where failing to yield to pedestrians is the most rampant.
-
-J You are recommending safety improvements to an interchange that was identified based on having a higher number of expected crashes than other interchanges of the same type.
-
-Given the scenario you selected above, how do you think the availability of other types of data could affect your recommendations? Such data may include any of the data types covered in Chapter 3.2. (e.g., EMS and hospital injury data, enforcement citations, public complaints, or other data). What additional information could this reveal?
-
-J **IDENTIFY** programs or policies that exist in your state or local agency to improve data. This may include any of the types of safety data covered in Chapter 3.2.
-
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-
-![](_page_97_Picture_0.jpeg)
-
-## **Solving Safety Problems UNIT 4**
-
-#### **LEARNING OBJECTIVES**
-
-After reading the chapters and completing exercises in Unit 4, the reader will be able to:
-
-- J **IDENTIFY** three major components of road safety management
-- J **DEFINE** the process of conducting site-level and system-level safety management
-- J **USE** safety data to identify safety issues and develop strategies to solving those issues
-
-## **Road Safety Management**
-
-Road safety management refers to the process of identifying safety problems, devising potential strategies to combat those safety problems, and selecting and implementing the strategies. Effective safety management is also proactive and looks for ways to prevent safety problems before they arise. High quality safety data should be used to determine the nature of the road safety problems and how best to solve them. As discussed in Unit 3, the clearest and most readily available indicators of road safety problems are crash data. These data can be used to identify safety problems on a large or a small scale. Other data, such as roadway characteristics, traffic volume, citations, and driver history, can be integrated with crash data to assist in identifying safety trends and high priority locations.
-
-## **Data quality issues should not prevent a data-driven process**
-
-Every transportation agency will acknowledge that it does not have perfect data. All data have issues related to accuracy, coverage, timeliness, and other factors. One agency's crash data may have an incomplete record of low severity crashes. Another agency may have very little data on the traffic volume on low volume rural roads. However, data quality issues should not prevent a transportation agency from using the data to drive its safety management efforts. Even
-
-![](_page_98_Picture_6.jpeg)
-
-while the agency strives to improve its data, the data on hand should be used in the process of identifying safety problems and devising solutions to those problems.
-
-## **Data needs for safety analysis**
-
-High quality safety analysis demands high quality data. Unfortunately, poor data availability and low quality limit the types of analyses that can be conducted. The data requirements depend on the type of analysis and what safety questions are being asked. Table 4-1 provides examples of various categories of safety analysis and lists the data that would be needed to conduct them.**<sup>1</sup>**
-
-**1**
-
-Applying Safety Data and Analysis to Performance-Based Transportation Planning, e-Guidebook, FHWA, [http://safety.](https://safety.fhwa.dot.gov/tsp/fhwasa15089/appb.cfm) [fhwa.dot.gov/tsp/](https://safety.fhwa.dot.gov/tsp/fhwasa15089/appb.cfm) [fhwasa15089/](https://safety.fhwa.dot.gov/tsp/fhwasa15089/appb.cfm) [appb.cfm](https://safety.fhwa.dot.gov/tsp/fhwasa15089/appb.cfm)
-
-|                                          | SAFETY ANALYSIS<br>QUESTION                                                                                                                                                                                   | D<br>A<br>T<br>A NEEDS                                                                                                                                                                                                                                                                                                                                                                                                                                  |
-|------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| MARKING<br>BENCH                         | How many fatalities<br>and serious injuries are<br>occurring in my area?<br>How does this<br>compare to other<br>areas of my State?                                                                           | Total crashes<br>Total fatalities and serious injuries<br>High-level roadway data — roadway ownership,<br>functional classification<br>Agency geographic boundary information                                                                                                                                                                                                                                                                           |
-| CONTRIBUTING FACTORS<br>CRASH TRENDS AND | What type of road users<br>are involved in crashes?<br>When are the<br>crashes occurring?<br>What are the major<br>contributing factors<br>to crashes?                                                        | Crash severity — fatality, injury type,<br>property damage only<br>Crash incidence data — time of day, day,<br>month, weather, etc.<br>Crash type — road departure, intersection,<br>head-on, angle, etc.<br>Contributing factors — age, impairment,<br>seatbelt usage, speed, etc.                                                                                                                                                                     |
-| MENT<br>MPROVE<br>SITES FOR SAFETY I     | What locations<br>(intersections or<br>segments) show the<br>most potential for<br>safety improvements?                                                                                                       | Crash severity<br>Crash location<br>Roadway and roadside characteristics — intersection<br>control, number of lanes, presence and type of<br>shoulder, presence and type of median, posted<br>speed, horizontal and vertical alignment, etc.<br>Traffic volume data — intersection total entering<br>traffic volume, roadway segment volume per<br>million vehicle miles.<br>Calibrated safety performance functions,<br>if predictive methods are used |
-| SAFETY RISK FACTORS                      | What are the common<br>characteristics of<br>locations with crashes?<br>What are the<br>countermeasures<br>to address these<br>characteristics?<br>How should we prioritize<br>system-wide<br>implementation? | Crash severity<br>Crash location<br>Roadway and roadside characteristics — intersection<br>control, number of lanes, presence and type of<br>shoulder, presence and type of median, posted<br>speed, horizontal and vertical alignment, etc.<br>Traffic volume data — intersection total entering<br>traffic volume, roadway segment volume per<br>million vehicle miles.                                                                               |
-|                                          |                                                                                                                                                                                                               | TABLE 4-1: Safety analysis categories, questions, tools and data needs.                                                                                                                                                                                                                                                                                                                                                                                 |
-
-## **Safety data as performance measures**
-
-A transportation agency has many types of data at its disposal for identifying safety problems, but the agency must select which type(s) of data will be the **performance measures** used to identify the road safety emphasis areas. Federal legislation has focused increasingly on fatal crashes and serious injury crashes as performance measures for road safety.
-
-Table 4-2 provides examples of performance measures developed by the National Highway Traffic Safety Administration (NHTSA) and the Governors Highway Safety Association (GHSA) that could be used to identify safety priorities.**<sup>2</sup>** The sources of the data could be State crash data files, the Fatality Analysis Reporting System (FARS), surveys conducted by the State, or grant applications from law enforcement and other departments. The section on Network Screening in Chapter 11 presents a more detailed discussion of crash-based performance measures and how they can be used to identify sites that are high priority for safety treatment.
-
-#### **Performance measure**
-
-A numerical metric used to monitor changes in system condition and performance against established visions, goals, and objectives.
-
-#### **2**
-
-Grant activity reporting
-
-Grant activity reporting
-
-reporting
-
-National Highway Traffic Safety Administration (NHTSA). 2007. Performance Measures Discussion. 408 Team Document #005, October 29, 2007. National Highway Traffic Safety Administration.
-
-| Number of traffic fatalities (three-year or five-year moving averages)                                                                | FARS                      |
-|---------------------------------------------------------------------------------------------------------------------------------------|---------------------------|
-| Number of serious injuries in traffic crashes                                                                                         | State crash<br>data files |
-| Fatalities/VMT (including rural, urban, and total fatalities)                                                                         | FARS, FHWA                |
-| Number of unrestrained passenger vehicle occupant fatalities, seat positions                                                          | FARS                      |
-| Number of fatalities in crashes involving a driver or motorcycle operator<br>with a blood alcohol concentration of .08 g/dL or higher | FARS                      |
-| Number of speeding-related fatalities                                                                                                 | FARS                      |
-| Number of motorcyclist fatalities                                                                                                     | FARS                      |
-
-Number of unhelmeted motorcyclist fatalities FARS
-
-Number of drivers 20 or younger involved in fatal crashes FARS
-
-Number of pedestrian fatalities FARS
-
-Observed seat belt use for passenger vehicles, front seat outboard occupants Survey
-
-Number of seat belt citations issued during grant-funded
-
-Number of impaired-driving arrests made during grant-funded
-
-**DESCRIPTION SOURCES**
-
-**TABLE 4-2**: Safety performance measures and data sources (*Source: NHTSA 2007*)
-
-Number of speed citations issued during grant-funded activities Grant activity
-
-enforcement activities
-
-enforcement activities
-
-![](_page_101_Picture_0.jpeg)
-
-## **Components of safety management**
-
-The safety management process can be viewed in three general components. These components are carried out by the agency (or agencies) responsible for managing the safety of the road system:
-
-- J **Identifying safety problems –** The agency uses crash data and other safety data to identify road safety problems or problem locations.
-- J **Developing potential safety strategies –** The agency develops potential strategies to address the identified safety problems. These strategies might also be referred to as countermeasures or treatments.
-- J **Selecting and implementing strategies –** The agency weighs the potential strategies and decides which ones to implement.
-
-## **Levels of safety management**
-
-Although all road safety management follows the same three general components listed above, the specific steps of the safety management process will be different depending on the scope. The process might be intended to address specific site-level issues, such as crash patterns at high priority intersections, curves, or corridors. On a larger scale, the process might be intended to address system-level issues, such as problems that can be addressed by policies, design standards, or broad ranging campaigns of education or enforcement. The following chapters will discuss safety management for these two levels: Chapter 11 presents site-level safety management; Chapter 12 presents system-level safety management.
-
-## **Site-Level Safety Management**
-
-Site-level safety management is the process of identifying and addressing safety issues at high priority **sites**. This contrasts with safety issues that are addressed for an entire transportation system (i.e., all roads in a city, county, or State). System-level safety management is covered in Chapter 12.
-
-Agencies responsible for road safety often conduct some form of site-level safety management. They identify particular sites of concern and determine how best to address the safety problems at these priority sites. The methods of identifying priority sites and the safety strategies used to treat the sites differ according to the type of agency. A department of transportation (DOT) may install a sign or pavement marking; a law enforcement agency might increase enforcement in the area of the site. Regardless of the type of agency, it is important to conduct site-level safety management in a manner that uses good analysis methods driven by safety data.
-
-Chapter 10 presented road safety management in terms of three general components:
-
-- J **Identifying safety problems**
-- J **Developing potential safety strategies**
-- J **Selecting and implementing strategies**
-
-![](_page_102_Figure_9.jpeg)
-
-**FIGURE 4-1**: Schematic Illustrating the Steps of Site-level Safety Management
-
-When discussing site-level safety management, these three components can be further divided into six distinct steps. This sixstep process is common to the engineering discipline and is presented in Part B of the first edition of the Highway Safety Manual**<sup>3</sup>** (HSM). The process, shown in Figure 4-1, will be the framework for the discussion of site-level safety management in this chapter. The material presented in this chapter is based on the guidance presented in the HSM and material from a series of documents entitled "Reliability of Safety Management Methods" published by FHWA. These FHWA
-
-#### **Site**
-
-A narrowly defined location of interest for safety analysis, such as an intersection, road section, interchange, or midblock crossing.
-
-**3**
-
-Highway Safety Manual, First edition, American Association of State Highway Transportation Officials, 2010.
-
-## **4**
-
-Srinivasan, R., F. Gross, B. Lan, G. Bahar (2016), *Reliability of Safety Management Methods: Network Screening*, Report No. FHWA-SA-16-037, Federal Highway Administration, Washington, D.C.
-
-#### **5**
-
-Srinivasan, R., G. Bahar, F. Gross (2016), *Reliability of Safety Management Methods: Diagnosis*, Report No. FHWA-SA-16-038, Federal Highway Administration, Washington, D.C.
-
-#### **6**
-
-Bahar, G. R. Srinivasan, F. Gross, (2016), *Reliability of Safety Management Methods: Countermeasure Selection*, Report No. FHWA-SA-16-039, Federal Highway Administration, Washington, D.C.
-
-#### **7**
-
-Srinivasan, R., F. Gross, G. Bahar (2016), *Reliability of Safety Management Methods: Safety Effectiveness Evaluation*, Report No. FHWA-SA-16-040, Federal Highway Administration, Washington, D.C.
-
-See next page.
-
-## **SAFETY MANAGEMENT**
-
-#### **COMPONENTS STEPS OF SITE-LEVEL SAFETY MANAGEMENT**
-
-#### **IDENTIFY SAFETY PROBLEMS**
-
-**Step 1. Network screening:** Identify locations that could benefit from treatments to reduce crash frequency and severity.
-
-**Step 2. Diagnosis:** Identify crash trends and patterns based on reported crashes, assess the crash types and severity levels, and study other elements that characterize the crashes.
-
-#### **DEVELOP POTENTIAL SAFETY SOLUTIONS**
-
-**Step 3. Countermeasure selection:** Identify appropriate countermeasures to target crash contributing factors and reduce crash frequency and severity at identified locations.
-
-**Step 4. Economic appraisal:** Estimate the economic benefit and cost associated with implementing a particular countermeasure or set of countermeasures.
-
-#### **SELECT AND IMPLEMENT STRATEGIES**
-
-**Step 5. Project prioritization:** Develop a prioritized list of safety improvement projects, considering available resources.
-
-**Step 6. Safety effectiveness evaluation:** Evaluate how a particular countermeasure (or group of countermeasures) has affected crash frequency and severity where it was installed.
-
-**TABLE 4-3**: Steps of the Site-level Safety Management Process
-
-documents provide in-depth guidance and examples on the following topics:
-
-- J **Network screening** The network screening guide describes various methods and the latest tools to support network screening.**<sup>4</sup>**
-- J **Diagnosis** The diagnosis information guide describes various methods and the latest tools to support diagnosis.**<sup>5</sup>**
-- J **Countermeasure selection** The countermeasure selection information guide describes various methods and the latest tools to support countermeasure selection.**<sup>6</sup>**
-
-### J **Safety effectiveness evaluation**
-
-- The safety effectiveness evaluation guide describes various methods and the latest tools to support safety effectiveness evaluation.**<sup>7</sup>**
-- J **Systemic safety programs** The systemic safety programs guide describes the state-of-the-practice and the latest tools to support systemic safety analysis.**<sup>8</sup>**
-
-The six steps of the site-level safety management process relate to the three general components of safety management as shown in Table 4-3. Each step is presented in more detail through the following sections in this chapter.
-
-## **Step 1. Network screening**
-
-Network screening refers to the process of selecting high priority sites that need safety treatment, often through an analysis of crash data. There are many ways in which an agency can use crash data to prioritize sites, ranging from simplistic methods, which are easy to understand and implement but can be inaccurate or ineffective, to more advanced methods, which require statistical expertise and more data but provide a better prioritization of sites.
-
-For many years, the most prevalent methods for ranking specific sites for safety improvements were based on historical crash data alone. Many agencies still use these methods to allocate their road safety funds. Agencies that prioritize sites by historical **crash frequency** identify those sites that have the highest number of crashes in a certain time period (typically three to five years). This serves to assist agencies in addressing the magnitude of the problem, that is, attempting to address the highest number of crashes. By its nature, this method typically identifies sites that have high amounts of traffic (either vehicles, pedestrians, or other road users). However, this method may miss abnormally hazardous sites that do not present a relatively large number of crashes. Another variation of the crash frequency method uses **crash severity**, in which agencies weight the crash frequency by giving greater weight to higher severity crashes. This method counteracts some of the bias in the crash frequency method. For example, a general high crash frequency may prioritize a busy
-
-intersection that has many crashes, but a closer examination reveals that most crashes are low speed, low severity rear-end crashes. The crash severity method would lower the priority of this intersection in favor of other sites where more serious crashes occur.
-
-Some agencies prioritize sites by the historical **crash rate**. This method incorporates traffic volume to augment the crash data. The crash frequency at a site is divided by the traffic volume – either the annual average daily traffic (for road segments), total entering volume (for vehicle traffic at intersections), or other volumes, such as pedestrian crossing volume. The typical unit for this method is crashes per 100 million vehicle miles traveled for road segments or crashes per 100 million entering vehicles for intersections. Crash rate in these units is calculated as:
-
-**Crash rate per 100 million vehicle miles traveled**
-
-**<sup>=</sup> (C×100,000,000) (V×365×N×L)**
-
-**C** = Number of crashes in the study period
-
-**V** = Traffic volumes using average annual daily traffic (AADT) volumes
-
-**N** = Number of years of data
-
-**L** = Length of the roadway segment in miles
-
-This approach of prioritizing sites by crash rate serves to counteract the bias of crash frequency that overly prioritizes sites with high volume, since higher volume decreases the crash rate. However, it may inefficiently prioritize sites with very low volumes.
-
-**8**
-
-(from previous page) Gross, F., T. Harmon, G. Bahar, K. Peach (2016), *Reliability of Safety Management Methods: Systemic Safety Programs*, Report No. FHWA-SA-16-041, Federal Highway Administration, Washington, D.C.
-
-#### **Crash frequency**
-
-The number of observed crashes per year.
-
-#### **Crash severity**
-
-The level of injury severity of the crash as an event, typically determined by the highest severity injury of any person involved in the crash.
-
-#### **Crash rate**
-
-The number of observed crashes per unit of traffic volume passing through the location.
-
-Agencies might use a combination of these two methods. They may set a minimum crash rate to generate an initial list of priority sites and then prioritize that group by crash frequency or severity. Regardless, these simplistic methods are known to have potential biases. One of the most prevalent biases is that the crash history used to prioritize sites with these methods usually reflects only the short-term trend of crashes. Given that the yearto-year occurrence of crashes at a location is random, it can be the case that a short-term crash history (one to three years) may be relatively high, but in the long run (ten years), the crashes would return to a lower amount, even if no safety improvements were done. This effect creates selection bias or **regression-to-the-mean** (RTM) bias in
-
-As the years progressed, many transportation safety professionals recognized that while these simplistic methods did identify sites that benefited from safety improvement, they were not the locations where safety funds could be spent the most effectively. The selection of high crash sites was subject to RTM bias. Also, sites with high numbers of crashes were typically complex and required expensive reconstruction in order to reduce crashes appreciably. The question became, "How could road safety funds be spent in a way that provided the biggest bang for the buck?"
-
-the safety analysis of this location.
-
-As the science of road safety advanced, researchers developed more advanced approaches for prioritizing sites for safety improvements. Dr. Ezra Hauer
-
-**Comparing road segments by crash frequency and rate**
-
-**Road Segment A:** A three-mile section of road that has had **four** crashes over five years and has a traffic volume of **4,000** vehicles per day.
-
-**Road Segment B:** A three-mile section of road that has had **10** crashes over five years and has a traffic volume of **12,000** vehicles per day.
-
-If an agency is comparing these segments based on crash frequency, they would prioritize road segment B for having 10 crashes compared to road segment A which had four crashes.
-
-If comparing these segments based on crash rate, the agency would calculate the crash rate of road segment A as (4 crashes x 100,000,000) / (4,000 vehicles per day x 365 x 5 years x 3 miles) = 18.2 crashes per 100 million vehicle miles traveled. Following the same calculation, road segment B has a rate of 15.2 crashes per 100 million vehicle miles traveled. According to crash rate, the agency would prioritize road segment A. The prioritization of these two segments changes when traffic volume is taken into account.
-
-pushed forward a movement to identify "sites with promise."**<sup>9</sup>** The main idea was to identify sites that experienced more crashes than would be expected from a site with that particular set of characteristics. In many cases, these abnormally performing sites could be addressed with low cost safety treatments, such as larger signs or pavement markings with greater visibility. This approach uses statistical regression models that predict crashes for a given set of characteristics. These models demonstrate the advantage of bringing together different types of safety data, which in this case could include crash data, roadway characteristics, and traffic volume.
-
-#### **Regressionto-the-mean**
-
-The fact that a short term examination of crash history at a location is likely inaccurate (e.g., lower or higher than its true safety performance). When a longer time period of crash history is examined, the crash frequency will "regress" to its "mean" and provide a better picture of the long term average crash frequency.
-
-**9**
-
-Hauer, E. (1997), *Observational Before After Studies in Road Safety*, Elsevier Science, New York.
-
-The most basic of these regression methods calculates **predicted crashes**. This method requires information about certain geometric and operational characteristics, such as traffic volume, number of lanes, and type of road.
-
-An SPF is developed or calibrated using data from an entire jurisdiction or State, so it is independent of the crash history
-
-of the specific site. This means that the predicted crash value is unaffected by the bias caused by RTM. Using SPFs, transportation agencies can predict crash values for many sites and prioritize the sites according to the highest predicted values. Another use of the predictive method is in systemic safety treatments, presented in Chapter 12 under Risk Based Prioritization.
-
-#### **Predicted crashes**
-
-The frequency of crashes per year that would be predicted for a site based on the result of a crash prediction model, called a safety performance function (SPF).
-
-| PERFORMANCE MEASURE                                                      | ACCOUNTS<br>FOR TRAFFIC<br>VOLUME | ACCOUNTS<br>FOR<br>RTM BIAS   | ACCOUNTS<br>FOR CRASH<br>SEVERITY |
-|--------------------------------------------------------------------------|-----------------------------------|-------------------------------|-----------------------------------|
-| 1. Average crash frequency                                               | No                                | No                            | Not explicitly*                   |
-| 2. Crash rate                                                            | Yes                               | No                            | Not explicitly*                   |
-| 3. Equivalent property damage only<br>(EPDO) average crash frequency     | No                                | No                            | Yes                               |
-| 4. Relative severity index                                               | No                                | No                            | Yes                               |
-| 5. Critical rate                                                         | Yes                               | No                            | Not explicitly*                   |
-| 6. Excess predicted average crash<br>frequency using method of moments   | No                                | No                            | Not explicitly*                   |
-| 7. Level of service of safety                                            | Yes                               | No                            | Not explicitly*                   |
-| 8. Excess predicted average crash<br>frequency using SPFs                | Yes                               | No                            | Not explicitly*                   |
-| 9. Probability of specific crash types<br>exceeding threshold proportion | No                                | Not affected<br>by RTM bias** | Not explicitly*                   |
-| 10. Excess proportion of<br>specific crash types                         | No                                | Not affected<br>by RTM bias** | Not explicitly*                   |
-| 11. Expected average crash frequency<br>with empirical Bayes adjustments | Yes                               | Yes                           | Not explicitly*                   |
-| 12. EPDO average crash frequency<br>with EB adjustment                   | Yes                               | Yes                           | Yes                               |
-| 13. Excess expected average crash<br>frequency with EB adjustment        | Yes                               | Yes                           | Not explicitly*                   |
-
-<sup>\*</sup> While these measures do not explicitly mention severity, analysts can adapt any of the measures to consider any severity level.
-
-**TABLE 4-4**: Performance Measures for Network Screening *(Source: Highway Safety Manual, 1st ed.)*
-
-<sup>\*\*</sup> These two measures will not be affected by RTM only if they are based on data from a long time period.
-
-#### **10**
-
-Srinivasan, R., D. Carter, and K.Bauer (2013), *Safety Performance Function Decision Guide: SPF Calibration vs SPF Development*, Report No. FHWA-SA-14-004, Federal Highway Administration, Washington, DC.
-
-#### **11**
-
-R. Srinivasan and K. Bauer (2013), *Safety Performance Development Guide: Developing Jurisdiction-Specific SPFs*, Report FHWA-SA-14-005, Federal Highway Administration, Washington, DC
-
-#### **12**
-
-Bahar, G (2014), *User's Guide to Develop Highway Safety Manual Safety Performance Function Calibration Factors*, HR 20- 7(332), National Cooperative Highway Research Program, American Association of State Highway and Transportation Officials, Standing Committee on Traffic Safety, Washington, DC.
-
-**Expected crashes, excess crashes**
-
-See next page.
-
-#### **Where can I get safety performance functions for my State?**
-
-SPFs can be obtained in two ways:
-
-- **1)** SPFs can be developed from scratch using crash, roadway, and traffic volume data from roads and intersections in the State. This requires significant data to be collected on hundreds of sites. A statistical expert must use these data to develop SPFs that are tailor made for that State.
-- **2)** SPFs can be obtained from national resources, such as the HSM; then calibrated for the particular State of interest. This requires data to be collected on a smaller number of sites than is required for developing a new SPF.
-
-The crashes predicted by the SPF are compared to the crashes observed on the State's roads, and an analyst calculates a calibration factor to adjust the SPF prediction appropriately for the State.
-
-SPF development or calibration is typically handled by the State DOT. FHWA provides guidance on deciding between developing a new SPF or calibrating an existing one.**<sup>10</sup>** States that decide to develop new SPFs can refer to guidance in a related FHWA publication.**<sup>11</sup>** NCRHP provides guidance for those who decide to calibrate existing SPFs.**<sup>12</sup>**
-
-The first edition of the HSM lists several benefits of the predictive method, including:
-
-- J RTM bias is addressed as the method concentrates on longterm expected average crash frequency rather than shortterm observed crash frequency.
-- J Reliance on availability of limited crash data for any one site is reduced by incorporating predictive relationships based on data from many similar sites.
-- J The method accounts for the fundamentally nonlinear relationship between crash frequency and traffic volume.
-
-Agencies can also use the predicted crashes in combination with actual crash history at the site of interest to calculate **expected crashes**. A method called empirical Bayes (EB) brings these two values together to reflect a crash frequency that incorporates the general crash prediction from the SPF with the real world experience of crash history at the site to provide an accurate estimation of how many crashes should be expected at the
-
-site (see more detailed discussion of the EB method later in this step). Some agencies may also calculate **excess crashes** as a measure for site prioritization. This is the difference between the expected crashes and the observed crash frequency at the site.
-
-## **Performance measures in network screening**
-
-The key to effective network screening is selecting an appropriate performance measure. Network screening methods should appropriately account for three major factors that can affect the screening outcome:
-
-- J **Differences in traffic volumes**
-- J **Possible bias due to RTM**
-- J **Crash severity**
-
-Table 4-4 lists the thirteen performance measures discussed in the HSM with an indication of their ability to account for these major factors. While some measures directly account for crash severity (e.g., relative severity index), analysts can adapt any of the measures to account for crash severity.
-
-## **Accounting for differences in traffic volumes**
-
-As discussed earlier, analysts have traditionally used crash rates to account for differences in traffic volume among sites. Crash rate is the ratio of crash frequency to exposure, which is typically the traffic volume. Crash rates implicitly assume a linear relationship between crash frequency and traffic volume; however, many studies have shown that the relationship between crashes and traffic volume is nonlinear, and the shape of this relationship depends on the type of facility. Nonlinear relationships,
-
-such as SPFs, are more appropriate than linear relationships, such as crash rates to account for differences in traffic volume among sites.
-
-SPFs are a more reliable method to account for differences in traffic volume among sites because they reflect the nonlinear relationship between crash frequency and traffic volume. The SPF is an equation that represents a best-fit model that relates annual observed crashes to the site characteristics including annual traffic volume and other site characteristics. Typically, SPFs are estimated for a particular crash type for a type of facility (e.g., run-off-road crashes on rural two
-
-#### **Expected crashes**
-
-The frequency of crashes per year that represents the combination of the predicted crashes and the observed crashes that actually occurred at the site.
-
-#### **Excess crashes**
-
-The difference between the expected crashes and the observed crash frequency at the site.
-
-![](_page_108_Figure_8.jpeg)
-
-**FIGURE 4-2**: Example of SPF for multi-vehicle crashes on rural, 4-lane freeways
-
-![](_page_108_Figure_10.jpeg)
-
-**FIGURE 4-3**: Example of SPF for single-vehicle crashes on rural, 4-lane freeways
-
-**13**
-
-R. Srinivasan and K. Bauer (2013), Safety Performance *Development Guide: Developing Jurisdiction-Specific SPFs*, Report FHWA-SA-14-005, Federal Highway Administration, Washington, DC.
-
-**14**
-
-[http://www.](http://www.safetyanalyst.org) [safetyanalyst.org/](http://www.safetyanalyst.org)
-
-**15**
-
-G. Bahar and E. Hauer (2014), *Users Guide to Develop HSM SPF Calibration Factors*, NCHRP Project 20-7(332).
-
-**16**
-
-Susan Herbel, Lorrie Laing, Colleen McGovern (2010), *Highway Safety Improvement Program (HSIP) Manual*, FHWA-SA-09-029, Federal Highway Administration, Washington, DC.
-
-lane roads) using data from an entire jurisdiction or State. Figure 4-2 and Figure 4-3 show example SPFs where the points represent observed crashes at specific traffic volumes for individual sites, and the solid line represents the bestfit model (i.e., the SPF). If the relationship between exposure and crash frequency were linear, then the solid line would be a straight line instead of a curve. These two figures also demonstrate the nature of SPFs – each curve is different. For the rural, four lane freeways used in this example, multi-vehicle crashes rise exponentially with more traffic volume (Figure 4-2) but singlevehicle crashes behave differently; they level off with increasing levels of traffic volume (Figure 4-3).
-
-An SPF produces the average number of crashes that would be predicted for sites with a particular set of characteristics. By comparing a site's observed number of crashes with the predicted number of crashes from an SPF, it may be possible to identify sites that experience more crashes than one would expect from a site with that particular set of characteristics. Sites where the observed number of crashes is larger than the predicted number of crashes from an SPF warrant further review and diagnosis. Two measures in Table 4-4, level of service of safety (LOSS) and the excess predicted average crash frequency using SPFs, use the observed crash frequency and predicted frequency from an SPF to identify sites with promise.
-
-Ideally, SPFs should be estimated using data from the same jurisdiction as the site(s) being studied.**<sup>13</sup>** However, that may
-
-#### **SPF Example 1**
-
-Some States use Safety Analyst, a software tool from AASHTO, to identify sites that may benefit from a safety treatment.**<sup>14</sup>** The following is an SPF from Safety Analyst that predicts the total number of crashes on rural multilane divided roads:
-
-$$P = L \times e^{-5.05} \times (AADT)^{0.66}$$
-
-**P** is the total number of crashes in one year on a segment of length **L**.
-
-This is a relatively simple SPF where the predicted number of crashes per mile is a function of just AADT. For example, if the AADT is 45,000, then the predicted number of crashes for a one mile segment based on the SPF will be the following:
-
-> **P = 1 × e-5.05 × 450000.66 =7.55 crashes per year**
-
-not always be possible due to the availability of data or lack of statistical expertise. In that case, the SPFs developed from another jurisdiction could be calibrated using data from the jurisdiction with the study sites.**<sup>15</sup>**
-
-## **Avoiding bias due to regression-to-the-mean**
-
-As previously discussed, RTM describes the situation when periods with relatively high crash frequencies are followed by periods with relatively low crash frequencies simply due to the random nature of crashes. Figure 4-4 illustrates RTM, comparing the difference between short-term average and long-term average crash history.**<sup>16</sup>** Due to RTM, the short-term average is not a reliable estimate of the long-term crash propensity of a particular site. If an agency selects sites based
-
-#### **SPF Example 2**
-
-Bauer and Harwood**<sup>17</sup>** provide a more complex SPF for fatal and injury crashes on rural two lane roads. This model provides
-
-a crash prediction that is more tailored to characteristics of the site, such as curve radius and vertical grade of the road:
-
-$$\begin{aligned} N_{\text{FI}} &= \exp\left[-8.76 + 1.00 \times \ln(\text{AADT}) + 0.044 \times \text{G} + 0.19 \times \ln(2 \times 5730/\text{R}) \times \text{I}_{\text{HC}} \\ &+ 4.52 \times (1/\text{R})(1/\text{L}_{c}) \times \text{I}_{\text{HC}}\right] \end{aligned}$$
-
-**NFI** = fatal-and-injury crashes per mile per year
-
-**AADT** = annual average daily traffic (vehicles/day)
-
-**G** = absolute value of percent grade; 0% for level tangents; ≥ 1% otherwise
-
-**R** = curve radius (ft); missing for tangents
-
-**I HC** = horizontal curve indicator: 1 for horizontal curves; 0 otherwise
-
-**LC** = horizontal curve length (mi); not applicable for tangents
-
-**ln** = natural logarithm function
-
-**17**
-
-Bauer, K. and Harwood, D., Safety Effects of Horizontal Curve and Grade Combinations on Two-Lane Highways, Federal Highway Administration, Report No. FHWA-HRT-13-077, January 2014.
-
-on high short-term average crash history, crashes at those sites may be lower in the following years due to RTM, even if the agency does not install countermeasures at those sites.
-
-If RTM is not properly accounted for, sites with a randomly high count of crashes in the short term could be incorrectly identified as having a high potential for improvement, and vice versa. In this case, scarce resources may be inefficiently used on such sites while sites with a truly high potential for cost effective safety improvement remain unidentified.
-
-One approach to address RTM bias is to use the EB method. The EB method is a statistical method that combines the observed crash frequency (obtained from crash reports) with the predicted crash frequency (derived from the appropriate SPF) to calculate the expected crash frequency for a site of interest. This method pulls the crash count towards the mean, accounting for the RTM bias.
-
-The EB method is illustrated in Figure 4-5, which illustrates how the observed crash frequency is combined with the predicted crash frequency based on the SPF.**<sup>18</sup>** The
-
-![](_page_110_Figure_17.jpeg)
-
-**FIGURE 4-4**: Chart to illustrate RTM phenomenon *(Source: HSIP Manual, 2010)*
-
-**18**
-
-Susan Herbel, Lorrie Laing, Colleen McGovern (2010), *Highway Safety Improvement Program (HSIP) Manual*, FHWA-SA-09-029, Federal Highway Administration, Washington, DC.
-
-![](_page_111_Figure_0.jpeg)
-
-**FIGURE 4-5**: Schematic to illustrate the empirical Bayes method *(Source: HSIP Manual, 2010)*
-
-EB method is applied to calculate an expected crash frequency or corrected value, which lies somewhere between the observed value and the predicted value from the SPF.
-
-Mathematically, the expected number of crashes can be written as a function of the predicted value from the SPF and the observed crashes in the following manner:
-
-$$N_{\text{expected}} = w \times N_{\text{predicted}} + (1 - w) \times N_{\text{observed}}$$
-
-**Nexpected** <sup>=</sup> expected average crash frequency for a certain study period
-
-**w** = weighted adjustment to be placed on the SPF prediction (0 < w < 1)
-
-**Npredicted** <sup>=</sup> predicted average crash frequency predicted using an SPF for the study period under the given conditions
-
-**Nobserved** <sup>=</sup> observed crash frequency at the site over the study period
-
-The weight w is a function of the predicted crash frequency (Npredicted) and a statistical parameter called the overdispersion parameter of the SPF. Procedures to estimate the expected
-
-average crash frequency are provided in Part B of the HSM. For example, if the observed crash frequency in a particular site was nine crashes per year, the predicted crash frequency from the SPF was 6.4 crashes per year, and the w was 0.3, then Nexpected will be as follows:
-
-$$N_{\text{expected}} = 0.3 \times 6.4 + (1 - 0.3) \times 9$$
-  
-= 8.22 crashes per year
-
-**Equation <sup>1</sup>** We can prioritize sites by calculating the difference between the EB expected crashes at a particular site and the predicted crashes from an SPF. By comparing EB expected crashes at a particular site instead of observed crashes, we account for possible bias due to RTM.
-
-> The first eight measures presented in Table 4-4 do not account for possible bias due to RTM. Measure 9 (probability of specific crash types exceeding threshold proportion) and measure 10 (excess proportion of specific crash types) are not affected by RTM unless they are based on short-term crash history. Measure 11 (expected average crash frequency with EB adjustments), measure 12 (EPDO average crash frequency
-
-with EB adjustment), and measure 13 (excess expected average crash frequency with EB adjustment) account for possible bias due to RTM using the EB adjustments.
-
-## **Accounting for crash severity**
-
-The severity of crashes at a location can (and should) have a bearing on the priority of the site for safety treatment. Three of the measures in Table 4-4, measure 3 (EPDO average crash frequency), measure 4 (relative severity index), and measure 12 (EPDO average crash frequency with EB adjustment), directly account for crash severity. Measures 3 and 12 use the EPDO method, which converts all crashes to a common unit, namely property damage only (PDO) crashes. Using these measures, the analyst assigns points to each crash based on its crash severity level. A PDO crash typically receives one point and the points increase as the severity of the crash increases.
-
-While other measures do not explicitly mention severity, analysts can adapt any of the measures to consider any severity level. For example, an analyst could use crash frequency and focus on the frequency of fatal and severe injury crashes to priority rank sites. It is important to note that the severity distribution of crashes may be a function of site characteristics including AADT. For example, sections with higher AADT values may be associated with lower speeds and consequently fewer severe crashes.
-
-## **Step 2. Diagnosis**
-
-Diagnosis is the second step in the roadway safety management process, following network
-
-screening. Diagnosis is the process of further investigating the sites and issues identified from network screening. The intent of diagnosis is to identify crash patterns and the factors that contribute to crashes at the identified sites. Thorough diagnosis can also identify potential safety issues that have not yet manifested in crashes. Diagnosis often involves a review of the crash history, traffic operations, and general site conditions. While safety professionals could review these data from the office, a field visit provides the opportunity to observe road user behavior and site characteristics that are not available in the data. Sometimes, safety professionals may also conduct a field review at night or at other times that crash history has indicated to be of concern. It is important to diagnose the cause of the problem before developing potential countermeasures, just as a doctor examines symptoms to diagnose an underlying disease before formulating a prescription. Otherwise, resources may be misallocated if a countermeasure that does not target the underlying issues is selected and implemented.
-
-The Haddon Matrix is a framework to identify possible contributing factors (e.g., driver, vehicle, and roadway/ environment) which are crossreferenced against possible crash conditions before, during, and after a crash to identify possible reasons for events. This comprehensive understanding of crash contributing factors is important for the diagnosis of safety problems. An example of the Haddon Matrix is presented later under Countermeasure Selection on page 4-20.
-
-The HSM recommends that diagnosis include the following parts:
-
-- J **A review of safety data**
-- J **An assessment of supporting documentation**
-- J **An assessment of field conditions**
-
-## **Safety data review**
-
-An analyst can conduct a detailed review of the crash data from police reports to identify patterns. This could involve reviewing the crash type, severity, sequence of events, and contributing circumstances. Different visualization tools, such pie charts, bar charts, or tabular summaries, can be used to display various crash statistics. In addition to reviewing descriptive statistics, analysts can use various methods to identify underlying safety issues based on the recognition of crash patterns.
-
-One method would be to identify locations that have a proportion of a specific collision type relative to the total collisions that is higher than some average or threshold proportion value for similar road types. Kononov found that looking at the percentage distribution of collisions by collision type can reveal the "existence of collision patterns susceptible to correction" that may or may not be accompanied by the overrepresentation in expected or expected excess collisions.**<sup>19</sup>** Heydecker and Wu originally proposed this method.**<sup>20</sup>** The method is identical for different location types. However, only similar location types should be analyzed together because collision patterns will naturally differ. For example, the collision patterns are different
-
-for stop-controlled intersections, signalized intersections, and twolane roads, so the method would be applied separately to the three types of facilities and separately for urban and rural environments. Another method would be to investigate sites that experience a gradual or sudden increase in mean collision frequency.**<sup>21</sup>**
-
-Following the detailed review of the crash data, the analyst can create collision diagrams, condition diagrams, and crash maps to summarize the crash information by location. A collision diagram is a tool to identify and display crash patterns. Many resources, including the HSM, provide guidance on developing collision diagrams. Examples of collision diagrams are shown in Figure 4-6 and Figure 4-7. Each crash at the site is displayed according to where it occurred, what type of crash it was, how severe it was, and various other characteristics. An analyst uses symbols to visually represent many of these characteristics.
-
-Condition diagrams include a drawing with information about the site characteristics including information about the roadway (e.g., number of lanes, presence of medians, pedestrian and bicycle facilities, shoulder information), surrounding land uses, and pavement conditions. Condition diagrams can be overlaid on top of collisions diagrams to gain further insight to the crash patterns.
-
-Crash mapping involves the use of geographic information systems (GIS) to integrate information from the roadway network with information from geocoded crash data. If the geocoded crash data are
-
-Kononov, J. (2002), Identifying Locations with Potential for Collision Reductions: Use of Direct Diagnostics and Pattern Recognition Methodologies, *Transportation Research Record* 
-
-*1784*, pp. 153-158.
-
-#### **20**
-
-**19**
-
-Heydecker, B. J., and J. Wu (1991), Using the Information in Road Accident Records Proc., 19th PTRC Summer Annual Meeting, London.
-
-#### **21**
-
-Hauer, E. (1996), Detection of Safety Deterioration in a Series of Accident Counts. *Transportation Research Record 1542*, 38-43.
-
-Hauer, E. (1996), Statistical Test of the Difference between Expected Accident Frequencies, *Transportation Research Record 1542*, 24-29.
-
-![](_page_114_Figure_0.jpeg)
-
-![](_page_114_Figure_1.jpeg)
-
-accurate, then crash mapping can provide valuable insights into crash locations and crash patterns.
-
-## **Assess supporting documentation**
-
-This step involves a review of documented information about the site along with interviews of local transportation professionals to obtain additional perspectives on the safety data review from the previous step. Examples of supporting documentation include traffic volumes, construction plans and design criteria, photos and maintenance logs, weather patterns, and recent traffic studies in the area.
-
-## **Assess field conditions**
-
-Field observations are useful for supplementing crash data and can help the analyst understand the behavior of drivers, pedestrians, and bicyclists. The first stage of the field investigation should be an on-site examination of a road user's experience. Those conducting the assessment should travel through the site at different times of the day using different modes of transportation (e.g., driving, walking, and bicycling). Assessors should observe the mix of vehicle traffic and other road users. They should also observe traffic movements, conflicts, and
-
-| ROAD SAFETY AUDIT                                                                              | TRADITIONAL SAFETY REVIEW                                                               |
-|------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|
-| Independent, multi-disciplinary team                                                           | Safety review team within the project team<br>with only safety and/or design experience |
-| Considers all potential road users (pedestrians,<br>bicyclists, motor vehicles, transit users) | Often concentrates only on motor vehicles                                               |
-| Accounts for road user capabilities<br>and limitations                                         | Safety reviews do not normally<br>consider human factor issues                          |
-| Always generates a formal report                                                               | Often does not generate a formal report                                                 |
-| Always generates a formal response report                                                      | Often does not generate a formal<br>response report                                     |
-| TABLE 4-5: Differences between Road Safety Audit and Traditional Road Safety Review            |                                                                                         |
-
-**TABLE 4-5**: Differences between Road Safety Audit and Traditional Road Safety Review *(Source: FHWA)*
-
-operating speeds. Those conducting the field review could determine whether the road and intersection characteristics are consistent with driver expectation and if roadside recovery zones are clear and traversable.
-
-## **Road safety audits**
-
-One method to assess field conditions is a road safety audit (RSA). This is the formal safety performance examination of an existing or future road or intersection by an independent, multidisciplinary team. An RSA qualitatively estimates and reports on existing and potential road safety issues and identifies opportunities for safety improvements for all road users. FHWA encourages States, local jurisdictions and tribal governments to integrate RSAs into the project development process for new roads and intersections and to conduct RSAs on existing ones.
-
-The purpose of an RSA is to answer the following questions:
-
-- J **What elements of the road may present a safety concern, and to what extent, to which road users, and under what circumstances?**
-- J **What opportunities exist to eliminate or mitigate identified safety concerns?**
-
-The multidisciplinary audit team consists of people who represent different areas of expertise, such as engineering (e.g., design, traffic, and maintenance), law enforcement, safety educators, public officials, community traffic safety advocates, and others. Any phase of project development (planning, preliminary engineering, design, construction) and any sized project from minor intersection and roadway retrofits to mega-projects are eligible for an RSA.
-
-Most State DOTs have established safety review processes. However, RSAs and a traditional safety reviews are different. Table 4-5 shows the difference between an RSA and a traditional safety review.**<sup>22</sup>**
-
-### **22**
-
-"Road Safety Audits (RSA)," accessed August 7, 2013, [http://safety.fhwa.](https://safety.fhwa.dot.gov/rsa/) [dot.gov/rsa/](https://safety.fhwa.dot.gov/rsa/)
-
-### **International Road Assessment Programme**
-
-The International Road Assessment Programme (iRAP) conducts safety inspections on high-risk roads in more than 70 countries. The iRAP inspectors perform a detailed road survey, focusing on road attributes that are known to be associated with crash risk. These include intersection design, number of lanes, roadside hazards, and provisions for pedestrian crossings. The inspectors use these data to develop a star rating, which reflects the level of safety of the road, and provide detailed feedback to the government agency in the form of an assessment report. iRAP also provides a Road Safety Toolkit, which helps engineers, planners, and policy makers develop safety plans for all road users.**23**
-
-## **Step 3. Countermeasure selection**
-
-After diagnosing the safety issues at the site, analysts select countermeasures to address the contributing factors for observed crashes. The first part of countermeasure selection is to identify countermeasures to target the underlying safety issues. Analysts can use tools like the Haddon Matrix and resources like the NCHRP Report 500 series to identify targeted countermeasures to address or mitigate underlying contributing factors.
-
-## **Identifying contributing factors**
-
-The Haddon Matrix is a tool originally developed for injury prevention, but it is directly applicable to highway safety in both diagnosis and countermeasure selection.**<sup>24</sup>** The Haddon Matrix is useful to gain a comprehensive understanding of crash contributing factors. Analysts can use the Haddon Matrix to identify human, vehicle, and roadway factors contributing to the frequency and severity of crashes prior to, during, and after the crash event. Then, analysts can identify targeted reactive and proactive countermeasures to address or mitigate the underlying contributing factors for the given site. Chapter 6 of the 1st edition of the HSM provides further discussion of the Haddon Matrix.
-
-The Haddon Matrix is comprised of nine cells to identify human, vehicle, and roadway factors contributing to the target crash type or severity outcome before, during, and after the crash. Precrash factors speak to the factors or actions prior to the crash that contributed to the occurrence of the crash. Crash factors speak to those factors or actions that occurred at the moment of the crash. Post-crash factors speak to factors that come into play after the crash that affect the severity of the injuries or speed of response. Examples of human factors include fatigue, inattention, age, and failure to wear a seat belt. Vehicle factors include bald tires, airbag operations, and worn brakes. Examples of roadway factors include pavement friction, weather, grade, and limited sight distance.
-
-Table 4-6 is an example application of the Haddon Matrix from the Highway Safety Improvement Program (HSIP) Manual for crashes in an urban area.**<sup>25</sup>** The top-left cell identifies driver behaviors or characteristics that may contribute to the likelihood or the severity of a collision, such as poor vision or reaction time, alcohol consumption, speeding, and risk taking. These
-
-**23**
-
-[org/](http://toolkit.irap.org/)
-
-<http://www.irap.net/> [http://toolkit.irap.](http://toolkit.irap.org/)
-
-**24**
-
-Haddon, W., Jr. (1972). A logical framework for categorizing highway safety phenomena and activity. *Journal of Trauma 12*: 193–207.
-
-**25**
-
-Susan Herbel, Lorrie Laing, Colleen McGovern (2010), *Highway Safety Improvement Program (HSIP) Manual,* FHWA-SA-09-029, Federal Highway Administration, Washington, DC.
-
-*Highway Safety Improvement Program Manual.*  Federal Highway Administration, Washington, D.C., 2010, Chapter 3
-
-factors should be considered when selecting countermeasures. For example, based on these human factors, successful countermeasures may be those that improve visibility or reduce speeding. The matrix in its entirety provides a range of potential issues that can be addressed through a variety of countermeasures including education, enforcement, engineering, and emergency response solutions.
-
-## **Countermeasure resources and tools**
-
-Diagnosing a roadway safety problem and identifying effective countermeasures is a skill developed through education, training, research, and experience. Many resources are available to help transportation professionals analyze and develop countermeasures. Since the transportation field continuously generates new knowledge and countermeasure approaches, it is
-
-important to stay informed of the available resources and tools.**<sup>26</sup>**
-
-Some of the most useful resources and tools for countermeasure guidance and selection are listed below (alphabetically):
-
-- J **Bicycle Safety Guide and Countermeasure Selection System (BIKESAFE, www.pedbikesafe. org/bikesafe)** – This resource provides practitioners with the latest information available for improving the safety and mobility of those who bike. The online tools provide the user with a list of possible engineering, education, or enforcement treatments to improve bicycle safety and/or mobility based on user input about a specific location.
-- J **Countermeasures That Work: A Highway Safety Countermeasure Guide for State Highway Safety Offices** – This document serves as a basic reference to help state
-
-| PERIOD        | HUMAN                                                                 | VEHICLE/<br>EQUIPMENT                                            | PHYSICAL<br>ENVIRONMENT                    | SOCIO<br>ECONOMIC                                                   |
-|---------------|-----------------------------------------------------------------------|------------------------------------------------------------------|--------------------------------------------|---------------------------------------------------------------------|
-| PRE<br>CRASH  | Poor vision or<br>reaction time,<br>alcohol, speeding,<br>risk taking | Failed brakes,<br>missing lights,<br>lack of warning<br>systems  | Narrow<br>shoulders,<br>ill-timed signals  | Cultural norms<br>permitting<br>speeding, red<br>light running, DUI |
-| CRASH         | Failure to<br>use occupant<br>restraints                              | Malfunctioning<br>safety belts,<br>poorly engineered<br>air bags | Poorly designed<br>guardrails              | Lack of<br>vehicle design<br>regulations                            |
-| POST<br>CRASH | High<br>susceptibility,<br>alcohol                                    | Poorly designed<br>fuel tanks                                    | Poor emergency<br>communication<br>systems | Lack of support<br>for EMS and<br>trauma systems                    |
-
-**TABLE 4-6**: Haddon Matrix for crashes in an urban area *(Source: HSIP Manual)*
-
-highway safety offices (SHSOs) select effective, evidence-based countermeasures for traffic safety problem areas related to user behaviors, such as alcohol-impaired and drugged driving, seat belts and child restraints, and aggressive driving and speeding.**<sup>27</sup>**
-
-- J **Crash Modification Factors Clearinghouse (www. cmfclearinghouse.org)** – This website offers transportation professionals a central, online repository of crash modification factors (CMFs) that indicate the safety effect on crashes due to infrastructure improvements. The website also provides additional information and resources related to CMFs. This site is funded by FHWA.
-- J **FHWA Proven Countermeasures (safety.fhwa.dot.gov/ provencountermeasures)** – FHWA regularly compiles a list of countermeasures that have been shown to be effective in reducing crashes but have yet to be widely applied on a national basis.
-- J **Handbook for Designing Roadways for the Aging Population (safety. fhwa.dot.gov/older\_users/ handbook)** – This FHWA guide provides practitioners with a practical information source that links aging road user performance to highway design, operational, and traffic engineering features. This handbook supplements existing standards and guidelines in the areas of highway geometry, operations, and traffic control devices.**<sup>28</sup>**
-
-- J **Highway Safety Manual (www. highwaysafetymanual.org)** – This document provides science-based knowledge and tools to conduct safety analyses, allowing for safety to be quantitatively evaluated alongside other transportation performance measures, such as traffic operations, environmental impacts, and construction costs.
-- J **National Cooperative Highway Research Program (NCHRP) Report 500 Series (safety. transportation.org/guides.aspx)** – This resource is a collection of 23 reports in which relevant information is assembled into single concise volumes, each pertaining to specific types of highway crashes (e.g., run-off-the-road, head-on) or contributing factors (e.g., aggressive driving) related to behaviors, vehicles, and roadways. Countermeasures are categorized as proven, tried, and experimental.
-- J **Pedestrian Safety Guide and Countermeasure Selection System (PEDSAFE, www.pedbikesafe. org/pedsafe)** – This resource provides practitioners with the latest information available for improving the safety and mobility of those who walk. The online tools provide the user with a list of possible engineering, education, or enforcement treatments to improve pedestrian safety and/ or mobility based on user input about a specific location.
-
-**27**
-
-Goodwin, A., Thomas, L., Kirley, B., Hall, W., O'Brien, N., & Hill, K. (2015). *Countermeasures that work: A highway safety countermeasure guide for State highway safety offices*, Eighth edition. (Report No. DOT HS 812 202). Washington, DC: National Highway Traffic Safety Administration. Accessed February 2017 at [www.nhtsa.](http://www.nhtsa.gov/staticfiles/nti/pdf/812202-CountermeasuresThatWork8th.pdf) [gov/staticfiles/](http://www.nhtsa.gov/staticfiles/nti/pdf/812202-CountermeasuresThatWork8th.pdf) [nti/pdf/812202-](http://www.nhtsa.gov/staticfiles/nti/pdf/812202-CountermeasuresThatWork8th.pdf) [Countermeasures](http://www.nhtsa.gov/staticfiles/nti/pdf/812202-CountermeasuresThatWork8th.pdf) [ThatWork8th.pdf](http://www.nhtsa.gov/staticfiles/nti/pdf/812202-CountermeasuresThatWork8th.pdf)
-
-**28**
-
-Brewer, M., D. Murillo, A. Pate (2014). *Handbook for Designing Roadways for the Aging Population*, Report No. FHWA-SA-14-015, Federal Highway Administration, Washington, D.C.
-
-**29**
-
-[www.](http://www.cmfclearinghouse.org) [cmfclearinghouse.](http://www.cmfclearinghouse.org) [org](http://www.cmfclearinghouse.org)
-
-## **Identifying and selecting countermeasures**
-
-After identifying potential countermeasures to target the underlying issues, safety professionals must estimate the safety impact of countermeasures, individually and in combination. It is important to consider positive and negative safety impacts. Subsequent steps of the roadway safety management process (i.e., economic appraisal and project prioritization) include the consideration of other parameters, such as constructability, environmental impacts, and cost.
-
-The agency that will be making the final decision on countermeasure selection should make sure to coordinate with other safety partners to ensure that the countermeasure is appropriate for all parties. For example, a DOT should coordinate with law enforcement and emergency response to make sure that a proposed engineering installation will interfere with enforcement activities or impede emergency responders.
-
-For infrastructure improvements, CMFs associated with different countermeasures provide a mechanism for determining the safety effect of different countermeasures. A CMF is a multiplicative factor used to compute the expected number of crashes after implementing a given countermeasure at a specific site.
-
-- J If the CMF for a particular treatment is less than 1.0, then that countermeasure is expected to reduce crashes.
-- J If the CMF for a particular treatment is greater than 1.0,
-
-### **Guidance on CMF application**
-
-FHWA provides an extensive selection of guidance on selecting and applying CMFs through the CMF Clearinghouse (**www.cmfclearinghouse.org**). They present answers to frequently asked questions, such as "How can I apply multiple CMFs?" and "How do I choose between CMFs in my search results that have the same star rating but different CMF values?" The website also houses an archive of annual webinars in which experienced CMF users talk about issues related to applying CMFs in real world situations.
-
-then that countermeasure is expected to increase crashes.
-
-J A CMF of 1.0 implies that a countermeasure will not have any effect on safety.
-
-For example, if the expected number of crashes without a countermeasure is 5.6 crashes per year, and the CMF for the particular countermeasure is 0.8, then the expected number of crashes with the countermeasure is:
-
-#### **5.6 crashes per year x 0.8 = 4.48 crashes per year**
-
-It is important to recognize that some countermeasures may decrease some types of crashes but increase other types. For example, installing a traffic signal would be expected to decrease severe collisions, such as right angle and left turn crashes, but it would be expected to increase less severe crashes, such as rear ends.
-
-The CMF Clearinghouse and the first edition of the HSM provide CMFs for a variety of countermeasures.**<sup>29</sup>** Only those CMFs that passed a set of inclusion criteria based on quality and reliability were included in the HSM. The CMFs in the clearinghouse are provided for any published study, regardless of quality, and are continuously updated based on the latest research. The CMFs in the clearinghouse are reviewed and given a star quality rating ranging from one to five stars, based on the quality of the study. Higher stars imply a better quality CMF.
-
-CMFs should be applied to situations that closely match those from which the CMF was developed. Several variables can be used to match a CMF to a given scenario including roadway type, area type, segment or intersection geometry, intersection traffic control, and traffic volume. However, it is
-
-critical for practitioners to use engineering judgment when a CMF is not available for the situations encountered as there are some cases for which a CMF that was developed for different conditions might be the best available.
-
-## **Step 4. Economic appraisal**
-
-An economic appraisal of alternative countermeasures should be conducted to ensure that safety funds are being used as efficiently as possible. This appraisal helps transportation agencies achieve their desired safety performance the fastest and at the lowest possible cost. An agency can compare
-
-#### **Calculating benefits due to crash reduction**
-
-A city has a stop-controlled intersection with an expected crash frequency of 10 crashes per year, consisting of one A-injury crash, one B-injury crash, two C-injury crashes, and six PDO crashes.
-
-The city is considering installing a roundabout at the intersection. Based on a search of the FHWA CMF Clearinghouse, they decide that they will use a CMF of 0.19 in the calculation of the crash reduction benefit.**<sup>30</sup>** This CMF applies only to serious and minor injury crashes, so they do not use it to estimate any reduction to fatal or PDO crashes (see note).
-
-They multiply the CMF by the expected crashes before roundabout installation to determine the expected crashes after installation:
-
-|     | CRASH SEVERITY                                         | FATAL | A<br>INJURY | B<br>INJURY | C<br>INJURY | PDO |
-|-----|--------------------------------------------------------|-------|-------------|-------------|-------------|-----|
-| I   | Expected Crashes per Year<br>before Roundabout         | 0     | 1           | 1           | 2           | 6   |
-| II  | CMF                                                    | N/A   | 0.19        | 0.19        | 0.19        | N/A |
-| III | Expected Crashes per Year<br>after Roundabout (I x II) | 0     | 0.19        | 0.19        | 0.38        | 6   |
-| IV  | Crash reduction benefit (I<br>minus III)               | 0     | 0.81        | 0.81        | 1.62        | 0   |
-
-Thus, the benefit of a roundabout installation is expected to be a reduction of 0.81 A-injury crashes, 0.81 B-injury crashes, and 1.62 C-injury crashes per year.
-
-*NOTE: A roundabout would also likely bring a reduction to fatal and PDO crashes (i.e., additional CMFs could be incorporated), but the example has been simplified to a single CMF for illustration purposes.*
-
-**30**
-
-Rodegerdts et al., "NCHRP Report 572: Applying Roundabouts in the United States." Washington, D.C., Transportation Research Board, National Research Council, (2007)
-
-the benefits expected from the countermeasure to the estimated costs of the countermeasure.
-
-Some safety countermeasures have a higher-cost value than others. Geometric improvements to the road, such as straightening a tight curve to reduce run-off-road crashes, tend to be very expensive. Installing a curve warning sign and in curve delineation may address the same problem, but at a much lower cost. Although both countermeasures address the same problem, the actual safety benefit may not be the same. Safety professionals take the relative costs and benefits into consideration when prioritizing among countermeasures. Part of calculating the cost of a countermeasure is considering how those costs vary over time, while taking into consideration any maintenance costs and long term effectiveness.
-
-## **Estimating benefits**
-
-The primary benefit of a countermeasure is a reduction in crash frequency or severity. To estimate the safety benefits, a safety professional should use CMFs, such as those discussed in the countermeasure selection step. CMFs can be applied to the actual crashes or expected crashes based on the EB method. Expected crashes are preferred because they account for possible bias due to RTM. The estimated change in crashes represents the expected benefit from the countermeasure.
-
-For each proposed countermeasure, the change in crash frequency and/ or severity needs to be converted to monetary value, based on the monetary value of the type of
-
-| INJURY<br>SEVERITY LEVEL | COMPREHENSIVE<br>CRASH COST |
-|--------------------------|-----------------------------|
-| Fatality (K)             | \$4,008,900                 |
-| Disabling Injury (A)     | \$216,000                   |
-| Evident Injury (B)       | \$79,000                    |
-| Fatal/Injury (K/A/B)     | \$158,200                   |
-| Possible Injury (C)      | \$44,900                    |
-| PDO (O)                  | \$7,400                     |
-
-**TABLE 4-7**: Crash Costs by Severity Level in the Highway Safety Manual, 1st ed.
-
-crashes reduced. This monetary value is also called the crash cost. Crash costs are based on costs to society, such as lost productivity, medical costs, legal and court costs, emergency service costs, insurance administration costs, congestion costs, property damage, and workplace losses.**<sup>31</sup>**
-
-The benefit from the countermeasure is the sum of the crash costs for crashes prevented by the countermeasure. Assigning costs to crashes is a topic that is under constant discussion and revision nationwide. States differ widely in the dollar amount that they assign to crashes, though all States apply higher values to more severe crashes. The CMF Clearinghouse provides a synthesis of crash costs that are used by various States.**<sup>32</sup>**
-
-Additionally, the first edition of the HSM provided a list of crash costs by severity level (Table 4-7). However, since the publication of the first HSM in 2010, the USDOT has issued periodic recommendations that dramatically raised the values. For instance, the monetary value of a
-
-### **31**
-
-Blincoe, L. J., Miller, T. R., Zaloshnja, E., and Lawrence, B. A. The economic and societal impact of motor vehicle crashes, 2010. (Revised), National Highway Traffic Safety Administration, Report No. DOT HS 812 013, Washington, DC, May 2015.
-
-#### **32**
-
-[http://www.](http://www.cmfclearinghouse.org/resources_servlifecrashcostguide.cfm) [cmfclearinghouse.](http://www.cmfclearinghouse.org/resources_servlifecrashcostguide.cfm) [org/resources\\_](http://www.cmfclearinghouse.org/resources_servlifecrashcostguide.cfm) [servlifecrash](http://www.cmfclearinghouse.org/resources_servlifecrashcostguide.cfm) [costguide.cfm](http://www.cmfclearinghouse.org/resources_servlifecrashcostguide.cfm)
-
-fatal crash was listed as \$4 million in the HSM, but recommended as over \$9 million in a 2013 policy memo from USDOT.**<sup>33</sup>**
-
-Although countermeasures are primarily expected to reduce crashes, there might be other benefits, including reduced travel times or lower fuel consumption. For example, a roundabout can decrease total delay at an intersection if applied and configured properly. An AASHTO publication provides guidance on estimating these other non-safety benefits.**<sup>34</sup>**
-
-## **Estimating costs**
-
-The costs of the proposed countermeasure include the startup cost and the ongoing operational and maintenance costs. These costs can usually be estimated based on costs of materials, labor cost per personhour, cost of additional right-ofway, and past experience with
-
-similar countermeasures. Table 4-8 illustrates the types of startup and ongoing costs that would be incurred for various countermeasures.
-
-## **Service life**
-
-Another important consideration when calculating the benefits and costs of a countermeasure is the length of time that the countermeasure will last. This is referred to as the service life. Countermeasures, such as road edgelines or pavement reflectors, will have a much shorter service life (e.g., three to five years) than countermeasures, such as traffic signal installation or sidewalk construction (e.g., 20 years or more). Many States have a standard list of the service life values used for common countermeasures. The CMF Clearinghouse provides a survey of service life values used by various States for many different countermeasures.**<sup>35</sup>**
-
-Trottenberg, Polly, and Robert Rivkin, "Revised Departmental Guidance 2013: Treatment of the Value of Preventing Fatalities and Injuries," USDOT Office of the Secretary of Transportation, 2013.
-
-**34**
-
-User and Non-User Benefit Analysis for Highways, American Association of State Highway Transportation Officials, September 2010
-
-**35**
-
-[http://www.](http://www.cmfclearinghouse.org/resources_servlifecrashcostguide.cfm) [cmfclearinghouse.](http://www.cmfclearinghouse.org/resources_servlifecrashcostguide.cfm) [org/resources\\_](http://www.cmfclearinghouse.org/resources_servlifecrashcostguide.cfm) [servlifecrash](http://www.cmfclearinghouse.org/resources_servlifecrashcostguide.cfm) [costguide.cfm](http://www.cmfclearinghouse.org/resources_servlifecrashcostguide.cfm)
-
-#### **Calculating monetary benefit of crash reduction**
-
-The previous example showed that a city calculated a crash savings of 0.81 A-injury crashes, 0.81 B-injury crashes, and 1.62 C-injury crashes per year by installing a roundabout. The city has examined guidance from the HSM, guidance from
-
-USDOT, and experiences of other cities and States and determined a standard set of crash costs they will use for all benefit/ cost calculations. They apply these costs to determine the monetary benefit of the expected crash reductions:
-
-|    | CRASH SEVERITY                                  | FATAL   | A<br>INJURY | B<br>INJURY | C<br>INJURY | PDO      |
-|----|-------------------------------------------------|---------|-------------|-------------|-------------|----------|
-| IV | Crash Reduction Benefit                         | 0       | 0.81        | 0.81        | 1.62        | 0        |
-| V  | This City's Standard Crash Cost                 | \$5 mil | \$400,000   | \$100,000   | \$60,000    | \$10,000 |
-| VI | Monetary benefit of crash<br>reduction (IV x V) | 0       | \$324,000   | \$81,000    | \$97,200    | 0        |
-
-Thus, the city expects a total monetary benefit of \$324,000+\$81,000+\$97,200 = **\$502,000** per year due to reduction in crashes.
-
-| COUNTERMEASURE                              | STARTUP COST                                                                                                           | ONGOING COST<br>DURING SERVICE LIFE                                               |  |
-|---------------------------------------------|------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|--|
-| Install curve<br>warning sign               | Low – sign material, minimal<br>labor for installation                                                                 | None                                                                              |  |
-| Install roundabout                          | High – Design plan, purchase<br>of additional right-of-way,<br>material, labor, traffic control<br>during construction | Low – maintenance of<br>grass and decorative<br>vegetation                        |  |
-| Install traffic signal                      | High – Timing plan, material,<br>labor for installation, traffic<br>control during construction                        | Moderate – electricity,<br>bulb replacements, repairs,<br>modifications to timing |  |
-| TABLE 4-8: Examples of Countermeasure Costs |                                                                                                                        |                                                                                   |  |
-
-The service life is used in the calculation of the present value of the benefits and costs of the proposed countermeasure. The calculation of present value includes a discount rate that reflects the time value of money (i.e., present dollars are worth more than future dollars). Present value of countermeasure benefits is calculated as follows:
-
-$$PV = A \times \frac{(1+i)^{y}-1}{i \times (1+i)^{y}}$$
-
-**PV =** present value of benefits
-
-**A =** annual benefit (i.e., monetary value of crashes prevented)
-
-**i =** discount rate
-
-**y =** service life of countermeasure
-
-Calculating present value in this way assumes a uniform annual benefit. The HSIP Manual demonstrates how to calculate present value if the benefits or costs each year are not the same.**<sup>36</sup>**
-
-The present value of annual costs (i.e., operational and maintenance costs) can be calculated in the same manner as for benefits. However, for
-
-### **Calculating the present value of a crash reduction benefit**
-
-From the previous example, the city plans to install a roundabout and expects to see a benefit from crash reductions resulting in savings of \$502,000 per year. They estimate that the roundabout will have a service life of 20 years and they determine that a discount rate of 5% is appropriate. They calculate the present value of benefits as:
-
-$$PV = $502,000 \times \frac{(1+0.05)^{20}-1}{0.05 \times (1+0.05)^{20}}$$
-$$= $6,256,030$$
-
-costs, the final present value must also include the startup cost in the year of installation (see examples in Table 4-8).
-
-## **Methods for economic appraisal**
-
-There are several methods for using the values of estimated benefits and costs to evaluate the economic effectiveness of safety improvement projects at a particular site. In particular, these methods
-
-**36**
-
-Herbel, Susan, Lorrie Laing, Colleen McGovern, Highway Safety Improvement Program (HSIP) Manual, Federal Highway Administration, FHWA-SA-09-029, January 2010
-
-are useful in situations where a safety professional is considering several alternatives and desires to choose the countermeasure with the greatest benefit for the cost.
-
-The HSIP Manual contains guidance on three methods - net present value, benefit/cost ratio, and cost effectiveness index.**<sup>37</sup>** Net present value (NPV) is generally regarded as the most economically appropriate method, though the other two methods have certain advantages, as discussed below. The following sections provide quoted guidance from the HSIP Manual on economic appraisal.
-
-#### **Net Present Value**
-
-The NPV method, also called the net present worth (NPW) method, expresses the difference between the present values of benefits and costs of a safety improvement project. The NPV method has two basic functions: 1) determining which countermeasure(s) is/are most cost efficient based on the highest NPV and 2) determining whether a countermeasure's benefits are greater than its costs (i.e., the project has a NPV greater than zero).
-
-The formula for NPV is:
-
-**NPV = PVB − PVC**
-
-**PVB =** Present value of benefits
-
-**PVC =** Present value of costs
-
-A countermeasure will result in a net benefit if the NPV is greater than zero. Table 4-9 summarizes the NPV calculations of four alternative countermeasures.
-
-For Alternative A, the NPV can be calculated as follows:
-
-**NPV = \$1,800,268 − \$500,000 = \$1,300,268**
-
-The same calculation is performed for the other three countermeasure alternatives, and rank each countermeasure based on its NPV. As shown, all four alternatives are economically justified with a NPV greater than zero. However, Alternative B has the greatest NPV for this site based on this method.
-
-#### **Benefit/Cost Ratio and Analysis**
-
-The benefit/cost ratio (BCR) is the ratio of the present value of a project's benefits to the present value of a project's costs.
-
-|  | 37 |
-|--|----|
-|  |    |
-
-Herbel, Susan, Lorrie Laing, Colleen McGovern, Highway Safety Improvement Program (HSIP) Manual, Federal Highway Administration, FHWA-SA-09-029, January 2010.
-
-| ALTERNATIVE<br>COUNTERMEASURE | PRESENT<br>VALUE OF<br>BENEFITS (I) | PRESENT<br>VALUE OF<br>COSTS (II) | NET<br>PRESENT<br>VALUE (I-II) | ALTERNATIVE<br>RANK |
-|-------------------------------|-------------------------------------|-----------------------------------|--------------------------------|---------------------|
-| A                             | \$1,800,268                         | \$500,000                         | \$1,300,268                    | 3                   |
-| B                             | \$3,255,892                         | \$1,200,000                       | \$2,055,892                    | 1                   |
-| C                             | \$3,958,768                         | \$2,100,000                       | \$1,858,768                    | 2                   |
-| D                             | \$2,566,476                         | \$1,270,000                       | \$1,296,476                    | 4                   |
-
-**TABLE 4-9**: Net Present Value *(Source: HSIP Manual, Chapter 4)*
-
-The formula for BCR is:
-
-**BCR = PVB / PVC**
-
-**PVB =** Present value of benefits
-
-**PVC =** Present value of costs
-
-Table 4-10 shows an example of using BCR to prioritize four alternatives.
-
-A project with a BCR greater than 1.0 indicates that the benefits outweigh the costs. However, the BCR is not applicable for comparing various countermeasures or multiple projects at various sites; this requires an incremental benefit/cost analysis.
-
-An incremental benefit/cost analysis provides a basis of comparison of the benefits of a project for the dollars invested. It allows the analyst to compare the economic effectiveness of one project against another; however, it does not consider budget constraints. Optimization methods are best for prioritizing projects based on monetary constraints. An in-depth explanation of incremental benefit/cost analysis and an example is provided in Chapter 4 of the HSIP Manual.
-
-When conducting a benefit/
-
-cost analysis, transportation professionals compare all of the benefits associated with a countermeasure (e.g., crash reduction), expressed in monetary terms, to the cost of implementing the countermeasure. A benefit/cost analysis provides a quantitative measure to help safety professionals prioritize countermeasures or projects and optimize the return on investment.
-
-#### **Cost-Effectiveness Index**
-
-In situations where it is not possible or practical to monetize countermeasure benefits, transportation professionals can use the cost-effectiveness index method in lieu of the NPV or BCR. Cost-effectiveness is simply the amount of money invested divided by the crashes reduced. The result is a number that represents the cost of the avoided crashes of a certain countermeasure. The countermeasure with the lowest value is the most cost-effective and therefore ranked first.
-
-#### **Cost-Effectiveness Index = PVC/CR**
-
-**PVC =** Present value of project cost
-
-| ALTERNATIVE<br>COUNTERMEASURE | PRESENT<br>VALUE OF<br>BENEFITS (I) | PRESENT<br>VALUE OF<br>COSTS (II) | BENEFIT/<br>COST<br>RATIO (I/II) | ALTERNATIVE<br>RANK |
-|-------------------------------|-------------------------------------|-----------------------------------|----------------------------------|---------------------|
-| A                             | \$1,800,268                         | \$500,000                         | 3.6                              | 1                   |
-| B                             | \$3,255,892                         | \$1,200,000                       | 2.7                              | 2                   |
-| C                             | \$3,958,768                         | \$2,100,000                       | 1.9                              | 4                   |
-| D                             | \$2,566,476                         | \$1,270,000                       | 2.0                              | 3                   |
-
-**TABLE 4-10**: Example of Benefit/Cost Ratio Prioritization *(Source: HSIP Manual, Chapter 4)*
-
-| ALTERNATIVE<br>COUNTERMEASURE | PRESENT<br>VALUE OF<br>COSTS | TOTAL<br>CRASH<br>REDUCTION | COST<br>EFFECTIVE-<br>NESS INDEX | ALTERNATIVE<br>RANK |
-|-------------------------------|------------------------------|-----------------------------|----------------------------------|---------------------|
-| A                             | \$500,000                    | 43                          | \$11,628                         | 1                   |
-| B                             | \$1,200,000                  | 63                          | \$19,048                         | 3                   |
-| C                             | \$2,100,000                  | 70                          | \$30,000                         | 4                   |
-| D                             | \$1,270,000                  | 73                          | \$17,397                         | 2                   |
-
-**TABLE 4-11**: Cost-Effectiveness Index *(Source: HSIP Manual, Chapter 4)*
-
-#### **CR =** Total crash reduction
-
-The Cost-Effectiveness Index is a simple and quick method that provides an indication of a project's value. Transportation professionals can use this formula and compare its results with other safety improvement projects. The Cost-Effectiveness Index method, however, does not account for value differences between reductions in fatal crashes compared to injury crashes, and whether a project is economically justified.**<sup>38</sup>**
-
-Table 4-11 summarizes the calculations using the costeffectiveness index method to rank alternative countermeasures, given the present value of the costs and the total crash reduction.
-
-For Alternative A, calculate the costeffectiveness index as follows:
-
-#### **Cost-effectiveness index = 500,000/43 = 11,628**
-
-Calculate the Cost-Effectiveness Index for the remaining alternatives and rank each countermeasure based on its Cost-Effectiveness Index value. With this method, the lowest index is the highest priority and
-
-therefore ranked first. Alternative A is ranked first, since it has the lowest cost associated with each crash reduction.
-
-The above example uses the number of crashes to determine the costeffectiveness index. Transportation professionals can use this same method using EPDO crash numbers, which has the advantage of considering severity.
-
-## **Step 5. Project prioritization**
-
-If a transportation agency is considering installing countermeasures at one or more sites out of a group of potential sites, they will need to prioritize which projects they will implement. Ideally, the agency would implement all projects that bring a safety benefit (e.g., all those with a NPV greater than zero or a BCR greater than one). However, all agencies work within a limited budget and must prioritize where safety funds are spent.
-
-The agency can use steps 1 through 4 of this process to determine which countermeasure(s) would be used at each potential treatment
-
-**38**
-
-*Highway Safety Improvement Program Manual.* Federal Highway Administration (Washington D.C., 2010), Chapter 4
-
-site and to conduct an economic appraisal of the expected effect of the countermeasure. The next step is to determine project priorities. The HSM discusses how projects can be prioritized by economic effectiveness, incremental benefit/cost analysis, or various optimization methods.
-
-## **Prioritizing by economic effectiveness**
-
-Projects can be prioritized by ranking projects or project alternatives by the economic appraisal values produced in step 4. An agency might select those projects with the highest NPV, the highest BCR, or the highest cost effectiveness index. When using NPV the goal of a safety professional should be to implement all projects that have an NPV greater than zero, since each one brings a safety benefit. However, this is not possible since funds are limited, thus the goal should be to implement the group of projects that have the greatest combined NPV when added together (NPV is an additive property). Maximizing the NPV of a group of projects is different from prioritizing projects with high NPV. In other words, it may be best to implement numerous low cost projects with low NPV than one high cost project with a high NPV – but not higher than the NPV of all the low cost projects added up.
-
-## **Prioritizing by incremental benefit/cost analysis**
-
-This method involves ranking all projects with benefit cost ratio greater than 1.0 in increasing order of their estimated cost. An analyst calculates an incremental BCR as such:
-
-**Incremental BCR =**
-
-**(Benefit of Project A − Benefit of Project B)** 
-
-**(Cost of Project A − Cost of Project B)** 
-
-If the incremental BCR is greater than 1.0, the project with the higher cost is compared to the next project on this list; however, if the incremental BCR is less than 1.0, the project with the lower cost is compared to the next project on the list. This process is repeated and the project selected in the last pairing is the considered the best economic investment.
-
-## **Prioritizing by optimization methods**
-
-Optimization methods take into account certain constraints when prioritizing projects. Linear programming, integer programming, and dynamic programming (refer to Chapter 8, Appendix A, HSM, 2010) are optimization methods consistent with an incremental benefit/cost analysis, but they also account for budget constraints in the development of the project list. These optimization methods are more likely to be incorporated into a software package, rather than manually calculated. Multiobjective resource allocation is another optimization method. It incorporates nonmonetary elements (including decision factors not related to safety) into the prioritization process.
-
-Safety professionals may use software applications to select and rank countermeasures. The SafetyAnalyst tool from AASHTO includes economic appraisal and priority ranking tools.**<sup>39</sup>** The economic appraisal tool calculates
-
-**39**
-
-[http://www.](http://www.safetyanalyst.org) [safetyanalyst.org/](http://www.safetyanalyst.org)
-
-![](_page_128_Picture_0.jpeg)
-
-the BCR and other metrics for a set of countermeasures. The priorityranking tool ranks proposed improvement projects based on the benefit and cost estimates from the economic appraisal tool. The priority-ranking tool can also determine an optimal set of projects to maximize safety benefits.
-
-## **Step 6. Safety effectiveness evaluation**
-
-Once a countermeasure has been implemented at a site, or group of sites, it is important to determine whether it was effective in addressing the safety problem. For a safety professional to evaluate the countermeasure, he or she must determine how the countermeasure affected the frequency, type, and severity of crashes. For example, did the installation of a roundabout reduce the frequency of angle crashes? If so, by how much? Did it cause an increase to any other types of crashes? A countermeasure evaluation can result in a CMF
-
-for the countermeasure, which quantifies the effect on crashes (see CMF discussion in Step 4).
-
-Two documents entitled A Guide to Developing Quality Crash Modification Factors**<sup>40</sup>** (from FHWA) and Recommended Protocols for Developing Crash Modification Factors**<sup>41</sup>** (from NCHRP) provide guidance on the different methods for conducting evaluations. The following is an overview of study designs and methods for conducting evaluations.
-
-## **Categories of Study Designs**
-
-Study designs fall into two broad categories - experimental and observational. Experimental studies are conducted when sites are selected at random for treatment. There is general consensus that experimental studies are the most rigorous way to establish causality.**<sup>42</sup>** In contrast, observational studies are conducted when sites are not selected as part of an experiment but selected for other reasons including
-
-**40**
-
-Gross, F., B. Persaud, and C. Lyon (2010), *A Guide for Developing Quality Crash Modification Factors*, Report FHWA-SA-10-032, Federal Highway Administration, Washington, D.C. Available at [http://www.](http://www.cmfclearinghouse.org/resources_develop.cfm) [cmfclearinghouse.](http://www.cmfclearinghouse.org/resources_develop.cfm) [org/resources\\_](http://www.cmfclearinghouse.org/resources_develop.cfm) [develop.cfm.](http://www.cmfclearinghouse.org/resources_develop.cfm) Accessed July 2016.
-
-**41**
-
-Carter, D., R. Srinivasan, F. Gross, and F. Council (2012), R*ecommended Protocols for Developing Crash Modification Factors*, Prepared as part of NCHRP Project 20-07 (Task 314), Washington, D.C. Available at [http://www.](http://www.cmfclearinghouse.org/resources_develop.cfm) [cmfclearinghouse.](http://www.cmfclearinghouse.org/resources_develop.cfm) [org/resources\\_](http://www.cmfclearinghouse.org/resources_develop.cfm) [develop.cfm.](http://www.cmfclearinghouse.org/resources_develop.cfm) Accessed July 2016.
-
-**42**
-
-Elvik, R. (2011a), Assessing Causality in Multivariate Accident Models, *Accident Analysis and Prevention*, Vol. 43, pp. 253-264.
-
-**43**
-
-Elvik, R. (2011a), Assessing Causality in Multivariate Accident Models, *Accident Analysis and Prevention*, Vol. 43, pp. 253-264.
-
-safety. Truly experimental studies are not common in road safety partly because of potential liability considerations (i.e., a random selection may result in an agency being held liable for failing to treat some sites that have demonstrated high crash history). Observational studies are more common in countermeasure evaluations because most transportation agencies prioritize installation sites based on some kind of past safety performance (see Step 1, Network Screening).
-
-Observational studies of countermeasures can be broadly classified into cross-sectional studies and before-after studies. In cross-sectional studies, an analyst compares a group of sites with a certain feature to a group of sites without that feature. For example, an analyst might compare the safety performance of a group of stop-controlled intersections to that of a group of yield-controlled intersections to determine the effect of the type of traffic control on crashes. Cross-sectional studies can also be thought of as "with/without" studies. In before-after studies, an analyst takes a group of sites and compares the safety performance in the period before a countermeasure is implemented to the period after the countermeasure is implemented. For example, in a before-after study, an analyst could evaluate the effect of converting a stop-controlled intersection to a roundabout by comparing safety data before the roundabout conversion to the safety data afterwards.
-
-CMFs that result from crosssectional studies are not considered to be as robust as those resulting
-
-from a before-after study. In a typical before-after study, an analyst deals with same roadway unit located in a particular place, most likely used by the same road users during the before and after period. Since most of these factors can be assumed to be constant or almost constant in the before and after periods, they are less likely to cause significant biases. On the other hand, "cross-sectional studies compare different roads, used by different road users, located at different places and subject to different weather conditions. Besides, these roads will differ in very many other ways that are not measured."**<sup>43</sup>** However, there are issues in both types of studies that need to be addressed, and they are briefly discussed below.
-
-## **Cross sectional studies**
-
-Analysts use cross-sectional studies to compare the safety of a group of sites with a feature with the safety of a group of sites without that feature. The resulting CMF can be derived by taking the ratio of the average crash frequency of sites with the feature to the average crash frequency of sites without the feature. For this method to work, the two groups of sites should be similar in their characteristics except for the feature. In practice, this is difficult to accomplish and multiple variable regression models are used. These cross-sectional models are also called SPFs. The coefficients of the variables from these equations are used to estimate the CMF associated with a treatment.
-
-Guidance from FHWA on developing CMFs says that "the basic issue with the cross-sectional design is
-
-### **Using cross-sectional modeling to calculate a CMF for widening shoulders**
-
-A CMF can be obtained from a cross sectional model. Suppose the intent is to estimate the CMF for shoulder width based on the following SPF, which was
-
-Y = exp [ 
-$$0.8727 + 0.4414 \times ln(\frac{AADT}{10000}) + 0.4293 \times (\frac{AADT}{10000}) - 0.0164 \times SW$$
-]
-
-Where, AADT is the annual average daily traffic and SW is the width of the paved shoulder in feet. If the intent is to estimate the CMF of changing the shoulder width from three to six feet, then the CMF can
-
-estimated to predict the number of crashes per mile per year on rural two-lane roads in mountainous roads with paved shoulders (Appendix B of Srinivasan and Carter, 2011**<sup>44</sup>**):
-
-$$+) + 0.4293 \times (\frac{AADT}{10000}) - 0.0164 \times SW$$
-
-be estimated as the ratio of the predicted number of crashes when the shoulder width is six feet to the predicted number of crashes when the shoulder width is three feet:
-
-$$\text{CMF} = \frac{\exp \left[ \ 0.8727 + 0.4414 \times \ln \left( \frac{\text{AADT}}{10000} \right) + 0.4293 \times \left( \frac{\text{AADT}}{10000} \right) - 0.0164 \times 6 \ \right]}{\exp \left[ \ 0.8727 + 0.4414 \times \ln \left( \frac{\text{AADT}}{10000} \right) + 0.4293 \times \left( \frac{\text{AADT}}{10000} \right) - 0.0164 \times 3 \ \right]}$$
-
-This ratio simplifies to:
-
-$$CMF = exp[-0.0164 \times (6-3)] = 0.952$$
-
-This CMF of 0.952 indicates that changing the shoulder width from three to six feet would be expected to reduce crashes (since the CMF is less than 1.0). Specifically, the expected change in crashes would be a 4.8% reduction (1.0 – 0.952 x 100 = 4.8).
-
-However, it is important to recognize that this CMF of 0.952 is the midpoint in a range of possible values (i.e., the confidence interval). This range can be calculated by using the standard deviation of the CMF. In order to estimate the standard deviation, the standard error of the coefficient of SW is needed, which was reported to be 0.0015 in the original study. The high and low ends of the confidence interval are calculated using -0.0164+0.0015, and then using -0.0165-0.0015, and the difference between the two is divided by two. The equation is given below:
-
-StDev(CMF) = 
-$$\frac{\exp \left[-0.0164 + 0.0015 \times (6-3)\right] - \exp \left[-0.0164 - 0.0015 \times (6-3)\right]}{2} = 0.004$$
-
-The approximate 95% confidence interval for the CMF is (0.952-1.96×0.004, 0.952+1.96×0.004), which translates to a range of 0.944 to 0.960. Since the entire 95% confidence interval is below 1.0, the CMF is statistically significant, thereby indicating that widening the shoulder from three to six feet is very likely to reduce crashes.
-
-that the comparison is between two distinct groups of sites. As such, the observed difference in crash experience can be due to known or unknown factors, other than the feature of interest. Known factors, such as traffic volume or geometric characteristics, can be controlled for in principle by estimating a multiple variable regression model and inferring the CMF for a feature from its coefficient. However, the
-
-issue is not completely resolved since it is difficult to properly account for unknown, or known but unmeasured, factors. For these reasons, caution needs to be exercised in making inferences about CMFs derived from crosssectional designs. Where there are sufficient applications of a specific countermeasure, the before-after design is clearly preferred."**<sup>45</sup>**
-
-**44**
-
-Srinivasan, R. and D. Carter (2011), *Development of Safety Performance Functions for North Carolina*, Report FHWA/NC/2010- 09, Submitted to NCDOT, December 2011.
-
-**45**
-
-Gross, F., B. Persaud, and C. Lyon (2010), *A Guide for Developing Quality Crash Modification Factors*, Report FHWA-SA-10-032, Federal Highway Administration, Washington, D.C. Available at [http://www.](http://www.cmfclearinghouse.org/resources_develop.cfm) [cmfclearinghouse.](http://www.cmfclearinghouse.org/resources_develop.cfm) [org/resources\\_](http://www.cmfclearinghouse.org/resources_develop.cfm) [develop.cfm.](http://www.cmfclearinghouse.org/resources_develop.cfm) Accessed July 2016.
-
-#### **46**
-
-Gooch, J.P., Gayah, V.V., and Donnell, E.T. (2016), Quantifying the Safety Effects of Horizontal Curves on Two-Way, Two-Lane Rural Roads, *Accident Analysis and Prevention*, Vol. 92, pp. 71-81.
-
-#### **47**
-
-Holmes, W.M., (2013). *Using Propensity Scores in Quasi-Experimental Designs.* SAGE Publications.
-
-#### **48**
-
-Wood, J., Porter, R., (2013). Safety impacts of design exceptions on non-freeway segments. *Transport. Res. Rec.: J. Transport.* Res. Board 2358, 29–37.
-
-#### **49**
-
-Gross, F., B. Persaud, and C. Lyon (2010), *A Guide for Developing Quality Crash Modification Factors*, Report FHWA-SA-10-032, Federal Highway Administration, Washington, D.C. Available at [http://](http://www.cmfclearinghouse.org/resources_develop.cfm) [www.cmfclearing](http://www.cmfclearinghouse.org/resources_develop.cfm)  [house.org/](http://www.cmfclearinghouse.org/resources_develop.cfm) [resources\\_ develop.](http://www.cmfclearinghouse.org/resources_develop.cfm) [cfm.](http://www.cmfclearinghouse.org/resources_develop.cfm) Accessed July 2016.
-
-#### **50**
-
-See next page.
-
-![](_page_131_Picture_10.jpeg)
-
-One way to account for some of the limitations of cross-sectional regression models is to use the propensity scores-potential outcome method. This method uses the "individual traits of a site to calculate its propensity score, defined as a measure of the likelihood of that site receiving a specific treatment. Sites with and without the treatment are then matched based on their propensity scores."**<sup>46</sup>** The matched data are then used to estimate a cross sectional regression model. The propensity score method has been shown to reduce selection bias by accounting for the non-random assignment of treatment sites.**<sup>47</sup>** Recently, the propensity score method is starting to be used in place of traditional cross-sectional methods to conduct evaluations.**<sup>48</sup>**
-
-Other types of cross-sectional methods include case control and cohort methods. "Case-control studies select sites based on outcome status (e.g., crash or no crash) and then determine the prior treatment (or risk factor) status within each outcome group."**<sup>49</sup>** Another critical component of many case-control studies is the matching of cases with controls in order to control for the effect of confounding factors. In cohort studies, sites are assigned to a particular cohort based on current treatment status and followed over time to observe exposure and event frequency. One cohort may include the treatment and the other may be a control group without the treatment. The time to a crash in these groups is used to determine a relative risk, which is the percentage change in the probability of a crash given the treatment.**<sup>50</sup>**
-
-## **Before after studies**
-
-An analyst can use a before-after study to evaluate a countermeasure by comparing the crashes before the countermeasure was installed to the crashes after installation. This study design is advantageous because the only change that has occurred at the site is the countermeasure installation (assuming the analyst has researched the site histories to discard any sites at which other significant changes occurred).
-
-There are issues for consideration with this study design as well. The analyst must know when the countermeasure was installed and must have data, such as crash and traffic volume, available in the before and after periods. For high-cost, high-profile countermeasures, such as road widening or traffic signal installation, the installation records will be readily available. However, for low-cost countermeasures, such as sign installations, there may be little to no documentation on when they were installed.
-
-The analyst might simply compare the number of crashes per year before the countermeasure to the number of crashes per year after the countermeasure, known as a simple or naïve before-after evaluation. Although a simple before-after evaluation can be done easily using only crash data, it is prone to significant bias. One of the most influential biases for this method is the possible bias due to RTM. As discussed earlier, RTM describes a situation in which crash rates are artificially high during the before period and would have been reduced even without an improvement to the site. Programs focused on highhazard locations are vulnerable to the RTM bias. This potential bias is greatest when sites are chosen because of their extreme value (e.g., high number of crashes or crash rate) in a given time period. A simple before-after evaluation has a high likelihood of showing a much greater benefit from the safety treatment than actually occurred.
-
-As discussed earlier under the network screening section, the EB method is one of the methods that has been found to be effective in dealing with the possible bias due to RTM. The following steps are needed to conduct an EB before-after evaluation:
-
-- **1. IDENTIFY** a reference group of sites without the treatment, but similar to the treatment sites in terms of the major factors that affect crash risk including traffic volume and other site characteristics. One way to identify a reference group that is similar to the treatment is to use the propensity score method discussed earlier under crosssectional studies.
-- **2.** Using data from the reference site, **ESTIMATE** SPFs using data from the reference sites relating crashes to independent variables, such as traffic volume and other site characteristics. As discussed in the following steps, SPFs are used in the EB method to predict the average number of crashes based on AADT and site characteristics. By selecting the reference group to be similar to the treatment group in terms of the major risk factors, we can reduce the possible bias due to confounding on these predictions.
-
-Carter, D., R. Srinivasan, F. Gross, and F. Council (2012), *Recommended Protocols for Developing Crash Modification Factors*, Prepared as part of NCHRP Project 20-07 (Task 314), Washington, D.C. Available at [http://www.](http://www.cmfclearinghouse.org/resources_develop.cfm) [cmfclearinghouse.](http://www.cmfclearinghouse.org/resources_develop.cfm) [org/resources\\_](http://www.cmfclearinghouse.org/resources_develop.cfm) [develop.cfm.](http://www.cmfclearinghouse.org/resources_develop.cfm) Accessed July 2016.
-
-#### **Using an EB before-after evaluation to develop a CMF for signal phasing changes**
-
-**51**
-
-Srinivasan, R. et al., *Evaluation of Safety Strategies at Signalized Intersections*, NCHRP Report 705, Washington, DC.
-
-This example is an illustration of an EB before-after evaluation that was conducted as part of NCHRP Project 17-35.**<sup>51</sup>** The countermeasure was a change from permissive to protected-permissive left turn phasing at signalized intersections in North Carolina. Data from twelve locations were used in this evaluation. A reference group of 49 signalized intersections was identified for the development of SPFs. The analysis looked at total intersection crashes, injury and fatal crashes, rear end crashes, and left turn opposing through (LTOT) crashes. In this example, only the data for LTOT crashes will be used.
-
-The SPF for LTOT crashes based on the data from the reference group was:
-
-**LTOT crashes/intersection/year = e-0.3696 (MajAADT/10000) 0.5564 e0.6585×(MinAADT / 10000)**
-
-Where, MajAADT is the major road AADT and the MinAADT is the minor road AADT. The overdispersion parameter (k) for this SPF was 0.5641.
-
-In the first site of this study, there were 10 observed crashes in the before period (Xb ), and the predicted number of crashes from the SPF in the before period was 5.535 (Pb ). The formula for obtaining the EB estimate of the expected crashes in the before period (EBb ) is as follows:
-
-$$EB_b = w \times P_b + (1 - w) \times X_b$$
-
-Where, Xb is the observed crashes in the before period, and w is the EB weight that is calculated as follows:
-
-$$w = \frac{1}{(1 + k \times P_b)}$$
-
-Where, k is the overdispersion parameter for the estimated SPF.
-
-In this example:
-
-$$W = \frac{1}{(1 + 0.5641 \times 5.535)} = 0.243$$
-
-The EB estimate of the crashes in the before period (EBb ) = 5.535\*0.243 + 10\*(1-0.243) = 8.917 crashes.
-
-The predicted number of crashes from the SPF in the after period was 11.391 (Pa ).
-
-The formula for the EB expected number of crashes that would have occurred in the after period had there been no countermeasure is given by:
-
-$$\pi = EB_b \times (P_a/P_b)$$
-
-In this example, the EB expected number of crashes in the after period had the countermeasure not been implemented (π) is equal to:
-
-**8.917 × ( 11.391 / 5.535 ) = 18.350 crashes**
-
-The variance of this expected number of crashes is also estimated in this step:
-
-$$Var(\pi) = \pi \times (P_a/P_b) \times (1 - w)$$
-
-Where, Pa is the SPF predictions in the after period. In this example, the variance of ππ is estimated as follows:
-
-Var(
-$$\pi$$
-) = 18.350 × (11.391 / 5.535)  
-× (1 - 0.243) = 28.603
-
-This process was repeated for all 12 sites. Based on the data for all the 12 sites that were used in the evaluation, the actual crashes in the after period were 115, the EB expected crashes had the countermeasure not been implemented was 131.933 with a variance of 140.080.
-
-*(continued on next page)*
-
-The formula for the CMF and its standard deviation (StDev) are as follows:
-
-$$CMF = \frac{\frac{\lambda_{sum}}{\pi_{sum}}}{1 + \frac{Var(\pi_{sum})^2}{\pi_{sum}^2}}$$
-
-StDev(CMF) =
-$$\frac{\text{CMF}^2 \frac{\text{Var}(\lambda_{\text{sum}})}{\lambda_{\text{sum}}^2} + \frac{\text{Var}(\pi_{\text{sum}})}{\pi_{\text{sum}}^2}}{(1 + \frac{\text{Var}(\pi_{\text{sum}})}{\pi_{\text{sum}}^2})^2}$$
-
-Where, λsum is the total number of crashes that occurred in the after period, for all the treated sites in the sample, πsum is the total number of expected crashes in the after period had the countermeasure not been implemented, and Var represents the variance. Since crashes are assumed to be Poisson distributed, Var(λsum) is usually assumed to be equal to λsum. So, (Var(λsum))/ (λsum ) will be equal to 1/λsum.
-
-In this example, the overall CMF was calculated as:
-
-$$CMF = \frac{\frac{115}{131.933}}{1 + \frac{140.080}{131.933^2}} = 0.865$$
-
-This CMF of 0.865 indicates that the countermeasure (changing from permissive to protected-permissive left turn phasing) would decrease crashes, since the CMF is less than 1.0. It would be expected to decrease crashes by 13.5% (1.0 – 0.865 x 100 = 13.5).
-
-Again, it is important to recognize that the
-
-CMF is the midpoint of a range of possible values (i.e., the confidence interval). The standard deviation of the CMF can be estimated as follows:
-
-StDev(CMF) =
-$$\frac{0.865^{2} \left(\frac{1}{115} + \frac{140.080}{131.933^{2}}\right)}{\left(1 + \frac{140.080}{131.933^{2}}\right)^{2}} = 0.111$$
-
-Based on this standard deviation of the CMF, the approximate 95% confidence interval is (0.865-1.96×0.111, 0.865+1.96×0.111), which translates to a range of 0.647 to 1.083. Since this confidence interval includes values greater than 1.0, the CMF is not statistically different from 1.0 at the 95% confidence level. This indicates that there is less confidence that this countermeasure will reduce crashes compared to a countermeasure whose CMF is significantly different from 1.0.
-
-Gross, F., B. Persaud, and C. Lyon (2010), *A Guide for Developing Quality Crash Modification Factors*, Report FHWA-SA-10-032, Federal Highway Administration, Washington, D.C. Available at [http://www.](http://www.cmfclearinghouse.org/resources_develop.cfm) [cmfclearinghouse.](http://www.cmfclearinghouse.org/resources_develop.cfm) [org/resources\\_](http://www.cmfclearinghouse.org/resources_develop.cfm) [develop.cfm.](http://www.cmfclearinghouse.org/resources_develop.cfm) Accessed July 2016.
-
-#### **53**
-
-Carter, D., R. Srinivasan, F. Gross, and F. Council (2012), *Recommended Protocols for Developing Crash Modification Factors*, Prepared as part of NCHRP Project 20-07 (Task 314), Washington, D.C. Available at [http://www.](http://www.cmfclearinghouse.org/resources_develop.cfm) [cmfclearinghouse.](http://www.cmfclearinghouse.org/resources_develop.cfm) [org/resources\\_](http://www.cmfclearinghouse.org/resources_develop.cfm) [develop.cfm.](http://www.cmfclearinghouse.org/resources_develop.cfm) Accessed July 2016.
-
-#### **54**
-
-B. Persaud and C. Lyon (2007), Empirical Bayes Before After Studies: Lessons Learned from Two Decades of Experience and Future Directions, *Accident Analysis and Prevention*, 39(3):546-55.
-
-- **3.** In estimating SPFs, **CALIBRATE** annual SPF multipliers to account for the temporal effects (e.g., variation in weather, demography, and crash reporting) on safety. The annual SPF multiplier is the ratio of the observed crashes to the predicted crashes from the SPF. In using the annual SPF multipliers from the SPFs to account for temporal effects, it is assumed that the trends in the crash counts are similar in the treatment and reference groups.
-- **4. USE** the SPFs, annual SPF multipliers, and data on traffic volumes for each year in the before period for each treatment site to estimate the number of crashes that would be predicted for the before period in each site.
-- **5. CALCULATE** the EB estimate of the expected crashes in the before period at each treatment site as the weighted sum of the actual crashes in the before period and predicted crashes from Step 4.
-- **6.** For each treatment site, **ESTIMATE** the product of the EB estimate of the expected crashes in the before period and the SPF predictions for the after period divided by these predictions for the before period. This is the EB expected number of crashes that would have occurred had there been no treatment. The variance of this expected number of crashes is also estimated in this step.
-
-The expected number of crashes without the treatment along with the variance of this parameter and the number of reported crashes after the treatment is used to calculate the CMF and the standard deviation of the CMF. This procedure is repeated for each treated site. Once CMFs have been calculated for each individual site in a group of treated sites, the CMFs can be combined to calculate the overall effectiveness of the countermeasure. More details on this procedure are provided in the previously mentioned guidance documents.**52,53**
-
-In some cases, treatments may be installed system-wide for a particular type of facility. For example, a jurisdiction may decide to increase the retroreflectivity of all their stop signs. Since sites are not specifically selected based on their crash history, the bias due to RTM is minimal. However, it is still necessary to account for changes in traffic volume and other trends. To evaluate the safety of such installations, an EB method could still be used, and while a reference group is not necessary, a comparison group is necessary in order to account for trends. SPFs can be estimated using the before-data from the treatment sites and these SPFs can be used to account for changes in traffic volumes. In addition, SPFs could be estimated for a group of comparison sites and the annual factors from these SPFs can be used to account for trends. Further details about such evaluations can be found elsewhere.**<sup>54</sup>**
-
-## **System-Level Safety Management**
-
-System-level safety management involves addressing road safety issues that affect the broad transportation system, as opposed to treating specific high priority sites. The size and scope of the transportation system depends on the agency or jurisdiction. For a State DOT, the transportation system would consist of all Stateowned roads, signals, bridges, and other features across the entire State, whereas the transportation system for a town would consist of a much smaller area and roadway network. Road safety at a systemlevel often has to do with policies, whether design policies for the construction and operation of roads and intersections, driver policies for licensing, or vehicle policies that require certain safety technologies. Other system-level efforts would include broad media or enforcement campaigns.
-
-Recall that Chapter 10 presented road safety management in terms of three general components:
-
-- J **Identifying safety problems**
-- J **Developing potential safety strategies**
-- J **Selecting and implementing strategies**
-
-This chapter will discuss how each of these components can be addressed at a system-level.
-
-#### **System-wide vs. systemic?**
-
-System-wide is a general term that refers to treating safety issues across an entire transportation system using policies or campaigns. Systemic is a more specific term that refers to identifying a subset of a transportation system based on risk factors and implementing safety efforts that address the particular characteristics of that subset. See page 4-41 for more discussion on the systemic approach.
-
-## **Identifying safety problems**
-
-To identify safety problems on a system-level, safety professionals analyze safety data that apply to the entire jurisdiction. They examine crash data and link crashes to other safety data to determine the nature and locations of safety problems. Problem identification on a system-level involves identifying crash trends and using risk-based methods to prioritize safety efforts.
-
-## **Identifying crash type trends**
-
-Safety professionals can examine crash types and contributing factors to determine the nature of crashes within their agency's jurisdiction. This type of examination may reveal crash trends, such as those related to alcohol involvement, seat belt use, driver age, or vulnerable road users. For example, crash data might show that crashes involving unbelted occupants have been increasing over the past several
-
-years, or it might show that the number of crashes involving unbelted occupants is significantly higher than other nearby agencies, such as adjacent counties or States. This would lead an agency to consider how to increase seat belt use, perhaps through media campaigns, increased enforcement, or educational campaigns in schools. This type of agency-wide analysis of crash data can demonstrate broad scale trends that need to be addressed through broad scale efforts.
-
-It is important that safety professionals are specific when identifying safety problems in crash trends. For example, "crashes involving teen drivers" is not defined well enough, because the causes of crashes for 16 year-olds is markedly different from those of older, more experienced teens. Crashes in which teens are victims of other drivers' errors require different solutions from those where the teen was at fault. Similarly, the cause of crashes depends greatly on the specific time, place and driving environment. A better target crash type would be "crashes occurring between 7-9 a.m. involving 16-year old drivers."
-
-## **Example of safety problem identification in State Highway Safety Plans**
-
-A good example of identifying safety problems from crash type trends can be seen in how States develop **strategic highway safety plans** (SHSPs). The development of a SHSP involves the identification of safety problems on the State and local roads. A State analyzes safety data to determine the priorities, referred
-
-### **Florida's emphasis on motorcyclist safety**
-
-The State of Florida examined its crash data to identify emphasis areas in the development of their SHSP in 2012. One area that continued to be a focus was motorcyclist safety. The data indicated that crashes involving motorcycles had decreased somewhat during the time period analyzed (2006 to 1010) but remained a significant portion of the crashes on Florida roads. Florida's safety professionals recognized that since Florida hosts numerous national motorcycle events, the state's SHSP should have motorcycle safety as an emphasis area.
-
-![](_page_137_Figure_6.jpeg)
-
-**FIGURE 4-8.** Florida motorcycle crash trend 2006-2010
-
-to as emphasis areas. The analysis can involve an examination of crash proportions between categories of crashes, crash trends, crash severity (e.g., fatal and serious injury), or more advanced crash modeling techniques. As presented in the call-out boxes, Ohio and Florida conducted analyses of their crash data and identified areas of concern.
-
-#### **Strategic highway safety plan**
-
-A statewidecoordinated safety plan that provides a comprehensive framework for reducing highway fatalities and serious injuries on all public roads.
-
-### **Ohio's emphasis on older driver safety**
-
-Ohio developed a SHSP in 2014 in which they identified fifteen emphasis areas. One of the emphasis areas was the safety of older drivers (65 and older). The crash data showed that older driver-related crashes accounted for 18% of highway deaths and 16% of serious injuries. They recognized that these numbers would likely increase with an aging population. The crash trends over the time period examined (2003 to 2013) showed a slight upward trend to older driver serious injuries and a slight downward trend to older driver fatalities. This contrasted to other types of crashes that experienced significant declines. These reasons motivated Ohio to make older driver safety an emphasis area in their 2014 SHSP.
-
-![](_page_138_Picture_2.jpeg)
-
-## **Risk based prioritization – the systemic approach**
-
-Chapter 11 presented various methods of selecting high priority sites through a process of network screening based on crash data. Many safety professionals recognize that this process of identifying specific locations using past crash data does not adequately address the fact that there may be locations that pose a safety threat but have not yet experienced many (or any) crashes. This recognition led to an increased use of risk-based prioritization, also called the **systemic** approach.**<sup>55</sup>**
-
-In this approach, a transportation agency identifies priority locations based on the presence of risk factors rather than crashes. In the medical field, doctors pay attention to factors that may elevate a person's risk for disease. A history of smoking, poor eating habits, and a lack of exercise
-
-may indicate a higher-than-average risk for heart disease, even if the person has not yet experienced heart problems. Similarly, a section of road with certain characteristics, such as sharp curvature, old pavement, or lack of visibility, may be at risk for run-off-road crashes, even if none have occurred yet. Agencies can be proactive in their approach to safety management by identifying and treating these sites before crashes occur. These treatments are often low cost, such as signs and markings, so many systemic-identified locations can be treated within an agency's limited budget.
-
-An agency using the systemic approach selects the focus crash type(s) and identifies risk factors associated with the focus crashes. Risk factors are site characteristics (e.g., design and operational features) that are common across
-
-#### **Systemic**
-
-The process of identifying road or intersection characteristics that increase the risk of crashes and selecting locations for safety treatment based on the presence of these risk factors.
-
-**55**
-
-[http://safety.fhwa.](https://safety.fhwa.dot.gov/systemic/) [dot.gov/systemic/](https://safety.fhwa.dot.gov/systemic/)
-
-locations with the focus crash type(s). The agency can identify risk factors by analyzing crash data from their jurisdiction or by reviewing previous research studies. Using the list of risk factors as a guide, the agency identifies a list of sites with those specific characteristics, and then develops targeted treatments to address or mitigate the specific risk factors. The agency can apply crash history and other thresholds to reduce the list of sites based on available resources and program objectives.
-
-The systemic approach has two attractive features. First, an agency can employ the systemic approach even for roads or intersections where crash data are not fully available (e.g., where location accuracy is questionable or underreporting is a problem). For instance, locating crashes accurately and precisely in rural areas or on non-State owned urban roads can be difficult. Second, the systemic approach is useful for treating safety issues where crashes are highly dispersed, such as on rural or low volume roads. Specifically, agencies can use the systemic approach to address existing and potential safety issues across a large portion of the network (e.g., shoulder rumble strips on all rural, two-lane roads with a certain shoulder width and traffic volume level).
-
-## **Developing potential safety strategies**
-
-After safety professionals analyze data and identify safety problems, they must develop potential strategies to address the problems. It is important to engage safety stakeholders and other partners
-
-when selecting potential strategies as they may provide unique perspectives. Safety professionals should seek to involve local officials, citizens, and safety partners to produce effective multidisciplinary strategies. For example, addressing a particular safety problem with law enforcement and education can be far more economical than implementing a multimilliondollar engineering fix. On the other hand, law enforcement tends to be effective only during the time in which it is active, so a more permanent engineering measure may be needed in some cases. It is often the case that a combination of strategies is necessary to effectively address the multitude of contributing factors.
-
-On a system level, agencies must think broadly across the many disciplines represented by those who have a stake in road safety. Potential strategies might address infrastructure policies and practices (e.g., design standards, speed limits, etc.) or they may be directed at specific population focused efforts (e.g., seat belt laws, helmet laws, young driver restrictions, etc.).
-
-Just as the identification of problems was based on safety data, so too must the development and selection of strategies be driven by the data. If an agency identified concerning trends in certain types of crashes, then they should further examine the crash data to determine how best to address the safety problem. For example, Figure 4-9 shows an example of alcohol-related crashes where an agency identified a spike in frequency (or high pole) of crashes occurring near 2:00 AM. Further examination revealed that bars in
-
-#### **Case Study: Systemic Analysis in Thurston County, Washington**
-
-The Thurston County Public Works Department in Washington conducted a systemic safety analysis for their road network. Based on a review of severe crashes, Thurston County decided to focus on roadway departure crashes in horizontal curves on arterial and collector roadways when it found that:
-
-- **1.** Most of the severe crashes occurred due to roadway departures, and that
-- **2.** 81% of the severe curve /roadway departure crashes occurred on arterial and collector roads. Because this effort coincided with ongoing efforts to identify and upgrade warning signs for horizontal curves on their County road system, Thurston County chose to focus on currently signed horizontal curves.
-
-Thurston County accessed an inventory of their roads and intersections through a database maintained by the Statewide County Road Advisory Board. In addition, Thurston County assembled crash data for the 2006-to-2010 timeframe from the Washington State DOT crash database. They linked the road, intersection, and curve data with crash data and used these data to identify risk factors. Thurston County assembled a list of 19 potential risk factors and then performed a descriptive statistics analysis to identify 9 risk factors for use in screening and prioritizing candidate locations. The identified risk factors were:
-
-- J **Roadway class of major rural collector**
-- J **Presence of an intersection**
-- J **Traffic volume of 3,000 to 7,500 annual average daily traffic**
-- J **Edge clearance rating of 3**
-- J **Paved shoulders equal to or greater than 4 feet in width**
-- J **Presence of a vertical curve**
-- J **Consecutive horizontal curves (windy roads)**
-
-- J **Speed differential between posted approach speed and curve advisory speed of 0, 5, and 10 miles per hour**
-- J **Presence of a visual trap (a minor road on the tangent extended)**
-
-Thurston County decided that a risk factor could be worth one point or a one-half point. Those factors present in at least 30% of the severe (fatal and injury) crashes and overrepresented by at least 10% (when comparing the proportion of all locations with the proportion of severe crash locations) were used as a guideline to have a high confidence and assigned one point in the risk assessment process. The risk factors that had a lower confidence in their relative data were assigned one-half point.
-
-Thurston County then tallied the number of risk factors present for each of the curves. The risk factor totals for the ten curves with the highest scores ranged from 4.5 to 6.0. All 270 signed curves were prioritized for potential low cost safety investments. They identified the following low-cost, low-maintenance countermeasures with documented crash reductions to implement at the selected locations:
-
-- J **Traffic signs** enhanced curve delineation with the addition of chevrons and larger advance warning signs
-- J **Pavement markings** dotted extension lines at intersections and recessed raised pavement markers
-- J **Shoulder rumble strips**
-- J **Roadside improvements** object removal, guardrail, and slope flattening
-
-Systemic analysis provided Thurston County a proactive, data-driven, and defensible approach to identifying curves for improvement prior to a severe crash occurring, rather than reacting after an incident has occurred.**<sup>56</sup>**
-
-**56**
-
-"Thurston County, Washington, Public Works Department Applies Systemic Safety Project Selection Tool" FHWA-SA-13-026, June 2013. [http://](https://safety.fhwa.dot.gov/systemic/) [safety.fhwa.dot.gov/](https://safety.fhwa.dot.gov/systemic/) [systemic/](https://safety.fhwa.dot.gov/systemic/)
-
-![](_page_141_Figure_0.jpeg)
-
-**FIGURE 4-9.** Example of "high pole" in crash data *(Source: NCHRP Report 501)*
-
-that jurisdiction closed at 2:00 AM. This could lead to potential strategies, such as increased enforcement of impaired driving at that time of night and in the vicinity of bars.
-
-It is also important to use the data to determine the necessary scope of the intervention. If the data show that the problem exists year-round, then the solution needs to match that. For example, a "safe ride program" for drinkers to get home on New Year's Eve is not going to significantly impact the problem of impaired driving overall.
-
-Critical thinking is needed to develop effective solutions to the safety problems at hand. Analysts should look for characteristics of crash trends that could be addressed by practical strategies. An NCHRP report on an integrated safety management process states that safety professionals should use safety data to perform "further analyses of those characteristics that are found to be significantly or practically over-represented on a percentage or rate basis."**<sup>57</sup>** The report gives a set of guidelines to be considered in analyzing crash data to identify trends and develop potential safety strategies:
-
-#### **Consider How Specific Behaviors Influence the Safety Problem**
-
-Safety professionals must identify and target road user behaviors that contribute to the identified safety problem. The target behavior should be specific. For example, "safe driving" is not a specific behavior that can be changed because it involves a number of different behaviors. However, "speeding on Main Street" is a specific behavior that can be targeted. It is also important to consider the factors influencing this behavior. Why are people speeding on Main Street? Which social, cultural, or environmental factors are influencing this behavior? Does it vary by time of day or week, perhaps reflecting the kind of drivers who are speeding?
-
-- **1. ASK** the questions, "Is this information sufficient for action item development? If not, what further information is needed to act on this finding?"
-- **2. CONSIDER** cross tabulations of two variables within the subset of data that pertains to the activities under consideration if one or more of the following types of conditions hold:
-  - J If the activities are time critical (e.g., all selective
-
-**57**
-
-Bahar, G., M. Masliah, C. Mollett, and B. Persaud, Integrated Safety Management Process, National Cooperative Highway Research Program, Report 501, Transportation Research Board of the National Academies, Washington, D.C., 2003
-
-enforcement strategies), perform a time-of-day by day-of-the-week analysis. As an example, alcoholrelated crashes will likely be over-represented in the early morning and on weekend days. A logical approach is to perform a cross tabulation of time-of day by day-of-theweek to determine the best times and days for driving under the influence (DUI) selective enforcement. The goal of the procedure at this point is to determine additional details (who, what, where, when, and how) for those crash types identified by the analyses performed to this point.
-
-- J If the over-represented variable is not constant over all crash severities, cross tabulate the variable by severity (e.g., nighttime, rural, and older-driver crashes tend to be more severe).
-- J If the activities can be targeted to geographic location, age group, gender, race, or any other demographic factor within the crash records, consider these variables for cross tabulation with other overrepresented variables.
-- **3. CONSIDER** creating subsets of the data for additional comparisons where activities are to be targeted to a particular subgroup of the population. For example, insight into a graduated driver's license strategy can be obtained by comparing 16-yearold causal driver crashes against
-
-17- to 20-year-old causal driver crashes. As another example, insight into youth alcohol enforcement activities can be attained by comparing alcohol-related crashes of 16 to 20-year-old causal drivers against alcohol-related crashes of their 21-year-old and older counterparts. Each of these types of comparisons can show differences between the respective subpopulations.
-
-- **4. USE** the results of each analysis to determine what further information is needed before the best decision can be made, and repeat the analysis with the additional information.
-- **5. PERSIST** and maintain a thread of evidence until the information available has been exhausted. If the information generated indicates a significant factor, create further subsets of the data (e.g., youth-pedestrian crashes), and repeat the entire analysis.
-- **6. REJECT** any strategies and activities at this point that the data clearly show to be counterproductive (i.e., activities that will consume resources that could be better applied elsewhere). Maintain a list of all potential strategies and corresponding activities that will be subjected to further analysis in the optimization procedure.**<sup>58</sup>**
-
-Many system-level safety strategies focus on behaviors of drivers and other road users. Resources like Countermeasures That Work provide a useful listing of potential safety strategies for system-level safety management.**<sup>59</sup>** The excerpt from *Countermeasures That Work* in
-
-**58**
-
-Bahar, G., M. Masliah, C. Mollett, and B. Persaud, Integrated Safety Management Process, National Cooperative Highway Research Program, Report 501, Transportation Research Board of the National Academies, Washington, D.C., 2003
-
-**59**
-
-Goodwin, A., Thomas, L., Kirley, B., Hall, W., O'Brien, N., & Hill, K. *Countermeasures That Work: A Highway Safety Countermeasure Guide for State Highway Safety Offices*, Eighth edition, National Highway Traffic Safety Administration, Report No. DOT HS 812 202, Washington, DC, 2015.
-
-#### **FIGURE 4-10.** Potential Safety Strategies to Address Speeding and Aggressive Driving
-
-#### **1. Laws**
-
-| COUNTERMEASURE              | EFFECTIVENESS | COST | USE  | TIME  |
-|-----------------------------|---------------|------|------|-------|
-| 1.1 Speed limits            | †<br>    | \$   | High | Short |
-| 1.2 Aggressive driving laws |          | \$   | Low  | Short |
-
-- † When enforced and obeyed
-- **2. Enforcement**
-
-| COUNTERMEASURE                  | EFFECTIVENESS | COST        | USE       | TIME   |
-|---------------------------------|---------------|-------------|-----------|--------|
-| 2.1 Automated enforcement       |          | †<br>\$\$\$ | Medium    | Medium |
-| 2.2 High-visibility enforcement |          | \$\$\$      | ††<br>Low | Medium |
-| 2.3 Other enforcement methods   |          | Varies      | Unknown   | Varies |
-
-- † Can be covered by income from citations
-- †† For aggressive driving, but use of short-term, high-visibility enforcement campaigns for speeding is more widespread
-- **3. Penalties and Adjudication**
-
-| COUNTERMEASURE                    | EFFECTIVENESS | COST   | USE     | TIME   |
-|-----------------------------------|---------------|--------|---------|--------|
-| 3.1 Penalty types and levels      |          | Varies | High    | Low    |
-| 3.2 Diversion and plea agreements |          | Varies | Unknown | Varies |
-
-#### **4. Communications and Outreach**
-
-| COUNTERMEASURE                                   | EFFECTIVENESS | COST   | USE    | TIME   |
-|--------------------------------------------------|---------------|--------|--------|--------|
-| 4.1 Public Information<br>supporting enforcement |          | Varies | Medium | Medium |
-
-### **Effectiveness:**
-
-|  | Demonstrated to be effective by several<br>high-quality evaluations with consistent results                          |
-|-------|----------------------------------------------------------------------------------------------------------------------|
-|  | Demonstrated to be effective in certain situations                                                                   |
-|  | Likely to be effective based on balance of evidence<br>from high-quality evaluations or other sources                |
-|  | Effectiveness still undetermined; different methods of<br>implementing this countermeasure produce different results |
-|  | Limited or no high-quality evaluation evidence                                                                       |
-
-Figure 4-10 gives a list of potential strategies for addressing speedingrelated crashes, from either laws, enforcement, penalties and adjudication, or communications and outreach. The list also includes an indication of the effectiveness, cost, current usage, and time of each strategy, which are all important considerations when selecting safety strategies to implement.
-
-If the agency identifies safety problems from a systemic analysis, the potential safety strategies should address the types of crashes that were related to the roadway characteristic risk factors. These strategies may often be engineering improvements related to the risk factors. For example, if an examination of crash trends may highlight run-off-road crashes, and a systemic analysis would identify the type(s) of road on which run-off-road crashes are likely to occur. Table 4-12 shows a list of potential safety strategies that could be implemented for engineering treatments for a run-off-road crash problem. In a systemic approach, these engineering treatments would be implemented across some or all roads meeting the risk factors that increase the likelihood of run-offroad crashes.
-
-## **Example of system-level safety strategies in state highway safety plans**
-
-SHSPs provide many good examples of system-level strategies that address safety problems identified through analysis of crash and other safety data. The previous section showed how Ohio and Florida had identified safety priorities on older drivers and motorcyclists,
-
-### **Florida's strategies for motorcyclist safety**
-
-After identifying motorcyclist safety as an emphasis area in their 2012 SHSP, Florida identified a list of strategies to address motorcyclist safety. Example strategies include:
-
-- J **Promote personal protective gear and its value in reducing motorcyclist injury levels and increasing rider conspicuity**
-- J **Promote adequate rider training and preparation to new and experienced motorcycle riders by qualified instructors at Stateapproved training centers**
-- J **Incorporate motorcycle-friendly policies and practices into roadway design, traffic control, construction, operation, and maintenance**
-- J **Develop and implement communications strategies that target high-risk populations and improve public awareness of motorcycle crash problems and programs.**
-
-respectively. The SHSPs from these States also demonstrated the types of safety strategies each State intended to pursue to combat the safety problems in these areas.
-
-## **Selecting and implementing strategies**
-
-A transportation agency must determine which of the potential strategies they will implement to address the identified safety problems. Since system-level safety solutions can involve broad changes to policies, design practices, or jurisdiction-wide road user behavior, there are different issues to consider compared to implementing a safety countermeasure at a specific
-
-| OBJECTIVES                                                                | COUNTERMEASURES                                                                                                                                                     | RELATIVE<br>COST TO<br>IMPLEMENT<br>AND OPERATE | EFFECTIVE-<br>NESS |
-|---------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------|--------------------|
-| 15.1 A: KEEP<br>VEHICLES<br>FROM<br>ENCROACHING                           | 15.1A1: Install shoulder<br>rumble strips                                                                                                                           |                                                 | Tried              |
-| ON THE<br>ROADSIDE                                                        | 15.1 A2: Install edgelines<br>"profile marking", edgeline<br>rumble strips or modified<br>shoulder rumble strips on<br>section with narrow or no<br>paved shoulders | Low                                             | Experimental       |
-|                                                                           | 15.1 A5: Provide improved<br>highway geometry for<br>horizontal curves                                                                                              | High                                            | Proven             |
-|                                                                           | 15.1 A6: Provide enhanced<br>pavement markings                                                                                                                      | Low                                             | Tried              |
-|                                                                           | 15.1 A7: Provide skid<br>resistance pavement surfaces                                                                                                               | Moderate                                        | Proven             |
-| 15.1 B:<br>MINIMIZE THE<br>LIKELIHOOD<br>OF CRASHING<br>INTO AN           | 15.1 B1: Design safer slopes<br>and ditches to prevent<br>rollovers                                                                                                 | Moderate                                        | Proven             |
-| OBJECT OR<br>OVERTURNING<br>IF THE VEHICLE<br>TRAVELS OFF<br>THE SHOULDER | 15.1 B2: Remove/relocate<br>objects in hazardous locations                                                                                                          | Moderate<br>to High                             | Proven             |
-| 15.1 C: REDUCE<br>THE SEVERITY<br>OF THE CRASH                            | 15.1 C1: Improve design of<br>roadside hardware                                                                                                                     | Moderate<br>to High                             | Tried              |
-|                                                                           | 15.1 C2: Improve design and<br>application of barrier and<br>attenuation systems                                                                                    | Moderate<br>to High                             | Tried              |
-
-**TABLE 4-12.** Potential Safety Strategies for Run-Off-Road Crashes *(Source: NCHRP 500, Volume 6)*
-
-location. Many more people will be affected by the system-level changes. This carries great promise in that safety might be improved across an entire system, but it also carries unique challenges.
-
-Agencies will need to consider the following questions when selecting strategies to implement:
-
-- J **Safety effectiveness**  How likely will it address the safety problem?
-- J **Public acceptance**  How will the strategy be accepted by the public? What kind of marketing will be needed to communicate the intent and benefit of the strategy?
-- J **Stakeholders and partners** Which parties will need to be involved in implementing the strategy?
-- J **Cost efficiency** What kind of return on the dollar would be expected?
-- J **Time** How long will it take to implement the strategy?
-
-Communication is critically important for system-level safety strategies. Both the general public and road users affected by the strategy must understand the benefits. Other public agencies may need to integrate their efforts with the proposed safety strategy. Administrators, lawmakers, and other key decision-making personnel must understand how the strategy will improve road safety for their constituency and bring an overall financial benefit. Unit 5 provides more discussion on communication, marketing, and
-
-### **Ohio's strategies for older driver safety**
-
-Ohio identified three strategies to address the older driver emphasis area in their 2014 SHSP:
-
-- J Coordinate older driver messages developed by multi-agency communication committee.
-- J Create a comprehensive and coordinated outreach effort that educates older drivers and their caregivers on driving risks and remedies.
-- J Encourage roadway design and engineering measures that reduce the risks of traffic crashes for older drivers.
-
-outreach for agencies who seek to implement system-level safety strategies.
-
-Evaluating a system-level strategy (e.g., program or intervention) to determine its effectiveness is a critical but often overlooked step. The transportation agency in charge should evaluate the effect of the safety strategy using good quality data; ideally the same type of data that was used to identify the safety problem initially. If a program or intervention is not effective, the overseeing agency should consider why this might be the case. Can the program be improved, or should other approaches be considered instead? If successful, how can the intervention be institutionalized to ensure long term support (and therefore lasting change)? Finally, it is important to remember that success or failure in one location does not guarantee the same results at a different location.
-
-## **Example of System-Level Safety Management**
-
-The following provides an example of using system-level safety management to address a specific problem. This example demonstrates the three general components of safety management presented in this unit.
-
-### **1. Identify the safety problem.**
-
-County A noticed a large number of crashes involving 16-17 year old drivers occurring weekdays between 11:00am and 1:00pm. Neighboring counties have not experienced this problem. County officials coordinate with school district staff to tackle this issue.
-
-In exploring the problem, the officials discover that County A is the only jurisdiction that has an open campus lunch policy allowing students to leave school during their lunch period. Allowing teens to leave campus during lunch means there are many young, inexperienced drivers on the roads at the same time. They may be carrying additional passengers which research has established leads to an increased risk of a fatal crash.**60,61** The brief lunch period also results in pressure to get back in time for the next class. Combined, these factors lead to a risky driving situation and an increased risk of crashing.
-
-**61**
-
-**60**
-
-Chen, L., Baker, S.P., Braver, E.R., & Li, G. (2000). Carrying Passengers as a Risk Factor for Crashes Fatal to 16- and 17-Year-Old Drivers. Journal of the American Medical Association, 283, 1578-1582.
-
-Tefft B.C., Williams A.F., & Grabowski J.G. (2013). Teen driver risk in relation to age and number of passengers, United States, 2007-2010. Traffic Injury Prevention, 14, 283-292.
-
-## **2. Develop potential safety strategies.**
-
-In this situation, an informational approach that simply tells teenagers about the problem would likely not make a difference. Teens are not crashing because they lack information about the importance of safe driving or the consequences of
-
-unsafe driving. Teens are crashing largely because they lack the driving experience that equips most drivers to intuitively/near instantaneously do the things necessary to avoid crashing. Because of this, changing the environment is more likely to be effective.
-
-The officials recognize that eliminating the policy that allows students to leave campus during lunch would lead to a reduction in crashes during this time. This policy would eliminate exposure to the risky driving situation and reduce the potential for crashes.
-
-#### **3. Select and implement strategies.**
-
-The school districts accordingly eliminate the policy allowing students to leave campus during lunch. They recognize that this policy change should be evaluated to determine its safety effect. Crash data would be needed to examine whether the closed school lunch policy has an effect on weekday crashes between 11:00am and 1:00pm. However, it will take many years to accumulate enough data for this evaluation. In this example, there is a proxy measure that can be used in the interim. A before and after observational survey with an appropriate control could quantify the number of students leaving campus during lunch before and after the change. In this case the officials know that the proxy measure (reduced driving from 11:00am to 1:00 pm) is a guaranteed indicator of crash reduction for this specific problem. However, it is not often the case that proxy measures are so closely aligned to the outcome of interest.
-
-![](_page_148_Picture_0.jpeg)
-
-## **Unit Summary**
-
-Solving road safety problems requires a comprehensive process to identify safety problems, develop potential safety strategies, and select and implement those strategies. To get the most effective results, this process must be based on solid safety data, particularly good quality crash data. The methods of undertaking the safety management process will depend on the scope of the effort.
-
-Safety management of individual sites involves a six-step process of screening the network for highpriority sites, diagnosing the safety issues at those sites, selecting appropriate countermeasures,
-
-conducting an economic appraisal for all options, prioritizing the countermeasure projects based on estimated costs and benefits, and evaluating the countermeasure performance afterwards. Safety management at a system-level involves identifying safety problems by examining crash trends or using a systemic approach to identifying high-risk road characteristics. State agencies who are developing system-wide safety strategies must examine the data trends and the road users involved. They must consider factors, such as how system-wide policies and programs will be accepted by the public and who will be the partners to involve in implementing the safety strategy.
-
-#### **EXERCISES**
-
-- J **PRESENT** an example road safety problem and compare and contrast the ways in which the problem could be addressed at a system-level vs. site-level.
-- J Your state has a small, rural, mountainous county where a large number of motorcycle crashes are happening. The crash rate per registered motorcycle in this county is nearly 10 times the state average. Upon further investigation you learn that this county is a popular motorcycling tourist destination. People come from all over the country to ride the curvy mountain roads. In fact, the majority of people involved in crashes are not from that area at all. Clusters of crashes occur on certain curves. What are some approaches that could be used to reduce crashes in this county? How could these approaches be evaluated? In particular, **DETAIL** how you would apply the three major components described in this unit:
-  - J Identify the safety problem
-  - J Develop potential safety strategies
-  - J Selecting and implement strategies
-
-When you work through this process, recall the discussion of human behavior from Unit 2. What are possible behaviors leading to the safety problem? What other factors could be influencing this behavior? How does this affect your identification and selection of potential safety strategies?
-
-J If possible, **OBTAIN** three to five years of crash data for an intersection or section of road in your area. You will likely need to contact the controlling agency – the State DOT, county, or city. Describe how you would apply the steps in Chapter
-
-- 11 on site-level safety management to this location (the network screening step would not apply since this location is already identified). Consider safety strategies across a range of disciplines (e.g., engineering, law enforcement, public communication and education, etc.).
-- J This exercise should be conducting using the Excel spreadsheet that accompanies this book. The goal of this exercise is to **USE** selected performance metrics to create a ranked list of sites for further investigation as part of a network screening effort. The Excel spreadsheet includes nearly 1,400 intersections, or sites. Each site has a unique ID number, traffic volume data, and other information about its location and characteristics. Three performance metrics have been calculated for each site. These have been calculated using five years of data (2010-2014) and one year of data (2014), resulting in a total of six performance metrics per site. Your assignment is to rank the sites using these various performance metrics and document the results. Document the twenty highest priority sites based on each method. Use the results to answer the following questions:
-  - J What were some of the sites that routinely ranked in the top twenty? What were some of their characteristics (volumes, number of lanes, stop/signal control)?
-  - J Were there any sites that were only occasionally present in the top twenty? What were some characteristics of these sites?
-
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-
-![](_page_151_Picture_0.jpeg)
-
-## **Implementing Road Safety Efforts UNIT 5**
-
-#### **LEARNING OBJECTIVES**
-
-After reading the chapters and completing exercises in Unit 5, the reader will be able to:
-
-- J **IDENTIFY** the current road safety partner agencies and define their role in addressing safety problems
-- J **DEFINE** three areas of road safety research
-- J **DEFINE** the characteristics of strategic communications
-- J **RECOGNIZE** potential avenues for advancing road safety efforts
-
-## **Who Does What**
-
-The greatest gains in road safety occur when transportation agencies work together rather than tackling problems alone. This can be challenging given the fragmented nature of transportation governance in the U.S. There are numerous agencies operating in different focus areas and at different levels. This chapter presents an overview of the various agencies and organizations that have a direct hand in advancing road safety and the initiatives that they undertake.
-
-U.S. transportation agencies are typically structured around particular focus areas, such as roadway, vehicles, or road users. This approach is also seen on the
-
-international scale – the United Nations (U.N.) used a generalization called the "Five Pillar" structure as part of the Decade of Action for Road Safety. **<sup>1</sup>** The U.N. recognized that efforts to improve road safety must address various pillars including road safety
-
-management, safer roads and mobility, safer vehicles, safer road users, and better post-crash response. **<sup>2</sup>**
-
-Likewise, U.S. transportation agencies are organized to address focus areas that are similar in theme to the U.N. five pillars, though not the same. Table 5-1 shows how agencies at all levels (Federal, State, and local) address five focus areas in road safety.
-
-**1**
-
-United Nations, A/RES/64/255, Geneva, 2010
-
-**2**
-
-Global Status Report on Road Safety 2013: Supporting a Decade of Action, World Health Organization, ISBN 978 92 4 156456 4, 2013.
-
-| FOCUS AREA                     | FEDERAL                                                               | STATE                                                                            | LOCAL                                                                |
-|--------------------------------|-----------------------------------------------------------------------|----------------------------------------------------------------------------------|----------------------------------------------------------------------|
-| Road Design /<br>Environment   | Federal Highway<br>Administration                                     | Departments of<br>transportation                                                 | City public works<br>Metropolitan/rural<br>planning organizations    |
-| Road User<br>Behavior          | National Highway<br>Traffic Safety<br>Administration<br>Federal Motor | Highway safety offices<br>Departments of<br>motor vehicles<br>Health departments | No specific agency                                                   |
-| Vehicle Design /<br>Technology | Carrier Safety<br>Administration                                      | Departments of<br>motor vehicles                                                 | No specific agency                                                   |
-| Law Enforcement                | No specific agency                                                    | State police /<br>highway patrol                                                 | Police departments                                                   |
-| Transit Safety                 | Federal Transit<br>Administration                                     | No specific agency                                                               | Metropolitan planning<br>organizations<br>Municipal transit agencies |
-
-**TABLE 5-1**: Transportation Agencies by Focus Area
-
-## **Federal Agencies**
-
-The Federal role in implementing road safety initiative is carried out largely through the U.S. Department of Transportation (USDOT) and the many agencies under that department. These agencies are each tasked with a specific focus area as the Federal Government seeks to address safety issues for various modes of travel. These agencies include:
-
-- J **Federal Highway Administration**
-- J **National Highway Traffic Safety Administration**
-- J **Federal Motor Carrier Safety Administration**
-- J **Federal Transit Administration**
-- J **Federal Railroad Administration**
-
-## **Federal Highway Administration**
-
-**www.fhwa.dot.gov**
-
-**Focus Area: Road Design and Environment**
-
-The Federal Highway Administration (FHWA) works to reduce highway fatalities through partnerships with State and local agencies, community groups, and private industry. The FHWA Office of Safety advocates designs and technologies that improve road safety and administers safety programs, such as the Highway Safety Improvement Program (HSIP). FHWA's Resource Center also provides technical assistance, technology deployment, and training. FHWA has a significant role in safety research through the Office of Safety Research and Development, which develops and implements safety innovations through teams of research engineers, scientists, and psychologists.
-
-In addition, FHWA oversees the Local and Tribal Technical Assistance Program (LTAP/TTAP), which provides information and training programs to local agencies and Native American Indian tribes to improve road safety.**<sup>3</sup>** Further, FHWA maintains division offices in each state to deliver assistance to partners and customers in highway transportation and safety services at the State level.
-
-## **National Highway Traffic Safety Administration**
-
-**www.nhtsa.gov**
-
-**Focus Areas: Road User Behavior and Vehicle Design and Technology**
-
-The National Highway Traffic Safety Administration (NHTSA) focuses on the safety of the vehicle, driver, and road user. NHTSA investigates safety defects in motor vehicles, establishes and enforces safety performance standards for motor vehicles and motor vehicle equipment, sets and enforces fuel economy standards, collects data, and conducts research on driver behavior and traffic safety, and helps states and local communities reduce the threat of impaired driving and other dangerous road user behaviors.
-
-NHTSA carries out research and demonstration programs in many behavioral areas including impaired driving, occupant protection, speed management (shared with FHWA), pedestrian, motorcycle and bicycle safety, older and younger road users, drowsy, and distracted driving. NHTSA is also the lead Federal agency for emergency medical services (EMS) and 9-1-1 systems.
-
-**4**
-
-"Who We Are and What We Do," accessed June 20, 2013, [https://www.](https://www.nhtsa.gov/about-nhtsa) [nhtsa.gov/about](https://www.nhtsa.gov/about-nhtsa)[nhtsa](https://www.nhtsa.gov/about-nhtsa).
-
-"About the National Program," last updated June 20, 2013, accessed June 20, 2013, [http://](http://www.ltap.org/about/) [www.ltap.org/about/](http://www.ltap.org/about/).
-
-## **Federal Motor Carrier Safety Administration**
-
-**www.fmcsa.dot.gov**
-
-**Focus Areas: Road User Behavior and Vehicle Design and Technology**
-
-The Federal Motor Carrier Safety Administration (FMCSA) focuses on reducing crashes, injuries, and fatalities involving commercial use of large trucks and buses. FMCSA develops and enforces researchbased regulations that balance safety and efficiency. The agency manages safety information systems to enforce safety regulations with regards to drivers who have high risk in factors, such as health, age, experience, and education.
-
-FMCSA also targets educational messages to carriers, commercial drivers, and the public.**<sup>5</sup>** Some key programs administered by the agency include:
-
-J **Commercial Driver's License Program:** FMCSA develops,
-
-- monitors, and ensures compliance with the commercial driving licensing standards for drivers, carriers, and States.
-- J **Motor Carrier Safety Identification and Information Systems:** FMCSA provides safety data, State and national crash statistics, current analysis results, and detailed motor carrier safety performance data to industry and the public. This data allows Federal and State enforcement officials to target inspections and investigations on higher risk carriers, vehicles, and drivers.
-- J **Safety education and outreach:** FMCSA implements educational strategies to increase motor carrier compliance with the safety regulations and reduce the likelihood of a commercial vehicle crash. Messages are aimed at all highway users including passenger car drivers, truck drivers, pedestrians, and bicyclists.**<sup>6</sup>**
-
-**5**
-
-"About FMCSA," accessed June 20, 2013, [https://www.](https://www.fmcsa.dot.gov/mission/about-us) [fmcsa.dot.gov/](https://www.fmcsa.dot.gov/mission/about-us) [mission/about-us](https://www.fmcsa.dot.gov/mission/about-us).
-
-**6**
-
-"Key FMCSA Programs," accessed October 15, 2013, [https://](https://www.fmcsa.dot.gov/mission/we-are-fmcsa-brochure) [www.fmcsa.dot.gov/](https://www.fmcsa.dot.gov/mission/we-are-fmcsa-brochure) [mission/we-are](https://www.fmcsa.dot.gov/mission/we-are-fmcsa-brochure)[fmcsa-brochure.](https://www.fmcsa.dot.gov/mission/we-are-fmcsa-brochure)
-
-![](_page_154_Picture_13.jpeg)
-
-The Federal Motor Carrier Safety Administration develops, monitors, and ensures compliance with commercial driving licensing standards.
-
-![](_page_155_Picture_0.jpeg)
-
-The Federal Transit Administration seeks to improve public transportation, such as buses.
-
-## **Federal Transit Administration**
-
-**www.transit.dot.gov**
-
-**Focus Area: Transit Safety**
-
-The Federal Transit Administration (FTA) seeks to improve public transportation by assisting State and local governments with planning, implementation, and financing of public transportation projects.**<sup>7</sup>** FTA manages many transit-oriented safety programs including:
-
-- J **Bus and Bus Facilities:** This program provides capital funding to replace, rehabilitate, and purchase buses and related equipment and to construct busrelated facilities.
-- J **Public Transportation Emergency Relief Program:** This program helps States and public transportation systems pay
-
-for protecting, repairing, and/ or replacing equipment and facilities that may suffer or have suffered serious damage because of an emergency including natural disasters.
-
-- J **Research, Development, Demonstration, and Deployment Projects:** This program supports research activities that improve the safety, reliability, efficiency, and sustainability of public transportation.
-- J **Transit Safety and Oversight:** FTA has the authority to establish and enforce a new comprehensive framework to oversee the safety of public transportation throughout the United States as it pertains to heavy rail, light rail, buses, ferries, and streetcars.**<sup>8</sup>**
-
-**7**
-
-"Federal Transit Administration," last updated September 6, 2013, [https://](https://www.usa.gov/federal-agencies/federal-transit-administration) [www.usa.gov/](https://www.usa.gov/federal-agencies/federal-transit-administration) [federal-agencies/](https://www.usa.gov/federal-agencies/federal-transit-administration) [federal-transit](https://www.usa.gov/federal-agencies/federal-transit-administration)[administration](https://www.usa.gov/federal-agencies/federal-transit-administration).
-
-**8**
-
-"MAP-21 Programs," accessed October 16, 2013, [https://](https://www.transit.dot.gov/regulations-and-guidance/legislation/map-21/map-21-program-fact-sheets) [www.transit.dot.](https://www.transit.dot.gov/regulations-and-guidance/legislation/map-21/map-21-program-fact-sheets) [gov/regulations](https://www.transit.dot.gov/regulations-and-guidance/legislation/map-21/map-21-program-fact-sheets)[and-guidance/](https://www.transit.dot.gov/regulations-and-guidance/legislation/map-21/map-21-program-fact-sheets) [legislation/map-21/](https://www.transit.dot.gov/regulations-and-guidance/legislation/map-21/map-21-program-fact-sheets) [map-21-program](https://www.transit.dot.gov/regulations-and-guidance/legislation/map-21/map-21-program-fact-sheets)[fact-sheets.](https://www.transit.dot.gov/regulations-and-guidance/legislation/map-21/map-21-program-fact-sheets)
-
-## **Federal Safety Programs**
-
-Federal agencies advance road safety through numerous programs and initiatives. Federal programs can be very influential due to large funding sources provided by Federal legislation. These funds are often distributed to State and local levels to implement various improvements to roads and intersections.
-
-Specific funding programs can change with each new piece of transportation legislation. However, it may be useful to look at an overview of some of the types of current and past funding programs. Below are listed a few programs that have been widely used through the years to develop and implement improvements to road safety:
-
-- J **Highway Safety Improvement Program (FHWA)**
-- J **Traffic Records Improvement Grants (NHTSA)**
-- J **Safety Data Improvement Program Grant (FMCSA)**
-
-## **Highway Safety Improvement Program (FHWA)**
-
-HSIP is a Federal program focused on infrastructure improvements that will lead to significant reduction in traffic fatalities and serious injuries on all public roads. HSIP is Federally funded and administered by FHWA, but it is implemented by the State departments of transportation per the strategies laid out in the State's Strategic Highway Safety Plan (SHSP). Funding is provided for safety-related infrastructure improvements, such as sidewalks, traffic calming, or signing upgrades. The States are required to develop a **data-driven**, strategic approach
-
-for improving highway safety through the implementation of such infrastructure improvements.**<sup>9</sup>** States are also required to report to the U.S. Secretary of Transportation on progress made implementing highway safety improvements and the extent to which fatalities and serious injuries on all public roads have been reduced.
-
-## **Traffic Records Improvement Grants (NHTSA)**
-
-NHTSA administers Federal funding to encourage States to implement programs that will improve the timeliness, accuracy, completeness, uniformity, integration, and accessibility of State data used in traffic safety programs. The Federal SAFETEA-LU legislation established this program of incentive grants, and the funding continued under subsequent legislation. The funds were to be used to evaluate the effectiveness of efforts to make safety data improvements, to link safety data systems within the State, and to improve the compatibility of the State data system with national data systems and data systems of other States. A State may use these grant funds only to implement such data improvement programs. To qualify, a State must meet certain requirements including a functioning Traffic Records Coordinating Committee (TRCC), a strategic plan to address data deficiencies, and a regular traffic records assessment.**<sup>10</sup>**
-
-## **Safety Data Improvement Program Grant (FMCSA)**
-
-The Safety Data Improvement Program (SaDIP), administered by FMCSA, provides financial and **9**
-
-Source: "Highway Safety Improvement Program (HSIP)," accessed August 12, 2013, http://safety. fhwa.dot.gov/hsip/ and "HSIP History," accessed August 12, 2013, [http://](https://safety.fhwa.dot.gov/hsip/gen_info/hsip_history.cfm) [safety.fhwa.dot.](https://safety.fhwa.dot.gov/hsip/gen_info/hsip_history.cfm) [gov/hsip/gen\\_info/](https://safety.fhwa.dot.gov/hsip/gen_info/hsip_history.cfm) [hsip\\_history.cfm.](https://safety.fhwa.dot.gov/hsip/gen_info/hsip_history.cfm)
-
-#### **10**
-
-NHTSA, Section 408 SAFETEA-LU Fact Sheet, [https://one.](https://one.nhtsa.gov/Laws-&-Regulations/Section-408-SAFETEA–LU-Fact-Sheet) [nhtsa.gov/Laws-](https://one.nhtsa.gov/Laws-&-Regulations/Section-408-SAFETEA–LU-Fact-Sheet) [&-Regulations/](https://one.nhtsa.gov/Laws-&-Regulations/Section-408-SAFETEA–LU-Fact-Sheet) [Section-408-](https://one.nhtsa.gov/Laws-&-Regulations/Section-408-SAFETEA–LU-Fact-Sheet) [SAFETEA%](https://one.nhtsa.gov/Laws-&-Regulations/Section-408-SAFETEA–LU-Fact-Sheet) [E2%80%93](https://one.nhtsa.gov/Laws-&-Regulations/Section-408-SAFETEA–LU-Fact-Sheet) [LU-Fact-Sheet](https://one.nhtsa.gov/Laws-&-Regulations/Section-408-SAFETEA–LU-Fact-Sheet)
-
-#### **Data-driven**
-
-An approach of which the priorities are determined by examination of crash data or other objective and reliable safety data, rather than priorities set by preferences of a few parties, current "hot" topics, or high profile rare events.
-
-technical assistance to States to improve data collected on truck and bus crashes that result in injuries or fatalities. The assistance is provided to State departments of public safety, departments of transportation, or State law enforcement agencies. SaDIP funds have been used to hire staff to code safety performance data, purchase software for field data collection, and revise outdated crash forms.
-
-FMCSA maintains the Motor Carrier Management Information System (MCMIS) and supplies access to the system for designated employees in each State through SAFETYNET, an online network of safety data. States use MCMIS and SAFETYNET to enter data on motor carriers, drivers, compliance reviews, inspections, and crashes. At the national level, FMCSA uses the data to characterize the safety experience of commercial motor vehicles, and to help States with the task of identifying high risk carriers and drivers. The data are also used by motor carrier companies, safety researchers, advocacy groups, insurance companies, the public, and a variety of other entities.
-
-## **State Agencies**
-
-All fifty States, the District of Columbia, and Puerto Rico administer road safety programs. State agencies administer roadway systems, driver licensing, injury prevention programs, traffic law enforcement, and other road safety activities. However, assignment of these responsibilities varies widely from State to State. In many cases, two or three government agencies are responsible for most or all of these activities. Other States distribute these responsibilities to numerous agencies and offices. Regardless of how these responsibilities are distributed, States share a vital role in improving road safety for all citizens.
-
-In general, State agencies that address road safety issues include:
-
-- J State departments of transportation
-- J State highway safety offices
-- J State departments of motor vehicles
-- J State highway patrols
-- J State health departments
-
-State departments of transportation oversee design, construction, maintenance, and operation of roads.
-
-![](_page_157_Picture_11.jpeg)
-
-## **State Departments of Transportation**
-
-#### **Focus Area: Road Design and Environment**
-
-Each State has a department of transportation (DOT), which oversees the design, construction, maintenance, and operation of the State's roads. This agency may also be called the State Highway Administration or Department of Roads. State DOTs have many official responsibilities. For highway safety issues, State DOTs have official transportation planning, programming, and project implementation responsibility. These agencies typically oversee all Interstate highways and most primary highways (State highways). State DOTs focus on roadway safety, and thus work in direct partnership with FHWA. They serve as liaisons between the Federal and local transportation agencies and provide resources and technical assistance to local agencies. State DOTs coordinate the use of Federal HSIP funds to improve roads and intersections on the local level.
-
-In some States, the DOT administers, maintains, and operates county and city streets or secondary roads. State DOTs also work cooperatively with tolling authorities, ports, local agencies, and special districts that own, operate, or maintain portions of the transportation network.
-
-The State DOT typically leads the development of the SHSP, a statewide-coordinated safety plan that provides a comprehensive framework for reducing highway fatalities and serious injuries on all public roads. The State DOT
-
-also develops long-range (20 to 30 year) transportation plans and short range (five to 10 year) plans that outline the vision of the transportation network and which projects will be constructed to fulfill that vision.
-
-## **State Highway Safety Offices**
-
-#### **Focus Area: Road User Behavior**
-
-State Highway Safety Offices (SHSOs) administer a variety of national highway safety grant programs authorized and funded through Federal legislation.**<sup>11</sup>** The governor of each State appoints a highway safety representative to administer the Federal Highway Safety Grant Program and numerous other highway safety programs designated by Congress. The governor's representative promotes safety initiatives in the State, such as high visibility enforcement campaigns like Click It or Ticket.
-
-The State Highway Safety Office focuses on behavioral aspects of roadway users, and thus works in direct partnership with NHTSA. Safety programs implemented by the SHSO include:
-
-- J Encouraging safety belt, child car seat, and helmet use
-- J Discouraging impaired driving
-- J Promoting motorcycle safety
-- J Improving the skills of younger and older drivers
-
-## **State Departments of Motor Vehicles**
-
-**Focus Area: Vehicle Design and Technology**
-
-State Departments of Motor Vehicles
-
-**11**
-
-"SHSO Programs & Funding," accessed June 20, 2013, [http://www.ghsa.](http://www.ghsa.org/about/federal-grant-programs) [org/about/federal](http://www.ghsa.org/about/federal-grant-programs)[grant-programs](http://www.ghsa.org/about/federal-grant-programs).
-
-#### **Coordinated**
-
-People from many agencies come together to develop an SHSP, including those from the department of transportation, department of motor vehicles, state highway patrol, public health, universities, and others.
-
-#### **Comprehensive**
-
-Using all types of strategies to improve road safety, such as infrastructure improvements, law enforcement, and campaigns to change driver behavior. This is seen in the types of crashes which serve as the focus areas of an SHSP, such as speeding related crashes, which are most effectively addressed through a combination of speed enforcement, engineering modifications, and behavioral campaigns.
-
-State highway patrols enforce motor vehicle laws and regulations, investigate crashes, and work to identify enforcement needs.
-
-![](_page_159_Picture_5.jpeg)
-
-(DMVs) administer State programs for driver licensing, and automobile inspection and registration. Generally, DMVs reside either within the State DOT or a department of public safety. A few States have a cabinet-level DMV.
-
-The DMV is responsible for identifying at-risk drivers and maintaining driver records. The agency also implements driver license standards, monitors graduated licensing programs, and establishes requirements for driver education. Some State DMVs also serve as the primary owners of the statewide crash data.
-
-## **State Highway Patrols**
-
-#### **Focus Area: Law Enforcement**
-
-State highway patrols (also known as State police and State patrols) operate in every State except Hawaii. State police patrol highways and enforce motor vehicle laws and regulations.
-
-State law enforcement agencies play an important role in reducing the frequency and severity of crashes. At the scene of crashes, State police direct traffic, administer first aid, call for emergency equipment, write traffic citations, and complete crash reports.
-
-State highway patrols also
-
-investigate motor vehicle crashes, which are important sources of State and Federal crash data. They work closely with State highway safety representatives to identify enforcement needs. They play a vital role in implementing impaired driving laws, safety belt use, and other safety programs. Certain troopers in the State highway patrol are tasked with inspecting large trucks to ensure the driver and the vehicle comply with safety regulations, such as vehicle size and weight, driver hours of service, and medical fitness.
-
-## **State Health Departments**
-
-#### **Focus Area: Road User Behavior**
-
-State health departments also play an important role in reducing crash severity. These agencies are typically responsible for statewide trauma center planning. State health departments provide training, certification, and technical assistance for EMS providers, administer injury prevention programs, and maintain trauma and injury databases. Some State health departments coordinate with other public agencies and community groups to promote young driver safety, older driver safety, child passenger safety, and pedestrian and bicycle safety.
-
-## **State Safety Plans and Programs**
-
-State agencies use numerous approaches to improve road safety within their State. Often, they seek to identify State-specific safety issues and direct Federal or State funding to solve those issues. State agencies take the lead in developing statewide safety plans or programs, many of which are encouraged or required by the Federal Government. Although States differ in their specific safety improvement efforts, the following plans or programs are developed in every State:
-
-- J **Strategic Highway Safety Plan**
-- J **Long Range Transportation Plan**
-- J **Statewide Transportation Improvement Program**
-- J **Railway-Highway Crossing Program**
-- J **Highway Safety Program**
-
-Although the HSIP was previously covered under Federal safety programs, it should be noted that the State plays the major role in selecting locations that need safety improvement, designing, and implementing the safety improvement, and reporting annually on all the HSIP funded projects that were constructed that year.
-
-## **Strategic Highway Safety Plan**
-
-A State's SHSP is a statewide**coordinated** safety plan that provides a **comprehensive** framework for reducing highway fatalities and serious injuries on all public roads. An SHSP identifies a State's key safety needs and guides investment decisions toward strategies with the
-
-highest potential to save lives and prevent injuries. Since 2005, Federal legislation has required States to develop, implement,evaluate, and update their SHSP.**12,13**
-
-The State DOT develops an SHSP in a cooperative process with local, State, Federal, tribal, and private sector safety stakeholders. It is a data-driven, multi-year plan that establishes statewide goals, objectives, and key emphasis areas. The development of an SHSP provides a venue for highway safety partners in the State to align goals, leverage resources (i.e., combine Federal and State resources), and collectively address the State's safety challenges.**<sup>14</sup>**
-
-An ideal SHSP meets several criteria:
-
-- J It addresses engineering, management, operation, education, enforcement, and emergency service elements of highway safety as key factors in evaluating highway projects.
-- J It considers safety needs of, and high-fatality segments of, all public roads.
-- J It considers the results of State, regional, or local transportation and highway safety planning processes.
-- J It describes strategies to reduce or eliminate safety hazards.
-- J It gains approval of the governor of the State or a responsible State agency.**<sup>15</sup>**
-
-As mentioned on page 5-5, in order to spend HSIP funds, a State must have a current SHSP, produce a program of projects or strategies to reduce safety problems, and evaluate the SHSP on a regular basis.**<sup>16</sup>**
-
-**Coordinated, Comprehensive**
-
-See previous page.
-
-**12**
-
-"Strategic Highway Safety Plans: A Champion's Guidebook to Saving Lives 2nd ed.," Federal Highway Administration (Washington, D.C., October 2012), History and Background, [http://](https://safety.fhwa.dot.gov/shsp/guidebook/) [safety.fhwa.dot.](https://safety.fhwa.dot.gov/shsp/guidebook/) [gov/hsip/shsp/](https://safety.fhwa.dot.gov/shsp/guidebook/) [guidebook/ovrvw.](https://safety.fhwa.dot.gov/shsp/guidebook/) [cfm](https://safety.fhwa.dot.gov/shsp/guidebook/)
-
-**13 14**
-
-"Strategic Highway Safety Plan (SHSP)," accessed August 13, 2013, [http://safety.](https://safety.fhwa.dot.gov/shsp/) [fhwa.dot.gov/hsip/](https://safety.fhwa.dot.gov/shsp/) [shsp/.](https://safety.fhwa.dot.gov/shsp/)
-
-**15**
-
-"Map-21," Title 23 U.S.C. (2012), accessed August 19, 2013, [http://www.](http://www.gpo.gov/fdsys/pkg/PLAW-112publ141/pdf/PLAW-112publ141.pdf) [gpo.gov/fdsys/pkg/](http://www.gpo.gov/fdsys/pkg/PLAW-112publ141/pdf/PLAW-112publ141.pdf) [PLAW-112publ141/](http://www.gpo.gov/fdsys/pkg/PLAW-112publ141/pdf/PLAW-112publ141.pdf) [pdf/PLAW-](http://www.gpo.gov/fdsys/pkg/PLAW-112publ141/pdf/PLAW-112publ141.pdf)[112publ141.pdf](http://www.gpo.gov/fdsys/pkg/PLAW-112publ141/pdf/PLAW-112publ141.pdf)
-
-**16**
-
-"Highway Safety Improvement Plan (HSIP)," accessed August 14, 2013, [http://www.fhwa.](https://www.fhwa.dot.gov/map21/factsheets/hsip.cfm) [dot.gov/map21/hsip.](https://www.fhwa.dot.gov/map21/factsheets/hsip.cfm) [cfm](https://www.fhwa.dot.gov/map21/factsheets/hsip.cfm).
-
-## **Long-Range Transportation Plans**
-
-Long-range transportation plans (LRTPs) identify transportation goals, objectives, needs, and performance measures over a 20- to 25-year horizon and provide policy and strategy recommendations for accommodating those needs. LRTPs are prepared at both the State and MPO level. LRTPs are fiscally-unconstrained and typically present a systems-level approach that considers roadways, transit, pedestrian, and bicycle facilities. LRTP's have wide scopes; the components of the plan may be policy-oriented and strategic or focused on specific projects. The types of improvements range widely, as well. Safety-focused improvements in a LRTP may be directed at infrastructure, such as building new interchanges or bringing certain highways up to current design standards, or behavioral efforts, such as addressing seat belt use or
-
-**18**
-
-**17**
-
-"Railway-Highway Crossings Program," accessed August 9, 2013, [http://](https://www.fhwa.dot.gov/map21/factsheets/rhc.cfm) [www.fhwa.dot.gov/](https://www.fhwa.dot.gov/map21/factsheets/rhc.cfm) [map21/rhc.cfm.](https://www.fhwa.dot.gov/map21/factsheets/rhc.cfm)
-
-Title 49, United States Code, § 5304
-
-**19**
-
-Railroad-Highway Grade Crossing Handbook 2nd ed. Federal Highway Administration (Washington D.C., August 2007)
-
-## **Statewide Transportation Improvement Programs**
-
-aggressive driving.
-
-The Statewide Transportation Improvement Program (STIP) identifies the funding and scheduling of transportation projects throughout the State that support the goals identified in the LRTP. STIPs are short-range (typically an outlook of five to ten years) and fiscally constrained, meaning that the projects must have designated funding. While many STIP projects are constructed for capacity or mobility reasons (i.e., build a bypass or widen a road), there are also STIP projects that are
-
-focused on improving safety, such as widening shoulders or installing rumble strips. Projects included in STIPs must have identified funding sources (e.g., HSIP, State, or local funding). The State DOT identifies projects in areas outside MPOs, such as rural areas and smaller urban jurisdictions, for inclusion in the STIP.**<sup>17</sup>**
-
-## **Railway-Highway Crossings Program**
-
-The Railway-Highway Crossings Program funds safety improvements to reduce the number of fatalities, injuries, and crashes at public grade crossings.**<sup>18</sup>** A grade crossing is a location where a public highway, road, street, or private roadway (including associated sidewalks and pathways) crosses a railroad track at the same level as the street. These locations are high-risk spots for road users. The United States has more than 200,000 grade crossings.**<sup>19</sup>** Types of crossing improvements that the Railway-Highway Crossings Program implements include:
-
-- J **Crossing approach improvements:** projects such as channelization, new or upgraded signals on the approach, guardrail, pedestrian/ bicycle path improvements near the crossing, and illumination
-- J **Crossing warning sign and pavement marking Improvements:** projects such as signs, pavement markings, and/or delineation where these project activities are the predominant safety improvements
-- J **Active grade crossing equipment installation/upgrade:** projects such as new or upgraded flashing lights and gates, track circuitry,
-
-![](_page_162_Picture_0.jpeg)
-
-The Railway-Highway Crossings Program funds safety improvements at crossings.
-
-wayside horns, and signal improvements such as railwayhighway signal interconnection and pre-emption.**<sup>20</sup>**
-
-## **Highway Safety Program**
-
-Each State administers a Highway Safety Program, approved by the U.S. Secretary of Transportation. The program is designed to reduce deaths and injuries on the road by targeting user behavior through education and enforcement campaigns.**<sup>21</sup>** The State conducts this program through the State highway safety office. A State is eligible for SHSP grants by having and implementing an approved
-
-Highway Safety Plan (HSP). The HSP establishes goals, performance measures, targets, strategies, and projects to improve highway safety in the State. It also documents the State's efforts to coordinate with the goals and strategies in the SHSP. However, the Highway Safety Program is distinct from an SHSP. SHSPs target improvements to infrastructure and road users, and are broad in content and context, while highway safety programs focus more on road user behavior.**<sup>22</sup>** An HSP might address issues such as excess speeds, proper use of occupant protection devices, driving while impaired, and quality of traffic records data.**<sup>23</sup>**
-
-**20**
-
-"Railway-Highway Crossings Program Reporting Guidance," accessed August 9, 2013, [http://](https://www.fhwa.dot.gov/map21/guidance/guiderhcp.cfm) [www.fhwa.dot.gov/](https://www.fhwa.dot.gov/map21/guidance/guiderhcp.cfm) [map21/guidance/](https://www.fhwa.dot.gov/map21/guidance/guiderhcp.cfm) [guiderhcp.cfm.](https://www.fhwa.dot.gov/map21/guidance/guiderhcp.cfm)
-
-**21 23**
-
-"MAP-21," Title 23, U.S.C. (2012), accessed August 16, 2013, [http://](https://www.fhwa.dot.gov/map21/docs/title23usc.pdf) [www.fhwa.dot.](https://www.fhwa.dot.gov/map21/docs/title23usc.pdf) [gov/map21/docs/](https://www.fhwa.dot.gov/map21/docs/title23usc.pdf) [title23usc.pdf, Sec.](https://www.fhwa.dot.gov/map21/docs/title23usc.pdf)  [402.](https://www.fhwa.dot.gov/map21/docs/title23usc.pdf)
-
-**22**
-
-"MAP-21," Title 23, U.S.C. (2012), accessed August 16, 2013, [http://](https://www.fhwa.dot.gov/map21/docs/title23usc.pdf) [www.fhwa.dot.](https://www.fhwa.dot.gov/map21/docs/title23usc.pdf) [gov/map21/docs/](https://www.fhwa.dot.gov/map21/docs/title23usc.pdf) [title23usc.pdf](https://www.fhwa.dot.gov/map21/docs/title23usc.pdf), Sec. 148.
-
-## **Local Agencies**
-
-intersections.
-
-Local agencies, such as city and county governments, play an important role in improving road safety and identifying and selecting transportation projects. These agencies that administer roads at the local level may be called by various names: public road agency, department of public works, departments of transportation, or road commissions. Due to their smaller size, many local agencies may not have a staff member who is specifically focused on road safety. The urban nature of their jurisdiction naturally causes their efforts to be focused on different types of road safety topics than a State DOT. For example, a city transportation department would typically focus on urban elements such as sidewalks, transit accommodations, and high density access management, where as a State DOT would typically be focused on more rural elements, such as high speed curves and isolated
-
-Most safety issues for local streets, intersections, or corridors are the responsibility of the city or county government. These agencies supplement State laws, establish traffic laws in their jurisdictions, and determine penalties for noncompliance. Local law enforcement agencies investigate crashes and submit crash data to State and Federal agencies. City and county planning and engineering staff help plan and design roads, bike lanes, and sidewalks. Many local police departments partner with State police to implement impaired driving, work zone safety, motorcycle safety, heavy truck, and safety belt education and enforcement programs. In some States, the State DOT owns many of the major roads in the city
-
-### **Charlotte Pedestrian Safety**
-
-The City of Charlotte developed its Transportation Action Plan (TAP) in 2011 to describe how to reach its safety and mobility transportation goals. The plan emphasized the safety of all road users and included objectives such as constructing 375 miles of new sidewalk by 2035.**<sup>24</sup>** To support the goal of pedestrian safety, Charlotte developed Charlotte WALKS, the city's first comprehensive Pedestrian Plan. This plan identified new strategies to meet the pedestrian safety and walkability goals in Charlotte's TAP.**<sup>25</sup>**
-
-and may coordinate with the local agency to identify potential safety improvements and get them installed. Many local agencies also collaborate with the State DOT to develop a Local Road Safety Plan.
-
-At the regional level, metropolitan planning organizations (MPOs) plan, program, and coordinate Federal highway and transit investments. When an urban area meets certain minimum characteristics (e.g., population), Federal law requires the creation of an MPO for the region to qualify for Federal highway or transit funds in urbanized areas. MPOs do not typically own or operate the transportation systems in their jurisdiction. MPOs play a coordination and consensus-building role in planning and programming funds for capital improvements, maintenance, and operations. MPOs involve local transportation providers in the planning process by coordinating with transit agencies, State and local highway departments, airport authorities, maritime operators, rail-freight operators, Amtrak, port operators, private providers of public transportation, and others within the MPO region.**<sup>26</sup>**
-
-#### **24**
-
-The City of Charlotte Transportation Action Plan Policy Document, 5 Year Update, August 22, 2011.
-
-#### **25**
-
-[http://charlottenc.](http://charlottenc.gov/Transportation/Programs/Pages/default.aspx) [gov/Transportation/](http://charlottenc.gov/Transportation/Programs/Pages/default.aspx) [Programs/Pages/](http://charlottenc.gov/Transportation/Programs/Pages/default.aspx) [default.aspx](http://charlottenc.gov/Transportation/Programs/Pages/default.aspx)
-
-#### **26**
-
-The Transportation Planning Process Briefing Book, Federal Highway Administration and Federal Transit Administration, 2015 Update, [http://www.fhwa.](https://www.fhwa.dot.gov/planning/publications/briefing_book/fhwahep15048.pdf) [dot.gov/planning/](https://www.fhwa.dot.gov/planning/publications/briefing_book/fhwahep15048.pdf) [publications/](https://www.fhwa.dot.gov/planning/publications/briefing_book/fhwahep15048.pdf) [briefing\\_book/](https://www.fhwa.dot.gov/planning/publications/briefing_book/fhwahep15048.pdf) [fhwahep15048.pdf](https://www.fhwa.dot.gov/planning/publications/briefing_book/fhwahep15048.pdf)
-
-![](_page_164_Picture_0.jpeg)
-
-## **Other Safety Partners**
-
-Although government agencies have the support of large budgets and institutional authority to implement improvements to road safety, they are not the only entities working to improve road safety. Government agencies work with partners from a variety of fields to improve the nation's roadways including those in private industry, special interest groups, and professional organizations.
-
-## **Private industry**
-
-#### **Automobile Manufacturers**
-
-Auto manufacturers have a critical effect on road safety by designing vehicles that assist the driver in avoiding crashes and that absorb energy in crashes that do occur. Federal standards stipulate that vehicles must have certain safety improvements, such as seat belts, air bags, and electronic stability control.**<sup>27</sup>** However, auto manufacturers have also implemented various non-required safety improvements. These improvements typically make use of emerging technologies or materials.
-
-For instance, in the early 2000's, auto manufacturers began
-
-manufacturing vehicles that had "smart" technologies to improve safety, such as collision warnings and assisted braking. These technologies, which were not required by the government, addressed some of the most common crash types, such as rear end crashes. These improvements also set the stage for more advanced automated vehicle designs and technologies.
-
-### **Insurance Companies**
-
-Insurance companies often assist in identifying ways to improve safety on the nation's roads. The most notable organization in this respect is the Insurance Institute for Highway Safety (IIHS) and its sister organization, the Highway Loss Data Institute (HLDI). These are organizations that study road safety issues and use insurance data to provide data-based evidence of safety by vehicle make and model. These organizations are funded by a pooled group of insurance companies and associations. IIHS runs the Vehicle Research Center, which conducts crash tests of many vehicle types to encourage auto manufacturers to produce safer vehicles and inform the consumer on vehicle safety ratings.
-
-**27**
-
-Title 49 of the United States Code, Chapter 301, Motor Vehicle Safety; and Federal Motor Vehicle Safety Standard (FMVSS) No. 218
-
-## **Special interest groups**
-
-Special interest groups are associations of individuals or organizations that promote their common interests by influencing the legislative process at the local, State, and/or Federal levels of government. Many interest groups also serve other functions, such as providing services and information to their members. Interest groups fill a vital role in advancing the cause of safety-related legislation.
-
-A few examples of road safetyfocused interest groups include the AAA Foundation for Traffic Safety, National Safety Council, Mothers Against Drunk Driving (MADD), the American Council of the Blind, and the National Federation of the Blind. There are many other interest groups involved in influencing transportation and safety legislation. These groups engage in a variety of activities, such as sponsoring independent research and evaluation, mobilizing citizens to contact their legislators in support of or against certain pieces of legislation, and disseminating policy reports in support of or against legislation affecting the safety of the road users they represent.
-
-## **Professional organizations**
-
-Professional organizations bring together road safety professionals from common backgrounds or spheres of influence to foster discussion and advancement of safety issues. Members of these organizations who recognize emerging road safety issues can use the power of the group to advocate legislation or sponsor research to address these issues.
-
-![](_page_165_Picture_5.jpeg)
-
-#### **American Council of the Blind (ACB) and National Federation of the Blind (NFB)**
-
-The ACB and NFB represent the interests of people who are blind or visually impaired. These organizations work to inform legislators, city and State agencies, and the public about road safety issues that are unique to individuals with visual impairment. They promote policies and practices that assist visually impaired individuals in traveling safety and independently. These include enhancements, such as auditory stop announcements on buses and accessible pushbuttons that provide information about street crossing signals.
-
-These organizations often hold regular conferences that allow safety professionals to network, share ideas, and gain knowledge from others in their field. The organizations also provide training opportunities that help advance and disseminate road safety knowledge.
-
-There are many professional organizations covering many disciplines that relate to road safety. While this textbook is not intended to provide an encyclopedic listing, a few examples are listed below.
-
-### **American Association of State Highway Transportation Officials (AASHTO)**
-
-AASHTO members consist of representatives from highway and transportation departments in the 50 States, the District of Columbia, and Puerto Rico. AASHTO provides tools such as Safety Analyst, an analytical software package, and publishes the Highway Safety Manual, which provides an analytical and quantitative framework for analyzing a road's safety performance. AASHTO focuses on emerging safety issues through its Standing Committee on Highway Traffic Safety. AASHTO inspired the development of a national safety plan called Toward Zero Deaths, committed to reducing the number of highway fatalities to zero.
-
-#### **Institute of Transportation Engineers (ITE)**
-
-ITE is an association of transportation professionals who are responsible for meeting mobility and safety needs. ITE promotes professional development of its members and facilitates the application of technology and scientific principles to the safety of ground transportation. ITE's Transportation Safety Council covers issues, such as roadside safety, pedestrian and bicyclist safety, and work zone safety.
-
-### **Association of Transportation Safety Information Professionals (ATSIP)**
-
-ATSIP focuses on data and is the leading advocate for improving the quality and use of transportation safety information. ATSIP furthers the development and sharing of traffic records system procedures, tools, and professionalism. Its goal is to improve the quality of safety data and encourage their use in safety programs and policies.
-
-### **Governors Highway Safety Association (GHSA)**
-
-GHSA represents the State and territorial highway safety offices that implement programs to address behavioral highway safety issues including occupant protection, impaired driving, and speeding. GHSA provides leadership and advocacy for the States and territories to improve traffic safety, influence national policy, enhance program management, and promote best practices.
-
-## **International Organizations**
-
-As of 2017, there is no U.S. organization that combines the narrow focus of improving road safety and a broad multidisciplinary approach. The current inventory of U.S. professional organizations is usually specific to a type of discipline, such as engineering or behavioral science. Looking beyond the U.S. borders shows that other countries have formed organizations that bring together many different disciplines to address road safety including the following:
-
-- J World Road Association (PIARC, after its former name Permanent International Association of Road Congresses), **www.piarc.org/en/**
-- J La Prévention Routière Internationale (PRI), **www.lapri.org**
-- J United Nations Road Safety Collaboration, **www.who.int/roadsafety/en/**
-- J Global Road Safety Partnership, **www.grsproadsafety.org**
-- J International Road Federation, **www.irf.global**
-
-#### **EXERCISES**
-
-- J **RESEARCH** a recent road safety project in your city, county, State, or region. Identify the roles and responsibilities of the Federal, State, and/or local governmental agencies in the project. Prepare a brief report for a class presentation.
-- J **USE** your local, State, or regional highway department's website to determine its most pressing road safety concerns. Prepare a brief report that explains the concerns, how the agency identified them, the proposed remedies, and the agency's next steps.
-- J **PREPARE** a brief presentation that summarizes how your local government administers roadway programs. Identify the form of government (city, county, municipality, parish, etc.), the local agencies, and their responsibilities to roadway safety programs.
-- J **RESEARCH** a private or nonprofit interest group that works to improve road safety. Prepare a class presentation that includes a brief history of the interest group, a synopsis of important and successful campaigns, and a summary of its current work. Some examples of private or nonprofit interest groups include the Automobile Association of America (AAA), Mothers against Drunk Driving (MADD), the Insurance Institute for Highway Safety (IIHS), and the American Bikers Aimed Toward Education (ABATE), and many others.
-- J **RESEARCH** your local, State, or regional transportation safety planning group. Prepare a brief report that identifies the group's current safety goal(s), explains how the group plans to alleviate the safety challenge(s), and describes the group's strategic communications plan.
-
-- J **RESEARCH** a private sector or industry association that works to improve road safety. Prepare a class presentation that includes a brief history of the interest group, a synopsis of important and successful campaigns, and a summary of its current work. Some examples of private sector or industry associations include the American Insurance Association (AIA), the American Traffic Safety Services Association (ATSSA), the American Road and Transportation Builders Association (ARTBA), the National Association of County Engineers (NACE), the American Public Works Association (APWA), and many others.
-- J **FIND OUT** how professional associations like the National League of Cities, the American Association of State Highway Transportation Officials (AASHTO), the Governor's Highway Safety Association (GHSA), and the Standing Committee on Highway Traffic Safety (SCOHTS) influence road safety policy. What are some examples of past successful campaigns?
-- J Using the State DOT website, **RESEARCH** your home State's Highway Safety Program. Prepare a brief presentation that identifies the State and describes how it is working to improve roadway safety. You may want to focus on the State's efforts with a particular road user or road safety provider. Be sure to include details about how the State identified the safety challenge(s), how the State chose its countermeasure(s), and how its efforts measure against national performance criteria.
-
-## **Road Safety Research**
-
-Road safety practitioners are constantly seeking the most effective means for preventing injuries and fatalities on the road. To accomplish this, they need to understand the nature of the road safety problems and which behavioral or infrastructure countermeasures are the best at addressing these problems. When weighing two possible safety treatments, they need to know which one is more effective (would prevent more crashes) and more efficient (preventing crashes at a lower cost). Additionally, safety practitioners work with a limited budget, so they need to know which safety treatment is more cost effective, that is, how many crashes can be prevented for the same dollar spent. These goals lead to many questions, such as:
-
-- J What age range should be targeted in a young driver safety program?
-- J What is an effective strategy for preventing run-off-road crashes?
-- J How many crashes would be expected on one type of road versus another type?
-- J How many serious injuries could be prevented by installing additional safety measures at a signalized intersection?
-
-Research is the key to answering these questions and providing quality information to the safety
-
-![](_page_168_Picture_9.jpeg)
-
-practitioner. Unit 3 of this textbook provides a look at the various types of safety data and how they can be used together. Good research analyzes one or more kinds of safety data to gain knowledge on ways to prevent crashes or decrease injuries when crashes do occur.
-
-## **General Types of Road Safety Research**
-
-The intention of this chapter is not to summarize the entire field of road safety research, but rather to provide an overview that will show what types of research have been conducted within the topic of road safety.
-
-|                     | RESEARCH QUESTION EXAMPLES                                              |                                                                                    |  |  |  |  |
-|---------------------|-------------------------------------------------------------------------|------------------------------------------------------------------------------------|--|--|--|--|
-|                     | PROBLEM IDENTIFICATION                                                  | EVALUATION                                                                         |  |  |  |  |
-|                     | What are characteristics of crashes<br>involving teen drivers?          | What is the safety effect of instituting<br>a graduated driver license law?        |  |  |  |  |
-| PERSON              | What type of driver is overrepresented<br>in alcohol-related crashes?   | Has the arrival of ride-sharing apps in<br>cities reduced alcohol-related crashes? |  |  |  |  |
-|                     | What models of vehicle are more<br>prone to run-off-road crashes?       | What is the effect of an antilock<br>braking system on run-off-road crashes?       |  |  |  |  |
-| VEHICLE             | What factors are associated with<br>large truck-related crashes?        | What is the effect of airbags on<br>injury severity?                               |  |  |  |  |
-|                     | What road features are associated<br>with run-off-road crashes?         | What is the safety effect of installing<br>rumble strips?                          |  |  |  |  |
-| MENT                | How does lane width influence<br>driver speed?                          | What is the safety effect of narrowing<br>lanes on urban roads?                    |  |  |  |  |
-| ENVIRON<br>AD<br>RO | Are some land development patterns<br>riskier from a safety standpoint? | What is the safety effect of controlling<br>road access in dense urban areas?      |  |  |  |  |
-
-**TABLE 5-2**: Safety Research Categorization
-
-One way that these road safety research projects can be generally categorized is by grouping them by the safety factor they address: the person, the vehicle, or the roadway. For each of these categories, safety research typically focuses either on identifying safety problems or on evaluating solutions to safety problems. Table 5-2 demonstrates this classification of safety research areas and provides example questions that would drive research studies in each area.
-
-While this chapter presents road safety research in terms of identifying problems or evaluating solutions, researchers also play key roles in developing new solutions to road safety problems. For example, the Florida Department
-
-of Transportation (FDOT) was experiencing thousands of crashes, including many fatalities, in construction work zones and sought an alternative to the traditional work-zone barrier. University of Florida civil engineering researchers hired by FDOT developed a new type of portable temporary lowprofile barrier that can redirect cars and small trucks, preventing them from crashing into the work zone and protecting the passengers in the vehicle. The barrier was advantageous in that it could be broken down into small inexpensive segments that are easy to install and move around.**<sup>26</sup>** Additional research was carried out to evaluate this new type of barrier in terms of criteria like crash performance and durability.
-
-**26**
-
-University of Florida, Office of Technology Licensing, "Portable Temporary Low-Profile Barrier (PTB) for Roadside Safety", UF #11052, US Patent 6,767,158
-
-## **Examples of Road Safety Research**
-
-The following sections provide descriptions and examples of research studies. These examples will give the reader a look at the types of studies that are conducted within each of the six categories shown in Table 5-2.
-
-## **Research on the Person**
-
-#### **Problem Identification**
-
-The IIHS sponsored a study to examine the characteristics of crashes involving 16-year old drivers. The researchers used crash data from NHTSA's General Estimates System (a national crash database built on sampling from police agencies around the U.S.). They compared crash involvement of sixteen-year-olds to that of other age categories of drivers and found that sixteen-year-olds were more likely to be involved in single-vehicle crashes and night time crashes (6:00pm to 11:59pm). Sixteen-year-olds were also more likely to have received a moving violation and been at fault for a crash. They were also more likely to be accompanied by other teenage passengers. Researchers also found some indications that drivers with less on-the-road experience (females in this study) were proportionately more involved in crashes.**<sup>28</sup>**
-
-#### **Evaluation of Solutions**
-
-In 1997, Michigan instituted its Graduated Driver Licensing (GDL) program to address the high rate of fatal crashes involving teen drivers. This program required teen drivers to gain driving experience under
-
-relatively low risk conditions before obtaining full driving privileges. A new driver would progress through Level 1 (a learner's stage requiring extensive supervised practice), Level 2 (an intermediate stage which prohibited teenage passengers and driving at night), and Level 3 (full licensure with no restrictions). NHTSA funded a group of researchers to evaluate the effect of the GDL program on the crash risk of 16-year-old drivers. The researchers examined Michigan statewide crash data from 1996 (pre-GDL) and 1998 and 1999 (post-GDL). They analyzed the pre-GDL and post-GDL rates of 16-yearold drivers involved in crashes by unit of the statewide population. Researchers also compared to crash rates of drivers over age 25 to control for any other trends. They found that the overall crash risk for 16-year-old drivers decreased 25% by the year 1999 (two years after GDL was implemented). They also found significant reductions in many specific crash types, such as night crashes and single vehicle crashes. These findings showed a significant benefit to the GDL program and served to support GDL implementation in other States.**<sup>29</sup>**
-
-## **Research on the Vehicle**
-
-#### **Problem Identification**
-
-The design of a vehicle, particularly how well it protects the occupants in the event of a crash, can have a significant effect on injuries sustained in the crash. A group of researchers from the IIHS investigated the relation of vehicle roof strength to occupant injury during crashes. They examined crash data from fourteen States
-
-**28**
-
-Ulmer, Robert G., Allan F. Williams, and David F. Preusser, Crash Involvements of 16-Year-Old Drivers, Journal of Safety Research, Vol. 28, No. 2, pp 97-103, 1997. [http://www.](http://www.sciencedirect.com/science/article/pii/S0022437596000412) [sciencedirect.com/](http://www.sciencedirect.com/science/article/pii/S0022437596000412) [science/article/pii/](http://www.sciencedirect.com/science/article/pii/S0022437596000412) [S0022437596000412](http://www.sciencedirect.com/science/article/pii/S0022437596000412)
-
-**29**
-
-Shope, J., L. Molnar, M. Elliott, P. Waller. Graduated Driver Licensing in Michigan: Early Impact on Motor Vehicle Crashes Among 16-Year-Old Drivers. Journal of the American Medical Association, Vol. 286, No. 13, October, 2001. [http://jama.](https://jamanetwork.com/journals/jama/fullarticle/194251) [jamanetwork.](https://jamanetwork.com/journals/jama/fullarticle/194251) [com/article.aspx?](https://jamanetwork.com/journals/jama/fullarticle/194251)  [articleid=194251](https://jamanetwork.com/journals/jama/fullarticle/194251)
-
-#### **30**
-
-Brumbelow, M., E. Teoh, D. Zuby, and A. McCartt. Roof Strength and Injury Risk in Rollover Crashes. Traffic Injury Prevention, 10:252-265, 2009. DOI: 10.1080/ 15389580902781343
-
-Kahane, C., and J. Dang. The Long-Term Effect of ABS in Passenger Cars and LTVs. National Highway Traffic Safety Administration, Report DOT HS 811 182, August 2009.
-
-**32**
-
-Bauer, K.M. and Harwood, D.W. Safety Effects of Horizontal Curve and Grade Combinations on Rural Two-Lane Highways, Report No. FHWA-HRT-13-077, Federal Highway Administration, Washington, DC, 2013.
-
-for single-vehicle rollover crashes involving midsize SUVs. They also used a rating of roof strength for each vehicle type in the crash data. Their findings showed that the crush resistance of a vehicle's roof was strongly related to the risk of fatal or incapacitating injury to the occupants. This research identified one statistically significant factor (roof strength) in the severity of rollover crashes. The researchers also recommended the study of other vehicle factors to determine their effect on the severity of rollover crashes.**<sup>30</sup>**
-
-#### **Evaluation of Solutions**
-
-Anti lock braking system (ABS) is a technology that was developed to combat the problem of drivers losing control of their vehicle during hard braking due to locked wheels. ABS modulates braking power to prevent a vehicle's wheels from locking up. Since this technology requires drivers to employ it correctly (i.e., step and hold on the brake rather than pumping), the Federal Government conducted a public information campaign in 1995 to inform drivers how to use ABS correctly. The NHTSA sponsored a study that took a long term look at the effect of ABS from 1995 to 2007. The researchers used crash data from two Federal databases (the Fatality Analysis Reporting System and the General Estimates System of the National Automotive Sampling System) to estimate the long-term effectiveness of ABS for passenger cars, light trucks, and vans. They found that ABS reduced fatal crashes with pedestrians but increased fatal run-off-road crashes. ABS proved to be quite effective in reducing nonfatal crashes in all types of vehicles.
-
-This result is for the ABS alone; the authors recognized that electronic stability control (a technology which automatically applies brakes to individual wheels to keep the driver on the road) would soon be paired with ABS for potentially greater crash reductions.**<sup>31</sup>**
-
-## **Research on the Road Environment**
-
-#### **Problem Identification**
-
-Curves on the road, both horizontal and vertical, are known to be problem spots for road safety. They are particularly concerning when they occur together, such as a horizontal curve at the peak of a hill. FHWA sponsored a study to identify and quantify the road and curve characteristics that are associated with higher instances of crashes. The researchers examined curves in Washington State using crash data and roadway characteristics contained in the Highway Safety Information System (HSIS). They analyzed locations where horizontal and vertical curves occurred independently, as well as locations where both occurred in the same place. The researchers developed equations that predicted the effect on crash frequency for each type of curve combination. These predictive equations included road and curve characteristics found to affect the frequency of crashes. These characteristics included the sharpness of the vertical curve and the radius of the horizontal curve.**<sup>32</sup>**
-
-#### **Evaluation of Solutions**
-
-Rumble strips are expected to decrease crashes by generating noise to alert sleepy or inattentive drivers that they are about to leave
-
-#### **How Research Affects Implementation: Motorcycle Helmets**
-
-Motorcycle helmets are now a common road safety element to reduce serious head injuries, but they were not always so. The original interest in motorcycle helmets began when Colonel T.E. Lawrence (better known as Lawrence of Arabia) died after suffering a head injury in a motorcycle crash. One of the physicians who attended him, Hugh Cairns, was moved by the incident and began studying the prevalence of head injuries among motorcyclists in the British Army. His work ultimately led to helmets becoming mandatory in the British Army and the U.K.
-
-Early motorcycle helmets were leather caps that did little to protect riders. Then in the 1950s, Roth and Lombard came up with the idea of using a crushable, energy absorbing
-
-![](_page_172_Picture_3.jpeg)
-
-material (Styrofoam) inside the helmet. A study in 1957 by a physician named George Snively helped this new type of helmet take hold through unusual means – testing six popular motorcycle helmets on human cadavers. The Roth and Lombard helmet with the protective lining was by far the most effective in preventing head injuries.
-
-Many more studies have documented the effectiveness of motorcycle helmets. This eventually led to standards for motorcycle helmets and universal helmet laws.**33,34**
-
-Maartens, N., A. Wills, and C. Adams. Lawrence of Arabia, Sir Hugh Cairns, and the Origin of Motorcycle Helmets. Neurosurgery, Vol. 50, No. 1, January 2002.
-
-## **Major Road Safety Research Sponsors and Research Programs**
-
-Most road safety research is funded by the government, through state or federal agencies. However, some research is also funded by privately run companies or foundations. The organizations that sponsor research projects are often focused on one category of research. The list below presents a look at some of the major research sponsors and their area of focus in road safety research.
-
-## **Federal Research Sponsors**
-
-#### **Federal Highway Administration**
-
-FHWA sponsors research studies on a variety of road safety topics, and their primary focus is on the roadway or the built environment. This research investigates the impact of road characteristics on
-
-**34**
-
-Liu, B., R. Ivers, R. Norton, S. Blows, S.K. Lo. Helmets for preventing injury in motorcycle riders (review). The Cochrane Collaboration, John Wiley and Sons, Ltd. January 2008. DOI: 10.1002/14651858. CD004333.pub3
-
-**35**
-
-Lyon, Craig; Bhagwant Persaud; and Kimberly Eccles. "Safety Evaluation of Centerline Plus Shoulder Rumble Strips." Federal Highway Administration, Report FHWA-HRT-15-048, 2015.
-
-the travel lane. Rumble strips on both the centerline and shoulder ensure that drivers are alerted no matter which side of the lane they depart. FHWA, through a pooled fund from 38 States, conducted a study to determine the safety effect of installing centerline and shoulder rumble strips on rural two-lane roads. The researchers gathered data on roads in three States and compared crash performance for roads where the rumble strips were installed versus roads without rumble strips. The analysis showed that the combined rumble strip strategy was effective at reducing crashes. As expected, the greatest crash reductions were for crash types that were related to lane departure including headon crashes (37% decrease), runoff-road crashes (26% decrease), and sideswipe-opposite-direction crashes (24% decrease).**<sup>35</sup>**
-
-**36**
-
-[https://www.fhwa.](https://www.fhwa.dot.gov/research/tfhrc/programs/safety/index.cfm) [dot.gov/research/](https://www.fhwa.dot.gov/research/tfhrc/programs/safety/index.cfm) [tfhrc/programs/](https://www.fhwa.dot.gov/research/tfhrc/programs/safety/index.cfm) [safety/index.cfm](https://www.fhwa.dot.gov/research/tfhrc/programs/safety/index.cfm)
-
-**37**
-
-[https://www.nhtsa.](https://www.nhtsa.gov/research-data) [gov/research-data](https://www.nhtsa.gov/research-data)
-
-road safety and seeks solutions to known safety problems. The FHWA Offices of Safety and Safety Research and Development conduct research to address issues including driver interaction with the roadway, intersection safety, pedestrian and bicycle safety, and keeping vehicles on the roadway.**36** The FHWA Turner-Fairbank Research Center houses more than 20 laboratories, data centers, and support facilities, and conducts applied and exploratory advanced research in road safety, among other topics. Additionally, FHWA staff participates and provides input to many other venues of research around the nation.
-
-#### **National Highway Traffic Safety Administration**
-
-NHTSA studies behaviors and attitudes in road safety, focusing on drivers, passengers, pedestrians, and motorcyclists. Research sponsored by NHTSA identifies and measures behaviors involved in crashes or associated with injuries, and develops and refines countermeasures to deter unsafe behaviors and promote safe alternatives. The research topics include occupant protection, distracted driving, motorcycle safety, speeding, and young drivers.**<sup>37</sup>**
-
-#### **Transportation Research Board**
-
-The Transportation Research Board (TRB) is part of the National Academies of Sciences and provides advice to the nation and informs public policy decisions. TRB plays a major role in road safety research. It hosts an annual meeting where transportation professionals
-
-#### **How Research Affects Implementation: Safety EdgeSM**
-
-The ultimate intent of safety research is to provide solid data to affect the way that safety measures are carried out in the real world. The development of Safety EdgeSM is a good example of this.
-
-Drivers who run off the road and then try to regain control often go too far and over-steer, leading to veering into the opposite lane or running off the road on the other side. This problem is made worse when the soil is eroded away from the pavement edge, creating a drop off. This safety concern was recognized in the 1980s, and the 1989 AASHTO Roadside Design Guide included a recommendation for adding a sloped edge to the pavement to assist drivers in regaining control onto the roadway. A few States attempted this treatment, but it was not widely implemented. Through the following years, other research showed a correlation between drop off crashes and fatalities.
-
-![](_page_173_Picture_13.jpeg)
-
-In the early 2000s, several States (Georgia, New York, Colorado, and Indiana) decided to install several miles of the sloped edge as demonstration project. The FHWA sponsored a research study to evaluate the effect of the sloped edge, now called Safety EdgeSM, on run-off-road crashes. The results showed a positive effect, and this finding swayed many safety offices in favor of the treatment. Those now in favor of Safety EdgeSM worked to get other offices, such as pavement offices, on board with the idea. Eventually it became a widespread practice, and by 2015, forty States required Safety EdgeSM to some degree in their design policies.**<sup>38</sup>**
-
-**38**
-
-Graham, J.L., Richard, K.R. , O'Laughlin, M.K., Harwood, D.W., "Safety Evaluation of the Safety Edge Treatment" Report No. FHWA-HRT-11-024, Federal Highway Administration, Washington, DC. (2011) [http://www.](https://www.fhwa.dot.gov/publications/research/safety/11024/11024.pdf) [fhwa.dot.gov/](https://www.fhwa.dot.gov/publications/research/safety/11024/11024.pdf) [publications/](https://www.fhwa.dot.gov/publications/research/safety/11024/11024.pdf) [research/](https://www.fhwa.dot.gov/publications/research/safety/11024/11024.pdf) [safety/11024/11024.](https://www.fhwa.dot.gov/publications/research/safety/11024/11024.pdf) [pdf](https://www.fhwa.dot.gov/publications/research/safety/11024/11024.pdf)
-
-from around the world present new research on many different transportation topics. The papers presented at the annual meeting are peer-reviewed, and a portion of them are published in the Transportation Research Record.
-
-TRB also maintains standing committees that provide direction to the research field and assist in disseminating research findings. Committees such as Transportation Safety Management, Highway Safety Performance, Pedestrians, Occupant Protection, and many others specifically address topics related to road safety. TRB manages the National Cooperative Highway Research Program (NCHRP), described below.
-
-## **State Research Sponsors**
-
-#### **American Association of State Highway Transportation Officials (AASHTO)**
-
-AASHTO is the organization behind NCHRP. The NCHRP funds many road safety research projects each year on a variety of topics that are integral to the State departments of transportation (DOTs) and transportation professionals at all levels of government and the private sector. The NCHRP is administered by the Transportation Research Board (TRB) and sponsored by individual State departments of transportation. The research projects are conducted in cooperation with FHWA (FHWA). Individual projects are conducted by contractors with oversight provided by volunteer panels of expert stakeholders. NCHRP projects cover a wide range of highway topics, but there is a specific focus area for safety.**<sup>39</sup>** AASHTO also maintains
-
-#### **Other Research Sponsors**
-
-Although the majority of road safety research is funded by government sources, private companies and organizations also participate in funding road safety research. Prominent examples of these are the IIHS and the American Automobile Association Foundation for Traffic Safety.
-
-several committees that oversee road safety topics including the Standing Committee on Highway Traffic Safety and the Subcommittee on Safety Management. These groups decide which research topics should be prioritized for funding under NCHRP.
-
-#### **State Research Programs**
-
-In addition to participating in large scale research efforts, such as NCHRP, State departments of transportation often fund road safety research projects on topics that are of particular interest to their State. They use portions from Federal funds that are specially designated for research projects. Every State has a different process for how the research projects are conceived and conducted, but a common arrangement is that the State DOT contracts with universities in the State to conduct the research. The research topics typically pertain to current road safety issues that are high priority within the State or issues related to geography, terrain, weather, driver population, or other such factors that may be particular to that State. For example, a State in a snowy climate may sponsor a research project on how snowplowing affects the visibility and durability of inpavement reflective markers.
-
-[http://www.trb.org/](http://www.trb.org/NCHRP/NCHRP.aspx) [NCHRP/NCHRP.](http://www.trb.org/NCHRP/NCHRP.aspx) [aspx](http://www.trb.org/NCHRP/NCHRP.aspx) **39**
-
-## **Strategic Communications**
-
-A key part of many efforts to improve road safety is sharing safety messages through strategic communications. This chapter provides an outline of the most basic elements of strategic communications that transportation safety professionals should use when working with their communications teams to craft and disseminate messages that seek to improve traffic safety culture. As mentioned in Unit 1, strategic communications programs like public education campaigns are commonly used to improve road user attitudes and awareness.
-
-A strategic communications program involves elements of communications, marketing, and public outreach. These components often overlap and are not easily separated into distinct categories with unique functions. Strategic communications is more than the sum of its parts. Rather, it is a structured methodology that fuses messaging with marketing while garnering public support.
-
-Several examples demonstrate that strategic communications can result in behavioral changes. An effort in the late 1980s to stop impaired driving resulted in substantial decreases in Driving Under the Influence (DUI) citations. NHTSA saw successful results from the implementation of the "Buckle Up America Campaign" and the National Safety Council's "Airbag and Seat Belt Safety Campaign" and the high-
-
-![](_page_175_Picture_6.jpeg)
-
-Public education campaigns like this one are commonly used to improve road user attitudes and awareness. *(Source: NHTSA)*
-
-visibility public information program "Click It or Ticket" safety belt enforcement campaign. Strategic communications was a key element of all of these efforts.
-
-Other examples of communications efforts include:
-
-- J Campaigns with careful pre-testing and delineation of a target group that receives the messages.
-- J Longer-term programs that deliver a message in sufficient intensity over time.
-
-- J Education programs built around behavioral change models, using interactive methods to teach skills to resist social influences through role playing.
-- J Public information campaigns that accompany other ongoing prevention activities.
-- J Programs conducted as part of a broader community effort or in support of law enforcement.
-
-An effective strategic communications program, like any effective endeavor, needs a plan. The following are key steps in developing a strategic communications plan that every road safety professional should know:
-
-- **1. Develop objectives**
-- **2. Identify target audience**
-- **3. Design messaging**
-- **4. Select communications channels**
-- **5. Determine budget and resources**
-- **6. Measure results**
-
-Each step of this communications plan outline is discussed in the sections below.
-
-## **1. Develop Objectives**
-
-The first phase in creating a strategic communications plan is to determine objectives. A welldesigned communications strategy may achieve multiple goals, such as informing the public about transportation safety issues, educating key political leaders on their roles in saving lives, and encouraging active participation from safety partners.
-
-To develop objectives, safety professionals must start by defining what "success" should look like. Important questions include:
-
-- J What are we trying to achieve?
-- J Do we want people to take a new action, or do we want people to modify an old behavior?
-- J How do we know when we have achieved our goal (i.e., what are our target metrics)?
-
-Establishing measurable goals is an important piece that should be not be overlooked. Having a target or measurable goal (metric) makes it simpler to gauge the success of a campaign.
-
-## **2. Identify Target Audience**
-
-After developing communications objectives, the next step is deciding what populations to target. For example, safety professionals must decide if the program should be aimed at the general public or a specific sub-set of the population, such as young drivers, pedestrians, or people who drive aggressively. A message could also be aimed at road safety professionals and government officials. In addition to a target audience or audiences, secondary audiences may be included. For instance, if the program targets young drivers, a secondary audience may include the parents of these novice drivers.
-
-## **3. Design Messaging**
-
-Once safety professionals identify the target audience, they must determine what the message will be. A key step is to define the problem that needs to be solved in order to craft the message. Messaging should be designed so that it encourages specific actions, and draws upon established
-
-#### **Watch for Me NC**
-
-Watch for Me NC is a comprehensive program run by the North Carolina Department of Transportation (NCDOT) in partnership with local communities. It is aimed at reducing the number of pedestrians and bicyclists crashes.
-
-The Watch for Me NC program involves two key elements: 1) safety and educational messages directed toward drivers, pedestrians, and bicyclists, and 2) enforcement efforts by area police to crack down on some of the violations of traffic safety laws. Local programs are typically led by municipal, county, or regional government staff with the involvement of many others including pedestrian and bicycle advocates, city planners, law enforcement agencies, engineers, public health professionals, elected officials, school administrators and others.
-
-All North Carolina communities are encouraged to use Watch for Me NC campaign materials to improve pedestrian and bicyclist safety in their communities.
-
-objectives to determine what those actions should be. Designing the message will bring about questions like these: "Is the intended action a change in safety funding or policies? Or is the goal a change in user behavior?" Focusing efforts on strategies that connect with the target audience is particularly important in today's environment of tight budgets and scarce resources.
-
-After message development, safety professionals should consider pretesting the message with the target audience. A pre-test can identify points of view of the target audience, provide unexpected insights or reactions that can help further refine messaging, and help determine if the communications plan will improve the chance of accomplishing the stated objectives.
-
-![](_page_177_Picture_6.jpeg)
-
-## **4. Select Communications Channels**
-
-Carefully crafted messages need to be conveyed through appropriate channels in order to be effective. Communications channels include the personal and the non-personal. Personal channels include the advocate channel (advocates championing the objectives of a campaign), expert channel (independent experts making statements to the target audience), and social channel (word of mouth communications). Non-personal channels include media, events, and public outreach – examples of common non-personal dissemination techniques include, but are not limited to, the following:
-
-J Brochures
-
-- J Conferences/workshops
-- J Dynamic message signs
-- J Media advisories
-- J New media (e.g., blogs, podcasts, Facebook pages)
-- J News media events
-- J Newsletters and press releases
-- J Public service announcements
-- J Print, radio, and television advertising
-- J Social media
-- J Websites
-
-Safety professionals must work with the communications team to determine the right media mix, which is the combination of communication channels needed to meet communications objectives. Together they must figure out which channels -- personal and/ or non-personnel – and which tactics will help meet the stated road safety goals. For example, personal channels may work well with government officials, while non-personnel channels may work best with reaching out to the public. Also, a detailed timeline of channels and tactics to employ, is crucial for implementing a strategic communications plan.
-
-## **5. Determine Budget and Resources**
-
-Implementing a comprehensive communications program requires resources like money, staff, and time. Therefore, developing a budget for the program is an integral part of carrying out the plan. Safety professionals should consider every element of the proposed plan, and make sure there are resources to
-
-cover them all. For instance, it may be necessary to hire an outside firm to help develop and refine messages and tactics, and that should be factored into the budget. All program expenses should be tracked to determine how well the budget has been met when measuring results.
-
-## **6. Measure Results**
-
-Evaluation of a strategic communications program is essential. Without evaluation, current programs may waste resources and fail to contribute to a road safety program's goals. Evaluations help safety professionals learn from outcomes and help them allocate funds in the most efficient manner. If a program does not meet expected metrics, they should re-examine the program and/or move the resources to other efforts.
-
-To measure the results of the program, safety professionals must go back to the objectives and predetermined measurable goals and targets. The original questions in Step 1 will guide the evaluation of the success of the program. Surveys, focus groups, and/or one-on-one interviews are often good ways of measuring the performance of a strategic communications program.
-
-As far as data allow, safety professionals should also attempt to compare the effectiveness of communication campaigns in the same way that they compare the performance of traditional infrastructure countermeasures. The comparison will help identify campaigns that are both effective and efficient.
-
-## **Advancing Road Safety**
-
-This chapter covers the crucial role that safety leaders, champions, and coalitions play in road safety management. While the various agencies and organizations involved in road safety bring unique and valuable perspectives to the problem of road safety, they also bring competing philosophies and problem-solving approaches. Bringing these various entities together to develop and implement an effective road safety program is a challenge.
-
-The major topics include:
-
-- J **Leaders**
-- J **Champions**
-- J **Coalitions**
-
-## **Leaders**
-
-Leadership is essential in any field, but it is particularly important in road safety for three major reasons:
-
-- J The safety field is diverse. It draws on the skills of educators, politicians, advocates, bureaucrats, public servants, and others. These groups sometimes work in harmony, but too often, they work in isolation. Leaders are necessary for cohesion in a complex, multi-stakeholder environment.
-- J A great deal of technical knowledge is available in the safety field regarding the most effective means of addressing the contributing factors to crashes.
-
-- However, technical knowledge is not sufficient for change to occur. Leadership is necessary to ensure technical knowledge is used.
-- J Road safety programs and projects compete with other public sector priorities. Without strong leaders to support the safety cause, public sector decision makers might not consider or prioritize safety.
-
-#### **Leadership Activities**
-
-Leaders are an instrumental part of any planning process, and road safety programs are no exception. Leaders bring people together, provide essential direction, and motivate people to participate in and implement the program. Leaders should be engaged and actively involved in the process.
-
-Consider a few examples of leadership in safety:
-
-- J State DOT leaders decide that reducing fatal and serious crashes should be the first priority of the department. They garner staff support to make funding and structural changes to the organization to support the shift in priorities.
-- J Law enforcement officers develop new incentive programs to encourage officers to identify and arrest impaired drivers. They successfully sell the program to their managers.
-
-![](_page_180_Picture_0.jpeg)
-
-Leaders bring people together, provide essential direction, and motivate people.
-
-- J A trauma nurse initiates a "teachable moments" campaign in which nurses teach patients about the risks associated with drinking and driving.
-- J A mayor recognizes that many streets in her city are not suitable for safe travel by pedestrians and garners support and funding from the city council to build sidewalks and improve the safety of transit stops.
-
-In each of these examples, the leaders recognized a need for change, acted on the need, and inspired others to follow. Anyone with drive, dedication, and a good idea can lead.
-
-#### **Leadership Traits and Skills**
-
-Good leaders influence policy direction, set priorities, and define performance expectations. They energize the road safety process and see to it that a plan is developed, and once developed, is implemented. They are risk takers, problems solvers, and creative thinkers committed to doing what is necessary to advance the cause, which sometimes means breaking traditional institutional barriers and balancing competing agency priorities.
-
-Leaders are often known as program managers, and their activities keep the implementation process on track. They manage the process and attend to the day-to-day tasks of arranging, facilitating, and documenting meetings, tracking progress, and moving discrete activities through to completion.
-
-Leaders are needed throughout all stages of road safety development, implementation, and evaluation. They communicate the safety vision and support a collaborative framework that enables safety stakeholders to participate actively in implementation.
-
-#### **Leadership Development**
-
-To expand leadership support, begin with the safety partners already committed to the safety concept and process. Encourage the leadership of those partners to contact their peers, explain the significance of their efforts, and marshal support. Their endorsement of the safety vision should include encouraging staff to stay engaged and building relationships across organizational boundaries and traditional areas of responsibility.
-
-Leadership support affects agencies or organizations internally by granting permission to dedicate time and resources to the safety effort.
-
-It also holds those responsible for safety accountable. Leadership should recognize that this is an ongoing process and institutionalize the change in the safety decisionmaking culture.
-
-The safety program manager may perform either as a part- or fulltime permanent role; experience demonstrates a dedicated role is preferable.
-
-## **Champions**
-
-Successful road safety programs call for at least one champion to assist in gathering all critical safety partners into a collaborative group. Champions provide enthusiasm and support for the safety programs.
-
-![](_page_181_Picture_7.jpeg)
-
-Like leaders, champions must be credible and accountable, have excellent interpersonal and organizational skills, and be skilled expediters.
-
-#### **Champion Activities**
-
-Safety champions help secure the necessary leadership, resources, visibility, support, and commitment of all partners. Sometimes the DOT leadership, or the leadership of the primary sponsoring agency, appoints the champion. A safety champion can reside at any level within the organizational structure and can perform various functions.
-
-For example, a safety champion may lead the executive committee that meets periodically to solve problems, remove barriers, track progress, and recommend further action. The role of the executive committee is to decide which projects or strategies are funded based on input from the emphasis area teams, and to prioritize them based on benefit/cost analysis, expected fatality reductions, and the extent to which they address the project's goals and objectives.
-
-Where relationships are not fully developed, the champion may have to put in additional effort to keep the full range of safety partners committed and actively participating.
-
-#### **Champion Traits and Skills**
-
-FHWA published the "Strategic Highway Safety Plan Implementation Process Model" that identifies two types of transportation safety champions.**<sup>39</sup>** The first have access to resources and the ability to implement change. In other words, they may
-
-not be involved in the day-today management responsibility for program development and implementation, but they are able to "move mountains" in terms of resource allocation and policy support. The second are leaders who inspire others to follow their direction. These champions are people who provide enthusiasm and support to transportation project implementation. They tend to be subject matter experts and highly respected within their own agencies and in the safety community.
-
-#### **Succession Planning**
-
-All agencies and organizations undergo staff changes, and it is essential to train the leaders of tomorrow to ensure that the focus on safety continues into the future. This can be accomplished by assigning leadership responsibilities for program implementation to newer staff and by ensuring that all staff have opportunities to engage and lead during meetings and other activities.
-
-To ensure continuity when an individual champion or leader retires, takes a position in another organization, or moves out of State, a systematic approach to identifying their replacement is necessary. The selection process should be based on individual skills, leadership traits, and the position held within a stakeholder organization. One way to institutionalize the selection of safety committee members is to link their selection to the position they hold within the stakeholder organization. For example, whoever assumes the previous safety champion or committee member's position should also become the new committee member.
-
-**39**
-
-[http://safety.fhwa.](http://safety.fhwa.dot.gov/hsip/shsp/) [dot.gov/hsip/shsp/](http://safety.fhwa.dot.gov/hsip/shsp/)
-
-## **Coalitions**
-
-Safety partners and organizations bring unique and valuable perspectives to bear on the transportation safety problem. However, differing philosophies, competing priorities, and varying business cultures may make collaboration a challenge. Coalitions are an opportunity for road safety leaders and champions to bring together the various disciplines and agencies and focus on the shared goal of reducing crashes. Whether coalitions are short-term, longterm, or permanent, they offer road safety collaborators the prospect of solving complex issues through partnerships.
-
-#### **Safety Partners**
-
-The organizational structure of agencies and interagency working relationships are important factors to consider when bringing safety partners together. Rather than create entirely new committees, a champion should build upon existing relationships, interagency working groups, and committees.
-
-Many States have functioning transportation safety committees, such as a TRCC, an Executive Committee for Highway Safety, or a Towards Zero Deaths (TZD) coalition. Regardless of how safety partners are brought together and organized to contribute to the safety process, champions should look for ways to expand membership to include a broad range of partners, such as insurance, trucking and motor coach companies, fire and rescue, local businesses, and others.
-
-When States implement the Federally required SHSPs, their safety partners typically include those that are Federally required, such as EMS providers, health and education departments, Motor Carrier Safety Assistance Program (MCSAP) managers, local agencies, tribal governments, special interest groups (e.g., MADD), and others.
-
-With an emphasis on wide-ranging collaboration that includes many external partners, it can be easy to overlook the importance of broad DOT involvement, as well. Early involvement of planning, design, operations, and maintenance will enhance the implementation of safety strategies, especially if they are new or experimental.
-
-Some champions bring partners together by convening a safety summit or meeting. This could be a large initial kickoff meeting or a meeting of the safety working group or steering committee. It provides an opportunity to learn about each of the safety partner priorities and understand what they contribute. Coalitions should give participants the opportunity to describe their safety concerns and current programs. This may advance the discussion of critical safety issues, identify opportunities, and forge an agreement on how to proceed.
-
-#### **Benefits of Collaboration**
-
-Engineering, planning, emergency response, and behavioral approaches all have roles to play in addressing road safety. Professionals in these various disciplines have different skill sets, and they approach solutions using different methods. Dramatic improvements in roadway safety are more likely to result through a combination of techniques than from techniques from a single discipline. This need
-
-for multidisciplinary solutions necessitates collaboration.
-
-## **Unit Summary**
-
-Improving road safety is a complex endeavor requiring the knowledge and expertise of a wide range of professionals. Road safety improvement is a joint effort across many Federal, State, and local agencies, each with their own particular focus area.
-
-Federal agencies often play a role in providing funding and national cooperation for focused road safety efforts. State and local agencies implement road safety improvements, either for roads and intersections in their jurisdiction, or on the road users themselves. The efforts of these government agencies is supported by a broad base of
-
-road safety research, which provides knowledge on identifying safety problems or evaluating potential solutions. Strategic communications allow all road safety partners to be effective in their efforts to improve road safety, either in communicating to the public or to transportation professionals.
-
-Leadership is essential in the road safety field. The diversity of the field, the importance of coordination among disciplines, and the need to defend safety programs among a host of competing public sector priorities all contribute to the need for strong leadership. Successfully implementing road safety efforts relies on safety champions and coalitions that bring together safety partners from all agencies and disciplines.
-
-#### **EXERCISES**
-
-- J **IDENTIFY** a road safety leader in your community, State, or region. Determine the leader's area(s) of concern, observe how the leader engages with the transportation safety community, and assess the leader's success. Write a brief report summarizing your research. Include an explanation for why or why not the leader was successful.
-- J FHWA has explicitly stated that a "safety champion" should lead safety efforts, specifically development of State SHSPs. Using your home State's DOT website, **RESEARCH** past or present safety champions. Prepare a brief class presentation that identifies the champion, explains why he or she was chosen for the role, what the requirements of the position were, and
-
-- what projects the State implemented under the champion's guidance. Be sure to include an assessment of the champion's tenure.
-- J Coalition building requires a thoughtful, reasoned approach. **CHOOSE** an important road safety issue that the transportation community is still studying. Propose a list of coalition members that could best approach the issue from all available disciplines and interests. Be sure to keep the list reasonable. In a report, summarize the safety issue and introduce the coalition members. Explain why each member is critical to the coalition and the expectations for each member's contributions.
-
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-![](_page_186_Picture_0.jpeg)
-
-**Road Safety Fundamentals is available for free at: https://rspcb.safety.fhwa.dot.gov/rsf/**
-
-![](_page_187_Picture_1.jpeg)
-
-**FHWA, Office of Safety**
-
-Felix H. Delgado, P.E. Felix.Delgado@dot.gov

Zotero/001_artiklid/Road Safety Indicators/cenajroadaccidentfatalitiesforecasting2024.md

@@ -1,310 +0,0 @@
----
-category: literaturenote
-citekey: cenajroadaccidentfatalitiesforecasting2024
-title: "Road Accident Fatalities Forecasting Models using Smeed’s Regression Analysis: A Case Study"
-authors: "Cenaj, Erjola; Dervishi, Raimonda"
-year: 2024
-date: 2024-00-00 2024
-doi: 10.24018/ejeng.2024.9.6.3213
-publication: European Journal of Engineering and Technology Research
-url: "https://doi.org/10.24018/ejeng.2024.9.6.3213"
-zotero_key: 2SGNNE3S
-zotero_storage: VEQ5AUQ2
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Cenaj, Erjola"
-status: converted
----
-## RESEARCH ARTICLE
-
-![](_page_0_Picture_2.jpeg)
-
-# Road Accident Fatalities Forecasting Models using Smeed's Regression Analysis: A Case Study
-
-Erjola Cena[j\\*](#page-0-0) and Raimonda Dervishi
-
-#### *ABSTRACT*
-
-**Fatalities and injuries triggered by road traffic accidents are worthwhile traffic safety problems, especially for developing and developed countries. Several academic works have convinced that using data from a given group, a weighty relationship between the number of road traffic fatalities (RTFs) and registered vehicles and population size is designed. This paper uses historical data from over ten years in Albania to identify the relationship between the same factors. The study divulges that such a relationship cannot be impacted with a good degree of exactness by Smeed's Equation (SE). Additionally, road accident prediction models by the structural form of Smeed's regression method enable estimating RTFs tied to population size and number of active vehicles. The modified forms of SE are the key to a satisfactory match in predicting RTFs in Albania.**
-
-**Keywords:** Number of vehicles, regression analysis, Smeed's equation, traffic accident fatalities.
-
-**Submitted:** October 10, 2024
-
-**Published:** November 28, 2024
-
-*[10.24018/ejeng.2024.9.6.3213](http://dx.doi.org/10.24018/ejeng.2024.9.6.3213)*
-
-*Department of Mathematical Engineering, Faculty of Mathematical and Physical Engineering, Polytechnic University of Tirana, Albania.*
-
-<span id="page-0-0"></span>\**Corresponding Author:* e-mail: [erjola\\_cenaj@yahoo.com](mailto:erjola_cenaj@yahoo.com)
-
-# 1. Introduction
-
-Fatalities and injuries due to road traffic accidents are substantial traffic safety problems for both developing and well-constructed countries. According to the statistics of the World Health Organization (WHO), worldwide, 1.3 million people die, and as many as 20–50 million people are also injured annually linked with traffic accidents. The same statistics point out that approximately 90% of fatalities from road traffic accidents transpire in low-andmiddle-income countries, despite the fact they hold a 50% share of motor vehicle ownership in the world [\[1\]](#page-3-0).
-
-On the other hand, road traffic accidents are the leading cause of all injuries and are representative of most deaths, crafting road traffic injuries the 9th leading matter of disability-adjusted life years lost worldwide [\[2\]](#page-3-1). Impetuosity and insanity, in many cases, are the foremost sources, especially in Balkan countries, and lack of proper sense of using traffic signs are some of RTFs sources. However, it is effective, entitizing other components such as population size, the number of vehicles in use, the total length of roads, and the population density, as if these factors can be translocated to reduce the future number of accidents [\[3\]](#page-3-2), [\[4\]](#page-3-3).
-
-Many studies have shown that population size and number of registered vehicles are dominant factors affecting road traffic fatalities worldwide [\[5\]](#page-3-4), [\[6\]](#page-3-5).
-
-Smeed's Equation (SE) is the earliest model used to approximate the number of fatalities in road traffic accidents, comprising the independent variables of population and number of registered vehicles and the dependent variable of the number of fatalities [\[7\]](#page-3-6). It has been found that SE is adequate for utilization in some developed countries [\[8\]](#page-3-7), and according to Ponnaluri [\[9\]](#page-3-8), it is a poorly method in developing countries [\[10\]](#page-3-9)–[\[12\]](#page-3-10).
-
-The process of RTFs evaluation was introduced for the first time in 1949, by Smeed. Smeed started to work based on available data linked to road traffic fatalities (RTFs), official data on active vehicle numbers, and population size referring to 1938 from the group of 20 countries selected (mostly EU). Then, he discovered a nonlinear relationship among RTFs and influencing demographic and motorization rates through the mathematical expression given in [\(1\)](#page-0-1) [\[13\]](#page-3-11):
-
-<span id="page-0-1"></span>
-$$\frac{F}{V} = 0.0003 \left(\frac{V}{P}\right)^{\frac{-2}{3}} \tag{1}$$
-
-where
-
-*F –* number of road traffic fatalities (RTFs)
-
-*V –* number of registered vehicles
-
-*P –* population size
-
-The Smeed equation provides a good match referring to available data from the selected countries for that year. The factors of experiencing possible road traffic accidents are coupled to RTFs, population size, and the number of registered vehicles. This approach is used to carry out important sizes handling identical factors as Smeed. We used the number of RTFs, official vehicle number registered vehicles, and population size, including the time fraction 2014–2023. As in many other countries worldwide, the population size and number of registered vehicles are dominant factors affecting road traffic fatalities in Albania. The study shows that Smeed's Equation is inadequate to be used for definitively predicting RTFs in Albania over the study period. The model is tested using the statistics from INSTAT Albania. It has been shown that these modified forms of SE lead to a satisfactory fit for the tested period.
-
-# 2. Data Source
-
-In this research work, the factors mentioned in the Smeed equation, such as population size, number of registered vehicles, and number of road traffic fatalities for the tested period under consideration, 2014–2023, are provided from the domestic official sources, Albanian Statistical Institute (INSTAT) [\[14\]](#page-3-12).
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-According to INSTAT, around 16,192 road traffic accidents in Albania have been declared during the last 10 years. Furthermore, around 24,020 people were involved in road accidents, split into 2199 cases of fatalities, and 19,622 were injured between the 2014–2023 period, as depicted in [Table I.](#page-1-0)
-
-#### 3. Methodology
-
-## *3.1. Application of Smeed's Regression Model to Estimate RTFs*
-
-Smeed's model is an empirical rule that binds RTFs to variables, the vehicle registered, and the population size for a given region, or country. As it is expected, increasing the number of vehicles will decrease fatalities per vehicle. This principle was first introduced based on 1938 work, which was employed later to test RTFs in 1960 up to1967 period or even later in many other scientific efforts included in the research work of [\[15\]](#page-4-0).
-
-<span id="page-1-0"></span>TABLE I: Statistical Data for the Tested Period (2014–2023)
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-| Year | Fatalities | Population | No. of vehicle |
-|------|------------|------------|----------------|
-| 2014 | 264        | 2,895,947  | 415,121        |
-| 2015 | 270        | 2,893,005  | 443,227        |
-| 2016 | 269        | 2,875,592  | 479,217        |
-| 2017 | 222        | 2,876,591  | 460,299        |
-| 2018 | 213        | 2,870,324  | 500,894        |
-| 2019 | 227        | 2,862,427  | 499,779        |
-| 2020 | 181        | 2,845,955  | 539,497        |
-| 2021 | 197        | 2,829,741  | 593,280        |
-| 2022 | 164        | 2,793,592  | 639,379        |
-| 2023 | 192        | 2,761,785  | 699,337        |
-
-Smeed's equation model can be given from [\(2\)](#page-1-1) [\[13\]](#page-3-11):
-
-<span id="page-1-1"></span>
-$$\frac{F}{V} = \alpha \cdot \left\{ \left( \frac{V}{P} \right)^{\beta} \right\} \cdot \varepsilon \tag{2}$$
-
-where
-
-*F –* number of RTFs
-
-*V –* number of registered vehicles
-
-*P –* population size
-
-*E –* error term
-
-*α* and *β –* parameters that are a matter of estimation
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-<span id="page-1-2"></span>On the other hand, [\(2\)](#page-1-1) can be expressed as following in [\(3\):](#page-1-2)
-
-$$Y = aX^{\beta}\varepsilon\tag{3}$$
-
-where
-
-*X* – the predictor variable is *V/P* (vehicle density)
-
-*Y* the dependent variable, *F/V* (fatality rate per vehicle)
-
-In the process of evaluating RTFs, Smeed was based on the least squares regression (LSR) techniques to assess the parameters linked to [\(3\).](#page-1-2) However, the LSR approach does not allow parameter variability and it is very sensitive to infraction of the normality assumption.
-
-## *3.2. Literature Review on the Application of Modified Smeed's Regression Model*
-
-A lot of efforts are undertaken by many authors in the field of RTFs theory. The research work and contribution of Koren and Borsos have led to a modified form of the original Smeed's equation as can be given in the following mathematical expression given in [\(4\)](#page-1-3) [\[16\]](#page-4-1):
-
-<span id="page-1-3"></span>
-$$\frac{F}{P} = \alpha \left(\frac{V}{P}\right)^{\beta} \varepsilon \tag{4}$$
-
-where *α* and *β* are the parameters necessary to be determined, for the known variables, such as the number of road traffic fatalities (*F*), the number of registered vehicles (*V*), and the population (*P*).
-
-After transforming [\(4\)](#page-1-3) and supposing both side logarithms, to base e, the nonlinear model in [\(4\)](#page-1-3) can be expressed in a linear form as follows in [\(5\):](#page-1-4)
-
-<span id="page-1-4"></span>
-$$Y_i = \alpha_0 + \beta X + e_i \tag{5}$$
-
-where
-
-*Yi* – ln(F/P), Xi = ln(V/P)
-
-*α<sup>0</sup>* – ln*α*
-
-*ei* – ln*ε*
-
-*i –* 1, 2, *...* , n
-
-Moreover, during this transformation, it is required that *ei*, *i* = *1, 2 ... , n* are *iid* distributed *N(0, σ2)*.
-
-Further on, using least squares regression (LSR) method to estimate the mentioned above parameters, should pass through employing expression in [\(6\):](#page-1-5)
-
-$$\hat{\beta} = \frac{\sum_{i=1}^{n} x_i y_i - \frac{1}{n} \sum_{i=1}^{n} x_i \sum_{i=1}^{n} y_i}{\sum_{i=1}^{n} x_i^2 - \frac{1}{n} (\sum_{i=1}^{n} x_i)^2}$$
-(6)
-
-Or *α*ˆ <sup>0</sup> is linked as following in [\(7\):](#page-1-6)
-
-<span id="page-1-6"></span><span id="page-1-5"></span>
-$$\hat{\alpha}_0 = \overline{y} - \hat{\beta}\overline{x} \tag{7}$$
-
-Using the equation *α*<sup>0</sup> = *lnα*, then the least squares estimation of the parameter *α* can be evaluated using [\(8\):](#page-2-0)
-
-$$\hat{\alpha} = e^{\hat{\alpha}_0} \tag{8}$$
-
-Thus, in terms of initial variables, the expression given in [\(9\)](#page-2-1) is carried out:
-
-<span id="page-2-1"></span>
-$$\frac{F}{P} = \hat{\alpha} \left(\frac{V}{P}\right)^{\hat{\beta}} \tag{9}$$
-
-#### *3.3. Test of Regression*
-
-The regression hypothesis that none of the *x*'s predict *y* can be expressed as *H0*: *β* = *0*. The modeling process does not include *β* = 0 as part of our hypothesis, enabling the restriction of *y* to ensure realizing an intercept of zero.
-
-We can test *H0: β* = *0* by [\(10\):](#page-2-2)
-
-<span id="page-2-2"></span>
-$$F = \frac{SSR}{SSE/(n-2)} \tag{10}$$
-
-It is simply distributed as *F* (*1, n*−*2*) when *H0*: *β<sup>1</sup>* = *0* is true. If *F* > *F* (*α; 1, n*−*2*), then it is worth rejecting *H0* from simulations.
-
-#### 4. Discussion and Results
-
-# *4.1. Estimation of RTFs using Smeed's Regression Model in Albania*
-
-The results of our research work are introduced in [Table II.](#page-2-3)
-
-As it can be depicted from the results [\(Table II\)](#page-2-3), the application of Smeed's equation leads to an overestimation of the number of RTFs in Albania.
-
-The result shows that on average, the expected fatalities estimated by Smeed's formula exceed the observed values by 4.8%, which compared to official data's (2.19%) is higher. Given the relatively large deviations between observed and expected values of fatalities as stated in [Table II,](#page-2-3) Smeed's equation is ineffective in predicting RTFs in Albania.
-
-Using [\(2\)](#page-1-1) and performing LSR technique on the data given in [Table I,](#page-1-0) we get the mathematical expression as given in [\(11\):](#page-2-4)
-
-<span id="page-2-4"></span>
-$$\frac{F}{V} = 0.0000232 \left(\frac{V}{P}\right)^{-1.6992} \tag{11}$$
-
-<span id="page-2-3"></span>TABLE II: Simulation Results on Estimated Fatalities based on Smeed's Regression Models
-
-| Year  | Fatalities | F<br>ˆ | Error (%)   | F<br>˜ | Error       |
-|-------|------------|--------|-------------|--------|-------------|
-| 2014  | 264        | 454    | 190 (71.9%) | 261    | 3 (1.2%)    |
-| 2015  | 270        | 464    | 194 (71.8%) | 249    | 21 (7.7%)   |
-| 2016  | 269        | 474    | 205 (76.2%) | 233    | 36 (13.4%)  |
-| 2017  | 222        | 468    | 246 (110%)  | 240    | −18 (8.1%)  |
-| 2018  | 213        | 481    | 268 (125%)  | 226    | −13 (6.1%)  |
-| 2019  | 227        | 479    | 252 (111%)  | 225    | 2 (0.8%)    |
-| 2020  | 181        | 490    | 309 (170%)  | 211    | −30 (16.5%) |
-| 2021  | 197        | 504    | 307 (155%)  | 196    | 1 (0.5%)    |
-| 2022  | 164        | 512    | 348 (212%)  | 182    | −18 (10.9%) |
-| 2023  | 192        | 560    | 368 (191%)  | 167    | 26 (13.5%)  |
-| Total | 2199       | 4886   |             | 2190   |             |
-
-Note: *F* = number of RTFs, *F* (as in the 5th column title) = estimate of F from Smeed's formula.
-
-<span id="page-2-5"></span><span id="page-2-0"></span>TABLE III: ANOVA Test: The Estimated Smeed's Regression Model ANOVA df SS MS F Significance F Regression 1 0.859457 0.859457 76.82324 2.25E-05 Residual 8 0.0895 0.011187 Total 9 0.948956
-
-TABLE IV: ANOVA Test: The Estimated Modified Smeed's
-
-<span id="page-2-7"></span>
-
-| Regression Model |    |          |          |          |                |
-|------------------|----|----------|----------|----------|----------------|
-| ANOVA            | df | SS       | MS       | F        | Significance F |
-| Regression       | 1  | 0.145557 | 0.145557 | 13.01249 | 0.006909       |
-| Residual         | 8  | 0.089488 | 0.011186 |          |                |
-| Total            | 9  | 0.235045 |          |          |                |
-
-Using [\(11\),](#page-2-4) the result of this application, the estimated *F*˜ , which portrays the estimate of *F* from [\(11\)](#page-2-4) is introduced in [Table II.](#page-2-3)
-
-From the simulation results it is shown that the application of Smeed's regression model is adequate for assessing the RTFs in Albania [\(Table II\)](#page-2-3). The result shows that on average, the expected fatalities as estimated by Smeed's formula exceeded the observed by 2.19% but results tightly to the average of real fatalities.
-
-[Table III](#page-2-5) presents the analysis of variance from the estimated Smeed's regression model. From the test of regression, the statistic:
-
-$$F = \frac{regression \, mean \, square}{residual \, mean \, square}$$
-
-where H0 is true for a significance level of 0.05, then it has Fisher distribution with 1 and n-2 degrees of freedom. If the calculated value of *F* is greater than F(0.05; 1, n-2), it necessitates the rejection of H0. Since Fcalculated = 76.82 > 5.32 = F(0.05;1,8), the test is significant at the 5% accuracy level. From the simulation results and analyses, it is concluded that a linear relationship between the expected value of *y* = *ln(F/V)* and *x* = *ln(V/P)* is observed.
-
-## *4.2. Estimation of RTFs using Modified Smeed's Regression Model in Albania*
-
-Using [\(4\)](#page-1-3) and LSR method based on [Table I,](#page-1-0) we get the mathematical expression as follows in [\(12\):](#page-2-6)
-
-<span id="page-2-6"></span>
-$$\frac{F}{P} = 0.0000231 \left(\frac{V}{P}\right)^{-0.6992} \tag{12}$$
-
-TABLE V: Estimated Fatalities from the Modified Smeed's
-
-<span id="page-2-8"></span>
-
-| Regression Model |            |        |             |  |  |
-|------------------|------------|--------|-------------|--|--|
-| Year             | Fatalities | F<br>ˆ | Error       |  |  |
-| 2014             | 264        | 260    | 4 (1.5%)    |  |  |
-| 2015             | 270        | 248    | 22 (8.15)   |  |  |
-| 2016             | 269        | 232    | 36 (13.4%)  |  |  |
-| 2017             | 222        | 239    | −17 (7.6%)  |  |  |
-| 2018             | 213        | 225    | −12 (5.6%)  |  |  |
-| 2019             | 227        | 224    | 3 (1.3%)    |  |  |
-| 2020             | 181        | 210    | −29 (16%)   |  |  |
-| 2021             | 197        | 195    | 2 (1%)      |  |  |
-| 2022             | 164        | 181    | −17 (10.4%) |  |  |
-| 2023             | 192        | 167    | 25 (13%)    |  |  |
-| Total            | 2199       | 2181   |             |  |  |
-
-<span id="page-3-13"></span>
-
-|    | Coefficient    |                            | Standart error |                            | R square       |                            |
-|----|----------------|----------------------------|----------------|----------------------------|----------------|----------------------------|
-|    | Smeed's method | Modified Smeed's<br>method | Smeed's method | Modified Smeed's<br>method | Smeed's method | Modified Smeed's<br>method |
-| αˆ | −10.6722       | −10.6724                   | 0.331394       | 0.331372                   | 0.90           | 0.62                       |
-| βˆ | −1.69923       | −0.69929                   | 0.193868       | 0.193855                   |                |                            |
-
-TABLE VI: Regression Results: Estimated Coefficients (*α*ˆ;*β*ˆ*)*
-
-[Table IV](#page-2-7) presents the analysis of variance from the estimated modified Smeed's regression model. From the regression test, if the calculated value of F is greater than F(0.05; 1, n-2), it is recommended to reject H0. Since Fcalculated = 13.01 > 5.32 = F(0.05;1,8) the test is significant at the 5% level. We conclude that there is a linear relationship between the expected value of *y* = *ln(F/P)* and *x* = *ln(V/P)*.
-
-The estimated *F*ˆ , which represents the estimate of F evaluated from [\(12\),](#page-2-6) is given in [Table V.](#page-2-8) From the simulation results [\(Table V\)](#page-2-8), it is shown that the application of modified Smeed's regression model is adequate for [Table V.](#page-2-8) Estimated fatalities from the modified Smeed's regression model assessing RTFs in Albania case. The result shows that, on average, the expected fatalities, as estimated by Smeed's formula, exceeded the observed by 2.181% but results tightly to the average of real fatalities.
-
-In [Table VI,](#page-3-13) the coefficients estimated from both regression models, the standard errors for the LSM, and the R Square coefficient are given. From the simulation results included in [Table VI,](#page-3-13) the estimated coefficient *α*ˆ, β , and standard error for both coefficients are almost equal. The R<sup>2</sup> coefficients of the determination chosen model results are 0.90 and 0.62, respectively. These values recommend that Smeed's regression model properly fit the data of the Albanian case referring to the 2014–2023 period.
-
-# 5. Conclusions
-
-Smeed's equation is a valuable tool to forecast fatalities linking to independent predictors, population size, and number of registered vehicles based on a non-linear mathematical expression form. Referring to official data within the period 2016–2023, Smeed's Equation cannot be specified as a good approximation due to its low degree of accuracy. Based on the approved index of safety (0.0003) in SE [\[8\]](#page-3-7), and compared to other case studies, it is clearly stated that the number of fatalities in Albania is apparently away from the international average. Furthermore, in this research work, the structural form of Smeed's regression method to assess fatalities was employed for estimating RTFs in Albania. Once again, referring to official data within the period 2016–2023, Smeed's Equation can be specified as a good approximation due to its high degree of accuracy. Performing F tests, a high statistical significance (*p <* 0.001) for both developed models are observed. Moreover, the results carried out in this scientific work are a good starting point for predicting traffic safety conditions in the future.
-
-The prediction of RTFs is a major issue for the safety of human life, which should be addressed more carefully together with the problems identified in studies [\[17\]](#page-4-2)–[\[19\]](#page-4-3) constitute the full dimensions of safety towards a reliable transport sector that meets the general notion of reliability in developing countries in 2030 and beyond.
-
-In conclusion, the developed models led to the conclusion that increasing the number of registered vehicles may decrease fatalities.
-
-## Conflict of Interest
-
-The authors declare that they do not have any conflict of interest.
-
-## References
-
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-category: literaturenote
-citekey: chapelonroadsafetyfrancehard2010
-title: "Road safety in France: The hard path toward science-based policy"
-authors: "Chapelon, Jean; Lassarre, Sylvain"
-year: 2010
-date: 2010-00-00 2010
-doi: 10.1016/j.ssci.2010.04.015
-publication: Safety Science
-url: "https://doi.org/10.1016/j.ssci.2010.04.015"
-zotero_key: X7GDB7CB
-zotero_storage: XNJZCH6R
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Chapelon, Jean"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-### Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/09257535)
-
-# Safety Science
-
-![](_page_0_Picture_4.jpeg)
-
-![](_page_0_Picture_5.jpeg)
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-# Road safety in France: The hard path toward science-based policy
-
-Jean Chapelon a,1, Sylvain Lassarre b,\*
-
-- <sup>a</sup> Conseil général de l'Environnement et du Développement durable, Paris la Défense, France
-- <sup>b</sup> Institut National de Recherche dans les Transports et leur Sécurité, Champs sur Marne, France
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-#### article info
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-Article history: Received 30 July 2009 Received in revised form 12 March 2010 Accepted 25 April 2010
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-Keywords: Road safety Policy Management Attributable risk Accident data
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-#### abstract
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-Very considerable advances have been made in road safety over the long term and especially in the recent years. This improvement was achieved in part due to the effectiveness of French decision-making system, even if it remained very perfectible. An analysis of the road safety management system is carried out in order to understand the strengths and weaknesses of the organisation of road safety in France.
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-The organisation of road safety in France is extremely centralised. The basis of road safety policy at the national level rests on an extensive information system (covering accidents, risk exposure, speed, utilisation of mobile phones) and on analyses of road risk (the risks attributable to alcohol, speed and the use of mobile phones).
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-This statistical information and these risk models are integrated in risk management tools such as monitoring, ranking and policing. Monitoring makes it possible to track the development of road safety, bench-marking to compare the performance of the country's different departments with each other, and policy making to refine the details of a policy.
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-The development of the governance of road risk is leading managers and decision-makers to perfect data-gathering procedures, standardise and simplify the analytical tools used, and broaden the range of risks covered.
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-> -2010 Elsevier Ltd. All rights reserved.
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-# 1. Introduction
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-Very considerable advances have been made in road safety over the past 30 years. In France, these advances have resulted in a fall in the numbers killed on the roads by about 2% a year, whereas the volume of traffic grew by around 2.5% annually, signifying an improvement of about 4.5% in the effectiveness of road safety policies. This is in line with the long-term model of fatality risk developed on an annual basis, over 50 years, which shows a decreasing trend over time accompanied by sharp falls in risk due to national road safety measures such as speed cameras (a 48% drop between 2002 and 2008) [\(Chapelon, 2006; Lassarre and Hoyau, 2008](#page-8-0)).
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-This improvement was achieved despite the fact that the French decision-making system remained very perfectible.
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-This article aims to demonstrate the strengths and weaknesses of the organisation of road safety in France and to show how decision-makers are made aware of the findings of research reports and surveys and the extent to which decisions are taken and followed up on a scientific basis.
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-E-mail addresses: [Jean.Chapelon@developpement-durable.gouv.fr](mailto:Jean.Chapelon@developpement-durable.gouv.fr) (J. Chapelon), [sylvain.lassarre@inrets.fr](mailto:sylvain.lassarre@inrets.fr) (S. Lassarre).
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-After outlining in Section [2](#page-1-0) the organisation of road safety in France and the role played by the National Inter-Ministerial Road Safety Observatory (Observatoire National Interministériel de Sécurité Routière), we will start, in Section [3.1](#page-2-0), by discussing the basis of a policy founded on scientific findings, including the role of essential information systems such as accident records, risk exposure data, speed measurements and measurements of other behavioural phenomena, including the use of mobile phones. The evaluation of ''objective" risk through the calculation of attributable risk will be taken in Section [3.2](#page-3-0) as an example for three factors: alcohol, speeding and mobile phones. The risk models adopted are based on epidemiological methods and time series analysis, as recommended by the experts of the [OECD \(2008\).](#page-8-0)
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-Secondly, the article will show in Section [3.3](#page-5-0) how this information and these models are integrated in three basic risk management tools:
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-- monitoring, which makes it possible to track the development of road safety,
-- bench-marking, which enables different departments to be compared with each other, and
-- policy making, which makes it possible to refine the details of a policy.
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-<sup>\*</sup> Corresponding author. Tel.: +33 1 4592 5764.
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-<sup>1</sup> Tel.: +33 1 4081 2364.
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-<span id="page-1-0"></span>We will end in Section [4](#page-7-0) by highlighting the inadequacies of the present structure, despite the progress made, and pointing to the avenues for improvement in the future as part of a discussion on the governance of road risk.
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-### 2. French organisation of road safety and the role played by the Road Safety Observatory
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-The Inter-Ministerial Road Safety delegate is in charge of road safety in France. He prepares the Inter-Ministerial Road Safety Committee (Comité Interministériel de Sécurité Routière – CISR) which takes decision, and implements these decisions. The interministerial character of the delegation is more paraded than real. Of course, there are five or six advisors detached from different ministries, such as Interior, Justice, Education and Health, but their ties with their ministries of origin are loose and they have very little influence over their ministry's actions in the area of road safety, which remains in the hands of the new Ministry of Sustainable Development, assisted by a Secretariat of Transport.
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-On the other hand, the strong point in the French organisation is its hierarchical nature, with the important role played by the Prefect of each department (Fig. 1) who, together with the head of road safety projects, one of his close collaborators, coordinates the local actions of the different ministries and encourages the activities of local community groups.
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-At the same time, the majority of road safety decisions are taken at meetings of officials on the basis of administrative and legal rather than technical and scientific factors. Scientific arguments hardly penetrate the decision-making body (CISR) or the adviser body (National Road Safety Council: Conseil National de Sécurité Routière – CNSR). The cost and effectiveness of measures are only two criteria among others. Comprehensive studies of the costeffectiveness or cost–benefit outcomes are rare, whereas this was a common practice in the 1970s.
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-As these measures form part of the government's action programme, the way they are presented to the press and the public is extremely important. Since this tends to be determined by the political agenda, the attempt to create an effect of surprise takes precedence over in-depth studies and discussions with all the parties involved.
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-Our knowledge of the risks and the effectiveness of measures is evolving. The effectiveness of vehicle testing, which was introduced in France in 1985 and is now in use throughout Europe, has been brought into question ([Christensen and Elvik, 2007\)](#page-8-0).
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-Decision-making is not a regular and linear process and may be subject to comings and goings. The principle of regular medical checks decided in March 2003 was dropped a few years later, when its systematic nature raised questions about its cost and the ensuing debate highlighted the fact that the risk of error (a false negative) represented a major shortcoming of the measure, since it
-
-![](_page_1_Figure_10.jpeg)
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-Fig. 1. Organisation of road safety in France.
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-prevented perfectly fit drivers from driving, including some elderly people, with disastrous consequences for their daily lives ([Leproust](#page-8-0) [et al., 2008a,b\)](#page-8-0).
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-The mission assigned to the National Inter-Ministerial Road Safety Observatory, which is under the authority of the road safety delegate, is to gather scientific information for the benefit of decision-maker in order to improve the quality of decisions, as well as to guide the activities of departmental road safety observatories (Observatoires Départementaux de Sécurité Routière – ODSR).
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-To do that, it relies in particular on a committee of experts (Fig. 1) composed of a small number of specialists, who are not necessarily the most qualified experts themselves but are able to call on the most competent specialists in every area they have to deal with.
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-Matters may be referred to this committee of experts by the Chairman of the National Road Safety Council or the minister responsible for road safety, or the committee may act on its own initiative.
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-Among the most recent reports produced by the committee experts, one can cite two examples:
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-- An in-depth study on drinking and driving, which required more than 6 months' work and for which the committee heard evidence from a score of outside personalities [\(Chapelon et al.,](#page-8-0) [2007](#page-8-0)). Produced on the committee's own initiative, the report contained some 10 recommendations, a good number of which have since been acted on (see Section 3.2.1.4).
-- A more immediate study on the advisability of introducing a specific regulation limiting young people's access to powerful vehicles, which was carried out at the request of the minister. It gave rise to a recommendation (no prohibition) that was approved by the National Road Safety Council and was adopted by the government.
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-The last element in the organisation of road safety in France is the National Road Safety Council (Fig. 1), which is a sort of Road Safety Parliament. It enables all the parties involved in road safety (Members of Parliament, highway administrators, insurance companies, motor manufacturers, accident victim defence associations, associations representing particular categories of road user) to meet and debate all road safety issues. As far as our area of interest is concerned, it has the enormous advantage of disseminating the culture of road safety in such a way that all the parties involved have the same level of information.
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-# 3. What is a science-based policy?
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-Road safety policy is founded above all on the input of a system of reliable information composed of, on the one hand, quick indicators, accident records, safety performance indicators (SPI: risk exposure data, and measurements of speed and of the use of mobile phones obtained from roadside surveys) and on the other hand, estimates of the risks attributable to major factors, such as alcohol consumption, speeding, and the use of mobile phones. Lastly, it is based on the management of road risk using three preferred instruments: monitoring, bench-marking, and policy making.
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-The following section presents the information system (Section [3.1](#page-2-0)), the attributable risk (Section [3.2](#page-3-0)), and the risk management (Section [3.3](#page-5-0)). All these analysis and tools are enforced in routine by the ONISR. The results are presented on the web-site of ONISR and CNSR.2
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-<sup>2</sup> [www.securiteroutiere.gouv.fr/observatoire](http://www.securiteroutiere.gouv.fr/observatoire) and [www.securiteroutiere.gouv.fr/](http://www.securiteroutiere.gouv.fr/cnsr) [cnsr](http://www.securiteroutiere.gouv.fr/cnsr).
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-#### <span id="page-2-0"></span>3.1. The information system
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-The rule that there is no management without measures applies to the management of road safety and necessitates very broadbased information gathering [\(OECD, 2008](#page-8-0)), with some national specificities.
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-# 3.1.1. Quick indicators (accidents, deaths, injuries and hospitalised victims)
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-Due to the complexity of accident records (which comprise some 70 items) and of our organisation, the data took, until now before the improvement that are made (see end of Section 3.1.2), around 5 months to arrive. That is why we utilised a very simple system of quick indicators that was confined to the monthly numbers of accidents, fatalities, and injured and hospitalised victims. This system enables departmental police forces to return information to the national level within 3 or 4 days of the end of the month, making it possible for the results to be published around the seventh day of the following month.
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-We were obliged to adjust corrector coefficients because of a number of errors, but the margin of error is less than 2%.
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-The advantage of this organisation is that the monthly publication of road accident data by means of a press release is a big event that interests the media. As a result, the coverage given in the media is equivalent to roughly three times of the media impact of our public information campaign.
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-#### 3.1.2. Accident records
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-Every personal injury traffic accident must be recorded in a BAAC (analytical bulletin of a personal injury road accident), which has to be filled in by the relevant police force and comprises very full information broken down into four main parts: the characteristics and location of the accident, and the vehicles and users involved, knowing that the users are attached to the vehicles that are attached to the location.
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-The importance of accident records for the study of accidents requires one to be concerned about their quality. That is why a project was launched in 2000 for modernising accident records, which comprised four elements: a quality plan, the simplification and updating of the form (a new BAAC-2002 standard was introduced on 1 January 2004), the harmonisation of the data (including a definition of the injury severity), and the decentralisation of the architecture (database shared between local and national levels).
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-The first problem in quality terms relates to under-reporting: too many accidents are not recorded on a BAAC form. The reason for that may be that police forces have not been informed of the accident: that is the case in particular of minor accidents that do not disrupt the traffic, accidents involving a single vehicle, and accidents involving bicycles ([Amoros et al., 2008\)](#page-8-0).
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-To improve the degree of under-reporting, the definition of an injured person was restated and clarified: it is a person requiring medical attention. The definition of a seriously injured person was also simplified: before, it was defined as someone who was hospitalised for at least 6 days; now, it is defined as someone who is hospitalised for at least 24 h, making it easier for the police to record him. Lastly, to reduce losses in transmission, the BAAC's indicators are now compared with the quick indicators for the numbers of killed and injured produced each day or each week by the different police forces.
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-The decentralisation of the information-gathering architecture: in the current architecture, the record is validated at the central level and the corrections made at the local level3 are not used at the national level. In the new computer-based architecture, the mode of transmission is to remain centralised, but the creation of a shared database will enable the departmental level (prefecture, departmental development directorate, local authorities) to dispose of directly exploitable information at the earliest. The idea is to bring the producers (police forces) and consumers (prefecture, DDE, local authorities) closer together in order to improve quality. This BAAC portal has been in use since March 2009.
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-# 3.1.3. Risk exposure
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-The number of vehicles times the kilometres driven is the most commonly used indicator of exposure to risk, but its accuracy varies according to road network categories. It can be measured very accurately on motorways and main roads using automatic counting devices, while on departmental roads it can be extrapolated using an abacus on the basis of the number of kilometres driven on the busiest network.4 On the other hand, there is rarely a figure available for traffic on urban roads.
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-In terms of user category, a reliable risk exposure indicator is obtained from surveys of private motor cars, commercial vehicles and heavy goods vehicles. On the other hand, only a very poor indicator is available for motor cycles, and none at all for moped, bicycles and pedestrians.
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-# 3.1.4. Speed measurements
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-For the past few years, the Observatory has been using investigators from a private firm to measure speed and seatbelt or crash helmet use, on the basis of a sampling plan made by INRETS.5 The data represent around 200,000 observations a year and are regularly published, in particular in the Observatory's annual report (ONISR, 2008).
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-The survey protocol was defined as follows: the only roads retained were straight, level roads without any disruptions to traffic, with no crossroads or traffic lights over a distance of at least one kilometre before and after the measuring point, or a few hundred meters in urban areas. The idea is to choose observation sites where the drivers have no natural limit of speed and can choose their speeds. To measure speed, 362 observation points representative of the French road network were retained (285 during the day and 77 at night). The investigators – around 50 of them – proceeded during half an hour (motorways) or 1 hour (others roads) to all the points in turn to record observations following a predefined breakdown of days of the month and hours of the day, then every 4 months at the same points at the same hour and on the same day of the week. The observations were distributed so they were spread uniformly over the 4-month period and covered every day and every period of the day (between 9.30 am and 4.30 pm in the daytime and between 10.00 pm and 2.00 am at night). Almost all the measurements during the day and all the measurements at night were thus taken outside peak hours. Furthermore, given that each observation point necessitated a sequence of measurements in each direction and in each lane, more than 2000 observation sessions took place each year and more than 200,000 speed measurements were recorded and processed (the year 2008 was incomplete, with 137,507 observations having been recorded during the first 8 months).
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-#### 3.1.5. Measuring mobile phone use
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-Measurements were taken by investigators positioned on the roadside at 81 different locations that represented the different types of network: connecting motorways (15 locations), relief
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-<sup>3</sup> Recovering of some lost accidents, and improving localisation, or some variables' contents (type of vehicle, date of driving licence...).
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-<sup>4</sup> The statistical department of the ministry of ecology, energy and sustainable development is in charge of statistics for motorways and main roads, ONISR gathers data for secondary roads.
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-<sup>5</sup> National Institute for Transport and Safety Research.
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-<span id="page-3-0"></span>motorways (8 locations), interurban roads and country roads (23 locations), and urban roads (35 locations). These measurements were taken between 1 December 2007 and 31 March 2008, on weekdays between 10.00 am and 5.00 pm, and the observation time was 25 min per location. The vehicles observed were motor vehicles with four wheels or more: light vehicles, vans, heavy goods vehicles and buses.
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-The investigators gathered the following information:
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-- the driver is holding the telephone to his ear,
-- the driver is holding the telephone in his hand but not to his ear, and
-- the driver is not holding a telephone.
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-A total of 15,335 vehicles were observed in the whole survey. It was not possible to measure the use of hands-free telephone kits reliably, however, despite the use of an electronic gadget that can detect the utilisation of a mobile phone. Additional tests will be conducted to try and develop a protocol for measuring the use of hands-free mobile phone kits.
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-#### 3.2. Evaluating the risks
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-Evaluating risk at a national level entails estimating the risk attributable to a road safety threat in the broadest sense of the term: either a cause, an exacerbating factor or an attenuating factor. It relies on epidemiological methods and covers general factors that are usually taken into account in matters to do with public health, but also factors that are specific to road transport in terms of infrastructure and vehicles. We then turn to a sequential accident model with temporal divisions determined by events (a sequential model), which are more suitable for formulating action plans at local level.
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-Three factors are taken as examples: two major ones, which are drinking and driving and speeding, and a new one, the use of mobile phones, knowing that other factors could have been retained as safety belt, although the rate of use is very high in France, and in the area of infrastructure, such as the effect of passing from a section of the network in the countryside to a dual-carriageway road network.
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-### 3.2.1. Risk attributed to alcohol
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-Starting from 2008 data on the relationship between the amount of alcohol a driver has consumed and the risk of a road accident, we look for estimating the number of human lives that could have been saved if no one had driven with a blood–alcohol level higher than the authorised limit (0.5 g/l) which is equal by definition to the attributable risk.
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-To do that, one has to know how much alcohol drivers have consumed.
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-3.2.1.1. Alcohol level of drivers on the road. Little is known about the blood–alcohol level of drivers on the road (which is to say not involved in accidents). The prevalence among drivers stopped at preventive police checks in 2007 was 2.8%, but these drivers cannot be regarded as a representative sample, since the police generally target their checks.
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-In the BAAC form, the ''person presumed responsible", information filled by the policeman,6 enables us to know the alcohol levels of drivers who are not responsible. One may consider that the characteristics of the drivers who are not responsible are representative
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-Table 1 Percentage of drivers not responsible who are over the blood–alcohol limit (>0.5 g/l) during the week and at weekends in 2008.
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-|               | Injury accidents (%) | Fatal accidents (%) |
-|---------------|----------------------|---------------------|
-| In the week   | 0.6                  | 1.6                 |
-| At weekends   | 1.9                  | 8.3                 |
-| All accidents | 0.9                  | 3.25                |
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-Source: ONISR, accident records.
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-of the characteristics of all drivers on the road, since these are drivers drawn by chance while driving. Using this method, the prevalence in 2008 was 0.9% in personal injury accidents and 3.25% in fatal accidents, the difference being due to the fact that these accidents did not happen at the same time, with the fatal accidents having tended to take place at night and during the weekend. It can be seen that the rate observed in preventive checks (2.8%) is intermediate between the two, which signifies a high degree of targeting in the checks carried out by the police.
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-One relies that much more easily on these data insofar as a large number of pointers show that data which only interest the police, such as responsibility or alcohol levels, are very well furnished by police forces.
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-The percentage of drivers involved in personal injury accidents who are over the blood–alcohol limit is three times greater at weekends than during the week. Furthermore, the percentage of drivers who are over the blood–alcohol limit at the time of fatal accidents is still higher (Table 1). This is attributable to the fact that these accidents happen at certain times and especially at night.
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-3.2.1.2. Evaluating the number of fatal accidents that could be avoided. To calculate the number of fatal accidents that could be avoided, one starts from accidents in which blood–alcohol levels are known7 thanks to the use of an estimate of the relative risk. When the risk is low, the odds-ratio method is a statistical method for estimating relative risk that is based on the yield of crossover products.
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-The percentages used are, on the one hand, the previously calculated proportion of drivers who are presumed not to be responsible and, on the other hand, the share of fatal accidents in which alcohol is involved. This is equivalent to the proportion of drivers presumed responsible who are over the blood–alcohol limit, if one makes the assumption that, where a driver presumed responsible is over the blood–alcohol limit, it is the alcohol that is mainly responsible for the accident.
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-The proportion of drivers who are over the blood–alcohol limit is almost 10 times greater among those presumed responsible than among the reference drivers who are not responsible ([Table 2\)](#page-4-0). We select as reference drivers those who are not responsible at the time of a fatal accident, since we have seen that the day and the hour play a major role in the distribution of injury and fatal accidents.
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-The relative risk of being responsible for a fatal accident when one has drunk, as estimated by the odds-ratio calculated from accidents with known blood–alcohol levels, is equal to 11.5. One can calculate the ''efficiency" of a driver with a blood–alcohol level below the legal limit in order to ensure that the accident in which he was involved was not fatal.
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-His ''efficiency" would be:
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-1 -1=odds-ratio ¼ 91%:
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-<sup>6</sup> ONISR considers that this variable is correctly filled. In 20% of accidents, the policeman does not indicate any responsible user.
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-<sup>7</sup> The proportion of fatal accidents with alcohol status known (either no alcohol drivers or at least one positive) is very high: 77%.
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-<span id="page-4-0"></span>Table 2 Breakdown of drivers presumed not responsible and drivers presumed responsible in fatal accidents according to whether alcohol is present or not.
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-| Fatal accidents                  | Alcohol (%) | No alcohol (%) |
-|----------------------------------|-------------|----------------|
-| Drivers presumed not responsible | 3.25        | 96.75          |
-| Drivers presumed responsible     | 27.9        | 72.1           |
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-Hence, the maximum number of fatal accidents that could have been avoided if no driver had driven with a blood–alcohol above the legal limit would be:
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-878 91.3% = 802, or 25.4% (802/3151) of the fatal accidents where blood–alcohol levels are known.
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-3.2.1.3. Evaluating the number of human lives saved. The 878 fatal accidents involving drivers with blood–alcohol levels above the legal limit and who are presumed responsible resulted in the deaths of 972 people. In this case, one thus has an average of 1.11 people killed per accident. Applying that multiple to the 802 fatal accidents that could have been avoided, one obtains a potential minimum reduction in deaths caused by drinking and driving of:
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-802 1.11 = 890, or 26.08% (890/3412) of those killed in fatal accidents where alcohol levels are known.
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-Extrapolating from all accidents and assuming that accidents with indeterminate or unknown alcohol levels break down into accidents with and without alcohol in exactly the same way as those where alcohol levels are known, one could avoid around 1000 fatal accidents a year and save the lives of 1200 people.
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-3.2.1.4. Analysing the risk attributed to alcohol. Beyond the calculation of risk, the analysis of this risk can be made: the alcohol impaired drivers represent a very small part of drivers (1%), a different situation from speed impairment (30% of drivers over speed limit and about 10% 10 km/h above speed limit). The average alcohol rate is very high: 1.6 g/l for all accidents and 1.8 g/l for fatal accidents. During the last ten years, despite all the measures taken, there has been no progress in alcohol risk reduction. This leads the expert committee to propose a new set of safety actions: confiscation of the vehicle (confiscation of the driving licence is not enough: alcoholic driver uses to drive without driving licence), improving relationship between medical driving licence committee and the doctors expert on alcohology.
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-#### 3.2.2. Risk attributed to speeding
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-In a certain number of cases, it is not known whether a factor of risk existed or not at the time of the accident and the odds-ratio method cannot be used to estimate the relative risk. That is the case, for example, of speeding and the use of mobile phones.
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-In effect, very little is known about the involvement of speed in individual accidents. What is know about the effect of speed on accidents does not result directly from our knowledge of the role of speed, therefore, but from a statistical analysis of the evolution of accidents on road networks as a result of the actual speeds practised, which may rise or fall over time as a result of a change in regulations.
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-In such a case, one method for evaluating what is at stake is to use data derived from international scientific literature, obtaining a relative risk factor from analyses of experiments on speed limits around the world.
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-To the extent that the relationship between average speed and accidents is well known [\(Nilsson, 1974; Cohen et al., 1998; Aarts](#page-8-0) [and van Schagen, 2006\)](#page-8-0) and that it consists of a power of 4 between the variation in average speed and the variation in the rate of fatal accidents, it is possible, by calculating the relative theoretical reduction in average speed if everyone abided by the speed limit, to multiply it by a factor of 4 in order to arrive at the relative fall in fatal accidents.
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-One could object that this distribution of speed is very theoretical and very far removed from the normal curve that is usually observed. This is what is shown by the chart below, which depicts the development of the distribution of speed on French motorways (Fig. 2). One sees that, even after the installation of speed cameras, there remains a non-negligible proportion of drivers who exceed speed limits, while at the same time a significant proportion of drivers who already abided by speed limits before 2002 reduced their speed in the following years.
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-However, even if one knows that, whatever measures are taken, the distribution of speeds will remain very Gaussian, a definition of gains in average speed that limits the reductions in speed only to those who exceeded the speed limit seems to us to be an indicator which, although theoretical, is useful for road safety policy. In any event, road safety decision-makers would not understand an ideal objective indicator starting from the principle that there would remain an intransigent fringe when it comes to abiding by speed limits.
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-Moreover, the same is true of the risk attributable to alcohol: there is no chance that all drivers will abide by legal blood–alcohol limits. In addition, this indicator is not that far removed from reality, since even if it is certain that one will never get all drivers to abide by speed limits, the impact on road safety will be amplified by the reductions in speed of those who already abided by the limits, as we saw in the 2002–2006 period.
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-Retrospectively, the 10% decrease between 2001 and 2008 of the average speed contributes to a 34.5% decrease of the number of fatalities, which represents nearly three quarters (exactly 72%) of the total decrease of fatalities (48.2%).
-
-![](_page_4_Figure_19.jpeg)
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-Fig. 2. Share of drivers below a given speed on motorways in 2001 and 2006.
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-<span id="page-5-0"></span>Prospectively, the average speed would decrease if every driver respects speed limits by 4.8%, which would lead to a decrease of 18% of the number of fatalities, which represents the attributable risk to speed.
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-3.2.3. Risk attributed to the use of telephones while driving
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-3.2.3.1. General outline. As far as mobile phones are concerned, we use a method that is similar to the one used for speed, by multiplying the added risk by the risk exposure.
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-In 2005 an Australian study [\(McEvoy et al., 2005\)](#page-8-0) sounded the alarm about the importance of the extra risk of telephoning while driving: 3.8 for hands-free sets and 4.9 for hand-held phones, thereby corroborating other studies such as the Canadian study ([Redelmeier and Tibshirani, 1997](#page-8-0)).
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-To evaluate the risk attributable to mobile phones in France, there was a tendency not to go back to this evaluation of the added risk, taking the view that it had been well covered by McEvoy, but rather to look at the prevalence of this risk in France by observing drivers' use of telephones at the wheel.
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-3.2.3.2. Frequency of use of mobile phones while driving. In terms of types of network, the proportion of drivers observed holding a telephone to their ear varies from 1.8% on country roads and 1.9% on urban streets to 2.6% on connecting motorway network, on which it seems most difficult to monitor this practice. Taking account of the distribution of traffic between the different networks, the average is estimated to 1.9%.
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-When the driver is holding a telephone in his hand but not to his ear (0.5%), one may assume that it is in one of the following situations: he is reading or writing an SMS, he is dialling a number, or he is using a hands-free telephone or kit.
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-These findings confirm that the use of handheld mobile phones while driving is not a rare event. Among motoring offences for which points are deducted from driving licences, it is the next most commonly observed one after speeding offences. The fact that a specific measure is going to be available for road traffic will make it possible to improve the monitoring of this practice and better estimate what is at stake for road safety.
-
-It should also be said that these observations were made during the day, between 10.00 am and 5.00 pm. For that reason, the use of handheld telephones may possibly have been overestimated, since one might think that drivers telephone less while driving at night, making the assumption that it is harder to find someone to talk to at that time of day. Nevertheless, as the proportion of night traffic is low (10% of the total), the results would only be marginally altered.
-
-3.2.3.3. Estimate of the potential consequences of using mobile phones while driving. Drawing on the findings of the McEvoy study, the potential consequences of the use of handheld telephones alone represent more than 7% of additional accidents, equal to 1.9% (the proportion of users) multiplied by 4.9 (relative risk) minus 1 (normal risk).
-
-On the other hand, there are more uncertainties when it comes to estimating the implications of using hands-free phone kits, given that it was not possible to measure this practice from the roadside. The results of an opinion survey carried out by the ONISR in March 2008 show that people who use mobile phones the most while driving are generally equipped with hands-free kits. One might thus think that what is at stake is certainly not negligible, and the ONISR is thinking about the best way to proceed in order to gain more reliable knowledge about the proportion of drivers who telephone using hands-free kits.
-
-Lastly, the March 2008 survey showed how the use of SMSs while driving was developing. It found that an SMS was read every 350 km and an SMS sent every 670 km by drivers who said they telephoned while driving.
-
-#### 3.3. Risk management
-
-Risk management is the way to drive the road safety policy from a sound risk assessment process which identifies the main characteristics of the road risks and their degree of acceptability. Different strategies are available to reduce the frequency and the severity of the traffic accidents: prevention, mitigation, risk avoidance, ... Risk management is based on three components:
-
-- monitoring, which makes it possible to track what is happening, once safety options have been implemented, and understand, and even anticipate, the development of the situation;
-- bench-marking, which enables the performance of each department to be compared with that of the others, when the implementation of safety measures is delegated to them, and then to benchmark the results; and
-- policy making which enables the details of a policy and of the safety options to be refined so as to make it more effective.
-
-# 3.3.1. Monitoring: monthly analysis of the situation taking account of the effects of weather and seasonal and time-of-year factors
-
-In view of the random nature of the numbers of accidents and victims, an analysis of time series is recommended to extract the trend and the seasonal effects from the irregular part.
-
-In effect, monthly data are characterised by substantial seasonal variations: traditionally, there are fewer accidents during the winter months, but they are followed by a steady increase during the spring, with the number of accidents rising to a peak during the three summer months and remaining at a high level in the autumn.
-
-These seasonal variations do not make it possible to make a direct comparison between some month and the previous month. As a result, a month is always compared with the same month the year before.
-
-The disadvantage of comparing monthly results at 12-month intervals is that there is no reference level to determine whether it was a ''good" or a ''bad" month. In particular, weather conditions have a considerable influence on the results, mainly as a result of their effect on traffic: generally speaking, good weather conditions bring an increase in traffic and hence a rise in the number of accidents. Furthermore, we do not know whether behaviour during the month in question was good or bad, whereas it would be more logical to compare it directly with the previous month, when behaviour patterns can be supposed to be similar.
-
-To facilitate comparisons between monthly results, the Observatory uses the Giboulée model developed by the SETRA8 and INRETS [\(Bergel et al., 1994](#page-8-0)) which calculates risk indicators corrected for the variations year-of-effects of weather and seasonal and time.
-
-With this correction, the Observatory calculates a monthly risk indicator in the form of an ''annual equivalent", which is interpreted in the following way: for example, given that the annual equivalent for May 2008 is 4520 and assuming that behaviour patterns remain the same during the year and that weather is neutral, the result in May 2010 will be 4520 fatalities, which makes it possible to evaluate the results for each month in comparison with an annual target.
-
-[Fig. 3](#page-6-0) shows that, whereas the results in early 2008 appeared to be positive when compared to 2007, it would be wrong to be optimistic since the outcome for 2008 reflected a bad year-end perfor-
-
-<sup>8</sup> Road and Motorway Research Agency.
-
-<span id="page-6-0"></span>![](_page_6_Figure_2.jpeg)
-
-Fig. 3. Annual equivalent of the number of deaths per month.
-
-mance, and if the same trend were to continue, the number of deaths will have increased by some 3% or 4% by the end of 2009.
-
-To sum up, we used a very sophisticated model but applied it in a very simple and pragmatic manner so as to produce clear conclusions for decision-makers.
-
-# 3.3.2. Bench marking: a method for comparing the performance of each department by standardising the risk
-
-To judge the road safety performance of a department, one traditionally has a choice between two methods: either one tracks developments from year to year, or one compares it with the performance of other departments.
-
-The drawback of the first method is that, not knowing whether the results of the previous year were good or bad, one cannot interpret developments during the current year.
-
-The drawback of the second method is that one cannot be content with a comparison between raw figures (there are large and small departments) and that one has to take account of risk exposure. Population is a totally inadequate indicator of risk exposure, since the number of kilometres driven per inhabitant is known to be very different from one department to another, ranging from 1230 vehicle-kilometres per inhabitant in Paris to 29,380 vehiclekilometres per inhabitant in the Landes ([ONISR, 2009](#page-8-0)).
-
-3.3.2.1. Basic principle of local accident indicators (LAIs). The purpose of LAIs is to provide a departmental indicator that takes account of the volume of traffic and its distribution between the different types of road network (motorways, country roads, urban areas). An LAI compares the level of risk of a department with what it would be if the department had the same levels of risk on its different networks as the whole of France.
-
-From that standpoint, the LAI is based on a classic method of standardisation, since it relates an observed degree of risk to an expected degree of risk that is estimated on the basis of the number of road deaths over five years.
-
-We distinguish between five categories of network: motorways, interurban roads, secondary roads, other country roads, and urban areas.
-
-The risk exposure indicator is the vehicles kilometres travelled or, in the case of urban areas, the population of urban districts of over 5000 inhabitants that have a central town with 5000 inhabitants or more.
-
-An LAI is defined for each department using the following formula, with j the department (from 1 to 96) and i the network (from 1 to 5), such that:
-
-- t(i, j) is the number of deaths on network i in department j;
-- e(i, j) is the risk exposure on network i in department j; and
-- r(i) is the average risk on network i throughout France.
-
-Thus
-
-$$r(i) = \frac{\sum_{j=1}^{96} t(i,j)}{\sum_{j=1}^{96} e(i,j)}$$
-
-$$LAI(j) = \frac{\sum_{j=1}^{5} t(i,j)}{\sum_{j=1}^{5} t(i,j)}$$
-
-One recognises in the LAI the classic relationship between the observed number of deaths and the expected number of deaths. The LAI makes it possible to go from an analysis in terms of an accident rate related to the population to an analysis in terms of an accident rate related to distance, applying the following simplified formula:
-
-LAI ¼ deaths=vehicles kilometres travelled
-
-- ¼ ðdeaths=inhabitantsÞ=ðvehicles
-  - kilometres travelled=inhabitantsÞ
-
-where the vehicles kilometres travelled are a weighting of the vehicles kilometres travelled observed by the average risks by network in France as a whole.
-
-Standardisation by vehicles kilometres travelled (and hence the LAI approach) provides a lot of ''explanations", since the maximum difference falls from 1 to 10 to 1 to 3 and the variance narrows from 0.36 to 0.25.
-
-Interpreting LAIs is more difficult since, to the extent that the distance factor is already taken into account, one has to turn to other explanatory factors. As a first approach, some factors grab the attention immediately. One can see, for example, that among the highest-ranked departments, the majority are departments with very marked urban characteristics and that the best results for the trunk road network are often scored by departments that have the largest networks of fast four-lane dual carriageways ([Fig. 4\)](#page-7-0).
-
-However, one may also notice that, among departments sharing similar characteristics, LAI values differ considerably, whether it is the overall LAI or LAIs by networks. With the distance factor neutralised, other factors have to be sought to interpret the differences. These variables have to be selected for their accidentinducing potential and have to be documented at the departmental level. In the current state of knowledge, the variables on which researchers are working are alcohol, seatbelts, roadside trees and shrubs, motorcycles, and the proportion of traffic on country roads.
-
-## 3.3.3. Policy making
-
-All speed measurements showed the extent to which drivers exceed speed limits in France. At the end of 2001, about 35% of motorists were found to drive at 10 km/h more than the speed limit. This highlighted what was at stake as regards speeding in France and enabled us to understand why the country was so far behind other comparable European countries [\(Delorme and Lassarre,](#page-8-0) [2005](#page-8-0)). In the light of these findings, traffic victims' associations called for tougher speed controls, which led the government to start testing automatic speed cameras in 2001–2002 and then to introduce them from 2003 onwards.
-
-The installation of 1400 fixed cameras and the deployment of 700 mobile cameras brought a substantial reduction in speed: the average speed declined by 10 km/h in 2008, with 10% of drivers driving at 10 km/h more than the speed limit. As a result, we observed a sharp fall in the number of deaths on the road – by 48% between 2001 and 2008. This example shows that knowledge had a particularly powerful impact on policy.
-
-According to the calculation of attributable risks, alcohol has now become the first factor of accidents in France, a fact that impressed the policy-makers. The analysis also underlies the main characteristics of drink driving (very small number of drivers but
-
-<span id="page-7-0"></span>![](_page_7_Figure_2.jpeg)
-
-Fig. 4. Map of the standardised rates of fatalities by department.
-
-very high rate of alcohol) what oriented the propositions of the expert committee.
-
-It's the same for the telephone when driving whose effect was before totally under-evaluated.
-
-#### 4. What governance?
-
-In terms of governance, we view the role played by the Observatory as an interface between experts and decision-makers in the transmission of information of a scientific nature for the purpose of formulating road safety policy. Information is disseminated to the media through press conferences that are programmed as a function of the political agenda. The results of expert studies are debated in more broadly-based organisations, such as the National Scientific Research Centre (CNSR), where other interested parties participate, such as motor manufacturers, insurance companies, victim defence associations and particular categories of user. This led to the decision taken in 2000 to seek to improve the quality of the information gathered and of the risk analyses carried out to guide decision-makers. Two action plans are currently being implemented to improve this process of risk evaluation: data from the road accident victims register of the department of the Rhône are being used to provide more rigorous estimators of the numbers of injured according to seriousness; and research in road safety economics is being encouraged in order to reintroduce monetary profitability criteria into decision-making, criteria that could thus be integrated into a broader, long-term sustainable approach to the road system.
-
-Additionally, the Observatory has developed a programme for evaluating the policies pursued with teams of researchers and technicians from the Ministry, and the resulting reports have been published – one on automatic speed cameras (ONISR, 2008) and one on drinking and driving (ONISR, 2007).
-
-The experience of the past few years has shown the importance of developing simple but rigorously constructed methods and tools, providing telling results for decision-makers and the public.
-
-Nevertheless, the situation is far from perfect. Firstly, these tools are perfectible, and bench-marking the methods promoted by the European Commission as part of projects like SafetyNet ([Thomas et al., 2005](#page-8-0)) will help get them shared more widely.
-
-In terms of risk selection, the Observatory does not cover all the issues, far from it. We are starting to invest in risk factors such as falling asleep at the wheel and taking medicines. The construction of risks is determined in part by networks of experts belonging to the different parties concerned, but will also depend on events, such as serious accidents, and on political and social considerations.
-
-Furthermore, experience shows that, over and beyond the results obtained and the tools used, a permanent concern is getting decision-makers to assimilate and validate them. Road safety managers need to watch out increasingly for the legalisation of the organisational process of risk management in order to preserve their own legitimacy and reputation.
-
-#### Acknowledgements
-
-We thank Nicole Muhlrad (INRETS) for assisting us in improving the english expression.
-
-#### <span id="page-8-0"></span>References
-
-- Aarts, L., van Schagen, I., 2006. Driving speed and the risk of road crashes: a review. Accident Analysis and Prevention 38 (2), 215–224.
-- Amoros, E. et al., 2008. Actual Incidences of Road Casualties, and Their Injury Severity, Modelled from Police and Hospital Data. EJPH, France.
-- Bergel, R., Girard, B., Lassarre, S., Le Breton, P., 1994. Un modèle de désaisonnalisation des indicateurs de sécurité routière. In: Carré, J.R., Lassarre, S., Ramos, M. (Eds.), Modélisation de l'insécurité routière. Tome II. Actes du séminaire 1993. Paradigme, Caen.
-- Chapelon, J., 2006. Impact du contrôle sanction automatisé sur la sécurité routière (2003–2005), ONISR. [<http://www.securiteroutiere.gouv.fr/cnsr/travaux\\_cnsr.](http://www.securiteroutiere.gouv.fr/cnsr/travaux_cnsr.htm) [htm>](http://www.securiteroutiere.gouv.fr/cnsr/travaux_cnsr.htm) (séance du 15 mars 2006).
-- Chapelon, J., Biecheler, M.B., Facy, F., Fontaine, H., Got, C., Perez-Diaz, C., Sibi, P., 2007. L'alcool sur la route: état des lieux et propositions, Comité des experts de la sécurité routière. [<http://www.securiteroutiere.gouv.fr/cnsr/travaux\\_cnsr.](http://www.securiteroutiere.gouv.fr/cnsr/travaux_cnsr.htm)
-- [htm>](http://www.securiteroutiere.gouv.fr/cnsr/travaux_cnsr.htm) (séance du 9 juillet 2007). Cohen, S., Duval, H., Lassarre, S., Orfeuil, J.-P., 1998. Limitations de vitesse – Les décisions publiques et leurs effets. Hermès, Paris.
-- Christensen, C., Elvik, R., 2007. Effects on accidents of periodic motor vehicle inspection in Norway. Accident Analysis and Prevention 39 (1), 47–52.
-- Delorme, R., Lassarre, S. (Eds.), 2005. L'insécurité routière en France dans le miroir de la comparaison internationale. La comparaison entre la France et la Grande-Bretagne. Rapport no. 216, Les collections de l' INRETS, Bron, France.
-- Lassarre, S., Hoyau, P.-A., 2008. Evolution récente du risque routier en France et en Grande-Bretagne. In: Delorme, R., Lassarre, S. (Eds.), Les régimes français et
-
-- britannique de régulation du risque routier: la vitesse d'abord. Synthèse no. 57, Les collections de l' INRETS, Bron, France.
-- Leproust, S., Lagarde, E., Salmi, L.-R., 2008a. Systematic screening for unsafe driving due to medical conditions. BMC Public Health 8, 27.
-- Leproust, S., Lagarde, E., Salmi, L.-R., 2008b. Risks and advantages of detecting individuals unfit to drive: a Markov decision analysis. Journal of General Internal Medicine 23 (11), 1796–1803.
-- McEvoy, S.P., Stevenson, M.R., McCartt, A.T., Woodward, M., Haworth, C., Palamara, P., Cercarelli, R., 2005. Role of mobile phones in motor vehicle crashes resulting in hospital attendance: a case-cross over study. British Medical Journal 331, 428–430.
-- Nilsson, G., 1974. The effects of speed limits on traffic crashes in Sweden. In: Proceedings of the International Symposium on the Effects of Speed Limits on Traffic Crashes and Fuel Consumption, Dublin, Organisation for Economy Cooperation and Development (OECD), Paris.
-- OECD, 2008. Towards Zero: Ambitious Road Safety Targets and the Safe System Approach. Paris.
-- ONISR, 2009. La sécurité routière en France: bilan de l'année 2008. La Documentation Française, Paris.
-- Redelmeier, D., Tibshirani, R., 1997. Associations between cellular–telephone calls and motor vehicle collisions. The New England Journal of Medicine 336 (7), 453–458.
-- Thomas, P. et al., 2005. Designing the European Road Safety Observatory. International Journal of Injury Control and Safety Promotion 12 (4), 251–253.

Zotero/001_artiklid/Road Safety Indicators/chenglobalmacroeconomicburdenroad2019.md

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----
-category: literaturenote
-citekey: chenglobalmacroeconomicburdenroad2019
-title: "The global macroeconomic burden of road injuries: estimates and projections for 166 countries"
-authors: "Chen, Simiao; Kuhn, Michael; Prettner, Klaus; Bloom, David E."
-year: 2019
-date: 2019-09-01 2019-09-01
-doi: 10.1016/S2542-5196(19)30170-6
-publication: The Lancet Planetary Health
-url: "https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196%2819%2930170-6/fulltext"
-zotero_key: MX4TN5E2
-zotero_storage: 9NND7AWJ
-collections: doktoritöö / HLO
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Chen, Simiao"
-status: converted
----
-# **The global macroeconomic burden of road injuries: estimates and projections for 166 countries**
-
-![](_page_0_Picture_2.jpeg)
-
-*Simiao Chen, Michael Kuhn, Klaus Prettner, David E Bloom*
-
-# **Summary**
-
-**Background Road injuries are among the ten leading causes of death worldwide and also impede economic wellbeing and macroeconomic performance. Beyond medical data on the incidence of road injuries and their resulting morbidity and mortality, a detailed understanding of their economic implications is a prerequisite for sound, evidence-based policy making. We aimed to determine global macroeconomic costs of road traffic injuries and their cross-country distribution.**
-
-**Methods We calculated the economic burden of all road traffic-related injuries for 166 countries by use of a macroeconomic model that accounts for the effect of fatal and non-fatal injuries on labour supply, age-specific differences in education and experience of those who are affected by road accidents, and the diversion of injury-related treatment expenses from savings, which results in lower investment.**
-
-**Findings We estimated that road injuries will cost the world economy US\$1·8 trillion (constant 2010 US\$) in 2015–30, which is equivalent to an annual tax of 0·12% on global gross domestic product. Although low-income and middleincome countries have the largest health burden, their share of the economic burden of road injuries is only 46·4% of the global loss, reflecting in part higher productivity (and earnings) in high-income countries, but also prominently higher treatment costs. Our results also indicate that treatment costs account for a greater proportion of the economic burden in high-income countries than in low-income countries.**
-
-**Interpretation The macroeconomic burden of road injuries is sizeable and distributed unequally across countries and world regions. This finding suggests a case for nuanced policy making. Our framework should provide a good starting point for the more detailed analysis of policies both at country level and across different countries.**
-
-**Funding National Institute on Aging.**
-
-**Copyright © 2019 The Authors(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.**
-
-# **Introduction**
-
-Road traffic injuries are among the ten leading causes of death worldwide, and they are the leading cause of death among young adults aged 15–29 years.1 Such accidents also lead to 20–50 million non-fatal injuries, and many people incur a disability as a result of their injury.2 According to WHO, 1·25 million people worldwide died in road traffic accidents in 2013.1 To provide some context, this figure is more than five times the death toll of the 2004 Indian Ocean tsunami,3 one of the deadliest natural disasters ever recorded. The worldwide prevalence, incidence, and mortality of road injuries are shown in detail in the appendix (pp 1–2).
-
-Although the human burden in terms of pain and suffering of those affected by road accidents—the victims, their families, and their friends—is beyond quantification, these accidents also inflict a large economic toll. Understanding the macroeconomic burden of road injuries and how they are distributed among world regions and countries is essential for policy making.
-
-The published literature on the consequences of road injuries broadly comprises two strands. One strand deals with the effectiveness (in terms of saving lives) of global interventions in reducing road injuries.4 Although some studies derive measures of cost-effectiveness,5 they focus on the cost of the intervention but do not assign a monetary value to the loss arising from road injuries. Establishing such a value (ie, the economic burden of road injuries) is the focus of the second strand. Although a few studies6–12 have estimated the economic burden of road injuries for one or a small number of countries, most of these approaches are based on aggregating the direct and indirect costs of road traffic accidents in different countries (the cost of illness approach) or on multiplying the cases of injuries and deaths due to road traffic accidents by the willingness of individuals to pay to avoid risks (the value of statistical life approach). However, in real economies, jobs do not remain vacant indefinitely, because companies substitute lost labour with new workers or machines (physical capital). Furthermore, approaches to date have been static and have, therefore, failed to account for the dynamics of morbidity-related and mortality-related changes in the population and the implications of treatment costs for savings (and, thus, the accumulation of capital).
-
-*Lancet Planet Health* **2019; 3: e390–98**
-
-**Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany** (S Chen ScD)**; Wittgenstein Centre, Vienna Institute of Demography, Vienna, Austria**  (M Kuhn PhD)**; Institute of Economics, University of Hohenheim, Stuttgart, Germany** (Prof K Prettner PhD)**; and Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA**  (Prof D E Bloom PhD)
-
-Correspondence to: Dr Simiao Chen, Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg 69120, Germany **simiao.chen@uni-heidelberg.**
-
-**de**
-
-See **Online** for appendix
-
-### **Research in context**
-
-#### **Evidence before this study**
-
-We searched MEDLINE, PubMed, Google Scholar, and references from relevant articles using the search terms "road injury" (or "road traffic injury") and "economic burden" (including the variants "economic cost" and "economic loss") in the title or abstract. Articles dated between Jan 1, 1960, and March 1, 2018, were included in this search. Previous approaches were based on aggregating the direct and indirect costs of road traffic accidents in different countries (the cost of illness approach) or on multiplying the cases of injuries and deaths due to road traffic accidents by the willingness of individuals to pay to avoid risks (the value of statistical life approach). However, in real economies, jobs do not remain vacant indefinitely, because companies substitute lost labour with new workers or machines (physical capital). Furthermore, approaches to date were static and failed to account for the dynamics of morbidity-related and mortality-related changes in the population and the implications of treatment costs for savings (and, thus, the accumulation of capital). One study improved on previous approaches by implicitly considering some of these effects and using growth regressions to estimate the macroeconomic effects of road injury for five countries. However, growth regressions might lead to biased estimates because of reverse causality and omitted variables. Use of a simulation model grounded in dynamic macroeconomic theory can address these disadvantages and provide complementary estimates. Additionally, we did not encounter any research that estimated and projected the macroeconomic costs for all the countries in the world.
-
-# **Added value of this study**
-
-We used a theory-based simulation model that describes how the sum of workers, weighted by their human capital in terms of education and experience, combine with physical capital in producing goods and services to estimate the macroeconomic burden of road injuries for 166 countries. We simulated the projected effects of road injuries on the underlying economies' production potential (ie, measuring the cost of injuries in terms of gross domestic product [GDP]) and accounted for economic adjustments in response to road injury casualties, more inclusive measures of economic loss than earnings, which constitute only one part of GDP, the effects on human capital differentiated by age-specific experience and education levels,
-
-A World Bank study13 infers growth effects from the coefficient estimate of mortality in growth regressions. The advantage of this approach is that, when the regression is appropriately specified, the estimated growth effect is clear from the final result, which already incorporates economic adjustment mechanisms. Because they use panel data, these growth regressions are naturally dynamic. Consequently, this method overcomes some of the crucial shortcomings of the cost of illness and value of statistical life approaches. However, this approach only and the effects on physical capital caused by a reduction in savings. We found that road injuries will cost the world economy about US\$1·8 trillion (measured in constant prices as of 2010) in 2015–30. The macroeconomic tolls of road injuries distribute differently among regions and countries. The highest aggregate economic burdens occur in the USA (\$487 billion), China (\$364 billion), and India (\$101 billion), which have the three largest populations in the world. Our results also indicate that treatment costs and their effects on savings and physical capital accumulation have a greater role in high-income countries than in low-income countries. More than 30% of the total economic burden from road injuries derives from physical capital loss in high-income countries, whereas physical capital loss accounts for less than 5% of the total economic burden in low-income countries. The macroeconomic burden of road injuries varies by World Bank income group. Despite the large fraction of disability-adjusted life-years (DALYs) that accrue in low-income and middle-income countries (almost 90%), their share of the economic burden of road injuries is only 46·4% of the global loss, reflecting not only higher productivity (and earnings) in high-income countries, but also higher treatment costs.
-
-#### **Implications of all the available evidence**
-
-This analysis suggests that the macroeconomic burden of road injuries is large and distributed unequally across countries and world regions. High-income countries have the highest macroeconomic burden of road injuries, whereas low-income and middle-income countries bear the greatest health burdens. This disparity of distribution might be driven by differences in economic development, where the productivity is higher and the workforce is better educated in high-income countries, leading to a larger loss of human capital from road injury, and more advanced health-care systems reduce the loss in DALYs but come at much higher treatment costs. Although the macroeconomic burden of road injuries on low-income countries is relatively light, it is likely to rise in the course of economic development if the growth in motorisation and traffic density outpaces development of infrastructure and law enforcement. The fact that low-income and middle-income countries bear the majority of the human toll underscores the need for improvements on multiple fronts, including infrastructure, law enforcement, public awareness, and emergency response systems.
-
-allows for assessment of severe diseases that affect many people (such as cardiovascular diseases). Detecting a significant growth effect for less impactful diseases is difficult, given the small sample sizes that typically underly growth regressions.14 Thus, the World Bank study13 does not include road traffic mortality directly but instead includes overall mortality in the regressions and infers from this number the effect of road traffic-related mortality. Furthermore, growth regressions are susceptible to imprecise parameter estimation and to various biases when sparse data on important control variables (eg, fertility and trade openness) are available. 15 This World Bank study13 also focused on the macroeconomic effects of road injuries for five countries; therefore, a comprehensive global estimate of the macroeconomic burden of road injuries, based on the simulation of an economy's productive capacity at the aggregate level and the extent to which road injuries affect the productive capacity, is still needed.
-
-To fill this gap, we aimed to use a theory-based simulation model that describes how the sum of workers, weighted by their human capital in terms of education and experience combines with physical capital in producing goods and services16 to estimate the macroeconomic burden of road injuries.
-
-# **Methods**
-
-# **Model description**
-
-We simulated the projected effect of road injuries on the underlying economy's production potential (ie, measuring the cost of injuries in terms of gross domestic product [GDP]). In doing so, we accounted for (1) economic adjustments in response to road injury casualties; (2) more inclusive measures of economic loss than earnings, which constitute only a part of GDP; (3) the effect on human capital differentiated by agespecific experience and education; and (4) the effect on physical capital caused by reduced savings from those injured individuals, because a proportion of the costs of treating road accident-related injuries could have gone into savings if no road injury had occurred.17 This approach has previously been used to assess the macroeconomic burden of non-communicable diseases in east Asian countries and the USA.18–20
-
-We estimated road injuries' effect on economic output for 166 countries. The definition of a road injury follows the Global Burden of Diseases, Injuries, and Risk Factors Study's (GBD's) injury codes and categories.21 Of these 166 countries, 138 countries have all data inputs necessary for our projections (appendix pp 5–6). We directly calculated the macroeconomic burden of road injuries for these 138 countries using the health macroeconomic model described in detail in Bloom et al 18 and in the appendix (pp 2–5). In applying the model, we first recognised that injuries from road accidents affect the economy through the loss of effective labour supply due to mortality and morbidity. Higher injury-induced mortality rates reduce the population, and therefore the number of working-age individuals, and non-fatal injuries reduce productivity and increase absenteeism. Additionally, a certain share of household resources is diverted from savings to finance out-of-pocket treatment costs. At the same time, insurance-funded coverage of road injury treatment costs translates into higher private health insurance premia and public health insurance taxes. Both channels lead to a loss of aggregate savings or investment across the population and hamper
-
-|                         | Economic burden, millions<br>of constant 2010 US\$<br>(lower and upper bound) | Percentage of total gross<br>domestic product in<br>2015–30<br>(lower and upper bound) | Per capita loss,<br>constant 2010 US\$<br>(lower and upper<br>bound) |
-|-------------------------|-------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|----------------------------------------------------------------------|
-| East Asia and Pacific   |                                                                               |                                                                                        |                                                                      |
-| Australia               | 25 539 (21 505–30447)                                                         | 0·099% (0·084–0·118)                                                                   | 979 (825–1167)                                                       |
-| Brunei                  | 402 (285–540)                                                                 | 0·146% (0·103–0·196)                                                                   | 909 (644–1221)                                                       |
-| Cambodia                | 893 (574–1343)                                                                | 0·208% (0·134–0·313)                                                                   | 52 (33–78)                                                           |
-| China                   | 363978 (337 113–389896)                                                       | 0·154% (0·143–0·165)                                                                   | 255 (236–273)                                                        |
-| Fiji                    | 58 (40–84)                                                                    | 0·077% (0·053–0·111)                                                                   | 63 (43–91)                                                           |
-| Indonesia               | 22 280 (19734–25 133)                                                         | 0·094% (0·083–0·106)                                                                   | 80 (71–90)                                                           |
-| Japan                   | 69326 (66377–73 312)                                                          | 0·067% (0·064–0·071)                                                                   | 554 (530–586)                                                        |
-| Laos                    | 543 (271–823)                                                                 | 0·189% (0·094–0·287)                                                                   | 74 (37–112)                                                          |
-| Malaysia                | 13064 (9815–16620)                                                            | 0·168% (0·126–0·214)                                                                   | 386 (290–491)                                                        |
-| Mongolia                | 372 (269–502)                                                                 | 0·134% (0·097–0·181)                                                                   | 114 (82–153)                                                         |
-| New Zealand             | 4742 (4165–5401)                                                              | 0·138% (0·121–0·158)                                                                   | 965 (848–1099)                                                       |
-| Philippines             | 6064 (4613–7899)                                                              | 0·084% (0·064–0·109)                                                                   | 53 (41–70)                                                           |
-| Singapore               | 2683 (2286–3160)                                                              | 0·046% (0·039–0·054)                                                                   | 447 (381–527)                                                        |
-| South Korea             | 22 745 (19754–26387)                                                          | 0·090% (0·078–0·104)                                                                   | 439 (381–510)                                                        |
-| Thailand                | 15097 (11994–18459)                                                           | 0·179% (0·142–0·218)                                                                   | 217 (173–266)                                                        |
-| Vietnam                 | 7826 (5164–10938)                                                             | 0·189% (0·125–0·264)                                                                   | 78 (51–109)                                                          |
-| Europe and central Asia |                                                                               |                                                                                        |                                                                      |
-| Albania                 | 274 (187–397)                                                                 | 0·098% (0·067–0·141)                                                                   | 94 (64–136)                                                          |
-| Armenia                 | 272 (229–322)                                                                 | 0·101% (0·085–0·119)                                                                   | 93 (79–111)                                                          |
-| Austria                 | 5359 (4648–6188)                                                              | 0·069% (0·060–0·080)                                                                   | 608 (527–702)                                                        |
-| Azerbaijan              | 812 (590–1115)                                                                | 0·078% (0·057–0·108)                                                                   | 79 (58–109)                                                          |
-| Belarus                 | 1191 (973–1470)                                                               | 0·105% (0·086–0·130)                                                                   | 128 (104–157)                                                        |
-| Belgium                 | 7219 (6141–8486)                                                              | 0·079% (0·067–0·093)                                                                   | 618 (526–726)                                                        |
-| Bosnia and Herzegovina  | 378 (295–482)                                                                 | 0·099% (0·077–0·126)                                                                   | 109 (85–139)                                                         |
-| Bulgaria                | 1596 (1372–1875)                                                              | 0·142% (0·122–0·167)                                                                   | 235 (202–276)                                                        |
-| Croatia                 | 1421 (1208–1678)                                                              | 0·123% (0·104–0·145)                                                                   | 351 (299–414)                                                        |
-| Cyprus                  | 423 (341–523)                                                                 | 0·086% (0·070–0·107)                                                                   | 349 (282–432)                                                        |
-| Czech Republic          | 5494 (4816–6301)                                                              | 0·120% (0·105–0·138)                                                                   | 519 (455–595)                                                        |
-| Denmark                 | 4180 (3540–4941)                                                              | 0·066% (0·056–0·078)                                                                   | 715 (606–846)                                                        |
-| Estonia                 | 444 (352–565)                                                                 | 0·091% (0·072–0·116)                                                                   | 349 (276–443)                                                        |
-| Finland                 | 2818 (2358–3372)                                                              | 0·061% (0·051–0·073)                                                                   | 503 (421–602)                                                        |
-| France                  | 37847 (32 157–44671)                                                          | 0·075% (0·064–0·089)                                                                   | 554 (471–654)                                                        |
-| Germany                 | 54069 (45 530–64565)                                                          | 0·079% (0·067–0·095)                                                                   | 657 (553–784)                                                        |
-| Greece                  | 3164 (2697–3705)                                                              | 0·072% (0·062–0·085)                                                                   | 288 (245–337)                                                        |
-| Hungary                 | 2869 (2459–3413)                                                              | 0·098% (0·084–0·117)                                                                   | 302 (259–359)                                                        |
-| Iceland                 | 212 (168–265)                                                                 | 0·065% (0·052–0·082)                                                                   | 633 (503–794)                                                        |
-| Ireland                 | 5356 (4711–6175)                                                              | 0·074% (0·065–0·085)                                                                   | 1081 (951–1246)                                                      |
-| Italy                   | 23 554 (20353–27 259)                                                         | 0·066% (0·057–0·076)                                                                   | 400 (346–463)                                                        |
-| Kazakhstan              | 4773 (3987–5685)                                                              | 0·124% (0·103–0·147)                                                                   | 249 (208–297)                                                        |
-| Kyrgyzstan              | 201 (166–243)                                                                 | 0·151% (0·125–0·183)                                                                   | 31 (26–38)                                                           |
-| Latvia                  | 602 (469–771)                                                                 | 0·102% (0·080–0·131)                                                                   | 327 (254–418)                                                        |
-| Lithuania               | 978 (840–1145)                                                                | 0·109% (0·094–0·128)                                                                   | 349 (299–408)                                                        |
-|                         |                                                                               |                                                                                        |                                                                      |
-| Luxembourg              | 892 (726–1090)                                                                | 0·072% (0·058–0·088)                                                                   | 1465 (1192–1791)                                                     |
-| Moldova                 | 167 (142–196)                                                                 | 0·108% (0·092–0·127)                                                                   | 42 (36–50)                                                           |
-| Netherlands             | 9791 (8503–11290)                                                             | 0·058% (0·051–0·067)                                                                   | 567 (492–654)                                                        |
-| Norway                  | 5854 (5596–6140)                                                              | 0·068% (0·065–0·071)                                                                   | 1052 (1005–1103)                                                     |
-| Poland                  | 15674 (13 556–18231)                                                          | 0·134% (0·116–0·156)                                                                   | 417 (361–485)                                                        |
-| Portugal                | 2505 (2071–3030)                                                              | 0·059% (0·049–0·072)                                                                   | 247 (205–299)<br>(Table 1 continues on next page)                    |
-
-|                                | Economic burden, millions<br>of constant 2010 US\$<br>(lower and upper bound) | Percentage of total gross<br>domestic product in<br>2015–30<br>(lower and upper bound) | Per capita loss,<br>constant 2010 US\$<br>(lower and upper<br>bound) |
-|--------------------------------|-------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|----------------------------------------------------------------------|
-| (Continued from previous page) |                                                                               |                                                                                        |                                                                      |
-| Romania                        | 4539 (3907–5252)                                                              | 0·106% (0·091–0·122)                                                                   | 237 (204–274)                                                        |
-| Russia                         | 50547 (48746–54386)                                                           | 0·172% (0·166–0·185)                                                                   | 354 (341–381)                                                        |
-| Serbia                         | 832 (685–1011)                                                                | 0·100% (0·083–0·122)                                                                   | 97 (80–118)                                                          |
-| Slovakia                       | 2557 (2112–3123)                                                              | 0·119% (0·099–0·146)                                                                   | 472 (390–576)                                                        |
-| Slovenia                       | 1207 (1017–1453)                                                              | 0·117% (0·099–0·141)                                                                   | 585 (493–704)                                                        |
-| Spain                          | 18200 (15919–20832)                                                           | 0·067% (0·058–0·076)                                                                   | 393 (344–450)                                                        |
-| Sweden                         | 6988 (6222–7931)                                                              | 0·068% (0·060–0·077)                                                                   | 682 (607–774)                                                        |
-| Switzerland                    | 8530 (7438–9914)                                                              | 0·074% (0·064–0·086)                                                                   | 971 (847–1129)                                                       |
-| Tajikistan                     | 124 (95–160)                                                                  | 0·064% (0·049–0·082)                                                                   | 13 (10–16)                                                           |
-| Turkey                         | 18195 (14069–22 502)                                                          | 0·079% (0·061–0·098)                                                                   | 215 (167–266)                                                        |
-| Ukraine                        | 3804 (2938–4658)                                                              | 0·157% (0·121–0·192)                                                                   | 89 (68–108)                                                          |
-| UK                             | 24048 (23002–25 103)                                                          | 0·049% (0·047–0·051)                                                                   | 353 (338–368)                                                        |
-| Latin America and Caribbean    |                                                                               |                                                                                        |                                                                      |
-| Argentina                      | 7342 (5879–9183)                                                              | 0·093% (0·075–0·117)                                                                   | 158 (127–198)                                                        |
-| The Bahamas                    | 217 (161–288)                                                                 | 0·117% (0·087–0·155)                                                                   | 542 (400–717)                                                        |
-| Barbados                       | 62 (46–82)                                                                    | 0·080% (0·059–0·105)                                                                   | 227 (169–298)                                                        |
-| Belize                         | 54 (41–68)                                                                    | 0·190% (0·146–0·239)                                                                   | 134 (103–168)                                                        |
-| Bolivia                        | 658 (274–1048)                                                                | 0·117% (0·049–0·186)                                                                   | 55 (23–88)                                                           |
-| Brazil                         | 56988 (52 347–60648)                                                          | 0·140% (0·128–0·149)                                                                   | 263 (242–280)                                                        |
-|                                |                                                                               |                                                                                        |                                                                      |
-| Chile                          | 4241 (3422–5252)                                                              | 0·082% (0·066–0·101)                                                                   | 226 (183–280)                                                        |
-| Colombia                       | 7763 (5906–9984)                                                              | 0·108% (0·082–0·139)                                                                   | 152 (116–196)                                                        |
-| Costa Rica                     | 1161 (898–1448)                                                               | 0·125% (0·096–0·155)                                                                   | 227 (175–283)                                                        |
-| Dominican Republic             | 4947 (3506–6714)                                                              | 0·296% (0·210–0·401)                                                                   | 436 (309–592)                                                        |
-| Ecuador                        | 2322 (1884–2833)                                                              | 0·152% (0·123–0·185)                                                                   | 130 (105–158)                                                        |
-| El Salvador                    | 629 (409–928)                                                                 | 0·156% (0·101–0·230)                                                                   | 96 (62–142)                                                          |
-| Guatemala                      | 1178 (862–1559)                                                               | 0·115% (0·084–0·152)                                                                   | 63 (46–83)                                                           |
-| Honduras                       | 399 (238–603)                                                                 | 0·098% (0·058–0·148)                                                                   | 40 (24–60)                                                           |
-| Jamaica                        | 173 (106–254)                                                                 | 0·071% (0·044–0·105)                                                                   | 60 (36–87)                                                           |
-| Mexico                         | 21026 (19903–22 202)                                                          | 0·089% (0·084–0·094)                                                                   | 153 (145–162)                                                        |
-| Panama                         | 826 (667–1006)                                                                | 0·079% (0·063–0·096)                                                                   | 187 (151–227)                                                        |
-| Paraguay                       | 921 (634–1296)                                                                | 0·169% (0·116–0·238)                                                                   | 127 (87–179)                                                         |
-| Peru                           | 2298 (1669–3086)                                                              | 0·058% (0·042–0·078)                                                                   | 67 (49–90)                                                           |
-| Suriname                       | 118 (87–155)                                                                  | 0·142% (0·104–0·187)                                                                   | 206 (151–271)                                                        |
-| Uruguay                        | 1011 (796–1262)                                                               | 0·108% (0·085–0·134)                                                                   | 288 (227–360)                                                        |
-| Middle East and north Africa   |                                                                               |                                                                                        |                                                                      |
-| Bahrain                        | 295 (240–365)                                                                 | 0·047% (0·039–0·059)                                                                   | 170 (138–210)                                                        |
-| Egypt                          | 10674 (6584–15 169)                                                           | 0·177% (0·109–0·252)                                                                   | 100 (62–142)                                                         |
-| Iraq                           | 2060 (1646–2534)                                                              | 0·053% (0·043–0·066)                                                                   | 46 (37–57)                                                           |
-| Israel                         | 4089 (3459–4844)                                                              | 0·071% (0·060–0·085)                                                                   | 454 (384–537)                                                        |
-| Jordan                         | 544 (405–708)                                                                 | 0·093% (0·069–0·122)                                                                   | 53 (39–69)                                                           |
-| Kuwait                         | 1833 (1590–2086)                                                              | 0·072% (0·062–0·082)                                                                   | 414 (359–471)                                                        |
-| Lebanon                        | 846 (434–1253)                                                                | 0·108% (0·056–0·161)                                                                   | 147 (76–218)                                                         |
-| Malta                          | 204 (177–236)                                                                 | 0·078% (0·067–0·090)                                                                   | 483 (419–559)                                                        |
-| Morocco                        | 3890 (2468–6522)                                                              | 0·163% (0·103–0·273)                                                                   | 102 (65–172)                                                         |
-| Oman                           | 4304 (3180–5581)                                                              | 0·321% (0·237–0·416)                                                                   | 819 (605–1063)                                                       |
-| Qatar                          | 2497 (2020–3076)                                                              | 0·078% (0·063–0·096)                                                                   | 866 (700–1066)                                                       |
-| Saudi Arabia                   | 24328 (14997–33 171)                                                          | 0·202% (0·124–0·275)                                                                   | 679 (418–925)                                                        |
-| Tunisia                        | 1681 (1165–2267)                                                              | 0·177% (0·122–0·238)                                                                   | 139 (96–187)                                                         |
-
-economy-wide physical capital accumulation. However, any shifts from consumption into health care do not count as a loss, because they are simply a sectoral reallocation of resources within a full-employment economy, of which the health-care sector forms a part. In other words, health system costs are costs for services provided that have a return to the economy: for example, salaries for nurses and physicians, returns to investments in building hospitals and training residents, or returns to providers of medical devices and pharmaceuticals. In our model, these are not losses but rather part of the economic cycle.
-
-To quantify the macroeconomic burden of road injuries, we compared aggregate output (GDP) across the following two scenarios over the period 2015–30: the status quo scenario, in which no interventions are implemented that could reduce the mortality rate of road injuries relative to current and projected rates, and a counterfactual scenario, in which we assumed the complete elimination of road injuries at zero cost. We then calculated the macroeconomic burden of road injuries as the cumulative difference in projected annual GDP between these two scenarios. Although the baseline estimates are undiscounted, we also provide the figures subject to discount rates of 2% and 3%.
-
-#### **Data sources**
-
-We considered data for 166 countries and for a set of World Bank regions. The GDP projections for the status quo scenario and the saving rate are taken from the World Bank's database.22–24 The mortality and morbidity data (years of life lost due to premature mortality and years lost due to disability) are from GBD 2017.21 We relied on the International Labour Organization for agespecific labour force projections,25 the Barro-Lee education database for age-specific data on average years of schooling,26 and a 2014 World Bank report for returns on education.27 Using these data sources, we calculated human capital according to the Mincer equation28 and inferred the experience-related human capital component from the corresponding estimates of Heckman and colleagues.29 The physical capital data are taken from the Penn World Table projections,30 with the value for the output elasticity of physical capital (the percentage change in output for a 1% change in the physical capital stock) following standard economic estimates.31
-
-The total treatment cost of road injuries in the USA is based on the Cost of Injury Reports from the Centers for Disease Control and Prevention;32 their total treatment cost estimate includes the medical cost for fatal injuries, non-fatal hospitalised injuries, and non-fatal emergency department visits due to road accidents. We calculated the per case costs for the countries with data (ie, the USA) and extrapolated costs for countries without data, under the assumption that the per case costs are proportional to the per capita health expenditure. This technique has been used in previous studies.33,34 Further details on assumptions and other parameter values and data sources used in the macroeconomic model are provided in the appendix (pp 5–6). To make estimates among countries comparable, all costs were converted to US\$ with 2010 constant prices.
-
-For 28 countries, some data (mostly on education, physical capital, and the saving rate) are incomplete, although reliable data are available on GDP and the mortality and incidence of road injuries. We used a linear projection to approximate the economic burden of road injuries for these countries (appendix pp 6–7).
-
-#### **Sensitivity analysis**
-
-We also did sensitivity analyses (for the 138 countries that had all the data necessary to compute the costs directly on the basis of our model; appendix p 7) by varying the mortality and morbidity rates. The baseline estimates were calculated with the mean mortality and morbidity data from GBD. In the sensitivity analysis, best-case and worst-case estimates were calculated on the basis of the lower and upper bounds of GBD mortality and morbidity data. In the main analyses, we provided the undiscounted estimates following previous studies.18,20,33 In additional analyses, we also presented the discounted estimates for each country by World Bank region and by World Bank income group, assuming a discount rate of 2% and 3%.
-
-# **Role of the funding source**
-
-The funder had no role in the data collection, study design, analysis, interpretation, writing of the manuscript, or the decision to submit. The corresponding author had full access to all the data and had final responsibility for the decision to submit for publication.
-
-# **Results**
-
-We calculated the macroeconomic burden of road injuries as the difference in total GDP in 2015–30 between the status quo scenario and the counterfactual scenario (in which all road accidents are eliminated) for the 138 countries with complete data, representing more than 90% of the world's population (table 1). We also calculated the indirect estimates for the 28 countries for which we did not have full data (appendix p 7) and the discounted estimates (appendix pp 7–10). Among all countries, the USA has the largest economic burden of road injuries of \$487 billion, followed by China (\$364 billion) and India (\$101 billion; figure 1). In terms of percentage of GDP, Yemen (0·33%) and Oman (0·32%) have the largest burden (figure 2), whereas the per capita figures are highest in Luxembourg with \$1465, the USA with \$1444, Ireland with \$1081, and Norway with \$1052.
-
-Globally, we estimated the macroeconomic loss of road injuries to be \$1·797 trillion over 2015–30 (table 2). This number is \$1·460 trillion if discounted at 2% or \$1·317 trillion if discounted at 3% (appendix pp 10–11). Our result implies that the burden of road injuries is
-
-|                                | Economic burden, millions<br>of constant 2010 US\$<br>(lower and upper bound) | Percentage of total gross<br>domestic product in<br>2015–30<br>(lower and upper bound) | Per capita loss,<br>constant 2010 US\$<br>(lower and upper<br>bound) |
-|--------------------------------|-------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|----------------------------------------------------------------------|
-| (Continued from previous page) |                                                                               |                                                                                        |                                                                      |
-| Yemen                          | 880 (591–1373)                                                                | 0·328% (0·220–0·512)                                                                   | 28 (19–43)                                                           |
-| North America                  |                                                                               |                                                                                        |                                                                      |
-| Canada                         | 27 573 (22 790–33 318)                                                        | 0·082% (0·067–0·099)                                                                   | 719 (594–869)                                                        |
-| USA                            | 487147 (45 3399–51 3884)                                                      | 0·157% (0·146–0·165)                                                                   | 1444 (1344–1523)                                                     |
-| South Asia                     |                                                                               |                                                                                        |                                                                      |
-| Bangladesh                     | 2793 (1613–4032)                                                              | 0·063% (0·036–0·091)                                                                   | 16 (9–23)                                                            |
-| Bhutan                         | 47 (21–70)                                                                    | 0·083% (0·037–0·125)                                                                   | 55 (25–83)                                                           |
-| India                          | 100933 (87476–112 282)                                                        | 0·153% (0·133–0·170)                                                                   | 71 (62–79)                                                           |
-| Nepal                          | 778 (321–1342)                                                                | 0·167% (0·069–0·288)                                                                   | 25 (10–43)                                                           |
-| Pakistan                       | 13426 (6349–20587)                                                            | 0·274% (0·130–0·421)                                                                   | 62 (29–95)                                                           |
-| Sri Lanka                      | 2035 (1301–3062)                                                              | 0·120% (0·077–0·180)                                                                   | 96 (61–145)                                                          |
-| Sub-Saharan Africa             |                                                                               |                                                                                        |                                                                      |
-| Angola                         | 2867 (1871–4247)                                                              | 0·155% (0·101–0·230)                                                                   | 80 (52–119)                                                          |
-| Benin                          | 409 (176–664)                                                                 | 0·183% (0·079–0·297)                                                                   | 31 (14–51)                                                           |
-| Botswana                       | 217 (164–285)                                                                 | 0·062% (0·047–0·082)                                                                   | 87 (65–114)                                                          |
-| Burkina Faso                   | 371 (233–544)                                                                 | 0·123% (0·077–0·180)                                                                   | 16 (10–24)                                                           |
-| Burundi                        | 43 (26–73)                                                                    | 0·115% (0·068–0·193)                                                                   | 3 (2–6)                                                              |
-| Cameroon                       | 782 (494–1198)                                                                | 0·104% (0·066–0·159)                                                                   | 28 (18–43)                                                           |
-| Cape Verde                     | 26 (17–36)                                                                    | 0·066% (0·044–0·093)                                                                   | 45 (30–64)                                                           |
-| Comoros                        | 12 (8–18)                                                                     | 0·102% (0·067–0·151)                                                                   | 13 (9–20)                                                            |
-| Congo (Brazzaville)            | 421 (251–665)                                                                 | 0·190% (0·113–0·301)                                                                   | 69 (41–109)                                                          |
-| DR Congo                       | 1475 (964–2196)                                                               | 0·218% (0·142–0·324)                                                                   | 15 (10–23)                                                           |
-| Ethiopia                       | 1034 (829–1281)                                                               | 0·067% (0·054–0·083)                                                                   | 9 (7–11)                                                             |
-| Gabon                          | 368 (253–518)                                                                 | 0·100 (0·069–0·140)                                                                    | 164 (112–230)                                                        |
-| The Gambia                     | 23 (12–37)                                                                    | 0·094% (0·051–0·154)                                                                   | 9 (5–15)                                                             |
-| Ghana                          | 1870 (1239–2730)                                                              | 0·156% (0·104–0·228)                                                                   | 58 (38–84)                                                           |
-| Guinea-Bissau                  | 41 (24–64)                                                                    | 0·168% (0·099–0·263)                                                                   | 19 (11–30)                                                           |
-| Kenya                          | 890 (776–1104)                                                                | 0·067% (0·058–0·083)                                                                   | 16 (14–19)                                                           |
-| Lesotho                        | 153 (96–229)                                                                  | 0·280% (0·176–0·419)                                                                   | 64 (40–96)                                                           |
-| Liberia                        | 23 (14–35)                                                                    | 0·066% (0·040–0·103)                                                                   | 4 (3–7)                                                              |
-| Madagascar                     | 241 (152–367)                                                                 | 0·104% (0·066–0·159)                                                                   | 8 (5–12)                                                             |
-| Malawi                         | 159 (100–237)                                                                 | 0·082% (0·052–0·122)                                                                   | 7 (5–11)                                                             |
-| Mali                           | 273 (168–440)                                                                 | 0·090% (0·056–0·145)                                                                   | 12 (8–20)                                                            |
-| Mauritania                     | 100 (64–142)                                                                  | 0·080% (0·051–0·114)                                                                   | 20 (12–28)                                                           |
-| Mauritius                      | 221 (177–274)                                                                 | 0·085% (0·068–0·106)                                                                   | 175 (140–217)                                                        |
-| Mozambique                     | 307 (196–445)                                                                 | 0·095% (0·061–0·137)                                                                   | 9 (6–13)                                                             |
-| Namibia                        | 296 (190–470)                                                                 | 0·109% (0·070–0·173)                                                                   | 105 (67–167)                                                         |
-| Niger                          | 116 (74–181)                                                                  | 0·061% (0·039–0·095)                                                                   | 4 (3–7)                                                              |
-| Nigeria                        | 4977 (3052–7847)                                                              | 0·060% (0·037–0·094)                                                                   | 23 (14–36)                                                           |
-| Rwanda                         | 347 (184–604)                                                                 | 0·149% (0·079–0·259)                                                                   | 25 (13–44)                                                           |
-| Senegal                        | 254 (162–407)                                                                 | 0·058% (0·037–0·094)                                                                   | 14 (9–22)                                                            |
-| Sierra Leone                   | 47 (28–74)                                                                    | 0·065% (0·039–0·102)                                                                   | 6 (3–9)                                                              |
-| South Africa                   | 14216 (11905–16891)                                                           | 0·191% (0·160–0·227)                                                                   | 236 (198–281)                                                        |
-| Sudan                          | 2144 (1380–3703)                                                              | 0·177% (0·114–0·306)                                                                   | 46 (30–80)                                                           |
-| Tanzania                       | 887 (581–1300)                                                                | 0·076% (0·050–0·112)                                                                   | 13 (9–19)                                                            |
-| Togo                           | 129 (80–195)                                                                  | 0·133% (0·082–0·201)                                                                   | 14 (9–22)                                                            |
-| Uganda                         | 670 (390–1038)                                                                | 0·103% (0·060–0·159)                                                                   | 13 (8–20)                                                            |
-| Zambia                         | 578 (380–855)                                                                 | 0·101% (0·066–0·149)                                                                   | 29 (19–42)                                                           |
-|                                |                                                                               |                                                                                        |                                                                      |
-
-*Table 1:* **Economic burden attributable to road injuries in 2015–30, by country and World Bank region**
-
-![](_page_5_Figure_1.jpeg)
-
-*Figure 1:* **Macroeconomic burden due to road injuries in 2015–30 (in billions of US\$ with constant prices as of 2010)** Grey areas represent countries with insufficient data.
-
-![](_page_5_Figure_3.jpeg)
-
-*Figure 2:* **Macroeconomic burden due to road injuries as a percentage of total GDP in 2015–30** Grey areas represent countries with insufficient data. GDP=gross domestic product.
-
-equivalent to an annual tax of 0·12% on global output, with an average per capita burden of \$231.
-
-By World Bank region, the aggregate macroeconomic burden of road injuries is highest in east Asia and the Pacific with a total economic loss of \$560 billion (table 2). North America has the second largest aggregate total economic loss of \$515 billion, but the highest per capita loss of \$1370 (table 2). This loss corresponds to an annual tax of 0·15% on the region's aggregate output. The economic burden of road injuries increases as the income group escalates: high-income countries bear the greatest burden with a total economic loss of \$963 billion and a per capita loss of \$779 (table 2). By contrast, road injuries cost low-income countries \$11 billion in total and \$14 per person (table 2). In terms of percentage loss of (cumulative) GDP, all countries have a relatively similar burden: 0·106% of GDP for high-income countries, 0·120% for low-income countries, and 0·138–0·144% for middle-income countries (table 2). Discounted estimates by World Bank region and World Bank income group are shown in the appendix (pp 10–11).
-
-Road injuries resulted in 70 million disability-adjusted life-years (DALYs) worldwide in 2015.21 The economic burden is not distributed in proportion with population size and DALYs (table 3). For example, south Asia accounts for 23·8% of the DALYs, but only 6·7% of the economic loss, whereas North America accounts for only 3·9% of the DALYs, but 28·6% of the economic loss (table 3). Notably, despite the relatively low economic burden of road injuries in low-income and middleincome countries (46·4% of the global economic loss), the disease burden, as measured in DALYs, is very large (89% of global DALYs; table 3).
-
-We also explored the importance of treatment costs in the economic burden of road injuries. A previous empirical analysis35 produced a bell curve when plotting traffic fatalities against GDP. Given that accident and injury rates are not declining with income, the downward segment of this curve seems to be mostly due to better life-saving treatments in high-income countries. This disparity might also be evident in treatment costs, where countries with a higher income conceivably face a higher burden. Our results show that treatment costs account for a greater proportion of the total economic burden in high-income countries than in low-income countries. In high-income countries, physical capital loss (because a proportion of the costs of treating road injuries could have gone into savings if no road injury had occurred) accounts for 31·5% of the total economic burden due to road injuries, but this number decreases to 13·9% for upper-middle-income countries, 6·2% for lower-middleincome countries, and 3·9% for low-income countries (appendix p 11).
-
-# **Discussion**
-
-This study estimates the macroeconomic burden of road injuries for 166 countries and shows that between 2015 and 2030, road injuries will cost the world economy \$1·8 trillion through a combination of diversion—healthcare expenditures that would otherwise have been used for savings or investment—and losses in employment due to mortality and morbidity. This figure is more than the aggregate GDP of Canada (the world's tenth largest economy) in 2017.36 The economic burden of road injuries is equivalent to an annual tax of 0·12% on global GDP during this period.
-
-The health and economic burdens of road injuries are distributed unequally across countries and regions. Of the 70 million DALYs lost to road injuries worldwide in 2015, nearly 90% occurred in low-income and middleincome countries. This distribution might be due to a higher proportion of vulnerable road users (including pedestrians, cyclists, and riders of motorised two-wheelers and their passengers) in lower-income countries.37 Moreover, low-income countries are more likely to lack good-quality prehospital care, the mandatory use of seatbelts for drivers and helmets for motorcyclists, appropriate speed limits, and effective laws against drunk driving.37 Responsive policy strategies for low-income and middle-income countries could include improving road conditions, lighting, traffic lights, and signage, building paved and level roads with more clearly demarcated traffic lanes, installing dedicated bike lanes, pedestrian crossings, and raised and protected pavements, enforcing the mandatory use of seatbelts in cars and helmets for motorcyclists, establishing laws against drunk driving, vehicle inspection laws, and specific speed limits appropriate to the type of road, and strengthening traffic law enforcement overall.
-
-Despite the large burden in terms of DALYs, the economic burden of road injuries in low-income and middle-income countries only accounts for 46·4% of the global economic cost. The disparity relates to differences in economic development. First, the workforce in
-
-|                                    | Economic loss, billions of<br>constant 2010 US\$ | Percentage of total<br>gross domestic<br>product in 2015–30 | Per capita loss,<br>constant 2010 US\$ |
-|------------------------------------|--------------------------------------------------|-------------------------------------------------------------|----------------------------------------|
-| By World Bank region               |                                                  |                                                             |                                        |
-| East Asia and Pacific              | 560                                              | 0·123%                                                      | 240                                    |
-| Europe and central Asia            | 345                                              | 0·082%                                                      | 374                                    |
-| Latin America and Caribbean        | 115                                              | 0·116%                                                      | 184                                    |
-| Middle East and north Africa       | 103                                              | 0·166%                                                      | 227                                    |
-| North America                      | 515                                              | 0·149%                                                      | 1370                                   |
-| South Asia                         | 121                                              | 0·155%                                                      | 64                                     |
-| Sub-Saharan Africa                 | 38                                               | 0·120%                                                      | 33                                     |
-| By World Bank country income group |                                                  |                                                             |                                        |
-| Low income                         | 11                                               | 0·120%                                                      | 14                                     |
-| Lower-middle income                | 202                                              | 0·138%                                                      | 64                                     |
-| Upper-middle income                | 621                                              | 0·144%                                                      | 237                                    |
-| High income                        | 963                                              | 0·106%                                                      | 779                                    |
-| Global (166 countries)             | 1797                                             | 0·120%                                                      | 231                                    |
-
-*Table 2:* **Economic cost attributable to road injury mortality and morbidity, by World Bank region and World Bank country income group**
-
-|                                    | Population in<br>2015, million<br>(global %) | Gross domestic<br>product in 2015,<br>billions of<br>constant 2010<br>US\$ (global %) | Economic loss in<br>2015–30,<br>billions of<br>constant 2010<br>US\$ (global %) | Disability<br>adjusted life<br>years in 2015,<br>million<br>(global %) |
-|------------------------------------|----------------------------------------------|---------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|------------------------------------------------------------------------|
-| By World Bank region               |                                              |                                                                                       |                                                                                 |                                                                        |
-| East Asia and Pacific              | 2251 (31·3%)                                 | 20236 (27·3%)                                                                         | 560 (31·1%)                                                                     | 21·5 (32·2%)                                                           |
-| Europe and central Asia            | 906 (12·6%)                                  | 22466 (30·3%)                                                                         | 345 (19·2%)                                                                     | 5·8 (8·7%)                                                             |
-| Latin America and Caribbean        | 584 (8·1%)                                   | 5339 (7·2%)                                                                           | 115 (6·4%)                                                                      | 5·8 (8·7%)                                                             |
-| Middle East and north Africa       | 404 (5·6%)                                   | 3146 (4·2%)                                                                           | 103 (5·8%)                                                                      | 5·8 (8·6%)                                                             |
-| North America                      | 356 (4·9%)                                   | 18500 (25·0%)                                                                         | 515 (28·6%)                                                                     | 2·6 (3·9%)                                                             |
-| South Asia                         | 1744 (24·2%)                                 | 2796 (3·8%)                                                                           | 121 (6·7%)                                                                      | 15·9 (23·8%)                                                           |
-| Sub-Saharan Africa                 | 950 (13·2%)                                  | 1621 (2·2%)                                                                           | 38 (2·1%)                                                                       | 9·5 (14·1%)                                                            |
-| By World Bank country income group |                                              |                                                                                       |                                                                                 |                                                                        |
-| Low income                         | 621 (8·6%)                                   | 374 (0·5%)                                                                            | 11 (0·6%)                                                                       | 6·8 (10·1%)                                                            |
-| Lower-middle income                | 2856 (39·7%)                                 | 5812 (7·8%)                                                                           | 202 (11·2%)                                                                     | 26·8 (40·1%)                                                           |
-| Upper-middle income                | 2521 (35·0%)                                 | 18952 (25·6%)                                                                         | 621 (34·6%)                                                                     | 26·0 (38·9%)                                                           |
-| High income                        | 1196 (16·6%)                                 | 48966 (66·1%)                                                                         | 963 (53·6%)                                                                     | 7·3 (10·9%)                                                            |
-| Global (166 countries)             | 7195 (100%)                                  | 74103 (100%)                                                                          | 1797 (100%)                                                                     | 70·0 (100%)                                                            |
-|                                    |                                              |                                                                                       |                                                                                 |                                                                        |
-
-*Table 3:* **Comparison of macroeconomic loss and lifetime disease burden by World Bank region and country income group**
-
-high-income countries is typically better educated, which implies that, for the same loss of DALYs due to road injuries, the loss of human capital will be larger. Second, high-income countries have advanced healthcare systems (eg, in terms of ambulance response times and accident and emergency departments), implying a smaller loss of DALYs due to lower morbidity and mortality associated with road injuries, but also much greater treatment costs. Our results show that physical capital loss due to diversion of savings to pay for treatment has a more important role in high-income countries than in low-income countries. More than 30% of the total economic burden due to road injuries comes from physical capital loss in high-income countries, whereas physical capital loss accounts for less than 5% of the total economic burden in low-income countries. Although the economic burden of road injuries on low-income countries is relatively light at present, it is likely to rise in the course of economic development if growth in motorisation and traffic density outpaces development of infrastructure and law enforcement levels.38–40
-
-Promotion of the use of self-driving cars might be a potential solution for reducing road accidents and lowering the burden of road injuries. Articles in *The Economist*41,42 and findings by the Boston Consulting Group43 argue that 90–94% of all accidents are due to human error and are, therefore, preventable through the use of self-driving cars. We calculated the potential cost savings from the adoption of autonomous cars by considering a conservative scenario of a 50% reduction in accidents and an optimistic scenario of a 90% reduction in accidents (as determined by *The Economist* and by the Boston Consulting Group). In the conservative scenario, autonomous cars could save \$0·9 trillion at a global level, whereas savings of \$1·6 trillion were produced in the optimistic scenario. However, because a country's infrastructure must be well developed to use self-driving vehicles successfully, promotion of their use might only be possible in high-income countries in the near future. The scientific research on autonomous cars and accident prevention is in its infancy; autonomous cars, for example, might lead to greater traffic density such that the number of accidents could increase.
-
-Our model has several limitations (appendix pp 11–13). First, we had to rely on imputations to calculate road injury-related health expenditures. This could either underestimate or overestimate the country-specific treatment cost of road injuries. However, this technique is a widely used approach to deal with lack of data and has also been adopted in other studies calculating the economic burden of other health outcomes.33,34 Second, owing to missing data, we had to impute the economic burden of road injuries for a subset of 28 of 166 countries. However, this does not substantially compromise our results, given that the 138 countries for which we had complete data cover more than 90% of the world population. Third, we did not account for the behavioural changes of family members, including their participation in the labour force when traffic accidents happen. Nevertheless, our analysis has many strengths and is a first step in understanding the global macroeconomic burden of road injuries using a simulation model rigorously grounded in dynamic macroeconomic theory.
-
-This study shows that high-income countries have the largest macroeconomic costs of road injuries, whereas low-income and middle-income countries bear sizeable health burdens. The fact that low-income and middle-income countries bear the majority of the human toll underscores the need for improvements on multiple fronts, including infrastructure, law enforcement, public awareness, and emergency response systems. Otherwise, as these countries develop, the human cost they already bear will be accompanied by economic hardship.
-
-#### **Contributors**
-
-SC, KP, MK, and DEB contributed to the study concept and design. SC did data analysis and wrote the first draft of the manuscript. SC, KP, MK, and DEB contributed to literature review and the interpretation of the data. KP, MK, and DEB critically revised the manuscript for important intellectual content. All authors approved the final version.
-
-#### **Declaration of interests**
-
-We declare no competing interests.
-
-#### **Acknowledgments**
-
-This research received funding from National Institute on Aging, National Institutes of Health (award numbers P30AG024409 and R01AG048037)
-
-#### **References**
-
-- 1 WHO. Global status report on road safety 2015. Geneva: World Health Organization, 2015.
-- 2 WHO. Road traffic injuries. Geneva: World Health Organization, 2018. http://www.who.int/news-room/fact-sheets/detail/roadtraffic-injuries (accessed June 20, 2018).
-- 3 Telford J, Cosgrave J. Joint evaluation of the international response to the Indian Ocean tsunami: synthesis report. Tsunami Evaluation Coalition, 2006.
-- 4 Vecino-Ortiz AI, Jafri A, Hyder AA. Effective interventions for unintentional injuries: a systematic review and mortality impact assessment among the poorest billion. *Lancet Glob Health* 2018; **6:** e523–34.
-- 5 Chisholm D, Naci H, Hyder AA, Tran NT, Peden M. Cost effectiveness of strategies to combat road traffic injuries in sub-Saharan Africa and South East Asia: mathematical modelling study. *BMJ* 2012; **344:** e612.
-- 6 Viscusi WK, Aldy JE. The value of a statistical life: a critical review of market estimates throughout the world. *J Risk Uncertainty* 2003; **27:** 5–76.
-- 7 Rice DP. Cost of illness studies: what is good about them? *Inj Prev* 2000; **6:** 177–79.
-- 8 Blincoe L, Miller T, Zaloshnja E, Lawrence BA. The economic and societal impact of motor vehicle crashes, 2010 (revised). Washington, DC: National Highway Traffic Safety Administration, 2015.
-- 9 Elvik R. An analysis of official economic valuations of traffic accident fatalities in 20 motorized countries. *Accid Anal Prev* 1995; **27:** 237–47.
-- 10 Elvik R. How much do road accidents cost the national economy? *Accid Anal Prev* 2000; **32:** 849–51.
-- 11 Milligan C, Kopp A, Dahdah S, Montufar J. Value of a statistical life in road safety: a benefit-transfer function with risk-analysis guidance based on developing country data. *Accid Anal Prev* 2014; **71:** 236–47.
-- 12 Peden M, Scurfield R, Sleet D, et al. World report on road traffic injury prevention. Geneva: World Health Organization, 2004.
-- 13 Fumagalli E, Bose D, Marquez P, et al. The high toll of traffic injuries: unacceptable and preventable. Washington, DC: World Bank, 2017.
-- 14 Durlauf S, Johnson P, Temple J. Growth econometrics. In: Aghion P, Durlauf S, eds. Handbook of economic growth. Amsterdam: Elsevier, 2005.
-- 15 Weil DN. Health and economic growth. In: Aghion P, Durlauf S, eds. Handbook of economic growth. Amsterdam: Elsevier, 2013.
-- 16 Zhang W, Bansback N, Anis AH. Measuring and valuing productivity loss due to poor health: a critical review. *Soc Sci Med* 2011; **72:** 185–92.
-- 17 Lucas RE. On the mechanics of economic development. *J Monet Econ* 1988; **22:** 3–42.
-- 18 Bloom DE, Chen S, Kuhn M, McGovern ME, Oxley L, Prettner K. The economic burden of chronic diseases: estimates and projections for China, Japan, and South Korea. *J Econ Ageing* 2018; published online Sep 26. DOI:10.1016/j.jeoa.2018.09.002.
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-- 19 Chen S, Kuhn M, Prettner K, Bloom DE. The macroeconomic burden of noncommunicable diseases in the United States: estimates and projections. *PLoS One* 2018; **13:** e0206702.
-- 20 Chen S, Bloom DE. The macroeconomic burden of noncommunicable diseases associated with air pollution in China. *PLoS One* 2019; **14:** e0215663.
-- 21 James SL, Abate D, Abate KH, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. *Lancet* 2018; **392:** 1789–858.
-- 22 World Bank. World Bank database, gross savings (% of GDP). World Bank, 2018. https://data.worldbank.org/indicator/NY.GNS. ICTR.ZS (accessed March 20, 2018).
-- 23 World Bank. World Bank database, GDP (constant 2010 US\$). World Bank, 2018. https://data.worldbank.org/indicator/NY.GDP. MKTP.KD?view=chart. (accessed March 20, 2018).
-- 24 World Bank. Global economic prospects. World Bank, 2017. https://data.worldbank.org/data-catalog/global-economic-prospects (accessed Aug 1, 2017).
-- 25 ILO. Labour force by sex and age (thousands). International Labour Organization, 2017. http://ilo.org/global/statistics-and-databases/ lang--en/index.htm (accessed Jun 15, 2017).
-- 26 Barro RJ, Lee JW. A new data set of educational attainment in the world, 1950–2010. *J Devel Econ* 2013; **104:** 184–98.
-- 27 Montenegro CE, Patrinos HA. Comparable estimates of returns to schooling around the world. Washington, DC: World Bank Group, 2014.
-- 28 Mincer J. Schooling, experience, and earnings. New York, NY: National Bureau of Economic Research, 1974.
-- 29 Heckman JJ, Lochner LJ, Todd PE. Earnings functions, rates of return and treatment effects: the Mincer equation and beyond. In: Hanushek E, ed. Handbook of the economics of education. Amsterdam: Elsevier, 2006: 307–458.
-- 30 University of Groningen and University of California. Capital stock at constant national prices for United States (RKNANPUSA666NRUG). Federal Reserve Bank of St Louis, 2017. https://fred.stlouisfed.org/series/RKNANPUSA666NRUG (accessed Aug 23, 2017).
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-- 31 Jones CI. R&D-based models of economic growth. *J Polit Economy* 1995; **103:** 759–84.
-- 32 Centers for Disease Control and Prevention. Cost of injury data. Centers for Disease Control and Prevention, 2010. https://www.cdc.gov/injury/wisqars/cost/index.html (accessed March 20, 2018).
-- 33 Bloom DE, Cafiero E, Jané-Llopis E, et al. The global economic burden of noncommunicable diseases. Geneva: World Economic Forum, 2011.
-- 34 Ding D, Lawson KD, Kolbe-Alexander TL, et al. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. *Lancet* 2016; **388:** 1311–24.
-- 35 Bishai D, Quresh A, James P, Ghaffar A. National road casualties and economic development. *Health Econ* 2006; **15:** 65–81.
-- 36 The World Bank. Canada. The World Bank, 2018. https://data. worldbank.org/country/canada (accessed Sep 3, 2019).
-- 37 WHO. 10 facts on global road safety. Geneva: World Health Organization, 2017. http://www.who.int/features/factfiles/ roadsafety/en/ (accessed June 20, 2018).
-- 38 WHO. Global status report on road safety 2013: supporting a decade of action. Geneva: World Health Organization, 2013.
-- 39 Ameratunga S, Hijar M, Norton R. Road-traffic injuries: confronting disparities to address a global-health problem. *Lancet* 2006; **367:** 1533–40.
-- 40 Chandran A, Sousa TRV, Guo Y, Bishai D, Pechansky F, Vida No Transito Evaluation Team. Road traffic deaths in Brazil: rising trends in pedestrian and motorcycle occupant deaths. *Traffic Injury Prev* 2012; **13** (suppl 1)**:** 11–16.
-- 41 Reinventing wheels. Autonomous vehicles are just around the corner. *The Economist,* March 1, 2018.
-- 42 A different world. Self-driving cars will profoundly change the way people live. Special report on autonomous cars. *The Economist*, March 1, 2018.
-- 43 Lang N, Rüßmann M, Mei-Pochtler A, et al. Self-driving vehicles, robo-taxis, and the urban mobility revolution. Boston Consulting Group, 2016. https://www.bcg.com/publications/2016/automotivepublic-sector-self-driving-vehicles-robo-taxis-urban-mobilityrevolution.aspx (accessed Sep 3, 2019).

Zotero/001_artiklid/Road Safety Indicators/chengpoissonweibullgeneralizedlinear2013.md

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----
-category: literaturenote
-citekey: chengpoissonweibullgeneralizedlinear2013
-title: The Poisson–Weibull generalized linear model for analyzing motor vehicle crash data
-authors: "Cheng, Lingzi; Geedipally, Srinivas Reddy; Lord, Dominique"
-year: 2013
-date: 2013-00-00 2013
-doi: 10.1016/j.ssci.2012.11.002
-publication: Safety Science
-url: "https://doi.org/10.1016/j.ssci.2012.11.002"
-zotero_key: AGS3NFM5
-zotero_storage: N78RI3GX
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Cheng, Lingzi"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-Contents lists available at [SciVerse ScienceDirect](http://www.sciencedirect.com/science/journal/09257535)
-
-# Safety Science
-
-journal homepage: [www.elsevier.com/locate/ssci](http://www.elsevier.com/locate/ssci)
-
-![](_page_0_Picture_5.jpeg)
-
-# The Poisson–Weibull generalized linear model for analyzing motor vehicle crash data
-
-Lingzi Cheng <sup>a</sup> , Srinivas Reddy Geedipally b,1 , Dominique Lord a,⇑
-
-<sup>a</sup> Zachry Department of Civil Engineering, Texas A&M University, 3136 TAMU College Station, TX 77843-3136, USA
-
-<sup>b</sup> Texas Transportation Institute, 110N Davis Drive, Suite 101, Room 124, Arlington, TX 76013-1877, USA
-
-#### article info
-
-#### Article history: Received 25 July 2012 Received in revised form 31 August 2012 Accepted 4 November 2012 Available online 23 December 2012
-
-Keywords: Poisson–Weibull Poisson–gamma Generalized linear model Traffic crashes
-
-#### abstract
-
-Over the last 20–30 years, there has been a significant amount of tools and statistical methods that have been proposed for analyzing crash data. Yet, the Poisson–gamma (PG) is still the most commonly used and widely acceptable model. This paper documents the application of the Poisson–Weibull (PW) generalized linear model (GLM) for modeling motor vehicle crashes. The objectives of this study were to evaluate the application of the PW GLM for analyzing this kind of dataset and compare the results with the traditional PG model. To accomplish the objectives of the study, the modeling performance of the PW model was first examined using a simulated dataset and then several PW and PG GLMs were developed and compared using two observed crash datasets. The results of this study show that the PW GLM performs as well as the PG GLM in terms of goodness-of-fit statistics.
-
-> -2012 Elsevier Ltd. All rights reserved.
-
-## 1. Introduction
-
-Over the last 20–30 years, there has been a significant amount of tools and statistical methods that have been proposed for analyzing crash data. These tools are needed because of the nature of the data, which are characterized by the random and (assumed) independent discrete non-negative events. [Lord and Mannering](#page-4-0) [\(2010\)](#page-4-0) have recently documented the latest regression models that have been developed or applied for modeling crash data. Statistical models can be used for various purposes, such as establishing relationships between motor vehicle crashes and different covariates (i.e., understanding the system), predicting values or screening variables.
-
-As documented in the literature, crash data are characterized by unique attributes. They include datasets that contain a limited number of observations which can also be subjected to low sample mean values (see [Lord and Mannering, 2010; Maher and Sum](#page-4-0)[mersgill, 1996](#page-4-0), and references herein). It has been noted that these unique attributes could negatively influence regression models by providing biased estimates of regression coefficients or predicted values ([Lord, 2006](#page-4-0)). As a result, a few researchers have examined the application of alternative regression models that could be used for minimizing these problems. Some of the models include the Poisson–lognormal, the negative binomial (NB) bootstrap maximum likelihood estimation (MLE) method and the NB-Lindley model [\(Miaou et al., 2003; Zhang et al., 2007; Geedipally et al.,](#page-4-0) [2012\)](#page-4-0).
-
-In the line of the discussion above, this paper documents the application of the Poisson–Weibull (PW) regression model for analyzing crash data. Before examining the stability of this model for datasets characterized by small sample sizes and low sample mean values, there is a need to determine whether or not the PW model performs as well as the traditional NB or Poisson–gamma (PG) model. If it does not, there is no need to evaluate it in the context of the issues described above. Although not very popular in traffic safety (due to the complexity of parameterization – described below), the PW model has been applied in the other areas (see, e.g., [Wagner and Steenbakkers, 1989; Phoenix et al., 1997](#page-4-0)). In highway safety, the PW model has only been applied once [\(Maher and](#page-4-0) [Mountain, 2009\)](#page-4-0). Thus, there is a need to fully describe its modeling performance using simulated and observed datasets.
-
-The study objective was accomplished using both simulated and observed datasets. First, the general performance of the PW model was shown using the simulated data. Then the modeling performance of the PW model was compared to the PG model using two observed datasets. Several goodness-of-fit (GOF) measures were used for evaluating the models.
-
-The next section describes the characteristics of the PW distribution and its generalized linear model (GLM).
-
-<sup>⇑</sup> Corresponding author. Tel.: +1 (979) 458 3949; fax: +1 (979) 845 6481. E-mail addresses: [lingzi\\_cheng@tamu.edu](mailto:lingzi_cheng@tamu.edu) (L. Cheng), [srinivas-g@ttimail.tamu.](mailto:srinivas-g@ttimail.tamu. edu) [edu](mailto:srinivas-g@ttimail.tamu. edu) (S.R. Geedipally), [d-lord@tamu.edu](mailto:d-lord@tamu.edu) (D. Lord).
-
-<sup>1</sup> Tel.: +1 (817) 462 0519x11519.
-
-#### <span id="page-1-0"></span>2. Background
-
-This section describes the characteristics and parameterization of the PW GLM. First, the statistical background about the PW is provided. Then, the second part illustrates the parameterization of the PW model.
-
-## 2.1. Characteristics of the PW GLM
-
-The PW distribution is a mixture of Poisson and Weibull distributions, as the name implies. Similar to most Poisson-based distributions (e.g., Poisson–gamma, Poisson–lognormal, etc.), the PW model is also designed to accommodate the over-dispersion ([McCullagh and Nelder, 1993; Raghavachari et al., 1997; Lord](#page-4-0) [et al., 2005; Maher and Mountain, 2009](#page-4-0)).
-
-First, the Poisson and Weibull distributions need to be defined, respectively. Crash data can be characterized as the product of Bernoulli trials with unequal probability of events (also known as Poisson trials). As the number of trials increases the distribution may approximately follow a Poisson process and the amount of dispersion is governed by the characteristics of this process ([Lord](#page-4-0) [et al., 2005\)](#page-4-0). Thus, the number of crashes at ith roadway entity Yi is assumed to be Poisson distributed with mean l<sup>i</sup> and independent over all entities:
-
-$$Y_i | \mu_i \sim \text{Poisson}(\mu_i) \quad i = 1, 2, 3, \dots, I$$
- (1)
-
-The Poisson mean l<sup>i</sup> is structured as:
-
-$$\mu_i = \hat{\mu}_i \varepsilon_i = f(\mathbf{X}; \mathbf{\beta}) \cdot \varepsilon_i \tag{2}$$
-
-and,
-
-$$\hat{\mu}_i = f(\mathbf{X}; \mathbf{\beta}) = \exp(\beta_0 + \sum_{j=1}^q \beta_j X_j), j = 1, 2, 3, \dots, q$$
- (3)
-
-where Xs are the independent variables; q represents the total number of independent variables; bs are the regression coefficients; and e<sup>i</sup> is the model error independent of all covariates ([Cameron and](#page-4-0) [Trivedi, 1998; Miaou and Lord, 2003; Lord and Park, 2010\)](#page-4-0).
-
-For the PW model, it is assumed that e<sup>i</sup> is independent of all covariates and Weibull distributed. This is used for capturing the extra variation that the traditional Poisson cannot fully handle. The Weibull probability density function (PDF) is given as follows:
-
-$$f(x) = \frac{k}{\lambda} \left( \frac{x}{\lambda} \right)^{k-1} \exp\left[ -\left( \frac{x}{\lambda} \right)^k \right] \quad x > 0, k > 0, \lambda > 0$$
- (4)
-
-where k and k are scale and shape parameters, respectively. The PDF of the Weibull distribution has a wide variety of shapes depending on the k values and the shape can be similar to that of the gamma, gamma-like, exponential or approximate normal distributions. This characteristic gives the model a lot of flexibility to fit different kinds of data.
-
-The mean and variance of the Weibull distribution are:
-
-$$E(\varepsilon) = \lambda \Gamma \left(1 + \frac{1}{k}\right)$$
-
-$$Var(\varepsilon) = \lambda^2 \Gamma \left(1 + \frac{2}{k}\right) - \left[\lambda \Gamma \left(1 + \frac{1}{k}\right)\right]^2$$
-(5)
-
-Given the statistical characteristics of Poisson and Weibull distributions, the PW distribution is defined as the mixture of those two distributions such that
-
-$$P(Y = y; \mu, \lambda, k) = \int Poisson(y; \hat{\mu}\varepsilon) Weibull(\varepsilon; \lambda, k) d\varepsilon$$
- (6)
-
-The mean or expected value of the PW distribution is given as:
-
-$$E(Y) = \hat{\mu}E(\varepsilon) = \hat{\mu} \times \lambda \Gamma\left(1 + \frac{1}{k}\right)$$
- (7)
-
-and the variance is given by:
-
-$$Var(Y) = \hat{\mu} \times \lambda \Gamma \left( 1 + \frac{1}{k} \right) + \hat{\mu}^2 \times \lambda^2 \Gamma \left( 1 + \frac{2}{k} \right) - \hat{\mu}^2$$
-
-$$\times \left[ \lambda \Gamma \left( 1 + \frac{1}{k} \right) \right]^2$$
-(8)
-
-#### 2.2. Parameter estimation
-
-As discussed above, the likelihood function for the PW GLM was given by Eq. (6). An important characteristic of the PW distribution is that the likelihood function does not have a closed form. However, this can be solved by using a hierarchical representation of this function. As discussed above, the PW model is conditional on the site-specific error term e which explains the additional heterogeneity. Therefore, the PW model can be also written as:
-
-$$P(Y = y; \mu | \varepsilon) = Poisson(y; \hat{\mu}\varepsilon)$$
-  
- $\varepsilon \sim Weibull(\varepsilon; \lambda, k)$  (9)
-
-The formulation above has a nice Bayesian interpretation and hierarchical structure. Therefore, the parameter estimation and inference can be obtained by using Markov chain Monte Carlo (MCMC) and software such as WinBUGS [\(Spiegelhalter et al.,](#page-4-0) [2003](#page-4-0)). Since the Bayesian formulation requires priors for all unknown parameters, non-informative normal priors for bs and gamma priors for xð¼ 1=k<sup>k</sup> Þ and k were used.
-
-The total number of iterations was determined using the Gelman–Rubin (G–R) statistic ([Gelman and Rubin, 1992; Brooks and](#page-4-0) [Gelman, 1998\)](#page-4-0). The G–R convergence statistic is generally used to verify that the simulation runs converged properly and is based on analyzing multiple simulated MCMC chains (usually 3–5 chains) by comparing the variances within each chain and the variance between chains. A total of three Markov chains were used in the coefficient estimation process so that the G–R diagnostic was performed accurately. For model comparison, it was suggested that convergence was achieved when the G–R statistic was less than 1.2 ([Mitra and Washington, 2007\)](#page-4-0). The first half of iterations was used as burn-in samples and was discarded. Thus, the remaining half of the iterations was used for estimating the coefficients.
-
-# 3. Methodology
-
-This section describes the functional form and the GOF statistics that were used for comparing the PW GLM with the PG GLM. The PG model is one of the most commonly used models in highway safety application ([Hauer, 1997; Lord, 2006; Lord and Mannering,](#page-4-0) [2010](#page-4-0)). The characteristics of the PG GLM can be found in other documents (see, e.g., [Hilbe, 2011](#page-4-0)).
-
-The functional form used for model development is as follows:
-
-$$\hat{\mu}_i = \beta_0 \times L_i \times F_i^{\beta_1} \times y \times e^{j-2} X_{ij} \beta_j$$
-(10)
-
-where l<sup>i</sup> is the estimated number of crashes per year for site i; Fi the vehicles per day (ADT) for segment i; Li the length of segment i in miles; y the number of years of crash data; Xij the a series of covariates (e.g., shoulder width, lane width, etc.) for site i; n the number of covariates; and b<sup>s</sup> is the estimated coefficients.
-
-The GOFs used in this study for comparing the PW and PG GLMs included the Deviance Information Criterion (DIC), the Mean Absolute Deviance (MAD), the Mean Squared Predictive Error (MSPE) and the Pearson Chi-square (v<sup>2</sup> ). The DIC is often used as a GOF measurement when Bayesian estimation method is used ([Mitra](#page-4-0) [and Washington, 2007; Lord and Park, 2010; Geedipally et al.,](#page-4-0) [2012](#page-4-0)). In general, a smaller DIC value refers to a better model fit. As a rule of thumb, differences in the DIC values more than 10 will rule out the model with a higher DIC and a difference between 5 and 10 can be considered substantial [\(Spiegelhalter et al., 2002\)](#page-4-0). However, it is important to point out that the DIC is dependent on the structure of the model. Thus, as discussed by [Mitra and](#page-4-0) [Washington \(2007\)](#page-4-0), and [Lord and Park \(2008\),](#page-4-0) it is recommended to use more than one GOF measure for evaluating regression models.
-
-The MAD, MSPE and Pearson v<sup>2</sup> are three common GOF measurements that are also utilized for assessing how well the model fit the data. Generally, a model with a smaller value shows a better statistical fit. Interested readers are referred to [Oh et al. \(2003\)](#page-4-0) for additional information about these GOF measures.
-
-#### 4. Data description
-
-Two datasets are used in this study for comparing the modeling performance of the PW and PG GLMs. This section describes the characteristics of these two datasets.
-
-## 4.1. Indiana data
-
-Five years (1995–1999) of crash and traffic data collected at 338 rural interstate road segments in the state of Indiana were used for the model development. This dataset was utilized by [Anastasopo](#page-4-0)[ulos et al. \(2008\), Washington et al. \(2011\) and Geedipally et al.](#page-4-0) [\(2012\)](#page-4-0) for developing tobit and NB-Lindley models. Table 1 summarizes the key traffic and geometric variables. For a complete and detailed description of the variables, the interested reader is referred to [Washington et al. \(2011\).](#page-4-0)
-
-### 4.2. Texas data
-
-Crash data from 1997 to 2001 collected at 4-lane rural undivided in Texas were provided by the Texas Department of Public Safety (DPS) and the Texas Department of Transportation (TxDOT). This dataset was used by [Lord et al. \(2008a,b\)](#page-4-0) for the project titled ''NCHRP 17-29: Methodology to Predict the Safety Performance of Rural Multilane Highways'' and the evaluation of the Conway– Maxwell–Poisson GLM, respectively. The final database included 1499 segments (P0.1 mile). [Table 2](#page-3-0) presents the summary statistics of the data.
-
-#### 5. Modeling results
-
-This section presents the modeling results for the PW and PG GLMs based on simulated and observed data.
-
-#### 5.1. Simulated data
-
-This section presents the modeling results for the simulated data that are intended to illustrate the general performance of the PW model. The simulation design was carried out in several steps:
-
-Table 1 Indiana data summary.
-
-| Traffic variable                                       | Minimum | Maximum | Average (std. dev.) | Total  |
-|--------------------------------------------------------|---------|---------|---------------------|--------|
-|                                                        |         |         |                     |        |
-| Crashes (for 5 years)                                  | 0       | 329     | 16.97(36.30)        | 5737   |
-| Average daily traffic (ADT) (veh./day)                 | 9442    | 143,422 | 30237.56(28776.43)  | _      |
-| Minimum friction on road segment (FR) (0–100 scale)    | 15.90   | 48.20   | 30.51(6.67)         | _      |
-| Pavement surface (PS) (asphalt = 1, concrete = 0)      | 0       | 1       | 0.77(0.42)          | _      |
-| Median width (MW) (ft)                                 | 16      | 194.7   | 66.98(34.71)        | _      |
-| Median barrier (MB) (present = 1, absent = 0)          | 0       | 1       | 0.16(0.37)          | _      |
-| Interior rumble strips (IRS) (Present = 1, absent = 0) | 0       | 1       | 0.72(0.45)          | _      |
-| Segment length (L) (miles)                             | 0.009   | 11.53   | 0.89(1.48)          | 300.09 |
-
-(1) Generate a true mean value (qi) for roadway entity i from a fixed sample population mean (d):
-
-$$\rho_i = \delta \tag{11}$$
-
-(2) Generate a value (ei) from a Weibull distribution with parameters k and k. Here, the mean of the Weibull distribution is equal to 1. The values for k and k are determined based on the dispersion parameter (DP) of the NB model (VarðYÞ ¼ l þ al2, where a is the DP) in order to better illustrate the level of dispersion based on a regression model commonly used in highway safety:
-
-$$\varepsilon_i \sim \text{Weibull}(k, \lambda)$$
- (12)
-
-(3) Calculate the mean ðl<sup>i</sup> Þ for roadway entity i: l<sup>i</sup> ¼ q<sup>i</sup> e<sup>i</sup> ð13Þ
-
-(4) Generate a discrete value ðYiÞ for entity i from a Poisson dis-
-
-tribution with mean equal to 
-$$\mu_i$$
-:  
- $Y_i \sim \text{Poisson}(\mu_i)$  (14)
-
-(5) Repeat steps 1–4 ''N'' times, where ''N'' is the required number of observations (which is also the sample size).
-
-To avoid the potential bias associated with the small sample size and low sample mean (see [Lord, 2006\)](#page-4-0), the following values were considered:
-
-- (a) Sample size ''N'' = 300.
-- (b) Sample population mean ''d'' = 10.
-- (c) DP ''a'' = 0.5, 1, 2, 3, 5.
-
-The theoretical values for k and k were calculated and labeled as ''true'' parameters for each dispersion level. By following the steps above, crashes or counts are then simulated using the PW distribution. Once the crash dataset was created, the parameters were reestimated using the PW model and then compared with the ''true'' parameters.
-
-[Table 3](#page-3-0) presents the modeling results for the simulated dataset. This table shows that PW model was able to reproduce the ''true'' parameter values. All parameter were statistically significant at the 5% level.
-
-#### 5.2. Indiana data
-
-[Table 4](#page-3-0) summarizes the coefficient estimates and GOF statistics of the PG and PW models for the Indiana data. The segment length was treated as an offset and thus the crash frequency increases linearly with the increase in segment length. For both PG and PW models, the estimated coefficient for the traffic flow is less than 1. This indicates that with the increase in the traffic flow, the crash risk increases as the flow increases. It should be noted that the 95% marginal posterior credible intervals for each coefficient estimate did not include the origin. [Table 4](#page-3-0) shows that all the estimated coefficients between the two models have the same sign, and most of their coefficients are very close. The standard errors for both
-
-<span id="page-3-0"></span>Table 2 Texas data summary.
-
-| Traffic variable                       | Minimum | Maximum | Average (std. dev.) | Total  |
-|----------------------------------------|---------|---------|---------------------|--------|
-| Crashes (for 5 years)                  | 0       | 97      | 2.84(5.69)          | 4253   |
-| Average daily traffic (ADT) (veh./day) | 42      | 24,800  | 6613.61(4010.01)    | _      |
-| Lane width (LW) (ft)                   | 9.75    | 16.5    | 12.57(1.59)         | _      |
-| Total shoulder width (SW) (ft)         | 0       | 40      | 9.96(8.02)          | _      |
-| Curve density (CD) (curves/mile)       | 0       | 18.07   | 1.43(2.35)          | _      |
-| Segment length (L) (miles)             | 0.1     | 6.28    | 0.55(0.67)          | 830.49 |
-
-Table 3 Modeling results for the simulated data.
-
-| Dispersion Level             | a = 0.5 |         | a = 1.0 |         | a = 2.0 |         | a = 3.0 |         | a = 5.0 |         |
-|------------------------------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|
-| Parameters                   | k       | x       | k       | x       | k       | x       | k       | x       | k       | x       |
-| Theoretical values           | 1.436   | 0.871   | 1.000   | 1.000   | 0.721   | 0.936   | 0.607   | 0.935   | 0.500   | 1.414   |
-| Estimated values (std. dev.) | 1.446   | 0.873   | 1.000   | 0.993   | 0.731   | 0.931   | 0.607   | 0.936   | 0.506   | 1.414   |
-|                              | (0.080) | (0.061) | (0.053) | (0.065) | (0.038) | (0.060) | (0.032) | (0.061) | (0.029) | (0.084) |
-
-x ¼ 1=k<sup>k</sup> where k; k are the shape and scale parameters respectively in Eq. [\(4\).](#page-1-0) The Weibull distribution was re-parameterized since WinBUGS uses a slightly different parameterization than the one discussed above. This did not influence the model performance.
-
-Table 4 Modeling results for the Indiana data.
-
-| Variable          | PG       |          | PW       |          |  |  |
-|-------------------|----------|----------|----------|----------|--|--|
-|                   | Value    | S.E.     | Value    | S.E.     |  |  |
-| Intercept (ln b0) | 4.627    | 1.354    | 4.022    | 1.377    |  |  |
-| ADT (b1)          | 0.7029   | 0.1254   | 0.6428   | 0.1243   |  |  |
-| FR (b2)           | 0.02589  | 0.01048  | 0.02713  | 0.01128  |  |  |
-| PS (b3)           | 0.4226   | 0.1874   | 0.4267   | 0.1992   |  |  |
-| MW (b4)           | 0.005169 | 0.001906 | 0.005489 | 0.002007 |  |  |
-| MB (b5)           | 3.035    | 0.3047   | 2.99     | 0.3093   |  |  |
-| IRS (b6)          | 0.3901   | 0.1866   | 0.4113   | 0.1987   |  |  |
-| /                 | 1.089    | 0.1392   |          |          |  |  |
-| xa                |          |          | 0.9959   | 0.316    |  |  |
-| k                 |          |          | 0.9805   | 0.07021  |  |  |
-| DIC               | 1486.47  |          | 1450.67  |          |  |  |
-| Pearson v2        | 1009.406 |          | 1003.086 |          |  |  |
-| MAD               | 6.919    |          | 7.014    |          |  |  |
-| MSPE              | 209.909  |          | 231.476  |          |  |  |
-
-<sup>a</sup> x ¼ 1=kk.
-
-models are also very similar. Though the PG and PW model have the same hierarchical structure, there is an extra parameter in the PW model. This is because the shape and scale parameters are assumed to be the same in the PG model, but not for the PW model. However, this extra parameter does not add any additional burden with regards to coding and running the PW model in Win-BUGS [\(Spiegelhalter et al., 2003](#page-4-0)) compared to the PG model.
-
-# 5.3. Texas data
-
-Table 5 summarizes the modeling results for the Texas data. The functional form used is similar to the one used for Indiana dataset. Like the previous dataset, the segment length was considered as an offset in the model. All the coefficients are significant at 5% level. In general, all the estimated coefficients are logical and consistent with the existing literature. The increase in the traffic flow and the horizontal curve density increases the crash risk, whereas the increase in lane width and shoulder width decreases the risk.
-
-# 6. Discussion
-
-There are two relevant points that need to be discussed. First, the results using the simulated data have shown that the PW model was able to reproduce the ''true'' parameters. Second, it was
-
-Table 5 Modeling results for the Texas data.
-
-| Variable          | PG      |        | PW      |        |  |
-|-------------------|---------|--------|---------|--------|--|
-|                   | Value   | S.E.   | Value   | S.E.   |  |
-| Intercept (ln b0) | 6.3590  | 0.3907 | 6.2280  | 0.4298 |  |
-| ADT (b1)          | 0.9770  | 0.0424 | 0.9682  | 0.0430 |  |
-| LW (b2)           | 0.0532  | 0.0169 | 0.0509  | 0.0163 |  |
-| SW (b3)           | 0.0100  | 0.0033 | 0.0101  | 0.0033 |  |
-| CD (b4)           | 0.0674  | 0.0121 | 0.0694  | 0.0122 |  |
-| /                 | 2.5510  | 0.2338 |         |        |  |
-| xa                |         |        | 1.0130  | 0.3068 |  |
-| k                 |         |        | 1.6290  | 0.0817 |  |
-| DIC               | 4784.45 |        | 4771.61 |        |  |
-| Pearson v2        | 1798.14 |        | 1869.32 |        |  |
-| MAD               | 1.70    |        | 1.69    |        |  |
-| MSPE              | 11.24   |        | 11.43   |        |  |
-
-<sup>a</sup> x ¼ 1=kk.
-
-shown that the PW GLM offers potential for modeling traffic crash data and performs as well as the PG model. As detailed in the modeling results for two observed datasets, both models provided similar GOF statistics. In addition, the computational time for both the models in WinBUGS is very similar.
-
-Given the fact that the PW GLM has so far not been applied often, there are many lines of research activities that could be investigated. First, as discussed above, crash data can sometimes be subjected to very low sample mean values or very sample sizes. Consequently, the PG model do not perform well with such datasets since the inverse dispersion parameter can be significantly biased (or mis-estimated). This can negatively influence the standard errors associated with the model's coefficient. Therefore, the further work is needed on examining the stability of the PW model when the data are characterized by low sample mean and small sample size.
-
-Second, since the empirical Bayes (EB) method is now frequently used in highway safety, an EB modeling framework should be development for the PW model. The approach used by [Zou et al.](#page-4-0) [\(2013\)](#page-4-0) to develop the EB framework for the Sichel model could be utilized. Third, the use of the PW GLM to identify hazardous sites should also be investigated. Fourth, even though the analysis was executed by assuming a fixed shape parameter (independent of covariates) in the Weibull distribution, further work should be accomplished to examine the effects of a variable-dependent shape parameter. Finally, a well-defined likelihood function and the <span id="page-4-0"></span>related moments for the PW model should be built. This way, the MLE method could be used for estimating PW GLMs.
-
-#### 7. Summary and conclusions
-
-This paper has described the application of the PW GLM for modeling traffic crashes. Although the PW GLM has been used in other fields, it has only been applied once in traffic safety (Maher and Mountain, 2009). The PW model was developed using a Bayesian framework and its general modeling performance was examined using the simulated dataset. The comparison of the PW and PG models was performed using two observed crash datasets. The comparison analysis was carried out using the most common functional form used by transportation safety analysts, which link crashes to the traffic flows and geometric variables. The results of this study show that the PW GLMs perform as well as the PG model in terms of GOF statistics. If the PW model performs well and offers stability when the data are characterized by low sample mean and small sample size values, then it could potentially offer a better alternative over the PG model for modeling motor vehicle crashes.
-
-#### References
-
-- Anastasopoulos, P., Tarko, A., Mannering, F.L., 2008. Tobit analysis of vehicle accident rates on interstate highways. Accident Analysis and Prevention 40 (2), 768–775.
-- Brooks, S.P., Gelman, A., 1998. General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7 (4), 434–455.
-- Cameron, A.C., Trivedi, P.K., 1998. Regression Analysis of Count Data. Cambridge University Press, Cambridge, UK.
-- Geedipally, S.R., Lord, D., Dhavala, S.S., 2012. The negative binomial-Lindley generalized linear model: characteristics and application using crash data. Accident Analysis and Prevention 45 (2), 258–265.
-- Gelman, A., Rubin, D.B., 1992. Inference from iterative simulation using multiple sequence. Statistical Science 7 (4), 457–511.
-- Hauer, E., 1997. Observational before-after studies in road safety: estimating the effect of highway and traffic engineering measures on road safety. Elsevier Science Ltd., Oxford.
-- Hilbe, J.M., 2011. Negative Binomial Regression, second ed. Cambridge University Press, Cambridge, UK.
-- Lord, D., 2006. Modeling motor vehicle crashes using Poisson–gamma models: examining the effects of low sample mean values and small sample size on estimation of the fixed dispersion parameter. Accident Analysis and Prevention 38 (4), 751–766.
-- Lord, D., Mannering, F.L., 2010. The statistical analysis of crash frequency data: a review and assessment of methodological alternatives. Transportation Research Part A: Policy and Practice 44 (5), 291–305.
-
-- Lord, D., Park, B.-J., 2010. Appendix D-negative binomial regression models and estimation methods. In: CrimeStat Version 3.3 Update Notes: Part 2: Regression Modeling, The National Institute of Justice, Washington, DC.
-- Lord, D., Geedipally, S.R., Persaud, B.N., Washington, S.P., Schalkwyk, I., Ivan, J.N., Lyon, C., Jonsson, T., 2008a. NCHRP Web-only document 126: methodology to predict the safety performance of rural multilane highways. [<http://](http://www.onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w126.pdf) [onlinepubs.trb.org/onlinepubs/nchrp/nchrp\\_w126.pdf>](http://www.onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w126.pdf).
-- Lord, D., Guikema, S.D., Geedipally, S.R., 2008b. Application of the Conway– Maxwell–Poisson generalized linear model for analyzing motor vehicle crashes. Accident Analysis and Prevention 40 (3), 1123–1134.
-- Lord, D., Park, P.Y.-J., 2008. Investigating the effects of the fixed and varying dispersion parameters of Poisson–gamma models on empirical Bayes estimates. Accident Analysis and Prevention 40 (4), 1441–1457.
-- Lord, D., Washington, S.P., Ivan, J.N., 2005. Poisson, Poisson–gamma and zeroinflated regression models of motor vehicle crashes: balancing statistical fit and theory. Accident Analysis and Prevention 37 (1), 35–46.
-- Maher, M., Mountain, L., 2009. The sensitivity of estimates of regression to the mean. Accident Analysis and Prevention 41 (4), 861–868.
-- Maher, M.J., Summersgill, I., 1996. A comprehensive methodology for the fitting predictive accident models. Accident Analysis and Prevention 28 (3), 281–296. Miaou, S.-P., Song, J.J., Mallick, B.K., 2003. Roadway traffic-crash mapping: a space-
-- time modeling approach. Journal of Transportation and Statistics 6 (1), 33–57. McCullagh, P., Nelder, J.A., 1983. Generalized Liner Models. Chapman and Hall,
-- London, UK. Miaou, S.-P., Lord, D., 2003. Modeling traffic-flow relationships at signalized intersection: dispersion parameter, functional form, and Bayes versus empirical Bayes. Transportation Research Record No.1840, 31–40.
-- Mitra, S., Washington, S., 2007. On the nature of over-dispersion in motor vehicle crash prediction models. Accident Analysis and Prevention 39 (3), 459–468.
-- Oh, J., Lyon, C., Washington, S., Persaud, B., Bared, J., 2003. Validation of FHWA crash models for rural intersections: lessons learned. Transportation Research Record 1840, 41–49.
-- Phoenix, S.L., Ibnabdeljalil, M., Hui, C.-Y., 1997. Size effects in the distribution for strength of brittle matrix fibrous composites. International Journal of Solids Structures 34 (5), 545–568.
-- Raghavachari, M., Srinivasan, A., Sullo, P., 1997. Poisson mixture yield models for integrated circuits: a critical review. Microelectronics Reliability 37 (4), 565– 580.
-- Spiegelhalter, D.J., Best, N.G., Carlin, B.P., Linde, A., 2002. Bayesian measures of model complexity and fit (with discussion). Journal of the Statistical Society: Series B 64 (4), 583–639.
-- Spiegelhalter, D.J., Thomas, A., Best, N.G., Lun, D., 2003. WinBUGS Version 1.4.1 User Manual. MRC Biostatistics Unit, Cambridge. Available from: <http:// www.mrcbsu. cam.ac.uk/bugs/welcome.shtml>.
-- Wagner, H.D., Steenbakkers, L.W., 1989. Stochastic strength and size effect in ultrahigh strength polyethylene fibres. Philosophical Magazine Letters 59 (2), 77–85. Washington, S.P., Karlaftis, M.G., Mannering, F.L., 2011. Statistical and Econometric
-- Methods for Transportation Data Analysis, second ed. Chapman and Hall/CRC, Boca Raton, FL.
-- Zhang, Y., Ye, Z., Lord, D., 2007. Estimating the dispersion parameter of the negative binomial distribution for analyzing crash data using a bootstrapped maximum likelihood method. Transportation Research Record 2019, 15–21.
-- Zou, Y., Lord, D., Zhang, Y., Peng, Y., 2013. Comparison of sichel and negative binomial models in estimating empirical Bayes estimates. Paper 13-2938. Presented at the 92nd Annual Meeting of the Transportation Research Board, 2013, Washington, D.C.

Zotero/001_artiklid/Road Safety Indicators/choidosilverzonesreduce2018.md

@@ -1,336 +0,0 @@
----
-category: literaturenote
-citekey: choidosilverzonesreduce2018
-title: "Do Silver Zones reduce auto-related elderly pedestrian collisions? Based on a case in Seoul, South Korea"
-authors: "Choi, Yunwon; Yoon, Heeyeun; Jung, Eunah"
-year: 2018
-date: 2018-00-00 2018
-doi: 10.1016/j.aap.2018.07.005
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2018.07.005"
-zotero_key: RGDSQTFM
-zotero_storage: SK96UGPA
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Choi, Yunwon"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/00014575)
-
-# Accident Analysis and Prevention
-
-journal homepage: [www.elsevier.com/locate/aap](https://www.elsevier.com/locate/aap)
-
-![](_page_0_Picture_5.jpeg)
-
-# Do Silver Zones reduce auto-related elderly pedestrian collisions? Based on a case in Seoul, South Korea
-
-![](_page_0_Picture_7.jpeg)
-
-Yunwon Choi[a](#page-0-0) , Heeyeun Yoon[b,](#page-0-1)[c,](#page-0-2) [\\*](#page-0-3), Eunah Jung[c](#page-0-2)
-
-- <span id="page-0-0"></span><sup>a</sup> *Interdisciplinary Program in Landscape Architecture, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea*
-- <span id="page-0-1"></span><sup>b</sup>*Department of Landscape Architecture and Rural Systems Engineering, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-921, Republic of Korea*
-- <span id="page-0-2"></span>c *Research Institute of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-921, Republic of Korea*
-
-#### ARTICLE INFO
-
-*Keywords:* Silver Zone Pedestrian safety Generalised linear model Difference-in-difference Bivariate Moran's I Kernel density mapping
-
-#### ABSTRACT
-
-Inaugurated in 2007, in Seoul, South Korea, the Silver Zone is a designated pedestrian safety zone for the elderly that adopts speed limit measures such as traffic signage and road surface markings. In this study, we empirically investigate the effectiveness of the Silver Zone in two respects: first, whether the establishment of the Silver Zone has lowered the number of elderly pedestrian collisions, and second, whether Silver Zones are established in the appropriate areas, that is, those with the highest frequency of such collisions. From our quasi-experimental statistical analysis, Difference-in-Difference, we learn that the Silver Zone has no effects on reducing elderly pedestrian collisions. From our spatial statistical analyses—Kernel Density mapping and Bivariate Moran's I—we found a spatial mismatch between the frequency of senior pedestrian-vehicular collisions and the location of Silver Zones. For better performance of the Silver Zone system, we suggest additional types of physical measures to be integrated into the Silver Zone system. Municipal-level comprehensive master plan for Silver Zone system is also necessary, under which local governments should use periodic surveys to inventory and prioritise the locations of highest elderly pedestrian-vehicular collisions.
-
-# **1. Introduction**
-
-As the world's population rapidly ages, with the ratio of the elderl[y1](#page-0-4) growing from the current 11% to 22% of the population by 2050 ([United Nations, 2015](#page-9-0)), many societies have made efforts to create healthier and safer environments for senior citizens ([Zhao, 2014;](#page-9-1) [Li and](#page-8-0) [Zhao, 2015\)](#page-8-0). Improving neighbourhood walkability, as an integral part of daily life, is one strategy towards this goal ([Hahm et al., 2017;](#page-8-1) [Kim](#page-8-2) [et al., 2014](#page-8-2)). Walking is both a means of exercise and transportation for the elderly. As they experience physical and cognitive losses, they forgo driving and taking public transit to avoid collisions, while maintaining physical activity [\(Piatkowski et al., 2015;](#page-8-3) [Burbidge and Goulias, 2009](#page-8-4)).
-
-Elderly pedestrian-vehicular collisions, however, are a serious problem [\(Rosenbloom et al., 2016;](#page-9-2) [Cœugnet et al., 2017\)](#page-8-5). While pedestrian deaths account for 22% of all vehicle-related casualties, approximately 40% of those involve the elderly[2](#page-0-5) [\(International Transport Forum,](#page-8-6) [2014\)](#page-8-6). This number has been increasing along with the aging population in many member countries of the Organization for Economic Cooperation and Development (OECD). Especially, increase of the traffic volume in high-density cities in the East Asia is a threat to elderly pedestrian safety ([Zhao et al., 2014](#page-9-3)). South Korea is no exception. Currently, elderly pedestrian collisions account for 40% of all vehicle-related casualties ([International Transport Forum, 2014;](#page-8-6) [Whi, 2015](#page-9-4)).
-
-In order to protect elderly pedestrians from collisions, the South Korean government established the Silver Zone system in 2007. The system mandates areas as safety zones where speed-limit measures, such as traffic signage and road surface markings, caution drivers about the presence of elderly pedestrians. The similar systems in other countries are Silver Zone[3](#page-0-6) and Green Man Plus system[4](#page-0-7) in Singapore and Safe Routes for Seniors program[5](#page-0-8) in the U.S.A. ([Fwa, 2016;](#page-8-7) [Tan,](#page-9-5)
-
-*E-mail addresses:* [yunwon.choi@snu.ac.kr](mailto:yunwon.choi@snu.ac.kr) (Y. Choi), [hyyoon@snu.ac.kr](mailto:hyyoon@snu.ac.kr) (H. Yoon), [jea0610@snu.ac.kr](mailto:jea0610@snu.ac.kr) (E. Jung). 1Age group of 60+
-
-<span id="page-0-3"></span><sup>⁎</sup> Corresponding author at: Department of Landscape Architecture and Rural Systems Engineering, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-921, Republic of Korea.
-
-<span id="page-0-4"></span>
-
-<span id="page-0-5"></span><sup>2</sup>Age group of 65+
-
-<span id="page-0-6"></span><sup>3</sup> Singapore's National project to enhance road safety for seniors, since 2014.
-
-<span id="page-0-7"></span><sup>4</sup> Technology to help the elderly cross the road more comfortably without adversely affecting the traffic flow by giving more green light time.
-
-<span id="page-0-8"></span><sup>5</sup> City-wide project to improve pedestrian infrastructure for seniors in the U.S.A.
-
-#### [2008;](#page-9-5) [Transportation Alternatives, 2009\)](#page-9-6).
-
-Since the inception and spreading implementation of the Silver Zone, however, questions have been raised about its effectiveness in reducing collisions. Although some analytical research has pointed to the ineffectiveness of these approaches ([Park and Oh, 2011;](#page-8-8) [Lee, 2012](#page-8-9)), and the media has speculated about possible reasons for its failures, the lack of relevant data has prevented a full assessment of its performance. The accumulated number of Silver Zones in Seoul reached 80 in early 2016, but it was not until 2010 that the database on the pedestrian collisions became available by the Korea Road Traffic Authority.
-
-In this study, we empirically investigate the effectiveness of the Silver Zone in two respects: first, whether the establishment of Silver Zones has lowered the number of elderly pedestrian-vehicular collisions, and second, whether Silver Zones were established in the appropriate areas, that is, those with the highest frequency of such collisions. To better evaluate Silver Zone program, we not only focused on the effect of Silver Zone, but also the effect of ten other physical elements in the zone, which may have effects on the frequency of collisions. With the analytical results, we further explore ways to improve the performance of Silver Zones.
-
-For the first question, we use generalised linear modelling: Zero Inflated Poisson (ZIP), Zero Inflated Negative Binomial (ZINB), Zero Truncated Poisson (ZTP) and Zero Truncated Negative Binomial (ZTNB) in Difference-in-Difference, one of the quasi-experimental specifications. To answer the second question, we use spatial statistical analyses, namely, the Kernel Density estimation and Bivariate Local Moran's I.
-
-The present study is the first attempt to investigate the environmental components of the Silver Zones that explain the elderly pedestrian-vehicular collisions, and the spatial alignment of the zones and the collision locations. Our multi-pronged evaluation will provide a more comprehensive understanding of the performance of Silver Zones, and thereby be a useful reference for municipalities wanting to improve the current system.
-
-The structure of this study is as follows. In the next section, we briefly review the literature on Silver Zones in South Korea, followed by a description of our analytical methods. After presenting our analytical design, we describe the results, including an overview of the actual effects of Silver Zones as well as the other environmental factors on pedestrian collisions, and the degree of spatial alignment between the designated Silver Zones and senior pedestrian-vehicular collision spots. The final section discusses the implications of our findings and future research direction.
-
-## **2. Background**
-
-#### *2.1. Pedestrian safety*
-
-Safety zones are purposed to protect pedestrians from vehicles, adopting a number of safety measures and techniques. School Zone and Silver Zone are some of those examples, made for protecting children and the elderly, respectively, and are established in areas where a large volume of such populations gather. School Zones are commonly located around schools and day-care facilities to help children's commuting safer. Silver Zone is relatively newer than the School Zone system, conceived in response to the increasing concern on the aging society. Japan is a pioneer of Silver Zone since 1986, and South Korea and Singapore have adopted the system recently [\(Ibrahim, 2003](#page-8-10); [Fwa,](#page-8-7) [2016;](#page-8-7) [KoRoad, 2012\)](#page-8-11).
-
-Safety Zones adopt traffic calming measures and techniques, to limit the speed of vehicles. In most of the safety zones, cars are not allowed to drive faster than 20mph (30 km/h). To inform drivers about the condition, speed signs are installed in the entrance and the exit of the zone. Within the zones, speed humps, smaller corner radii, pavement treatment, and chicane are deployed to physically and visually deter drivers from speeding. Also, more frequent and brighter lighting and fences are installed to help pedestrians walk with caution. Occasionally, surveillance cameras are operated to warn the drivers and detect the violation ([Lee et al., 2013](#page-8-12)).
-
-#### *2.2. Silver Zone*
-
-In South Korea, to address the increasing elderly pedestrian-vehicular collisions, the Ministry of Government Administration and Home Affairs (MOGAHA) established the legal framework for the Silver Zone system, with the passage of the Road Traffic Act in May 2007. Since then, as of September 2016, a total of 746 zones have been created in South Korea, of which 80 are located in Seoul [\(Ministry of Government](#page-8-13) [Administration and Home Affairs, 2016;](#page-8-13) [Kim, 2015\)](#page-8-14).
-
-The Silver Zone designation is initiated by the requests from the heads of public or private facilities for seniors, such as senior welfare centres, medical institutions, sports centres and parks. Where the request is granted, a safety zone is created with special treatment to vehicular roads within a 300- to 500-metre radius outward from the facility [\(Ministry of Public Administration and Security, 2012](#page-8-15)). The borough, the city government and the police department coordinate efforts to manage the Silver Zones thereafter.
-
-In the Silver Zones, the speed is limited to below 30 km/h, and the zone-designation and speed-limit signage is installed. Road surface is marked with colouring in red-brown and lettering the phrase "Silver Zone". Occasionally, extra measures are added, such as fences, speed bumps, elevated crosswalks, reduced crosswalk slope, realignment to one-way traffic, widened pedestrian pathways, and speed and signal cameras [\(Ministry of Public Administration and Security, 2012\)](#page-8-15).
-
-However, previous studies, using Before-After evaluation methods, suggested that Silver Zones have no or only a slight effect in reducing collisions [\(Lee, 2012](#page-8-9); [Park and Oh, 2011](#page-8-8)). [Lee \(2012\)](#page-8-9) analysed the effects of Silver Zone by types of senior care facility, shapes of Silver Zone boundary (rectangular, T, Y, L, and I shape), and the locations of Silver Zone (at the corner or along the straight road). In [Lee \(2012\)](#page-8-9), Silver Zone was partially effective in reducing collisions at some locations. [Park et al. \(2010\)](#page-8-16) found that Silver Zone is effective in reducing the severity of collision by 9.5% but showed only a slight effect, 1.6%, in reducing the number of collision. Anecdotal evidence points to the impracticality of the speed reduction measures the system currently adopts, and pressing the necessity of more vigorous enforcements such as speed and signal cameras, speed bumps and radar speed signs [\(Kim,](#page-8-17) [2017\)](#page-8-17). Some editorials also argue that the lack of consideration on the previous collision records leads to failure in prioritizing Silver Zones in the collision-prone spots ([Choi, 2015](#page-8-18); [Yoon, 2013;](#page-9-7) [Lim, 2015](#page-8-19); [Lee,](#page-8-20) [2015\)](#page-8-20). Silver Zones are established only when the requests are made, thus locations with high collision rates could have been left untreated if such request was not made ([Busan Ilbo, 2016](#page-8-21); [Park, 2016\)](#page-8-22).
-
-#### *2.3. Analytical methodology*
-
-## *2.3.1. Poisson, negative binomial, ZIP, ZINB, ZTP and ZTNB in differencein-difference approach*
-
-The Difference-in-Difference (DID) approach is one of the quasiexperimental analyses frequently used to evaluate the impact of policy interventions, since it is useful for inferring near-causality in observational studies ([Li et al., 2012;](#page-8-23) [Chabé-Ferret, 2015](#page-8-24); [Dempsey and](#page-8-25) [Plantinga, 2013](#page-8-25); [Wang and Shi, 2012;](#page-9-8) [Pope and Pope, 2012](#page-9-9)). DID estimates the effects of interests by comparing the outcome from the treatment and control groups in two different time periods: usually before and after the treatment. While the treatment group is exposed to the intervention only in the after period, the control group does not receive treatment during either period or receives treatment during both periods. By deducting the difference in the outcomes between the two groups with and without the treatment, in the periods before and after the treatment, we can understand the remaining "difference-indifference" as a pure impact of the treatment, controlling for all other factors that simultaneously affect both groups, in the before and after
-
-#### periods [\(Pope and Pope, 2012\)](#page-9-9).
-
-Data of collisions have special characteristics: The number of collisions per street segment during a certain time period is discrete and generally small or zero. Since collisions are rare events, the count of segments with zero collisions tends to be high. Poisson and Negative Binomial, and their zero-truncated and zero-inflated options are such distribution explaining collision data. The Poisson distribution illustrates the probability of observing discrete numbers of events in a given time [\(Bradshaw et al., 2009](#page-8-26)), and thus, has been adopted in many transportation studies ([Pérez et al., 2007;](#page-8-27) [Dong et al., 2014](#page-8-28); [Lord et al.,](#page-8-29) [2005\)](#page-8-29): The distribution, however, assumes the mean and variance to be identical, and for that to be the case, the probability of an event occurrence is identical and independent throughout the entire time period ([Chang et al., 2014;](#page-8-30) [Hardin and Hilbe, 2012](#page-8-31)). The strict assumption of this single-parameter model is often inappropriate to apply to real-life data. The Negative Binomial distribution relaxes this assumption, and has been used for accommodating the condition of overdispersion ([Wooldridge, 2016;](#page-9-10) [Hardin and Hilbe, 2012](#page-8-31)).
-
-In case of excessive or non-zero counts in data, zero-inflated and zero-truncated specifications of the Poisson or Negative Binomial modelling are adopted. These specifications identify the probability of the outcome value zero, then separate or exclude it, respectively, from the distribution of the rest of the outcome values. In zero-inflated approaches, the model is divided into two parts: first, the binary part to determine whether observations fall in a condition of taking only zero value, and second, the Poisson or Negative Binomial part to predict the rest of the counts. In zero-truncated models, the probability of the outcome value zero is taken out by adjustment on their original–Poisson or Negative Binomial–distribution ([Nelson, 1977;](#page-8-32) [Keeley et al., 1978](#page-8-33); [Hardin and Hilbe, 2012](#page-8-31)).
-
-Transportation and crime research have used these models conventionally. [Tipakornkiat \(2014\)](#page-9-11) used both the Poisson and negative binomial models to find the positive impact of safety rest areas for drivers in reducing the number of road accidents. Because of the overdispersion, the negative binomial model turned out to fit the data better. [Schneider et al. \(2010\)](#page-9-12) also compared the Poisson and negative binomial models in assessing the association between the characteristics of roadway intersections and the risk of pedestrian collisions in Alameda County, California. For the same reason stated above, the negative binomial model outperformed the Poisson model. [Ayati and](#page-8-34) [Abbasi \(2011\)](#page-8-34) also selected the negative binomial over the Poisson model to find the impact of traffic volume in collisions on urban highways.
-
-These two models are often compared with their zero-inflated versions. For example, [Mouatassim and Ezzahid \(2012\)](#page-8-35) compared Poisson and zero-inflated Poisson (ZIP) to model the number of claims from Moroccan private health insurance. As a substantial portion of policyholders had not filed insurance claims during the study period, the ZIP regression performed better. [Kim et al. \(2015\)](#page-8-36), using the ZINB model, suggested that drivers' demographic characteristics and the record of past traffic violations predict the number of collisions in South Korea. Since the data included a large portion of drivers who had never been involved with collisions, the zero-inflated model was a better choice for the dataset. The zero-truncated version of the Poisson and negative binomial models are used when the data do not contain any zero values. For example, [Van Der Heijden et al. \(2003\)](#page-9-13) used ZTP to estimate the size of the criminal population. The crime report, the main dataset of the analysis, listed individuals who had been apprehended at least one time, thus the data did not include a zero value.
-
-# *2.3.2. Spatial analysis: kernel density and bivariate local Moran's I*
-
-Kernel Density Estimation is a non-parametric statistical method used to visualise point patterns of data. By using moving windows focused on data points, so-called kernels, with a specific radius this analytical method generates density surfaces that show where the points are concentrated [\(Bailey and Gatrell, 1995](#page-8-37)). Bivariate Local Moran's I is an analytical tool that examines whether two characteristics are spatially correlated. With bivariate local Moran's I, we can identify the degree of such a correlation in the four categories: first, High–High where both values of the variables are high; second, Low–Low where both are low; and Low–High and High–Low where only one is high, while the other is low. Moran's I index of 1 and -1 represents the perfect positive and negative correlation between the two variables, respectively, with values close to zero implying weak correlations. The absolute value of .3 or higher is considered a fairly strong correlation ([Yoon and Srinivasan, 2014\)](#page-9-14).
-
-## **3. Analytical design**
-
-## *3.1. Study site*
-
-Our study site consists of 80 Silver Zones in Seoul, South Korea. Assuming that the effect of the Silver Zone may extend beyond this immediate area, we include 300-metre buffers around the zones themselves, which implies an average of a 5-minute walking distance ([Azmi et al., 2012](#page-8-38)). For the DID analysis, we analysed 58 zones established either from 2007 to 2009 or from 2011 and 2014. For the DID analysis with the environmental factors, we excluded zones that had no collision incidence and analysed the rest 27 zones. For the spatial analyses, 32 zones established from 2011 to 2015 were considered. [Fig. 1](#page-3-0) describes our study area.
-
-## *3.2. Dataset and sample*
-
-The two primary datasets were Silver Zone data collected by the Seoul Metropolitan Government from 2007 to 2014 and Elderly Pedestrian Vehicular Collision data from the Korea Road Traffic Authority (KoRoad) from 2010 to 2015. The Silver Zone data include locations, configurations and installation dates of each Silver Zone, and the name and type of facilities that it centres on. The Elderly Pedestrian Vehicular Collision dataset provides detailed information on collisions, including locations, dates, times, the age and gender of the injured party and driver, seriousness of the injury/damages, road types and the weather at the time of the incident. The number of elderly pedestrians injured in vehicular collisions totals 3009, with 478 occurring in 2010, 390 in 2011, 549 in 2012, 564 in 2013, 711 in 2014 and 668 in 2015.
-
-For the supplementary information about the physical environments of the zones, we used Daum, a web portal that provides eye-level maps, satellite imageries and street views. We included nine different characteristics: distance from the centre of Silver Zone, percentage of coloured road surface in the entire Silver Zone, the number of lanes, distance from a collision to the nearest crosswalk, speed bump and traffic light, presence and location of fencing, no-sidewalk road signage and land use (e.g., residential and commercial). The descriptive statistics is in Appendix I.
-
-# **4. Analytical plan**
-
-# *4.1. Difference-in-Difference*
-
-With DID approach, we compare the count of collisions in the treatment and control groups before and after their designation as Silver Zones. Our treatment group comprises collisions that occurred in the areas designated as Silver Zones and the 300-metre buffer area in 2011, 2012, 2013 or 2014. Our control group is the collision count in the areas that were designated as Silver Zones and the 300-metre buffer areas in 2007, 2008 or 2009. Control group is supposed to be a group of samples that are closely matched with treatment group, whereby the only difference is whether the treatment was imposed or not. Therefore, the areas designated as a Silver Zone in the earlier years can be a more appropriate control group for the areas designated as such in the later years, than the randomly selected, non-Silver Zone areas. To allow for a
-
-<span id="page-3-0"></span>![](_page_3_Figure_2.jpeg)
-
-**Fig. 1.** Study Site: Seoul, South Korea.
-
-1-year stabilisation period, and to measure the full-year effects of those Silver Zones, the collisions were counted in the year 2010 for the before condition and during 2015 for the after condition.
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-If the Silver Zone designation improved pedestrian safety, the number of collisions would go down more or show less of an increase from 2010 to 2015 in the treatment group, compared with the number counted in the control group. Since new Silver Zones are advertised to be safer and easier to walk, it has potential to attract more pedestrians. If this were the case, pedestrian collisions would have impacted by the increased exposure. To rule out this possibility, we conducted two sets of *t*-tests to verify whether the changes of the pedestrian volume differ or not in the two groups (control and treatment groups), in the two points of the collision measurement. Due to data availability, we used data of 2009 and 2015. We could conclude that the treatment and control groups have maintained the same level of senior pedestrians in both years, and the level of change between those two years in those two groups are identical. This implies that the exposure of the senior pedestrians to collisions have changed in a similar way in both of the groups. The result of the *t*-test is available from the authors upon request.
-
-The full DID model is shown below:
-
-$$\begin{split} g\left(E(Num\_Collision_i)\right) &= \ \beta_0 + \beta_1 Treatment_i + \beta_2 After_i \\ &+ \ \beta_3 (Treatment_i\_After_i) + \beta X_i \end{split}$$
-
-Function (1) Difference in Differences (DID) model
-
-Our dependent variable, *Num Collision* \_ , is the number of collisions occurred in the each Silver Zone in the aforementioned years (mean: 0.83); *Treatment* is a binary variable indicating whether the collision occurred within the treatment group or the control group (*Treatment* = 1 if established in 2011, 2012, 2013 and 2014 or *Treatment* = 0 if established in 2007, 2008 and 2009). *After* is a binary variable indicating whether the collision occurred in 2015 or in 2010 (*After* = 1 if occurred in 2015 or *After* = 0 if occurred in 2010). By including the interaction term, *Treatment\_After*, as a product of the group and time indicator, we allow differential changes from the before and after periods in the number of pedestrian-vehicular collisions between those two groups. *Xi* is a vector of the aforementioned environmental characteristics of the Silver Zone. The detailed explanation of the variables is in Appendix I.
-
-We fit the ZIP, ZINB, ZTP and ZTNB models: first, ZIP and ZINB with the three aforementioned variables directly relevant to the DID inference, and then ZTP and ZTNB with all other additional variables representing environmental characteristics of the Silver Zone. In the first analysis, all Silver Zone samples are included, and many of them have not accommodated any collision in either 2010 or 2015. Due to the high frequency of zero value, ZIP and ZINB are utilized to handle the data. In the second analysis, on the other hand, the data is zero truncated because the Silver Zones with collision records are included in the sample. ZTP and ZTNB are utilized to address the zero truncated characteristic of the data.
-
-*g(.)* is a link function that relates the expected dependent variable to the linear predictors [\(Fox, 2016](#page-8-39)). For the Poisson and negative binomial family, a canonical link function–natural log is used. Consequently, the relationship between the dependent and independent variables in Poisson, negative binomial models are shown below:
-
-$$ln(E(Num\_Collision_i)) = \beta_0 + \beta_1 Treatment_i + \beta_2 After_i + \beta_3 (Treatment_i\_After_i) + \beta X_i$$
-
-Function (2) Poisson model
-
-For the negative binomial model, as its variance is allowed to differ from the mean, a latent heterogeneity term *<sup>i</sup>* is introduced in the conditional mean of the model, where exp( *<sup>i</sup>*) follows gamma distribution [\(Shankar et al., 1995\)](#page-9-15):
-
-$$\begin{split} ln(E(Num\_Collision_i)) = & \ \beta_0 + \beta_1 Treatment_i + \beta_2 A fter_i \\ & + \beta_3 (Treatment_i\_A fter_i) + \beta X_i + \ \varepsilon_i \end{split}$$
-
-Function (3) Negative binomial model
-
-#### *4.2. Spatial analysis*
-
-We use two spatial analyses, Kernel Density Estimation (KDE) and Bivariate Local Moran's I, to further investigate the geographical relationship between Silver Zones and sites with a high occurrence of senior pedestrian car-related collisions, and to determine whether the collision points and Silver Zone locations overlap.
-
-First, we plotted a series of KDE maps to visualise the locations of the new Silver Zones established in each year from 2011 to 2015, and the locations of frequent senior pedestrian car-related collisions in the prior year. If the Silver Zones were established close to the previous year's sites of highest-frequency collisions, the maps should show a geographical match between them.
-
-To achieve a more rigorous assessment of the degree of spatial match than a visual comparison can provide, we used the Bivariate Local Moran's I analysis. We created the two variables aggregated at the level of the census tract[6](#page-4-0) to measure their correlation in this fashion: first, the cumulative number of senior pedestrian-vehicular collisions from 2010 to 2014, aggregated by census tract, and second, the total number of Silver Zones in the same census tract as of 2015. The bivariate Local Moran's I function is presented below in Function (4):
-
-$$I_i = A_i \times \sum_{i=1}^n \omega S_i$$
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-Function (4) Bivariate Local Moran's I *Ai* is the number of such collisions in the census tract i (up to n), *Si* is the number of Silver Zones in the same census tract and is a spatial weight matrix. We chose a distance-based weight matrix over contiguity-based ones, since the analytical units vary in size and configuration, thus could not be consistent in defining neighbouring units. We used a threshold distance of 300 m because it represents an average 5-minute walking distance for elderly and could be considered a maximum distance seniors are willing to walk as part of their daily routine.
-
-#### **5. Findings**
-
-*5.1. Result 1: DID*
-
-In [Table 1,](#page-5-0) we present the analytical results of the ZIP, ZINB, ZTP and ZTNB regression.
-
-For the first DID analysis, in order to select the best model for testing the effects of Silver Zone, we conducted two steps of tests; first, we tested zero-inflation with Vuong test to select a model between standard Poisson and ZIP models, and second, we tested overdispersion with a likelihood ratio test to select a model between ZIP and ZINB model ([Desmarais and Harden, 2013](#page-8-40); [Zuur et al., 2009\)](#page-9-16). The Vuong test statistics were significantly positive (z = 6.64, Pr > z = 0.00), led us to ZIP over the standard Poisson model. The likelihood ratio test indicates overdispersion, led us to ZIP over ZINB model. Therefore, we selected ZIP as the final model. Also, smaller AIC for ZIP reaffirmed our decision.
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-For the second DID analysis, since we excluded all Silver Zones that had no collision history in 2010 or 2015, the data became zero-truncated. To select between ZTP and ZTNB model, we again used the likelihood ratio test and confirmed that our data is not overdispersed, consequently, we selected ZTP as our final model.
-
-From both the ZIP and ZINB models, we learned that the Silver Zone is ineffective in reducing collisions. All variables directly relevant to DID - *After, Treatment* and their interaction, *After\_Treatment* - explain that the change in the number of collisions in the treatment and control groups from the before and after period is not different at 5% significance level.
-
-Among other environmental factors, the *Locational range*–a binary variable indicating whether a collision happened in the Silver Zone, *Distance to traffic lights, Distance to crosswalk* and *Distance to intersections* are demonstrated to be a predictor of senior pedestrian collisions.
-
-First, we found that senior pedestrian collisions are more likely to occur in the Silver Zone compared to their 300-m buffer. The expected number of collisions in the Silver zone is 684% (=exp(1.923)) of that of the 300-m buffered area. The Silver Zone is established in front of senior care facilities where senior pedestrians tend to concentrate, and the chance of collision involving them would increase. In addition, the variable *Distance to traffic lights* suggests that more collisions would occur in locations farther from vehicle traffic lights. When moving 10 m away from the vehicle traffic light, the expected number of collisions is increased by 109.86% (=exp(0.094)) of that of otherwise cases. Longer distance of traffic intervals may encourage drivers to drive faster and also encourage pedestrians to jaywalk.
-
-*Distance to crosswalks* is also a factor related to the number of senior pedestrian-vehicular collisions. When moving 10 m away from crosswalks, the expected number of collisions is reduced to 88% (=exp (-0.117)) of that of the otherwise cases, holding all other variables constant. This means that collisions have occurred more frequently near crosswalks. This result may imply that the areas near crosswalks are relatively unsafe for senior pedestrians due to drivers' frequent violation of traffic signals or their tendency to make sudden stops. However, it could also be true that simply a higher density of pedestrians is exposed to collisions near crosswalks. The variable *Distance to intersections* can be understood in a similar manner: intersections are also collisionprone spots ([Lee and Abdel-Aty, 2005](#page-8-41); [Lee et al., 2017;](#page-8-42) [Miranda-](#page-8-43)[Moreno et al., 2011\)](#page-8-43). When moving 10 m away from intersections, the expected number of collisions is reduced to 97% (=exp(−0.028)) of that of the otherwise cases. In intersections with vehicles coming from many different directions, the chance of collisions naturally increases: where cars make multi-directional turns simultaneously, drivers tend to pay less attention to pedestrians. As mentioned above, it is also possible that increased exposure of the pedestrians in the intersections or crosswalks could have impacted on change in the number of collisions.
-
-<span id="page-4-0"></span><sup>6</sup> In South Korea, the census unit is equivalent to the census tract in the U.S. It is called *Jigyegu*, and contains approximately 500 residents.
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-<span id="page-5-0"></span>**Table 1** Parameter estimates, standard errors and approximate p-values from the ZIP, ZINB, ZTP and ZTNB models, describing the DID relationships between the number of collisions in treatment and control groups in the before and after period, along with other selected predictors.
-
-|                       | Effects of Silver Zone            |                   |                                             |                   | Effects of Silver Zone and Physical Elements associated with Senior Pedestrian Collisions |                                              |
-|-----------------------|-----------------------------------|-------------------|---------------------------------------------|-------------------|-------------------------------------------------------------------------------------------|----------------------------------------------|
-|                       | Zero Inflated<br>Poisson<br>Model |                   | Zero Inflated<br>Negative Binomial<br>Model |                   | Zero Truncated Poisson<br>Model                                                           | Zero Truncated Negative<br>Binomial<br>Model |
-| Variables             | ZIP with DID                      | inflate           | ZINB with DID                               | inflate           | ZTP with<br>DID and Physical Variables                                                    | ZTNB with<br>DID and Physical Variables      |
-| After                 | 0.265<br>(0.292)                  | −0.287<br>(0.607) | 0.265<br>(0.292)                            | −0.287<br>(0.607) | 0.202<br>(0.390)                                                                          | 0.202<br>(0.390)                             |
-| Treatment             | 0.233<br>(0.368)                  | 1.200*<br>(0.698) | 0.233<br>(0.368)                            | 1.200*<br>(0.698) | 0.241<br>(0.615)                                                                          | 0.241<br>(0.615)                             |
-| After_Treatment       | 0.032<br>(0.461)                  | 0.055<br>(0.947)  | 0.032<br>(0.461)                            | 0.055<br>(0.947)  | 0.082<br>(0.472)                                                                          | 0.082<br>(0.472)                             |
-| Loc_range             | –                                 | –                 | –                                           | –                 | 1.923**<br>(0.757)                                                                        | 1.923**<br>(0.757)                           |
-| Percent_colour        | –                                 | –                 | –                                           | –                 | 0.611<br>(0.685)                                                                          | 0.611<br>(0.685)                             |
-| Number_lane           | –                                 | –                 | –                                           | –                 | 0.371<br>(0.236)                                                                          | 0.371<br>(0.236)                             |
-| Dist_crosswalk        | –                                 | –                 | –                                           | –                 | −0.117**<br>(0.054)                                                                       | −0.117**<br>(0.054)                          |
-| Dist_light            | –                                 | –                 | –                                           | –                 | 0.094***<br>(0.032)                                                                       | 0.094***<br>(0.032)                          |
-| Fence                 | –                                 | –                 | –                                           | –                 | −0.658<br>(0.479)                                                                         | −0.658<br>(0.479)                            |
-| Dist_intersection     | –                                 | –                 | –                                           | –                 | −0.028**<br>(0.0133)                                                                      | −0.028**<br>(0.0133)                         |
-| Speedlimit<br>Signage | –                                 | –                 | –                                           | –                 | −0.0675<br>(0.335)                                                                        | −0.0675<br>(0.335)                           |
-| No_sidewalk           | –                                 | –                 | –                                           | –                 | 0.404<br>(0.896)                                                                          | 0.404<br>(0.896)                             |
-| Residential           | –                                 | –                 | –                                           | –                 | −0.709<br>(0.786)                                                                         | −0.709<br>(0.786)                            |
-| Commercial            | –                                 | –                 | –                                           | –                 | 0.248<br>(0.427)                                                                          | 0.248<br>(0.427)                             |
-| Constant              | 0.985***<br>(0.233)               | 0.706<br>(0.445)  | 0.985***<br>(0.233)                         | 0.706<br>(0.445)  | −0.050<br>(0.952)                                                                         | −0.050<br>(0.952)                            |
-| Observations          | 116                               | 116               | 116                                         | 116               | 27                                                                                        | 27                                           |
-| −2LL                  | −0.0264                           |                   | −0.0264                                     |                   | −0.2345                                                                                   | −0.8303                                      |
-| df                    | 8                                 |                   | 9                                           |                   | 15                                                                                        | 15                                           |
-| AIC                   | 230.4553                          |                   | 232.455                                     |                   | 117.868                                                                                   | 117.868                                      |
-| BIC                   | 252.484                           |                   | 257.238                                     |                   | 137.306                                                                                   | 137.306                                      |
-| Prob > chi2           | 0.4160                            |                   | 0.4160                                      |                   | 0.0404                                                                                    | 0.6987                                       |
-
-Robust standard errors in parentheses.
-
-#### *5.2. Result 2: Spatial analysis with kernel density and bivariate local Moran's I*
-
-From the Kernel Density maps, we found no apparent relationship between the locations of higher numbers of senior pedestrian collisions in a given year and the locations of newly established Silver Zones in the next year. In general, no spatial match is visible except for some small areas. For example, the northwestern area in [Fig. 2\(](#page-6-0)a), and the south western parts in [Fig. 2\(](#page-6-0)c), (d), (e) whereby Silver Zones seem to have been inaugurated at sites of higher collision frequency in the
-
-<sup>\*\*\*</sup> p < 0.01, \*\* p < 0.05, \* p < 0.1.
-
-<span id="page-6-0"></span>![](_page_6_Figure_2.jpeg)
-
-**Fig. 2.** Kernel Density Estimation of Senior Pedestrian-Vehicular Collisions and New Silver Zones.
-
-previous years.
-
-From the Bivariate Local Moran's I analysis, we found a similar result as above. The bivariate local Moran's I value of -0.004 (p = 0.05) implies very little negative or no correlation between the density of senior pedestrian-vehicular collisions and Silver Zone locations ([Fig. 3](#page-7-0)). If Silver Zones had been established where such collisions were the most prevalent, then the areas marked as High–High and Low–Low should be dominant in the map. In our analysis, only 50 are marked as High–High and 1062 are marked as Low–Low out of a total of 16,230 census tracts in Seoul. Indeed, there is a higher occurrence of spatial mismatch: 1,221 are marked as High–Low, meaning Silver Zones were not established at sites that coincided with a higher number of collisions; and 634 were marked as Low–High, meaning the opposite, that Silver Zones were implemented in areas with low rates of elderly pedestrian-vehicular collisions.
-
-We should note that there is a chance of misreading this result as the real effects of the system. If Silver Zones actually reduce the number of collisions, then the census tracts with a higher number of Silver Zones would have fewer collisions, and thus be categorised as Low–High. Therefore, the large number of census tracts in this category presented in our bivariate local Moran's I result could be read as supporting the effectiveness of the Silver Zone system. However, through our carefully designed quasi-experiment, the Difference-in-Difference analysis and the Kernel Density map, we previously demonstrated the ineffectiveness of the Silver Zone system and therefore eliminate the other interpretation of this analytical result.
-
-<span id="page-7-0"></span>![](_page_7_Figure_2.jpeg)
-
-**Fig. 3.** Bivariate Local Moran's I Cluster Map (Threshold 300).
-
-## **6. Conclusion**
-
-In this study, we investigate the effectiveness of the Silver Zone in two respects. First, we assess the changes in the number of elderly pedestrian-vehicular collisions in the two groups through the DID approach, and second, we assess the appropriateness of their locations to address highly collision-prone spots through spatial modelling.
-
-As discussed above, our statistical analyses demonstrate that the Silver Zone, as currently implemented, is an ineffective measure in reducing senior pedestrian-vehicular collisions. The number of collisions increased from 2010 to 2015 without statistical difference in both treatment and control areas; in other words, the Difference-in-Difference was not supported at a statistically significant level. The analysis also highlights which types of physical characteristics contribute to senior pedestrian collisions. Areas bounded as a Silver Zone have more collisions than their 300-metre buffers. In addition, areas farther away from traffic lights and areas closer to crosswalks or intersections invite more of such collisions.
-
-Our analytical results also suggest that current Silver Zone locations do not spatially coincide with areas of higher senior pedestrian-vehicular collisions. The Kernel Density Estimation of senior pedestrian collision density shows that yearly records of such collisions are not closely reflected in new Silver Zone designations the following year. The Bivariate Local Moran's I analysis provides similar results in that the cumulative number of collisions do not overlap with designated Silver Zones. As mentioned in the background of this study, Silver Zones are initiated at the request of individual operators of senior facilities, without a city-level master planning process incorporating collision pattern studies ([Ministry of Public Administration and](#page-8-15) [Security, 2012](#page-8-15)). The current process creates several problems. On the one hand, the city government could easily overlook areas with high rates of elderly pedestrian-vehicular collisions were it not for the request of Silver Zone by the head of senior citizen facilities. Without a more comprehensive system, the city is not able to coordinate individually requested zone designations with other dangerous sites beyond the vicinity of senior facilities, such as the dangers of long distance of traffic intervals, identified above.
-
-Although the system was originally conceived for seniors, Silver Zones could be used for greater pedestrian safety for all age groups. However, for this system to function effectively, a more comprehensive approach is needed, both for locating these special zones and designing their safety measures. Instead of leaving it to civil petition, the city government should take responsibility for inventorying pedestrian collisions, including locations, through periodic surveys. Based on this information they should prioritise the most critical sites for Silver Zone designation. Moreover, in addition to the signage and road marking that currently comprises the system, other design features and measures could be integrated into the system, such as longer green lights for slow pedestrians, fences to prevent jaywalking, high friction pavement, speed cameras, in-roadway warning flashing light (IRWL) systems to slow the speed of vehicles and part-time pedestrianization [\(Park et al.,](#page-8-16) [2010;](#page-8-16) [Abdelrahman et al., 2011](#page-8-44); [Zhang and Ma, 2014](#page-9-17); [Castillo-](#page-8-45)[Manzano et al., 2014](#page-8-45)).
-
-#### **Acknowledgements**
-
-This work was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning [grant number NRF-2015R1C1A2A01055615], Social Science Program through the National Research Foundation of Korea funded by the Ministry of Education [grant number NRF-2017S1A3A2066771], the Korea Environmental Industry and Technology Institute (KEITI) [grant number 2014-001-310007] and the Creative-Pioneering Researchers Program in Seoul National University (number 500-20170162).
-
-### **Appendix A. Supplementary data**
-
-Supplementary material related to this article can be found, in the online version, at doi:[https://doi.org/10.1016/j.aap.2018.07.005.](https://doi.org/10.1016/j.aap.2018.07.005)
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-- <span id="page-9-6"></span>Transportation Alternatives, 2009. Safe Routes for Seniors [Final Report]. Retrieved from. . [https://www.transalt.org/sites/default/files/news/reports/2009/Safe\\_](https://www.transalt.org/sites/default/files/news/reports/2009/Safe_Routes_for_Seniors.pdf) [Routes\\_for\\_Seniors.pdf](https://www.transalt.org/sites/default/files/news/reports/2009/Safe_Routes_for_Seniors.pdf).
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-- criminal population from police records using the truncated poisson regression model. Statistica Neerlandica 57, 289–304. [https://doi.org/10.1111/1467-9574.](https://doi.org/10.1111/1467-9574.00232) [00232.](https://doi.org/10.1111/1467-9574.00232)
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Zotero/001_artiklid/Road Safety Indicators/corbenstrengtheningroadsafetystrategy2010.md

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-category: literaturenote
-citekey: corbenstrengtheningroadsafetystrategy2010
-title: Strengthening road safety strategy development ‘Towards Zero’ 2008–2020 – Western Australia’s experience scientific research on road safety management SWOV workshop 16 and 17 November 2009
-authors: "Corben, Bruce F.; Logan, David B.; Fanciulli, Lisa; Farley, Roger; Cameron, Iain"
-year: 2010
-date: 2010-00-00 2010
-doi: 10.1016/j.ssci.2009.10.005
-publication: Safety Science
-url: "https://doi.org/10.1016/j.ssci.2009.10.005"
-zotero_key: 4DRNCUW9
-zotero_storage: 5H83CVWN
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Corben, Bruce F."
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-# Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/09257535)
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-# Safety Science
-
-![](_page_0_Picture_4.jpeg)
-
-![](_page_0_Picture_5.jpeg)
-
-# Strengthening road safety strategy development 'Towards Zero' 2008–2020 – Western Australia's experience scientific research on road safety management SWOV workshop 16 and 17 November 2009
-
-Bruce F. Corben a,\*, David B. Logan <sup>a</sup> , Lisa Fanciulli <sup>b</sup> , Roger Farley <sup>b</sup> , Iain Cameron b,1
-
-### article info
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-Article history: Received 1 July 2009 Received in revised form 24 September 2009 Accepted 4 October 2009
-
-Keywords: Safe System Vision Zero Sustainable Safety Computer modelling Prediction Serious casualties Strategy performance Road safety Community consultation
-
-### abstract
-
-With a significant list of achievements in traffic safety over some 40 years, Australia's most successful initiatives have emerged from consideration of the scientific basis for achieving effective results. Over time, the increasing use of scientific methods has matured to an extent whereby road safety strategies for entire jurisdictions are now being formed and optimised on the basis of evidence-based mathematical modelling of predicted strategy performance over periods of ten or more years ahead. This paper describes the approach used in the State of Western Australia (WA) to support the development of its new strategy, 'Towards Zero', addressing the 12-year period from 2008 to 2020. Also described is the early development of the modelling approach in the State of Victoria, Australia, subsequently applied, refined and adapted to Western Australia. The 'Towards Zero' strategy aims to ensure that road safety policies continue to evolve within a strategic framework, based on Australasia's Safe System framework, which was derived from the best elements of the Swedish Vision Zero and the Dutch Sustainable Safety visions.
-
-The process for the development of the strategy involved a greater degree of community and stakeholder engagement than has been the case previously, based on the fundamental belief that the community should be provided with the best evidence about what works no matter how controversial, so that it can debate and consider the options available to improve safety.
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-It is equally important to have a strategy based on the best possible evidence. The Monash University Accident Research Centre (MUARC) was appointed by the WA Office of Road Safety to identify, describe and recommend proven counter-measures within the Safe System framework and develop a series of scientifically sound directions and options.
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-This paper outlines the modelling technique used by MUARC along with results and summarises the community consultation process.
-
-> -2009 Elsevier Ltd. All rights reserved.
-
-# 1. Background
-
-The Western Australian government and Road Safety Council of Western Australia undertook to develop a new road safety strategy for the period 2008–2020 within Australasia's Safe System framework, with the addition of a bold, long term level of aspiration or vision. The Safe System vision had already been formulated from the best elements of the Swedish Vision Zero and the Dutch Sustainable Safety visions, however Western Australia had never before had a long term level of aspiration or vision. Such an approach has rarely been tried around the world. This project, therefore, should be recognised as an ambitious, highly-challenging initiative of the WA government. MUARC was commissioned by the government to provide the objective research basis for the strategy which, subject to successful implementation, can be expected to deliver large and lasting road safety benefits for Western Australia. A key aspect of the approach was to develop a model to estimate the projected benefits of a combination of best-practice Safe System countermeasures. The METS (Macro Estimates for Target Setting) model was developed for Victoria and, with the permission of Victoria's state road authority, VicRoads, the underlying model structure was used as the basis for substantial further development, culminating in the model now known as METS–WA ([Corben et al., 2008](#page-12-0)).
-
-Given the challenge of implementing such an approach, the WA Road Safety Council determined that the strategy should be developed in a consultative and transparent way to maximise
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-<sup>a</sup> Monash University Accident Research Centre, Building 70 Monash University, Victoria 3800, Australia
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-<sup>b</sup>Office of Road Safety, Main Roads Western Australia, Government of Western Australia, Perth, Australia
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-<sup>\*</sup> Corresponding author. Tel.: +61 3 9905 4375; fax: +61 3 9905 4363. E-mail addresses: [Bruce.Corben@muarc.monash.edu.au](mailto:Bruce.Corben@muarc.monash.edu.au) (B.F. Corben), [Lisa.](mailto:<xml_chg_old>Lisa.Fanciulli@dpc.wa.gov.au</xml_chg_old><xml_chg_new>Lisa. Fanciulli@mainroads.wa.gov.au</xml_chg_new>) [Fanciulli@mainroads.wa.gov.au](mailto:<xml_chg_old>Lisa.Fanciulli@dpc.wa.gov.au</xml_chg_old><xml_chg_new>Lisa. Fanciulli@mainroads.wa.gov.au</xml_chg_new>) (L. Fanciulli).
-
-<sup>1</sup> Tel.: +61 8 9222 8781.
-
-stakeholder and community acceptance. As such, the process for the development of the strategy for 2008–2020 involved a greater degree of community and stakeholder consultation than has been the case with previous strategies.
-
-### 2. The Safe System
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-The adoption of the Safe System road safety philosophy represents a profound shift in thinking and insight in the Australian society's efforts to curb road trauma. Translating the Safe System philosophy and principles into real-world practice is believed to have the potential to deliver major advances in the safety performance of Australia's road-transport system over coming years and decades.
-
-The Safe System framework was created specifically for Australia and New Zealand by combining the best elements of Sweden's Vision Zero and the Netherlands' Sustainable Safety road safety philosophies. Of the highly motorised countries, Sweden and the Netherlands are acknowledged world leaders in road safety performance and have each created and adopted during the 1990s bold, fundamentally new approaches to reducing road trauma. While these philosophies differ in their relative emphases, they are both founded on a strong ethical platform and both acknowledge the vital importance of explicitly recognising the limitations of humans using the road-transport system.
-
-Like its Swedish and Dutch counterparts, Australasia's Safe System philosophy also aspires to eventually eliminating death and serious injury within its road-transport systems. Serious casualties are defined as the sum of deaths and serious injuries. While some will regard this goal as unrealistic, it is important to realise that:
-
-- - Designing and operating a system that tolerates consistently high-levels of serious trauma is both unethical and at risk of perpetuating the acceptance of failure by system designers and operators.
-- - New, fundamentally safe, designs will result from viewing the safety of the road-transport system in this new light. Intrinsically safe designs and protocols for operation can shift system safety a large step forward, in contrast with the incremental progress characterising traditional approaches.
-
-The Safe System is underpinned by four key principles:
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-- 1. The limits of human performance Traditional approaches to road safety focus heavily on preventing human failures within the system. For example, there has been considerable success with programs targeting drink-driving, speeding, and restraint non-use. However, despite this partial success, these and other forms of human failure, such as poor gap selection at complex or simple intersections or while overtaking, failure to stay within travel lanes, even on high-speed high-standard roads, and driving while fatigued, have so far proven beyond our capabilities to eliminate through behaviour change programs. A guiding philosophy that explicitly acknowledges the limited intrinsic capabilities of humans is essential to future success.
-- 2. The limits of human tolerance to violent forces Humans are limited in their biomechanical tolerance to the violent forces and energy exchanges that commonly occur in traffic crashes. When tolerance limits are exceeded serious injury or death results. The Safe System explicitly acknowledges these scientific facts by seeking to create a road-transport system that is forgiving of human failures within the system, such that any foreseeable collisions occur within the biomechanical limits of the
-
-humans involved. To achieve this in practice, the ability of a vehicle to protect its occupants in common crash types and at typical impact speeds (resulting from legal travel speeds) must be known and taken into account in decisions about system design and operation. It is also vital to understand the biomechanical limits of unprotected road users (e.g., pedestrians, bicyclists and motorcyclists) in collisions across the full range of vehicle types and speed zones.
-
-- 3. Safe road use While the ultimate responsibility for the safety performance of the road-transport system resides with the system designers and operators, it can only be possible to move progressively towards a situation of no deaths or serious injuries in the long-term provided road users comply with key rules such as speed limits, restraint-use, driving unimpaired and in vehicles with good safety features. Thus, under a shared responsibility for road safety, the system designers depend on compliance by road users for achieving a truly Safe System, while individual, complying users depend on inherently safe design standards and operation for their safety.
-- 4. Creating a forgiving road-transport system The road-transport system is, fundamentally, a real-world illustration of the basic laws of nature governing the movement of objects, be they vehicles or humans, relative to the physical environment. How these objects interact is, ultimately, a matter of physics varied in time and space according to human intervention, which is particularly difficult to predict or control.
-
-Safe outcomes are largely determined by the ability of individual road users to control kinetic energy, which is the physical energy gained by an object (most significantly a vehicle) as a result of its motion. Kinetic energy is a function of both the mass of an object, but more importantly, its speed at any instant in time. Unlike mass, which is linearly related to kinetic energy, speed has a squared (i.e. 2nd power) relationship with kinetic energy. A safe road-transport system depends, therefore, on the successful separation of sources of kinetic energy (crash avoidance) and, where this cannot be achieved, the controlled dissipation of kinetic energy to avoid exceeding human biomechanical limits (injury prevention or mitigation). Given the inherent limitations of human performance within the road-transport system, the Safe System seeks to create roads and roadsides that are tolerant of human error and reasonable levels of impairment. This is achieved by ensuring that sources of kinetic energy remain separated or, in worst cases, can be managed within the limits of human tolerance to serious injury.
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-A major challenge for the future is to identify and implement current initiatives that meet this goal, as well as to generate new initiatives consistent with these Safe System principles.
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-### 3. Approach to developing the optimal strategy
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-A key early challenge was to identify a way to apply the Safe System to a large geographic jurisdiction with a low population.
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-Western Australia has the largest land area of all the Australian States and Territories comprising 2.5 million sq km and 13,000 km of coastline. Of the population of 2.1 million people, some 1.6 million live in the capital city, Perth. The road network is 179,000 km long, of which 126,000 km is unsealed with 227 million vehicle kilometres travelled annually.
-
-In 2007 the fatality rate was 112 per million people, of which about two-thirds were killed on regional roads at a rate of about 220 per million compared to a rate of 50 deaths per million people in Perth. A total of about 3000 people are seriously injured each year on Western Australian roads.
-
-To facilitate the identification of the best mix of initiatives for Western Australia's next road safety strategy, the concept of a ''Safe System Matrix" was created. The matrix aimed to direct attention primarily to a relatively small number of the larger and most severe traffic injury problems, using proven high-impact solutions. This approach still permits other problems of lower priority to be addressed (e.g., where specific user groups are at especially high risk, even though their absolute numbers of injuries may be relatively low), but at a level commensurate with the potential savings in serious trauma.
-
-The Safe System Matrix served two important purposes:
-
-- 1. It provided a systematic, structured approach to identifying strategically important road safety initiatives, consistent with the principles of the Safe System. In particular, it ensured that each of the main components of the Safe System, namely, safe roads and roadsides, safe travel speeds, safe vehicles and safe road use, were comprehensively addressed within the strategy. These main components have become known as the Safe System Cornerstones.
-- 2. The matrix was structured to ensure that the differing road safety problems and priorities between Western Australia's three main geographic areas – Metropolitan Perth, Regional WA and Remote WA – could be more precisely targeted within the strategy to meet these differing needs. 'Regional WA' represents the road network within a 300 km (approximately) radius of metropolitan Perth, while 'Remote WA' encompasses the rest of the state. These divisions were chosen to acknowledge the substantial differences in road, vehicle and other characteristics between each of these areas.
-
-The Safe System Matrix is shown in generic form in Table 1. The matrix does not contain all of the proposed countermeasures, but rather those that are anticipated to have a significant effect upon serious casualties. There are a variety of other worthwhile initiatives, including additional bicycle paths, traffic calming, improved pedestrian separation and lighting (especially in remote areas of WA) included in the final strategy. A brief description of each of the major areas of the matrix follows.
-
-### 3.1. Safe roads and roadsides
-
-The Safe Infrastructure Cornerstone consists of countermeasures aimed at improving the safety of roads and roadsides. It al-
-
-Table 1 The WA Safe System Matrix.
-
-Safe roads and roadsides Safe speeds Safe vehicles Safe road use All of WA Statewide Accident 'Black Spot'<sup>a</sup> program safer roads programs Enhanced speed enforcement Promotion of crash avoidance features Development of aggregate behaviour change programs Review and fine tune speed limits Promotion of advanced crashworthiness features Metro Perth Intersection countermeasures Adjustment of speed limits to complement infrastructure measures No metro-specific countermeasures Development of specific behaviour change for Metro issues Run-off-road countermeasures Regional WA Safe System transformation of strategically important routes radiating from metro boundary Rezone all limits downwards by 10 km/h Specific promotion of ESC in 4WDs Development of specific behaviour change programs for regional issues Promote ESC selection/fitment to heavy vehicles Remote WA Safe System transformation of strategically important routes around remote centres Rezone all limits downwards by 10 km/h Specific promotion of ESC in 4WDs Development of specific behaviour change programs for issues in remote areas Promote ESC selection/fitment to heavy vehicles
-
-lows for the continuation of existing Accident Black Spot and Safer Roads programs across the whole of WA, provided the selected sites and routes do not overlap with the specific regional and remote treatments detailed below.
-
-One of the key countermeasures for Metro Perth is a 'Safe System Transformation' of intersections. This would involve the implementation of such measures as roundabouts, full control of right-turns at signals, combination red light/speed cameras and others at key intersections. Sites would be selected on the basis of numbers of serious casualties, regardless of exposure, in order to maximise the potential savings in serious casualties as a result of countermeasures being implemented. The use of serious casualties, normalised for traffic volume, may redirect attention away from locations with the worst safety records in terms of absolute numbers of deaths and serious injuries. Run-off-road crashes are also a significant problem in urban areas, but few effective solutions currently exist. Ongoing research should be conducted into the development of new barrier solutions for urban applications, especially on higher speed roads such as those zoned at or above 70 km/h. For roads currently zoned at 60 km/h, reduced speed limits, coupled with enhanced enforcement, have the potential to address this problem without the need for barrier solutions.
-
-In addition to the whole of WA programs, a Safe System Transformation was proposed for routes radiating from Perth. This aimed to address the run-off-road and potentially, head-on crash problem on key strategic routes from the Metro Perth boundary as far out into regional WA as practicable. A similar strategy could be adopted for key rural centres. Traditionally, specific road sections would be treated piecemeal on the basis of crash history. In the case of a Safe System Transformation, however, the goal is to start from the metropolitan end of each route (where crash densities tend to be higher) and work outwards 'injury-proofing' entire road lengths rather than individual sections.
-
-### 3.2. Safe speeds
-
-Reducing travel speeds across Western Australia is one area where significant serious casualty savings are possible at relatively low cost. This potential has been demonstrated clearly through Nilsson's power laws ([Nilsson, 2004](#page-12-0)) reinforced with a variety of evaluations of reduced speeds conducted internationally (e.g. [Aarts](#page-12-0) [and van Schagen, 2006](#page-12-0) and references therein). This is particularly true for a very large, sparsely populated jurisdiction like Western Australia where it will be practically impossible in the foreseeable future to provide best-practice infrastructure solutions across much of the state. For the whole of WA, a program of enhanced
-
-<sup>a</sup> In Australasia, 'Black Spot' has a similar meaning to high crash location/high risk location, commonly used internationally.
-
-enforcement of speed limits was recommended, involving measures such as better-targeted police coverage and increased use of speed cameras. It was also recommended that a systematic program of 'fine tuning' of existing speed limits be pursued, particularly in metropolitan Perth as well as regional and remote centres. This involved redefining a change in speed limit so as to incorporate nearby known hazardous locations, such as intersections or high-risk curves, within the lower speed limit.
-
-In addition to the above measures, two speed-related countermeasures were recommended for Metro Perth. The first of these was to reduce urban speed limits from 60 km/h to 50 km/h. Currently 27% of all serious casualties occur in 60 km/h speed zones within the metropolitan area and even small reductions in travel speed can be expected to lead to significant serious casualty savings. Another situation where lower speed limits can bring about significant trauma reductions is in areas of high pedestrian activity such as Perth's central business district and urban strip shopping centres. It was therefore recommended that these areas be rezoned either to 40 or 30 km/h to improve Safe System compatibility in both avoiding and mitigating pedestrian-vehicle conflicts.
-
-In regional WA, it was recommended that speed limits be reduced by 10 km/h, with progressive reinstatement possible as the Safe System Transformation program is completed along the selected key routes. It was also deemed highly desirable for WA's open road speed limit to be reduced from 110 km/h to 100 km/h, where the vast majority of roads may never be practically able to be lifted to Safe System standards.
-
-#### 3.3. Safe vehicles
-
-This Safe System Cornerstone aimed to capitalise on the proven safety performance of a number of vehicle safety features that, in general, are not currently-fitted as standard on many vehicles available on the Australian market. These features include electronic stability control (ESC) and Intelligent Speed Adaptation (ISA), which help to prevent crashes occurring and side-impact and curtain air bags, and active head restraints, which help to reduce occupant injury severity when a crash occurs. This initiative aimed to accelerate the introduction and rate of penetration of these features into Western Australia's vehicle population by requiring government fleet purchasers to specify selected features when renewing the government fleet and by encouraging corporate fleet purchasers to do likewise. Not only will this approach accelerate the take-up of these proven features in WA's vehicle fleets, it will also put much-needed pressure on vehicle manufacturers to provide these features as standard in new vehicles, or at least as options that can be specified as stand-alone features in their base models. These benefits will flow on to WA's private vehicle purchasers as government and fleet vehicles are sold into the general fleet.
-
-Based on current and proposed levels of availability of these features, a timeline for their introduction to government and corporate fleet purchases was proposed as follows:
-
-- -ESC from Year 1.
-- -Curtain and side-impact airbags from Year 1.
-- -Intelligent Speed Adaptation from Year 3.
-- -Active head restraints from Year 5.
-
-### 3.4. Safe road use
-
-For a best-practice strategy for WA, the Safe Road Use Cornerstone would involve the creation of an integrated suite of campaigns that present and promote the Safe System road safety philosophy as well as targeting traditional and new problem areas. The main features would include:
-
-- - An emphasis on the Safe System being a new, ambitious approach capable of achieving major improvements in traffic safety over the next decade and beyond.
-- - The importance of shared responsibility, with road users to comply with key laws including drink-driving, drug-driving, seat belt use, fatigue-driving, etc. Correspondingly, the obligation of the system designers and operators to create a low-risk road-transport system would also be reinforced.
-- - A series of problem-specific education, promotion and publicity campaigns targeting key problem types amenable to improvement through behaviour change programs including drink-driving, drug-driving, speeding, restraint-use, distraction/inattention, fatigue-driving, etc. Furthermore, as part of each issue-specific campaign ongoing reinforcement of Safe System principles and the concept of shared responsibility.
-
-### 4. Modelling strategy performance
-
-Central to the overall approach to developing an optimal road safety strategy for Western Australia was the modelling of strategy performance using evidence-based estimates of the effectiveness of the individual road safety initiatives described in the previous section as well as combinations of initiatives. The modelling approach operates at a macro-level and so includes only those initiatives expected to have a sizeable impact on the reduction in severe road trauma. Using evidence-based estimates of effectiveness and actual crash data for Western Australia over recent years, the model used mathematical methods to forecast future savings in serious casualties in each year of the 12-year period from 2008 to 2020. These predicted savings in serious casualties were summed over the strategy life for each individual initiative, assuming it alone was implemented. These savings were also summed for combinations of initiatives implemented as part of a strategy, allowing differentiation between options in terms of their potential contribution to preventing road fatalities and serious injuries. The principal measure of worth of a strategy option is the total number of serious casualties prevented over the strategy life.
-
-These savings are estimated relative to the level of serious casualties that could be expected to occur in the absence of a significant road safety strategy.
-
-Forecasting future road trauma occurrence and trends is a challenging exercise, characterised by uncertainty and assumptions about the nature of the road-transport system, now and in the future. However, to plan for success, it is necessary to follow such a path, while reducing as far as practicable, the potential for error. The approach taken with modelling for the WA strategy has attempted to account for two main, whole-of-system, influences.
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-- - Future growth in serious casualties as a result of increasing exposure, reflected by vehicle kilometres travelled (VKT). This figure was estimated at 2.6% per annum for the life of the strategy, based on growth between 2001 and 2005, and could be an underestimate depending upon the duration and size of the economic boom experienced by WA at the time of modelling.
-- - Future improvement in serious casualty rates due to the gradual effects of increasing motorisation (e.g. [Jacobs et al.,](#page-12-0) [2000](#page-12-0)). This value was estimated at 1.6% per annum for WA, derived from the trend in serious casualties per vehicle-kilometre travelled using annual police-reported data for the period 1990–1996.
-
-<span id="page-4-0"></span>These two influences, while tending to counteract each other, resulted in a forecast general upward trend of an average 1.0% per annum in serious casualties through to 2020 due to the higher rate of traffic growth than decline in serious casualties associated with motorisation. Together, the combined effect of traffic growth and serious casualty reduction with increasing motorisation was used to define a continuous point of reference against which potential savings in serious casualties due to the proposed strategy and its individual elements were estimated.
-
-The model produces two principal outputs:
-
-- 1. The estimated total number of serious casualties saved over the life of the strategy (relative to the forecast numbers of serious casualties in the absence of a significant strategy).
-- 2. The percentage reduction in serious casualties in the final year of the strategy compared with a selected baseline period, which would be the most recent year, or an average of the most recent three years (if substantially different from the most recent year) for which full serious casualty data were available. The total number of serious casualties saved over the life of a strategy can be used to identify the most effective strategy, while the percentage reduction in the final year of the strategy (Year 12) provides a target for achievement by the end of the strategy (and at intermediate milestones throughout).
-
-The model also produces a number of additional output measures for each initiative and each strategy option. These included the estimated cost (in present-day values), as provided by agencies responsible for implementation, allowing the average cost of implementation per serious casualty saved, predicted numbers of fatalities and serious injuries in the final year of the strategy, among others to be estimated.
-
-# 5. Model structure
-
-### 5.1. Description
-
-The METS (Macro Estimates for Target Setting) model was originally developed over a period of 16 months from early 2006, funded by VicRoads as part of the development of the next Victorian road safety strategy. Although the Western Australian version was based on the latest version of METS, METS-WA has undergone extensive development and customisation with the aim of tailoring it to the Western Australian road safety environment, as well as incorporating significant improvements to the underlying model. The model is a numerical implementation of concepts developed in the late 1990s by MUARC researchers Peter Vulcan and Bruce Corben (e.g. [Vulcan and Corben, 1998](#page-12-0)). It starts with a base number of annual serious casualties (killed and hospitalised) derived from police-reported data. The effectiveness of individual countermeasures in reducing the proportion of total serious casualties is used to predict serious casualty numbers from the effects of the countermeasure in isolation for each year for the duration of the strategy. Underlying trends are also taken into account as detailed above. Individual countermeasures are then combined into strategy 'packages', either additively or multiplicatively, the latter avoiding double-counting of savings when different initiatives act upon the same 'pool' of serious casualties.
-
-# 5.2. Estimating scope for reduction in serious casualties
-
-This section presents a brief overview of the mathematical basis for estimating the reductions in serious casualties possible from specific initiatives. In essence, this section summarises the work of [Vulcan and Corben \(1998\),](#page-12-0) adapting it to the present application. As noted above, fatalities and serious injuries can be reduced by initiatives undertaken within the four Safe System Cornerstones. If no improvements are made in any of these areas, it is considered reasonable to assume that total fatalities (and serious injuries) would change according to an annual average serious casualty growth factor, g, defined as the percentage growth in traffic minus underlying rate of improvement due to the general effects of increased motorisation. As with [Vulcan](#page-12-0) [and Corben \(1998\),](#page-12-0) the effects of traffic congestion and differential growth on different parts of the road network were neglected.
-
-$$SC_t = SC_0 \left( 1 + \frac{g}{100} \right)^t \tag{1}$$
-
-where SC<sup>t</sup> = serious casualties in year t, SC0 = serious casualties in base year, g = annual average serious casualty growth factor, as defined above.
-
-To take account of the empirical finding that even in the absence of any additional road safety measures, fatality rate gradually decreases as motorisation (vehicles per head of population) increases and the road, vehicles and the road user population ''matures". While such effects are difficult to measure allowance has been made for this effect.
-
-Then if the total percentage reduction in fatalities and serious injuries resulting from all safety measures combined is R percent
-
-$$SC_R = SC_t \left( 1 - \frac{R}{100} \right) \tag{2}$$
-
-where SC<sup>R</sup> = number of serious casualties in the final year of the strategy (of duration t years) during which all the safety measures were applied.
-
-To ensure that savings in serious casualties are not counted more than once, they are either added or multiplied together, depending upon the actual mechanisms for achieving the savings. As described earlier, some initiatives act on the same set of crashes as other initiatives and therefore the savings need to be combined multiplicatively, while other initiatives can act on separate crash sets and so have been combined additively. [Vulcan and Corben](#page-12-0) [\(1998\)](#page-12-0) present a number of examples to illustrate this point, namely:
-
-$$1 - \frac{R}{100} = \left(1 - \frac{r_b}{100}\right) \left(1 - \frac{r_s}{100}\right) \tag{3}$$
-
-where rb = percent reduction from better vehicle brakes, rs = percent reduction from increased seat belt wearing, R = total percent reduction from these two measures.
-
-Similarly, if there are four sets of measures (r1–r4), the formula for calculating the total percentage reduction from all measures R is
-
-$$1 - \frac{R}{100} = \left(1 - \frac{r_1}{100}\right) \left(1 - \frac{r_2}{100}\right) \left(1 - \frac{r_3}{100}\right) \left(1 - \frac{r_4}{100}\right) \tag{4a}$$
-
-where r<sup>1</sup> = percentage reduction from all safer vehicle measures, r<sup>2</sup> = percentage reduction from all safer road measures, r<sup>3</sup> = percentage reduction from all safer road user measures, r<sup>4</sup> = percentage reduction from improved emergency medical services.
-
-Or, more generally, for n road safety countermeasures, each resulting in a percentage reduction, rn:
-
-$$1 - \frac{R}{100} = \prod_{i=1}^{n} \left( 1 - \frac{r_n}{100} \right) \tag{4b}$$
-
-Interactions between these four categories of measures have been ignored.
-
-Each of the above four percentage reductions represents the net effect of several measures some of which are additive, some are multiplicative and some of which overlap or interact (e.g. speeding and drink-driving enforcement).
-
-It can be seen from Eq. [\(1\)](#page-4-0) that the extent to which a reduction in fatality rate translates to a reduction in the number of fatalities also depends on traffic growth. Thus influencing the rate of growth or fall in road use also represents scope for further improvements in road safety as well as vehicle emissions.
-
-A unique feature of METS-WA is its ability to allow different levels and combinations of initiatives to be relatively easily compared.
-
-# 5.3. Key assumptions
-
-METS uses a worksheet with more than 150 input variables, both user-defined and calculated. Variables are separated into general parameters as well as parameters relating to each of the Safe System Cornerstones, namely infrastructure (safe roads and roadsides), speed, vehicles and behaviour (safe road use). Where possible, input variables were derived from either reliable agency-supplied statistical data or the results of sound research. For a number of minor variables, however, neither of these sources was able to provide the specific data required. Where this was the case, conservative consensus estimates were used, based on the best evidence currently available.
-
-#### 5.4. Outputs
-
-The two principal outputs of the model are: (a) cumulative numbers of serious casualties saved over the life of the strategy and (b) performance of the strategy in its final year, as well as selected milestone points. Strategy performance was measured as the percentage reduction in the number of serious casualties in the selected year compared with the chosen baseline period (as defined earlier), usually the most recent year for which full serious casualty data are available. The number of cumulative serious casualties saved is a relative figure calculated by projecting the future state of baseline serious casualties in the absence of a specific strategy. Although it is a somewhat arbitrary baseline, it allows different strategy options to be compared with one another. Moreover, cumulative savings are important to consider, as strategy performance in a single year may give little indication of overall strategy performance.
-
-Where the costs of implementing initiatives were provided, the model also provides approximate indications of: (a) total strategy cost, (b) cost per serious casualty saved, (c) the monetary valuation by society resulting from the serious casualty savings, and (d) the ratio of this monetary valuation to strategy cost. Costs are all expressed in present-day values, with future cost increases not taken into account.
-
-To illustrate the use of the equations presented in Section [5.2](#page-4-0) (refer to [Vulcan and Corben, 1998](#page-12-0) for original explanation), consider the case of initiatives being assessed to improve road infrastructure safety, accelerate the take-up of proven vehicle safety features and strengthen speed management. Set out below are three charts which show the estimated annual savings in serious casualties from each of the above initiatives, as well as the yearby-year build-up in the cumulative savings in serious casualties over the period of the 12-year strategy developed for WA. These savings were calculated using the above equations, from knowledge of the:
-
-- average annual number of serious casualties in the chosen base period;
-- effectiveness of individual initiatives in reducing serious casualties; in some cases it is important to distinguish between the different levels of effectiveness of initiatives on fatalities and on serious injuries;
-
-- annual growth in traffic over the strategy life;
-- annual reduction in the rate of serious casualties per vehicle kilometre of travel over the strategy life;
-- extent of implementation of the initiative, either as a onceonly decision or in terms of the annual (or other periodic) level of implementation;
-- timing and the means by which the initiative has its effect, especially with respect to the cumulative effects over time (this is described more fully below).
-
-# 5.5. Example 1 – Safe Roads and Roadsides Cornerstone
-
-The effectiveness of black spot and other safety-based road investment undertaken in Australia over many years were used to estimate savings in serious casualties from improving road and roadside safety. Estimates of effectiveness of these types of program were obtained from local evaluations. Applying these findings to the WA trauma problem ([Meuleners et al., 2005\)](#page-12-0), and adjusting for the actual level of investment each year across a number of safe infrastructure investment options, it was estimated that 58 serious casualties would be prevented each year if \$135 million (in present-day values) is invested annually over the life of the strategy. The estimated annual savings of 58 serious casualties for an annual investment of \$135 million is calculated from a weighted average of the individual levels of effectiveness of different types of infrastructure investment (i.e., accident black spot program, safer roads program, Safe System Transformation of metropolitan intersections, and regional and remote routes).
-
-Because infrastructure programs of this scale require a significant lead time to reach full implementation capacity, no savings were allowed in the first two years of the strategy and only half of annual savings were counted in the third year, before building to full delivery of annual savings from Year 4 for the life of the strategy. Such assumptions attempt to be both realistic about potential savings in serious casualties and conservative in nature, so that implementing agencies are confident they are not being asked to perform beyond their true capacity.
-
-A major feature of investment in safe infrastructure is that savings in serious casualties gained in the first year continue to accrue in each of subsequent years until the theoretical, useful lives of these projects have expired. During this period the savings which accrued from investment in the Years 2, 3, 4, etc. also accrue in the same basic way so that the net effect is a rapidly building profile of savings in serious casualties that actually extend well beyond the 12-year life of the strategy. Because only the savings accrued during the life of the strategy are counted in the strategy assessment method, the model is quite conservative in its estimation of strategy benefits. It is, however, assumed that the effectiveness of safety measures implemented within this cornerstone remain fully effective throughout the life of the strategy (see [Fig. 1](#page-6-0)).
-
-# 5.6. Example 2 – Safe Vehicles Cornerstone
-
-The individual estimates of effectiveness of proven vehicle safety features are used to estimate the rate of accumulation of savings in serious casualties resulting from these features finding their way into a jurisdiction's vehicle fleet. For example, it has been found that Electronic Stability Control (ESC) reduces single-vehicle serious casualties involving passenger vehicles by 35% (after [Lie et al. \(2006\)](#page-12-0), and [Scully and Newstead, 2008\)](#page-12-0), while curtain airbags are effective in reducing all serious casualties from side-impacts and rollovers involving passenger vehicles by 20% (reference: MUARC estimate). Similarly, research has found that Intelligent Speed Adaptation (ISA) is effective in
-
-<span id="page-6-0"></span>![](_page_6_Figure_2.jpeg)
-
-Fig. 1. Estimated cumulative serious casualty savings by year compared with ''business as usual" for the modelled package of infrastructure investment.
-
-reducing all serious casualties involving passenger vehicles by an estimated 10% [\(Regan et al., 2006](#page-12-0)) and that Active Head Restraints are effective in reducing all serious casualties from rear-impacts involving passenger vehicles by 15% (MUARC estimate). These estimates of effectiveness were then applied to actual Western Australian data on each target crash type to estimate savings in serious casualties for a given rate of introduction of the various technologies.
-
-The rates of penetration of the technologies into the WA fleet was based on the assumption that from a specified date for each technology, government and corporate vehicle purchasing policies and practices will change to ensure that such features would always be specified from the assumed start date forward. It was therefore also necessary to obtain information on the current rate of purchase and sale of government and corporate fleet vehicles, and project these rates throughout the 12-year life of the strategy.
-
-In order to maintain an acceptable degree of conservatism in the setting of an overall strategy target, the start dates for each vehicle safety feature were chosen to enable adequate time for implementing agencies to prepare for the required changes in fleet purchasing policy. For largely practical reasons, it was assumed that take-up of both ESC and side/curtain airbags would increase from actual 2008 levels to 100% by 2011 and 2012 respectively, ISA from Year 3 and active head restraints from Year 7, and that only 80% of purchased vehicles in first year of implementation would have the specified safety features, 90% in second year of implementation and 100% thereafter. It was also hoped that the collective actions of fleet purchasers would be sufficient to bring about a change in the policy of many vehicle manufacturers to make these features standard in base models so that from that time forward the occupants of all new vehicles would enjoy the safety benefits possible from these features. The added savings in serious casualties derivable, should such a decision be taken by the manufacturers of vehicles sold in Australia, have not been counted in the total benefits possible during the life of the strategy.
-
-Likewise, in keeping with the conservative nature of this modelling approach, savings in serious casualties from private purchases have not been counted; such savings are regarded as a bonus to overall strategy performance. Notwithstanding, ongoing efforts should be made to promote the purchase by private buyers of vehicles with these proven features. Similarly, the benefits able to be derived from improved vehicle designs offering greater protection in impacts with pedestrians and cyclists, while strongly supported, have not been quantified (see Fig. 2).
-
-### 5.7. Example 3 – Safe Speeds Cornerstone
-
-Two main initiatives were considered under the Safe Speeds Cornerstone; first, achieving improved driver and rider compliance with existing speed limits and, secondly, reducing speed limits on selected types of road. The methods used to estimate savings in serious casualties by implementing the latter of these two initiatives are presented here to illustrate the approach taken.
-
-The work of [Nilsson \(2004\)](#page-12-0) and [Elvik et al. \(2004\)](#page-12-0) resulted in the establishment and widespread use of scientifically robust relationships between changes in mean travel speeds and changes in the incidence of fatalities, serious injuries and other injuries. In essence, Nilsson found that a decrease in mean travel speeds resulted in decreases in the incidence of fatalities, serious injuries and other injuries according to power functions. The decrease in injuries is a function of the decrease in mean speed raised to the fourth, third and second powers, for fatalities, serious injuries and other injuries, respectively.
-
-To estimate the potential savings in serious casualties as a result of reducing the speed limits in existing 60 km/h zones to 50 km/h, Nilsson's relationships were applied to actual injury data for WA, on the basis that this potential reduction in speed limit would reduce the mean speed in 60 km/h zones from 40 km/h to 37 km/h. This estimate was based on actual mean travel speed data and previous research by [Haworth et al. \(2001\)](#page-12-0) which estimated that such a speed limit reduction would produce a 3 km/ h reduction in mean speeds. No additional enforcement is expected to be needed in order to achieve this reduction in mean speeds. These assumptions resulted in the calculation of an overall serious casualty reduction factor of 0.21, derived as a weighted average of the fourth power exponent applied to the actual average annual number of fatalities and the third power exponent applied to the actual average annual number of serious injuries occurring throughout WA's 60 km/h speed zones. It was also assumed that this change in speed limits would not take place until Year 5 of the strategy to enable agencies to prepare for the change, including supporting the somewhat controversial initiative (refer to the section on community consultation) with appropriate education and publicity in advance of the change. The model also included a reduction in 110 km/h posted speed limits to 100 km/h (Year 5) as well as the remainder of state speed limits being reduced by 10 km/h in Year 8 (see [Fig. 3](#page-7-0)).
-
-![](_page_6_Figure_12.jpeg)
-
-Fig. 2. Estimated cumulative serious casualty savings by year compared with ''business as usual" for the modelled package of vehicle safety countermeasures.
-
-<span id="page-7-0"></span>![](_page_7_Figure_2.jpeg)
-
-Fig. 3. Estimated cumulative serious casualty savings by year with respect to ''business as usual" for a reduction of 60 km/h speed limits to 50 km/h and 110 km/ h posted speed limits to 100 km/h in Year 5, with the remainder of state speed limits reduced by 10 km/h in Year 8.
-
-# 5.8. Combining the effects of all initiatives
-
-As noted earlier, if initiatives target the same crashes, then the total savings from the combined effects may be estimated by multiplying together the individual estimates, whereas when the initiatives target different crashes, then the savings may be added to estimate the overall effect.
-
-In the case of the initiatives illustrated above, all vehicle safety features except for ISA target different crash types and therefore their combined effect can be estimated by adding the individual effects. ISA, on the other hand, targets all crash types and so the total savings in serious casualties from all vehicle safety features would be estimated by multiplying the sum of the other three vehicle safety initiatives by the reduction factor for serious casualties from ISA. The overall saving from the initiatives spanning the three Safe System Cornerstones would then be estimated by multiplying the total savings from vehicle safety features by the reduction factors for serious injuries for the infrastructure investment initiative and for the speed limit reduction initiative, as these latter initiatives act on the same crashes.
-
-### 6. Results
-
-### 6.1. MUARC's recommended Safe System strategy
-
-Fig. 4 shows the relative performance of each of the groups of initiatives (as shown in the WA Safe System Matrix section) that were selected for consideration for inclusion in the strategy. From left to right, the bars represent the projected cumulative serious casualties estimated over the 12-year life of the strategy for infrastructure (safer roads and roadsides), enhanced enforcement, speed limit reductions, vehicle safety measures and an aggregate behaviour change program.
-
-The above initiatives were combined to create what was called the 'Optimum' Safe System Option (OSSO). Here, 'optimum' is defined as a pragmatic optimum, derived from consultation with representatives from responsible agencies in WA. Defining this optimum had regard for what would be realistic levels of input from agencies within each cornerstone, while still recognising practical constraints on agency delivery, levels of maturity of new vehicle technology and the influences of political and community views. The OSSO was estimated to yield cumulative savings of 16,000 serious casualties over the 12-year duration of the strategy, reducing annual serious casualties from 2962 in
-
-![](_page_7_Figure_11.jpeg)
-
-Fig. 4. Performance of individual initiatives, if implemented in isolation.
-
-2006 to around 1420 serious casualties per annum by Year 12 in 2020. This represents a 52% reduction in serious casualties compared with 2006. It is estimated that by the conclusion of Year 12 the number of state-wide fatalities annually could reach as low as 100 (c.f. an average annual base level of 202 for the 3 year period from 2005 to 2007).
-
-As traffic growth has a significant influence on model outputs, as well as being somewhat variable, a range of traffic growth figures were investigated. The results, shown in [Table 2](#page-8-0), demonstrate how the model is easily able to show how serious casualty outcomes might change with movements in other variables. The effect of lower traffic growth is to improve the performance of the strategy, while higher traffic growth results in greater cumulative serious casualty savings, but worse strategy performance overall, highlighting the potential risks in strategy implementation.
-
-## 6.2. Strategy alternatives
-
-Several variations to the Safe System strategy were modelled, to investigate the effects of some of the compromises that might be considered for each of the main Safe System initiative groups. Each change was modelled while keeping the others at levels specified by the OSSO. The following delays were examined:
-
-- - Infrastructure: reduce annual investment by 30% (found to result in a similar number of savings in serious casualties forgone as if the investment in infrastructure were delayed by 2 years).
-- - Speed: delay 60–50 km/h, 110–100 km/h until Year 5 (from Year 1); delay 10 km/h reduction until Year 7 (from Year 3).
-- - Enforcement: delay enhanced enforcement until Year 5 (from Year 1).
-- - Vehicles: delay ESC, side/curtain airbags until Year 3 (from Year 1); delay ISA until Year 7 (from Year 3); no change for Active Head Restraints (Year 5)
-
-No changes were made to the aggregate behaviour change program. [Fig. 5](#page-8-0) shows the results of each change, based on the effects of delaying one initiative at a time. Should multiple delays occur, the cumulative effects on serious casualty savings would be considerably greater.
-
-# 6.3. Limitations of modelling approach
-
-With any form of forecasting, there will potentially be a degree of uncertainty about the direction, magnitude and sustainability of predicted change. The modelling method described
-
-<span id="page-8-0"></span>Table 2 Effects of varying traffic growth on model outputs.
-
-| Output estimate                                                               | Traffic growth |        |        |
-|-------------------------------------------------------------------------------|----------------|--------|--------|
-|                                                                               | 2.6%<br>(base) | 2.0%   | 3.0%   |
-| Cumulative serious casualty savings over 12<br>years (rounded to nearest 500) | 16,000         | 15,500 | 17,000 |
-| Percentage reduction in Year 12                                               | 52%            | 57%    | 49%    |
-| Estimated serious casualties in Year 12 (rounded<br>to nearest 10)            | 1420           | 1290   | 1520   |
-| Estimated fatalities in Year 12 (rounded to<br>nearest 10)                    | 100            | 90     | 110    |
-
-![](_page_8_Figure_4.jpeg)
-
-Fig. 5. Effects of varying initiative commitment on cumulative serious casualties saved.
-
-here clearly has a number of limitations on its reliability and accuracy. Some of the main limitations are discussed briefly below, though a full discussion of the effects of these limitations is not practical here.
-
-- quality and consistency of reported crash data describing fatalities and serious injuries;
-- accuracy of estimates of effectiveness of initiatives and countermeasures being modelled, including the transferability of non-local research findings to other jurisdictions;
-- quality of other, non-safety, data such as traffic growth, vehicle sales and future investment in general road improvements;
-- expectations that historical trends in society in general and the transport system in particular will continue to be highly influential in defining future trends;
-- future trauma savings due to infrastructure investment are substantially underestimated as only the savings accrued during the life of the strategy (usually 10–12 years) are counted, whereas much of the infrastructure investment will continue to deliver trauma savings for up to 30 years. A similar argument applies to vehicle safety, though the average life of a passenger vehicle is about 10–12 years, thereby limiting this effect as it relates to vehicles.
-
-To date, the model has not been developed to investigate the effects of shifts in travel mode, such as the growth in motorcycling, walking and public transport use, for example. New projects are currently exploring these possibilities.
-
-To address these and other concerns, the modelling approach has been highly conservative in estimating savings in severe trauma. For example, the general philosophy of the modelling process assumes:
-
-- lower levels of effectiveness of initiatives;
-- slower implementation/take-up of initiatives;
-- higher costs to achieve the predicted savings;
-- sensitivity testing of predicted traffic growth, generally adopting conservative values.
-
-The overall effect of this typically conservative approach has been to ensure governments are not unwittingly promising to deliver more than is realistically possible for an agreed level of investment. In jurisdictions with highly ambitious aspirations, modelling can readily be undertaken in a way that would genuinely 'stretch' the available resources to maximise the achievement of trauma reductions. There appears to be no single correct approach; instead, the modelling can be carried out to suit the aspirational preferences of a particular jurisdiction.
-
-Finally, it is important to recognise that the greater value of this form of modelling lies in establishing the relative benefits of alternative strategy options rather their absolute values. The METS modelling approach is presented as a macro-level approach rather than creating the unjustified 'illusion of precision' ([Sherwood, 2009](#page-12-0)). As a result, there are many road safety initiatives that were not modelled explicitly, primarily because, individually, the predicted savings are small and therefore incompatible with the 'macro' nature of the overall approach. Their aggregate effect, however, can be significant, yet is not counted here and instead is regarded as a 'bonus' contribution to the adopted strategy. In the case of continuing initiatives in place prior to the strategy, some of these benefits will be implicit in the adjustments made for the effects of increased motorisation.
-
-### 7. The community consultation
-
-To promote community, stakeholder and government acceptance of the ambitious approach taken to the development of the strategy, the Road Safety Council (RSC) undertook an extensive consultation process parallel to MUARC's modelling work. The consultation had as its basis the fundamental belief that the community should be provided with the best evidence about what works no matter how controversial that may be so that it can debate and consider the options available to improve safety. This approach was strongly supported by the Road Safety Minister.
-
-### 7.1. Process
-
-The RSC called on the expertise of community engagement specialists Estill and Associates and market research company Synovate to develop, undertake and analyse the community consultation. Over 4000 people directly participated in the consultation.
-
-The consultation process involved three phases:
-
-- - Phase 1 gathered the community's views on road safety and introduced it to the Safe System philosophy and the concept of shared responsibility through a series of community forums hosted by local Members of Parliament in their constituencies and an online survey.
-- - Phase 2 presents the findings of the first phase of consultation and the recommended package of initiatives developed by MUARC to the community for comment through a discussion paper (with online and paper based feedback options), community forums and a survey of a scientifically representative sample of the broader population.
-- - Phase 3 involves the communication of the endorsed strategy to the public, stakeholders and the Parliament.
-
-A bipartisan Parliamentary Reference Group with an independent chairperson was also established and consulted in each phase.
-
-The three phases of the consultation process are further detailed in Appendix A.
-
-### 7.2. Results
-
-It was considered likely that the people who took the time to attend a forum or complete the online or paper-based questionnaire would have a stronger interest in the road safety initiatives proposed by MUARC than would the wider community. It was possible that they would be either more strongly in favour or more strongly against them. In contrast, the representative population sample was selected to be representative of the broader community's age, gender and geographical location and was considered to more accurately reflect the opinions held about road safety by the population at large. On the whole, as shown in Table 3, both groups supported the majority of the strategy proposals made by MUARC.
-
-The research showed that the community was highly concerned about reducing death and serious injury on our roads and recognised that this is a shared responsibility of the entire community. The majority of the community supported MUARC's recommended road safety strategy; however, the level of support varied for individual initiatives and amongst different demographics. In particular there was strong support for initiatives aimed at maximising compliance with the road rules among road users, improving Western Australia's road network and enhancing the safety of vehicles travelling on our roads.
-
-The representative sample indicates that the community was more divided on reducing speed limits in regional, urban and shopping areas, though it was far less resistant towards these changes than the group that completed the available, open survey. To add further complexity to the issue, approximately half of those who did not support speed limit reductions still believe these changes would be at least somewhat effective in reducing death and serious injury on the roads. Support for speed limit reductions is higher among women and those with dependents and lower among men, people without dependents and motorcyclists.
-
-There is a statistically significant difference between the views of the representative population sample and the views of those with an existing interest in road safety who attended the forums or completed the open questionnaire. It appears that those with a passion for road safety are actually less open to speed reductions than the general population.
-
-The community's main concerns surrounding speed limits changes were that irresponsible road users would continue to speed without better enforcement, while responsible road users would feel the impact of reduced travel times.
-
-# 7.3. Implications of community consultation results for MUARC's recommended strategy
-
-In light of the scientific evidence the RSC was convinced that the initiatives recommended by MUARC would effectively reduce the level of death and serious injuries on WA roads. With the high level of community support for MUARC's recommended initiatives to improve road user behaviour, the safety of roads and roadsides, the enforcement of speeding and vehicle safety, the RSC endorsed those initiatives as recommended by MUARC. In addition, the RSC endorsed additional research and evaluation projects for each of the cornerstones and undertaking to acknowledge and encourage some of the programs the community would like to see (such as, first aid training and attitude enhancement programs), provided they are not measures with proven negative impacts.
-
-However, in responding to the divided community views on the desirability of speed limit reduction, the RSC did not adopt MUARC's recommendation to reduce most of the state's speed limits by 10 km/h. Instead, the RSC determined that it should undertake a range of demonstration projects to illustrate the effects of speed limit reductions; adjust speed limits to complement road infrastructure; introduce more consistent speed limits and develop a community education program to enhance community understanding of the benefits of speed limit reduction.
-
-MUARC found that this amendment to its Optimal Safe System Option will reduce the strategy's potential to save people from being killed or seriously injured on WA roads from 12,600 to 11,000 over the 12 year life of the strategy. Nonetheless, by being able to clearly demonstrate the benefits and community views on this recommended initiative, the RSC has been able to give the Government and Parliament a high level of assurance that the strategy will be widely supported by the community which has led them to be more receptive and supportive of it.
-
-Following a change of Government in September 2008, the Minister for Road Safety announced the new Government's endorsement of the policy direction established in Towards Zero in Parliament on 19 March 2009 and received bipartisan support. Implementation is now progressing with widespread community and political support.
-
-Table 3 Snapshot of results of community consultation<sup>a</sup> .
-
-| Safe road use                                                              | Safe roads and roadsides                                                                                                                                                                                                                                                                        | Safe speeds – enforcement                                                                                                                                                                                                                                                                                             | Safe vehicles                                                                                 |
-|----------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|
-| 94% supported or strongly supported<br>education programs                  | 95% supported or strongly supported<br>improving intersections                                                                                                                                                                                                                                  | 74% supported or highly supported this initiative<br>(with an additional 18% somewhat supporting<br>the measure)                                                                                                                                                                                                      | 90% supported or strongly<br>supported the ongoing<br>promotion of vehicle safety<br>features |
-| 86% supported or strongly supported<br>improved policing of the road rules | 91% supported or strongly supported<br>improving lengths of road or roadsides<br>and black spots<br>74% supported or strongly supported<br>building roadside barriers<br>68% thought that improving the roads<br>would be an effective or highly effective<br>approach to improving road safety | Safe speeds – limit reductions<br>48% supported or strongly supported reducing<br>speed limits near shopping centres<br>40% supported or strongly supported reducing<br>regional speed limits from 110 km/h to 100 km/h<br>40% supported or strongly supported reducing<br>urban speed limits from 60 km/h to 50 km/h | 79% supported or strongly<br>supported safety features on<br>all Government vehicles          |
-|                                                                            |                                                                                                                                                                                                                                                                                                 | 31% believed speed limit reductions would be<br>effective or highly effective (with an additional<br>27% thinking it would be somewhat effective).                                                                                                                                                                    |                                                                                               |
-
-<sup>a</sup> Unless otherwise specified, the percentages shown represent the responses of the representative population group – who were randomly sampled by Synovate. The full report outlining the results of the community consultation can be found at: <http://www.officeofroadsafety.wa.gov.au>.
-
-### 8. Conclusion
-
-The Western Australian government decided to take an ambitious approach by developing a new road safety strategy in accordance with the principles of the Safe System, rarely attempted elsewhere. Based on the underlying structure of a mathematical model developed by the Monash University Accident Research Centre in Melbourne, Australia, the METS–WA model allowed comparisons in terms of cumulative serious casualties saved to be made between the various elements of the Safe System, including the Cornerstones incorporating safe roads and roadsides, safe speeds, safe vehicles and safe road use. METS–WA was also used to combine these initiatives to predict overall road safety strategy performance over its 12-year duration and investigate the effects of delays in implementation or changes in predicted traffic growth.
-
-The strategy subsequently developed aimed to represent the best-practice solution achievable and has the potential to result in large reductions in severe trauma in Western Australia if adopted fully, with annual serious casualties estimated to be reduced by around 50% over 2006 levels ([Office of Road Safety, 2008\)](#page-12-0).
-
-This is the first time Western Australia has conducted widespread community consultation during the development of a road safety strategy and it is regarded as crucial to establishing the foundations for endorsement and implementation.
-
-To promote community acceptance of the Safe System philosophy and a bold, long term vision for road safety, it has been important to share the research with the community and listen to its views. The views of the community and the scientists on reducing road trauma were important considerations for Government in endorsing the recommended Towards Zero strategy.
-
-The successful adoption of the Safe System framework depends upon all participants in the road-transport system accepting their share of the responsibility for its safe operation. This community and stakeholder consultation process increases the chance that those objectives will be met as Western Australia now begins to build its capacity to implement Towards Zero.
-
-#### Acknowledgements
-
-The development of the original METS model was funded by VicRoads. Its permission to use the underlying model in Western Australia is gratefully acknowledged, as is the permission of the Western Australia Office of Road Safety to publish this paper.
-
-### Appendix A. Summary of community consultation
-
-| What                             | Purpose                                                                                                                                                                                                                                                                                                                                            | How                                                                                                                                                                                                                                                          | Where                                                | Respondents | Note                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-|----------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Community<br>forums<br>(phase 1) | To inform the<br>participants of road<br>safety statistics in<br>their local area and<br>help participants<br>gauge their own<br>awareness of local<br>road safety issues and<br>driving behaviours<br>In addition the<br>workshops allowed<br>for community<br>debate on road safety<br>and promoted<br>community<br>engagement in road<br>safety | Hosted by members of<br>state Parliament in<br>their constituencies –<br>all members of the<br>community were<br>invited to attend<br>through media,<br>advertising and direct<br>invitations (from the<br>MP)                                               | 36 forums<br>were<br>conducted<br>in 19<br>locations | 1326        | Two workshops were held in most<br>of the locations. The morning<br>session was for community<br>representatives (mainly students<br>and apprentices). The afternoon<br>session was for community leaders<br>and 'influences', including those<br>that, as employers, have some<br>responsibility for the safety of their<br>employees. Handheld electronic<br>devices, which ensured anonymity,<br>were used to record (and capture)<br>participants' responses                                                                                                                                                                                                                                           |
-| Online survey<br>(phase 1)       | This was an internet<br>based version of the<br>questionnaire<br>presented at the<br>phase 1 community<br>forums, allowing<br>interested people<br>who had not been<br>able to attend a<br>forum to still have<br>input to the first<br>phase of community<br>consultation                                                                         | Advertisements were<br>placed in The West<br>Australian and the<br>details were emailed<br>to an extensive<br>database of people<br>who had previously<br>indicated an interest<br>in road safety<br>The survey also<br>received extensive<br>media coverage | Online                                               | 1188        | Of the respondents 71% were male.<br>The majority of the sample (43%)<br>was aged between 40 and 59. A<br>large proportion held a motorcycle<br>licence (40%). This group cannot be<br>considered a representative sample<br>as there are too many males, too<br>many respondents in full-time<br>employment and the age groups do<br>not represent the wider community<br>However, the fact that the<br>respondents took the trouble to<br>complete the online survey suggests<br>that they hold considered opinions<br>and are concerned about road<br>safety. Hence, while not a<br>representative sample of the<br>broader population, their views are<br>being considered<br>(continued on next page) |
-
-## Appendix A (continued)
-
-| What                                                                                                                                                                                  | Purpose                                                                                                                                                                                 | How                                                                                                                                                                                                                                                   | Where                                                                                                                                                                                              | Respondents             | Note                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Community and<br>stakeholder<br>forums (phase<br>2)                                                                                                                                   | This phase involved the<br>community and<br>stakeholders in providing<br>feedback on the MUARC<br>recommendations, which<br>were presented as the<br>Toward Zero draft                  | Advertisements<br>and some articles<br>were placed in<br>local newspapers.<br>Invites were sent<br>to stakeholders<br>and relevant                                                                                                                    | In eight locations<br>across the state<br>(metropolitan,<br>regional and<br>remote)                                                                                                                | 807                     | The attendees of these<br>forums were largely<br>stakeholders directly<br>involved and<br>concerned about road<br>safety                                                                                                                                                                                                                                                                                                                                                                   |
-| Online feedback<br>on 'Towards<br>Zero' – MUARC's                                                                                                                                     | discussion paper<br>recommendations<br>(phase 2)                                                                                                                                        | database contacts<br>The Towards Zero<br>draft discussion<br>paper was<br>presented online,<br>together with the<br>feedback form<br>allowing people<br>who were unable<br>to attend the<br>community<br>forums to submit<br>their feedback<br>online | Advertisements<br>and some articles<br>were placed in<br>local newspapers.<br>Details were sent<br>to stakeholders<br>and relevant<br>database contacts.<br>Promoted at the<br>community<br>forums | Online                  |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| This was an open<br>'survey'. Any<br>interested<br>person could<br>complete it                                                                                                        |                                                                                                                                                                                         |                                                                                                                                                                                                                                                       |                                                                                                                                                                                                    |                         |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| Independent<br>representative<br>sample                                                                                                                                               | The primary purpose of<br>this research was to<br>obtain feedback from the<br>larger community and not<br>just from those with an<br>interest or with strong<br>opinions on road safety | Recruited via a<br>phone survey<br>conducted<br>through an<br>independent social<br>research company<br>– Synovate                                                                                                                                    | Respondents were<br>randomly<br>contacted by<br>phone and were<br>given the option to<br>receive and then<br>submit their<br>feedback online or<br>as a hard copy via<br>post                      | 649 (correct to<br>±3%) | The independent<br>research company<br>ensured this sample<br>was representative of<br>the community gender<br>and age spread and<br>included metro,<br>regional and remote<br>participants<br>This representative<br>community survey<br>ensured a balanced<br>view from across the<br>community and all<br>voices are heard – not<br>only the loudest<br>This is considered to be<br>the most reliable<br>representation of the<br>thoughts and opinions<br>of the general<br>population |
-| Additional specific<br>forums<br>(industry<br>leaders, special<br>interest groups<br>(including<br>cyclists and<br>pedestrians)<br>and Road Safety<br>Council agency<br>stakeholders) | These forums encouraged<br>direct feedback from<br>specific groups including<br>vulnerable road users<br>such as motorcyclists, the<br>police and representatives<br>of large companies | Invitations were<br>sent to specific<br>groups,<br>companies,<br>organisations, etc.                                                                                                                                                                  | Perth convention<br>and Exhibition<br>centre                                                                                                                                                       | 200                     |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-
-### <span id="page-12-0"></span>References
-
-- Aarts, L., Van Schagen, I., 2006. Driving speed and the risk of road crashes: a review. Accident Analysis and Prevention 38, 215–224.
-- Corben, B.F., Logan, D.B, Johnston, I.R., Vulcan, A.P., 2008. Development of a New Road Safety Strategy for Western Australia 2008–2020, Report Number 282, Monash University Accident Research Centre, June.
-- Elvik, R., Christensen, P., Amundsen, A., 2004 Speed and road accidents: an evaluation of the Power Model. Oslo.
-- Haworth, N., Ungers, R., Vulcan, A.P., Corben, B.F., 2001. Evaluation of a 50 km/h Default Urban Speed Limit for Australia. Monash University Accident Research Centre, Melbourne.
-- Jacobs, G., Aeron-Thomas, A., Astrop, A., 2000. Estimating global road fatalities. TRL Report 445. Transport Research Laboratory, Berkshire, UK.
-- Lie, A., Tingvall, C., Krafft, M., Kullgren, A., 2006. The effectiveness of electronic stability control (ESC) in reducing real life crashes and injuries. Traffic Injury Prevention 7, 38–43.
-- Meuleners, L., Hendrie, D., Legge, M., Cercarelli, L.R., 2005. An Evaluation of the Effectiveness of the Black Spot Programs in Western Australia, 2000–2002.
-
-- Injury Research Centre, School of Population Health, The University of Western Australia, RR155. Crawley, WA.
-- Nilsson, G., 2004. Traffic safety dimensions and the power model to describe the effect of speed on safety, Doctoral thesis, Department of Technology and Society, Traffic Engineering, Lund, Sweden. [http://www.lub.lu.se/luft/diss/](http://www.lub.lu.se/luft/diss/tec_733/tec_733.pdf) [tec\\_733/tec\\_733.pdf](http://www.lub.lu.se/luft/diss/tec_733/tec_733.pdf) [30.6.09].
-- Office of Road Safety, W.A., 2008. Towards Zero Recommended Strategy. Perth, Western Australia.
-- Regan, M.A., Triggs, T.J., Young, K.L., Tomasevic, N., Mitsopoulos, E., Stephan, K., Tingvall, C., 2006 On-Road evaluation of Intelligent Speed Adaptation, Following Distance Warning and seatbelt reminder systems: final results of the TAC Safecar project. IN CENTRE, M.U.A.R. (Ed.) MUARC Report Series, Clayton, Monash University Accident Research Centre.
-- Scully, J., Newstead, S., 2008. Evaluation of electronic stability control effectiveness in Australasia. Accident Analysis & Prevention 40, 2050–2057.
-- Sherwood, O., 2009. ''The illusion of precision". Personal communication, 8 April.
-- Vulcan, A.P., Corben, B.F., 1998. Prediction of Australian road fatalities for the year 2010. Presented to: National Road Safety Summit, Canberra, ACT.

Zotero/001_artiklid/Road Safety Indicators/cottrillevaluatingpedestriancrashesareas2010.md

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----
-category: literaturenote
-citekey: cottrillevaluatingpedestriancrashesareas2010
-title: Evaluating pedestrian crashes in areas with high low-income or minority populations
-authors: "Cottrill, Caitlin D.; Thakuriah, Piyushimita (Vonu)"
-year: 2010
-date: 2010-00-00 2010
-doi: 10.1016/j.aap.2010.04.012
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2010.04.012"
-zotero_key: PFMRW59R
-zotero_storage: S997TRUT
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Cottrill, Caitlin D."
-status: converted
----
-Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/00014575)
-
-## Accident Analysis and Prevention
-
-journal homepage: [www.elsevier.com/locate/aap](http://www.elsevier.com/locate/aap)
-
-![](_page_0_Picture_5.jpeg)
-
-# Evaluating pedestrian crashes in areas with high low-income or minority populations
-
-Caitlin D. Cottrill <sup>a</sup>,∗, Piyushimita (Vonu) Thakuriah<sup>b</sup>,<sup>1</sup>
-
-- <sup>a</sup> Urban Transportation Center, University of Illinois, Chicago, 412 South Peoria Street, Suite 340, Chicago, IL 60607-7036, United States
-- <sup>b</sup> Department of Urban Planning and Policy, University of Illinois, Chicago, 412 South Peoria Street, Suite 340, Chicago, IL 60607-7036, United States
-
-#### article info
-
-Article history: Received 14 May 2009 Received in revised form 26 April 2010 Accepted 29 April 2010
-
-Keywords: Pedestrian Crash Income Minority Crime Transit
-
-### abstract
-
-In this paper, we present an analysis of the relationship between pedestrian–vehicle crashes and characteristics of areas with high low-income and minority populations in the Chicago metropolitan area (also called environmental justice or EJ areas in the United States). While related research has indicated that pedestrian crashes occur more frequently in these areas than in non-EJ areas, this paper attempts to relate the incidence to environmental characteristics and behavioral factors through a better understanding of the contributing factors present in crash occurrences in EJ versus non-EJ areas. Specially constructed small-area factors from a Spatial Decision Support System (SDSS) are used to explain pedestrian–vehicle crashes.
-
-Using a Poisson model that corrects for underreporting, we find that pedestrian crash incidents in EJ areas are related to variables of exposure (including the suitability of the area for walking and transit accessibility), crime rates, transit availability, and general population demographics such as income and presence of children. Results suggest that it may be necessary to better incorporate a safety perspective or measures of safety improvements in pedestrian and transit improvements and expansion programs within EJ areas.
-
-© 2010 Elsevier Ltd. All rights reserved.
-
-#### **1. Introduction**
-
-In 2005, motor-vehicle crashes killed 4881 pedestrians in traffic in the United States and sent 120,815 pedestrians to emergency medical departments (National Highway Traffic Safety Administration [\[NHTSA\], 2006\).](#page-10-0) While the contributing causal factors to these crashes vary widely, previous studies have indicated that certain factors relating to the socio-demographics and the physical environment of neighborhoods may heighten the risk of pedestrian–vehicle crashes. These initial indications are particularly relevant in regards to environmental justice (EJ) guidelines which broadly require each U.S. federal agency to make the alleviation of inequitable environmental burden a part of its mission by identifying and addressing, as appropriate, disproportionately high and adverse human health or environmental effects of its programs, policies, and activities on minority and low-income populations (Environmental Protection Agency, [EPA, Executive Order, 1994\).](#page-10-0) This paper is intended to specifically address pedestrian–vehicle crashes within the framework of EJ guidance to determine if pedestrian safety is an issue that should be specifically addressed within EJ activities.
-
-Several authors have noted that there is an increased incidence of pedestrian–vehicle crashes in EJ areas [\(Mid-America Regional](#page-10-0) [Council, 2007; Murtha, 2005\).](#page-10-0) EJ areas are those with high proportions of minority and low-income households (the specific definition used in this paper is given in Section [3.1\).](#page-2-0) While numbers gathered from these evaluations are of interest, what is notable is that occurrences of crashes are often cited while paying little attention to the question of general exposure to risk, including such factors as mixture of land uses, the number of potential pedestrians in an area, or the overall character of the surrounding environment and whether it is inviting to and safe for pedestrian travel.
-
-The current study more extensively explores some geographic correlates of pedestrian–vehicle crashes, through an analysis of data from the Chicago metropolitan area in the State of Illinois, USA, with particular emphasis on spatial variations on these events by EJ versus non-EJ area. We examine 2005 Chicago vehicle–pedestrian crash data, gathered from the [Illinois Department of Transportation](#page-10-0) [\(IDOT\)](#page-10-0) as a case study in order to hold factors beyond socio-demographics constant and to provide information relating to risk levels in environmental justice areas. Statistical evidence, as presented below, shows that an elevated risk is
-
-<sup>∗</sup> Corresponding author. Tel.: +1 312 996 4820; fax: +1 312 413 0006.
-
-E-mail addresses: [ccottr2@uic.edu](mailto:ccottr2@uic.edu)(C.D. Cottrill), [vonu-pt@uic.edu](mailto:vonu-pt@uic.edu)(P. Thakuriah).
-
-<sup>1</sup> Tel.: +1 312 996 4820; fax: +1 312 413 0006.
-
-present, perhaps indicating that policies and programs addressing pedestrian–vehicle crashes in these areas should be targeted to those factors that are likely to account for such increases in risk.
-
-The objective of the paper is to report on a comparative analysis of motor-vehicle–pedestrian crashes in EJ versus non-EJ areas and to understand which factors contribute to variations in pedestrian crashes in those areas. We have taken the view that two groups of geographic factors can potentially affect observed differences – those relating to the physical environment (called environmental factors) of the areas and those relating to social behaviors, cognitive and demographics (called behavioral factors) of the residents of the areas. These measures are a part of an ongoing research project to develop a Spatial Decision Support System on the Chicago metro area for planning purposes by the authors' research team. The construction and potential use of these measures in accident analysis are described. The importance of these factors in explaining EJ versus non-EJ differences in pedestrian crashes are demonstrated using exploratory methods as well as regression. An issue of concern that is addressed in the paper is underreporting of pedestrian crashes – we have adopted a statistical approach to address potential underreporting in the Chicago vehicle–pedestrian crash data.
-
-The paper is organized as follows: prior literature on environmental justice and pedestrian crashes is reviewed in Section 2. The study area, the method by which EJ areas were determined and the environmental and behavioral factors considered are presented in Section [3. S](#page-2-0)ection [4](#page-3-0) presents an exploratory analysis of crash trends in EJ versus non-EJ areas and reports summary statistics on the factors and on crash patterns in the two types of areas. Statistical models of pedestrian crashes are given in Section [5. S](#page-7-0)ummary and conclusions are drawn in Section [6.](#page-9-0)
-
-#### **2. Background and prior literature**
-
-In 1994, President Clinton signed Executive Order 12898, "Federal Actions to Address Environmental Justice in Minority Populations and Low-Income Populations". As part of this order, it was required that, "each Federal agency shall make achieving environmental justice part of its mission by identifying and addressing, as appropriate, disproportionately high and adverse human health or environmental effects of its programs, policies, and activities on minority populations and low-income populations..." (1994). Partially in response to this order, and partially in response to growing awareness by residents and activists in environmental justice communities, pedestrian issues in low-income and minority areas have of late been gaining increased visibility and dialogue.
-
-Several authors have examined geographic variations in pedestrian crash rates at both a macro (state or regional) level as well as a micro (intra-regional) level. For example, [Paulozzi \(2006\)](#page-10-0) found that the highest pedestrian fatality rates concentrate in Sunbelt states that have experienced rapid population growth in the past 50 years. This pattern may result from at least three features of these states: (a) a high percentage of urban vehicle miles traveled; (b) urban sprawl; and (c) a high prevalence of alcohol use—especially heavy use—among Sunbelt pedestrians. [Beck et al.](#page-10-0) [\(2007\), i](#page-10-0)n a comparison of the Atlanta metro region with the rest of the U.S., reported that from 2000 to 2004, pedestrian fatality rates declined in the United States, but the Atlanta metropolitan statistical area did not experience the same decline. Pedestrian fatality rates for males, Hispanics, and the 15–34 and 35–54 year age groups were higher in Atlanta than in the United States overall.
-
-Of direct relevance to this paper is a stream of literature on intra-regional variations in pedestrian crash rates. [Dougherty et al.](#page-10-0) [\(1990\)](#page-10-0) found that for Montreal, neighborhoods with income levels in the lowest quintile had death and accident rates that were at least four times the rates for children living in the least disadvantaged neighborhoods. Several authors have written about the complex interaction of factors that put pedestrians in poor neighborhoods, especially child pedestrians, at greater risk from moving vehicles ([Padilla et al., 1976; Brown and Davidson, 1978;](#page-10-0) [Bijur et al., 1986; Lyons et al., 2008; Guyer et al., 1985\)](#page-10-0) including "the lack of secure play space; dwellings adjacent to busy traffic flows; hazardous journeys to school and other facilities; less developed cognitive skills, which would imply risk-taking; and greater stress (physiological, social, and psychological)." ([Bagley,](#page-10-0) [1992\)](#page-10-0) [LaScala et al. \(2004\), u](#page-10-0)sing data from four California communities, found that annual numbers of pedestrian injuries were greater in areas with higher youth population densities, more unemployment, fewer high-income households, and greater traffic flow.
-
-[Loukaitou-Sideris and Liggett \(2007\)](#page-10-0) evaluated pedestrian accidents in Los Angeles in the context of socio-demographic and land use characteristics, eventually concluding that, "The coefficients on these variables [percent of the population below poverty and percent Latino] in each model were positive, supporting the assumption that pedestrian accidents are more likely to occur in low-income, minority neighborhoods once other aspects of risk are controlled for." Similar results were found in Nassau County, New York (pertaining to risk of hospitalization for pedestrian injuries). Additionally, the Surface Transportation Policy Project's (STPP) report, Mean Streets 2002 [\(STPP, 2002\) s](#page-10-0)tated that,
-
-Nationwide, non-Hispanic whites comprise 69 percent of the population. However, only 60 percent of pedestrian deaths for which ethnicity is known are non-Hispanic whites. In contrast, African-Americans make up more than one in five (20 percent) pedestrian deaths, though they represent only 12 percent of the total U.S. population. Likewise, Latino pedestrians comprise 13.5 percent of deaths, but only 12.5 percent of the total U.S. population. However, although the current literature includes studies of individual and environmental characteristics related to injury rates, there exist only a handful of neighborhood studies that introduce specific geographic components with general applicability across communities [\(LaScala et al., 2000, 2001, 2004\).](#page-10-0)
-
-While it is not necessarily the case that these pedestrian deaths took place near the location of residence, it is critical to evaluate the potential that the socio-demographics and the nature of the built environment of the home location may play a part in the relative imbalance of the ethnicity of crash victims.
-
-Several international studies have also indicated a relationship between pedestrian crashes and areas of high low-income and/or minority populations. For example, in a scan of literature addressing the issue from a number of countries, including the US, the United Kingdom, and Australia, among others, [Laflamme and](#page-10-0) [Diderichsen \(2000\)](#page-10-0) found that for most types of traffic injuries, including pedestrian injuries, "...mortality and morbidity are often higher among children from lower social positions and in more deprived socio-economic areas." In a study of English and Welsh families, [Edwards et al. \(2006\)](#page-10-0) found that the pedestrian death rate of children of parents classified as long-term unemployed or never employed was 20.6 times higher than that of children of parents in higher managerial or professional occupations. Finally, a study of traffic crashes involving Maoris in New Zealand conducted by [Sargent et al. \(2004\)](#page-10-0) found a higher proportion of traffic casualties and injuries for all crashes (including pedestrian) for those Maoris living in areas of higher relative deprivation. These findings indicate that potential linkages between socio-economic variables such as income and race and pedestrian crashes are a worldwide phenomena and require additional study.
-
-#### <span id="page-2-0"></span>**3. Study area**
-
-In 2005, the [Illinois Department of Transportation \(IDOT\)](#page-10-0) reported 4886 vehicle crashes in the Chicago region in which a pedestrian was involved. Of those crashes, 120 (or about 3%) resulted in the death of the pedestrian. [Fig. 1](#page-3-0) shows the distribution of vehicle–pedestrian crashes in the six-county Chicago metro area (consisting of Cook, McHenry, Kane, Will, Lake, and DuPage counties; Cook county includes the City of Chicago). Overall, pedestrian–vehicle crashes accounted for about 1% of all 2005 Illinois crashes and about 2% of all crashes in the Chicago region. [Fig. 1](#page-3-0) shows that the distribution of pedestrian–vehicle crashes are not random throughout the region; rather, it appears that crashes are concentrated in the downtown business district (the "Loop") and areas in south and west Chicago. The goal of this paper is to utilize the Chicago region as a case study to analyze pedestrian–vehicle crashes, focusing on those factors that are considered under environmental justice regulations and to determine if issues beyond pedestrian exposure play a part in influencing pedestrian–vehicle crash rates.
-
-#### 3.1. Determination of EJ areas
-
-EJ and non-EJ areas are defined at the level of census tracts. There are several pros and cons in using census tracts as the spatial unit for equity studies. Census tracts have a relatively homogeneous population of about 4000 and are delimited by local persons and thus "reflect the structure of the metropolis as viewed by those most familiar with it" ([Bogue, 1985\).](#page-10-0) They are small enough to reduce the possibility of "aggregation errors" and "ecological fallacies" [\(Anderton et al., 1994\) b](#page-9-0)ut large enough to potentially avoid impacts that spill over beyond smaller spatial units such as block or block groups ([Been, 1994\),](#page-10-0) although other authors have noted that impact areas may run well beyond the boundaries of individual tracts in metropolitan areas [\(Mohai, 1995\).](#page-10-0) Further, it is the most commonly used geographic unit of analysis [\(Anderton](#page-9-0) [et al., 1994\)](#page-9-0) – in our case, the spatial level corresponds to the unit of analysis in the Chicago area Spatial Decision Support System and it is deemed to be a reasonable approximation of the concept of a neighborhood ([Denton and Massey, 1991\).](#page-10-0) Although there are several studies that compare outcomes related to mobility and transportation conditions in EJ versus non-EJ areas, there is no consistency among studies about how EJ areas are defined. In safety research, [Wier et al. \(2009\)](#page-10-0) recently used census tracts in the San Francisco area to determine the relationship between street, land use, and population characteristics, including commute behaviors and census tract-level pedestrian–vehicle injury collisions.
-
-The use of census tracts as the level of analysis also introduces the need to examine the way in which population exposure is evaluated. Because, as noted above, census tracts are fairly homogenous in population size, for purposes of this analysis we have chosen to use population density as a measure of exposure. While the use of actual pedestrian counts would be useful, such information is not generally available on an area-wide basis. The use of density in this calculation is consistent with methods proposed by such entities as the [FHWA \(1999\), w](#page-10-0)hich suggests population density as a method of evaluating potential travel behavior from a contextoriented perspective, and such researchers as [Wedagama et al.](#page-10-0) [\(2006\)](#page-10-0) and [Graham and Glaister \(2003\), w](#page-10-0)ho use population density as an explanatory variable in models of crash frequencies and who suggest that increased urban density may be both an indicator of deprivation, which has been shown as influencing pedestrian crashes, and as an influence on traffic volumes and speed, which may also have an influence. [Raford and Ragland \(2003\), a](#page-10-0)dditionally state that, "Physical factors such as residential population density, mixed land use, street connectivity, and adequate pedestrian facilities have been identified as key physical variables that influence the number and types of walking trips." By using density, we are able to better represent the relative distribution of pedestrian crashes across the Chicagoland region. As the region includes a large variety of census tract types (ranging from rural to highly urban), the spatial area of each tract also varies greatly. It may be misleading to directly compare incidents of pedestrian crashes in highly rural tracts with those in highly urban tracts, as the spatial density will vary greatly. By using population density, we are better able to perform comparative analyses.
-
-The definition of EJ areas adopted here replicates the rule given in [Sriraj et al. \(2003\). E](#page-10-0)J neighborhood criteria are determined in this paper by computing demographic and economic information for Cook County, and basing the threshold analysis for EJ census tracts on the results. Using this method, the following rules are determined to define EJ tracts: (a) Black – according to the 2000 Census, 26% of all residents of Cook County are Black. All census tracts with a Black population greater than 26% qualify as EJ neighborhoods; (b) Hispanic – according to the 2000 Census, 20% of all residents of Cook County are Hispanic. All census tracts with a Hispanic population greater than 20% qualify as EJ neighborhoods; (c) low-income – all households that are within 150% of the poverty threshold are considered low-income. Using the reference area technique, this population is 21.5% of the total population in Cook County. Accordingly, census tracts that have more than 21.5% of their population within the low-income threshold, qualify as EJ neighborhoods.
-
-#### 3.2. Environmental and behavioral factors relating to pedestrian crashes
-
-The Chicago area Spatial Decision Support System (SDSS) integrates a Multi-Criteria Decision-Making (MCDM) environment with small-area spatial data to integrate transportation decision-making with housing, community development, economic development and physical planning. Using a variety of data sources and transportation modeling outputs, it consists of a number of indicators on traffic and transportation, accessibility measures, regional employment opportunities, small-area employment estimates based on forecasted job openings and actual jobs, affordable housing, school quality, crime, health, land use and the built environment.
-
-The factors within the SDSS, which were used in this study, can be categorized into two groups: the first group of factors relate to the physical environment of the area including the level of vehicular traffic flow, quality and complexity of the walking environment, pedestrian crowding possibilities (at transit stations or commercial developments), road crossing opportunities which present possibilities of conflict with vehicular traffic, population density and the extent of crime (called environmental factors). The second group of factors relate to social behaviors, cognitive and demographics (called behavioral factors), which lead to variations in the ability to cope effectively with these hazards, including percent of population with little or no English-speaking ability as indicators of unfamiliarity with the environment, percent of school-going children and percent of carless households.
-
-[Table 1](#page-4-0) presents the indicators used with a brief explanation of each indicator. Indicators were chosen mostly on the basis of prior, published research as well as local knowledge derived from professionals in the Chicago metro area, who work in the area of transportation, safety and public health. Using a methodologically rigorous approach to ensure that all possible factors that account for systematic differences between EJ and non-EJ tracts and omitted variable bias, is left for future research.
-
-<span id="page-3-0"></span>![](_page_3_Figure_2.jpeg)
-
-**Fig. 1.** Map showing 2005 pedestrian crashes in the Chicago metro area.
-
-### **4. Exploratory analysis of pedestrian crashes in EJ versus non-EJ areas**
-
-[Tables 2 and 3](#page-4-0) show summary statistics on variables that are of importance in the analysis. [Table 2](#page-4-0) shows that the number of pedestrian crashes in EJ areas are well over twice that in non-EJ areas and that the pedestrian crash rate per 1000 population is 9.66 in EJ areas compared to 3.37 in non-EJ areas. Large differences do not exist in the proportion of injuries of different types between EJ and non-EJ areas. Injuries are classified as follows [\(IDOT Crash](#page-10-0) [Report Data Dictionary, 2005\):](#page-10-0)
-
-- - A Injuries: incapacitating but not fatal injuries, including those which prevent the injured party from walking, driving, or normally continuing the activities her or she was capable of performing before the injury occurred.
-- - B Injuries: nonincapacitating and non-fatal injuries, evident to observers at the crash scene.
-
-- C Injuries: possible injuries, which are not classified as type A or B above.
-
-Fatal crashes are 2.2% of all crashes in EJ areas compared to 3% in non-EJ areas.
-
-Pedestrian crash victims tend to be younger in EJ areas (close to 28% of pedestrians involved in crashes in EJ areas are less than 16 years of age, compared to about 10% of pedestrians in non-EJ areas) and where the gender of the crash victim is known, involve a slightly larger share of males (56% compared to 55%). [Table 3](#page-5-0) shows that the median household income in EJ areas is \$36,274.79 compared to \$61,414 in non-EJ areas. A larger proportion of EJ area residents have "low" levels of English-speaking ability and also a greater share of school-age children (30% compared to 26% in non-EJ areas). About 24% of households in EJ areas have no cars, compared to only 8% of households in non-EJ areas.
-
-[Table 3](#page-5-0) shows that the mean pedestrian crash rate in EJ tracts is 3.26 and that in non-EJ tracts is 1.94, with a difference of 1.32.
-
-<span id="page-4-0"></span>**Table 1** Environmental and behavioral Indicators used in the study.
-
-| Indicator                                               | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
-|---------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Environmental Indicators at census tract-level          |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| Transit availability index (TAI)                        | Composite index giving the extent to which residents have access to transit (bus and rail); based<br>on three input measures – frequency (person-minutes served), hours of service (number of hours)<br>and service coverage (percentage of census tract area covered). See Minocha et al. (2008) for more<br>details                                                                                                                                                                                                 |
-| Pedestrian accessibility index (PED)                    | Composite index ranking tract suitability for pedestrian travel; based on input values of<br>population, income, number of households, amount of commercial and residential land uses as a<br>percentage of census tracts, weighted trip origins and destinations, and Pedestrian Environment<br>Factor (PEF) values, where PEF's are the average number of blocks for the quarter section within<br>each census tract and the eight adjacent quarter sections. See Cottrill and Thakuriah (2008) for<br>more details |
-| Sum of annual average daily traffic (SUM AADT)          | Total annual average daily traffic on links of all highway functional classes within census tract;<br>output from regional traffic assignment model and GIS                                                                                                                                                                                                                                                                                                                                                           |
-| Total miles of roads (SUM LENGTH)                       | Total miles of roads of all functional classifications within census tract; output from GIS                                                                                                                                                                                                                                                                                                                                                                                                                           |
-| Total number of schools (NO SCHOOLS)                    | Count of all schools within census tract                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
-| Crime rate (CRIME)                                      | Total violent and non-violent crimes in census tracts                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-| Behavioral indicators at census tract-level             |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| Median household income (MEDINC)                        | Median household income; based on census 2000                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
-| Population density (POP SQMILE)                         | Population per square mile in census tract (census 2000 population)                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Percent with no cars (PERNOCAR)                         | Percentage of population without access to a vehicle (census 2000 data)                                                                                                                                                                                                                                                                                                                                                                                                                                               |
-| Percent commercial (PER COMM)                           | Percentage of census tract used for commercial purposes (based on CATS land use 1990 file)                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| Percent children (PERCHILDREN)                          | Percentage of population under the age of 16 (census 2000 data)                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| Percent who speak limited or no English (PERLOWENGLISH) | Percentage of persons who speak no English or limited English (Census 2000 data)                                                                                                                                                                                                                                                                                                                                                                                                                                      |
-
-Our objective in Section [5](#page-7-0) is to examine if and how much, ceteris paribus, environmental and behavioral factors explain the crash rate difference between EJ and non-EJ areas.
-
-The built environment in EJ areas differs from that in non-EJ areas in significant ways. EJ areas are of much higher density (13,910 population per square mile versus 7111 per square mile in non-EJ areas) but have fewer schools per census tract (1.89 on the average, compared to 2.68 in non-EJ areas). EJ tracts are estimated to have lower Average Annual Daily Traffic (with a SUM AADT value of 229,599 miles) compared to 340,739 miles in non-EJ areas. EJ areas also have lower highway length (with a SUM LENGTH value about 3.5 miles on the average per census tract) compared to non-EJ areas (with an average of 9.84 miles).
-
-The average pedestrian accessibility rank (PED) is slightly higher in EJ areas (the t-test of difference between PED in EJ versus non-EJ areas is significant only at the .10 level). The transit availability
-
-**Table 2** Comparison of EJ and non-EJ tracts on pedestrian crash victim socio-demographics and injury severity.
-
-|                           | EJ tract  |         | Non-EJ tract |         | All tracts |         |
-|---------------------------|-----------|---------|--------------|---------|------------|---------|
-|                           | Number    | Percent | Number       | Percent | Total      | Percent |
-| Crash totals              | 3367.00   | 100.00% | 1501.00      | 100.00% | 4868.00    | 100.00% |
-| Crash rates               |           |         |              |         |            |         |
-| Pop 2001 Census Est.      | 3,486,832 |         | 4,458,285    |         | 4,458,285  |         |
-| Count of Ped crashes      | 3367      |         | 1501         |         | 4868       |         |
-| Crash rate                | 0.000966  |         | 0.000336677  |         | 0.000337   |         |
-| Crashes/10,000            | 9.66      |         | 3.37         |         | 6.13       |         |
-| Injury severity           |           |         |              |         |            |         |
-| A Injury crash*           | 664       | 19.72%  | 331          | 22.05%  | 995        | 20.44%  |
-| B Injury crash**          | 1717      | 50.99%  | 746          | 49.70%  | 2463       | 50.60%  |
-| C Injury crash***         | 875       | 25.99%  | 371          | 24.72%  | 1246       | 25.60%  |
-| Fatal crash               | 74        | 2.20%   | 45           | 3.00%   | 119        | 2.44%   |
-| No injuries               | 37        | 1.10%   | 8            | 0.53%   | 45         | 0.92%   |
-| Hit and run               |           |         |              |         |            |         |
-| No                        | 2276      | 67.60%  | 1190         | 79.28%  | 3466       | 71.20%  |
-| Yes                       | 1091      | 32.40%  | 311          | 20.72%  | 1402       | 28.80%  |
-| Age (in years) of victims |           |         |              |         |            |         |
-| 0–5                       | 132       | 3.92%   | 26           | 1.75%   | 158        | 3.26%   |
-| 6–16                      | 824       | 24.47%  | 247          | 16.61%  | 1071       | 22.06%  |
-| 17–35                     | 899       | 26.70%  | 485          | 32.62%  | 1384       | 28.51%  |
-| 36–50                     | 666       | 19.78%  | 313          | 21.05%  | 979        | 20.17%  |
-| 51–70                     | 446       | 13.25%  | 262          | 17.62%  | 708        | 14.59%  |
-| 71 and more               | 400       | 11.88%  | 154          | 10.36%  | 554        | 11.41%  |
-| Gender                    |           |         |              |         |            |         |
-| Female                    | 1314      | 39.03%  | 624          | 41.57%  | 1938       | 39.81%  |
-| Male                      | 1709      | 50.76%  | 774          | 51.57%  | 2483       | 51.01%  |
-| Unknown                   | 344       | 10.22%  | 103          | 6.86%   | 447        | 9.18%   |
-
-<sup>\*</sup> A Injuries: Incapacitating but not fatal injuries, including those which prevent the injured party from walking, driving, or normally continuing the activities her or she was capable of performing before the injury occurred.
-
-<sup>\*\*</sup> B Injuries: Nonincapacitating and non-fatal injuries, evident to observers at the crash scene.
-
-<sup>\*\*\*</sup> C Injuries: Possible injuries, which are not classified as type A or B above.
-
-<span id="page-5-0"></span>**Table 3** Variables utilized in the analysis, tests of significance, effect size and correlations.
-
-| Variable              | EJ tract                                                                  |                    |          | Test of significance and effect size |          |
-|-----------------------|---------------------------------------------------------------------------|--------------------|----------|--------------------------------------|----------|
-|                       | "No"  d  ≤ .20: small; .20 <  d  ≤ .50: medium;<br>.50 <  d  ≤ .80: large | "Yes"<br>(N = 946) | t value* | Cohen's d**                          |          |
-|                       | (N = 886)                                                                 |                    |          |                                      |          |
-|                       | Mean                                                                      | Mean               |          |                                      |          |
-| PED CRASH             | 1.94                                                                      | 3.26               | −7.87    | 0.37                                 |          |
-| Environmental factors |                                                                           |                    |          |                                      |          |
-| TAI                   | 0.61                                                                      | 0.67               | −10.86   | 0.51                                 | 0.20206  |
-| PED LOW (binary)      | 0.26                                                                      | 0.25               | 0.47     | −0.02                                | −0.03654 |
-| SUM AADT              | 340,739.10                                                                | 229,599.87         | 5.44     | −0.26                                | 0.03028  |
-| SUM LENGTH            | 9.84                                                                      | 3.49               | 11.7     | −0.56                                | −0.12138 |
-| NOOFSCHOOLS           | 2.68                                                                      | 1.89               | 6.83     | −0.32                                | 0.29065  |
-| POP00 SQMI            | 7,111.12                                                                  | 13,910.09          | −14.26   | 0.67                                 | 0.29913  |
-| PERCRIME              | 3.03                                                                      | 4.91               | −22.16   | 0.74                                 | 0.27328  |
-| PED                   | 35.31                                                                     | 36.23              | −1.8     | 0.68                                 | 0.31226  |
-| Behavioral factors    |                                                                           |                    |          |                                      |          |
-| WLKTOWRK              | 79.25                                                                     | 52.59              | 3.24     | −0.15                                | 0.29943  |
-| MEDHHINC99            | 67,414.06                                                                 | 36,274.79          | 32.85    | −0.78                                | −0.20477 |
-| PERNOCAR              | 0.08                                                                      | 0.24               | −24.05   | 1.12                                 | 0.31196  |
-| PER COMM              | 0.04                                                                      | 0.09               | −9.08    | 0.42                                 | 0.21792  |
-| PERCHILDREN           | 0.26                                                                      | 0.30               | −10.19   | 0.48                                 | 0.00721  |
-| PERLOWENGLISH         | 0.25                                                                      | 0.43               | −12.41   | 0.57                                 | 0.10207  |
-
-<sup>\*</sup> Italicized and bold estimates are significant at the .01 level.
-
-index (TAI) is significantly larger in EJ areas, indicating that the built environment, land uses and transit access levels may be more conducive to walking in the EJ areas. Figs. 2 and 3 show Trellis plots of variations in pedestrian crashes by EJ and non-EJ census tract, for different levels of TAI and PED. The top panel of the Trellis plots in Fig. 2 shows variations in pedestrian crashes by EJ versus non-EJ census tracts for "High" levels of TAI (a TAI value of "High" indicates census tracts with the top 25% of values of transit availability, "Medium High" indicates census tracts with the second highest 25% of TAI values and so on). [Fig. 3](#page-6-0) displays similar information on PED. The plots also show the mean pedestrian crash rates for each TAI/PED and EJ condition.
-
-Overall, the mean pedestrian crash rates for both EJ and non-EJ tracts decrease with decrease in TAI levels. In the case of non-EJ
-
-![](_page_5_Figure_10.jpeg)
-
-**Fig. 2.** Trellis plot of variations in count of crashes by census tract for combinations of EJ and transit availability index (TAI).
-
-<sup>\*\*</sup> The interpretation of effect sizes given by Cohen's d for difference in means with pooled variance is generally difficult due to "the low level of consciousness of the magnitude of phenomenon" ([Cohen, 1992\),](#page-10-0) but may be interpreted generally as follows: <sup>|</sup>d| ≤ .20: small; .20 < <sup>|</sup>d| ≤ .50: medium; .50 < <sup>|</sup>d| ≤ .80: large. # Correlation with PED CRASH.
-
-<span id="page-6-0"></span>![](_page_6_Figure_2.jpeg)
-
-**Fig. 3.** Trellis plots of variations in counts of crashes by census tracts for combinations of pedestrian accessibility (PED) and EJ status of census tract.
-
-tracts with High TAI levels (on the left hand side of the top panel), the mean crash rate is 3.75, decreasing to 1.37 in the case of tracts with Low TAI levels. The mean crash rates for each TAI condition is higher for EJ tracts compared to non-EJ tracts; but in the case of EJ tracts too, the mean pedestrian crash rate decreases from 3.94 in the case of tracts with HIGH TAI levels to a mean of 2.56 in the case of EJ tracts with Low TAI levels. The difference between the mean crash rates of EJ and non-EJ tracts is substantially greater for tracts with Low–Medium and Low levels of TAI compared to tracts with High and High–Medium TAI levels.
-
-Further, the percent of both non-EJ and EJ tracts with zero crashes increases with decrease in their TAI level. The percent of non-EJ tracts that have zero pedestrian crashes increases from 15% of all non-EJ tracts that have High TAI levels, to about 26% for tracts with High–Medium TAI levels, to approximately 42% for tracts with Low–Medium TAI levels and leveling off at about 40% of non-EJ tracts with Low levels of TAI with zero crashes. The percent of EJ tracts (on the right hand side of the top panel) that have zero pedestrian crashes also share a similar relationship to TAI levels; the percent of EJ tracts with High TAI levels that have zero recorded pedestrian crashes is close to 15% and increases to about 24% for EJ tracts with Low TAI levels. The percent of non-EJ tracts with low levels of pedestrian crashes (1–3 crashes) remains constant (at about 32–45% of all non-EJ tracts) for each TAI level; the same trend can be noticed for EJ tracts (between 32 and 42% of EJ census tracts have 1–3 crashes, irrespective of TAI levels). However, the percent of both EJ and non-EJ tracts with High or Very High crash rates (over 9) occur only where TAI levels are High or High–Medium. For each TAI level, the percent of EJ tracts with Low crashes is lower than in non-EJ tracts while the percent of EJ tracts with High–Medium crashes is greater than in non-EJ tracts.
-
-[Table 3](#page-5-0) also shows the effect sizes of the environmental and behavioral factors, as measured by Cohen's d index ([Cohen, 1992\).](#page-10-0) Based on this measure, the following environmental factors – TAI, SUM LENGTH, POP00 SQMI, PERCRIME and PED – have large differences between pedestrian crashes in EJ and non-EJ tracts. Among behavioral factors, MEDHHINC99 and PERNOCAR have large differences between EJ and non-EJ areas.
-
-The table also shows that the environmental measures TAI, PER-CRIME and PED with large effect sizes are positively correlated with PED CRASH. Finally, MEDHHINC99 is negatively correlated, whereas PERNOCAR is positively correlated with PED CRASH.
-
-The relationship of the pedestrian accessibility index (PED) to pedestrian crashes is similar to that of TAIto crashes and can be seen in the Fig. 3. The mean pedestrian crash rates for both EJ and non-EJ tracts decrease with decrease in PED levels. In the case of non-EJ tracts with High PED levels (on the left hand side of the bottom panel), the mean crash rate is 3.60, decreasing to 1.21 in the case of tracts with Low PED levels. The mean crash rates for each PED condition is higher for EJ tracts compared to non-EJ tracts; but in the case of EJ tracts too, the mean pedestrian crash rate decreases from 5.13 in the case of tracts with HIGH PED levels to a mean of 1.84 in the case of EJ tracts with Low PED levels. Overall, in both EJ and non-EJ tracts, there is an increase in the percent of census tracts with zero recorded crashes as PED levels decrease. However, in contrast to the case of TAI, the percent of both EJ and non-EJ tracts with High or Very High crash rates (over 9) mostly occur only where PED levels are High, with just a minimal percentage of tracts in the High–Medium and Low PED categories, in which such crash rates were experienced.
-
-In summary, the mean crash rate of EJ tracts is estimated to be higher than in non-EJ tracts by 1.32, however, large differences do not exist between the two types of areas in the proportion of injuries of different types and in the proportion of fatalities. The mean crash rate and the percent of both non-EJ and EJ tracts with zero pedestrian crashes increase with decrease in their TAI and PED levels, and are highest in cases of tracts with High levels of TAI and PED. Further, the mean crash rates for each TAI and PED condition is higher for EJ tracts compared to non-EJ tracts. For each TAI and PED level, the percent of EJ tracts with Low crashes is lower <span id="page-7-0"></span>than in comparable non-EJ tracts while the percent of EJ tracts with High–Medium crashes is greater than in non-EJ tracts.
-
-#### **5. Statistical models of pedestrian crash counts**
-
-The basic form of the statistical models considered is PED CRASHj = f(EJ TRACT, environmental factors, behavioral factors)<sup>i</sup> where PED CRASHj is the count of motor-vehicle–pedestrian crashes in the jth tract and EJ TRACTj is a binary variable taking the value of 1 if the jth tract is considered to be an EJ tract based on the rules given in Section [3.1](#page-2-0) and the environmental and behavioral factors are as described in Section [3.2.](#page-2-0)
-
-The crash data considered in this paper poses several methodological challenges including (i) the possibility of variations in the estimated effects of models arising from the choice of census tracts as the spatial unit, (ii) the presence of spatial autocorrelation in safety data, (iii) presence of overdispersion and heterogeneity and (iv) underreporting of crashes. Several different models were estimated in an attempt to address these different aspects of the data. The final results presented in Table 4 are from a Poisson regression with heterogeneity and a Poisson regression with heterogeneity which accounts for exogenous underreporting that are most relevant in explaining the distribution of PED CRASH with census tract-level environmental and behavioral factors.
-
-Variations in the estimated model effects can arise from the choice of census tracts as the areal unit rather than zip code or some other spatial unit; this is referred to as the modifiable areal unit problem (MAUP) ([Openshaw, 1979; Holt et al., 1996; Anselin et al.,](#page-10-0) [2000\).](#page-10-0) Aside from the reasons given in Section [3.1, o](#page-2-0)ur preference behind using census tracts in this analysis is that most EJ analysis have used this unit ([Mid-America Regional Council, 2007; US](#page-10-0) [EPA, 2009\)](#page-10-0) and our results extend safety research into the emerging area of environmental justice analysis. The extent to which the model parameters are sensitive to the choice of this analysis is left for future research.
-
-The presence of spatial autocorrelation can violate the independence assumption made by many statistical models [\(LeSage and](#page-10-0) [Pace, 2004\).](#page-10-0) A Local Indicators of Spatial Association (LISA) significance map of PED CRASH locations (as shown in Fig. 4) provides an indication of the existence of clusters in the spatial arrangement of pedestrian crashes. The similarity of the maps after both 1 and 199 permutations indicates that the sensitivity is fairly low, and supports the existence of spatial clusters. A visual review of the residual versus predicted PED CRASH values from the OLS model also supports the presence of spatial autocorrelation, indicating that crashes tend to be overpredicted in some suburban locations, and underpredicted in the Chicago downtown business district (the "Loop") and in southern areas of the city of Chicago. The calculated Moran's I value of 0.3984 was also significant, indicating that
-
-**Table 4** Alternative models, parameter estimates and measures of fit.
-
-| Variable              | Model I                                          |                                                             |  |  |
-|-----------------------|--------------------------------------------------|-------------------------------------------------------------|--|--|
-|                       | Poisson with<br>heterogeneity<br>Marginal effect | Poisson with exogenous<br>underreporting<br>Marginal effect |  |  |
-| Intercept             | −4.21***                                         | −2.13***                                                    |  |  |
-| EJ TRACT (1 = "Yes")  | 0.65***                                          | 0.33***                                                     |  |  |
-| Environmental factors |                                                  |                                                             |  |  |
-| TAI2                  | 2.01***                                          | 1.01***                                                     |  |  |
-| PED LOW               | 1.61***                                          | 0.59***                                                     |  |  |
-| SUM AADT2             | 0.48e−06***                                      | 0.24e−06***                                                 |  |  |
-| SUM LENGTH2           | −0.28e−03***                                     | −0.14e−03***                                                |  |  |
-| NO SCHOOLS            | 0.19**                                           | 0.09**                                                      |  |  |
-| POP SQMILE            | 0.91e−05                                         | 0.46e−03                                                    |  |  |
-| PERCRIME              | 0.24**                                           | 0.12**                                                      |  |  |
-| PED                   | 0.09***                                          | 0.05***                                                     |  |  |
-| Behavioral factors    |                                                  |                                                             |  |  |
-| WLKTOWRK              | 0.0008***                                        | 0.0009                                                      |  |  |
-| MEDHHINC99            | −0.22e−06                                        | −0.11e−05                                                   |  |  |
-| PERNOCAR              | 2.60**                                           | 1.31**                                                      |  |  |
-| PER COMM              | 1.37                                             | 0.69                                                        |  |  |
-| PERCHILDREN           | −2.09                                            | −1.05                                                       |  |  |
-| PERLOWENGLISH         | 0.21                                             | −0.10                                                       |  |  |
-|                       |                                                  | Probit reporting equation                                   |  |  |
-| Intercept             |                                                  | 0.54e−07                                                    |  |  |
-| COUNTY (1 = "Cook")   |                                                  | 0.018**                                                     |  |  |
-| R2                    | 0.58#                                            | 0.61#                                                       |  |  |
-| Log–likelihood        | −1763.25                                         | −1511.36                                                    |  |  |
-| 2/df                  | 136.8                                            | 93.76                                                       |  |  |
-| Vuong's statistic     | –                                                | −60.75                                                      |  |  |
-|                       | 0.13 (p < 0.0001)                                | 0.18 (p < 0.517)                                            |  |  |
-|                       | –                                                | 0.00                                                        |  |  |
-
-<sup>\*\*</sup> Significant at 0.05. \*\*\* Significant at 0.01.
-
-there is spatial autocorrelation in the residuals. While accounting for such spatial autocorrelation is important for this problem and a spatial autoregressive model was presented in an earlier version of this paper [\(Cottrill and Thakuriah, 2008\),](#page-10-0) that model was limited in that the distribution of PED CRASH was assumed to be normal and further work is necessary to develop a spatial model with the appropriate distributional assumption for PED CRASH. Since there is, simultaneously, evidence of spatial autocorrelation, underreporting and heterogeneity in the count data under consideration in this paper, in the most ideal case, our requirement is to use a count data model that explicitly takes into account the spatial structure, underreporting and evidence of heterogeneity. In the model con-
-
-![](_page_7_Figure_13.jpeg)
-
-**Fig. 4.** LISA cluster map: pedestrian crashes.
-
-<sup>#</sup> Pearson's R<sup>2</sup> given as R<sup>2</sup> <sup>p</sup> = 1 − -J j=1 yj ln(yj/j)/ J j=1 yj ln(yj/y¯) 
-
-sidered here, we account for three out of the four requirements – and leave the explicit introduction of spatial dependence in count models of PED CRASH with heterogeneity and underreporting for future research.
-
-[Table 4](#page-7-0) reports the marginal effects of a Poisson regression that accounts for heterogeneity and a Poisson regression that accounts for both heterogeneity and potential underreporting of crashes. Associated statistics and measures of fit are also presented. Diagnosis of a preliminary OLS model showed there was no evidence of multicollinearity, which may be due to combining many different individual factors into the composite indices, in contrast to introducing them as separate effects. Partial residual plots suggested that several variables need transformation; in particular, TAI, and SUM AADT were squared, leading to improvement in model fit; SUM LENGTH, NO SCHOOLS and RANKTOT were also candidates for transformation, but we chose not to, as the parameter estimates and model fit did not substantially change with the transformations.
-
-Poisson regression is typically used to model crash counts. However, the presence of overdispersion in the data led us to estimate a Negative Binomial model. In the incremental model development process, we estimated Poisson and Negative Binomial models with heterogeneity, of which, on the basis of three measures: Pearson's R2, log-likelihood and 2/df-statistic, we keep the Poisson Model with heterogeneity in [Table 4](#page-7-0) (under Model 1) for further consideration. The Poisson model with heterogeneity has a mean of exp(xˇ + ε) where ε∼N(0, -2) where ˇ's are parameters to be estimated, x's are exploratory variables explaining PED CRASHES and ε's are unobserved heterogeneity that are normally distributed across the individual data with mean 0 and standard deviation -The estimate of is 0.13 and is statistically significant at the .01 level. However, an additional issue, that of underreporting, was taken into account in the final model development step, which leads to Model II in [Table 4.](#page-7-0)
-
-Underreporting in low-income neighborhoods has been noted to be important in several studies. For example, [Sciortino et](#page-10-0) [al. \(2005\)](#page-10-0) found that the California Statewide Integrated Traffic Reporting System (SWITRS) under-reported San Francisco pedestrian injuries by 21% (using San Francisco General Hospital medical records as a gold standard), with African-Americans and males less likely to have a SWITRS-reported injury; [Stutts et al. \(1998\)](#page-10-0) reported on the basis of specially-collected bicycle and pedestrian injury data from emergency hospitals that overall, 70% of the reported bicycle injury events and 64% of the reported pedestrian injury events did not involve a motor-vehicle.
-
-From a modeling perspective, count data models that take heterogeneity and underreporting into account have been given by [Winkelmann and Zimmermann \(1993\)](#page-10-0) and applied to safety data by [Kumara and Chin \(2005\). S](#page-10-0)uchmodels acknowledge that only a subset of the actual number of crashes that occurred are reported and that the count of reported accidents is smaller than the total number of events. Let y\* be the unobserved number of accidents and let y denote the recorded number of accidents. Let y\*|ε have a Poisson distribution with mean E(y∗|x, ε) = exp(x ˇ + ε), where, as before, ˇ's are parameters to be estimated, x's are exploratory variables explaining PED CRASHES and ε's are unobserved heterogeneity that are normally distributed across the individual data with mean 0 and standard deviation -. Further, let ci denote a binary variable which is 1 if the net utility from reporting the ith crash is positive; ci is determined by a reporting process such that c<sup>∗</sup> <sup>i</sup> = zi + ui and ci = 1 iff c<sup>∗</sup> <sup>i</sup> > 0. Hence the number of reported crashes in census tract <sup>j</sup> is yj <sup>=</sup> <sup>y</sup><sup>∗</sup> <sup>i</sup>=<sup>1</sup>ci.
-
-The vector **z** represents exploratory variables that determine the reporting mechanism; in the current case, the feasibility of several variables was examined, based on the literature. Ultimately, COUNTY, a binary variable, which is 1 in the case of Cook County, the most populous county, which includes the City of Chicago and includes over 88% of the EJ tracts, was included in **z**. The vector are parameters to be estimated and with a normality assumption for the ui's, we have a probit model for the selection mechanism. The Poisson probabilities in this model (Poisson with endogenous reporting), with the modified mean ¯ , are given by:
-
-$$\begin{split} P(Y_j = y_j | \varepsilon_j) &= \frac{\exp^{-\tilde{\lambda}_j} \tilde{\lambda}_j^{y_j}}{y_j!} \\ \text{where} \quad \tilde{\lambda}_j &= \exp(x_j \beta + \varepsilon_j) \times \varPhi\left(\frac{z_j \gamma + (\rho | \sigma) \varepsilon_j}{\sqrt{1 - \rho^2}}\right) \end{split}$$
-
-where ˚ is the CDF of the standard normal distribution, is the correlation between ε and u, i.e. is the correlation between the variables relating to crash occurrence and reporting. If = 0, i.e., if reporting is independent from crash occurrence, we have a case of a Poisson model with exogenous underreporting with the conditional mean function:
-
-$$\bar{\lambda}_j = \exp(x_j \beta + \varepsilon_j) \times \Phi(z_j \gamma)$$
-
-The Poisson model with endogenous underreporting led to an estimate of which is small and not significant at any reasonable level of significance. On this basis, we estimated a Poisson model with exogenous underreporting, the results of which are shown as Model II in [Table 4.](#page-7-0) In deciding whether to favor Model II or III, we use Vuong's test, along with three other measures of fit, including Pearson's R2, log-likelihood and 2/df. The Vuong's test statistic (V) is distributed as a standard normal variate and hence its value is comparable to the critical value of the standard normal distribution. Consequently, V > 1.96 favors the Poisson underreporting models, whereas V ≤ 1.96 favors the parent Poisson models. In the case of Model II, V = −60.75, which leads to the conclusion that Model II should be favored. Nevertheless, on the basis of the other measures: we explore the results of Model III further, since it leads to useful insights regarding underreporting and otherwise is a better fit compared to Model I (on the basis of the log-likelihood value at convergence, the Pearson's R<sup>2</sup> and the2/df value).
-
-The reporting equation estimates that crashes in non-Cook County are likely to be under-reported by 1.8% points more than in Cook county. Since most EJ tracts are in Cook county, this result is contradictory with previous literature, which reports pedestrian crashes are more likely to be under-reported by minority and lowincome populations. We believe that this contradictory finding has to do with differences in the nature of the crashes and the severity of the crashes in Cook versus the suburban counties (here referred to as "collar counties"). Whereas crashes per capita are higher in Cook County, compared to collar counties, minor crashes (those without fatalities, incapacitating injuries or with other notable injuries) as a percent of all crashes is higher in collar counties compared to Cook. Minor crashes as a percent of all crashes are higher in EJ tracts than non-EJ tracts in collar counties. This is in contrast to Cook County, where this quantity is lower for EJ tracts than for non-EJ tracts. It is possible that minor crashes that are reported in EJ tracts in collar counties are only a subset of the total number of such crashes occurring there.
-
-It may be recalled that the difference between mean PED CRASHES in EJ tracts and mean PED CRASHES in non-EJ tracts is 1.32. Controlling for environmental and behavioral factors, the OLS model estimates that this difference in average PED CRASH rates drops to 0.65. Across almost all the different models tested, the coefficient for EJ TRACT remains remarkably constant – between 0.62 to 0.67 – and statistically significant, indicating that holding the environmental and behavioral factors constant reduces the difference in PED CRASH between EJ and non-EJ tracts by about half.
-
-<span id="page-9-0"></span>With Model II, however, which corrects for underreporting, the estimate of the differences drops to 0.33. The discussion above, which attempts to relate differential reporting in EJ tracts between Cook and collar county, partly answers this reduction – once underreporting in non-Cook county tracts are accounted for, the difference in PED CRASH between EJ and non-EJ tracts reduces further.
-
-The coefficients for TAI merit further discussion. The TAI estimates are significant in the three models shown in [Table 4.](#page-7-0) In keeping with the Trellis plots in [Figs. 2 and 3](#page-5-0), as the value of these indicators increase, PED CRASH is estimated to increase. This finding implies that TAI is a potentially useful proxy measure to direct measurements on pedestrian exposure by tracts. PERCRIME is also an important predictor of PED CRASH; pedestrian crashes increase with recorded crime in the census tract. The exact mechanism of why this is so merits further research. It would also be interesting to find out whether some of the pedestrians involved in crashes in areas which are unsafe from a personal security perspective were in the process of avoiding crime being committed, or became careless with respect to the traffic environment, in attempts to deal with perceived unsafe situation that theymay have suddenly faced. This would require researchers to access detailed crime data from police department. The coefficient for SUM AADT<sup>2</sup> is as expected; however, the coefficient for SUM LENGTH<sup>2</sup> is negative, possibly because of the presence of freeway links in some of the census tracts, where there little possibility of walking. Whereas the coefficient of NO SCHOOLS is positive, as expected, that of PERCHIL-DREN is negative, indicating that child pedestrians may be more vulnerable to crashes in areas that are not within the immediate vicinity of their residential locations. WLKTOWRK is not significant at any level of significance. It is also likely that the effect of the density variable, POP00 SQMI, was explained by the variable EJ TRACT.
-
-#### **6. Summary and conclusions**
-
-This paper evaluates the incidence of pedestrian–vehicle crashes in high low-income and minority population (EJ) census tracts as compared to pedestrian–vehicle crashes in non-EJ tracts in the Chicago metropolitan area. The summary statistics presented above, primarily the overall incidence, are generally supportive of the finding that pedestrian–vehicle crashes are more prevalent in EJ census tracts, as indicated in previous research. By taking a two-pronged approach to the data set and addressing the issue from the standpoints of environmental characteristics and behavioral factors, these general findings may be expanded upon and more targeted methods of addressing this issue may be developed. While earlier studies have indicated an increased number of pedestrian–vehicle crashes in areas of high minority or low-income populations in comparison to those areas that do not meet these criteria, less attention has been paid to ascertaining contributing factors to this disparity. This paper has attempted to test both environmental and behavioral factors for their contributions to the perceived heightened incidence of these crashes in order to provide better guidance for potential needs for interventions and further study.
-
-In general, the summary statistics indicate that the pattern of pedestrian–vehicle crashes in EJ and non-EJ tracts as evaluated in relation to environmental conditions are broadly similar, with comparable patterns obtained when evaluating these crashes against the transit availability index (TAI) and the pedestrian accessibility index (PED). These findings indicate that the general geographic characteristics of the pedestrian environment have roughly similar impacts on the likelihood of pedestrian–vehicle crashes in terms of rate, if not incidence. The relationship between TAI and pedestrian crashes may be especially significant, as it would suggest that expansions of and improvements to pedestrian-accessed transit stops should be planned and implemented in conjunction with pedestrian safety improvements in order to reduce the risk of incidents. Additionally, the relationship between PED and crashes may indicate the need to more thoroughly address the safety impacts of pedestrian improvements, particularly in the relationship between potential pedestrian and vehicle interactions, in order to more safely encourage walking. These findings are especially relevant in the domain of EJ regulations, as the environmental analysis indicates that EJ tracts tend to have higher levels of both TAI and PED, indicating the potential for more crashes.
-
-The statistical analysis presented reveals a number of interesting relationships. First, the connection between crash underreporting and injury category within the collar counties surrounding the primary urban county in the metropolitan area suggests that there may be differences in these crashes unaccounted for in the model. Potential variables that may impact this underreporting and should be explored further include distance to police or hospital services and general insurance rates and coverage. It is possible, for example, that minor crashes in collar counties are less likely to impact travel flow or be observed by law enforcement, and so are not reported at the time of occurrence. Additionally, if either the driver or pedestrian is uninsured, it may be to their advantage to take care of the crash impacts outside of legal boundaries if the injuries are minor, or if no injury occurred. These and other potentially related variables should be explored further.
-
-A second relationship of note is related to proxies for exposure rates. As noted above, high TAI and PED scores were related to an increase in crash incidence, which may indicate that they reveal areas of increased pedestrianism, thus increased exposure. Two other variables that may also be related to exposure rates are RANKTOTAL and PERCRIME, increases of which were also related to an increased number of crashes. For RANKTOTAL, the relationship is fairly clear, as this variable represents a ranking of census tracts by those factors that are typically associated with high pedestrian rates. PERCRIME, on the other hand, presents a less clear relationship, as noted in Section [5. F](#page-7-0)urther analysis of pedestrian and driver behavior in areas of high crime levels should be conducted to more clearly evaluate how increased levels of crime impact pedestrian–vehicle crashes. Finally, the general spatial analysis presented here reveals the presence of spatial autocorrelation, indicating that a more thorough spatial analysis of the data should be conducted. Results from the initial spatial error model improved overall model performance, indicating that controlling spatial dependence would likely result in better overall model fit.
-
-The implications of the findings of the analysis are fairly wideranging. As noted, further examination of a number of relationships is needed; however, it is evident that a number of factors related to pedestrian–vehicle crashes in EJ areas should be addressed. First, pedestrian and transit improvements and expansions should better incorporate safety considerations at locations of potential pedestrian–vehicle interaction. The raised incidence of pedestrian crashes in areas that rank highly for pedestrian amenities indicates that further analysis of safety requirements is needed, beyond the current practices. Secondly, further analysis of pedestrian and driver behavior in areas of high crime rates should be conducted in order to encourage more safe behavior in these areas. Finally, environmental justice analyses should include a review of how pedestrian–vehicle crash rates may be impacted by projects or programs developed for these areas.
-
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Zotero/001_artiklid/Road Safety Indicators/coutonotemodelingroadaccident2011.md

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-category: literaturenote
-citekey: coutonotemodelingroadaccident2011
-title: "A note on modeling road accident frequency: A flexible elasticity model"
-authors: "Couto, António; Ferreira, Sara"
-year: 2011
-date: 2011-00-00 2011
-doi: 10.1016/j.aap.2011.05.033
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2011.05.033"
-zotero_key: F4KGU2P8
-zotero_storage: DHB3GHBT
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Couto, António"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/00014575)
-
-## Accident Analysis and Prevention
-
-j ou rnal home page: [www.elsevier.com/locate/aap](http://www.elsevier.com/locate/aap)
-
-![](_page_0_Picture_5.jpeg)
-
-# A note on modeling road accident frequency: A flexible elasticity model
-
-## António Couto∗, Sara Ferreira
-
-Civil Engineering Department, School of Engineering, Porto University, Rua Dr. Roberto Frias 4200-465 Porto, Portugal
-
-#### a r t i c l e i n f o
-
-Article history: Received 24 January 2011 Received in revised form 24 May 2011 Accepted 25 May 2011
-
-Keywords: Translog function Negative binomial Road accident models Regression models
-
-### a b s t r a c t
-
-Count data models and their variants have been widely applied in accident modeling. The traditional log-linear function is used to represent the relationship between explanatory variables and the dependent variable (accident frequency). However, this function assumes constant elasticity for the estimation parameters, which is a limitation in the analysis of the effects of explanatory variables on accident risk. Although interaction effects between explanatory variables have been studied in the road safety context (where they are normally assessed by logistic regression), no one has yet examined the possibility of using a flexible function form allowing non-constant elasticity values. This paper seeks to explore the use of the translog function usually used in the economics context to allow the elasticity to vary with the values of other explanatory variables. Therefore, the objective of this study was to evaluate the application of the translog function to accident modeling and to compare the results with those of the traditional log-linear function negative binomial (NB) model. The results show that, in terms of goodness-of-fit statistics and residual analysis, the NB model with the translog function performs better than the traditional NB model. Additional evaluations in terms of predictive performance, hotspot identification and uncertainty associated with the estimated values were taken into account. Although this study is exploratory in nature, it suggests that the translog function has considerable potential for modeling accident observations. It is hoped that this novel accident modeling methodology will open the door to the reliable interpretation and evaluation of the influence of explanatory variables on accident frequency.
-
-© 2011 Elsevier Ltd. All rights reserved.
-
-### **1. Introduction**
-
-In recent decades, there has been considerable research conducted on the development of accident prediction models, usually using generalized linear models (GLMs) to describe the influence of explanatory variables on road accidents ([Fridstrom](#page-7-0) et [al.,](#page-7-0) [1995;](#page-7-0) [Poch](#page-7-0) [and](#page-7-0) [Mannering,](#page-7-0) [1996;](#page-7-0) [Sawalha](#page-7-0) [and](#page-7-0) [Sayed,](#page-7-0) [2001;](#page-7-0) [Greibe,](#page-7-0) [2003;](#page-7-0) [Lord](#page-7-0) [and](#page-7-0) [Persaud,](#page-7-0) [2004\).](#page-7-0) To better understand the factors determining accidentfrequencies, additionaltechniques have been applied recently to the modeling of accidents, such as randomparameters count models. These particular models account for the presence of heterogeneity across observations by allowing some or all parameters to vary according to a random distribution [\(Anastasopoulos](#page-7-0) [and](#page-7-0) [Mannering,](#page-7-0) [2009\).](#page-7-0) Other studies using a modeling framework, such as zero-inflated negative binomial models [\(Carson](#page-7-0) [and](#page-7-0) [Mannering,](#page-7-0) [2001;](#page-7-0) [Lee](#page-7-0) [and](#page-7-0) [Mannering,](#page-7-0) [2002;](#page-7-0) [Kumara](#page-7-0) [and](#page-7-0) [Chin,](#page-7-0) [2003\),](#page-7-0) Conway–Maxwell–Poisson generalized linear models [\(Lord](#page-7-0) et [al.,](#page-7-0) [2008\)](#page-7-0) and generalized negative binomial models [\(Mitra](#page-7-0) [and](#page-7-0) [Washington,](#page-7-0) [2007;](#page-7-0) [Lord](#page-7-0) [and](#page-7-0) [Park,](#page-7-0) [2008\),](#page-7-0) have focused on the performance of the models.
-
-The GLM usually used to model accidents applies a logarithmic transformation to linearize the multiplicative equation and can be referred to as a log-linear function model. As is well known, these log-linear function models assume constant elasticity for the estimation parameters, and this can be a limitation to represent the relationships between explanatory variables and accident frequency. Moreover, why has traffic flow elasticity been assumed to be the same for both long and short segments? Additionally, why must the effect on accident risk of a percentage increase in traffic flow be the same in contexts of both high and low traffic flows?
-
-Infact, studies ([Lord,](#page-7-0) [2000;](#page-7-0) [LordandPark,](#page-7-0) [2008;Hadayeghi](#page-7-0) et [al.,](#page-7-0) [2010\)](#page-7-0) that have applied the CURE method proposed by [Hauer](#page-7-0) [and](#page-7-0) [Bamfo](#page-7-0) [\(1997\)t](#page-7-0)o analyze the cumulative residuals for each explanatory variable have revealed that for high values of traffic flow the model did not perform well (i.e., the cumulative residual curves were larger than the +2.0 boundary and/or did not end near the zero-residual line). Moreover, [Lord](#page-7-0) [\(2000\)](#page-7-0) suggested that separate models could be developed when the cumulative residuals indicate a change from a possible underestimation of accidents to an overestimation of accidents. This solution assumed different elasticities for the traffic volume. Nonetheless, if the model has more than one variable, then this is not an achievable solution.
-
-Although interaction effects between some explanatory variables have been used in many road safety studies ([Yannis](#page-7-0) et [al.,](#page-7-0) [2005;](#page-7-0) [Hill](#page-7-0) [and](#page-7-0) [Boyle,](#page-7-0) [2006;](#page-7-0) [Liu,](#page-7-0) [2007;](#page-7-0) [Sze](#page-7-0) [and](#page-7-0) [Wong,](#page-7-0) [2007;](#page-7-0) [Rhodes](#page-7-0)
-
-<sup>∗</sup> Corresponding author. Tel.: +351 225 082 110. E-mail addresses: [fcouto@fe.up.pt](mailto:fcouto@fe.up.pt) (A. Couto), [sara@fe.up.pt](mailto:sara@fe.up.pt) (S. Ferreira).
-
-[and](#page-7-0) [Pivik,](#page-7-0) [2011\),](#page-7-0) the purpose was only to analyze the difference in risk for two or more populations as they move from one condition to another. For instance, in the case of segment length, it is understandable that this factor can influence the impact of traffic flow on accident frequency. Indeed, traffic flow can have a different impact on accident frequency depending on whether the segment is longer or shorter. This interaction can be modeled by a flexible functional form instead of the constant elasticity model usually used to represent the effects of explanatory variables. Thus, even though the interaction effects of explanatory variables were not the main focus of this study, the flexible function form takes them into account. Because the main objective of this study is to assess the possibility that explanatory variables have non-constant elasticity, it is expected that the assumption adopted in this study will outperform the mathematical limitations of some popular function forms because the flexible function can allow for the free variation of elasticity and thus provides a well-structured function and better fitting performance.
-
-Flexible function forms are frequently used in econometrics ([Caves](#page-7-0) et [al.,](#page-7-0) [1980;](#page-7-0) [Diewert](#page-7-0) [and](#page-7-0) [Wales,](#page-7-0) [1987;](#page-7-0) [Oum](#page-7-0) [and](#page-7-0) [Tretheway,](#page-7-0) [1989;](#page-7-0) [Couto](#page-7-0) [and](#page-7-0) [Graham,](#page-7-0) [2009\)](#page-7-0) because they allow analysts to model second-order effects, such as elasticities of substitution, which are functions of the second derivatives of production, cost or utility functions ([Greene,](#page-7-0) [2008\).](#page-7-0) The most popular flexible functional form is the translog function originated in [Christensen](#page-7-0) et [al.](#page-7-0) [\(1973\).](#page-7-0) This function has been extensively applied in transportation research, especially in logistics and policy studies based on transportation costs ([Karlaftis](#page-7-0) [and](#page-7-0) [Mccarthy,](#page-7-0) [2002;](#page-7-0) [Link,](#page-7-0) [2006;](#page-7-0) [Satar](#page-7-0) [and](#page-7-0) [Peoples,](#page-7-0) [2010\).](#page-7-0)
-
-The translog function form can generalize negative binomial (NB) and Poisson models through the estimation of quadratic "cross variables," thereby allowing the elasticity to vary with the values of other explanatory variables.
-
-By using a flexible function, this paper provides a new interpretation of the relationship between explanatory variables and the dependent variable, and it tests the potential of this function form by applying it to accident modeling. To accomplish this objective, an NB model with a traditional log-linear functional form and an NB model with a translog function were developed and compared using a segment dataset. This dataset contains accident data collected at segments in Porto, Portugal, over a 5-year period. The models developed in this study consider two of the most widely used explanatory variables besides traffic flow (often referred to as general annual average daily traffic or AADT): segment length and the number of minor intersections per segment length. These explanatory variables are usually used to homogenize the segment and are easy to determine at all road safety study stages. The study will show that the model with a translog function performs better than the traditional log-linear function, producing statistically significant "cross variable" parameters.
-
-The next section presents a brief background related to the traditional log-linear functional form and the translog function. The third section describes the methodology used for estimating and comparing the NB model with and withoutthe second-order terms. The data and estimation results are summarized in the fourth and fifth sections, respectively. The last section presents a summary and conclusions of the analysis performed in this work.
-
-#### **2. Background**
-
-Count-data modeling techniques are commonly used for accident frequency analysis because the number of accidents on roadway segments per unit oftime is a non-negative integer. Count data are generally modeled with a Poisson regression. One requirement of the Poisson distribution is that the mean of the count process equals its variance.When the variance is significantly larger than the mean, the data are said to be overdispersed. Accident data have been shown to exhibit over-dispersion [\(Sawalha](#page-7-0) [and](#page-7-0) [Sayed,](#page-7-0) [2001;](#page-7-0) [Lord](#page-7-0) et [al.,](#page-7-0) [2005;](#page-7-0) [Caliendo](#page-7-0) et [al.,](#page-7-0) [2007\).](#page-7-0) In many cases, overdispersion count data are successfully modeled using an NB model ([Washington](#page-7-0) et [al.,](#page-7-0) [2003\).](#page-7-0)
-
-The probability density function of the NB distribution can be defined as:
-
-$$P(Y = y_i | \mu_i, \alpha) = \frac{\Gamma(y_i + (1/\alpha))}{\Gamma(1/\alpha) \cdot y_i!} \cdot \left(\frac{1/\alpha}{(1/\alpha) + \mu_i}\right)^{1/\alpha} \times \left(\frac{\mu_i}{(1/\alpha) + \mu_i}\right)^{y_i}$$
-(1)
-
-where ˛ is the dispersion parameter (when ˛<sup>=</sup> 1/ is equal to zero that is →∞, the NB distribution reverts back to the Poisson distribution) and (.) is <sup>a</sup> gamma function. Thus, the mean <sup>i</sup> <sup>=</sup> <sup>E</sup>{Y} can be smaller than the variance Var{Y} <sup>=</sup> <sup>i</sup> <sup>+</sup>˛<sup>i</sup> 2.
-
-In addition, the mean is linked to a vector of explanatory variables by a log-linear function:
-
-$$\mu_i = \exp(\beta X_i)$$
- or, equivalently  $\ln(\mu_i) = \beta X_i$ 
-
-where Xi is <sup>a</sup> vector of explanatory variables and <sup>ˇ</sup> is <sup>a</sup> vector of estimable parameters. In this formulation, the expected number of events per period is given by <sup>E</sup>[yi] <sup>=</sup> <sup>i</sup> <sup>=</sup> exp(ˇXi). The estimated parameters ˇ are used to make inferences about the explanatory variables thought to impact the accident process. To analyze the parameter estimation results, elasticities are determined to provide an estimate of the impact of a variable on the expected frequency, and they can be interpreted as the effect of a 10% change in the variable on the expected frequency <sup>i</sup> ([Washington](#page-7-0) et [al.,](#page-7-0) [2003\).](#page-7-0) The elasticity of frequency <sup>i</sup> is defined as
-
-$$E_{x_{ik}}^{\mu_i} = \frac{\partial \mu_i}{\mu_i} \times \frac{x_{ik}}{\partial x_{ik}} = \beta_k x_{ik} \tag{2}$$
-
-where E represents the elasticity, xik is the value of the kth explanatory variable for observation <sup>i</sup>, <sup>ˇ</sup><sup>k</sup> is the estimated parameter for the <sup>k</sup>th explanatory variable and <sup>i</sup> is the expected frequency for observation i.
-
-However, the traditional NB model (with a log-linear function) assumes constant elasticity. This limitation can bias the interpretation of the relationship between the explanatory variables and the expected accident frequency. Hence, a flexible function form has been considered to model second-order effects.
-
-The translog function originated in [Christensen](#page-7-0) et [al.](#page-7-0) [\(1973\)](#page-7-0) has become enormously popular in the economics context probably because itis easier to estimate and interpret as itis a quadratic function with all arguments in natural logarithms ([Oum](#page-7-0) [and](#page-7-0) [Waters,](#page-7-0) [1996\).](#page-7-0) As described in [Greene](#page-7-0) [\(2008\),](#page-7-0) one way to derive it is first to write <sup>y</sup> <sup>=</sup> <sup>g</sup>(x1, . . ., xk) of an unknown function form and then, ln <sup>y</sup> <sup>=</sup> ln <sup>g</sup>(x1, . . ., xk) <sup>=</sup> <sup>f</sup>(x1, . . ., xk). Hence, by the trivial transformation xk = exp(ln xk), one interprets the function as a function of the logarithms of <sup>x</sup>'s. Thus, ln <sup>y</sup> <sup>=</sup> <sup>f</sup>(ln <sup>x</sup>1, . . ., ln xk). Now, expand this function in a second-order Taylor series around the point x = [1,1, . . ., 1] so that at the expansion point, the log of each variable is <sup>a</sup> convenient zero, then:
-
-$$\ln y = f(0) + \sum_{k=1}^{K} [\partial f(.)/\partial \ln x_k]_{\ln x = 0} \ln x_k$$
-
-$$+ \frac{1}{2} \sum_{k=1}^{K} \sum_{l=1}^{K} [\partial^2 f(.)/\partial \ln x_k \partial \ln x_l]_{\ln x = 0} \ln x_k \ln x_l$$
-(3)
-
-Because the function and its derivates evaluated at the fixed value 0 are constants, one can interpret them as the parameters and they can be expressed as 1:
-
-$$\ln y = \beta_0 + \sum_{k=1}^K \beta_k \ln x_k + \frac{1}{2} \sum_{k=1}^K \sum_{l=1}^K \gamma_{kl} \ln x_k \ln x_l$$
- (4)
-
-It is interesting to note that if kl <sup>=</sup> 0, Eq. (4) becomes <sup>a</sup> function of the commonly used log-linear model. The second-order terms allow for possible cross-relationships among the variables and for possible non-linearities in the relationships [\(Oum](#page-7-0) [and](#page-7-0) [Waters,](#page-7-0) [1996\).](#page-7-0)
-
-The explanatory variable elasticity can be found by taking the differential of the function:
-
-$$\frac{\partial \ln y}{\partial \ln x_k} = \beta_k + \sum_{l=1}^K \gamma_{kl} \ln x_l \tag{5}$$
-
-From this, it is clear that, in general, the elasticity varies with the proportions of the explanatory variables and with their individual levels.
-
-The next section describes the methodology used to estimate and compare the NB model with a traditional log-linear function and the NB model with a translog function.
-
-#### **3. Methodology**
-
-Many different functional forms have been proposed to represent the relationship between accident frequency and explanatory variables. Two different types of functional form usually used by transportation safety analysts can be distinguished. One type is models that include several covariates that are related to the functional and geometrical characteristics of road networks [\(Poch](#page-7-0) [and](#page-7-0) [Mannering,](#page-7-0) [1996;](#page-7-0) [Sawalha](#page-7-0) [and](#page-7-0) [Sayed,](#page-7-0) [2001;](#page-7-0) [Greibe,](#page-7-0) [2003\).](#page-7-0) Such models can be used to analyze the factors affecting road safety, their influence and how they are related to the dependent variable. The other type of functional form (often referred to as AADT models) uses only a traffic flow variable. Authors [\(Lord](#page-7-0) et [al.,](#page-7-0) [2008\)](#page-7-0) have mentioned that these types of models are preferable to the former because they can be easily re-calibrated when they are applied in a jurisdiction other than the one in which it was developed.
-
-Because the purpose of the study is not to predict the number of accidents but rather to provide a methodological approach to gain new insights into the ways that explanatory variables influence accident frequency, a functional form was considered with more than the traffic flow variable. In addition to the traffic flow variable, explanatory variables usually used to characterize the segment and that have been reported by researchers to be significantly important in modeling accidents were included ([Fridstrom](#page-7-0) et [al.,](#page-7-0) [1995;](#page-7-0) [Mountain](#page-7-0) [and](#page-7-0) [Fawaz,](#page-7-0) [1996;](#page-7-0) [OCDE,](#page-7-0) [1997;](#page-7-0) [Lord,](#page-7-0) [2006\).](#page-7-0) Therefore, variables describing the number of minor intersections and segment lengths have been included in this segments model. These explanatory variables are easy to determine and consequently can be used at all road safety study stages.
-
-To select the proper relationship between the dependent variable and explanatory variables, a method proposed by [Hauer](#page-7-0) [and](#page-7-0) [Bamfo](#page-7-0) [\(1997\)](#page-7-0) was applied. Basically, the Integrate-Differentiate method consists of drawing an Empirical Integral Function for each explanatory variable and then comparing the shape with the pre-established graphs of well-known functions (power, gamma, exponential or polynomial functions). The function with the most similar shape is selected to represent the relationship between the dependent variable and the explanatory variable being investigated. By applying this method to the explanatory variables being used in this study, it was found that the relationships between accident frequency and both AADT and segment length could be described by the power function. In the case of the minor intersection density variable, the exponential function was found to be more suitable to describe the aforementioned relationship.
-
-A time trend variable has also been included in this study to reflect potential changes in the overall accident level over time. A number of studies have included a time trend variable that allows for changes in terms of risk over time [\(Fridstrom](#page-7-0) et [al.,](#page-7-0) [1995;](#page-7-0) [Mountain](#page-7-0) et [al.,](#page-7-0) [1998\).](#page-7-0) It has been previously demonstrated that this variable has a negative effect on the frequency of accidents, thus leading to overall improvement due to such factors as safety education, enforcement and vehicles.
-
-Thus, the NB model with a traditional log-linear function takes the form:
-
-$$\ln \mu_{it} = \beta_0 + \beta_1 \ln(F_{it}) + \beta_2 \ln(Leng_i) + \beta_3 IntDen_i + \ln(\gamma) TimeT_t$$
-(6)
-
-where it is the mean number of accidents in segment <sup>i</sup> at time period t, Fit is the annual average daily traffic in both directions (segment i; time period t), Lengi represents the length of the segment i, TimeTt is the time trend variable, IntDensi is the minor intersection density (segment <sup>i</sup>) and <sup>ˇ</sup><sup>k</sup> (<sup>k</sup> <sup>=</sup> 0–3) and are the estimated parameters.
-
-The translog functional form is expressed as:
-
-$$\ln \mu_{it} = \beta_0 + \beta_1 \ln(F_{it}) + \beta_2 \ln(Leng_i) + \beta_3 IntDen_i + \ln(\gamma) TimeT_t$$
-
-$$+ \beta_4 [\ln(F_{it})]^2 + \beta_5 [\ln(Leng_i)]^2 + \beta_6 (IntDen_i)^2 + \beta_7$$
-
-$$\times \ln(F_{it}) \ln(Leng_i) + \beta_8 \ln(F_{it}) IntDen_i$$
-
-$$+ \beta_9 \ln(Leng_i) IntDen_i$$
-(7)
-
-where it is the mean number of accidents per year, the explanatory variables are the same as in Eq. (6) and <sup>ˇ</sup><sup>k</sup> (<sup>k</sup> <sup>=</sup> 0–9) and are the estimated parameters. Note that the time trend variable does not take the form of a "cross variable," hence no interaction is expected between this variable and the other explanatory variables on accident frequency.
-
-To compare the two types of model applied in this study, several methods were used to estimate their goodness-of-fit (GOF). Four different GOF statisticaltests –Akaike's Information Criterion (AIC), Freeman–Tuckey <sup>R</sup><sup>2</sup> (R<sup>2</sup> FT), Elvik index (R<sup>2</sup> PE) and McFadden Pseudo R<sup>2</sup> as described in [Bozdogan](#page-7-0) [\(1987\),](#page-7-0) [Fridstrom](#page-7-0) et [al.](#page-7-0) [\(1995\)](#page-7-0) and [Greene](#page-7-0) [\(2008\)](#page-7-0) were employed.
-
-The model with the lowest AIC is considered the model with the best statistical fit. On the other hand, the model with the largest McFadden Pseudo R<sup>2</sup> value indicates the best fitting model. The Elvik index and Freeman–Tuckey R<sup>2</sup> are two measures of GOF that describe how much "variation" in the dependent variable is "explained" by the model estimate as compared to the total variation present in the data set.
-
-A GOF evaluation based on residual analysis was also used. This method is known as the CURE method and it was initially proposed by [Hauer](#page-7-0) [and](#page-7-0) [Bamfo](#page-7-0) [\(1997\).](#page-7-0) It can be used to evaluate the fit of models by examining the cumulative residuals for each explanatory variable separately. The cumulative residuals are plotted in increasing order and the closer the curve oscillates around the
-
-<sup>1</sup> Note that kl <sup>=</sup> lk.
-
-<span id="page-3-0"></span>![](_page_3_Figure_2.jpeg)
-
-**Fig. 1.** Accident number distribution.
-
-**Table 1** Summary statistics for the Porto data (5-year).
-
-| Variablea                                   | Min.   | Max.     | Mean     | S.D.     |
-|---------------------------------------------|--------|----------|----------|----------|
-| Dependent variable (accident number)        | 0      | 27       | 2.85     | 3.90     |
-| AADT                                        | 142.37 | 64067.80 | 15240.38 | 11699.49 |
-| Segment length (in m)                       | 20.71  | 3342.78  | 313.18   | 352.56   |
-| Number of minor intersections per kilometer | 0.00   | 32.04    | 4.57     | 5.18     |
-
-<sup>a</sup> <sup>1980</sup> observations (396 segments <sup>×</sup> <sup>5</sup> years).
-
-zero-residual line, the better the model fits the data (the reader is referred to [Hauer](#page-7-0) [and](#page-7-0) [Bamfo](#page-7-0) [\(1997\)](#page-7-0) for a more detailed description ofthis method). The CURE plot reveals how wellthe functionalform fits the data with respect to each individual explanatory variable.
-
-The next section describes the characteristics of the dataset.
-
-### **4. Data**
-
-To illustrate the application of the translog function to model accidents, data from Porto, Portugal, were used. These data consist of accident data from urban segments classified by local and principal distributor roads collected over a 5-year period (January 1st 2001 to December 31st 2005). The accident data were obtained from the official Portuguese Police Security database covering all police-recorded accidents with local information (injury accidents and property damage only accidents).Allthe accidents were related to a specific locality by applying a Geographic Information System (GIS)tool. The data consist of 5650 police records of accidents, 1183 of which were personal injury accidents and 4467 were property damage only accidents, and they are related to 396 segments resulting from a road network characterization as a series of nodes and segments from a previous work [\(Ferreira,](#page-7-0) [2010\).](#page-7-0) Fig. 1 shows the distribution of these accident numbers.
-
-The daily traffic (defined as the Annual Average Daily Traffic or AADT) was estimated by the Porto "SATURN" traffic model. This model was developed in 2005 for the peak hourly flow (PHF) of the Porto road network and involved all the recommend development stages (specification; coding; error checking; calibration; validation) ([Tavares,](#page-7-0) [2003\).](#page-7-0) The PHF was expanded to AADT every five years by applying zone factors that relate the PHF with the daily traffic flow calculated using data provided by permanent counting stations located throughout the principal city zones belonging to the Urban Traffic Center.
-
-In addition, GIS was used to find geometric characteristics of the segments, namely the number of minor intersections and the lengths. These data were processed in a doctoral study [\(Ferreira,](#page-7-0) [2010\).](#page-7-0) Table 1 presents the summary statistics for the dataset.
-
-#### **5. Estimation results**
-
-Given all the "cross variables" required by the translog function model, the parameters were estimated. However, the results
-
-**Table 2** Model estimation results for the translog function and log-linear function negative binomial models (t-statistics in parentheses).
-
-| Variable                           | Negative binomial parameter estimates |                         |  |
-|------------------------------------|---------------------------------------|-------------------------|--|
-|                                    | Log-linear function model             | Translog function model |  |
-|                                    | 0.7350 (14.141)                       | 0.6766 (11.734)         |  |
-| ˇ0                                 | 0.3701 (13.623)                       | 0.4249 (15.037)         |  |
-| ˇ1                                 | 0.8910 (31.28)                        | 1.0026 (28.109)         |  |
-| ˇ2                                 | 0.0380 (8.738)                        | 0.0233 (3.259)          |  |
-| ˇ3<br>ln()                         | −0.0583 (−3.674)                      | −0.0572 (−3.658)        |  |
-|                                    | –                                     | −0.0690 (−3.311)        |  |
-| ˇ5                                 | –                                     | 0.0018 (2.777)          |  |
-| ˇ6                                 | –                                     | −0.0780 (−2.851)        |  |
-| ˇ7                                 | –                                     | 0.0434 (5.672)          |  |
-| ˇ9<br>˛ (overdispersion parameter) | 0.4849 (15.263)                       | 0.4449 (14.687)         |  |
-| AIC                                | 3.73084                               | 3.70226                 |  |
-| McFadden Pseudo R2                 | 0.1096                                | 0.0984                  |  |
-| R2                                 | 63.7%                                 | 67.9%                   |  |
-| FT<br>R2<br>PE                     | 69.3%                                 | 71.8%                   |  |
-
-![](_page_4_Figure_2.jpeg)
-
-![](_page_4_Figure_3.jpeg)
-
-**Fig. 2.** Cumulative residual plots for the log-linear function and translog function NB models (parameter F).
-
-showed that two "cross variables" were not statistically significant. These variables correspond to [ln(Fit)]2 and ln(Fit)(IntDeni). In fact, the correlation matrix analysis reveals a strong possibility of correlation between [ln(Fit)]2 and ln(Fit) and between ln(Fit)(IntDeni) and (IntDeni). [Table](#page-3-0) 2 presents the estimationresults for both the traditional log-linear function NB model and the translog function NB model excluding the insignificant variables.2,3 As shown in [Table](#page-3-0) 2, excluding the McFadden Pseudo R<sup>2</sup> values, the translog model provides the best fit according to the three different test statistics (i.e., AIC, R<sup>2</sup> FT, R<sup>2</sup> PE), although they are all very similar. The <sup>R</sup><sup>2</sup> FT and <sup>R</sup><sup>2</sup> PE measures indicate that the translog model explains 68% and 72% of the systematic component, respectively, compared with 64% and 69% explained by the traditional NB model. These findings may be an outgrowth of the traditional NB model's restriction that the estimated elasticity is constant.
-
-In fact, the residual analysis stresses these conclusions. Comparing the CURE plotted for the two models (refer to Figs. 2–4), one can conclude that the translog function NB model, in general, fits the data better with respect to each individual explanatory variable than does the log-linear function NB model. The exception
-
-![](_page_4_Figure_9.jpeg)
-
-![](_page_4_Figure_10.jpeg)
-
-**Fig. 3.** Cumulative residual plots for the log-linear function and translog function NB models (parameter Leng).
-
-is Fig. 3b, because the cumulative residual line is slightly larger than the <sup>−</sup>2.0 boundary for segment length values above approximately 1200 m.
-
-An important issue in developing and using regression models is parameter interpretation. To analyze and to compare the influence of each explanatory variable on accident frequency in each of the two models, the elasticity values were analyzed. With regard
-
-![](_page_4_Figure_14.jpeg)
-
-![](_page_4_Figure_15.jpeg)
-
-**Fig. 4.** Cumulative residual plots for the log-linear function and translog function NB models (parameter IntDen).
-
-<sup>2</sup> The explanatory variables of the log-linear function NB model and the firstorder variables of the translog function NB model were previously "normalized" by dividing every variable observation by its respective sample mean to facilitate model comparisons and interpretation of the results.
-
-<sup>3</sup> Despite the panel nature of the data, measures to deal with panel data were not used because information on a range of exogenous variables has been incorporated that characterizes segment effects. These effects are quasi-invariant over time and the use of firm dummy variables was found to produce severe multicollinearity problems. Also, tests for possible temporal correlation and heteroskedasticity problems were conducted using a series of likelihood ratio tests as described by [Poch](#page-7-0) [and](#page-7-0) [Mannering](#page-7-0) [\(1996\),](#page-7-0) revealing that these issues do not significantly affect the resulting estimates.
-
-to the elasticity results from the log-linear function NB model, the values are consistent with the findings of previous research (see for example [Mountain](#page-7-0) [and](#page-7-0) [Fawaz,](#page-7-0) [1996;](#page-7-0) [Mountain](#page-7-0) et [al.,](#page-7-0) [1998;](#page-7-0) [Greibe,](#page-7-0) [2003\).](#page-7-0) Nevertheless,the AADT variable has low elasticity compared with similar studies (accidents increase by about 4% when the traffic volume increases by 10%). This resultindicates reducedinfluence of this variable on accident frequency. Because the segment length parameter is not significantly different from 1, the accident frequency is almost proportional to road length, as was expected. Consistent with expectations, the model also reveals that accident frequency increases when the minor intersection density increases (Eit xik <sup>=</sup> <sup>0</sup>.04(IntDeni)), although the impact on accident frequency is low (ˇ<sup>3</sup> <sup>=</sup> 0.04). Concerning the effects of local and/or national road safety policies and programs on accident frequency, parameter was estimated with statistical significance to be 0.95, implying that for the period studied the accident risk declined annually by an average of 5%, thereby accumulating to a reduction of 23% over the whole period.
-
-Comparing the parameters estimated by the traditional NB model with the translog function NB model (see [Table](#page-3-0) 2), it is clear that the effects of the explanatory variables on accident frequency are similar for both models, although some improvement can be observed in the case of the values estimated by the latter model. Indeed, the elasticities of the AADT and the segment length variables have increased slightly to values more similar to those of past research.
-
-Regardless of the better fit of the translog function NB model, its essential trait is that it adds a new approach to defining the mean function structure of an NB model in terms of the flexibility and interpretability of the explanatory variables' elasticities. In fact, a closer look atthe parameters estimated by the translog function NB model presented in [Table](#page-3-0) 2 reveals that, e.g., with increasing traffic volume, the accident frequency also increases, but when the segment length is higher, the magnitude of this effect is lower ceteris paribus To better illustrate the differences among these models, estimated accident frequencies from the two models are shown in Figs. 5 and 6. Fig. 5 compares the number of accidents estimated by the log-linear function NB model and the translog function NB model for three segment length values and considering a minor intersection density equal to the mean sample value. This figure shows that the estimated values are slightly different, with the translog function NB output being lower than the traditional NB output. The same can be illustrated with the minor intersection density variable interaction with segment length (see [Fig.](#page-6-0) 6) and considering the AADT mean sample value. In this case, the numbers of accidents estimated by the two models are very distinct, especially when IntDen is equal to 10 minor intersections per kilometer, with the translog function NB output being lower than the log-linear function NB output. For IntDen equal to 0, the models' estimated accident frequency outputs are reversed, i.e., the loglinear function NB output is higher than the translog function NB output.
-
-These figures clearly demonstrate that the translog function is particularly distinct from the log-linear function at the boundary values. This fact suggests that data heterogeneity can be considered in the translog function. As such, ignoring this variation can undermine the goodness of any estimate of individual sites as stated by [Miaou](#page-7-0) [and](#page-7-0) [Lord](#page-7-0) [\(2003\).](#page-7-0) Indeed, proper boundary values can be particularly important to modeling data with a large number of zeros (low samplemeanvalues) or withahighernumber of accidents. The latter can be referred to as a case of hotspot identification analysis. Due to the factthatthe residuals analysis ofthe translog function NB model showed lower values when the observations include a high number of accidents, this function is likely to be outperformed in the prediction of accidents at sites with a high probability of many accidents occurring.
-
-![](_page_5_Figure_6.jpeg)
-
-**Fig. 5.** Estimated values for the Porto dataset (with IntDen = 4.57 mean sample value). Log-linear function versus translog function NB models.
-
-Although the study was not motivated by hotspot identification methods, a comparative analysis between these two functions was considered. Using the model estimates from both functions as a criterion for the identification of potential hotspot, the top 10% of the 396 segments (considering a 5 year average) were selected. Then, the potential hotspots were compared with the hotspots identified through observed accident frequencies. As expected, the translog function NB model "correctly" identified more hotspots than the log-linear function NB (8% more sites). Moreover, following similar procedures as those applied in [Geedipally](#page-7-0) [and](#page-7-0) [Lord](#page-7-0) [\(2010\),](#page-7-0) outcome classification was used to compare the performance of the two models, using as a threshold value the mean of all the sites in the Porto data. These comparative results are presented in Table 3. As expected, the translog function NB model "correctly" identified more hotspots than the log-linear function NB. More-
-
-**Table 3** Evaluation of model performance at identifying the "true" hotspot.
-
-| Number of sites                                      | Log | Translog |
-|------------------------------------------------------|-----|----------|
-| Number of false positives (Type I errors)a           | 92  | 89       |
-| Number of false negatives (Type II errors)a          | 66  | 58       |
-| Number of sites correctly classified as non-hotspots | 155 | 158      |
-| Number of sites correctly classified as hotspots     | 83  | 91       |
-
-<sup>a</sup> As defined in [Geedipally](#page-7-0) [and](#page-7-0) [Lord](#page-7-0) [\(2010\).](#page-7-0)
-
-<span id="page-6-0"></span>![](_page_6_Figure_2.jpeg)
-
-![](_page_6_Figure_3.jpeg)
-
-![](_page_6_Figure_4.jpeg)
-
-**Fig. 6.** Estimated values for the Porto dataset (with F = 15240.38 mean sample value). Log-linear function versus translog function NB models.
-
-over, as a measure of the capacity of the method to detect "true" hotspots, the proportion of sites that were correctly detected by the translog function as hotspots was 61%, whereas the log-linear function detected 56%, indicating that the former performs slightly better. Furthermore, the smaller value of the proportion of the total number of errors (Type I and II) to the number of sites under analysis obtained by the translog function (37% compared to 40% for the log-linear function) confirms the previous results.
-
-In order to stress the potential of the translog function form, it wasdecidedto also evaluate itspredictiveperformance considering the methodology described in [Lord](#page-7-0) et [al.](#page-7-0) [\(2008\).](#page-7-0) Thus, two samples consisting of 80% of the original data were randomly extracted to fit the models. Then, the estimated models were applied to the remaining 20%. The mean absolute deviance (MAD) and the mean-squared predictive error (MSPE) were computed to assess the predictive performance and are presented in Table 4 (for a detailed description of this method, the readers are referred to [Lord](#page-7-0) et [al.](#page-7-0) [\(2008\)\).](#page-7-0) As can be seen in Table 4, the results for both models and for the full data model estimation exhibit similar characteristics, although with slight differences in the MAD and MSPE measures suggesting that the translog function NB model performs better.
-
-Finally, to examine the uncertainties associated with the estimated values, an empirical Bayes (EB) method was used in this study as an indirect method of evaluating uncertainty. This
-
-**Table 4** Predictive performance evaluation measures.
-
-| Measurea | Sample 1 |          | Sample 2 |          |  |
-|----------|----------|----------|----------|----------|--|
-|          | Log      | Translog | Log      | Translog |  |
-| MADFit   | 2.0      | 1.9      | 1.9      | 1.8      |  |
-| MSPEFit  | 9.9      | 8.3      | 8.7      | 7.2      |  |
-| MADPred  | 1.6      | 1.5      | 2.0      | 2.0      |  |
-| MSPEPred | 5.8      | 5.7      | 11.1     | 12.1     |  |
-
-<sup>a</sup> For a detailed description of these measures, the readers are referred to [Lord](#page-7-0) et [al.](#page-7-0) [\(2008\).](#page-7-0)
-
-approach is based on the fact that the EB method achieves a better estimate of the expected number of accidents, whereas the present procedure is a result of a combination accident counts with accident estimates. To provide a comparative measure, the EB values were computed and then compared with the estimated model (EM) values for both models analyzed. This comparison is based on the percentage difference calculated as (EM estimate − EB estimate) × 100/EB estimate. The averages ofthis percentage difference for the log-linear function NB model and the translog function NB model were found to be 22% and 19%, respectively.Although this particular formulation provides attractive statistical properties, an extensive investigation is needed using a full Bayesian treatment as a direct method of providing exact measures of uncertainty ([Mitra](#page-7-0) [and](#page-7-0) [Washington,](#page-7-0) [2007\).](#page-7-0)
-
-#### **6. Summary and conclusions**
-
-This paper introduces a novel accident modeling methodology using a flexible function named the translog function that allows future research to adopt a new interpretation of the elasticity of explanatory variables.
-
-This paper demonstrated the potential of the translog function NB model to provide new insights into the ways that explanatory variables influence accident frequency. The translog model is commonly used in econometric studies wherein it provides a flexible function form to model second-order effects, thus allowing the elasticity to vary according to the magnitude of the explanatory variable. In the context of road accident analysis, this innovative methodology can be seen as an extension of the Poisson or NB model in the case of accident modeling.
-
-Using 5 years of road accident data from urban segments (Porto, Portugal), the translog function NB model was compared with the log-linear function NB model commonly used to analyze road accidents. The results of this study show that the translog function NB model, in general, performs better than the log-linear NB model in terms of GOF statistics and residual analysis. Also, the estimation results provide some interesting findings. For instance, the traffic volume elasticity varied (although not substantially) with the segment length, revealing variable influences of these variables on accident frequency, especially for lower segment length values. In addition, it was found that the segment length elasticity changed in conjunction with the minor intersection density, with a higher effect when the number of intersections per segmentlength is high.
-
-These results suggest that the translog function provides proper boundary values and thus contributes a well-defined mean structure that fits the heterogeneous data usually exhibited by accident samples. A simple comparison between the two models was carried out suggesting that the translog function NB model has the potential to provide better estimates of the expected number of accidents at high-risk sites. Nonetheless, further research should be conducted to address data characterized by many zeros and to optimize the identification of hotspots.
-
-In addition, a preliminary study was conducted on the uncertainty associated with the estimates of the model parameters. <span id="page-7-0"></span>Using an EB estimation analysis, the translog function NB model appears to offer positive results. Nonetheless, for the purpose of measuring estimation uncertainty, a full Bayesian modeling framework should be developed for the translog function form in future research.
-
-Although the analysis conducted in this research was conducted for a segment dataset, the potential of flexible function forms to structure the mean function of intersection accident models should be analyzed as an alternative to the function forms usually postulated for the interaction between traffic flows in the minor and major approaches of intersections.
-
-Although this study is exploratory in nature, it suggests that the translog function has considerable potential for modeling accident observations. Finally, it is hoped that the new function represented by the translog model described in this paper may be adopted by other researchers, opening a door to the reliable interpretation and evaluation of the influence of explanatory variables on accident frequency.
-
-#### **References**
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-- Kumara, S.P., Chin, H.C., 2003. Modeling accident occurrence at signalized tee intersections with special emphasis on excess zeros. Accident Analysis and Prevention 4, 53–57.
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-- Lee, J., Mannering, F., 2002. Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. Accident Analysis and Prevention 34, 149–161.
-- Link, H., 2006. An econometric analysis of motorway renewal costs in Germany. Transportation Research Part A 40, 19–34.
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-- Lord, D., 2000. The prediction of accidents on digital networks: characteristics and issues related to the application of accident prediction models. Ph.D. Dissertation. University of Toronto.
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-- Lord, D., Washington, S.P., Ivan, J., 2005. Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory. Accident Analysis and Prevention 37, 35–46.
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-- Mitra, S., Washington, S., 2007. On the nature of over-dispersion in motor vehicle crash prediction models. Accident Analysis and Prevention 39, 459–468.
-- Mountain, L., Fawaz, B., 1996. Estimating accidents at junctions using routinelyavailable input data. Traffic Engineering & Control 27 (11), 624–628. Mountain, L., Maher, M., Fawaz, B., 1998. The influence of trend on estimates of
-- accidents at junctions. Accident Analysis and Prevention 30 (5), 641–649. OCDE, 1997. Road Safety Principles and Models: Review of Descriptive, Pre-
-- dictive, Risk and Accident Consequence Models. Organisation for Economic Co-operation and Development, Paris, p. 105.
-- Oum, T.H., Tretheway, M.W., 1989. Hedonic vs. general specifications of the translog cost function. Logistics and Transportation Review 25 (1), 3–21.
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-
-**António Couto** Assistant Professor; Ph.D., Faculty of Engineering of Porto University, Porto, Portugal, specializes in the economics of transport, focusing particularly on modeling the implications of transport provisions and accessibility for productivity and economic growth.
-
-**Sara Ferreira** Assistant Professor; Ph.D., Faculty of Engineering of Porto University, Porto, Portugal, is mainly engaged in road safety analysis, namely accident modeling, hotspot analysis and mitigating measures.

Zotero/001_artiklid/Road Safety Indicators/dinurandomparametermodelsaccident2011.md

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----
-category: literaturenote
-citekey: dinurandomparametermodelsaccident2011
-title: Random parameter models for accident prediction on two-lane undivided highways in India
-authors: "Dinu, R.R.; Veeraragavan, A."
-year: 2011
-date: 2011-00-00 2011
-doi: 10.1016/j.jsr.2010.11.007
-publication: Journal of Safety Research
-url: "https://doi.org/10.1016/j.jsr.2010.11.007"
-zotero_key: XUJ6PMQ5
-zotero_storage: 66JCVXKR
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Dinu, R.R."
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-# Journal of Safety Research
-
-j o u r n a l h om e p a g e : www. e l s ev i e r. c om / l o c a t e / j s r
-
-![](_page_0_Picture_5.jpeg)
-
-# Random parameter models for accident prediction on two-lane undivided highways in India
-
-R.R. Dinu a, ⁎, A. Veeraragavan b,1
-
-- <sup>a</sup> Doctoral Research Student, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai-600036, India
-- <sup>b</sup> Department of Civil Engineering, Indian Institute of Technology Madras, Chennai-600036, India
-
-### article info abstract
-
-Article history: Received 17 January 2010 Received in revised form 5 October 2010 Accepted 29 November 2010 Available online 22 January 2011
-
-Keywords: Road accident modeling Rural highway Mixed traffic Poisson regression Random parameter models
-
-Introduction: Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation. Method: The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models. Results: The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations. Conclusions: The paper is concluded with a discussion on modeling results and the limitations of the present study.
-
-© 2011 National Safety Council and Elsevier Ltd. All rights reserved.
-
-# 1. Introduction
-
-A considerable amount of research effort has gone into understanding factors that contribute to road accidents, and statistical models have been developed to predict accident frequencies under given traffic, road geometric, and environmental factors. The generalized linear modeling (GLM) approach proposed by [Nelder and Wedderburn \(1972\),](#page-3-0) with the assumption of Poisson or negative binomially distributed error, has been the choice in the development of most of these models. Early research in accident modeling [\(Miaou & Lum, 1993; Miaou, 1994; Maher & Summersgill,](#page-3-0) [1996; Poch & Mannering, 1996](#page-3-0)) established the adequacy of this modeling approach and proposed models to estimate expected accident frequencies on highway segments, given the traffic and geometrics of the highway element. The variables such as Average Annual Daily Traffic (AADT), lane and shoulder widths, horizontal curvature, section length, skew angle of intersection, land-use,
-
-<sup>1</sup> Tel.: +91 44 2257 4272.
-
-and so forth were found to be contributing to the variations in accident potential of highway elements [\(Abdel-Aty & Radwan, 2000;](#page-3-0) [Harwood, Council, Hauer, Hughes, & Vogt, 2000; Ivan, Wang, &](#page-3-0) [Bernardo, 2000; Kim, Chung, Song, & Chon, 2005; Ca](#page-3-0)fiso, Graziano, [Silvestro, & Cava, 2009](#page-3-0)).
-
-The models mentioned above come under the category of fixed parameter models, where the model parameters are assumed to be the same for any individual observation in the population. In other words, the magnitude of influence of given explanatory variable on outcome is the same for any individual observation. In the case of accident prediction models, this means that the contribution of given explanatory variable on accidents is the same on any highway segment. Recent research on accident modeling suggests that it would be more appropriate to model accident frequencies with random parameter modeling, which allows the model parameters to vary among individual observations in the population ([Anastasopoulos &](#page-3-0) [Mannering, 2009](#page-3-0)). The argument in favor of this approach is that there could be heterogeneity associated with the process of accident occurrence among highway segments due to variability among vehicle types, driver behavior, and so forth. Hence, the magnitude of influence of explanatory variables on accidents is more likely to be varying across locations than fixed.
-
-<sup>⁎</sup> Corresponding author. Tel.: +91 44 2257 5292.
-
-E-mail addresses: [rrdinu@iitm.ac.in](mailto:rrdinu@iitm.ac.in) (R.R. Dinu), [av@iitm.ac.in](mailto:av@iitm.ac.in) (A. Veeraragavan).
-
-<span id="page-1-0"></span>The aforementioned heterogeneity is more likely on Indian roads where there is large variability within and among vehicle types and driver population. Early research has established the significance of mixed nature of Indian traffic on accident occurrence, with proportions of various types of vehicles contributing to the variability in accident potential of highway elements ([Robert & Veeraragavan,](#page-3-0) [2007; Srinivas, Dinu, & Veeraragavan, 2007\)](#page-3-0). These studies used fixed parameter modeling approach and the appropriateness of random parameter modeling for the mixed traffic situation has not been investigated so far. The present study aims at modeling accident frequencies on Indian two-lane undivided rural highway segments, employing random parameter modeling.
-
-#### 2. Methodology
-
-Number of accidents yi occurring in highway segment i over period of time t follows a Poisson distribution with probability density function;
-
-$$P(y_i) = \frac{(\lambda_i t)^{y_i} e^{-(\lambda_i t)}}{y_i!} \tag{1}$$
-
-where λ<sup>i</sup> is the expected number of accidents in unit time, or in other words, the 'safety' associated with the highway segment. Poisson distributed accident frequencies can be modeled using generalized linear modeling approach, with a logarithmic link function as;
-
-$$\lambda_i = \exp(\beta x_i) \tag{2}$$
-
-where λ<sup>i</sup> is the expected accident frequency on highway segment i over unit period of time, β is the vector of estimable parameters and xi is the vector of explanatory variables. To account for the heterogeneity with random parameters, the estimable parameters can be written as;
-
-$$\beta_i = \beta_i + \nu_i \tag{3}$$
-
-where ν<sup>i</sup> is the randomly distributed term. The random parameter model for Poisson distributed accident frequencies takes the following form;
-
-$$\lambda_i | \nu = \exp(\beta' x_i) \tag{4}$$
-
-where λi|ν is the expected accident frequency on highway segment over unit period of time, conditional on probability distribution of random parameters ν, β' is the vector of estimable random parameters and xi is the vector of explanatory variables. This results in loglikelihood function;
-
-$$LL = \sum_{i} \log \int_{\nu} P(y_i | \nu) g(\nu) \partial \nu$$
- (5)
-
-where g(ν) is the probability density function of the random term. Maximum likelihood estimation of the above function is computationally cumbersome. Simulation assisted maximum likelihood estimation has been proposed in literature for evaluating likelihood functions that involve such high-dimensional integrals [\(Train, 2009](#page-3-0)). Here the integral, which is the probability of a random process, is approximated from repeated simulation of the random process.
-
-### 3. Data
-
-The data used in the present study were collected from nearly 200 km of highways around the city of Chennai, in the southern Indian state of Tamil Nadu. The highway segments were divided into 33 homogeneous sections, based on traffic volume, carriageway width, and shoulder width. The highway segments were two-lane undivided, with 6.5 m to 7 m wide carriageway and were of length varying from 1 km to 22 km. The accident history for a period of three years, from 2001 to 2003, was collected for these highway segments from the police records. Nearly 3,000 accidents occurred on the selected segments during this period. Traffic surveys carried out along midblock locations and intersections along the segments showed that there is considerable difference in both traffic volume and composition between day (6 a.m.–6 p.m.) and night (6 p.m.– 6 a.m.), with average hourly volume of 1,000 vph during the day and 300 vph during the night. Fig. 1 shows the average composition of day and night traffic. During the day, significant proportions of motorized twowheelers and non-motorized vehicles were present in the traffic, to the tune of an average of 26% and 5% of the traffic, respectively.
-
-The huge differences in traffic volume levels and composition suggested modeling of day-time and night-time accidents separately.
-
-Road geometric and environment data were collected for the selected highway segments through detailed inventory surveys, supplemented with GPS tracking. As the highway segments contained many horizontal (/vertical) curves in it, a weighted averaging procedure was used to arrive at a single representative index for all the horizontal (/vertical) curves within. The index for horizontal curves in a highway segment was calculated as;
-
-$$CI_{H} = \sum_{i} w_{i} DEG_{i} \tag{6}$$
-
-where wi is the weight assigned to i th horizontal curve of curvature DEGi degrees per 100 m. The curvature of horizontal curve was calculated as;
-
-$$DEG_i = \frac{\Delta_i}{l_i} \times 100 \tag{7}$$
-
-where Δ<sup>i</sup> is the central angle in degrees and ll is the length in m of i th horizontal curve. The weight wi was calculated as;
-
-$$w_i = \frac{l_i}{L} \tag{8}$$
-
-![](_page_1_Picture_25.jpeg)
-
-![](_page_1_Figure_26.jpeg)
-
-Fig. 1. (a) Average traffic composition during day (6 AM – 6 PM). (b) Average traffic composition during night (6 PM – 6 AM).
-
-where L is the length of highway segment under consideration. For vertical curves in a segment, the index was computed as;
-
-$$CI_V = \sum_i w_j GRAD_j$$
- (9)
-
-where wj is the weight for j th vertical curve with gradient GRADj% per 100 m. The gradient of vertical curve was computed as;
-
-$$GRAD_j = \frac{g_j}{l_i} \times 100 \tag{10}$$
-
-where gj is the gradient in % and lj is the length in m of the j th vertical curve. The weight wj is computed similar to that for horizontal curves, which is shown in Eq. [\(8\).](#page-1-0)
-
-## 4. Analysis
-
-The dependent variable modeled was the accident frequency on a given highway segment in a year. Separate models were developed for day time and night time accidents. The traffic variables considered in the modeling were logarithm of average hourly volume and proportion of the different vehicle types in traffic. Length of the highway segment was considered to account for vehicle-kilometers traveled. The road environment variable included in the modeling was driveway density per kilometer, which is the number of access roads joining the main roadway in one kilometer length. Among road geometric variables, the candidate variables were width of shoulder, weighted index for horizontal curvature CIH and weighted index for vertical curvature CIV along the segment.
-
-Simulation assisted maximum likelihood estimation approach was employed for parameter estimation. To account for possible heterogeneity in parameters, each parameter was initially assumed to be normally distributed. After trials with varying number of draws, 500 random draws of each parameter was found to result in stable estimate. The correctness of the assumption of heterogeneity in parameters was tested with the statistical significance of the estimate of standard deviation of parameter distribution. T-test was conducted at 95% level of significance and the parameter was treated as random when the standard deviation of parameter distribution was found significant. The parameters with statistically insignificant variance were treated as fixed parameters.
-
-The goodness-of-fit (GOF) of the model was tested with loglikelihood ratioρ<sup>2</sup> , computed as;
-
-$$\rho^2 = 1 - \frac{LL(\beta)}{LL(0)} \tag{11}$$
-
-where LL(β) is the log-likelihood at convergence of the full model and LL(0) is that of the constant-only model. A ρ<sup>2</sup> value closer to one suggests that the model explains most of the variability in the dependent variable. Out of the three years data, the data pertaining to the first two years is used as the calibration data set and the data for the third year is kept aside as the validation data set. The model parameters were estimated with the calibration data set, employing the simulation based maximum likelihood estimation procedure. The models were then validated by computing the X<sup>2</sup> statistic using the following relationship;
-
-$$X^{2} = -2[LL(\beta)_{c + \nu} - LL(\beta)_{c} - LL(\beta)_{\nu}]$$
-
-$$(12)$$
-
-where LL(β)c, LL(β)<sup>c</sup> and LL(β)c<sup>+</sup> <sup>v</sup> are the log-likelihood values at convergence for the full model, computed over the calibration data set, the validation data set, and both the data sets combined, respectively. The X<sup>2</sup> statistic follows a distribution with degrees of freedom equal to the number of parameters in the model under consideration. The statistic was tested against χ<sup>2</sup> distribution at 95% level of significance. Only those variables that were found significant at 0.05 level in the two-tailed t-test were included in the final models. Table 1 gives the summary of the final models.
-
-## 5. Discussion of results
-
-The results of model parameter estimation show that many of the explanatory variables, especially the ones related to traffic
-
-Table 1 Estimation results for model parameters.
-
-| Variable                                           | Estimate (t-statistic in parenthesis) |                               |
-|----------------------------------------------------|---------------------------------------|-------------------------------|
-| Dependent variable                                 | Day-time accidents per year           | Night-time accidents per year |
-| Constant                                           | -6.47 (-7.43)                         | 1.39 (2.35)                   |
-| Logarithm of hourly traffic volume, vph            | 1.41 (11.10)                          | 0.60 (5.56)                   |
-| Standard deviation of parameter distribution       | 0.02 (5.07)                           | 0.03 (6.17)                   |
-| Length of highway segment, km                      | 0.16 (28.60)                          | 0.13 (23.46)                  |
-| Proportion of buses in traffic, %                  |                                       | 2.75 (2.77)                   |
-| Proportion of cars in traffic, %                   | -8.43 (-13.54)                        | -11.18 (-11.13)               |
-| Standard deviation of parameter distribution       | 1.95 (17.91)                          | 2.85 (17.79)                  |
-| Proportion of motorized two-wheelers in traffic, % | 1.78 (3.97)                           |                               |
-| Standard deviation of parameter distribution       | 1.26 (14.24)                          |                               |
-| Proportion of trucks in traffic, %                 |                                       | -3.96 (-7.56)                 |
-| Standard deviation of parameter distribution       |                                       | 0.28 (4.76)                   |
-| Driveway density, per km                           | 0.05 (9.21)                           | 0.03 (4.64)                   |
-| Standard deviation of parameter distribution       |                                       | 0.04 (10.71)                  |
-| Width of shoulder, m                               | -0.21 (-4.51)                         |                               |
-| Weighted index for horizontal curvature, CIH       | 0.06 (3.16)                           |                               |
-| Standard deviation of parameter distribution       | 0.02 (2.88)                           |                               |
-| Weighted index for vertical curvature, CIV         | 0.19 (2.47)                           | 0.21 (2.36)                   |
-| Model summary                                      |                                       |                               |
-| LL(β)                                              | -219.49                               | -206.97                       |
-| LL(0)                                              | -527.53                               | -434.80                       |
-| ρ2                                                 | 0.58                                  | 0.52                          |
-| Model Validation                                   |                                       |                               |
-| LL(β)c                                             | -219.49                               | -206.97                       |
-| LL(β)v                                             | -104.73                               | -102.33                       |
-| LL(β)c+ v                                          | -326.68                               | -310.45                       |
-| 2<br>χcritical                                     | 18.31                                 | 15.51                         |
-| X2                                                 | 2<br>4.92 (bχcritical<br>)            | 2<br>2.3 (bχcritical<br>)     |
-
-<span id="page-3-0"></span>composition, have standard deviation significantly different from zero, when modeled as random. This suggests that there is heterogeneity associated with the variables, which are not implicitly accounted for in the data. The result of the random parameter modeling is a good reason to believe that the different explanatory variables do not influence accident occurrence by the same amount on all highway segments. As hypothesized earlier, this randomness could be due to the difference among vehicle types in terms of maneuverability, performance, and so forth, as well as the difference in driver behavior and other differences in road condition, weather, and so forth, which are not measured and included in the model.
-
-Hourly traffic volume, length of highway segment, proportion of cars and motorized two-wheelers in traffic, driveway density, width of shoulder, and horizontal and vertical curvatures were found to be significantly influencing day-time accident frequencies. While increase in proportion of cars and width of shoulder were found to decrease the accident frequencies, increase in the other variables resulted in an increase in accidents. In case of night-time accidents, hourly traffic volume, length of highway segment, proportion of buses, cars, and trucks, driveway density, and vertical curvature were found to be significant. Here the proportion of cars and trucks in traffic were found to cause a decrease in accidents, while all other variables had a positive coefficient, showing an increase in accident frequencies.
-
-In the model for day-time accidents, the parameters for logarithm of hourly volume, proportions of cars and motorized two-wheelers, and weighted index for horizontal curvature were found to be randomly distributed with standard deviation significantly different from zero. In the model for night-time accidents, the parameters for logarithm of hourly volume, proportion of cars and trucks, and driveway density were found to be randomly distributed. The randomness associated with the explanatory variables related to traffic volume and composition could be attributed to the variability among and within the different vehicle types in terms of dimensions and dynamics. The randomness of parameters of road geometrics and access could be due to variability in driver behavior. The significance of standard deviations of these variables in the models show that the random parameter modeling approach is promising in Indian context of mixed traffic as well.
-
-As the random parameters were assumed to be normally distributed, their range of influence on accident frequencies can be inferred from the estimates of mean and standard deviation. Thus the parameter for logarithm of hourly volume can be expected to vary between 1.37 and 1.48 in the day-time accidents model and between 0.54 and 0.66 in the night-time accidents model for 95% of the highway segments. The range of variability of other random parameters can also be estimated in a similar manner. This would help safety engineers in evaluating more realistic ranges of safety benefits that can be expected from a corrective measure.
-
-#### 6. Conclusions
-
-This study is an attempt to employ random parameter modeling to develop accident prediction models for Indian two-lane undivided rural highways that operate under mixed traffic conditions. The heterogeneity in the process of accident occurrence across locations due to a number of factors (e.g., variability among vehicle types, variability in driver behavior) is expected to be accounted for in this modeling approach. Accident prediction models were developed for day-time and night-time accidents over a three year accident history of nearly 200 km of two-lane undivided highway segments in India. The explanatory variables considered for modeling included hourly traffic volume, length of highway segment, proportion of buses, cars, motorized two-wheelers and trucks in the traffic, driveway density, shoulder width, and horizontal and vertical curvatures.
-
-The model coefficients were assumed to be normally distributed and a simulation based maximum likelihood method was used for parameter estimation. The results of the analysis showed that the model parameters for hourly volume, proportion of cars, two-wheelers and trucks in traffic, driveway density, and horizontal and vertical curvature had statistically significant standard deviation, suggesting that the parameters are randomly distributed. As the models proposed in the study accommodate for the unaccounted heterogeneity, they are expected to help safety engineers in computing more realistic ranges of safety benefits of remedial measures.
-
-Although this study employs random parameter modeling successfully to model accidents under mixed traffic conditions, it has the following limitations:
-
-- The comparison of random parameter models with fixed parameter counterparts has not been done in the study. Such a comparison would be necessary to assess whether the improvement in modeling with this approach would really result in significant differences when applied to a real world situation, considering the computational complexity involved.
-- The random parameters in the present study were assumed to be normally distributed. It is possible that the variables are indeed random, but the probability distribution is other than normal. Evaluating suitability of different possible probability distributions or having a methodology to identify the distribution is necessary here.
-
-#### References
-
-Abdel-Aty, M. A., & Radwan, A. E. (2000). Modeling traffic accident occurrence and involvement. Accident Analysis and Prevention, 32(5), 633−642.
-
-Anastasopoulos, P. C., & Mannering, F. L. (2009). A note on modeling vehicle accident frequencies with random-parameters count models. Accident Analysis and Prevention, 41(1), 153−159.
-
-Cafiso, S., Graziano, A. D., Silvestro, G. D., & Cava, G. L. (2009). Accident prediction models for evaluation of safety performance on two-lane rural highways. TRB 88th Annual Meeting Compendium of Papers DVD.
-
-Harwood, D. W., Council, F. M., Hauer, E., Hughes, W. E., & Vogt, A. (2000). Prediction of the expected safety performance of rural two-lane highways. Technical Report, FHWA-RD-99-207.
-
-Ivan, J. N., Wang, C., & Bernardo, N. R. (2000). Explaining two-lane highway crash rates using land use and hourly exposure. Accident Analysis and Prevention, 32(6), 787−795.
-
-Kim, S. H., Chung, S. B., Song, K. H., & Chon, K. S. (2005). Development of an Accident Prediction Model using GLIM (Generalized Log-linear Model) and EB method: A case of Seoul. Journal of the Eastern Asia Society for Transportation Studies, 6, 3669−3682.
-
-Maher, M. J., & Summersgill, I. (1996). A comprehensive methodology for the fitting of predictive accident models. Accident Analysis and Prevention, 28(3), 281−296.
-
-Miaou, S. (1994). The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accident Analysis and Prevention, 26(4), 471−482.
-
-Miaou, S., & Lum, H. (1993). Modeling vehicle accidents and highway geometric design relationships. Accident Analysis and Prevention, 25(6), 689−709.
-
-Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A (General), 135(3), 370−384.
-
-Poch, M., & Mannering, F. (1996). Negative Binomial Analysis of Intersection-Accident Frequencies. Journal of Transportation Engineering, 122(2), 105−113.
-
-Robert, V. R., & Veeraragavan, A. (2007). Accident prediction factors for rural highway segments in developing countries. TRB 86th Annual Meeting Compendium of Papers CD-ROM.
-
-Srinivas, C., Dinu, R. R., & Veeraragavan, A. (2007). Application of Poisson and Negative Binomial Regression for Modeling Road Accidents under Mixed Traffic Conditions. TRB 86th Annual Meeting Compendium of Papers CD-ROM.
-
-Train, K. (2009). Discrete Choice Methods with Simulation. Cambridge, UK: Cambridge University Press.
-
-Mr. Dinu holds a Master's Degree in Transportation Engineering from University of Kerala. He is pursuing his doctoral research in the Department of Civil Engineering, Indian Institute of Technology Madras. His research interests include road safety studies under mixed traffic flow and road accident modeling.
-
-Dr. Veeraragavan holds a Master's Degree in Transportation Engineering from Indian Institute of Technology Madras and a Ph.D degree in Civil Engineering from Bangalore University, India. He was employed at Bangalore University, Bangalore, India during 1985-2004, and since 2004 he has been employed as Professor of Civil Engineering at the Indian Institute of Technology Madras, Chennai, India. His research interests include road safety under mixed traffic flow, traffic management, and road asset management.

Zotero/001_artiklid/Road Safety Indicators/ekmekcieffectsroadwidthradii2024.md

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----
-category: literaturenote
-citekey: ekmekcieffectsroadwidthradii2024
-title: "Effects of road width, radii and speeds on collisions at three-arm priority intersections"
-authors: "Ekmekci, Mustafa; Woods, Lee; Dadashzadeh, Nima"
-year: 2024
-date: 2024-00-00 2024
-doi: 10.1016/j.aap.2024.107522
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2024.107522"
-zotero_key: 4EJRG565
-zotero_storage: BP4JZH48
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Ekmekci, Mustafa"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-Contents lists available at [ScienceDirect](www.sciencedirect.com/science/journal/00014575)
-
-# Accident Analysis and Prevention
-
-journal homepage: [www.elsevier.com/locate/aap](https://www.elsevier.com/locate/aap)
-
-![](_page_0_Picture_5.jpeg)
-
-![](_page_0_Picture_6.jpeg)
-
-## Effects of road width, radii and speeds on collisions at three-arm priority intersections
-
-Mustafa Ekmekci a,1,\* , Lee Woods b,2 , Nima Dadashzadeh c,3
-
-- <sup>a</sup> *School of Civil Engineering and Surveying, University of Portsmouth, Portsmouth PO1 3AH, UK*
-- <sup>b</sup> *Faculty of Technology, Portland Building, University of Portsmouth, Portsmouth PO1 3AH, UK*
-- <sup>c</sup> *Future Mobility Centre, Huddersfield Business School, University of Huddersfield, Huddersfield HD1 3DH, UK*
-
-#### ARTICLE INFO
-
-*Keywords:*  Visibility Priority intersection Safety Collisions 20 mph speed limit 30 km/h speed limit piecewise Structural Equation Modelling
-
-#### ABSTRACT
-
-Simulation and observational studies have identified the importance of intersection geometries and vehicle speeds in collisions. However, the causal mechanisms of such collisions in low-speed areas and for different collision types remain unclear. This observational study investigates the complex relationships between geometries, speeds, visibilities, and road traffic collisions in the context of low-speed urban areas. Data were collected from 120 three-arm priority intersections in Portsmouth, UK. In 2007, Portsmouth became the first city in the UK to adopt a 20mph speed limit on all residential streets. The city has also adopted the UK's Manual for Streets (MfS) as the design standard for all new priority intersections in low-speed residential areas. Piecewise structural equation models (pSEM) were developed to represent the causal mechanisms that relate to geometries, speeds, speed limits and collisions. Findings indicate the role of combinations of approach lane width, corner radii, speed limit, and type of collision. The interaction of wider approach lanes on the minor arm and larger radii of turns for left-turning vehicles (left-hand driving perspective) was associated with higher numbers of road traffic collisions for right-turning vehicles. It is posited here that this is due to the orientation of the left-turning vehicle blocking the left visibility of the right-turning vehicle. These results give weight to the introduction of the 20mph speed limit zone in Portsmouth and some of the changes brought about by MfS. However, the combined effect of approach width and radii on collisions is novel and could form the basis of further guidance on reducing specific types of collisions at three-arm priority intersections.
-
-## **1. Introduction**
-
-Globally, there is a growing awareness of the importance of road safety and the high number of road collisions leading to fatalities and severe injuries. According to the World Health Organisation [\(WHO,](#page-9-0)  [2018\)](#page-9-0), 1.35 million people worldwide are killed in road collisions. Among young people aged 15–29, road collisions are the leading cause of death [\(WHO, 2015](#page-9-0)). According to the Federal Highway Administration [\(FHWA, 2018](#page-8-0)), just under 37,000 fatal collisions occurred on the roads of the United States in one year. More than 50 % of the total fatal and injury crashes occur at or near intersections [\(FHWA, 2024](#page-8-0)). Furthermore, 18 % of the fatal collisions happen in unsignalized intersections, with 14 % involving pedestrians and 3 % cyclists.
-
-Intersections, at-grade or grade-separated, are locations of high collision concentration among European countries, 40–60 % of the total (European [Commission, 2024](#page-8-0)). In the UK, 49 % of collisions in urban areas happen at priority control intersections, of which 59 % occurred at T or staggered intersections (statistic calculated from STATS19 ([Department for Transport, 2022\)](#page-8-0) dataset using ArcGIS). These collisions are responsible for many casualties, 66 % of whom were drivers, cyclists or motorbike riders; 19 % passengers; and 15 % pedestrians.
-
-Priority intersection design standards and guidance have evolved in the UK from 1946 (Design & Layout of Roads in Built-Up Areas) ([Cook,](#page-8-0)  [1946\)](#page-8-0) to the most recent guidance published in Manual for Streets and Manual for Street 2 [\(Department for Transport, 2007; CHIT, 2010\)](#page-8-0). MfS has been adopted as the design standard by many highway authorities
-
-<sup>\*</sup> Corresponding author.
-
-*E-mail addresses:* [mustafa.ekmekci@port.ac.uk](mailto:mustafa.ekmekci@port.ac.uk) (M. Ekmekci), [lee.woods@port.ac.uk](mailto:lee.woods@port.ac.uk) (L. Woods), [n.dadashzadeh@hud.ac.uk](mailto:n.dadashzadeh@hud.ac.uk) (N. Dadashzadeh). 1 ORCID: 0000-0003-2636-1739. 2 ORCID: 0000-0002-8336-4939. 3 ORCID: 0000-0001-5425-0572.
-
-when designing new priority intersections, particularly in lower-speed urban areas. There was a recognition in MfS that providing excellent visibilities to drivers through Design Manual for Roads and Bridges (DMRB) ([Highways Agency, 1994\)](#page-8-0) and Design Bulletin 32 (DB32) ([Department of the Environment, 1992\)](#page-8-0) standards had a counterintuitive effect on safety. With excellent visibility, drivers' perceptions of risks reduce, which leads to increased vehicle speeds, less concentration, and greater risk-taking behaviours ([Henson et al., 1992](#page-8-0)). Rather than improving the intersections' safety, it is suggested that excessive visibility reduces the safety of intersections. As such, MfS reduces the minimum visibility requirements at priority intersections alongside guidance on reducing the geometric dimensions of these intersections. MfS also promotes the use of 20mph speed limits in built-up areas. Portsmouth in the UK became the first city where a 20mph city-wide speed limit was introduced across residential areas ([Bunce, 2009\)](#page-8-0).
-
-This study sets out to investigate the traffic safety of low visibility priority intersections at 120 locations in Portsmouth. This paper examines the effect of visibility distance, approach width, radii and speeds on road traffic collisions. Although many studies in the literature look at the effect of geometrical variables on road traffic collisions, these studies did not look at the combined effect of geometrical variables in the context of low-speed/low-speed-limit priority intersections.
-
-The remainder of this paper is structured as follows: Section 2 reviews the existing literature on contributory factors of road traffic collisions (RTC) at priority intersections, human factors of RTC, and visibility issues at priority three-arm intersections. [Section 3](#page-2-0) describes the study area, data collection and analytic methods. Results and discussions are presented in [Section 4,](#page-5-0) followed by the conclusions and suggestions for further work in [Section 5.](#page-7-0)
-
-### **2. Review of literature**
-
-#### *2.1. Contributory factors of road traffic collisions*
-
-A considerable body of literature exists on the causes of road traffic collisions, including several studies related to priority intersections in the UK and globally. It is essential to understand what kind of variables affect intersection safety in order to be able to discern the effects of visibilities, radii and approach width from the effects of all other variables likely to impact the numbers and types of collisions. Table 1 shows general contributory factors of RTCs in the literature.
-
-Many studies show that minor road width is associated with an increase in approach speed and collision frequency both globally (i.e. [Harnen et al., 2003; Vieira Gomes, 2013](#page-8-0)) and in the UK (i.e. [Summersgill](#page-9-0)  [et al., 2001\).](#page-9-0) However, these studies could not explain the wider minor road width, radii and visibility connections.
-
-Vehicle speed is the most commonly recognised contributory factor for collisions. [Ziolkowski \(2016\)](#page-9-0) examined the effect of geometric variables and road features on vehicle speeds in urban areas and found that an increase in road width was associated with an increase in speed. [Salifu \(2004\)](#page-9-0) observed that the major arm's standard deviation of the approach speed was significantly and positively correlated with collision frequency. However, the speed effect may also be contradictory for specific types of collisions, such as right-turning and left-turning movement collisions from minor roads. A microsimulation study of three-arm priority intersections by [Zhang et al. \(2017\)](#page-9-0) found that the number of collisions for turning vehicles from minor roads decreased with increased speed on the major road. The authors thought psychological factors could explain why drivers turning from the minor road may drive cautiously when traffic speed on the major arm is relatively high.
-
-Researchers have paid considerable attention to the relationship between traffic collisions and traffic flow (i.e. [Salifu, 2004; Vieira](#page-9-0)  [Gomes, 2013; Bonneson and McCoy, 1993; Hauer et al., 1988; Kulmala,](#page-9-0)  [1996; Golias, 1991\)](#page-9-0). The common sense of traffic safety concludes that collisions cannot occur with zero traffic flow ([Turner et al., 2003](#page-9-0)) and
-
-**Table 1**  General contributory factors of road traffic collisions at priority intersection.
-
-| Feature                                                      | Reference                                                                                                             |
-|--------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|
-| The number of arms/approach lanes<br>in junction             | Vieira Gomes (2013), Wang et al. (2015)                                                                               |
-| Channelisation (Ghost Island, Left                           | Summersgill et al. (1996), Abdel-Aty and                                                                              |
-| Turning Line, Right Turning Line)                            | Haleem (2011), Salifu (2004)                                                                                          |
-| The distance to the nearest other<br>intersection            | Haleem and Abdel-Aty (2010)                                                                                           |
-| Visibility                                                   | York et al. (2007), TMS Consultancy (2010),<br>Summersgill et al., (2001), Pickering et al.<br>(1986)                 |
-| Road Width                                                   | Ziolkowski (2016), Greibe (2003), Salifu<br>(2004), Harnen et al. (2003), Vieira Gomes<br>(2013), Ahmed et al. (2014) |
-| Road surface conditions                                      | Akin and Akbas (2010), Wang et al. (2015),<br>Hassan and Al-Faleh (2013)                                              |
-| Speed (Mean, St. deviation, 85th<br>percentage, speed limit) | Charlton (2003), Haleem and Abdel-Aty<br>(2010), Sarkar et al. (2012), Salifu (2004),<br>Wang et al. (2015)           |
-| Day or Night                                                 | Wang et al. (2015), Hassan and Al-Faleh<br>(2013)                                                                     |
-| Road Lighting                                                | Salifu (2004), Hassan and Al-Faleh (2013)                                                                             |
-| Weather Condition                                            | Akin and Akbas (2010), Wang et al. (2015)                                                                             |
-| Traffic Volume                                               | Salifu (2004), Vieira Gomes (2013),                                                                                   |
-|                                                              | Bonneson and McCoy (1993), Hauer et al.                                                                               |
-|                                                              | (1988), Kulmala (1996), Golias (1991),                                                                                |
-|                                                              | Abdel-Aty and Haleem (2011)                                                                                           |
-| Car Parking                                                  | Sarkar et al. (2012)                                                                                                  |
-| Guard, Pedestrian Crossing, Cycle<br>Lane                    | Salifu (2004), Summersgill et al. (1996)                                                                              |
-| Day of the Week, Time of Day                                 | Hassan and Al-Faleh (2013)                                                                                            |
-
-Annual Average Daily Traffic (AADT) flow is a significant predictor of collisions ([Kim et al., 2007\)](#page-8-0).
-
-The study of [Salifu \(2004\)](#page-9-0) at priority T intersections revealed that the absence of street lighting, right turn storage on major arms (left-hand drive perspective), the standard deviation of approach speed on the major arm and wider road width of the minor road were all significantly and positively correlated with the frequency of collisions [\(Salifu, 2004](#page-9-0)).
-
-#### *2.2. Human factors*
-
-The studies in section 2.1 considered factors associated with the intersection. However, research has shown that factors associated with the driver are the most common cause of road traffic collisions, such as speeding, careless driving, distraction due to the use of handheld devices, driving under the influence of alcohol or drugs and other risktaking behaviours. The characteristics of the driver are shown to relate to some of these behaviours, with young men being particularly prone to aggressive driving and higher risk-taking behaviours, which is believed to be the main reason why younger men are more likely to be involved in road traffic collisions than their female counterparts ([Stan](#page-9-0)[ton and Salmon, 2009;](#page-9-0) [Massie et al., 1995; Holland et al., 2010\)](#page-8-0). [Amin](#page-8-0)  [\(2020\)](#page-8-0) examined the effect of contributory factors on male and female older drivers in the West Midlands of the United Kingdom. Complex intersection geometry, extreme weather conditions and time of day were significant factors for older driver collisions.
-
-Human errors have the highest collision contributions depending on research outcomes, between 75 % and 95 % ([Stanton and Salmon, 2009](#page-9-0); [Kostelec, 2020](#page-8-0)).
-
-Whilst it can be seen from these two datasets that road conditions (including here road geometries) feature little in terms of the causes, it is highly plausible that many of the driver behaviours listed as causes are themselves influenced by the geometric design of intersections, particularly the level of care drivers show at an intersection, risk-taking behaviours and speed, which are some of the most critical factors in road traffic collisions.
-
-## <span id="page-2-0"></span>*2.3. Visibility issues at three-arm priority intersections*
-
-Some researchers have used microsimulation studies to explore the causes of road traffic collisions (i.e. [Darzentas et al., 1980; Zhang et al.,](#page-8-0)  [2017\)](#page-8-0). Microsimulation approaches depend on gap acceptance and other driver characteristics specified within the simulation. Hence, they may not be appropriate when investigating such behaviours. Moreover, some microsimulation models also assume junction visibilities [\(Dar](#page-8-0)[zentas et al., 1980](#page-8-0)) rather than investigating their effect.
-
-Most studies in the literature into the causes of collisions are observational rather than based on simulations. [Pickering et al. \(1986\)](#page-8-0)  observed frequencies and characteristics of collisions at priority T intersections in rural areas where the speed limit was 50mph and examined the effect of some features of T intersections, such as ghost islands (major arm to minor arm turning areas demarked with road markings only rather than physical barriers/kerbs) which were found to be associated with a reduction in collisions. The wider road width on the major arm had similar beneficial effects on collisions. The collision rate of turning right movements from the minor road was 40 % lower at intersections without merging and diverging lanes. [Pickering et al. \(1986\)](#page-8-0)  stated that there is no explanation for this but suggested this might be related to visibilities from the minor road. He used the reciprocal and identity forms of visibility variables to test this and found that their inclusion was significant for rear shunt collisions on minor arms only. Longer right-hand visibilities, 9 m back, were associated with higher numbers of minor rear shunt collisions.
-
-[Pickering et al. \(1986\)](#page-8-0) and the Cost Benefit Analysis of collisions (COBA) procedure requires at least a knowledge of traffic inflow to estimate traffic collisions [\(Highways Agency, 2022](#page-8-0)).
-
-[Arndt and Troutbeck \(2001\)](#page-8-0) concluded that the approaching visibilities at minor arms were not associated with collision rates. However, this study did not examine the causality of traffic collisions regarding visibility.
-
-Methodologically similar to Pickering's study (1986), [Summersgill](#page-9-0)  [et al. \(1996\)](#page-9-0) considered the visibility of intersections by measuring these from 2, 4.5 and 9 m back from the give-way line and the association with collisions. However, they did not assess collision types such as rear shunts, right-turn major arm movements. [Layfield et al. \(1996\)](#page-8-0) considered staggered intersections in the UK, highlighting that the inverse of visibility from minor roads can predict collisions instead of using the mean speed of major road vehicles. Another study by [Summersgill et al.](#page-9-0)  [\(2001\)](#page-9-0) compared the operational forms of one-way and two-way priority intersections in urban areas. They found that visibility was associated with speed, so these variables may affect collisions by modifying speed. However, the authors could not demonstrate the mechanism by which this might happen. [York et al. \(2007\)](#page-9-0) and TMS [Consultancy](#page-8-0)  [\(2010\)](#page-8-0) undertook research on visibilities at priority intersections to support the development of the MfS. The results showed that shorter visibility could lower vehicle speeds in line with [Summersgill et al.](#page-9-0)  [\(2001\). York et al. \(2007\)](#page-9-0) concluded that geometries could reduce both the likelihood and severity of collisions. Visibilities of just 40 m can be safe. However, caution was expressed that visibilities less than 20 m may not be safe as speeds may not be reduced sufficiently to offset the shorter visibility. TMS Consultancy concluded that visibility y distance on the major arms could not be seen as a single deterministic factor for road collisions in high-risk urban areas.
-
-The developing body of research into the negative impacts of excessive visibilities at intersections on collisions led to the guidance in the MfS. In contrast with earlier design guidance and standards (Design and Layout of Roads in Built-up Areas, 1946; Design Manual for Roads and Bridges, 1992), MfS promoted shorter visibilities and geometries in order to reduce vehicle speeds and therefore reduce the likelihood and severity of collisions. MfS requires stopping sight distances to the left and right to be provided from only 2.4 m back from the give-way intersection. This 2.4 m x-distance relates to the distance between the front bumper of a large car and the driver's eye position. In doing so, MfS intersections require the driver on the minor arm to stop at the give-way line, as it is only at this point that they have sufficient visibility to determine if there are gaps in the traffic they can turn into. This is a fundamental difference from earlier design standards that allowed drivers to make such judgements from 9 m back, meaning that they did not necessarily need to stop or slow down considerably at the give-way line as two measurements were illustrated in [Fig. 1.](#page-3-0)
-
-TMS [Consultancy \(2010\), York et al. \(2007\)](#page-8-0), and others provide evidence as to the association between visibilities and collisions and an indication that the effect may be through moderation of vehicle speeds, but there remain unanswered questions as to the relationships between geometries, speeds, speed limits and visibilities at intersections in the UK. Their analysis of these associations is problematic. They do not account for the nature of rare collision data, which may not be normally distributed with respect to visibilities. The simple linear regression of a count response variable may not present meaningful results. Generalised Linear Modelling (GLM) approaches taking account of the discrete nature of responses would be more logical. Such approaches have been applied in the UK ([Maher and Summersgill, 1996; Pickering et al., 1986\)](#page-8-0) and overseas (i.e. [Bonneson and McCoy, 1993; Hauer et al., 1988; Kul](#page-8-0)[mala, 1996; Poch and Mannering, 1996; Salifu, 2004; Sawalha and](#page-8-0)  [Sayed, 2006; Sayed and Rodriguez, 1999](#page-8-0)). However, GLM may not be able to represent the complex inter-relationships, and feedback loops between speeds, visibilities, speed limits and collisions posited in this paper. Moreover, the generalizability of previous research to the context of very low-speed limit areas, such as the Portsmouth 20mph city-wide scheme, is questionable. Recent changes to the Highway Code [\(DVSA,](#page-8-0)  [2022\)](#page-8-0), changes brought about by new in-vehicle technologies, driving styles and the change in design standards mean that new empirical research may uncover different associations and relationships to those presented in the current literature.
-
-This study, therefore, seeks to understand the complex interrelationships between visibilities, speeds, speed limits, geometries and different types of road traffic collisions in the context of low-speed (20mph) intersections through an analysis of data from Portsmouth, UK.
-
-## **3. Method and data**
-
-This section describes the study area, sampling strategy, data collection approach, and the analytic methods adopted.
-
-While an experimental approach may be beneficial in determining causality (from a statistical perspective), this has significant ethical issues. Changes in the visibilities at intersections to observe whether collision increases would not pass most ethical tests. Also, collisions are rare events, so the data collection for an experimental design may take several years. Microsimulation, which is beneficial if no collision data exists, can be used, but these require gap acceptance and other driver behaviours to be specified for the model to be run. These behaviours are not interesting to this study; hence, this approach was rejected. The overarching methodological approach deemed most appropriate was observational and quantitative due to the convenience, timescales and ethical considerations. The Department publishes detailed secondary datasets on collisions in the UK (STATS19) for Transport (DfT) which enables such observational studies possible across a large population of intersections (Department for Transport UK, 2022).
-
-#### *3.1. Study area*
-
-The City of Portsmouth was the first city in the UK to adopt a 20mph speed limit on all residential streets in 2007 ([Bunce, 2009\)](#page-8-0). The city is the most densely populated in the UK, with much of the city developed during the industrial Victorian period around the Royal Navy base. Large residential areas are typified by high-density terraced housing with minimal or no setback from the footway and low visibility priority junctions linking these roads to local distributor roads. Mid-century areas adhered to the design standards of the time, which promoted
-
-<span id="page-3-0"></span>![](_page_3_Figure_2.jpeg)
-
-**Fig. 1.** Illustration of how visibility distances can be drawn for different x-distances.
-
-high visibility on minor arms of priority intersections to aid traffic capacity. Most recently, the city has adopted the UK's Manual for Streets as the design standard for all new priority intersections in low-speed residential areas.
-
-#### *3.2. Sampling strategy of low visibility three-arm intersections*
-
-A stratified sampling strategy was used rather than random sampling to efficiently secure the diversity of variables required. Stratification of priority three-arm intersections was based on minor and major road speed limits: 20mph or 30mph, and the visibility of the intersection: low or high, as shown in Table 2.
-
-A standard way of identifying whether intersections had relatively low or high visibility is to compare the visibility of intersections and the corresponding Stopping Sight Distance (SSD). If the visibility of an intersection is more than the SSD of that intersection, it means that the minimum safety requirement related to visibility was met. Equation (1) shows the basic SSD formula. 'v' is speed (m/s), 't' is the reaction time (s) of drivers to recognise a hazardous situation, and 'd' represents deceleration of vehicles (m/s2 ). Stopping sight distances (SSD) were calculated with 1.5 s reaction time and 4.41 m/s2 deceleration coefficients provided by MfS for light vehicles. A spot speed camera measured the speed of vehicles going through the major roads and approaching the intersections. These speeds were used to calculate the SSD rather than speed limits. Intersection visibilities to the right or left, from 4.5 m back that were equal to or less than 1.1\*SSD, were considered low visibility intersections.
-
-$$SSD = v^*t + \frac{v^2}{2^*d}$$
- (1)
-
-#### *3.3. Data collection*
-
-More than 150 three-arm intersection locations in Portsmouth were reviewed between September 2020 and August 2021 of which 120
-
-**Table 2**  Sample Stratification.
-
-| Speed Limit on<br>Main Road | Speed Limit on<br>Minor Road | Low<br>Visibility | High<br>Visibility | Total |
-|-----------------------------|------------------------------|-------------------|--------------------|-------|
-| 20mph                       | 20mph                        | 20                | 20                 | 40    |
-| 30mph                       | 20mph                        | 20                | 20                 | 40    |
-| 30mph                       | 30mph                        | 20                | 20                 | 40    |
-| Total                       |                              | 60                | 60                 | 120   |
-
-priority three-arm intersections met the criteria set out in section 3.2 to be included in this study [\(Ekmekci et al., 2023](#page-8-0)). Primary data (Speed & Classified Manual Traffic Counts) were collected from these 120 locations in Portsmouth and Gosport, as shown in [Fig. 2](#page-4-0). Speed data for approaching vehicles at 360 intersection arms were measured by using an SL700 spot speed camera. Classified 15-minute traffic counts were undertaken in the am, pm or both peak periods. These were converted to AADT using the standard conversion factors suggested by the Chartered Institution of Highways and Transportation (CIHT, 1997). Secondary response variable data in this study was obtained from the STATS19 database downloaded from "[data.gov.uk](http://data.gov.uk)". The dataset includes separately documented collision, casualty and vehicle files for the base years between 2008 and 2020.
-
-Visibilities to the left and the right were measured in two ways. MfS requires visibilities to the left and the right to be measured to the nearside kerb from 2.4 m back from the give-way line. This is because vehicles approaching from the left may be overtaking vehicles or cars parked and hence be on the "wrong" side of the road. Additionally, in this study, visibilities were measured to the centre line of the major arm, as shown in [Fig. 3](#page-4-0) and also considered obstructions caused by parked vehicles. This study focuses on right-turning vehicles. These involve gaps being accepted to the left and right, hence visibilities to the left and the right. Both sets of visibility measurements were tested in the modelling described later.
-
-## *3.4. Piecewise structural equation modelling (pSEM)*
-
-Piecewise Structural Equations Modelling (pSEM) was developed by Shipley's studies in the 2000 s [\(Shipley, 2009\)](#page-9-0). The relationships between variables in the traditional SEM are simultaneously estimated in a single variance–covariance matrix [\(Stein et al., 2012\)](#page-9-0). However, in piecewise SEM (pSEM), each set of relationships are estimated locally (or, in other words, independently) [\(Shipley, 2009](#page-9-0)). The traditional SEM uses chi-square tests to compare observed and predicted covariance matrices, while the goodness-of-fit of a pSEM is measured as a "test of directed separation" ([Lefcheck, 2016\)](#page-8-0). [Shipley \(2009\)](#page-9-0) highlighted that this test does not depend on asymptotic methods, so it can be used with smaller sample sizes. Secondly, many problems involving non-normality and non-linearity can be overcome. Another aspect of the "test of directed separation" is that it does not conflict with some problems of covariance matrices involving a maximised loss function. Finally, it allows each piece of the local causal model to determine the extent to which it contributes to the lack of fit [\(Shipley, 2009\)](#page-9-0). Therefore, pSEM seems to be a robust technique, and the work presented here is believed
-
-<span id="page-4-0"></span>![](_page_4_Figure_2.jpeg)
-
-**Fig. 2.** Locations for 120 three-arm priority intersections in Portsmouth data collection.
-
-![](_page_4_Figure_4.jpeg)
-
-**Fig. 3.** Used variables in the initial causal path.
-
-to be the first study that models traffic collisions with pSEM.
-
-R Studio was selected as the analytical package, as it is an opensource programming language supported by various academic environments, with three extensions to run SEMs, including "piecewiseSEM" integrated with Generalized Linear Modelling (GLM) techniques [\(RC](#page-9-0)  [Team, 2020; Lefcheck et al., 2023\)](#page-9-0).
-
-As collisions are rare events, the frequencies will not follow normal distributions. One sample Kolmogorov-Smirnov test was used to check whether they followed Poisson distributions. The results show no significant evidence (Asymp. 2-tailed significance is 0.149) to reject the null hypothesis that "H0: The frequency of the sample significantly differs from a Poisson distribution." Therefore, it is assumed that the collisions follow Poisson distributions, and modelling the data with Poisson regression is appropriate.
-
-The associations between speed variables (mean) and other factors
-
-were modelled with pSEM through multiple linear regression, as speeds are dependent variables. Kolmogorov-Smirnov tests were used to check the normal distribution assumptions. As per the results in Table 3, the variables are assumed to be normally distributed.
-
-[Fig. 4](#page-5-0) below shows the path diagram of the pSEM model. Response variables are the mean of minor road speed, major road speeds (from right arms to the left and left arms to the right) and traffic collisions.
-
-**Table 3**  Test of Normality.
-
-|                                         | Kolmogorov-Smirnova |     |        |
-|-----------------------------------------|---------------------|-----|--------|
-|                                         | Statistic           | df  | Sig.   |
-| Mean of Minor Road Speed                | 0.075               | 120 | 0.094  |
-| Mean of Major Road Right to Left Speed  | 0.071               | 120 | 0.200* |
-| Mean of Major Right Left to Right Speed | 0.047               | 120 | 0.200* |
-
-<span id="page-5-0"></span>![](_page_5_Figure_2.jpeg)
-
-**Fig. 4.** Initial pSEM causal path.
-
-(2)
-
-(5)
-
-Predictors are traffic flow, the combination of minor road width and corner radii, visibilities, speed limit and major road width, the mean of minor road speed, and major road speeds (from right arms to the left and left arms to the right).
-
-The hypothesized model's equations can be written as follows:
-
-$$\begin{split} AvrSpeedMinor &= [TF.\beta_1 + Int.\beta_2 + VisR24.\beta_3 + VisR9.\beta_4 + VisL9.\beta_6 + SpeedLimit.\beta_7 + AvrSpeedMajRtoL.\beta_8 \\ &+ AvrSpeedMajLtoR.\beta_9 + \beta_0] \end{split}$$
-
-AvrSpeedMajRtoL = [TF*.*β10 + SpeedLimit*.*β11 + WidthMaj*.*β12 + β13] (3)
-
-$$AvrSpeedMajLtoR = [TF.\beta_{14} + SpeedLimit.\beta_{15} + WidthMaj.\beta_{16} + \beta_{17}]$$
- (4)
-
-$$\begin{split} E &= Exp(TF.\beta_{18} + Int.\beta_{19} + VisR24.\beta_{20} + VisR9.\beta_{21} + VisL24.\beta_{22} + VisL9.\beta_{23} \\ &+ SpeedLimit.\beta_{24} + AvrSpeedMinor.\mu_1 + AvrSpeedMajRtoL.\mu_2 \\ &+ AvrSpeedMajLtoR.\mu_3 + \beta_{25}) \end{split}$$
-
-Where,
-
-βi and μi : Estimated coefficients*.*
-
-TF: Traffic Flow (vehicle/hour). Int: Interaction of minor road width and corner radii (metres<sup>2</sup> ). VisR24: Right-hand visibility at 2.4 m back from give-way lines projected to the near side kerb of the major road (metres). VisR9: Right-hand visibility at 9 m back from give-way lines projected to the near side kerb of the major road (metres). VisL24: Left-hand visibility at 2.4 m back from give-way lines projected to the centre line of the major road (metres). VisL9: Left-hand visibility at 9 m back from give-way lines projected to the centre line of the major road (metres). SpeedLimit: Speed limit (variable is 1 if 20mph is applied; 0 if not). WidthMinor: Minor road width measured at 9 m back from give-way lines (metres). WidthMaj: Major road width (metres). AvrSpeedMinor: Average speed of vehicles approaching on the minor arms (mph). Avr-SpeedMajRtoL: Average speed of vehicles traveling from the major road's right arm to its left arm (mph). AvrSpeedMajLtoR: Average speed of vehicles traveling from the major road's left arm to its right arm (mph). E: Expected frequency of collisions predicted by the model (collision/year). Exp (): Exponential function of the natural number (e).
-
-## **4. Results and discussions**
-
-The connections in the initial model shown in Fig. 4 were step by step removed from the model using a backward elimination method. The final form of the pSEM path diagram is shown in [Fig. 5](#page-6-0). Traffic flow, minor road width, visibility to the right (R 2.4 m), and visibility to the left (L 2.4 m) have a statistically significant impact on the number of the turn right from minor motorised veh-veh collisions at the 95 % confidence level (P-value of 0.05).
-
-The parameters estimated and P-values for the latest form of the pSEM model are summarised in [Table 4,](#page-6-0) and estimates of model fit are shown in [Table 5](#page-6-0). The variables included in the final model were able to predict circa 60 % of the variation in the numbers of right-turn motorised veh-veh collisions. This compares to 37 % for the flow-based model and 50 % for the geometry-flow-based model by Salifu et al. (2004), and two models (multivariate adaptive regression splines (MARS) and negative binomial (NB)) produced by [Abdel-Aty and Haleem \(2011\)](#page-8-0)  with 39 % and 19 %, respectively.
-
-These results reinforce the findings in the literature that vehicle flows relate to the number of collisions at priority intersections [\(Hauer](#page-8-0)  [et al., 1988; Golias, 1991; Kulmala, 1996; Bonneson and McCoy, 1993;](#page-8-0)  [Salifu, 2004](#page-8-0); [Bonneson and McCoy, 1993; Hauer et al., 1988; Kulmala,](#page-8-0)  [1996; Golias, 1991; Salifu, 2004;](#page-8-0) [Vieira Gomes, 2013](#page-9-0)). This study has also confirmed that these findings remain true in the context of lowspeed limit (20mph) areas.
-
-Speed limit and a combination of approach width and corner radii on the left (geometries) affect approach speed. In low-speed limit areas, the effect of speed limits and geometries on vehicle speeds negates any apparent effects of visibilities on speeds on the minor approach. The lower visibilities are not slowing vehicles down further than the speed limits, and geometry has already slowed vehicles. This may suggest that there is a role for lower speed limits, even at intersections where visibilities are already low. However, visibilities do appear to have a direct relationship with the number of collisions rather than an indirect one through vehicle speeds. Visibility distances at 2.4 m back from the giveway line were directly associated with the number of collisions for rightturning vehicles. All else being equal, the greater the left-hand visibility at 2.4 m back, the better the safety, while greater right-hand visibility had a counter effect on safety. This is contrary to the literature ([York](#page-9-0)  [et al., 2007](#page-9-0); TMS [Consultancy, 2010;](#page-8-0) [Summersgill et al., 2001](#page-9-0)) and to the design standards ([Department for Transport, 2007; CHIT, 2010](#page-8-0);
-
-<span id="page-6-0"></span>![](_page_6_Figure_2.jpeg)
-
-**Fig. 5.** The latest form of pSEM after implementing the backward elimination method.
-
-**Table 4**  Results of pSEM Parameters estimate and P-value for the final pSEM model.
-
-| Response                                    | Predictor                         | Estimate | P.<br>Value |
-|---------------------------------------------|-----------------------------------|----------|-------------|
-| Mean of Major Road Right to<br>Left Speed   | Traffic Flow                      | 0.0005   | 0.0000      |
-| Mean of Major Road Right to<br>Left Speed   | Speed Limit                       | 3.9902   | 0.0000      |
-| Mean of Major Road Left to<br>Right Speed   | Traffic Flow                      | 0.0005   | 0.0000      |
-| Mean of Major Road Left to<br>Right Speed   | Speed Limit                       | 3.1690   | 0.0000      |
-| Mean of Minor Road Speed                    | Speed Limit                       | 2.3575   | 0.0000      |
-| Mean of Minor Road Speed                    | Corner Radii: Minor<br>Road Width | 0.0221   | 0.0028      |
-| Right Turn from Minor Veh<br>Veh Collisions | Vis. Right 2.4                    | 0.0026   | 0.0364      |
-| Right Turn from Minor Veh<br>Veh Collisions | Vis. Left 2.4                     | − 0.0035 | 0.0293      |
-| Right Turn from Minor Veh<br>Veh Collisions | Traffic Flow                      | 0.0002   | 0.0000      |
-| Right Turn from Minor Veh<br>Veh Collisions | Corner Radii: Minor<br>Road Width | 0.0113   | 0.0003      |
-
-**Table 5**  R-square values of response variables.
-
-| Response                                 | R-squared of pSEM |
-|------------------------------------------|-------------------|
-| Mean of Major Road Right to Left Speed   | 0.34              |
-| Mean of Major Road Left to Right Speed   | 0.34              |
-| Mean of Minor Road Speed (MMRS)          | 0.26              |
-| Right Turn from Minor Veh-Veh collisions | 0.60              |
-
-[Highways Agency, 1994\)](#page-8-0) that do not distinguish between left-hand and right-hand visibilities. The reasons behind this counterintuitive finding are not yet known. It may be the case that for right-turning vehicles, drivers first look for a gap to the right before looking for a gap to the left. When visibility to the right is good, this may encourage drivers to pull out into the intersection when a clear gap to the left is not yet apparent. Further work to investigate this phenomenon is required.
-
-Major road mean speeds are not significant predictors of turning
-
-movement collisions from the minor road. This result is not surprising. A microsimulation study of three-arm priority intersections by [Zhang et al.](#page-9-0)  [\(2017\)](#page-9-0) found that the number of conflicts for turning vehicles from minor roads decreased with increased speed on the major road. The authors thought that psychological factors could explain this situation that drivers turning from the minor road may drive cautiously when traffic speed on the major arm is relatively high.
-
-The results confirm the effects of speed limits and vehicle flows on the approach speeds as expected. On the other hand, Table 6. shows the effect of a + 1 standard deviation change in input variables on traffic collision frequency. The direct effect of traffic flow seems to be more potent than others. + 1 standard deviation change in the visibility (105.4 m) to the left, from the minor arm, was associated with a 31 % reduction of collisions over the observed period. To the contrary, the + 1 standard deviation change in the visibility (91.1 m) to the right was associated with an increase in the number of collisions by 27 %. Additionally, wider approaches and larger radii are associated with higher approach speeds on minor arms. + 1 standard deviation change in this combination caused by 45 % increase in the collision frequency. From a statistical perspective, [Fig. 6](#page-7-0) provides insight into why the interaction effect of corner radii and minor road width is significant. It is suggested here that this might be due to the fact that at wider minor arms, two cars can be at the give-way line together. Where the corner radii for the leftturning vehicle is small [\(Fig. 6](#page-7-0)A), the left-turning vehicle is likely to be
-
-**Table 6**  The effects of 1 SD change in independent variables on collision frequency.
-
-| Variable                              | Mean   | Standard<br>Deviation (SD) | Coefficient | Effect on<br>Collision (%)            |
-|---------------------------------------|--------|----------------------------|-------------|---------------------------------------|
-| Vis. Right 2.4                        | 80.5   | 91.1                       | 0.0026      | EXP (91.1x<br>0.0026) − 1 =<br>+27    |
-| Vis. Left 2.4                         | 104.8  | 105.4                      | − 0.0035    | EXP (105.4x<br>− 0.0035) − 1 =<br>-31 |
-| Traffic Flow                          | 3790.5 | 3295.6                     | 0.0002      | EXP (3295.6x<br>0.0002) − 1 =<br>+93  |
-| Corner Radii *<br>Minor Road<br>Width | 33.5   | 32.7                       | 0.0113      | EXP (32.7x<br>0.0113) − 1 =<br>+45    |
-
-<span id="page-7-0"></span>![](_page_7_Picture_2.jpeg)
-
-**Fig. 6.** The effect of the interaction of minor road width and turning radii.
-
-relatively perpendicular to the give-way line. In such an orientation, the driver of the right-turning vehicle sat at the same give-way line is clear as it passes over the bonnet of the left-turning vehicle. Where the corner radii for the left-turning vehicle is wider (Fig. 6B); due to the orientation of the left-turning vehicle, the vision of the right-turning vehicle is obstructed by the cabin space of the left-turning vehicle. See the diagram below.
-
-#### **5. Conclusion and future studies**
-
-The purpose of this study was to explain the complex interplay between approach width, radii, speed and visibility in the context of lowspeed limit priority intersections. This paper has provided a deeper insight and given novel evidence into visibility and traffic collision interactions. A novel pSEM approach was adopted to explore these complex interactions. The initial model's connections were refined using a backward elimination procedure to develop the final model. Results of the final pSEM model show that:
-
-- As expected, flows were found to be important, even in the context of low-speed limit areas.
-- Approach speed is affected by the speed limit and the combination of approach width and left corner radii.
-- On the minor approach, the effect of speed limits and geometry on vehicle speeds negates any apparent benefits of visibilities on speeds in low-speed limit locations. The lower visibility does not slow vehicles any more than the speed limits and geometry already do. This could imply that lower speed limits have a role to play, especially at intersections where visibility is already low. On the other hand,
-
-- visibilities appear to have a direct relationship with collision frequency rather than an indirect one via vehicle speeds.
-- Major road mean speeds are not significant predictors of the minor road turning movement collisions.
-- Visibilities did not moderate the effect of approaching vehicles' speed at low-speed intersections, while minor road width and turning radii are significant predictors of minor road speed.
-- The direct effect of traffic flow appears to be stronger than others.
-- Collisions are also related to the width and radius of the nearside kerb on minor arms at three-arm priority junctions.
-- Furthermore, wider minor road width and larger radii are related to faster minor arm approach speeds.
-- It is highly plausible that wider road width and corner radii allow a second vehicle wishing to turn left to be at the give-way line blocking the left-hand visibility.
-
-MfS promotes single-lane minor arm intersections and smaller turning radii (MfS section 9.4). However, this study found that the combination of these two factors appears to be significant rather than either factor on its own.
-
-The number of collisions for right-turning vehicles was directly related to visibility distances at 2.4 m back from the give-way line. All else being equal, higher left-hand visibility at 2.4 m increased safety, while greater right-hand visibility decreased safety. This contradicts the literature [\(York et al., 2007;](#page-9-0) TMS [Consultancy, 2010;](#page-8-0) [Summersgill et al.,](#page-9-0)  [2001\)](#page-9-0) and design standards [\(Department for Transport, 2007; CHIT,](#page-8-0)  [2010;](#page-8-0) [Highways Agency, 1994](#page-8-0)) that do not differentiate between leftand right-hand visibilities. The causes for this unexpected discovery are unknown. Drivers of right-turning vehicles may check for a gap to the
-
-<span id="page-8-0"></span>right before looking for a gap to the left. When visibility to the right is good, drivers may be tempted to pull out from the intersection due to a clear gap to the left is not yet visible. More research into this phenomenon is needed.
-
-Major and minor road speeds cannot be seen as a determining factor of right-turning from minor with major road motor vehicle collisions, even if it is a crucial aspect for another type of collision not included in this paper.
-
-As explanatory variables, visibilities at 9 m to the left and right did not significantly affect the type of collision presented in this paper. However, other types of collisions, such as motor vehicle-bicycle, should be analysed to understand the effect of visibilities at 9 m.
-
-While the study primarily focuses on driving behaviours in the United Kingdom where vehicles are driven on the left side of the road, it is essential to acknowledge the potential applicability of the findings to other high-income countries with right-hand driving practices such as United States. Despite the natural differences in traffic regulations and cultural norms between left and right-hand driving nations, there may be similarities in certain driving behaviours and decision-making processes. However, it is crucial to recognize that direct transferability may be limited due to contextual variations, such as infrastructure differences, road design, and socio-economic factors. Future research should explore how the findings can be estimated for diverse driving environments, thereby contributing to a more comprehensive understanding of global road safety dynamics.
-
-#### **CRediT authorship contribution statement**
-
-**Mustafa Ekmekci:** Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. **Lee Woods:** Conceptualization, Investigation, Methodology, Project administration, Supervision, Writing – review & editing. **Nima Dadashzadeh:** Conceptualization, Methodology, Supervision, Writing – review & editing.
-
-### **Declaration of competing interest**
-
-The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
-
-#### **Data availability**
-
-The data that has been used is confidential.
-
-#### **Acknowledgements**
-
-This work is a part of first author's PhD thesis which is fully funded by the Republic of Turkey Ministry of National Education, Çanakkale Onsekiz Mart University: Study Abroad Program.
-
-## **References**
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Zotero/001_artiklid/Road Safety Indicators/ekslermeasuringunderstandingroadsafety2010.md

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-category: literaturenote
-citekey: ekslermeasuringunderstandingroadsafety2010
-title: Measuring and understanding road safety performance at local territorial level
-authors: "Eksler, Vojtech"
-year: 2010
-date: 2010-00-00 2010
-doi: 10.1016/j.ssci.2009.12.010
-publication: Safety Science
-url: "https://doi.org/10.1016/j.ssci.2009.12.010"
-zotero_key: T7F962GA
-zotero_storage: 3A2M2QKK
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Eksler, Vojtech"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/09257535)
-
-# Safety Science
-
-![](_page_0_Picture_4.jpeg)
-
-![](_page_0_Picture_5.jpeg)
-
-# Measuring and understanding road safety performance at local territorial level Vojtech Eksler \*
-
-European Transport Safety Council (ETSC), 20 Avenue des Celtes, 1040 Bruxelles, Belgium
-
-#### article info
-
-Article history: Received 12 August 2009 Received in revised form 18 November 2009 Accepted 12 December 2009
-
-Keywords: Local territorial level Bayes risk Serious injuries Safety performance
-
-#### abstract
-
-Road safety performance has traditionally been measured at national level, because the national authorities have assumed main responsibility for managing road safety of citizens. With the rise of EU governance, the role of national government has slightly decreased while the role of local authorities has been reinforced in many countries, creating a new playfield for policy actions. Assessing road safety performance at local territorial level may provide new inputs needed to trigger further improvements in road safety, as it brings about a higher accountability of policy makers and brings relevant issues closer to citizens. The measures and methods for such evaluation have now become broadly available and their application may bring a difference in current pace of road safety improvements. An example of the application of full Bayes spatio-temporal model on local road risk data is provided, illustrating the potential of local areal analysis for a better road safety management.
-
-> -2009 Elsevier Ltd. All rights reserved.
-
-# 1. Introduction
-
-Historically, cities and regions have played an essential role in the formation of national-states and national societies. Their inhabitants gave the sense to the notion of national identity, cultural norms and habits. The sense of social identity, citizen responsibility and respect of authorities which are acquired through active community life have then a great impact on the way how people obey the law. Since behaviour and compliance with traffic rules are the key determinants for the occurrence of road crashes, it is straightforward to seek how to improve compliance with law at local level, at which many behaviour-shaping mechanisms operate.
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-The European integration has contributed to a certain relaxation of the 'constraints of state' and cities and regions have emerged as one possible level for regulation of interests, groups and institutions. Several European countries have recently seen their governance systems decentralized. Decentralized governance systems tend to have stronger accountability mechanisms and lower corruption ([Nas et al., 1986; Rose-Ackerman, 1999](#page-4-0)), which are the factors presumably related to compliance with road traffic law. [Lederman et al. \(2005\)](#page-4-0) argue that decentralization leads to a higher accountability, since easier monitoring means less severe informational problems at local level. In countries with decentralized administrative power, employment of the least amount of special legal powers of the state compatible with the task to be performed, are sought [\(Hood and Schuppert, 1988](#page-4-0)) to bring power as close to people as possible. Moreover, local governments are directly responsive to their citizens needs but may be subject to capture by elites [\(Bardhan and Mookherjee, 2006](#page-4-0)).
-
-A common feature of policy making in all so called SUN countries (Sweden, United Kingdom and the Netherlands, three countries with the best road safety record in the world) is a vital participation of civil society organized in associations and interest groups [\(Koornstra et al., 2002](#page-4-0)) and the proximity of policy making to citizens. These have a huge implication contributing to a higher accountability and responsiveness. It is indeed a common feature of road safety improvement strategies in countries with best road safety records not to rely on traditional topdown governance, in which actions at local level are governed exclusively from the national level to which they are subordinated. Specific arrangements and responsibility distributions could be tracked in SUN countries. While in the UK, the key areas of application were infrastructure improvements and traffic law enforcement including contracts between local police forces and local authorities on the number of checks of specific offences, in the Netherlands, the accountability was enhanced through the local transport plans, being regularly put under scrutiny by the central government. In Sweden, the local initiatives by civil society, companies and authorities were likely contributing to the improvements leading to its exceptional road safety record.
-
-Alongside with the natural interest in local territories as areas of policy actions in road safety, there are several other motifs to put local territorial level at the centre of attention of contemporary road safety research. First, it can be claimed that European and national authorities have already used a great part of the potential for interventions. The EU has already passed legislation on driver licensing, properties of vehicles, and safety of infrastructure.
-
-<sup>\*</sup> Tel.: +32 (0) 22 30 41 06. E-mail address: [vojtech.eksler@etsc.eu](mailto:vojtech.eksler@etsc.eu)
-
-National authorities have brought extensive legislation regarding road user behaviour, including tools for enhancing compliance with law, such as penalty point system. Fields of actions targeting citizens at local level have, at the same time, been left unexploited until now.
-
-Beside, territories have recently become the trial fields of numerous advances in road safety research, such as intelligent transport system applications (e.g. intelligent speed adaptation, alcohol interlocks, or the shared-space urban road design concept).
-
-To allow assessment to be made on the impact of these innovative concepts on road safety, there is a need for evaluation tools and techniques, which are reliable and robust, but at the same time simple and understandable. This contradiction is most likely the reason why assessment of road safety level is so rarely done at local level. However, exceptions exist and several observation and evidence based studies could be identified in the literature. Their authors realized that at specific cases, it is indeed more feasible to somewhat restrain the geographical area for running statistical analysis. For example [Servadei et al. \(2003\)](#page-4-0) assessed the effect of Italy's motorcycle helmet law on brain injuries performed at Romagna region and [Farchi et al. \(2007\)](#page-4-0) assessed the effectiveness of the new penalty point system in Italy in Lazio region. Similarly, there are studies showing the evidence of differences in road users' behaviour at sub-national level. For example, there are significant intra-national differences in terms of the usage rate of restraint systems between Italian ([Taggi](#page-5-0) [et al., 2006\)](#page-5-0) or Swiss regions ([Siegrist et al., 2006\)](#page-4-0). Similarly, the prevalence of driving while intoxicated varies between regions of France, Italy or Spain.
-
-To summarize, there is a growing interest in local territorial level as the area of various policy actions. There is also a great potential for road safety improvements through local policy actions, which should not be ignored. The thorough understanding of mechanisms and the availability of tools for the statistical analysis are the main prerequisites for allowing road safety practitioners to evaluate the effects of various policy measures applied at local level and give appropriate feedback to policy makers on the effectiveness of their actions.
-
-# 2. Framework for the analysis
-
-There are numerous consideration issues when it comes to assessing road safety performance at local territorial level. They include among else the choice of spatial unit, time frame, indicators and relevant statistical method, but all of these are driven by the general framework for the analysis.
-
-The assessment of road safety at local territorial level is of a particular interest where the main areas of policy actions are likely corresponding. The administrative areas of a country are to be considered first, as the areas of general actions, spending powers and targeted policy actions. As second, the administrative area of police forces is to be considered, as police enforcement is one of the most efficient road safety interventions. Last but not least, the invisible borders of cultural and normative background should be taken into account [\(Wegman et al., 2008\)](#page-5-0).
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-Ideally, these would correspond, allowing a non-distorted picture about the effectiveness of policies to be made on the background of road safety outcomes analysis. Recently, benchmarking as an evaluation tool has gained popularity also in road safety. The benchmarking as an integrated part of a continuous improvement plan should be viewed as a set of activities designed to bring gradual, but continual improvement to a process through constant review. It can be understood as an implementation tool of the Shewhart's cycle consisting of plan, do, check, act (PDCA), which is perhaps the best known theoretical framework for improvements [\(Shewhart, 1939\)](#page-4-0). Thus the analysis is the core of the improvement process.
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-The quantitative analysis of crash outcomes as the measure of road safety performance is thus the prerequisite for managerial approach in road safety. As lord Kelvin put it: ''I often say that when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science, whatever the matter may be" ([Stellman, 1998\)](#page-5-0).
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-The evaluation of road safety performance could be accomplished by analyzing the level and trend in road crash outcomes standardized per exposure. Crash outcomes traditionally considered by road safety practitioners are road fatalities. Road injuries are often missing, as the statistics suffer from incompleteness, due to underreporting and data handling deficiencies. The use of health system based data combined with police reports may overcome this weakness. While at national level, health care systems could provide data necessary for the estimation of indicators regarding injury; in some countries the emergency department is the most common source especially for slight injuries, while in others, there are general practitioners or primary care centres ([Gio](#page-4-0)[rgi Rossi et al., 2005](#page-4-0) or [MacKay, 2003\)](#page-4-0). While in case of fatal and serious injuries, the local registrations are forwarded to central registry, this is not always the case for other types of injury data. But road injury data are available at registry of all emergency medical care centres operating under given jurisdiction in a given territory. To that end, it can be argued that at local level, road injury data are easier to collect and link with road crashes. This is also the main reason why studies on underreporting of road crash injuries are usually carried out at local territorial level, such as regions ([Amoros et al., 2003](#page-4-0)). Thus ideally, the use will be made of both fatal and serious injury counts at local level. As regards exposure indicator, vehicular travel is preferred over a static use of population of vehicle registry data.
-
-It is a common practice to benchmark road safety performance of countries, despite obvious difficulties with the interpretation of differences identified. While a substantial part of the gap between any two countries can be explained by differences attributable to the application of different road safety policies, there is always a part of the gap caused by structural differences, such as road network composition, climate, urbanization, climate, culture and many others. Similarly, differences in the development of risk indicators at national level can be partly attributable to changes in mobility patterns, traffic performance and modal split.
-
-At local territorial level, many of these factors do not have to be taken anymore in consideration when searching for the explanation of various road safety performances ([Wegman et al., 2008\)](#page-5-0). Factors such as climate, culture or norms could be often considered the same for sub-national entities. At the same time, however, some physical structural factors could come to play a more important role, but they can be easily accounted for by a simple standardization. For example, [Eksler and Lassarre \(2008\)](#page-4-0) identified the population density and urbanization as the single most important synthetic factor suitable for standardizing risk rates at sub-national level, when a comparison of road safety levels is made. The trend in road safety indicators is likely less sensitive to certain structural factors, despite some evidence on the effect of urbanization on amenability to road safety intervention. Benchmark on the trend of the risk thus has a higher explanatory value compared to the benchmark made at national level, where the availability of effective interventions can make a crucial difference in the performance over the time.
-
-# 3. Analytical methods
-
-Injuries from road crashes had originally been understood as a part of public health problem. But due to their relatively limited weight on health system at beginning of 20th century, they were often marginalized and eventually became forgotten part of public health research and initiatives ([Porter, 1999\)](#page-4-0). As a consequence, modern statistical techniques developed in epidemiology were rarely applied in road safety research. More particularly, the methods for statistical analysis in epidemiology have developed considerably during the past century, including changes in basic concepts, methods of data analysis, and methods of exposure measurement [\(Pearce, 1996](#page-4-0)). For example, since early 1990s, we have been witnessing a broad application of Bayesian analysis in descriptive epidemiology contributing to major advances in ecological research.
-
-Such a gradual development in analytical methods has not always been a case in road safety research, where the basic concepts and methods have undergone a rather limited development. The use of modern statistical methods such as the use of Bayesian statistical techniques have been limited to the analysis of road crash data on road stretches rather than to general data on territorial level (see e.g. [Heydecker and Wu, 2001](#page-4-0) or [Qin et al., 2005\)](#page-4-0).
-
-When analyzing various road safety outcomes in terms of counts or rates, different statistical approaches must be considered for different spatial levels of analysis. More particularly, when analyzing scare events at local level, the rough rates are not more appropriate as the counts suffer from the randomness. For the difference in road deaths counts to be statistically significant, a large difference is needed. For example, if there were 20 deaths in one year, there must be maximum 12 deaths in another for the observed reduction to be statistically significant. This only demonstrates an obvious difficulty in working with crash outcomes of lower frequency such as road fatalities and trying to figure out the differences in their occurrence in time and space. This is particularly true for the analysis at sub-national level. Several spatial levels are usually considered in road safety analysis, from continent to the tiny communities. While the numbers analysed at the local level can be too small and subjected to random variations to produce reliable rates, the numbers at aggregated spatial level are usually enough stable and representative to be analysed directly as crude rates. Analysis of road risk rates at different levels of spatial aggregation may therefore require different statistical approaches and methods.
-
-While at the level of EU and its member states the use of crude rates appears satisfactory, at the level of small areal units, the use of relative rates estimated via Bayesian methods reveals to be utterly indispensable. Similarly, where the traditional methods encompassing crude rates are appropriate for the analysis done at continental and country level, the analysis performed at regional or community level necessitates special statistical methods to be applied.
-
-Similarly, different road safety outcomes should be considered at local territorial level, to overcome the problem with the scarcity of crashes and their outcomes. Counts of serious injuries on roads are the obvious additional measures beside traditionally used counts of road deaths (see Table 1).
-
-Table 1 Type of rates and outcomes for the assessment of road safety at local level.
-
-| Level          | Rates | Outcomes     |
-|----------------|-------|--------------|
-| EU             | Crude | Deaths (KSI) |
-| Countries      | Crude | Deaths (KSI) |
-| Regions        | Bayes | KSI          |
-| Municipalities | Bayes | KSI          |
-
-Applying absolute figures of fatalities or other road safety outcomes and dividing them by a chosen exposure is the simplest and most commonly used method for comparing road safety performance of countries. Epidemiologists often insist in the necessity to account for the demography by applying Standardized Mortality Ratio (SMR), but this approach is rarely used in road safety analysis. Epidemiologists insists that standardization is necessary when comparing several populations that differ with respect to age (and/ or gender), because age and gender have such a powerful influence on health risk. Despite the recent evidence of some epidemiological studies that such indirect standardization is inappropriate for small area comparisons (not necessarily in terms of area, or population size), since SMRs for different geographic areas have different denominators ([Julious et al., 2001](#page-4-0)), this is not a major obstacle for an analysis that includes regions, because fairly similar geographic units are usually subject to assessment.
-
-The risk faced by people in road traffic is not equal for men and women, and the degree of risk also varies significantly with age ([Massie et al., 1994; Evans, 2001](#page-4-0)). Given the demographical differences between sub-national units, it is straightforward to apply the sex–age standardization to road injury rates. The following formula is then used to calculate the SMR of a particular geographical unit (country, region):
-
-$$SMR_i = \frac{Y_i}{Y_i^*}$$
-
-where Yi is the observed number of road injuries in the unit i; Y<sup>i</sup> is the expected number of road injuries in the same unit i.
-
-$$Y_i^* = \sum_{jk} \tau_{jk} \cdot N_{ijk}$$
-
-where sjk, is the standardized injury rate of the age–sex group j, k; Nijk is the population count of this group in the geographical unit i.
-
-Comprehensive set of analyses unveiled that the demography accounts for up to 10% of the variance in road mortality registered within regions of some EU countries. The effect of population structure is usually insignificant and seems to be more relevant in larger countries such as Germany or Spain [\(Eksler, 2007](#page-4-0)).
-
-The concept of standardized ratios could be applied for all types of injury and indeed combined with different measures of exposure. However it has some limitations as the events under consideration are scarce and subjected to randomness. Given that the variance in rates is proportional to the square root of the mean, there will be high sampling variability for the small area units. To overcome this variability one can carry out smoothing of the raw rates via hierarchical modelling. This approach can be justified in a likelihood random effects context, or via a minimum mean squared error argument and it is now exclusively applied from a fully Bayesian perspective. The approach is more flexible in adapting to each unique situation, more efficient in using all available evidence and more useful in providing relevant quantitative arguments, than traditional methods [\(Spiegelhalter et al., 2004](#page-5-0)).
-
-Crash injuries, where the crash injury count is denoted Yi, expected number of injuries k<sup>i</sup> and the exposure (number of inhabitants or traffic volume in MVKMs) Ei are modelled assuming the data to be Poisson distributed:
-
-$$Y_i \sim Poisson(\lambda_i)$$
-
-This model can be rewritten in terms of rates rather than counts as:
-
-$$Y_i \sim Poisson(\theta_i E_i)$$
-
-where h<sup>i</sup> is the crash injury rate in area i and Ei is the exposure in the area i, here either the number of registered population of area i, or the estimated number of kilometers travelled by motorized vehicles within the area i. The k<sup>i</sup> = hiEi and the log(Ei) are treated as known offset terms. The maximum likelihood estimates h<sup>i</sup> = Yi/Ei of this classical model assumed in many disease mapping studies correspond to standardized ratios. These ratios would be highly unstable due to the use of sparse character of the counts modelled. In the generalized linear model the h<sup>i</sup> for each area is a function of covariates and parameters indicating the association between the covariates and the rate. This model can be extended to form a generalized mixed linear model incorporating a random effect within hi. The modelling is implemented for counts (Yi), assuming that the response Yi's given a vector m of random effects are conditionally independent.
-
-$$\lambda_i | v \sim \text{Poisson}(\theta_i^v E_i)$$
-
-hm <sup>i</sup> represents the expected rate of Yi/Ei given all the random effects. The log risk is modelled as:
-
-$$\log(\theta_i^{\nu}) = \alpha + \boldsymbol{X}_i \boldsymbol{\beta} + \nu_i$$
-
-where a is a fixed effect, X<sup>i</sup> represents a vector of explanatory variables (if considering an ecological model), b a vector of fixed effect parameters, m<sup>i</sup> is the unstructured heterogeneity (error) known also as random effects, therefore, this kind of model is commonly known as a mixture model where fixed and random effects are combined (mixed) together. The exp(mi)'s represent local area crash (injury) ratios adjusted for the explanatory variables. If m<sup>i</sup> is drawn from the zero mean normal distribution m = N(0, r<sup>2</sup> ), where r<sup>2</sup> is the variance parameter, the model takes on a pretty standard mixed form, with only r having to be estimated during the model fitting procedure. A prior gamma distribution Gamma (0.5, 0.0005) is assumed for the precision equal to the inverse variance.
-
-The application of the full Bayes modelling can be demonstrated by the example of a Bayesian analysis of road fatality risk at municipality level in Belgium over the period 2001–2006 [\(Eksler and](#page-4-0) [Lassarre, 2008\)](#page-4-0). Fatalities per vehicular travel in terms of motor vehicle kilometres were used here as the measure of road safety performance. The full Bayes spatio-temporal model has been run, allowing to account for the structure in underlying crash mechanisms distribution, resulting in a smoothed representation of fatal road injury risk distribution across the country. The model had a following form:
-
-$$\log(\theta_{ij}^{v}) = \alpha + \beta \cdot X_{ij} + v_i + (\varphi + \delta_i)t_j$$
-
-where Yij is the observed number of road fatalities for the ith area, (i = 1, ..., N), and the jth year, (j = 1, ..., T), a the constant term, xij the covariate for the ith area, in the jth year, b the regression coefficient, m<sup>i</sup> the total heterogeneity of the intercept (consisting of Vi the unstructured heterogeneity and Ui the spatially structured heterogeneity), u the mean linear time trend over all areas, tj the year j, and d<sup>i</sup> spatial structured heterogeneity of the time trend.
-
-It is possible to map the Bayes relative risk as the exponential of various random effects. The temporal trend estimate expressed as the 1 exp(di) could be mapped to provide an idea about the distribution of the trend in fatality risk across the country. The area specific time effect consists here again of the unstructured and structured component. In Fig. 1, the structured component is mapped only, as to highlight the underlying structure in road risk trend. When using the estimated vehicular travel as the exposure of risk, the effect of safe agglomeration when measured by road mortality disappears and the single spots are no more visible on the map. The nests of municipalities with similar trends appear, in most cases following the border of hierarchically superior regions. The latter allow a judgement to be made about the effectiveness of regional road safety policies in the country. The gradient appearing in the map of the trend in fatality risk points to the
-
-![](_page_3_Figure_12.jpeg)
-
-Fig. 1. Structured part of Bayes Relative Risks (BRR) and of the relative temporal trend estimates for Belgian municipalities (2000–2005).
-
-<span id="page-4-0"></span>north–south division of country in terms of reduction, which was in general very high in the Northern part of the country (Flanders) and much smaller in the Southern part of the country (Wallonia). This may point to different effectiveness of road safety policies in the two regions.
-
-When aiming at the comparison of risk levels, or providing full understanding on the distribution of risk and its trends, a direct standardization of risk rates could be useful. It is possible to standardize by a large number of variables depending on the exposure considered and explanation sought. For example, when analyzing mortality rates, population density could be used as suggested by numerous authors (e.g. Orselli, 2003; Baker et al., 1987 and Chapelon 2002). These authors identified an almost log linear relationship between the road mortality and population density at the level of countries and regions. The direct standardization by population density reduces the variance of mortality rates significantly. Eksler (2009) estimated that for different EU countries, on average 60% of the variance of road mortality comes from the sub-national differences in population density. The remaining difficulty is to correctly interpret rates standardized by explanatory factors such as population density and provide relevant recommendations for policy making, since some factors remains abstract and not directly linked with measures at the scope of policy makers.
-
-# 4. Discussion and conclusions
-
-There are two basic reasons for comparing safety performance of sub-national jurisdictions. In the first place, a ranking of relative performance of each are very useful for comparison within countries. To ensure a meaningful benchmarking process, factors that could enable safety practitioners to achieve similar safety improvements need to be included within the ranking process. This might be done by developing a model of safety outcomes that includes these factors. The second reason is to provide better understanding of the factors affecting safety improvement, so that safety practitioners can design more effective programmes. This requires greater focus on understanding how the effects of programmes are modified by the nature of the safety problems faced by each area. Lessons can not only be learned from comparison between areas within countries, but also from comparison between similar areas in different countries. This can probably be more effectively achieved by case studies comparing small groups of areas, than by attempting to model a wide range of areas. Evaluation of road safety performance at local territorial level requires an application of good measures of safety performance and of appropriate analytical tools. Most notably, the analysis should be based on fatal and serious injury figures and the application of robust statistical techniques shall be assured.
-
-The methods described in this paper are purely descriptive and cannot fully explain distribution of risk and of its trend at local territorial level. A direct standardization by explanatory factors may be necessary, but the choice of appropriate factors remains a difficult task. Analyses making use of highly sophisticated statistical techniques could bring new impetus to the work of road safety practitioners and policymakers, but their impact could be limited by the fact that the results of statistical modelling could be difficult to communicate to general public.
-
-The analysis of local area road safety data should be viewed as a complementary approach to local road crash data analysis. Recent advances and increased popularity of modern geographic information system applications made it possible to regularly analyse crash data in respect to road network leading to the identification of high risk sections. Such analysis is a common part of network safety management and a crucial tool for road administrators. Its benefits are concentrated around infrastructure improvements, while the benefits of local area analysis could potentially point to other target areas for safety improvements.
-
-When benchmarking the performance of local entities, the trend is preferred above the level, which is likely more sensitive to various structural factors attributable to local territories. The benchmark of the trend (progress in improving road safety) is also more appropriate measure in respect to policymakers' accountability. Benchmarking road safety performance at local territorial level has a great potential for road safety improvements. The methods and tools for such benchmarking have now become available and road safety authorities shall be encouraged to apply them alongside with traditional approaches.
-
-### References
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-Amoros, E., Martin, J.L., Laumon, B., 2003. Comparison of road crashes incidence and severity between some French counties. Accident Anal. Prev. 35 (4), 537–547. Baker, S., Whitfield, R.A., O'Neill, B., 1987. Geographic variations in mortality from
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-Chapelon, J., 2002. Un nouvel indicateur d'accidentologie locale. In: Courier des statistiques, vol. 103. September 2002.
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-Eksler, V., 2007. Road mortality in Europe: how sensitive is it to demographic structure and population dynamics? IATSS Res. 31 (1), 80–88.
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-Eksler, V., 2009. Road mortality in Europe: a regional approach, PhD thesis, University of Versailles St-Quentin, Versailles, France.
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-Giorgi Rossi, P., Farchi, S., Chini, F., Camilloni, L., Borgia, P., Guasticchi, G., 2005. Road Traffic Injuries in Lazio, Italy: a descriptive analysis from an emergency roombased surveillance system. Ann. Emerg. Med. 42, 152–157.
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-Heydecker, B.G., Wu, J., 2001. Identification of sites for road accident remedial work by Bayesian statistical methods: an example of uncertain inference. Adv. Eng. Soft. 32 (10–11), 859–869.
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-Hood, C., Schuppert, G.F. (Eds.), 1988. Delivering Public Services in Western Europe, Beverly Hills and London. SAGE Publications, London. 268 p.
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-Julious, S.A., Nicholl, J., George, S., 2001. Why do we continue to use standardized mortality ratios for small area comparisons? J. Public Health Med. 23, 40–46. Koornstra, M., Lynam, D., Nilsson, G., Noordzij, P., Petterson, H.E., Wegman, F.M., Wouters, P., 2002. SUNflower: a Comparative Study of the Development of Road Safety in Sweden, the United Kingdom, and the Netherlands. SWOV,
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-Liedschendam. Lederman, D., Loayza, N.V., Soares, R., 2005. Accountability and corruption: political institutions matters. Econo. Polit. 17 (1).
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-MacKay, M., 2003. National differences in European mass accident data bases. In: Gennarelli, T.A., Wodzin, E. (Eds.), Contemporary Injury Severity and Outcome Issues. a Joint Programme of the AAAM and IRCOBI 24 September. IRCOBI, Born France. pp. 51–55.
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-Massie, D.L., Campbell, K.L., Williams, A.F., 1994. Traffic accident involvement rates by driver age and gender. Accident Anal. Prev. 27, 73–87.
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-Orselli, J., 2003. L'analyse statistique des variations spatio-temporelles des accidents de la route, Conseil générale des Pont et Chaussées, No. 2002-0153- 01.
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-Pearce, N., 1996. Traditional epidemiology, modern epidemiology, and public health. Am. J. Public Health 86 (5), 678–683.
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-Porter, D., 1999. Health, Civilization, and the State: a History of Public Health from Ancient to Modern Times. Routledge, New York. vi + 376 pp.
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-Qin, X., Ivan, J.N., Ravishanken, N., Liu, J., 2005. Hierarchical bayesian estimation of safety performance functions for two-lane highways using Markov Chain Monte Carlo modeling. J. Transp. Eng. 131 (5), 345–351.
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-Rose-Ackerman, S., 1999. Corruption and Government: Causes, Consequences, and Reform. Cambridge University Press, Cambridge. 290 p.
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-Servadei, F., Begliomini, C., Gardini, E., Giustini, M., Taggi, F., Kraus, J., 2003. Effect of Italy's motorcycle helmet law on traumatic brain injuries. Inj. Prev. 9, 257–260. doi[:10.1136/ip.9.3.257.](http://dx.doi.org/10.1136/ip.9.3.257)
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-Shewhart, W.A., 1939. Statistical Method from the Viewpoint of Quality Control. Dover, New York.
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-Siegrist, S., Allenbach, R., Cavegn, M., Nieman, S. And Ackermann, Y., 2006. SINUS report 2006, Sicherheitsniveau und Unfallgeschehen im Strassenverkehr, 2005. Schweizerische Berungsstell für Unfallverhütun BfU, Bern.
-
-- <span id="page-5-0"></span>Spiegelhalter, D., Thomas, A., Best, N., Lunn, D., 2004. WinBUGS User Manual Version 2.0. MRC Biostatistics Unit, Cambridge, UK.
-- Stellman, J.M., 1998. Lecture on ''Electrical Units of Measurement" (3 May 1883), published in Popular Lectures vol. 1, p. 73, quoted in Encyclopaedia of Occupational Health and Safety (1998) by Jeanne Mager Stellman, p. 1973.
-- Taggi, F., Dosi, G., Giustini, M., Crenca, A., Cedri, C., Fondi, G., Iascone, P., Marturano, P. 2006. The Ulisse System for the monitoring and the use of the safety belts and
-- the helmets in Italy (2000–2005). Rapporti ISTIAN 06/39. Istituto Superiore di Sanita, In Italian.
-- Wegman, F., Commandeur, J., Doveh, E., Eksler, V., Gitelman, V., Hakker, S., Lynam, D., Oppe, S., 2008. SUNflowerNext: towards a Composite Road Safety Performance Index. SWOV Institute for Road Safety Research, Leidschendam, the Netherlands.

Zotero/001_artiklid/Road Safety Indicators/elbasyounyapplicationgeneralizedlinkfunctions2010.md

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-category: literaturenote
-citekey: elbasyounyapplicationgeneralizedlinkfunctions2010
-title: Application of generalized link functions in developing accident prediction models
-authors: "El-Basyouny, Karim; Sayed, Tarek"
-year: 2010
-date: 2010-00-00 2010
-doi: 10.1016/j.ssci.2009.09.007
-publication: Safety Science
-url: "https://doi.org/10.1016/j.ssci.2009.09.007"
-zotero_key: FDHG26KS
-zotero_storage: 5EWGTQW7
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "El-Basyouny, Karim"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/09257535)
-
-# Safety Science
-
-![](_page_0_Picture_4.jpeg)
-
-![](_page_0_Picture_5.jpeg)
-
-# Application of generalized link functions in developing accident prediction models
-
-Karim El-Basyouny \*, Tarek Sayed <sup>1</sup>
-
-Dept. of Civil Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4
-
-#### article info
-
-Article history: Received 29 September 2008 Received in revised form 8 September 2009 Accepted 16 September 2009
-
-Keywords: Canonical link Full Bayes estimation Generalized link Markov Chain Monte Carlo Poisson–gamma hierarchy
-
-#### abstract
-
-The Bayesian Poisson–Gamma hierarchy, leading to the negative binomial distribution, has been the standard practice in developing accident prediction models. To linearize the relationship connecting the mean of the negative binomial distribution to relevant covariates, a canonical log link has traditionally been used. Typically, little information is available regarding the choice of a particular link. To avoid link misspecification, it is proposed to nest the canonical log link model within a generalized link family and subsequently use the full Bayes method for parameter estimation, performance evaluation and inference. The proposed approach was applied to a sample of accident and traffic volume data corresponding to 99 intersections in the city of Edmonton, Alberta. The results showed that both the generalized link model and the traditional canonical link model provided adequate fit to the data. However, the Bayes factor provided a clear statistical support for the use of the generalized link approach. A procedure for link validation is also described. It allows the users (e.g., road authorities) to consider the changes in predicted accidents that will result if a generalized link is used instead of a canonical link. If a certain maximal change is tolerated, the canonical link can be used to analyze the data; otherwise the generalized link is worth the extra efforts and should be adopted. When compared with the traditional approach, the generalized link model was found to predict a lower number of accidents whenever there is a heavy traffic at the major approach, especially if combined with light flow on the minor approach. The paper concludes by identifying out areas for further research.
-
-> Crown Copyright -2009 Published by Elsevier Ltd. All rights reserved.
-
-#### 1. Introduction
-
-The application of accident prediction models (APMs) in assessing the safety of a road entity (intersection, road segment, etc.) has become a standard practice among safety researchers and practitioners. An APM is a mathematical model that relates the expected accident frequency at a road entity to the various traffic and geometric characteristics of that entity. Accident prediction models have several road safety applications such as: evaluating the safety of various road facilities, identifying and ranking accident prone locations and evaluating the effectiveness of road safety improvement measures ([Sawalha and Sayed, 2006](#page-6-0)).
-
-Current practice utilizes the negative binomial (NB) model, which results from a Poisson–Gamma hierarchy, to develop APMs. The log function (canonical link) is usually used to connect the random (accidents) and systematic (traffic and geometric characteristics) components of the NB model to produce a linear predictor. Nevertheless, there is often no or little information available concerning the appropriate link to use in a given application. An improper choice of link may result in a link misspecification leading to substantial bias in the model coefficients and the mean response estimates [\(Czado and Munk, 2000; Czado and Raftery,](#page-6-0) [2006;](#page-6-0) and the references therein). To avoid this problem, link families have been considered by [Pregibon \(1980\)](#page-6-0) and [Czado \(1992\).](#page-6-0) Such families generalize the canonical link through the use of an extra parameter h > 0 characterizing the generalized link. The basic idea has been to embed the canonical log link of the NB model, which corresponds to the choice h = 1, in a wide parametric class of links and allow for a data selected choice of the link function.
-
-This paper proposes that generalized links are considered while developing APMs. If the canonical link is validated against the generalized link, then it can be used, otherwise the generalized link is to be adopted. The proposed approach is applied to a sample of accident and traffic volume data corresponding to 99 intersections in the city of Edmonton, Alberta. The objective is to compare the generalized and canonical link functions with respect to accident prediction. The statistical software WinBUGS [\(Lunn et al., 2000\)](#page-6-0), which provides MCMC sampling for a variety of Bayes models, was used in the analysis.
-
-The rest of the paper is organized as follows: Section [2](#page-1-0) describes previous work. A family of generalized links is introduced in Section [3,](#page-1-0) where the issues of link misspecification and validation are discussed. Section [4](#page-2-0) describes the methodology used for
-
-<sup>\*</sup> Corresponding author. Tel.: +1 604 716 4470.
-
-E-mail addresses: [basyouny@civil.ubc.ca](mailto:basyouny@civil.ubc.ca) (K. El-Basyouny), [tsayed@civil.ubc.ca](mailto:tsayed@civil.ubc.ca) (T. Sayed).
-
-<sup>1</sup> Tel.: +1 604 822 4379.
-
-<span id="page-1-0"></span>estimating the models as well as goodness of fit measures. The data set and the model used in the analysis are described in Section [5.](#page-3-0) The results are presented in Section [6](#page-4-0) and are discussed in Section [7](#page-5-0). Section [8](#page-5-0) presents a summary and conclusions.
-
-#### 2. Previous work
-
-Some of the most commonly applied statistical models to accident data include: binomial, Poisson, Poisson–gamma (negative binomial), Zero-Inflated Poisson (ZIP), Zero-Inflated Negative Binomial (ZINB), and multinomial probability models [\(Lord et al., 2005\)](#page-6-0). They showed that the Poisson and NB models serve as statistical approximations to the accident process, but since over-dispersion is exhibited in most accident data the APM models based on NB are usually preferred over Poisson regression models.
-
-Let Y be the random variable that represents the accident frequency at a given location during a specific time period, and let y be a certain realization of Y. The mean of Y, denoted by K, is itself a random variable. For K = k, Y is Poisson distributed with parameter k. It is the usual practice to assume that the distribution of K can be described by a gamma probability density function with shape parameter j and scale parameter j/l. Then, the distribution of Y is negative binomial with a mean l and variance l + l<sup>2</sup> /j.
-
-Several other distributions were proposed for developing APMs. For instance, assuming a normal distribution for K leads to the Poisson-lognormal model. Since the lognormal tails are asymptotically heavier than those of the Gamma distribution [\(Kim et al.,](#page-6-0) [2002](#page-6-0)), the Poisson–lognormal is a good candidate for modeling accident rates in the presence of outliers. [Lord et al. \(2008\)](#page-6-0) used the Conway–Maxwell–Poisson distribution to develop APMs. Such a distribution is a generalization of the Poisson that can cope with both over- and under-dispersion. They found that the generalized linear models based on the Conway–Maxwell–Poisson distribution perform as well as NB models in terms of goodness of fit and predictive performance.
-
-Recently, researchers have tried to address a problem arising from the ''excess" zeroes in accident data, by applying zero-inflated probability models such as ZIP and ZINB. The models assume that a dual-state process ''safe" and ''unsafe" is responsible for generating the accident data. The general consensus indicated an improved fit to data compared to the regular Poisson and NB models. [Lord et al.](#page-6-0) [\(2005\)](#page-6-0) criticized the dual-state approach and argued that ''excess" zeroes arise from a combination of low exposure, high heterogeneity and sites categorized as high risk; inappropriate selection of time/space scales; under-reporting; and/or omitting important covariates. They recommended that a careful selection of the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small area statistical methods (for data characterized by low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.
-
-Developing APMs using data characterized by a low sample mean is problematic, especially if combined with a small sample size due to the expensive costs of collecting accident data and other relevant variables. The low mean problem has been studied in the traffic safety literature, see [Maher and Summersgill \(1996\),](#page-6-0) who showed how such a problem affects the goodness of fit of APMs, [Wood \(2002\),](#page-6-0) who proposed a method to test the fit of APMs developed using data exhibiting a low mean, and [Lord \(2006\)](#page-6-0), who confirmed that a low sample mean combined with a small sample size can seriously affect the estimation of the dispersion parameter and undermine common analyses performed in highway safety.
-
-The majority of APMs were developed using a NB model with a fixed dispersion parameter. Such an assumption was challenged and various dispersion parameter relationships were examined ([El-Basyouny and Sayed, 2006](#page-6-0)). In particular, [Mitra and Washing](#page-6-0)[ton \(2007\)](#page-6-0) indicated that the modeling of the dispersion parameter depends on how the mean structure is modeled. It appears that extra-variation is a function of covariates when the mean structure is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean extra-Poisson variation is not significantly related to these variables.
-
-Let Xj, j = 1, ... , p, represent the independent variables (e.g., traffic flow and other site characteristics). The log function, which is the canonical link for the NB distribution, is then used to relate the mean l to the covariates
-
-$$ln(\mu) = \beta_0 + \beta_1 X_1 + \dots + \beta_p X_p. \tag{1}$$
-
-The functional form in Eq. (1) has been extensively reviewed [\(Miaou](#page-6-0) [and Lord, 2003](#page-6-0)). The most common functional form used for developing APMs for road intersections is
-
-$$ln(\mu) = ln(\alpha_0) + \alpha_1 \, ln(V_{\it mj}) + \alpha_2 \, ln(V_{\it mi}) + \sum_{j=1}^{\it m} \gamma_j X_j, \eqno(2)$$
-
-where Vmj is the annual average daily traffic (AADT) on the major approach, Vmi is the AADT on the minor approach, the Xj are covariates representing certain traffic and geometric characteristics, and a0, a1, a2, c1, ... , c<sup>p</sup> are model parameters. The corresponding form used for developing APMs for road segments is
-
-$$\ln(\mu) = \ln(\alpha_0) + \alpha_1 \ln(L) + \alpha_2 \ln(V) + \sum_{i=1}^{m} \gamma_i X_i, \tag{3}$$
-
-where L is segment length and V is the annual average daily traffic (AADT). [Miaou and Lord \(2003\)](#page-6-0) examined the mathematical limitations of the various functional forms, particularly their properties at the boundaries. The authors demonstrated that, for a given data set, a large number of plausible functional forms with almost the same overall statistical goodness of fit are possible. Furthermore, they introduced an alternative class of logical formulations that may enable a richer interpretation of the data.
-
-Other methods for developing APMs that were recently researched include: the application of the multivariate Poisson-lognormal (MVPLN) to model accident count data at different levels of severity ([El-Basyouny and Sayed, 2009a](#page-6-0)), the use of a two-state Markov Switching model [\(Malyshkina et al., 2009\)](#page-6-0) to analyze collision frequencies, and the use of models with random parameters ([El-Basyouny and Sayed, 2009b\)](#page-6-0).
-
-#### 3. Generalized links
-
-As we have seen above, previous work in the traffic safety literature has investigated many aspects of APMs, except the link function. Canonical links in generalized linear models, such as the log link in NB regression, are used to linearize the relationship between the predicted value and the predictors. They are popular among practitioners as they provide maximum information and simple interpretation of the regression parameters ([McCullagh](#page-6-0) [and Nelder, 1989\)](#page-6-0). Yet, a canonical link may not furnish the best fit available for a given data set.
-
-It has been shown in the statistical literature that the use of an inappropriate link results in a link misspecification, which might lead to a substantial bias in the model estimates ([Czado and Munk,](#page-6-0) [2000; Czado and Raftery, 2006](#page-6-0); and the references therein). Noting that the choice of the link function is often made arbitrarily, recent research has: (i) permitted the data to estimate the link function by incorporating it as an unknown in the model [\(Mallick and Gelfand,](#page-6-0) [1994\)](#page-6-0), (ii) described full Bayesian inference for generalized linear models where uncertainty exists about the structure of the linear <span id="page-2-0"></span>predictor, the linear parameters and the link function [\(Ntzoufras](#page-6-0) [et al., 2003\)](#page-6-0), and (iii) used a non-parametric approach to estimate a generalized additive model with unknown link function ([Horowitz and Mammen, 2007](#page-6-0)).
-
-To guard against link misspecification, the canonical link is usually embedded into a wide class of links. One such useful family of link functions is given by [Czado \(1997\), Czado and Raftery \(2006\)](#page-6-0)
-
-$$\mu(\eta, \theta) = \exp\{[(1 + \eta)^{\theta} - 1]/\theta\}; \quad \eta \geqslant 0, \theta > 0,$$
- (4)
-
-where g = Xb, X = (1, X1, ... , Xp) and b ¼ ðb0; b1; - ; bpÞ 0 . In the generalized linear models terminology, Eq. (4) is actually an inverse link function corresponding to the following (forward) link function
-
-$$g(\mu, \theta) = (\theta \ln(\mu) + 1)^{1/\theta} - 1; \quad \mu \ge 0, \theta > 0.$$
- (5)
-
-Regarding notation, the simplified notation lh will be used instead of l(g, h) unless it is needed to stress the dependence of l on g. Note also that as h increases away from 1, lh increases away from l<sup>1</sup> on one hand, while the scale of the regression parameters b<sup>i</sup> is reduced on the other hand as a tradeoff in order to adequately fit the data. A similar tradeoff (albeit reversed) occurs as h decreases away from 1. Thereby, the notation bh will be used occasionally to highlight the fact that the regression parameters are on different scales for different link functions. Also, the notation g(b) will be used sometimes to emphasize the dependence of the linear predictor g on b.
-
-Let M<sup>h</sup> denote the NB APM based on g(l, h) and note that the canonical log link is the special case h = 1, corresponding to M1. Note also that M<sup>h</sup> is a generalized linear model when h is known, while this is no longer true if h, b and j are jointly estimated by the data.
-
-Eq. (4) defines a family of power transformations to impose linearity, i.e., to make the linear model more appropriate to the data in the sense that the transformation is approximately linear over the range of observations. To illustrate, let us consider the consequences of fitting a model with the canonical link g(l, 1) (=ln (l)) when the correct but unknown link function is indeed g(l, h ). Using a first order Taylor series expansion about the canonical link, we have the approximate equation
-
-$$g(\mu, \theta^*) \cong \ln(\mu) + (\theta^* - 1)[\ln(\mu) - \ln(\ln(\mu) + 1)(\ln(\mu) + 1)].$$
- (6)
-
-The correct link function g(l, h ) = g = Xb may thus be approximated by
-
-$$ln(\mu) = X\beta + Z\delta,$$
- (7)
-
-with an obvious definition for Z and d = (h 1). The term Zd, which describes the local differences between the two link functions, represents the misspecification of the model using the wrong link function.
-
-We propose to test M<sup>1</sup> against M<sup>h</sup> so that the traditional model M<sup>1</sup> is used only if supported by the data, i.e., if the hypothesis h = 1 is plausible. Otherwise M<sup>h</sup> should be adopted. Such a testing problem is discussed in the following subsection.
-
-## 3.1. Link validation
-
-The usual practice of treating model selection (M<sup>1</sup> vs. Mh) as a testing problem of a point null hypothesis h = 1 was criticized by many researchers [\(Czado and Munk, 2000](#page-6-0)). Alternatively, they proposed a hypothesis of the form
-
-$$H_0: \theta \notin [1 - \theta_l, 1 + \theta_u]$$
- vs.  $H_1: \theta \in [1 - \theta_l, 1 + \theta_u],$  (8)
-
-where (1 hl, 1+ hu) is a neighborhood of h within which the canonical link is validated. Eq. (8) represents the validation problem and its converse (H<sup>1</sup> vs. H0) represents the discrimination problem. The following p-value was then derived
-
-$$p(\hat{\theta}, s, \theta_l, \theta_u) = \Phi\{(\hat{\theta} - 1 - \theta_u)/s\} - \Phi\{-(\hat{\theta} - 1 + \theta_l)/s\}, \tag{9}$$
-
-where ^h and s denote the maximum likelihood estimate of h and its standard deviation. (For a full Bayes estimator, Eq. (9) can still be used to approximate the p-value due to the asymptotic normality of the posterior distribution.)
-
-The bounds 1 h<sup>l</sup> and 1 + h<sup>u</sup> depend on the APM and the interests of the practitioner. To fix ideas, assume that data are available on yi and xi = (xi1, ... , xip) for a sample of n locations (i = 1, ... , n). In the NB APM context, an appropriate discrepancy measure between the two links g(l, h) and g(l, 1) can be defined as the maximal change in the predicted accidents when switching from the canonical link to the generalized link
-
-$$\Delta(\theta) = \max_{i=1,\dots,n} |\mu(\eta_i(\beta_\theta), \theta) - \mu(\eta_i(\beta_1), 1)|. \tag{10}$$
-
-The next step is to determine a tolerance bound D<sup>0</sup> such that testing
-
-$$H_0: \Delta(\theta) > \Delta_0$$
- vs.  $H_1: \Delta(\theta) \leq \Delta_0$ , (11)
-
-is equivalent to (8). Since the criterion D(h) is convex with a minimum at h = 1, the bounds 1 h<sup>l</sup> and 1 + h<sup>u</sup> are uniquely determined by solving h = D<sup>1</sup> (D0).
-
-For a given data set, [Czado and Munk \(2000\)](#page-6-0) indicated that the two-dimensional surface p(...,hl, hu) is reduced to the one-dimensional curve p(h.,., D0), where the level sets a = p give the asymptotic minimal bound D<sup>0</sup> for which H<sup>0</sup> can be rejected at level a as well as the maximal bound for which H<sup>1</sup> can be rejected by the discrimination test at level 1 a.
-
-The performance of M<sup>1</sup> vs. M<sup>h</sup> can be also assessed by way of simulation. Such an assessment will have to be based on criteria similar to (10), e.g., mean absolute deviation (difference) or mean squared deviation, since the regression parameters b<sup>1</sup> and bh are on different scales and cannot be directly compared.
-
-#### 4. Methodology
-
-The joint maximum likelihood estimation of the parameters h, b and j of the NB APM based on the generalized link g(l, h) is outlined in [Czado and Munk \(2000\)](#page-6-0). The Empirical Bayes (EB) estimates may then be used in subsequent applications of APMs. Alternatively, the full Bayes estimates can be obtained using the MCMC method. For a discussion of ''EB versus full Bayes", the reader is referred to ([Miaou and Lord, 2003; Mitra and Washington,](#page-6-0) [2007\)](#page-6-0).
-
-Subsection 4.1 describes the full Bayes approach that was used for model specification, parameter estimation and performance evaluation in this paper, whereas Subsection [4.2](#page-3-0) is devoted to goodness of fit measures.
-
-#### 4.1. Full Bayes estimation
-
-The posterior distributions needed in the full Bayes approach were obtained using MCMC sampling of the NB APM in Eq. [\(1\)](#page-1-0) by means of the statistical software WinBUGS.
-
-MCMC methods were used to repeatedly sample from the joint posterior distribution of the parameters h, b and j. These techniques generate sequences (chains) of random points (h(t) , b(t) , j(t) t = 1, ... , T (iterations), whose distributions converge to the target posterior distributions. A sub sample (h(t) , b(t) , j(t) ), t = 1, ... , T<sup>0</sup> < T, was used to monitor convergence and was then excluded as a burn-in sample. The remaining iterations (t = T<sup>0</sup> + 1, ... , T) were used for parameter estimation, performance evaluation and inference.
-
-Monitoring convergence is important because it ensures that the posterior distribution has been ''found" and thereby indicates when sampling of parameters should begin. A common methodology to check convergence is by tracking two or more parallel <span id="page-3-0"></span>chains with diverse starting values to ensure full coverage of the sample space of the parameters. Convergence of multiple chains was assessed using the Gelman–Rubin convergence statistic as modified by [Brooks and Gelman \(1998\).](#page-6-0) For a given parameter monitored over J chains, the Brooks–Gelman–Rubin (BGR) statistic is
-
-$$BGR = I_P/I_U, (12)$$
-
-where IP is the length of the 100(1 a)% interval for the parameter based on the pooled TJ samples and IU is the mean of the J withinchain interval lengths. A value under 1.2 of the BGR statistic indicates convergence. The BGR diagnostics are available in WinBUGS.
-
-To obtain the full Bayes estimates the user is required to specify prior distributions for the parameters. In the absence of sufficient prior knowledge of the distributions for individual parameters on the risk factors, uninformative proper prior distributions are usually specified ([Miaou and Lord, 2003; Mitra and Washington,](#page-6-0) [2007](#page-6-0)).
-
-Under M1, it is usually assumed that j gamma (0.001, 0.001) and b<sup>j</sup> N(0, 1002 ), j = 0, 1, ... , p. Such priors represent proper distributions that are flat; hence the term uninformative (also called diffused) proper priors.
-
-Under Mh, the same uninformative prior was used for j. A truncated normal distribution is then used as a proper prior for h, namely, h N(1, r<sup>2</sup> <sup>h</sup> ), 0 < h < 1, as it seems natural to have the prior distribution of the link parameter centered at (or symmetric about) the canonical log link ([Czado and Raftery, 2006](#page-6-0)). It can be verified that, the symmetry of these prior choices requires small standard deviations (rh 0.3, or smaller). The authors considered rh = 1, 2, 4 and noted that link values (h) larger than 1 are favored when these parameter values are used. Alternatively, Pareto priors with high density at small values of h and monotonically decreasing tails were also considered, as the generalized link function in Eq. [\(4\)](#page-2-0) changes most rapidly for small values of h.
-
-The following development is along the lines of [Czado and Raf](#page-6-0)[tery \(2006\),](#page-6-0) who advocated a reference proper prior distribution for bh. These priors vary with h and assume little prior information. Let zh<sup>i</sup> = ln(lhi) + (yi lhi)/lh<sup>i</sup> denote the adjusted dependent variable, xh<sup>i</sup> = j lhi/(j + lhi) denote its weight and define the weighted summary statistics:
-
-$$\bar{z}_{\theta} = \sum_{i=1}^{n} \omega_{\theta i} z_{\theta i} / \sum_{i=1}^{n} \omega_{\theta i}, 
-s_{\theta 0}^{2} = \sum_{i=1}^{n} \omega_{\theta i} (z_{\theta i} - \bar{z}_{\theta})^{2} / \sum_{i=1}^{n} \omega_{\theta i}, \quad \bar{x}_{\theta j} = \sum_{i=1}^{n} \omega_{\theta i} x_{i j} / \sum_{i=1}^{n} \omega_{\theta i}, 
-s_{\theta j}^{2} = \sum_{i=1}^{n} \omega_{\theta i} (x_{i j} - \bar{x}_{\theta j})^{2} / \sum_{i=1}^{n} \omega_{\theta i}, \quad j = 1, \dots, p.$$
-(13)
-
-For the (p + 1)-dimensional regression coefficient vector, including an intercept, it is assumed that bh Np+1(th, Rh), where the mean vector is given by th ¼ ðzh; 0; - ; 0Þ <sup>0</sup> and the covariance matrix is given by
-
-$$\Sigma_{\theta} = s_{\theta 0}^{2} \begin{pmatrix} 1 + \sum_{j=1}^{p} \bar{\chi}_{\theta j}^{2} / s_{\theta j}^{2} & -\bar{\chi}_{\theta 1} / s_{\theta 1}^{2} & \cdots & -\bar{\chi}_{\theta p} / s_{\theta p}^{2} \\ -\bar{\chi}_{\theta 1} / s_{\theta 1}^{2} & 1 / s_{\theta 1}^{2} & \cdots & 0 \\ \cdots & \cdots & \cdots & \cdots \\ -\bar{\chi}_{\theta p} / s_{\theta p}^{2} & 0 & \cdots & 1 / s_{\theta p}^{2} \end{pmatrix}.$$
-(14)
-
-# 4.2. Goodness of fit measures
-
-Let D denote the un-standardized deviance of the negative binomial model. [Spiegelhalter et al. \(2002\)](#page-6-0) proposed the deviance information criteria (DIC) as a measure of model complexity and fit
-
-$$DIC = \bar{D} + p_D; \quad p_D = \bar{D} - \hat{D}, \tag{15}$$
-
-where D is the posterior mean of D, D^ is the point estimate obtained by substituting the posterior means of (li, j) in D and pD is a measure of model complexity estimating the effective number of parameters. As a goodness of fit measure, DIC is a Bayesian generalization of Akaike's information criteria (AIC) that is penalizing larger parameter models. According to the WinBUGS' DIC guide ([Spiegelhalter et al., 2005](#page-6-0)), it is difficult to determine what would constitute an important difference in DIC. Very roughly, differences of more than 10 might definitely rule out the model with the higher DIC. Differences between 5 and 10 are substantial. If the difference in DIC is less than 5, and the models make very different inferences, then it could be misleading just to report the model with the lowest DIC.
-
-The Bayes factor Bh<sup>1</sup> was also employed to assess the improvement in fit over a NB APM with a canonical link (M1) when a generalized link (Mh) is used. Following [Czado and Raftery \(2006\),](#page-6-0) Bh<sup>1</sup> is approximated by
-
-$$2 \ln(B_{\theta 1}) \approx D(M_1) - D(M_{\theta}) + (E_{\theta} - E_1),$$
- (16)
-
-where D(M1) and D(Mh) are obtained by substituting the corresponding posterior means of (li, j) in D. The computation of E<sup>h</sup> in Eq. (16) requires the maximum likelihood estimates of the parameters and their covariance matrix in addition to the prior means and covariance matrices specified for j and bh. E<sup>1</sup> is the corresponding function under the canonical link. The details are given in [\(Cza](#page-6-0)[do and Raftery, 2006\)](#page-6-0). According to [Raftery \(1996\)](#page-6-0), M<sup>1</sup> is supported if 2 ln(Bh1) is less than 0, otherwise M<sup>h</sup> is supported; with the range 0–2.2 indicating weak support, 2.2–6 representing positive evidence, 6–10 demonstrating strong evidence and higher values signifying very strong evidence.
-
-Other measures such as the model deviance G<sup>2</sup> and R<sup>2</sup> have been suggested ([Mitra and Washington, 2007\)](#page-6-0). Yet, the G<sup>2</sup> and R<sup>2</sup> measures proposed in their Eqs. (14) and (15) use a Poisson-distributed error structure where the mean is equal to the variance and hence need to be modified to account for the extra-variation ([Wood, 2002\)](#page-6-0). This was not pursued for the following reasons. According to the WinBUGS' DIC guide ([Spiegelhalter et al., 2005\)](#page-6-0), the posterior mean of the deviance (Dbar = G<sup>2</sup> ) should perhaps not really be considered as a measure of fit. The fit might better be measured by deviance evaluated at the parameter of interest (Dhat). As emphasized by [Linde \(2005\)](#page-6-0), the posterior mean of the deviance (Dbar) is more a measure of model 'adequacy'. This is evident since the addition of a covariate might in certain cases increase the Dbar value. On the other hand, the R<sup>2</sup> measure of fit has been criticized in the literature and it has been shown to provide misleading results [\(Miaou et al., 1996](#page-6-0)).
-
-#### 5. Data and model description
-
-A total of 99 signalized intersections in the city of Edmonton were investigated for the purpose of developing accident prediction models relating the safety of urban 4-leg intersections to their traffic flows. This data set was used as a reference group for the evaluation of the City of Edmonton Intersection Safety Camera ([Sayed and de Leur, 2007\)](#page-6-0). The data on accident frequencies and traffic volumes within the intersections were obtained from the city of Edmonton and covered the period from 1997 to 2003.
-
-Average values for the traffic volumes (over the 7 years period) are used to build APMs for predicting the total (aggregate) number of accidents using
-
-$$\eta = \ln(\alpha_0) + \alpha_1 \ln(V_{mj}) + \alpha_2 \ln(V_{mi}).$$
- (17)
-
-The aggregation is justified on several grounds. For instance, an aggregate APM was found to perform well compared with APMs <span id="page-4-0"></span>developed to handle temporal correlation [\(Lord and Persaud, 2000\)](#page-6-0). Also, the aggregation of accidents over a period of reasonable length helps to avoid confounding effects and such a phenomenon like regression-to-the-mean [\(Cheng and Washington, 2005\)](#page-6-0). A statistical summary of the data is shown in Table 1.
-
-The AADT functional form [\(17\)](#page-3-0) is popular among practitioners and is often preferred over models that include several covariates because it can be easily re-calibrated (Lord et al., 2008; and the references therein). Further, it will be adopted for estimating safety
-
-Table 1 Statistical summary of data set (99 intersections).
-
-|                                 | Minimum | Maximum | Mean   | Standard<br>deviation |  |
-|---------------------------------|---------|---------|--------|-----------------------|--|
-| AADT on minor<br>approach (Vmi) | 2809    | 29,128  | 16,043 | 6564                  |  |
-| AADT on major<br>approach (Vmj) | 5642    | 61,744  | 28,010 | 9705                  |  |
-| Total number of<br>accidents    | 0       | 193     | 70     | 39                    |  |
-
-Table 2 Model statistics.
-
-| Canonical link        | Estimate | Standard<br>deviation |                | 95% Credible intervals |  |  |  |
-|-----------------------|----------|-----------------------|----------------|------------------------|--|--|--|
-| (M1)                  |          |                       | Lower<br>limit | Upper<br>limit         |  |  |  |
-| Intercept             | 8.96     | 1.20                  | 11.28          | 6.64                   |  |  |  |
-| AADT major            | 0.88     | 0.13                  | 0.64           | 1.12                   |  |  |  |
-| AADT minor            | 0.44     | 0.09                  | 0.25           | 0.63                   |  |  |  |
-| j                     | 7.02     | 1.18                  | 4.95           | 9.58                   |  |  |  |
-| DIC                   | 924.87   |                       |                |                        |  |  |  |
-| Generalized link (Mh) |          |                       |                |                        |  |  |  |
-| Intercept             | 3.35     | 0.60                  | 4.41           | 2.25                   |  |  |  |
-| AADT major            | 0.34     | 0.06                  | 0.22           | 0.46                   |  |  |  |
-| AADT minor            | 0.29     | 0.04                  | 0.18           | 0.34                   |  |  |  |
-| j                     | 6.76     | 1.16                  | 4.78           | 9.28                   |  |  |  |
-| h                     | 1.48     | 0.14                  | 1.25           | 1.81                   |  |  |  |
-| DIC                   | 926.96   |                       |                |                        |  |  |  |
-|                       |          |                       |                |                        |  |  |  |
-| Bayes factor (Bh1)    |          |                       |                |                        |  |  |  |
-| 2ln (Bh1)             | 15.11    |                       |                |                        |  |  |  |
-
-performance in the forthcoming highway safety manual (HSM) ([Hughes et al., 2005\)](#page-6-0). It should be noted that AADT models may suffer from omitted variables bias since many non-flow related factors are known to affect accident frequency. Nevertheless, such models are well recognized in the traffic safety literature.
-
-#### 6. Results
-
-This section compares the results of the canonical log link and those of the generalized link function. The results were evaluated in terms of model prediction and goodness of fit.
-
-The posterior summaries in Table 2 were obtained using J = 2 chains, T = 10,000 iterations, T<sup>0</sup> = 5000 burn-in sample in Win-BUGS. The BGR statistics in Eq. [\(12\)](#page-3-0), for the parameters h, b and j were less than 1.2, indicating convergence.
-
-Table 2 summarizes the model parameter estimates and their associated statistics using the canonical and generalized links, respectively. The table shows that the parameter estimates are significant as the 95% credible intervals are bounded away from zero. The coefficients of the major and minor flows are all positive, indicating an increase in the mean accident frequency with traffic flow. The shape parameter j of the negative binomial model is significant, demonstrating the presence of over-dispersion in the data. The credible interval for the parameter h does not straddle 1, signifying the need for a generalized link function.
-
-The DIC statistics (Eq. [\(15\)](#page-3-0)) were 924.87 and 926.96 under M<sup>1</sup> and Mh, respectively, giving a slight advantage to M<sup>1</sup> over Mh. In contrast, the Bayes factor calculation (Eq. [\(16\)](#page-3-0)) produced
-
-$$2\ln(B_{\theta 1}) = 920.7 - 923.7 + (-39.64 + 57.75) = 15.11,$$
-
-providing very strong support for Mh.
-
-In order to validate the canonical model M<sup>1</sup> against Mh, the maximal change discrepancy measure in Eq. [\(10\)](#page-2-0) is adopted. PROC GENMOD in SAS was used, with the inverse and forward link functions in Eqs. [\(4\) and \(5\),](#page-2-0) to compute D(h), h 2 [0.5, 2.5]. The maximal change and p-value curves are portrayed in Figs. 1 and 2, respectively.
-
-The p-curve in [Fig. 2](#page-5-0) shows that using a generalized link will result in a maximal absolute difference of 2.4 in predicted accidents at a = 0.1 compared to a canonical link. A canonical link can be validated at a = 0.1, if one is willing to tolerate a change of 6.3 predicted accidents.
-
-![](_page_4_Figure_18.jpeg)
-
-Fig. 1. Maximal change in predicted accidents when switching from M<sup>1</sup> to Mh.
-
-<span id="page-5-0"></span>![](_page_5_Figure_2.jpeg)
-
-Fig. 2. p-value curve for assessing the maximal change in predicted accidents.
-
-# 7. Discussion
-
-For the current data set, convergence was indicated with 10,000 iterations, of which 5000 were discarded as a burn-in sample. For larger data sets and/or the addition of covariates, a larger number of iterations might be needed to achieve convergence. The user might also consider alternative priors, e.g., gamma (0.1, 0.1) for j, N(0, 102 ), for bj, a truncated normal with mean 1 and larger standard deviation or a Pareto prior for h.
-
-The two models M<sup>h</sup> and M1, may predict very different values depending on the traffic flow data as depicted in Fig. 3, which displays the difference between the two means lh and l<sup>1</sup> at various combinations of traffic flows (10,000–60,000 for the major approach and 5000–30,000 for the minor approach). The response
-
-![](_page_5_Figure_7.jpeg)
-
-Fig. 3. Response surface of differences in predicted accidents (canonical – generalized link functions) by major and minor flows.
-
-surface in Fig. 3 indicates that the difference l<sup>1</sup> lh increases considerably with the major flow. In contrast, it decreases as the minor flow rises (with few exceptions in the right upper corner of the graph). Consequently, the difference l<sup>1</sup> lh is positive and large whenever there is a heavy traffic at the major approach, especially if combined with light traffic at the minor approach. On the other hand, the difference l<sup>1</sup> lh is negative and smallest whenever the opposite holds true.
-
-For the current data set, the results of goodness of fit as measured by DIC seem to be similar under M<sup>h</sup> and M1. However, as pointed out by [O'Hara \(2007\),](#page-6-0) comparisons of DIC from different models may lead to similar results unless DIC is focused on the right part of the model, which is not a straightforward task. Alternatively, the Bayes factor can be used to assess the improvement in fit over M<sup>1</sup> when M<sup>h</sup> is used. In this case the Bayes factor lends strong support to the generalized link model Mh.
-
-On the other hand, link validation has indicated that an absolute difference of 2 predicted accidents is sufficient to discriminate between the two models, but the canonical link can be validated only if an absolute difference of 7 predicted accidents is tolerated. Whether or not a change of 7 predicted accidents is acceptable is entirely up to the practitioner (e.g., road authority). If a change of this magnitude is allowed, the canonical link can be used; otherwise the generalized link should be adopted.
-
-#### 8. Summary and conclusions
-
-The generalized link was employed in association with the traditional negative binomial model to predict the number of accidents at 99 intersections using traffic flows at the major and minor approaches as covariates.
-
-Using DIC as a goodness of fit measure, the NB APM adopting the generalized link was found to fit the data as well as the corresponding model based on the usual canonical log link. In contrast, the Bayes factor provided strong support to the generalized link over the canonical link.
-
-A procedure for link validation was also described. It allows the practitioners (road authorities) to consider the changes in predicted accidents that will result if a generalized link is used instead of a canonical link. If a certain maximal change is tolerated, the <span id="page-6-0"></span>canonical link can be used to analyze the data; otherwise the generalized link is worth the extra efforts and should be adopted.
-
-The two models differ in terms of prediction. Compared with the NB APM based on the canonical log link, the model associated with the generalized link seems to produce lower predictions whenever the major flow is heavy, especially if combined with light flow on the minor approach. It also seems to predict a higher number of accidents when the opposite holds true. The canonical and generalized links may also lead to large differences in the identification and ranking of accident prone locations.
-
-It should be emphasized that erroneous selection of one model over the other can lead to serious consequences. Thereby, it is crucial to assess the goodness of fit of the two models and to validate the generalized link as outlined above. Also, while fitting the generalized link may take longer (in terms of convergence), it is not markedly complicated (in terms of WinBUGS coding) than the canonical link. Finally, whether a specific accident difference (e.g., 7 or more or less) is worth the extra effort is completely up to the user.
-
-It should be noted that these findings maybe be valid to this data set only. In order to generalize the comparison of the two models, it is perhaps necessary to involve other datasets and to carry on a large simulation study considering different data scenarios (different roadway elements, sample sizes, period of time, etc.) for locations with known safety status.
-
-This study can be extended in many directions. For example, a log-normal prior could be used instead of the gamma prior leading to generalized links associated with Poisson log-normal models. Also, a generalized link could be developed to connect the dispersion parameter j to relevant covariates. Other extensions include applying the generalized link approach to other data sets, in order to evaluate its performance over a wide class of traffic environments, and conducting a simulation study as suggested above.
-
-## References
-
-- Brooks, S.P., Gelman, A., 1998. Alternative methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7, 434– 455.
-- Cheng, W., Washington, S.P., 2005. Experimental evaluation of hotspot identification methods. Accident Analysis and Prevention 37, 870–881.
-- Czado, C., Munk, A., 2000. Noncanonical links in generalized linear models when is the effort justified? Journal of Statistical Planning and Inference 87, 317–345.
-- Czado, C., Raftery, A.E., 2006. Choosing the link function and accounting for link uncertainty in generalized linear models using Bayes factors. Statistical Papers 47, 419–442.
-- Czado, C., 1992. On link selection in generalized linear models. In: Fahrmein, L., Francis, B., Gilchrist, R., Tutz, G. (Eds.), Advances in GLIM and Statistical Modeling, Lecture Notes in Statistics, vol. 78. Springer, New York, pp. 60–65.
-- Czado, C., 1997. On selecting parametric link transformation families in generalized linear models. Journal of Statistical Planning and Inference 61, 125–139.
-- El-Basyouny, K., Sayed, T., 2006. Comparison of two negative binomial regression techniques in developing accident prediction models. TRR: Journal of the TRB. 1950, 9–16.
-- El-Basyouny, K., Sayed, T., 2009a. Collision prediction models using multivariate Poisson–lognormal regression. Accidents Analysis and Prevention 41 (4), 820– 828.
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-- El-Basyouny, K., Sayed, T., 2009b. Accident prediction models with random corridor parameters. Accidents Analysis and Prevention 41 (5), 1118–1123.
-- Horowitz, J.L., Mammen, E., 2007. Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions. Annals of Statistics 35 (6), 2589–2619.
-- Hughes, W., Eccles, K., Harwood, D., Potts, I., Hauer, E., 2005. Development of a highway safety manual. Appendix C: highway safety manual prototype chapter: two-lane highways. NCHRP Web Document 62 (Project 17–18(4)). Washington, DC. [<http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp\\_w62.pdf>](http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w62.pdf).
-- Kim, H., Sun, D., Tsutakawa, R.K., 2002. Lognormal vs. gamma: extra variations. Biometrical Journal 44 (3), 305–323.
-- Linde, A., 2005. DIC in variable selection. Statistica Neerlandica 59 (1), 45–56.
-- Lord, D., Persaud, B., 2000. Accident prediction models with and without trend: application of the generalized estimating equation. Transportation Research Record 1717, 102–108.
-- Lord, D., 2006. Modeling motor vehicle crashes using Poisson–gamma models: examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter. Accident Analysis and Prevention 38 (4), 751–766.
-- Lord, D., Guikema, S.D., Geedipally, S.R., 2008. Application of the Conway–Maxwell– Poisson generalized linear model for analyzing motor vehicle crashes. Accident Analysis and Prevention 40, 1123–1134.
-- Lord, D., Washington, S.P., Ivan, J.N., 2005. Poisson, Poisson–gamma and zero inflated regression models of motor vehicle crashes: balancing statistical fit and theory. Accident Analysis and Prevention 37 (1), 35–46.
-- Lunn, D.J., Thomas, A., Best, N., Spiegelhalter, D., 2000. WinBUGS a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 10, 325–337.
-- Maher, M.J., Summersgill, I., 1996. A comprehensive methodology for the fitting of predictive accident models. Accident Analysis and Prevention 28 (3), 281–296.
-- Mallick, B., Gelfand, A.E., 1994. Generalized linear models with unknown link functions. Biometrika 81 (2), 237–245. Malyshkina, N.V., Mannering, F.L., Tarko, A.P., 2009. Markov switching negative
-- binomial models: an application to vehicle accident frequencies. Accident Analysis and Prevention 41 (2), 217–226.
-- McCullagh, P., Nelder, J.A., 1989. Generalized Linear Models, second ed. Chapman & Hall, London.
-- Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes. Transportation Research Record 1840, 31–40.
-- Miaou, S.P., Lu, A., Lum, H.S., 1996. Pitfalls of using R-squared to evaluate goodness of fit of accident prediction models. Transportation Research Record 1542, 6–
-- Mitra, S., Washington, S., 2007. On the nature of over-dispersion in motor vehicle crash prediction models. Accident Analysis and Prevention 39, 459–468.
-- Ntzoufras, I., Dellaportas, P., Forster, J., 2003. Bayesian variable and link determination for generalised linear models. Journal of Statistical Planning and Inference 111, 165–180.
-- O'Hara, B., 2007. Focus on DIC. [<http://www.deepthoughtsandsilliness.blogspot.](http://www.deepthoughtsandsilliness.blogspot.com/2007/12/focus-on-dic.html) [com/2007/12/focus-on-dic.html>,](http://www.deepthoughtsandsilliness.blogspot.com/2007/12/focus-on-dic.html) (accessed May 2008).
-- Pregibon, D., 1980. Goodness of link tests for generalized models. Journal of the Royal Statistical Society C 29, 5–24.
-- Raftery, A.E., 1996. Approximate Bayes factors and accounting for model uncertainty in generalized linear models. Biometrika 83, 251–266.
-- Sawalha, Z., Sayed, T., 2006. Traffic accidents modeling: some statistical issues. Canadian Journal of Civil Engineering 33 (9), 1115–1124.
-- Sayed, T., de Leur, P., 2007. Evaluation of Edmonton's intersections safety camera programs. In: Proceedings of the 2007 Annual meeting of the Transportation Research Board, Washington, DC.
-- Spiegelhalter, D., Thomas, A., Best, N., Lunn, D., 2005. WinBUGS user manual. MRC Biostatistics Unit, Cambridge. [<http://www.mrc-cam.ac.uk/bugs>](http://www.mrc-cam.ac.uk/bugs).
-- Spiegelhalter, D.J., Best, N.G., Carlin, B.P., Van der Linde, A., 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society B 64, 1–34.
-- Wood, G.R., 2002. Generalised linear accident models and goodness of fit testing. Accident Analysis and Prevention 34 (4), 417–427.

Zotero/001_artiklid/Road Safety Indicators/elbasyounycollisionpredictionmodelsusing2009.md

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----
-category: literaturenote
-citekey: elbasyounycollisionpredictionmodelsusing2009
-title: Collision prediction models using multivariate Poisson-lognormal regression
-authors: "El-Basyouny, Karim; Sayed, Tarek"
-year: 2009
-date: 2009-00-00 2009
-doi: 10.1016/j.aap.2009.04.005
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2009.04.005"
-zotero_key: 3UW6E3BC
-zotero_storage: Q3W57QTW
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "El-Basyouny, Karim"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/00014575)
-
-# Accident Analysis and Prevention
-
-journal homepage: [www.elsevier.com/locate/aap](http://www.elsevier.com/locate/aap)
-
-![](_page_0_Picture_5.jpeg)
-
-# Collision prediction models using multivariate Poisson-lognormal regression
-
-Karim El-Basyouny<sup>∗</sup>, Tarek Sayed<sup>1</sup>
-
-*Dept. of Civil Engineering, University of British Columbia, 2002-6250 Applied Science Lane, Vancouver, BC, Canada V6T 1Z4*
-
-# article info
-
-*Article history:* Received 24 December 2008 Received in revised form 20 March 2009 Accepted 2 April 2009
-
-*Keywords:* Collision prediction models Full Bayes estimation Markov Chain Monte Carlo Multivariate lognormal distribution Multivariate identification of hot spots
-
-# abstract
-
-This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only (*PDO*) and injuries plus fatalities (*I* + *F*). This correlation was estimated at 0.758, which is highly significant, suggesting that higher *PDO* rates are associated with higher *I* + *F* rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models.
-
-Crown Copyright © 2009 Published by Elsevier Ltd. All rights reserved.
-
-## **1. Introduction**
-
-The application of collision prediction models (CPMs) in assessing the safety of a road entity (intersection, road segment, etc.) has become a standard practice among safety researchers and practitioners [\(Hauer et al., 1988; Persaud and Dzbik, 1993; Lord, 2000;](#page-8-0) [Miaou and Lord, 2003; Sawalha and Sayed, 2006a,b\).](#page-8-0)
-
-Data on the number of collisions at a particular site are usually available where the collisions are classified by severity (e.g., fatal, minor injury, major injury or property damage only), by the number of vehicles involved (e.g., single or multiple), and/or by the type of collision (e.g., angle, head-on, rear-end, sideswipe or pedestrianinvolved), etc. In such cases, it is necessary to account for the likely correlations among collision counts at different levels of classification. These correlations may be caused by omitted variables, which can influence collision occurrence at all levels of classification, or
-
-In contrast to the above two-stage approach of using a model to predict total collisions and a conditional model to predict severity, recent models for unconditional accident severity analysis have been proposed in the safety literature [\(Tunaru, 2002; Ma and](#page-8-0) [Kockelman, 2006; Brijs et al., 2007; Park and Lord, 2007; Ma et](#page-8-0) [al., 2008; Ye et al., 2009; Aguero-Valverde and Jovanis, 2009\).](#page-8-0) These multivariate extensions are based on either multivariate Poisson (MVP) models ([Tsionas, 2001; Karlis, 2003; Karlis and](#page-8-0) [Meligkotsidou, 2005\)](#page-8-0) or multivariate Poisson-lognormal (MVPLN) models [\(Chib and Winkelmann, 2001\).](#page-7-0) The MVPLN regression is
-
-[tsayed@civil.ubc.ca](mailto:tsayed@civil.ubc.ca) (T. Sayed). <sup>1</sup> Tel.: +1 604 822 4379.
-
-from ignoring shared information in unobserved error terms. Several researchers such as [Hydén \(1987\)](#page-8-0) and [Sayed and Zein \(1999\)](#page-8-0) showed that severe conflicts and various collision severity levels did in fact share the same severity distribution. However, most studies have avoided the issue of correlations by using a frequency model to predict the total number of collisions and then conditional on collision occurring, severity can be predicted using techniques such as multinomial logit models, nested logit models, ordered probit models, sequential binary probit models, heteroskedastic multivariate generalized extreme value models and mixed logit models (e.g., see [Yamamoto et al., 2008; Kim et al., 2008; Milton et al., 2008; a](#page-8-0)nd the references therein).
-
-<sup>∗</sup> Corresponding author. Tel.: +1 604 716 4470. *E-mail addresses:* [basyouny@civil.ubc.ca](mailto:basyouny@civil.ubc.ca) (K. El-Basyouny),
-
-preferred to the MVP approach for the analysis of multivariate collision count data because (i) it accounts for over-dispersion (extra Poisson variation), which is often observed in collision data; and (ii) it allows for a full general correlation structure. Since the classical estimation of the parameters of the MVP and MVPLN regression models is not straightforward, the Markov chain Monte Carlo (MCMC) simulation method ([Gilks et al., 1996\)](#page-8-0) is typically used in the applications.
-
-This paper advocates the use of MVPLN for modeling collision count data. The MVPLN regression model presents an opportunity to corroborate the findings reported in the literature regarding the nature of the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate PLN models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency.
-
-The development of the MVPLN model is undertaken using WinBUGS [\(Lunn et al., 2000\)](#page-8-0) which is an open-source statistical software. WinBUGS is a flexible platform for the Bayesian analysis of complex statistical models using MCMC methods. The proposed implementation of the MVPLN regression model is relatively simpler than other MVPLN approaches using special codes written for the MATLAB and R computing environments [\(Park and Lord, 2007;](#page-8-0) [Ma et al., 2008\).](#page-8-0) WinBUGS has the additional advantage of providing the user with a goodness-of-fit measure (the deviance information criteria) that can be used for model comparisons.
-
-# **2. Previous work**
-
-To distinguish between the collision prediction models that use multivariate explanatory variables to predict a univariate dependent variable (e.g., the total number of collisions) and those which involve multivariate dependent as well as independent variables, the former will be termed univariate CPMs, while the latter will be termed multivariate CPMs. It should be noted that both univariate and multivariate CPMs use multivariate independent variables (covariates). In view of this terminology, the popular univariate approach for developing CPMs uses the Poisson-gamma hierarchy, which leads to the negative binomial regression model (e.g., [Poch](#page-8-0) [and Mannering, 1996; Hauer, 1997; Hinde and Demetrio, 1998;](#page-8-0) [Lord, 2000; Miaou and Lord, 2003\).](#page-8-0) The PLN regression represents a viable alternative for modeling the extra-Poisson variation ([Kim](#page-8-0) [et al., 2002; Miranda-Moreno, 2006\).](#page-8-0)
-
-The majority of CPMs were developed using models with a fixed dispersion parameter. Such an assumption was challenged and various dispersion parameter relationships were examined [\(Heydecker](#page-8-0) [and Wu, 2001; Miaou and Lord, 2003; Miranda-Moreno et al., 2005;](#page-8-0) [El-Basyouny and Sayed, 2006; Mitra and Washington, 2007; Lord](#page-8-0) [and Park, 2008\).](#page-8-0)
-
-There is extensive research addressing the problem of observing excessive zeroes in collision data (e.g., [Shankar et al., 2004; Lee and](#page-8-0) [Mannering, 2002; Kumara and Chin, 2006; Qin et al., 2004; Warton,](#page-8-0) [2005; Lord et al., 2005, 2007\).](#page-8-0) Another problem of considerable interest is the development of CPMs using data characterized by a low sample mean, especially if combined with a small sample size, (e.g., [Maycock and Hall, 1984; Fridstrøm et al., 1995; Wood, 2002;](#page-8-0) [Lord, 2006\).](#page-8-0) Other modeling techniques have also been proposed in the literature advocating the use of random parameter negative binomial regression model ([Anastasopoulos and Mannering, 2009\)](#page-7-0) or a two-state Markov Switching model [\(Malyshkina et al., 2008\)](#page-8-0) to analyze collision frequencies.
-
-Multivariate extensions of the simple Poisson distribution to account for the correlations among different count processes (such as counts of different crash severities) have been proposed in the literature. For instance, a model that assumes the same nonnegative covariance term for all pairs has been considered by [Tsionas](#page-8-0) [\(2001\)](#page-8-0) and [Karlis \(2003\).](#page-8-0) [Ma and Kockelman \(2006\)](#page-8-0) adapted a multivariate Poisson (MVP) regression approach to assess the effects of different covariates on collision counts at different severity levels. The assumption of equal covariance terms has been relaxed by [Karlis and Meligkotsidou \(2005\), b](#page-8-0)ut the restriction to nonnegative covariance terms was still maintained. [Brijs et al. \(2007\)](#page-7-0) used a similar non-regression model for ranking hazardous sites in Belgium.
-
-Multivariate zero-inflated Poisson (MVZIP) models were also considered ([Li et al., 1999\).](#page-8-0) Since the MVP regression models do not allow for over-dispersion, a multivariate negative binomial (MVNB) model was used to investigate the simultaneity of fatality and injury collision outcomes [\(Ladron de Guevara and Washington, 2004\).](#page-8-0) A major drawback of the MVNB models is its inability to tolerate negative correlations.
-
-To address the shortcomings of MVP and MVNB, [Chib and](#page-7-0) [Winkelmann \(2001\)](#page-7-0) developed the MVPLN regression approach. The MVPLN model does not only account for over-dispersion, but also it has a fairly general correlation structure allowing for (i) different covariance terms; and (ii) the possibility of negative correlations, which cannot be ruled out unquestionably for all classifications.
-
-Recently, applications of the MVPLN to model collision count data at different levels of severity were undertaken. [Tunaru \(2002\)](#page-8-0) used an MVPLN non-regression model implemented via WinBUGS for analyzing multiple response count data (classified by severity and the number of vehicles involved) taking into account complex correlation structures.
-
-[Park and Lord \(2007\)](#page-8-0) used MATLAB codes [MATLAB \(2006\), t](#page-8-0)ailored according to the MCMC algorithm of [Chib and Winkelmann](#page-7-0) [\(2001\), f](#page-7-0)or jointly modeling collision frequency by severity using data from 451 three-leg un-signalized intersections in California obtained through the Highway Safety Information System (HSIS). Their results showed promise toward obtaining more accurate estimates by accounting for correlations in the multivariate collision counts and over-dispersion.
-
-[Ma et al. \(2008\)](#page-8-0) coded a Gibbs sampler and two Metropolis–Hastings algorithms ([Gilks et al., 1996\)](#page-8-0) in the R language for the prediction of collision counts by severity using data collected from Washington State through the HSIS on 7773 rural two-lane roadways in the Puget Sound region. Their results indicated that the MVPLN approach offered better predictions than those from univariate Poisson and negative binomial models. The results indicated that there were statistically significant correlations between (as well as over-dispersion in) collision counts at different levels of injury severity. Several recommendations for highway safety treatments and design policies were suggested, e.g., they concluded that wide lanes and shoulders are key for reducing collision frequencies, as are longer vertical curves.
-
-[Ye et al. \(2009\)](#page-8-0) developed a simultaneous equations model of collision frequencies by collision type for a sample of 165 rural intersections in 38 counties of Georgia. The model was formulated using multivariate Poisson regression with multivariate normal heterogeneity. A simulation-based maximum likelihood approach was used to estimate the parameters. The model estimation results support the notion of the presence of significant common unobserved factors across collision types, although the impact of these factors on parameter estimates was found to be rather modest.
-
-[Aguero-Valverde and Jovanis \(2009\)](#page-7-0) used Full Bayes MVPLN models implemented via WinBUGS to estimate the expected col<span id="page-2-0"></span>lision frequency for different collision severity levels using data on 6353 rural two-lane segments in central Pennsylvania. They found high correlations among collision severities, with highest values between contiguous severity levels. In terms of goodness-of-fit, the multivariate model outperformed the independent univariate PLN models and provided improved precision for collision frequency estimates. The multivariate estimates were combined with cost data from PennDOT to rank sites for safety improvements via expected collision cost and excess expected cost per segment. The results showed that the multivariate-based top ranked segments have consistently higher costs and excess costs than the univariate estimates as a result of higher multivariate estimates of fatalities and major injuries. These higher estimates resulted in different rankings for the multivariate and independent models.
-
-### **3. Methodology**
-
-# *3.1. The multivariate Poisson-lognormal model*
-
-For a set of data on road collisions at *n* locations, where the collisions at each location are classified into *K* categories, define the vector *yi* = (*yi*<sup>1</sup> *yi*2...*yiK*) , where *yik* denote the number of collisions at the *i*th location in category *k*. It is assumed that the *yi* are independently distributed and that the Poisson distribution of *yik*, given *ik*, is
-
-$$f(y_{ik}|\lambda_{ik}) = \lambda_{ik}^{y_{ik}} \frac{e^{-y_{ik}}}{y_{ik}!}, \quad i = 1, 2, \dots, n, \quad k = 1, 2, \dots, K.$$
- (1)
-
-To model extra variation, assume further that ln(*ik*) = ln(*ik*) + ε*ik*, where
-
-$$\ln(\mu_{ik}) = \beta_{k0} + \beta_{k1}X_{i1} + \dots + \beta_{kj}X_{ij}, \qquad (2)$$
-
-the *Xij* denote relevant traffic and road characteristics and the ε*ik* denote multivariate normal errors distributed as ε*<sup>i</sup>* ∼ *Nk*(0, ˙), where
-
-$$\varepsilon_i = \begin{pmatrix} \varepsilon_{i1} \\ \varepsilon_{i2} \\ \vdots \\ \varepsilon_{iK} \end{pmatrix}, \quad \Sigma = \begin{pmatrix} \sigma_{11} & \sigma_{12} & \dots & \sigma_{1K} \\ \sigma_{21} & \sigma_{22} & \dots & \sigma_{2K} \\ \vdots & \vdots & \ddots & \vdots \\ \sigma_{K1} & \sigma_{K2} & \dots & \sigma_{KK} \end{pmatrix}.$$
-
-Let *X* and ˇ*<sup>k</sup>* denote the matrix of covariates and the vector of regression coefficients, respectively, and let ˇ denote the set {ˇ1, ˇ2,..., ˇ*K*}. Thus, given (*X*, ˇ, ˙), the *<sup>i</sup>* are independently distributed as
-
-$$f(\lambda_i|X,\beta,\Sigma) = \frac{\exp\{-0.5(\lambda_i^* - \mu_i^*)'\Sigma^{-1}(\lambda_i^* - \mu_i^*)\}}{(2\pi)^{K/2} \left(\prod_{k=1}^K \lambda_{ik}\right) |\Sigma|^{1/2}},$$
-(3)
-
-which is a *K*-dimensional log-normal distribution, where
-
-$$\begin{array}{lll} \lambda_i = (\lambda_{i1} & \lambda_{i2} \cdots \lambda_{iK})', & \lambda_i^* = (\ln(\lambda_{i1}) & \ln(\lambda_{i2}) \cdots \ln(\lambda_{iK}))', \\ \mu_i = (\mu_{i1} & \mu_{i2} \cdots \mu_{iK})', & \mu_i^* = (\ln(\mu_{i1}) & \ln(\mu_{i2}) \cdots \ln(\mu_{iK}))'. \end{array}$$
-
-It should be noted that the univariate PLN models are obtained as the special case where ˙ is a diagonal matrix.
-
-#### *3.2. Prior distributions*
-
-Let denote the set {-1, -2,..., *<sup>n</sup>*}. To obtain the full Bayes estimates of (-, ˇ, ˙), it is required to specify prior distributions for the hyper parameters (ˇ, ˙). Prior distributions are meant to reflect to some extent prior knowledge about the parameters of interest. If such prior information is available, it should be used to formulate the so-called informative priors. The specification of informative priors for generalized linear models was dealt with by [Bedrick et](#page-7-0) [al. \(1996\), w](#page-7-0)ho considered conditional means priors as well as data augmentation priors of the same form as the likelihood and showed that such priors result in tractable posteriors. A good discussion on the elicitation of priors in collision data analysis can be found in [Schluter et al. \(1997\).](#page-8-0)
-
-In the absence of sufficient prior knowledge of the distributions for individual parameters, uninformative (vague) proper prior distributions are usually specified. The most commonly used priors are diffused normal distributions (with zero mean and large variance) for the regression parameters and a Wishart (*P*, *r*) prior for ˙<sup>−</sup>1, where *P* and *r* ≥ *K* represent the prior guess at the order of magnitude of the precision matrix ˙−<sup>1</sup> and the degrees of freedom, respectively. The parameterization of the Wishart probability density function is
-
-$$f(\Sigma^{-1}|P, r) = |P|^{K/2}|\Sigma^{-1}|^{(r-K-1)/2} \exp\{-0.5Tr(P\Sigma^{-1})\}.$$
-
-Choosing *r* = *K* as the degrees of freedom corresponds to vague prior knowledge [\(Spiegelhalter et al., 1996; Tunaru, 2002\).](#page-8-0)
-
-# *3.3. Posterior distributions*
-
-Let*f*(ˇ) *k*,*<sup>j</sup> N*(0,10,000) and *f*(˙<sup>−</sup>1) Wishart (*P*, *K*) denote the hyper-prior distributions for the regression parameters and the precision matrix, respectively, Further, let *y* denote the set {*y*1, *y*2,..., *yn*}. Given *X*, the joint distribution of (*y*, -, ˇ, ˙) is
-
-$$f(y,\lambda,\beta,\Sigma|X) = \frac{f(\beta)f(\Sigma^{-1})}{(2\pi)^{nK/2}|\Sigma|^{n/2}\left(\prod_{i=1}^{n}\prod_{k=1}^{K}y_{ik}!\right)}g(y,\lambda,X,\beta,\Sigma),$$
-
-where
-
-$$\begin{split} g(y,\lambda,X,\beta,\Sigma) &= \exp\left\{-0.5\sum_{i=1}^{n} \left((\lambda_i^* - \mu_i^*)'\Sigma^{-1}(\lambda_i^* - \mu_i^*) + \sum_{k=1}^{K} [(y_{ik} - 1)\ln(\lambda_{ik}) - \lambda_{ik}]\right)\right\}. \end{split}$$
-
-The posterior distributions are given by
-
-$$f(\lambda_{i}|y,X,\beta,\Sigma) = \frac{g_{i}(y_{i},\lambda_{i},X,\beta,\Sigma)}{\int_{0}^{\infty} \int_{0}^{\infty} \dots \int_{0}^{\infty} g_{i}(y_{i},\lambda_{i},X,\beta,\Sigma) d\lambda_{i1} d\lambda_{i2} \dots d\lambda_{iK}},$$
-(4)
-
-where
-
-$$\begin{split} g_i(y_i, \lambda_i, X, \beta, \Sigma) &= \exp\left\{-0.5\lambda_i^* - \mu_i^*)'\Sigma^{-1}(\lambda_i^* - \mu_i^*) \\ &+ \sum\nolimits_{k=1}^K [(y_{ik} - 1)\ln(\lambda_{ik}) - \lambda_{ik}]\right\}, \end{split}$$
-
-$$f(\beta|y,\lambda,X,\Sigma)$$
-
-$$= \frac{g(y, \lambda, X, \beta, \Sigma)f(\beta)}{\int_{-\infty}^{\infty} \int_{-\infty}^{\infty} \dots \int_{-\infty}^{\infty} g(y, \lambda, X, \beta, \Sigma)f(\beta)d\beta_{10}d\beta_{11} \dots d\beta_{KJ}},$$
- (5)
-
-and
-
-$$f(\Sigma^{-1}|\mathbf{y},\lambda,X,\beta) \equiv Wishart\left(P + \sum_{i=1}^{n} (\lambda_i^* - \mu_i^*)(\lambda_i^* - \mu_i^*)', K + n\right). \tag{6}$$
-
-# *3.4. Full Bayes estimation*
-
-The posterior distributions needed in the full Bayes approach can be obtained using MCMC sampling. The Wishart distribution in (6) can be sampled using a Gibbs sampler. In contrast, the posterior distributions in (4) and (5) are not standard and require the <span id="page-3-0"></span>Metropolis–Hastings algorithm [\(Park and Lord, 2007; Ma et al.,](#page-8-0) [2008\).](#page-8-0)
-
-In this paper, the posterior distributions are sampled using the MCMC techniques available in WinBUGS 2.2.0; the windows interface of OpenBUGS. The techniques generate sequences (chains) of random points, whose distributions converge to the target posterior distributions. A sub-sample is used to monitor convergence and then excluded as a burn-in sample. The remaining iterations are used for parameter estimation, performance evaluation and inference.
-
-Monitoring convergence is important because it ensures that the posterior distribution has been "found". Thereby indicating when parameters sampling should begin. To check convergence, two or more parallel chains with diverse starting values are tracked to ensure full coverage of the sample space. Convergence of multiple chains is assessed using the Brooks–Gelman–Rubin (BGR) statistic ([Brooks and Gelman, 1998\).](#page-7-0) A value under 1.2 of the BGR statistic indicates convergence. Convergence is also assessed by visual inspection of the MCMC trace plots for the model parameters as well as by monitoring the ratios of the Monte Carlo errors relative to the respective standard deviations of the estimates; as a rule of thumb these ratios should be less than 0.05.
-
-# *3.5. Model comparisons*
-
-The Deviance Information Criteria (DIC) is used for model comparisons ([Spiegelhalter et al., 2002\).](#page-8-0) As a goodness of fit measure, DIC is a Bayesian generalization of Akaike's Information Criteria (AIC) that penalizes larger parameter models. According to [Spiegelhalter et al. \(2005\), i](#page-8-0)t is difficult to determine what would constitute an important difference in DIC. Very roughly, differences of more than 10 might definitely rule out the model with the higher DIC. Differences between 5 and 10 are considered substantial. If the difference in DIC is less than 5, and the models make very different inferences, then it could be misleading just to report the model with the lowest DIC.
-
-To show that DIC is additive under independent models and priors, let *f*(*y*|) and *f*(*y*) denote the conditional and marginal distributions of *y*, where denote the vector of parameters associated with *y*. Then, DIC = D¯ + p, where p = D¯ − D( ¯), ¯ = E[|y] and D¯ = E[D()|y] are the posterior means of and the Bayesian deviance
-
-$$D(\theta) = -2 \ln\{f(y|\theta)\} + 2\ln\{f(y)\}. \tag{7}$$
-
-For *K* collision categories, let *y* and be partitioned as (*y*1,..., *yk*) and (1,..., *k*). Define DICk <sup>=</sup> <sup>D</sup>¯ <sup>k</sup> <sup>+</sup> pk, pk <sup>=</sup> <sup>D</sup>¯ <sup>k</sup> <sup>−</sup> Dk(¯ <sup>k</sup>), D¯ <sup>k</sup> = <sup>E</sup>[Dk(k)|yk], ¯ <sup>k</sup> = E[k|yk] and Dk(k) = −2 ln{f(yk|k)} + 2 ln{f(yk)}. Under independent models and priors, we have that f(y|) = <sup>K</sup> <sup>k</sup>=1f(yk|k) and <sup>f</sup>(y) <sup>=</sup> <sup>K</sup> k=1f(yk). These multiplicative conditional and marginal distributions of *y* translate additively in the Bayesian deviance (7) leading to DIC <sup>=</sup> <sup>K</sup> <sup>k</sup>=1DICk.
-
-#### **4. Multivariate identification**
-
-The treatment in this section is not meant to account for the extensive literature on the selection of hazardous locations, but rather to illustrate the generalization to the multivariate setting of some of the techniques that were developed for univariate analysis.
-
-Several hazardous location identification techniques exist in the literature. These include the probability of worst site, posterior distribution of the ranks of the collisions costs or expected number of collisions, or through expected collision cost and excess expected cost [\(Tunaru, 2002; Brijs et al., 2007; Aguero-Valverde and Jovanis,](#page-8-0) [2009\).](#page-8-0)
-
-However, assigning costs to different types of injury can be controversial for a variety of reasons [\(Brijs et al., 2007\).](#page-7-0) These reasons include ethical arguments (e.g., can we assign a cost to a human life?) and economic arguments (what are the quantities that must be measured to estimate the cost of a seriously injured person?). To avoid such difficulties, an alternative approach of selecting hot spots is adopted which is based on the posterior probability of excess [\(Higle and Witkowski, 1988; Sayed and Abdelwahab, 1997;](#page-8-0) [Heydecker and Wu, 2001\).](#page-8-0) The Bayesian posterior probability that a site has an excessive mean collision frequency provides an indication of sites at which the future collision record is expected to be worse than usual. In the univariate analysis, this probability is expressed as:
-
-$$\int_{\mu_0}^{\infty} f(\lambda_i | y, X, \beta, \sigma^2) d\lambda_i > 1 - \delta, \quad i = 1, 2, \dots, n,$$
-(8)
-
-where 2, <sup>0</sup> and ı denote the extra-Poisson variation, an upper limit of the "acceptable" mean number of collisions and a threshold value between 0 and 1, respectively. In practice, <sup>0</sup> is typically specified as either the median or mean of the prior distribution, whereas ı has been arbitrarily assumed to equal 0.05 ([Higle and](#page-8-0) [Witkowski, 1988; Sayed and Abdelwahab, 1997\).](#page-8-0)
-
-Under the multivariate Poisson-lognormal model, the criterion (8) generalizes to
-
-$$\int_{\mu_{10}}^{\infty} \int_{\mu_{20}}^{\infty} \dots \int_{\mu_{K0}}^{\infty} f(\lambda_i | y, X, \beta, \Sigma) d\lambda_{i1} d\lambda_{i2} \dots d\lambda_{iK} > 1 - \delta,$$
-
-$$i = 1, 2, \dots, n,$$
-(9)
-
-where *f*(*<sup>i</sup>*|*y*, *X*, ˇ, ˙) is given by [\(4\)](#page-2-0) and *k*<sup>0</sup> denotes an upper limit of the "acceptable" mean number of collisions in category *k*. It should be noted that the univariate Eq. (8) is the special case *K* = 1. The evaluation of the multiple integral in (9) is rather complicated. Thereby, the following approximation, due to [Clayton and Kaldor](#page-7-0) [\(1987\), i](#page-7-0)s proposed to simplify the computation of (9),
-
-$$f(\lambda_i^*|y, X, \beta, \Sigma) \approx N_K(mi, Si)$$
- (10)
-
-where
-
-$$m_{i} = S_{i} \left( \Sigma^{-1} \mu_{i}^{*} + \begin{pmatrix} (y_{i1} + 0.5) \ln(y_{i1} + 0.5) - 0.5 \\ (y_{i2} + 0.5) \ln(y_{i2} + 0.5) - 0.5 \\ \dots \\ (y_{iK} + 0.5) \ln(y_{iK} + 0.5) - 0.5 \end{pmatrix} \right),$$
-(11)
-
-and
-
-$$S_{i} = \begin{pmatrix} \Sigma^{-1} + \begin{pmatrix} y_{i1} + 0.5 & 0 & \dots & 0 \\ 0 & y_{i2} + 0.5 & \dots & 0 \\ \dots & \dots & \dots & \dots \\ 0 & 0 & \dots & y_{iK} + 0.5 \end{pmatrix} \end{pmatrix}^{-1}. \quad (12)$$
-
-The approximation (10) can be used in the Bayesian probability of excess criterion (9) to compare the univariate and multivariate approaches of selecting hot spots.
-
-For univariate PLN models (*K* = 1) let <sup>∗</sup> <sup>0</sup> denote the natural logarithm of the average of the prior means. Then, the *i*th intersection would be selected as a hazardous location whenever
-
-$$\Phi(\mu_{i0}^*) < \delta, \tag{13}$$
-
-where ˚ denotes the univariate standard normal distribution function and
-
-$$\mu_{i0}^* = \frac{\mu_0^* - m_i}{\sqrt{S_i}}$$
-
-The standardized multivariate normal distribution function can be used in a similar procedure to select hot spots under MVPLN. Thus, using a similar notation, the *i*th intersection would be selected as a hazardous location whenever
-
-$$\Phi_{K}(\mu_{i10}^{*}, \mu_{i20}^{*}, \dots, \mu_{iK0}^{*}, \hat{R}) < \delta,$$
- (14)
-
-where ˚*<sup>k</sup>* denotes the multivariate standard normal distribution function,
-
-$$\mu_{ik0}^* = \frac{\mu_{k0}^* - m_{ik}}{\sqrt{S_{ikk}}}, \quad k = 1, 2, \dots, K,$$
-
-and Rˆ denotes the correlation matrix corresponding to ˙ˆ , the Bayesian estimate of the covariance matrix ˙.
-
-# **5. Data and model description**
-
-A total of *n* = 99 signalized intersections in the city of Edmonton were investigated for the purpose of developing collision prediction models relating the safety of urban 4-leg intersections to their traffic flows. This data set was used as a reference group for the evaluation of the City of Edmonton's Intersection Safety Camera program ([Sayed and de Leur, 2007\).](#page-8-0)
-
-The data on collision frequencies and traffic volumes within the intersections were obtained from the city of Edmonton. The traffic and collision data were provided for a 3-year period before the implementation of the program and were selected to ensure that no changes in either the intersection layout or traffic volumes occurred over the course of the 3 years. The term "collision" as defined by Edmonton's Transportation Department, refers to reportable onstreet collisions that do not occur on private property, include at least one motor vehicle, and result in injury, at least \$1,000 in property damage, or both. The database does not include non-vehicular collisions (e.g., cyclist hitting a pedestrian) and includes collisions that are forwarded to the department by the police service within a specified period. The term "intersection collisions" include collisions that occur inside and 10 m past the legally defined limits of the outer crosswalk lines of intersecting roads and include rightturn cut-offs. The collisions were classified into *K* = 2 categories: property damage only (*PDO*), *k* = 1, and injuries and fatalities (*I* + *F*), *k* = 2.
-
-Average values for the traffic volumes (over the 3 years period) are used to build CPMs for predicting aggregate number of collisions
-
-$$\ln(\mu_{ik}) = \beta_{k0} + \beta_{k1} X_{i1} + \beta_{k2} X_{i2}, \tag{15}$$
-
-where *Xi*<sup>1</sup> = ln(*Vi*1), *Vi*<sup>1</sup> = AADT at the major approach, and *Xi*<sup>2</sup> = ln(*Vi*2), *Vi*<sup>2</sup> = AADT at the minor approach. The aggregation is justified on several grounds. For instance, an aggregate CPM was found to perform well compared with CPMs developed to handle temporal correlation [\(Lord and Persaud, 2000\).](#page-8-0) Also, the aggregation of collisions over a period of reasonable length helps to avoid confounding effects and such a phenomenon like regression-tothe-mean [\(Cheng and Washington, 2005\).](#page-7-0) A statistical summary of the data is shown in [Table 1.](#page-5-0)
-
-To explore the potential relationships between *PDO*/*I* + *F* and major and minor traffic volumes, [Fig. 1](#page-5-0) displays the threedimensional relationships via wireframe plots.
-
-[Fig. 1](#page-5-0) lends support to the AADT functional form (15), as the relationship between each of *PDO* and *I* + *F* and major and minor traffic volumes, is effectively linear on the logarithmic scale, except for a very few cases where there were no collisions. The AADT functional form (15) is popular among practitioners and is often preferred over models that include several covariates because it can be easily re-calibrated ([Lord et al., 2008\).](#page-8-0) Further, it will be adopted for estimating safety performance in the forthcoming highway safety manual [\(Hughes et al., 2005\).](#page-8-0) It should be noted that AADT models may suffer from omitted variables bias since many non-flow related factors are known to affect collision frequency. Nevertheless, such models are well recognized in the traffic safety literature.
-
-# **6. Results and discussion**
-
-## *6.1. Model results*
-
-[Tables 2 and 3](#page-5-0) summarize the parameter estimates and their associated statistics using the univariate and multivariate Poissonlognormal models. In contrast to the univariate PLN which has multivariate explanatory variables, the MVPLN involves multivariate dependent as well as independent variables. It should also be noted that while MVPLN can handle more than two collision categories, the current application involves only two severity levels leading to a bivariate PLN.
-
-The posterior summaries in [Tables 2 and 3](#page-5-0) were obtained via two chains with 20,000 iterations 10,000 of which were excluded as a burn-in sample using WinBUGS. A Wishart prior with an identity scale matrix and two degrees of freedom was adopted [\(Chib](#page-7-0) [and Winkelmann, 2001; Congdon, 2006\).](#page-7-0) Examination of the BGR statistics, ratios of the Monte Carlo errors relative to the standard deviations of the estimates and trace plots for all model parameters indicated convergence.
-
-The results of [Tables 2 and 3](#page-5-0) show that: (i) the parameter estimates are significant, as the 95% credible intervals are bounded away from zero; (ii) the regression coefficients are positive, indicating an increase in the mean collision frequency with traffic volumes; (iii) the impact of traffic volumes at the major approach is larger than that at the minor approach; and (iv) traffic volumes have a larger effect on *PDO* collisions than on *I* + *F* collisions.
-
-Also, [Tables 2 and 3](#page-5-0) reveal small differences in the estimates of the regression parameters between the univariate and multivariate models. However, the estimates of the extra Poisson variation parameters (<sup>11</sup> and 22) were considerably smaller under MVPLN. In fact, the MVPLN 95% credible intervals were entirely shifted to the left of those obtained under the univariate PLN models, for both *PDO* and *I* + *F*.
-
-For *i* = 1, 2,..., *n* and *k* = 1, 2,..., *K*, the mean and variance of *Yik* are given by
-
-$$E(Y_{ik}) = \mu_{ik} \exp(0.5\sigma_{kk}), \tag{16}$$
-
-and
-
-$$Var(Y_{ik}) = E(Y_{ik}) + [E(Y_{ik})]^{2}(\exp(\sigma_{kk}) - 1).$$
-(17)
-
-Since the second term in Eq. (17) dominates the first, we have that
-
-$$Var(Y_{ik}) \cong [E(Y_{ik})]^2 (\exp(\sigma_{kk}) - 1),$$
-
-leading to the ratio
-
-$$\frac{Var(Y_{ik}|MVPLN)}{Var(Y_{ik}|PLN)} \cong \frac{(\exp(\hat{\sigma}_{kk}) - 1)}{(\exp(\hat{\sigma}_{kk}) - 1)},$$
-(18)
-
-where ˆkk and ˜kk are the posterior estimates of *kk* under MVPLN and PLN, respectively. As precision is inversely proportional to the variance of expected collision frequency, it is estimated that MVPLN is more than twice as precise as the independent univariate PLN models; the actual precision ratios were computed via Eq. (18) as 2.5 and 2.1 for *PDO* and *I* + *F*, respectively. The precision of the expected collision frequencies can be estimated also using the standard deviations via Eq.(17). [Fig. 2](#page-6-0) displays these standard deviations by severity and model.
-
-[Fig. 2](#page-6-0) reveals that (i) the standard deviations of *I* + *F* are smaller than those of *PDO*, (ii) the standard deviations of the multivariate model are uniformly smaller than those of the PLN models, (iii) the relationship between the two sets of standard deviations appears to be linear for the two severity levels, and (iv) the standard deviations of the multivariate model were decreased 41% and 36% for *PDO* and *I* + *F*, respectively. With a larger data set for rural two-lane segments in central Pennsylvania, [Aguero-Valverde and](#page-7-0)
-
-<span id="page-5-0"></span>**Table 1** Statistical summary of data set (99 intersections).
-
-|                                                      | Minimum | Maximum | Mean   | Standard deviation |
-|------------------------------------------------------|---------|---------|--------|--------------------|
-| AADT on major approach                               | 5,642   | 61,744  | 28,010 | 9,705              |
-| AADT on minor approach                               | 2,809   | 29,128  | 16,043 | 6,564              |
-| Number of property damage only (PDO) collisions      | 0       | 126     | 43.13  | 24.96              |
-| Number of injuries and fatalities (I + F) collisions | 0       | 78      | 26.99  | 15.59              |
-
-![](_page_5_Figure_4.jpeg)
-
-**Fig. 1.** Left: wireframe plot of ln(*PDO*) vs. ln(*V*1) and ln(*V*2). Right: wireframe plot of ln(*I* + *F*) vs. ln(*V*1) and ln(*V*2). *PDO*: property damage only, *I* + *F*: injuries and fatalities.
-
-**Table 2** Univariate models' statistics.
-
-| Univariate Poisson-lognormal    | Estimate | Standard deviation | 95% credible intervals |             |
-|---------------------------------|----------|--------------------|------------------------|-------------|
-|                                 |          |                    | Lower limit            | Upper limit |
-| Property damage only (PDO)      |          |                    |                        |             |
-| Intercept                       | −10.590  | 1.282              | −13.090                | −7.950      |
-| Ln(V1) (major AADT)             | 0.927    | 0.131              | 0.667                  | 1.188       |
-| Ln(V2) (minor AADT)             | 0.496    | 0.097              | 0.307                  | 0.686       |
-| 11                              | 0.361    | 0.036              | 0.297                  | 0.437       |
-| DIC                             | 730.3    |                    |                        |             |
-| Injuries and fatalities (I + F) |          |                    |                        |             |
-| Intercept                       | −9.267   | 1.447              | −12.180                | −6.440      |
-| Ln(V1) (major AADT)             | 0.754    | 0.148              | 0.480                  | 1.042       |
-| Ln(V2) (minor AADT)             | 0.493    | 0.105              | 0.292                  | 0.706       |
-| 22                              | 0.415    | 0.043              | 0.339                  | 0.507       |
-| DIC                             | 684.3    |                    |                        |             |
-
-[Jovanis \(2009\)](#page-7-0) obtained reductions in the standard deviations of 20.5% (*PDO*), 10.5% (minor injuries), 21.5% (moderate injuries), 48.1% (major injuries) and 40.7% (fatalities).
-
-The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables *<sup>i</sup>*<sup>1</sup> (*PDO*) and *<sup>i</sup>*<sup>2</sup> (*I* + *F*). This correlation ( ) was estimated at 0.758, which is highly significant. In essence, higher *PDO* rates are associated with higher *I* + *F* rates, as the collision likelihood for both types is likely to rise due to the same deficiencies in roadway design and/or other unobserved factors.
-
-As noted in previously, AADT-only models may suffer from omitted variables bias since the unobserved heterogeneity from other factors known to influence collision frequency (such as number of lanes, signal-control timing, speed limits, etc.) ends up in the
-
-**Table 3** Multivariate models' statistics.
-
-| Multivariate Poisson-lognormal  | Estimate | Standard deviation | 95% credible intervals |             |
-|---------------------------------|----------|--------------------|------------------------|-------------|
-|                                 |          |                    | Lower limit            | Upper limit |
-| Property damage only (PDO)      |          |                    |                        |             |
-| Intercept                       | −10.680  | 1.347              | −13.420                | −8.250      |
-| Ln(V1) (major AADT)             | 0.902    | 0.138              | 0.643                  | 1.179       |
-| Ln(V2) (minor AADT)             | 0.531    | 0.104              | 0.320                  | 0.723       |
-| 11                              | 0.163    | 0.030              | 0.113                  | 0.230       |
-| Injuries and fatalities (I + F) |          |                    |                        |             |
-| Intercept                       | −9.346   | 1.565              | −12.410                | −6.240      |
-| Ln(V1) (major AADT)             | 0.742    | 0.159              | 0.435                  | 1.059       |
-| Ln(V2) (minor AADT)             | 0.513    | 0.121              | 0.276                  | 0.761       |
-| 22                              | 0.217    | 0.042              | 0.145                  | 0.312       |
-| Covariance                      |          |                    |                        |             |
-| 12                              | 0.143    | 0.029              | 0.092                  | 0.208       |
-| Correlation                     |          |                    |                        |             |
-| √1122<br>= 12/                  | 0.758    | 0.056              | 0.634                  | 0.852       |
-| DIC                             | 1373     |                    |                        |             |
-
-<span id="page-6-0"></span>![](_page_6_Figure_2.jpeg)
-
-**Fig. 2.** Standard deviations (S.D.) of expected collision frequencies by severity and model. *PDO*: property damage only, *I* + *F*: injuries and fatalities.
-
-correlation structure, thus affecting the estimated correlation. To study the magnitude of this effect on the correlation, the literature was consulted where several independent variables were used to develop multivariate CPMs.
-
-[Park and Lord \(2007\)](#page-8-0) reported a number of correlations between *PDO* and four severity levels. Upon combining these 4 severity levels in one category (*I* + *F*) and using the covariance matrix, the correlation between *PDO* and *I* + *F* was estimated at 0.8608. A similar calculation using the results of [Aguero-Valverde and Jovanis \(2009\)](#page-7-0) produced a correlation estimate of 0.58. [Ma et al. \(2008\)](#page-8-0) did not report the variances associated with the various severity levels. However, assuming comparable precisions, their results produced a rough correlation estimate of 0.44.
-
-These results indicate that the current estimate of 0.758 is in the upper range of the estimates reported in the literature. Although the inclusion of additional relevant covariates is expected to reduce the correlation, it is conjectured to remain significant with crucial inferential impact.
-
-The DIC statistics were 730.3, 684.3 and 1373 under the *PDO* PLN, *I* + *F* PLN and MVPLN, respectively. Thus, the MVPLN model provides a superior fit over the two univariate models as its (multivariate) DIC is much less than the sum of their (univariate) DICs; a very significant drop off of 41.6.
-
-### *6.2. Multivariate identification of hazardous locations*
-
-Hazardous locations were identified via Eqs. [\(13\) and \(14\)](#page-3-0) using ı = 0.10, 0.05 and 0.01. For *K* = 2, the standardized bivariate normal distribution function PROBBNRM ([SAS, 2002\)](#page-8-0) was used in the implementation of Eq. [\(14\). T](#page-3-0)he PROBBNRM function required three input arguments (<sup>∗</sup> <sup>10</sup> − mi1)/ Si11,(<sup>∗</sup> <sup>20</sup> − mi2)/ Si22, and ˆ = 0.758. The outcomes appear in [Table 4, w](#page-7-0)here the results of the univariate Total PLN model were obtained through direct simulation of the sum *PDO* + *I* + *F*.
-
-According to [Table 4, t](#page-7-0)he numbers of intersections identified as hot spots were 31, 29, 35 and 41 under *PDO* PLN, *I* + *F* PLN, Total PLN and MVPLN, respectively, for ı = 0.10. The corresponding numbers were 30, 28, 32 and 37, for ı = 0.05, and 22, 20, 28 and 30 for ı = 0.01. As expected the numbers of hot spots was reduced as the threshold value decreased. However, several locations identified as hazardous by the MVPLN model were missed by the univariate models regardless of the choice of threshold value. It should also be noted that none of the intersections that were not identified as hazardous by MVPLN were identified by any of the univariate models except for one location under the *PDO* PLN model for ı = 0.10 and 0.05.
-
-The results of [Table 4](#page-7-0) demonstrate that some hazardous locations can be overlooked if the analysis was restricted to the univariate models. These results are consistent with those of the cost ranking approach of [Aguero-Valverde and Jovanis \(2009\)](#page-7-0) in that the MVPLN and independent PLN models yield different identification/ranking of hot spots.
-
-The advantage of MVPLN over separate univariate analyses is illustrated in Fig. 3, where posterior information is also contrasted to prior knowledge. The figure displays two credible ellipses and a credible rectangle for (ln(-*PDO*),ln(-*I+F*)), with 95% confidence. Fig. 3 was prepared using the data for Site 56, which was identified as hazardous by MVPLN and *I* + *F* PLN but neither by *PDO* PLN nor by Total PLN. Similar patterns were observed for other identified sites.
-
-![](_page_6_Figure_14.jpeg)
-
-**Fig. 3.** 95% credible ellipses and rectangle for (ln(-*PDO*),ln(-*I+F*)), using the data for Site 56. *PDO*: property damage only, *I* + *F*: injuries and fatalities.
-
-<span id="page-7-0"></span>**Table 4** Number of hot spots selected under different PLN models.
-
-|                                 | Hazardous? | Multivariate PLN |     |          |     |          |     |
-|---------------------------------|------------|------------------|-----|----------|-----|----------|-----|
-|                                 |            | ı = 0.10         |     | ı = 0.05 |     | ı = 0.01 |     |
-|                                 |            | No               | Yes | No       | Yes | No       | Yes |
-| Univariate PLN                  |            |                  |     |          |     |          |     |
-| Property damage only (PDO)      | No         | 57               | 11  | 61       | 8   | 69       | 8   |
-|                                 | Yes        | 1                | 30  | 1        | 29  | 0        | 22  |
-| Injuries and fatalities (I + F) | No         | 58               | 12  | 62       | 9   | 69       | 10  |
-|                                 | Yes        | 0                | 29  | 0        | 28  | 0        | 20  |
-| Total                           | No         | 58               | 6   | 62       | 5   | 69       | 2   |
-|                                 | Yes        | 0                | 35  | 0        | 32  | 0        | 28  |
-
-The larger ellipse corresponds to the prior distribution in Eq. [\(3\). T](#page-2-0)he area inside that ellipse corresponds to 95% probable values for (ln(-*PDO*),ln(-*I+F*)) before the sample data were collected. The smaller ellipse corresponds to the posterior distribution in Eq. [\(10\). T](#page-3-0)he area inside that ellipse corresponds to 95% probable values for (ln(-*PDO*),ln(-*I+F*)) after incorporating the sample data into the analysis. It is clear that the sample information has enhanced prior knowledge and resulted in a more focused "posterior" ellipse that is a (small) subset of the "prior" ellipse.
-
-The rectangle was constructed via the Bonferroni method using the two credible intervals obtained from the separate univariate models, *PDO* PLN and *I* + *F* PLN. The shaded area inside that rectangle corresponds to 95% probable values for (ln(-*PDO*),ln(-*I+F*)) obtained by combining the two separate univariate posterior intervals. The posterior ellipse is nearly contained in the rectangle indicating that some values that are improbable under the MVPLN posterior analysis would be probable under the separate univariate analyses and vice versa. Also, some of the probable values under the rectangle seem to contradict prior knowledge as they lie outside the prior ellipse.
-
-# **7. Conclusions and future research**
-
-A MVPLN regression was used to jointly analyze a sample of collision counts classified by two severity levels (*PDO* and *I* + *F*) at 99 urban intersections. To illustrate the importance of the multivariate technique, it was compared with the independent univariate PLN models with respect to model inference, goodness-of-fit, identification of hazardous locations and precision of expected collision frequency.
-
-The development of the MVPLN model is undertaken using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a measure for model comparisons.
-
-The estimates of the extra Poisson variation parameters were considerably smaller under MVPLN. As precision is inversely proportional to the variance of expected collision frequency, it is estimated that the MVPLN model is more than twice as precise as the univariate PLN models. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables (*PDO*) and (*I* + *F*). This correlation ( ) was estimated at 0.758, which is highly significant. The results indicate that higher *PDO* rates are associated with higher *I* + *F* rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. This correlation was identified by other researchers in the literature.
-
-In terms of the goodness-of-fit, the MVPLN provided a superior fit over the two univariate models as its (multivariate) DIC is much less than the sum of their (univariate) DICs. The differences between the DICs show a significant drop off of 41.6.
-
-In term of model application, the paper introduces a new multivariate technique for the identification of hazardous locations. The new technique generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature to fit the multivariate relationship between latent variables. The results showed that all the hazardous locations identified by the univariate models were identified by multivariate model (except one location under *PDO* PLN). The results also demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models.
-
-The results presented in this paper are based on a single dataset. Even though these results conform to those in the literature, further research with different datasets and infrastructures is required to confirm the paper's findings.
-
-In this paper two severity levels were investigated (namely: *PDO* and *I* + *F*). However, different severity levels could be incorporated (e.g., fatality, severe injury, light injury, PDO, etc.) or different collisions types could be considered (e.g., angle, head-on, rear-end, sideswipe, etc.). The multivariate nature of such data sets is both logical and intuitive. Recent improvements in statistical tools allows for a more precise analysis which takes into account the correlations that exist among the different levels. These tools should be used to assist transportation engineers to better understand the relationships between the different modeling variables and techniques.
-
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Zotero/001_artiklid/Road Safety Indicators/elvikstrengtheningincentivesefficientroad2010.md

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----
-category: literaturenote
-citekey: elvikstrengtheningincentivesefficientroad2010
-title: "Strengthening incentives for efficient road safety policy priorities: The roles of cost–benefit analysis and road pricing"
-authors: "Elvik, Rune"
-year: 2010
-date: 2010-00-00 2010
-doi: 10.1016/j.ssci.2010.01.005
-publication: Safety Science
-url: "https://doi.org/10.1016/j.ssci.2010.01.005"
-zotero_key: PQ258R6J
-zotero_storage: 7GTAAZ86
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Elvik, Rune"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-## Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/09257535)
-
-# Safety Science
-
-![](_page_0_Picture_4.jpeg)
-
-![](_page_0_Picture_5.jpeg)
-
-# Strengthening incentives for efficient road safety policy priorities: The roles of cost–benefit analysis and road pricing
-
-Rune Elvik \*
-
-Institute of Transport Economics, Gaustadalléen 21, NO-0349 Oslo, Norway
-
-#### article info
-
-Article history: Received 13 May 2009 Received in revised form 2 January 2010 Accepted 7 January 2010
-
-Keywords: Cost-benefit analysis Power Welfare Incentives Road safety policy Social dilemmas
-
-#### abstract
-
-This paper discusses how incentives for setting efficient priorities in road safety policy can be strengthened. Efficient priorities are characterised by the use of cost-effective road safety measures. Cost-effective road safety measures can be identified by means of cost-benefit analyses. Studies of the actual priorities in road safety policy, in particular in the Scandinavian countries, suggest that these priorities are inefficient, i.e. characterised by the non-use or sub-optimal use of cost-effective road safety measures as well as an extensive use of ineffective road safety measures. This occurs despite the fact that road safety policy analyses have included extensive cost-benefit analyses of road safety measures. It would thus appear that cost-benefit analyses do not necessarily generate a sufficient incentive to implement cost-effective road safety measures. Possible reasons for this are discussed in the paper. It is argued that a large part of the monetary benefits of road safety measures, as estimated in cost-benefit analyses, are not subject to market transactions, and do therefore not manifest themselves in the form of increased income or higher profits. While cost-benefit analyses are indispensable as a means of identifying cost-effective road safety measures, their influence on actual road safety policy needs to be strengthened by providing additional incentives for the use of cost-effective road safety measures. It is suggested that a system of road pricing could generate such incentives. A brief sketch of a hypothetical system of road pricing is given and some problems associated with the implementation of this system are discussed.
-
-> -2010 Elsevier Ltd. All rights reserved.
-
-## 1. Introduction
-
-In order to maintain progress in improving road safety, it is important to use cost-effective road safety measures. In this paper, the term cost-effective will be used to denote measures whose benefits (in monetary terms) are greater than their costs (in monetary terms). To identify cost-effective road safety measures, cost– benefit analysis is an indispensable tool [\(OECD, 2008\)](#page-7-0). Road safety policy analyses relying on cost-benefit analyses have been reported in several countries [\(Elvik, 1997, 2001, 2003, 2007; Elvik et al.,](#page-6-0) [1984; Elvik and Amundsen, 2000; OECD, 2008\)](#page-6-0). These analyses show that major improvements in road safety can be accomplished by implementing cost-effective road safety measures. The most recent analysis for Norway [\(Elvik, 2008a\)](#page-6-0) suggested that the number of road accident fatalities can be reduced by about 50% if all currently known cost-effective road safety measures are implemented by the year 2020.
-
-However, other studies [\(Elvik, 1993, 1995a, 2009a; Fridstrøm](#page-6-0) [and Elvik, 1997; Odeck, 1991, 1996; Jansson and Nilsson, 1989;](#page-6-0) [Nilsson, 1991; Nyborg, 1998; Elvik and Veisten, 2005; Helland](#page-6-0) [and Sørensen, 2009\)](#page-6-0) show that actual road safety policy, at least in Norway, is characterised by inefficient priorities. This means that policy involves both the use of measures that are not costeffective (i.e. benefits are smaller than costs) and the non-use or sub-optimal use of measures that are cost-effective. The existence of inefficient policies can be ascertained by examining the main findings of successive road safety policy analyses for Norway [\(Elvik](#page-6-0) [et al., 1984; Elvik, 1999, 2007\)](#page-6-0). In all these analyses, several main policy options were developed. Two of these options were to continue present policy or to base policy strictly on cost-benefit analysis.
-
-In the most recent of these analyses ([Elvik, 2007\)](#page-6-0), it was found that continuing present policy may reduce the number of road accident fatalities from 250 (mean 2003–2006) to 190 in 2020. Cost-effective use of road safety measures, based strictly on the results of cost-benefit analyses, may reduce the number of fatalities to 138 in 2020. In other words, current road safety policy in Norway does not make use of all opportunities for cost-effective road safety improvements. It should be added that this finding may not apply to all countries. A study in Great Britain [\(Nellthorp and Mac](#page-6-0)[kie, 2000](#page-6-0)) found that priorities between major road projects were to a large extent, although not slavishly, based on cost-benefit analysis. A recent Norwegian study [\(Odeck, 2009\)](#page-7-0) also indicated
-
-<sup>\*</sup> Tel.: +47 22 57 38 10; fax: +47 22 60 92 00. E-mail address: [re@toi.no](mailto:<xml_chg_old>kre@toi.no</xml_chg_old><xml_chg_new>re@toi.no</xml_chg_new>)
-
-that policy makers base their priorities on the factors that are included in cost-benefit analyses (travel time, safety, environmental amenities), but take account of these factors in non-monetary terms (impact assessments in Norway are required to state impacts both in natural units and in monetary terms). It is nevertheless relevant to ask the following questions:
-
-- 1. Why are the actual priorities in road safety policy inefficient? Does cost–benefit analysis generate sufficient incentives to implement cost-effective road safety measures?
-- 2. If cost–benefit analysis does not generate sufficient incentives for efficient policy priorities, how can stronger incentives for making policy conform more closely to the results of cost-benefit analyses be created?
-
-It should be noted that there are, in theory, very many reasons why actual road safety policy may not be based strictly on the results of cost–benefit analysis ([Elvik and Veisten, 2005\)](#page-6-0). It could, for example, be the case that implementing all cost-effective road safety measures would require substantially larger expenditures than current budgets allow for. However, road safety policy analyses in Norway [\(Elvik, 1999, 2007\)](#page-6-0) found that this was not the case. Current policy priorities in Norway are so inefficient that all costeffective road safety measures could be funded by reducing expenditures on ineffective measures and increasing expenditures on cost-effective measures, without having to increase total expenditures. This may of course be different in other countries. This paper will focus on two possible reasons why cost-benefit analyses may not create sufficient incentives to implement cost-effective road safety measures. The first of these concerns the nature of the monetary benefits of improved road safety and the possibilities of converting these benefits to increased income or larger profits. The second concerns the strength of the interests various stakeholders who may influence road safety have in improving road safety.
-
-## 2. Components of the monetary valuation of road safety and the nature of road safety benefits
-
-While different countries estimate the costs of road accidents in different ways, there is near unanimity among economists about how to estimate these costs for use in cost-benefit analysis. Guidelines for estimating road accident costs are given in a number of publications, including a report from the COST-programme [\(Alfaro](#page-6-0) [et al., 1994\)](#page-6-0) and reports from the ECMT and OECD [\(ECMT, 2001;](#page-6-0) [Pearce et al., 2006](#page-6-0)). These guidelines recommend using a combination of three approaches in order to cover all cost elements:
-
-- 1. The resource cost method, also referred to as the costs of restitution, is applied to estimate the direct monetary outlays associated with resources used as a result of accidents.
-- 2. The human capital method, which is applied to estimate the value of productive capacity lost as a results of accidents. Lost productive capacity is sometimes referred to as indirect costs of accidents.
-- 3. The willingness-to-pay method, which is used to estimate the ex ante demand for improving road safety, i.e. the gain in welfare (consumer surplus) that can be attained by reducing the number of accidents.
-
-[Fig. 1](#page-2-0) lists these components of the monetary valuation of road safety. The various components of valuation differ in terms of how they are manifested in economic transactions. The direct costs are real expenditures. In principle, these costs can be retrieved or at least roughly identified in the accounts of hospitals, insurance companies, the police, the courts, car repair shops and households. Estimates of lost productive capacity partly reflect monetary transactions; partly these costs are of a more abstract nature. In particular, lost productive capacity attributable to a fatality is usually estimated as the present value of future earnings. This represents the value of what the individual could have produced if alive; this can never be known with certainty and is therefore most appropriately interpreted as a loss of productive potential or capacity, not an actual loss of production.
-
-The actual loss of production resulting from accidents can be estimated by applying the friction cost method developed by Koopmanschap et al. ([Koopmanschap and Ineveld, 1992; Koopmanschap](#page-6-0) [et al., 1995](#page-6-0)). This method estimates the loss of production during the period it takes to hire new staff to replace an individual who leaves the labour force as a result of an accident. The estimates developed by Koopmanschap et al. indicate that the actual losses of production are only about 10% of the present value of the loss of productive capacity or potential. This does not mean that the loss of productive potential is not real; it simply means that this loss does not materialise as a decline in production.
-
-Willingness-to-pay for a reduced number of road accidents reflects the hypothetical demand for improved safety. It does not reflect any actual monetary transactions. This fact was clearly understood by the pioneers introducing the willingness-to-pay approach to the valuation of road safety. Thus, [Schelling \(1968\),](#page-7-0) page 143 wrote: ''Unexpected death has a hypothetical quality whether it is merely being talked about or money is being spent to prevent it." The willingness-to-pay for reducing statistical risks by an amount that corresponds to the prevention of one death has nothing to do with the ex post costs generated by the death of a known individual. The latter costs are at least two orders of magnitude smaller than the valuation of preventing a death. Thus, in countries that apply the willingness-to-pay approach for valuing road safety, this component normally represents more than half, in some cases nearly the whole, monetary value assigned to improving road safety [\(Elvik, 1995B\)](#page-6-0). The valuation of improved road safety in terms of willingness-to-pay is not subject to market transactions. This means that, although representing real benefits of improving road safety, it will not be realised in terms of added income or profits.
-
-## 3. Incentives and stakeholders in road safety policy
-
-#### 3.1. The concept of incentives
-
-Given the fact that a large part of the monetary benefits of improved road safety, as estimated in cost-benefit analyses, will not manifest itself as a monetary gain, it is necessary to ask whether various actors who can influence road safety have sufficiently strong incentives to do so, even if actual the monetary benefits obtained will be considerably smaller than the societal benefits as reflected in the valuation of improved safety. An incentive can be defined as any prospective benefit or reward favouring the choice of an option. Thus defined, an incentive can only motivate for action if it makes a noticeable difference in utility and if the actor is able to bring about the desired outcome of an action. The relevant questions therefore are:
-
-- 1. To what extent can various actors (households, businesses, health care providers, car manufacturers, government) influence road safety by means of actions under their control?
-- 2. How much would these actions influence the utility of these actors?
-
-The term ''utility" is applied in a very broad sense here: It refers to the level of satisfaction or welfare, including the level of support
-
-<span id="page-2-0"></span>![](_page_2_Figure_2.jpeg)
-
-Fig. 1. Components of the monetary valuation of improved road safety.
-
-given to actions taken by politicians. The following sections will briefly discuss the incentives of some actors to influence road safety.
-
-## 3.2. Household incentives to improve road safety
-
-Do households face strong incentives for improving road safety? According to the latest survey of household consumption expenditures in Norway [\(Statistics Norway, 2009\)](#page-7-0), only about 2.3% of expenditures can reasonably be interpreted as being attributable to road accidents. In other words, if road accidents were eliminated, an average Norwegian household would save about 2% on their household budget. No household can exercise 100% control of the risk it faces on the road. A more plausible estimate is that individual behaviour, with respect to, for example, speed [\(Elvik,](#page-6-0) [2008b](#page-6-0)), route choice or seat belt wearing can influence the risk of injury by, say, about 30%. Hence, by consistently adopting safe behaviour, a household might be able to save 30% of its accident costs, amounting to about 0.7% of the total household budget. This incentive must be regarded as quite weak.
-
-An additional problem is that some promising road safety measures that are cost-effective from a societal perspective are not cost-effective from a road user perspective. ISA (intelligent speed adaptation; technology making speeding impossible or at least unpleasant) is a case in point. The assessment of costs and benefits of ISA on new cars from a societal perspective and from a road user perspective are compared in [Table 1](#page-3-0).
-
-According to the societal perspective, the benefits consist of all savings in accident costs, from which the costs of additional travel time below the speed limit are subtracted. It was assumed that not all ISA-systems are perfect; hence, the mean speed of travel in a traffic stream in which all vehicles have ISA will be a little below the speed limit. This additional travel time has been included.
-
-According to a road user perspective, the analysis is quite different. Only 60% of accident savings are included, as the rest of accidents are external from the road user's point of view ([Elvik, 1994\)](#page-6-0). The full costs of reducing speed, including those attributable to the lowering of illegal speeds, have been included. While treating time saved by speeding (i.e. violating the speed limit) as a benefit is not appropriate in a cost–benefit analysis adopting a societal perspective ([Elvik, 2006\)](#page-6-0), it is clearly a benefit from a road user perspective; why else would drivers be speeding?
-
-The two perspectives give dramatically different results. From the societal perspective, benefits exceed costs by a wide margin. From a road user perspective, benefits are negative. Hence, unless a cost–benefit analysis somehow succeeded in persuading households to adopt a societal perspective, it is unlikely that it would make households buy ISA.
-
-#### 3.3. Business incentives to improve road safety
-
-Just as the average costs of road accidents represent a small share of an average household budget, the mean cost of an accident normally represents a very small share of the costs of a business firm. Thus, in a study of the costs of occupational accidents in the furniture industry, [Rundmo and Söderqvist \(1994\)](#page-7-0) found that the financial losses resulting from accidents amounted to 0.3% of the total payroll. Businesses themselves paid only about 20% of this; the rest was covered by the public sector. The purely financial incentive to prevent accidents was therefore almost non-existent; on the average a saving of 0.05% of the wage budget would be attained if accidents were eliminated.
-
-In short, businesses – at least in the furniture industry – hardly have noticeable costs as a result of accidents. According to [Rundmo](#page-7-0) [and Söderqvist \(1994\)](#page-7-0) firms kept a larger staff than they strictly speaking needed. They did so in order to keep production running smoothly in cases of illness. The normal rate of absence from work in many industries is between 5% and 10%; thus to keep activities progressing normally, surplus staff of at least this magnitude is needed. Keeping this staff of course costs a lot. But this cost has already been internalised by businesses; injuries that result from accidents have, in a sense, been paid for in advance and hardly generate any new cost. Since most of the cost of keeping surplus staff is attributable to illness not caused by accidents, the monetary incentive to improve safety is weak.
-
-[Osmundsen et al. \(2008\)](#page-7-0) discuss in general terms problems associated with implementing safety incentives in industry. They point out several difficulties that are likely to reduce the efficiency of incentives intended to promote safety, using Norwegian oil industry as a case.
-
-<span id="page-3-0"></span>Table 1 Costs and benefits of intelligent speed adaptation (ISA) as viewed from a societal and driver perspective.
-
-| Item of benefits or costs | Amounts in million NOK (1 NOK = 0.12 Euro in May 2009) if all cars have intelligent speed adaptation. Negative amounts are added costs |            |                                                                                  |  |  |
-|---------------------------|----------------------------------------------------------------------------------------------------------------------------------------|------------|----------------------------------------------------------------------------------|--|--|
-|                           | Societal perspective<br>Driver perspective<br>Comments                                                                                 |            |                                                                                  |  |  |
-| Accident savings          | 28.997                                                                                                                                 | 17.398     | About 40% of accident costs are external and not counted as a benefit by drivers |  |  |
-| Extra travel time         | <br>6402                                                                                                                               | <br>38.410 | Only reduction in legal speeds included according to societal perspective        |  |  |
-| Vehicle operating costs   | 720                                                                                                                                    | 720        | Car drivers will include all savings in vehicle operating costs                  |  |  |
-| Environmental benefits    | 2350                                                                                                                                   | 0          | Environmental benefits are external from drivers' point of view                  |  |  |
-| Total benefits            | 25.665                                                                                                                                 | <br>20.292 |                                                                                  |  |  |
-| Installation costs        | 9181                                                                                                                                   | 9181       | Costs are paid in full by drivers                                                |  |  |
-| Annual running costs      | 2333                                                                                                                                   | 2333       | Costs are paid in full by drivers                                                |  |  |
-| Total costs               | 11.514                                                                                                                                 | 11.514     |                                                                                  |  |  |
-| Benefit cost ratio        | 2.23                                                                                                                                   | <br>1.76   | Dramatic difference in results depending in perspective adopted                  |  |  |
-
-#### 3.4. Health care sector incentives to promote road safety
-
-Does the health sector have a strong incentive to improve road safety? A recent cost estimate for Norway ([Elvik, 2009B](#page-6-0)) indicates that the costs of treating traffic injury victims represent only about 1.5% of the total operating cost for hospitals in Norway. Thus, by eliminating road accidents, <2% of the total hospital budget could be saved.
-
-In practice, however, it is not very likely that hospital budgets would be cut by this amount if road accidents were eliminated. It is more likely that the resources now spent on treating traffic injury victims would be transferred to other types of medical treatment. There are waiting lists for many types of treatment; in principle these could be shortened if there were no road accidents. This is obviously a societal gain. But the gain would not materialise in the form of reduced spending on health care. Moreover, the health sector does not control any road safety measures and is therefore not in a position to reduce the costs by means of its own policies.
-
-## 3.5. Car manufacturer incentives to improve road safety
-
-Car manufacturers are today facing an incentive to improve road safety that is considerably stronger than it was only 15 years ago. One of the factors contributing to this is the remarkable success of the European New Car Assessment Programme (EuroNCAP) ([ETSC, 2009](#page-6-0)). EuroNCAP is an extensive programme for testing and rating the safety performance of new cars. The programme has become widely known among car owners, thanks in part to the support and publicity given to the programme by motoring organisations. EuroNCAP has attained high credibility because it is an independent programme providing objective assessments; it does not cater to the commercial interests of any particular car manufacturer. This gives the programme a vastly more authoritative status than any advertisement produced by a car manufacturer.
-
-EuroNCAP score has become a key element in the competition for car purchasers. Studies [\(Andersson, 2005\)](#page-6-0) show that consumers value car safety and are willing to pay for it. [Andersson \(2005\)](#page-6-0) estimated the implicit value of preventing a fatality to SEK 12 million (1 SEK = 0.10 Euro in December 2009). Car safety has improved greatly in recent years; occupants of new cars are far less likely to be killed or seriously injured in an accident of a given severity than occupants of older cars [\(Broughton, 2008; Martin and Len](#page-6-0)[guerrand, 2008](#page-6-0)).
-
-In principle, therefore, safety can be developed into a major competitive force in the marketplace, thus giving car manufacturers incentives to develop safer cars. Car manufacturers will, however, only offer safety technology they think will sell. ISAsystems, for example, are not offered by car manufacturers today.
-
-#### 3.6. Road authority incentives to promote road safety
-
-In all highly motorised countries, the road system has been greatly improved during the past 50 years or so. Many of these improvements are likely to have contributed to improving road safety. Do road authorities face strong incentives to improve road safety? Road authorities do face incentives, but these are partly of a different nature from those facing other stakeholders. In the first place, road authorities can be instructed by politicians to implement road safety measures. In the second place, the recent development of road assessment programmes, like EuroRAP, has made information more easily available to motorists about the safety of roads, thus putting pressure on road authorities to improve roads with a bad safety record. In the third place, it is not unreasonable to think that many traffic engineers take professional pride in designing safer roads and improving the safety of existing roads.
-
-Neither of these incentives is, however, derived from cost-benefit analysis. Although facing incentives to improve road safety, road authorities do not necessarily face a strong incentive to use their budgets as efficiently as possible. For Norway, there is evidence ([Nyborg, 1998](#page-6-0)) that road budgets are ample and clearly large enough to fund all cost-effective measures as well as quite a few measures that are not cost-effective. This may of course be different in other countries. Other studies ([Elvik, 1995a, 2009a](#page-6-0)) indicate that road authorities in Norway regard politicians as their principals and try to develop politically feasible investment programmes that will be widely supported by politicians. To a certain extent, policies designed to be widely supported will embody priorities not based strictly on efficiency.
-
-#### 3.7. Preliminary conclusions
-
-Based on the discussion above, it seems clear that cost-benefit analyses will not always create sufficient incentives for implementing cost-effective road safety measures. In the first place, the actual monetary gains resulting from road safety measures will be considerably smaller than the full societal benefits of such measures as estimated according to the willingness-to-pay for improving road safety. This does not mean that the benefits are not real, simply that they are not converted to a stream of cash by means of market transactions. In the second place, some of those who have a stake in road safety have such a small stake in it that they face weak incentives to act. Car manufacturers and road authorities face stronger incentives, and these incentives have probably become stronger in recent decades than they used to be. The incentives facing car manufacturers and road authorities are, however, of a general nature and do not ensure that these stakeholders will always promote the most efficient measures for improving road safety. It is reasonable to say that both car manufacturers and road authorities can be expected to continue their efforts to improve road safety, but not necessarily in the most cost-effective manner. Cost-benefit analyses therefore needs to be supported by stronger instruments of power in order to ensure that the priorities for improving road safety are as efficient as they can be. Can such instruments of power be imagined? Can a way be found to convert the hypothetical demand for road safety, as expressed in the willingness-to-pay for it, into an actual demand and thus generate stronger incentives for improving road safety?
-
-## 4. The prospects of a near perfect road pricing to generate demand for road safety
-
-## 4.1. Technology for road pricing
-
-Technological progress is rapidly making it possible to introduce a near perfect system of road pricing. In such a system, every road user would pay the marginal societal costs of road use per kilometre of travel. These costs would include:
-
-- 1. The cost of providing a certain road capacity.
-- 2. Road maintenance costs.
-- 3. Costs of specific road safety measures.
-- 4. Accident costs.
-- 5. Environmental costs.
-
-For each of these items a basic cost rate per kilometre of travel would be determined for each road. Each motor vehicle could then be fitted with a driving computer and a geographical positioning system that could record information regarding, for example:
-
-- 1. Route choice.
-- 2. Speed.
-- 3. Use of daytime running lights.
-- 4. Following distance.
-- 5. Use of indicators.
-- 6. Impact speed in case of an accident.
-
-The cost of the equipment needed to record these data is rapidly going down. The advantages of recording the data would be huge. One could, for example, in principle eliminate the problem of incomplete accident reporting and inaccurate information regarding where accidents take place.
-
-Enormous amounts of data would be produced by driving computers. A solution must be found to the problem of how to collect and process these data. There are many possibilities. One possibility would be to connect the fuel system electronically to the driving computer. The computer would write the data to a memory stick that would need to be removed from the car every time it needed to be refuelled. It would not be possible to open the lid to the fuel tank without removing the memory stick. To pay for the fuel, the memory stick would have to be inserted into a computer at each fuel station – the driver would then get a bill including both the fuel and the payment for using the road. A new memory stick would be handed to the driver as a receipt for the payment; this would then have to be inserted into the driving computer in order to be able to start the car.
-
-As outlined above, the driving computer would be an instrument for data collection only. It would not interfere with driver behaviour. Drivers would therefore still have the option of speeding; however, every instance of speeding would be detected and the driver would be charged a fee proportional to the risk involved in speeding plus a penalty intended as a deterrent.
-
-As far as pedestrians and cyclists are concerned, [Jansson \(1994\),](#page-6-0) in an analysis of the external cost of accidents, argues that these road users should not pay for using the roads, since motor vehicles are the main source of the risk they are facing. It will therefore be assumed that motorists will pay the costs of facilities intended for use by pedestrians or cyclists. This would be in their interest, as separate facilities for pedestrians and cyclists make it less likely that motorists will cause injuries to these road users.
-
-## 4.2. A simple illustration
-
-To illustrate how a system of road pricing might be operating, consider the estimates of the societal costs of travel given in [Table](#page-5-0) [2](#page-5-0). The table is based on [Erke and Elvik \(2006\)](#page-6-0) and on [Samstad et al.](#page-7-0) [\(2005\)](#page-7-0). The estimates in [Table 2](#page-5-0) include only the external effects of motorised travel by road, not the costs of road investments and maintenance.
-
-Consider, as an illustration a driver who is driving 1 km on a rural two lane road with a speed limit of 80 km/h. The basic charge for this driver would be NOK 0.973 km-<sup>1</sup> (1 NOK = 0.12 Euro as of December 2009). If the driver was speeding by 10%, i.e. going at a speed of 88 km/h, a surcharge would be added to reflect the cost of speeding. This cost would include an increase in accident risk, increased contribution to global warming and an increase of air pollution. Applying the Power Model ([Elvik, 2004, 2005\)](#page-6-0), the surcharge can be estimated to about 0.30 NOK for accidents and in total about 0.02 NOK for global warming and air pollution. The total charge per kilometre would then be about NOK 1.30 km-To this should be added a surcharge for road maintenance, as road wear increases as a function if speed.
-
-Faced by prices per kilometre of driving that vary depending on the baseline level of accident risk and depending on individual behaviour, drivers would soon discover that safe behaviour brings monetary rewards in the form of lower charges per kilometre. As part of such a system, road authorities could offer specific services to road users every year on every road. These services might, depending on the status of the road, include: resurfacing, renewing traffic signs, installing road lighting, changing the speed limit, or any other measure. At the beginning of each year, road authorities would contact all car owners who used the road at least once the past year and offer them a package of services and an estimate of the cost of these services. Payment for major investments would take the form of down-payments during a number of years, to avoid suddenly increasing road user charges drastically to fund a major project.
-
-Road users would indicate their willingness-to-pay for the services offered by purchasing memory sticks that contain different combinations of the measures. To prevent strategic behaviour in the form of free riding, services should be priced so that they could only be offered if everybody paid for them. The amount paid would depend on the frequency of use of the road. In case of insufficient demand, the services would not be performed.
-
-To stimulate demand, road users should be offered rebates once the measures offered had been implemented and found to reduce the accident rate. This would create strong incentives for road authorities not to introduce measures that would not be expected to reduce the accident rate and to evaluate rigorously whether measures implemented did in fact reduce the accident rate. A potential problem would be that a low number of accidents would make it difficult to evaluate the effects of small road safety schemes at a local level. To create a basis for meaningful evaluation, all projects of the same nature should therefore be pooled into a national data base. Evaluation studies should be performed by an independent body employing state-of-the-art techniques. Findings should always be published.
-
-## 5. Discussion
-
-Road safety has been greatly improved in many highly motorised countries the past 40 years, following a peak in the number of road accident fatalities around 1970. However, the current
-
-<span id="page-5-0"></span>Table 2 External costs of use of motor vehicles on public roads in Norway. NOK per kilometre of driving.
-
-| Traffic environment | Type of road           | Marginal external costs of motorised travel – NOK per vehicle kilometre (1 NOK = 0.12 Euro in May 2009) |                |               |               |
-|---------------------|------------------------|---------------------------------------------------------------------------------------------------------|----------------|---------------|---------------|
-|                     |                        | Accidents                                                                                               | Global warming | Air pollution | Traffic noise |
-| Rural               | Motorway, 100 km/h     | 0.224                                                                                                   | 0.176          | 0.047         | 0.000         |
-| Rural               | Motorway, 90 km/h      | 0.206                                                                                                   | 0.176          | 0.047         | 0.000         |
-| Rural               | Two lane road, 80 km/h | 0.750                                                                                                   | 0.176          | 0.047         | 0.000         |
-| Rural               | Two lane road, 70 km/h | 0.700                                                                                                   | 0.176          | 0.047         | 0.000         |
-| Rural               | Two lane road, 60 km/h | 0.650                                                                                                   | 0.176          | 0.047         | 0.000         |
-| Urban, small city   | Two lane road, 50 km/h | 0.900                                                                                                   | 0.176          | 0.173         | 0.510         |
-| Urban, major city   | Two lane road, 50 km/h | 0.900                                                                                                   | 0.176          | 0.397         | 0.518         |
-
-number of fatalities is still regarded as unacceptably high in most countries and ways of improving the efficiency of road safety policies are sought. Policy analyses indicate that, at least in Norway and Sweden, current road safety priorities are not as effective as theoretically possible, i.e. ineffective measures continue to be used and not all cost-effective measures are introduced.
-
-Cost-benefit analyses do not seem to generate sufficient incentives to implement cost-effective road safety measures. The purely monetary incentives for improving road safety facing households, business firms and the health care sector are weak. Most of the monetary benefits of improving road safety as estimated in costbenefit analyses represent a hypothetical demand for improved road safety. To generate stronger incentives for improving road safety, this hypothetical demand needs to be converted to an actual demand. In principle, this is now becoming possible in the form of road pricing systems in which road users pay all costs of road use. By applying information technology, it is in principle even possible to device road pricing systems that are responsive to road user behaviour. As an example, a computer recording speed would make it possible to put a price tag on speeding by applying known relationships between speed and the risk and severity of accidents.
-
-In a fully implemented road pricing system, road authorities would get all their funding from fees paid by road users. The level of service offered to road users would then depend directly on how much road users were willing to pay. Road authorities could test this by offering alternative packages of measures carrying different price tags. Successful road safety measures would lead to lower road user charges at a later date.
-
-If comprehensive road pricing were introduced, there would no longer be a need for cost-benefit analysis. The provision of road safety would ideally speaking match the demand for it, resulting in an optimal level of safety. Road users would be rewarded for improving safety by paying less for the use of roads; the safer the roads, the lower the road user charges. The system would be fair in sense that money would be raised from those who benefitted from the improvements.
-
-There are, however, several problems related to road pricing that need to be considered carefully before introducing it. More specifically, the following problems need to be discussed:
-
-- 1. Acceptability of the system and the need for designing it so that it does not impose inequitable financial burdens on road users.
-- 2. Whether a detailed monitoring of individual behaviour represents an unacceptable invasion of privacy.
-- 3. How to fund the costs for starting up the system.
-- 4. The prices to be charged by the system are not very precisely known.
-- 5. The need for charging prices which are higher than the marginal costs of road use.
-- 6. How to dispose of the revenue generated by the system.
-
-It is well known that road pricing is very unpopular and not always accepted by road users [\(Button and Verhoef, 1998; Schade](#page-6-0) [and Schlag, 2003\)](#page-6-0). Road pricing is often met with the objection that road users are already paying the societal costs of transport in terms of taxes on vehicles and motor fuel. At the aggregate level, this may well be correct, but the taxes imposed on vehicles and fuel do not reflect the marginal costs of driving. If correctly designed, a road pricing system would generate sufficient revenue to operate and extend the road system and compensate for the external effects of road use. To keep the system revenue neutral and to prevent it from adding to the total costs of keeping and operating a motor vehicle, offsetting changes in taxes on the purchase and ownership of motor vehicles, as well as motor fuel taxes could be introduced. The total economic burden placed on households and businesses would then remain unchanged.
-
-As discussed in this paper, the system would include a fairly detailed monitoring of road user behaviour. This is intended to generate incentives for safer behaviour, since unsafe road user behaviour is a major road safety problem. A detailed monitoring of road user behaviour may, however, be regarded as an invasion of privacy. There are legitimate concerns that the data generated by the system could be abused. If, as an example, a road user forgot to use the indicator when turning right at a junction, and then struck a pedestrian – who did not believe the car was turning as it did not indicate that it would – this fairly minor error could be used by an insurance company to deny the driver coverage for the damages to the pedestrian. This is clearly a serious concern, and the system should be designed so as to prevent such abuses. One way of doing so, would be to require the informed consent of every road user before data generated by the system were made available to, for example, insurance companies.
-
-There would be significant initial costs to start-up a system of road pricing. Installing driving computers and digital maps in every motor vehicle might cost, perhaps, a few hundred Euros per vehicle. Many road users would regard these expenses as a complete waste; they would see no benefits of a system whose most obvious effect would be to make them pay more for using the roads. If government, based on a careful impact assessment, found the benefits of road pricing to clearly exceed the costs, the possibility of subsidising the start-up cost of the system should be considered; in other words government would pay the start-up cost and road users would get it for free.
-
-The prices to be charged by a road pricing system are not very well known. In an initial phase, equilibrium prices would have to be determined by a process of trial-and-error. At the outset, prices should be based on the results of surveys of the willingness-to-pay for improving road safety, saving travel time and improving the environment. The system would then, in a sense, represent an empirical testing of the validity of the currently mostly hypothetical valuations of safety, travel time and environmental amenities. Willingness-to-pay surveys have been criticised for hypothetical bias: since respondents do not have to pay the amounts stated, they can be tempted to state higher amounts than they are really willing to pay, in order to be politically correct or satisfy their own notions of what a good citizen should do. There is, therefore, a significant risk that prices based on the results of willingness-to-pay surveys will be too high and that few will actually be willing to pay these <span id="page-6-0"></span>prices. The result might be a reduced traffic volume – which by itself might improve road safety – but which would not be the major objective of the system.
-
-Can a system of road pricing fund all the costs of maintaining, improving and expanding the road system? Jansson (1992) discussed this question in 1992, asking if it was at all conceivable that roads could be run commercially, by road users paying for using the roads. He concluded that this was in principle possible, but that strict marginal cost pricing would not be feasible, as it would lead to huge deficits. This is because there are large fixed costs in any road system; strict marginal cost pricing would not cover all fixed costs. To cover the fixed costs, prices higher than the marginal costs would have to be charged; alternatively government would have to subsidise the fixed costs.
-
-The revenue generated by a road pricing system should be spent in a way that did not undermine or distort the incentives generated by the system. The income generated by the road pricing system would be allocated between different recipients. Road authorities would get what they proposed in their budgets, provided road users were willing to pay for these budgets. The health care sector would be reimbursed for treating accident victims. Victims would be compensated both by insurance companies and the public sector, as is the case today. An option that deserves consideration is to overcharge speeding and other violations in order to generate a surplus. This surplus would then be paid back to law-abiding road users in order to reward them for their safe behaviour. That would make the reward for safe behaviour more tangible than it is when it manifests itself merely in the form of paying lower charges than violators do. Moreover, part of a surplus could be used to fund police enforcement.
-
-#### 6. Conclusions
-
-The main conclusions of the analyses presented in this paper can be summarised as follows:
-
-- 1. Current road safety policy is inefficient: not all road safety measures whose benefits are greater than the costs are implemented, and resources are spent on measures whose benefits are smaller than the costs.
-- 2. Cost–benefit analysis does not by itself generate sufficient incentives to implement cost-effective road safety measures. One reason for this is the fact that most of the benefits of improving road safety consist of gains in welfare that are not readily payable by means of market transactions. These gains are nevertheless real and improve social welfare.
-- 3. Households, business firms and the health sector in general face very weak incentives to improve road safety.
-- 4. Car manufacturers face incentives to improve safety to the extent that greater safety promotes the sales of cars.
-- 5. Road authorities face incentives to improve road safety, but these are of a general nature and do not necessarily favour using the most cost-effective measures to improve road safety.
-- 6. Road pricing is rapidly becoming more technically feasible. In principle, it is possible to design a system for road pricing that will generate incentives for improving road safety.
-- 7. There are, however, many potential problems associated with road pricing that need careful discussion before introducing such a system.
-
-## References
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Zotero/001_artiklid/Road Safety Indicators/evansfatalaccidentsrailwaylevel2011.md

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-category: literaturenote
-citekey: evansfatalaccidentsrailwaylevel2011
-title: Fatal accidents at railway level crossings in Great Britain 1946–2009
-authors: "Evans, Andrew W."
-year: 2011
-date: 2011-00-00 2011
-doi: 10.1016/j.aap.2011.04.019
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2011.04.019"
-zotero_key: 9432RQ2M
-zotero_storage: GRPARC3D
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Evans, Andrew W."
-status: converted
----
-Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/00014575)
-
-# Accident Analysis and Prevention
-
-journal homepage: [www.elsevier.com/locate/aap](http://www.elsevier.com/locate/aap)
-
-![](_page_0_Picture_5.jpeg)
-
-# Fatal accidents at railway level crossings in Great Britain 1946–2009
-
-Andrew W. Evans <sup>∗</sup>
-
-Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, United Kingdom
-
-# article info
-
-Article history: Received 15 November 2010 Received in revised form 17 February 2011 Accepted 14 April 2011
-
-Keywords: Railways Roads Level crossings Safety Accidents Fatalities
-
-# abstract
-
-This paper investigates fatal accidents and fatalities at level crossings in Great Britain over the 64-year period 1946–2009. The numbers of fatal accidents and fatalities per year fell by about 65% in the first half of that period, but since then have remained more or less constant at about 11 fatal accidents and 12 fatalities per year. At the same time other types of railway fatalities have fallen, so level crossings represent a growing proportion of the total. Nevertheless, Britain's level crossing safety performance remains good by international standards.
-
-The paper classifies level crossings into three types: railway-controlled, automatic, and passive. The safety performance of the three types of crossings has been very different. Railway-controlled crossings are the best-performing crossing type, with falling fatal accident rates. Automatic crossings have higher accident rates per crossing than railway controlled or passive crossings, and the accident rates have not decreased. Passive crossings are by far the most numerous, but many have low usage by road users. Their fatal accident rate has remained remarkably constant over the whole period at about 0.9 fatal accidents per 1000 crossings per year.
-
-A principal reason why fatal accidents and fatalities have not fallen in the second half of the period as they did in the first half is the increase in the number of automatic crossings, replacing the safer railway controlled crossings on some public roads. However, it does not follow that this replacement was a mistake, because automatic crossings have advantages over controlled crossings in reducing delays to road users and in not needing staff.
-
-Based on the trends for each type of crossing and for pedestrian and non-pedestrian accidents separately, in 2009 a mean of about 5% of fatal accidents were at railway controlled crossings, 52% were at automatic crossings, and 43% were at passive crossings. Fatalities had similar proportions. About 60% of fatalities were to pedestrians.
-
-A simple comparison of automatic railway level crossings and signalised road intersections found that in 2005 the numbers of fatalities per 1000 crossings or intersections were similar.
-
-© 2011 Elsevier Ltd. All rights reserved.
-
-# **1. Introduction**
-
-This paper investigates the safety performance of railway level crossings in Great Britain over the long-term from 1946 to 2009. A reason for concern about level crossings is that their safety performance is not improving, or improving only slowly. The lower curve in [Fig. 1](#page-1-0) is a simple plot of the average number of fatalities per year at level crossings in Great Britain in five-year intervals from 1946 to 2009. The number of fatalities per year fell substantially in the first half of this period from 42.6 in 1946–1950 to 11.8 in 1971–1975,
-
-Abbreviations: AHB, automatic half barrier (level crossing); ALCRM, all level crossings risk model; CSI, Common Safety Indicator; DfT, Department for Transport; ERA, European Railway Agency; GB, Great Britain; HMRI, Her Majesty's Railway Inspectorate; LC, level crossing; Nped, non-pedestrian accident; ORR, Office of Rail Regulation; Ped, pedestrian accident; RSSB, Rail Safety and Standards Board.
-
-but it has remained more or less constant since then, and was 10.8 in 2006–2009. At the same time, other railway fatalities have fallen, so level crossing fatalities are becoming more prominent and represent an increasing proportion of the total. The 42.6 level crossing fatalities per year in 1946–1950 represented 11% of all railway fatalities (excluding trespassers and suicides), but the 10.8 level crossing fatalities per year in 2006–2009 represented 46% of all railway fatalities. Good quality data on level crossing numbers, fatalities and fatal accidents are available for Great Britain since 1946, and the aim of this paper is to explore what can be learned from a longterm analysis of these data. The paper is confined to fatal accidents and fatalities to avoid complications from changing definitions and under-recording of non-fatal injuries.
-
-Although 95% of fatalities at level crossings are to road users, either pedestrians or road vehicle occupants, the long-term development of level crossing fatalities does not mirror those in road accidents generally. The upper curve in [Fig. 1](#page-1-0) plots the average number fatalities per year in road accidents, not including
-
-<sup>∗</sup> Corresponding author. Tel.: +44 020 7594 6043; fax: +44 020 7594 6102. E-mail address: [a.evans@imperial.ac.uk](mailto:a.evans@imperial.ac.uk)
-
-<span id="page-1-0"></span>![](_page_1_Figure_2.jpeg)
-
-**Fig. 1.** Level crossing and road fatalities per year in five-year intervals: Great Britain: 1946–2009.
-
-level crossing accidents, measured in units of 100. Road fatalities increased in the first half of the period at the time when level crossing fatalities were falling, and decreased in the second half of the period at the time when level crossing accidents were about constant. Another notable difference is that the proportion of fatalities to pedestrians is higher at level crossings than in road accidents generally. (The proportions of fatalities to pedestrians in 1946–2009 were 56% at level crossings and 36% in other road accidents.) Therefore, it seems that the factors influencing level crossing fatalities are different from those influencing road accidents.
-
-This paper is about Great Britain, but it is useful at the outset to place the performance of Great Britain in a European context, using data from the 'Common Safety Indicators' (CSIs) assembled by the European Railway Agency [\(ERA, 2010\).](#page-8-0) Compared with most other European countries, the United Kingdom (which is Great Britain plus Northern Ireland) has a good record. In 2008, the UK had fewer level crossing (LC) fatalities than France or Germany (14 compared with 42 and 52, respectively), fewer LC fatalities per million population (0.26 compared with 0.65 and 0.67), fewer LC fatalities per 1000 level crossings (2.1 compared with 2.9 and 4.8), fewer LC fatalities per railway route-kilometre (0.45 compared with 1.46 and 1.67), and fewer LC fatalities per billion train-kilometres (26 compared with 79 and 50).
-
-The paper continues as follows. Section 2 considers the numbers and types of level crossings in Britain. Section [3](#page-2-0) considers fatalities and fatal accidents at level crossings. Section [4](#page-3-0) uses a simple statistical model to analyse the data. Section [5](#page-5-0) presents the results of the modelling. Section [6](#page-7-0) compares fatal accidents and fatalities at automatic level crossings and signalised road intersections in 2005. Section [7](#page-7-0) presents conclusions. [Appendix A](#page-7-0) gives additional statistical results.
-
-## **2. Numbers and types of level crossings**
-
-The railway safety regulator in Britain, formerly HM Railway Inspectorate (HMRI) and now the Office of Rail Regulation (ORR), records the numbers and types of level crossings on the national railway system in its annual report ([HMRI, annual\),](#page-8-0) and has done so at least since 1946. The official classification of level crossing types has changed over the years: in 1946 there were four types; in recent years there have been twelve. However, they may reasonably be grouped into three broad types, which are used in this paper. These are the following.
-
-![](_page_1_Figure_9.jpeg)
-
-**Fig. 2.** Numbers of level crossings: Great Britain: 1946–2009.
-
-- (1) Railway-controlled crossings. At these crossings, the opening and closing of the crossing to the railway or the road is controlled by a member of the railway staff, who is a signaller or crossing-keeper. The operation of the crossing is now almost always interlocked with the railway signalling, so that it is not possible to clear the signals for a train unless and until the crossing is closed to the road. Such crossings traditionally had large swinging gates, some of which remain in service. Modern railway-controlled crossings have lifting full barriers, either operated by a signaller at the site, or operated remotely and supervised by Closed Circuit Television.
-- (2) Automatic crossings. These crossings are operated by the passage of trains without the intervention of railway staff. When a train approaching the crossing reaches a 'strike-in' point, it triggers the operational cycle: first there is a warning to road users; then the crossing is closed to road users; then the train passes; and finally the crossing opens to the road again. The minimum permissible time in Britain between the train striking-in and its reaching the crossing is 27 s ([Health](#page-8-0) [and Safety Executive, 1996, p](#page-8-0)age 15). The best-known type of automatic crossing is the automatic half barrier (AHB), which has flashing lights and barriers covering only the approach side of the road. The reason for having only half barriers is to provide an escape route for any vehicle or person already on the crossing when the barriers fall. There are various other types of automatic crossing, including some with lights but no barriers.
-- (3) Passive crossings, including footpath crossings. Some of these are called 'user-worked' crossings in Britain. These crossings have no active warnings of the approach of trains, so that the safety of persons or vehicles crossing depends on their own vigilance and behaviour. Sometimes telephones are provided to enable users to phone the railway signaller to check that the line is clear. Passive crossings account for the great majority of level crossings: they are typically on private roads, on farms, or on footpaths. Many, but not all, are little used; some usage is seasonal or can change with land use.
-
-Almost all the crossings on public roads are either railwaycontrolled or automatic. They typically have much higher usage than passive crossings.
-
-[Table 1](#page-2-0) and Fig. 2 show the average numbers of crossings of each type on the main line railway system of Great Britain in 5-year
-
-<span id="page-2-0"></span>**Table 1** Average number of level crossings in 5-year periods: 1946–2009.
-
-| Period          | Railway controlled | Automatic | Passive | All    |
-|-----------------|--------------------|-----------|---------|--------|
-| 1946–1950       | 4148               |           | 22,902  | 27,050 |
-| 1951–1955       | 4186               |           | 22,124  | 26,310 |
-| 1956–1960       | 4360               |           | 20,298  | 24,658 |
-| 1961–1965       | 3823               | 14        | 19,225  | 23,062 |
-| 1966–1970       | 2036               | 220       | 12,884  | 15,141 |
-| 1971–1975       | 1635               | 307       | 9820    | 11,762 |
-| 1976–1980       | 1582               | 394       | 9004    | 10,980 |
-| 1981–1985       | 1325               | 523       | 8475    | 10,323 |
-| 1986–1990       | 1021               | 729       | 7379    | 9129   |
-| 1991–1995/96    | 930                | 779       | 7254    | 8963   |
-| 1996/97–2000/01 | 880                | 782       | 6656    | 8318   |
-| 2001/02–2005    | 865                | 800       | 6293    | 7958   |
-| 2006–2009       | 813                | 720       | 5144    | 6677   |
-
-**Table 2** Fatalities at level crossings: all railways: Great Britain: 1946–2009.
-
-| Period          | Pedestrians | Occupants of road vehicles | Railway staff | Railway passengers | All  |
-|-----------------|-------------|----------------------------|---------------|--------------------|------|
-| 1946–1950       | 126         | 78                         | 9             | 0                  | 213  |
-| 1951–1955       | 100         | 59                         | 1             | 0                  | 160  |
-| 1956–1960       | 70          | 59                         | 9             | 0                  | 138  |
-| 1961–1965       | 68          | 72                         | 4             | 0                  | 144  |
-| 1966–1970       | 45          | 52                         | 9             | 8                  | 114  |
-| 1971–1975       | 37          | 19                         | 3             | 0                  | 59   |
-| 1976–1980       | 37          | 25                         | 5             | 0                  | 67   |
-| 1981–1985       | 26          | 23                         | 0             | 0                  | 49   |
-| 1986–1990       | 40          | 33                         | 1             | 8                  | 82   |
-| 1991–1995/96    | 42          | 24                         | 0             | 0                  | 66   |
-| 1996/97–2000/01 | 41          | 10                         | 0             | 0                  | 51   |
-| 2001/02–2005    | 37          | 21                         | 3             | 5                  | 66   |
-| 2006–2009       | 31          | 12                         | 0             | 0                  | 43   |
-| 1946–2009       | 700         | 487                        | 44            | 21                 | 1252 |
-
-periods over the 64 years 1946–20091. There were about 27,050 in 1946–1950, and about 6680 in 2006–2009. The reduction is partly the result of long-term policies to eliminate crossings where possible, and partly because the national railway route length roughly halved during the period. The fastest reduction in the numbers of level crossings occurred in the late 1960s, when many railway routes were closed as a matter of policy [\(British Railways](#page-8-0) [Board, 1963\).](#page-8-0) The number of crossings per route-km was 0.85 in 1946–1950 and 0.42 in 2006–2009.
-
-Table 1 shows that in 1946–1950 all crossings were either railway-controlled or passive. Automatic crossings were not introduced into Great Britain until the early 1960s, although they were then fairly common elsewhere in Europe (see [Ministry of](#page-8-0) [Transport, 1968, S](#page-8-0)ection I; [Hall and Van der Mark, 2008\).](#page-8-0) The number of railway-controlled crossings steadily declined from about 4150 in 1946–1950 to 813 in 2006–2009. This was because of a mixture of crossing closures, line closures, and conversions of crossings to automatic. The remaining number of railway-controlled crossings in Britain is high by European standards, many countries having none ([Rail Safety and Standards Board, 2006\).](#page-8-0) It will be seen later that railway-controlled crossings are relatively safe, and that may partially account for the good safety record at level crossings in Britain. The number of automatic crossings grew from zero in 1956–1960 to 800 in 2001/02–2005, and fell back to 720 in 2006–2009. The number of passive crossings has steadily declined, from about 22,900 in 1946–1950 to 5140 in 2006–2009.
-
-# **3. Fatalities and fatal accidents at level crossings**<sup>2</sup>
-
-Fatalities to level crossing users and staff are classified and recorded by HMRI in the following four groups.
-
-- (1) Pedestrians
-- (2) Occupants of road vehicles
-- (3) Railway staff
-- (4) Railway passengers
-
-In this context railway passengers are those on board trains: people outside trains who are intending to travel or who have travelled are classified as pedestrians.
-
-Table 2 gives the numbers of fatalities at level crossings by 5 year period in each of these groups. There were a total of 1252 fatalities in the 64 years, an average 19.6 per year. Of these, 700 (56%) were pedestrians, 487 (39%) were occupants of road vehicles, including two-wheeled vehicles and farm vehicles, 44 (4%) were railway staff, and 21 (2%) were railway passengers. The railway passenger fatalities occurred in just three accidents—at Hixon in 1968, Lockington in 1986 and Ufton Nervet in 2004. The proportion of pedestrian fatalities in Britain may be relatively high by international standards; there are little data on this, but one international example is the USA, where only 18% of road user fatalities at level crossings were pedestrians in 2001 [\(Mok and Savage, 2005,](#page-8-0) page 868). That may be because of relatively less pedestrian activity in the USA.
-
-<sup>1</sup> In fact, as indicated by the row labels, Table 1 and later tables include 10 periods of 5 years, 1 period of 5¼years, 1 period of 4¾years and 1 period of 4 years. The¼ and ¾ years arise because HMRI switched its reporting period from calendar years up to 1990 to fiscal years (1 April to 31 March) from 1991/92 to 2003/04, and back to calendar years in 2005. The changing periods have been taken into account in the calculations of the paper.
-
-<sup>2</sup> It should be noted that there is a slight mismatch between the number of level crossings discussed in Section [2](#page-1-0) and the numbers of fatalities and fatal accidents in
-
-Section 3. The former are confined to the national rail system, but the latter cover all railways. There are a sizeable number of level crossings off the national system, but many of these are on heritage railways and are little used. A small number of the fatalities discussed in Section 3 were not on the national system. In the rest of the paper we disregard this distinction.
-
-<span id="page-3-0"></span>The HMRI annual reports do not explicitly give the numbers of fatal accidents at level crossings, as distinct from fatalities, but they do mention many individual accidents, especially those with more than one fatality. In a previous project, [Evans \(2003\)id](#page-8-0)entified all multiple-fatality accidents mentioned in HMRI reports, including those at level crossings. If one makes the assumption that all multiple-fatality accidents are explicitly mentioned, it is then possible to deduce the numbers of single-fatality accidents, and hence the total numbers of fatal accidents.
-
-[Table 3](#page-4-0) shows the results of doing this. The bottom line of [Table 3](#page-4-0) shows the distribution of fatalities in fatal level crossing accidents in Great Britain in 1946–2009: there were 76 accidents with more than one fatality, from which it is deduced that there were 1029 single-fatality accidents and 1105 fatal accidents overall. This implies that the mean number of fatalities per fatal accident was 1.133. The worst accident was in 1947 at Burton Agnes level crossing, on the Hull–Scarborough line, when a train struck an army lorry, with 12 fatalities in the lorry. The second worst accident was in 1968 at Hixon level crossing, on the Colwich–Stoke-on-Trent line, when a train struck a slow-moving road transporter loaded with an electrical transformer, leading to 11 fatalities on the train.
-
-Multiple-fatality accidents may have fatalities to people in more than one of the four categories above: for example, an accident may cause fatalities both to occupants of road vehicles and to staff or passengers on trains. However, it happens that there have been no accidents in which there were fatalities both to pedestrians and to people in other categories. This makes it possible unambiguously to divide accidents into those with pedestrian fatalities and those with other fatalities, labelled henceforth as 'pedestrian' and 'nonpedestrian' accidents. The great majority of the fatalities of the nonpedestrian accidents are occupants of vehicles (road or rail), but these accidents also include a small number of fatalities to railway staff struck by trains while manually operating gates, especially in the early years. [Table 3](#page-4-0) shows the distribution of fatalities in the two classes of accident. Almost all the pedestrian accidents were singlefatality, and all the most serious accidents were non-pedestrian. Of all fatal accidents, 692 (63%) were pedestrian and 413 (37%) were non-pedestrian.
-
-[Table 4](#page-4-0) shows the numbers of pedestrian and non-pedestrian accidents at each of the three types of crossing in 5-year periods from 1946 to 2009. The right-hand panel of [Table 4](#page-4-0) shows that the total numbers of accidents per year in both categories fell in the first 30 years 1946–1975, but did not fall much in 1976–2009. The overall numbers of accidents per year are plotted in [Fig. 4](#page-6-0) (below).
-
-[Table 5](#page-4-0) gives the number of fatal accidents at each type of crossing per year per 1000 crossings. It can be seen that the overall fatal accident rate has not changed very much over time from its long-term average of 1.17 accidents per year per 1000 crossings. The accident rate at railway-controlled crossings has fallen; that at passive crossings has remained more or less constant at about 0.9 accidents per year per 1000 crossings; that at automatic crossings is both higher and, if anything, rising over time. Fig. 3 shows the data points for each type of crossing.
-
-#### **4. Analysis**
-
-The analysis in this paper considers fatal accident rates per 1000 crossings per year and trends in these over time by level crossing type and accident type (pedestrian and non-pedestrian). The analysis does not aim to estimate risks at individual crossings, for which various models have been developed, including [Austin and](#page-8-0) [Carson \(2002\)](#page-8-0) for the United States and the All Level Crossing Risk Model (ALCRM) ([Rail Safety and Standards Board, 2010\)](#page-8-0) for Great Britain. These models are used for informing decisions about which level crossings should be upgraded. The ALCRM has some 200 input
-
-![](_page_3_Figure_9.jpeg)
-
-**Fig. 3.** Fatal accidents per 1000 crossings: 1946–2009.
-
-variables, including the numbers of trains, road vehicles and pedestrians using the crossing per day.
-
-For this paper we have little data on the usage of crossings, and in any case usage varies greatly from one crossing to another. Nevertheless, in interpreting the results, it is useful to be aware that usage systematically varies between crossing types. The Rail Safety and Standards Board [\(RSSB, 2004,](#page-8-0) [Table 4\)](#page-4-0) gives some useful information on average crossing usage by crossing type, excluding footpath crossings. Their data suggest that for railway-controlled crossings, the average number of trains per day is about 70, the average number of road vehicle users is about 1200 and the average number of pedestrians is about 160. For automatic crossings, the average number of trains per day is about 60, the average number of road vehicle users is about 1000 and the average number of pedestrians is about 80. After making reasonable guesses of the amount of footpath use, the average number of trains at passive crossings is about 55 per day, the average number of road vehicle users is only about 4, and the average number of pedestrians is about 25. Therefore, the most marked difference in usage is between controlled and automatic crossings on one hand, with an average of 1000–1200 road vehicles per day and passive crossings on the other hand, with only 4 road vehicles per day. This is a reflection of the fact that controlled and automatic crossings are generally on public roads, whereas passive crossings are generally on private roads, farms, and footpaths. Passive crossings have low numbers of fatal accidents per crossing per year, as shown in Fig. 3, but their low usage implies that they have the highest risk per road vehicle-traverse.
-
-In order to model accident trends by crossing type and accident type, we have fitted the following model to the data in [Table 4.](#page-4-0)
-
-$$y_{ijt} = \alpha_{ij}c_{it} \exp(\beta_{ij}t) \tag{1}$$
-
-where yijt is the mean number of accidents of type j per year at crossings of type i in year t (measured with 1966 = 0); (i = 1, 2, 3; j = 1, 2). cit is the number of crossings of type i in year t; ˛ij are parameters measuring the mean number of accidents of type j per year at crossings of type i in year 0. ˇij are parameters measuring the annual proportionate rate of change in the number of accidents of type j per crossing per year at crossings of type i.
-
-The model assumes that the mean numbers of fatal accidents at crossings of each type are directly proportional to the numbers of crossings of that type, cit. Following [Mok and Savage \(2005\), w](#page-8-0)e have also investigated fitting the model with accidents assumed proportional to a power of cit, not necessarily equal to 1. However, for no type of crossing or type of accident was the power significantly different from 1, so we have retained direct proportionality.
-
-<span id="page-4-0"></span>**Table 3** Distributions of fatalities in fatal level crossing accidents: Great Britain: 1946–2009.
-
-|                          | Number of accidents with given number of fatalities |    |    |   |   |   |   |   |   |    |    | Fatalities | Fatalities/accident |      |       |
-|--------------------------|-----------------------------------------------------|----|----|---|---|---|---|---|---|----|----|------------|---------------------|------|-------|
-|                          | 1                                                   | 2  | 3  | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12         | All                 |      |       |
-| Pedestrian accidents     | 685                                                 | 6  | 1  |   |   |   |   |   |   |    |    |            | 692                 | 700  | 1.012 |
-| Non-pedestrian accidents | 344                                                 | 43 | 15 | 3 | 1 | 3 |   |   | 1 | 1  | 1  | 1          | 413                 | 552  | 1.337 |
-| All accidents            | 1029                                                | 49 | 16 | 3 | 1 | 3 | 0 | 0 | 1 | 1  | 1  | 1          | 1105                | 1252 | 1.133 |
-
-**Table 4** Fatal accidents at level crossings: all railways: Great Britain: 1946–2009.
-
-| Period          |     | Railway controlled |     |     | Automatic |     |     | Passive |     |     | All crossings |      |  |
-|-----------------|-----|--------------------|-----|-----|-----------|-----|-----|---------|-----|-----|---------------|------|--|
-|                 | Ped | Nped               | All | Ped | Nped      | All | Ped | Nped    | All | Ped | Nped          | All  |  |
-| 1946–1950       | 47  | 25                 | 72  | 0   | 0         | 0   | 79  | 28      | 107 | 126 | 53            | 179  |  |
-| 1951–1955       | 35  | 9                  | 44  | 0   | 0         | 0   | 65  | 41      | 106 | 100 | 50            | 150  |  |
-| 1956–1960       | 17  | 17                 | 34  | 0   | 0         | 0   | 53  | 38      | 91  | 70  | 55            | 125  |  |
-| 1961–1965       | 26  | 15                 | 41  | 0   | 0         | 0   | 42  | 41      | 83  | 68  | 56            | 124  |  |
-| 1966–1970       | 12  | 7                  | 19  | 0   | 4         | 4   | 32  | 36      | 68  | 44  | 47            | 91   |  |
-| 1971–1975       | 11  | 5                  | 16  | 4   | 0         | 4   | 22  | 12      | 34  | 37  | 17            | 54   |  |
-| 1976–1980       | 7   | 6                  | 13  | 1   | 7         | 8   | 29  | 13      | 42  | 37  | 26            | 63   |  |
-| 1981–1985       | 2   | 2                  | 4   | 3   | 11        | 14  | 21  | 7       | 28  | 26  | 20            | 46   |  |
-| 1986–1990       | 1   | 1                  | 2   | 9   | 21        | 30  | 27  | 8       | 35  | 37  | 30            | 67   |  |
-| 1991–1995/96    | 3   | 0                  | 3   | 10  | 12        | 22  | 29  | 7       | 36  | 42  | 19            | 61   |  |
-| 1996/97–2000/01 | 4   | 0                  | 4   | 8   | 7         | 15  | 27  | 3       | 30  | 39  | 10            | 49   |  |
-| 2001/02–2005    | 5   | 0                  | 5   | 15  | 15        | 30  | 16  | 6       | 22  | 36  | 21            | 57   |  |
-| 2006–2009       | 3   | 0                  | 3   | 9   | 8         | 17  | 18  | 1       | 19  | 30  | 9             | 39   |  |
-| 1946–2009       | 173 | 87                 | 260 | 59  | 85        | 144 | 460 | 241     | 701 | 692 | 413           | 1105 |  |
-
-Ped = accidents involving fatalities only to pedestrians.
-
-Nped = accidents involving fatalities only to other classes of person.
-
-**Table 5** Fatal accidents per year per 1000 crossings: Great Britain: 1946–2009.
-
-| Period          | Railway-controlled | Automatic | Passive crossings | All  |
-|-----------------|--------------------|-----------|-------------------|------|
-| 1946–1950       | 3.47               |           | 0.93              | 1.32 |
-| 1951–1955       | 2.10               |           | 0.96              | 1.14 |
-| 1956–1960       | 1.56               |           | 0.90              | 1.01 |
-| 1961–1965       | 2.15               | 0.00      | 0.86              | 1.08 |
-| 1966–1970       | 1.87               | 3.63      | 1.06              | 1.20 |
-| 1971–1975       | 1.96               | 2.60      | 0.69              | 0.92 |
-| 1976–1980       | 1.64               | 4.06      | 0.93              | 1.15 |
-| 1981–1985       | 0.60               | 5.35      | 0.66              | 0.89 |
-| 1986–1990       | 0.37               | 7.83      | 0.90              | 1.40 |
-| 1991–1995/96    | 0.65               | 5.65      | 0.99              | 1.36 |
-| 1996/67–2000/01 | 0.91               | 3.84      | 0.90              | 1.18 |
-| 2001/02–2005    | 1.22               | 7.89      | 0.74              | 1.51 |
-| 2006–2009       | 0.92               | 5.91      | 0.92              | 1.46 |
-| 1946–2009       | 1.89               | 5.62      | 0.90              | 1.17 |
-
-This differs from Mok and Savage, who found powers of less than 1 in the USA.
-
-The results in this paper are based on the assumption that the accident data are Poisson-distributed, though because the data are 'overdispersed' for the Poisson distribution we have also explored the use of the negative binomial distribution. [Appendix A](#page-7-0) gives results for the negative binomial corresponding to those given in this section for the Poisson distribution. The use of the negative binomial distribution makes no difference to the results of the statistical tests, and only small differences to the parameter estimates.
-
-In fitting the model, we have assumed that all the ˛ijs are different, with no particular structure. We then explore the structure of the trend parameters ˇ: in particular we explore whether the trend parameters are different for the different types of crossing and accidents. [Table 6](#page-5-0) gives the main statistical results and test statistics for variants of the model used for estimating these trends. Model variant (a) assumes that the trend ˇ is zero for all types of crossings and accidents; variant (b) assumes a common non-zero ˇ for all types of crossing and accidents; variant (c) assumes differentˇs for different accident types but not for different crossing types (ˇ1<sup>j</sup> =ˇ2<sup>j</sup> =ˇ3j); variant (d) assumes different ˇs for different crossing types but not for different accident types (ˇi<sup>1</sup> =ˇi2); variant (e) assumes that all six ˇs are different.
-
-The scaled deviance in [Table 6](#page-5-0) is the measure of goodness of fit of the model variants to the data; a large scaled deviance indicates a poor fit. If the data are indeed generated in the way presumed in the model (that is Poisson-distributed with a time-dependent mean), the scaled deviance would be approximately -2-distributed with mean equal to the number of degrees of freedom. It can be seen that the fit is not good for any of the variants: the data are 'overdispersed'. To test whether one model variant fits the data significantly better than another, we compare the difference in the scaled deviances of the two variants with the -2-distribution with degrees of freedom equal to the difference in degrees of freedom between the variants. For example, variant (e) fits significantly better than variant (d) because the reduction in the scaled deviance of 20.0 in moving from (d) to (e) is larger even than the upper 0.1% point of the -2-distribution with 3 degrees of freedom (which is 16.27). On the basis of such tests, variant (e) is significantly the best-fitting model, indicating that the trends are different for
-
-<span id="page-5-0"></span>**Table 6** Statistical results from fitting Poisson log-linear models to the accident data.
-
-| Variant of model                                       | Degrees of freedom | Scaled deviance | Estimate of trend parameter (standard error in brackets)                                                                                                                                                                                                                                                                                                                                   |
-|--------------------------------------------------------|--------------------|-----------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| (a) No trend                                           | 66                 | 171.4           | Assumed zero for all types of crossing and accidents                                                                                                                                                                                                                                                                                                                                       |
-| (b) Common trend                                       | 65                 | 162.2           | −0.57% (0.19%) p.a. for all types of crossing and accidents                                                                                                                                                                                                                                                                                                                                |
-| (c) Separate trends for accident types                 | 64                 | 147.6           | −0.05% (0.23%) p.a. for pedestrian accidents<br>−1.59% (0.33%) p.a. for non-pedestrian accidents                                                                                                                                                                                                                                                                                           |
-| (d) Separate trends for LC types                       | 63                 | 132.2           | −2.58% (0.47%) p.a. for railway-controlled crossings<br>+1.34% (0.77%) p.a. for automatic crossings<br>−0.19% (0.22%) p.a. for passive crossings                                                                                                                                                                                                                                           |
-| (e) Separate trends for LC types and<br>accident types | 60                 | 112.2           | −2.17% (0.55%) p.a. for pedestrians at controlled crossings<br>−3.50% (0.89%) p.a. for non-pedestrians at controlled crossings<br>+3.49% (1.30%) p.a. for pedestrians at automatic crossings<br>+0.00% (0.97%) p.a. for non-pedestrians at automatic crossings<br>+0.37% (0.26%) p.a. for pedestrians at passive crossings<br>−1.39% (0.41%) p.a. for non-pedestrians at passive crossings |
-
-each combination of crossing type and accident type. In the work below we adopt model (e), and estimate the mean numbers of accidents per crossing as the sum of those to pedestrians and nonpedestrians.
-
-Conclusions from the modelling in Table 6 on the trends in fatal accidents per crossing per year are the following.
-
-- Taken together, fatal accidents per crossing per year show a significant downward trend, but the trend is small, especially compared with other long-term improvements in rail safety (see, for example, [Evans, 2007\).](#page-8-0)
-- The trend for pedestrian accidents per crossing per year taken together is significantly worse than that for non-pedestrian (mostly road vehicle) accidents
-- The trend in accidents per crossing per year is significantly downward at railway-controlled crossings, about flat at passive crossings, and non-significantly upward (as well as higher) at automatic crossings.
-
-[Fig. 3](#page-3-0) plots the modelled and observed numbers of fatal accidents per crossing per year by type of crossing. The observed data are from [Table 5.](#page-4-0) The modelled curves are the sum of those for pedestrian and non-pedestrian accidents from model variant (e). The figure clearly illustrates the different trends for the different types of crossing.
-
-The following are plausible explanations for the different levels and trends in fatal accidents per crossing per year.
-
-- On railway-controlled crossings, the accident rates have declined. This may be first because the methods operating crossings have improved: in particular, barriers have replaced many gates, some of which require staff to go on to the tracks. Secondly, the protection offered by the railway signalling systems, which are interlocked with the crossings, has improved, just as railway signalling has improved generally.
-- On automatic crossings accident rates are higher than on railwaycontrolled crossings and have not declined, even though the average crossing usage is broadly similar. Accidents rates are higher because the primary responsibility for the safe operation of automatic crossings rests with road users in observing the warnings indicating approaching trains. Furthermore, if there is a mishap, such as a road vehicle stalling or a pedestrian falling on the crossing, the users are not protected by the railway signals as they are at railway-controlled crossings.
-- The safe operation of passive crossings also depends on the vigilance and use of correct procedures by road and footpath users. However, their accident rate per crossing is much lower than for automatic crossings, primarily because, not being on public roads, their usage is lower. However, their accident rate has not declined: it is a remarkable empirical finding in [Fig. 3](#page-3-0) and
-
-[Table 5](#page-4-0) that the fatal accident rate has remained about constant at about 0.9 per 1000 passive crossings over the whole period of 64 years, despite investigations to improve them ([Rail Accident](#page-8-0) [Investigation Branch, 2009\).](#page-8-0)
-
-As an extension to fitting model (1) to the accident data, we explored adding further explanatory variables to supplement or replace the time trend. Variables tested were train-kilometres per route-kilometre (as an indicator of the intensity of trains), road motor vehicle-kilometres (as an indicator of the intensity of road traffic), and road fatalities per year (as an indicator of general road safety performance). We have not found convincing relationships between accidents per crossing and these variables. Some reasons may be the following. First, as noted above and shown in [Fig. 3,](#page-3-0) the safety performance and trends of the different crossing types have been different, so one might not expect accidents per crossing to be well modelled by general variables such as those above. Secondly, a majority of the level crossing accidents are to pedestrians, but variables related to traffic and road accidents are dominated by motor vehicles. Thirdly, there is an interesting argument put forward by [Stott \(1987\)](#page-8-0) and again by [Heavisides and Barker \(2008\)](#page-8-0) and by [RSSB \(2010\)](#page-8-0) that one should not expect a linear relationship between motor vehicle accidents and road traffic flow at automatic level crossings. Rather, the relationship is gamma-shaped, in which accidents rise linearly with traffic at low flows, but then reach a maximum and fall off at higher flows. This is because after a train has initiated the crossing sequence, at high road traffic flows the first road vehicle to arrive is likely to arrive before the train; it then stops and its presence to some degree protects later road vehicles against colliding with the train. At lower road traffic flows, it is more likely that the first vehicle arrives at the same time as the train, with greater risk of collision.
-
-## **5. Mean accidents and fatalities per year**
-
-[Fig. 4](#page-6-0) plots the results of combining the modelled trends in accidents per crossing from [Fig. 3](#page-3-0) with the numbers of crossings in [Table 1](#page-2-0) and [Fig. 1](#page-1-0) to give the modelled trends in fatal accidents per year by type of crossing and in total. [Fig. 4](#page-6-0) also shows the overall observed numbers of fatal accidents per year from the data in [Table 4.](#page-4-0)
-
-[Table 7](#page-6-0) shows modelled trends, accident rates per thousand crossings, numbers of crossings, modelled numbers of accidents, and estimated fatalities in 2009. The estimated numbers of fatalities per fatal accident are assumed to be different for the two types of accident, and constant over time. They are taken from [Table 3.](#page-4-0)
-
-Conclusions from [Fig. 4](#page-6-0) and [Table 7](#page-6-0) are the following.
-
-• The estimated mean numbers of fatal accidents and fatalities at level crossings in 2009 were 10.0 and 11.2, respectively. (The
-
-<span id="page-6-0"></span>**Table 7** Modelled fatal accidents and fatalities: Great Britain 2009.
-
-| Type of crossing                    | Type of fatal<br>accident           | Average rate of<br>change in fatal<br>accidents per<br>crossing | Estimated mean<br>fatal accidents per<br>1000 crossings<br>2009 | Number of<br>crossings 2009 | Estimated mean<br>fatal accidents per<br>year 2009 | Fatalities per fatal<br>accident | Estimated mean<br>fatalities per year<br>2009 |
-|-------------------------------------|-------------------------------------|-----------------------------------------------------------------|-----------------------------------------------------------------|-----------------------------|----------------------------------------------------|----------------------------------|-----------------------------------------------|
-| Railway controlled                  | Pedestrian                          | −2.2% p.a.                                                      | 0.47                                                            | 808                         | 0.38                                               | 1.01                             | 0.39                                          |
-| Railway controlled                  | Non-pedestrian                      | −3.5% p.a.                                                      | 0.13                                                            | 808                         | 0.10                                               | 1.34                             | 0.14                                          |
-| Railway controlled                  | All                                 |                                                                 |                                                                 |                             | 0.48                                               |                                  | 0.52                                          |
-| Automatic                           | Pedestrian                          | +3.5% p.a.                                                      | 3.92                                                            | 717                         | 2.81                                               | 1.01                             | 2.84                                          |
-| Automatic                           | Non-pedestrian                      | 0.0% p.a.                                                       | 3.32                                                            | 717                         | 2.38                                               | 1.34                             | 3.18                                          |
-| Automatic                           | All                                 |                                                                 |                                                                 |                             | 5.19                                               |                                  | 6.03                                          |
-| Passive                             | Pedestrian                          | +0.4% p.a.                                                      | 0.68                                                            | 5067                        | 3.44                                               | 1.01                             | 3.49                                          |
-| Passive                             | Non-pedestrian                      | −1.4% p.a.                                                      | 0.17                                                            | 5067                        | 0.87                                               | 1.34                             | 1.16                                          |
-| Passive                             | All                                 |                                                                 |                                                                 |                             | 4.31                                               |                                  | 4.64                                          |
-| All types<br>All types<br>All types | Pedestrian<br>Non-pedestrian<br>All |                                                                 |                                                                 | 6592<br>6592                | 6.64<br>3.35<br>9.99                               | 1.01<br>1.34                     | 6.72<br>4.47<br>11.19                         |
-
-![](_page_6_Figure_4.jpeg)
-
-**Fig. 4.** Fatal accidents per year at level crossings: 1946–2009.
-
-actual numbers in that year were 11 and 13, respectively, but there is considerable variation from year to year.)
-
-- At railway-controlled crossings the number of fatal accidents per year has fallen greatly over the long term, and had reached low levels in 2009. The fall is partly because the number of such crossings has fallen (shown in[Table 1an](#page-2-0)d[Fig. 2\),](#page-1-0) and partly because the accident rate has fallen (shown in [Fig. 3](#page-3-0) and discussed in Section [4\).](#page-3-0) Railway-controlled crossings are estimated to have accounted for a mean of only about 5% of level crossing fatal accidents and fatalities in 2009. Most of these are pedestrians.
-- At automatic level crossings the number of fatal accidents per year has risen over the long term. This is partly because of the increase in the numbers of such crossings since their introduction in the early 1960s, and partly because their accident rate has been rising for pedestrians. As shown in [Fig. 3, t](#page-3-0)heir accident rate per crossing is higher than for the other types of crossing. In 2009, automatic crossings accounted for means of 52% of fatal accidents and 54% of fatalities at level crossings.
-- At passive crossings (including footpath crossings), the number of fatal accidents per year has fallen over the long term. This is entirely due to the fall in the numbers of such crossings. Their mean accident rate remained remarkably constant at rather less
-
-**Table 8** Fatal accidents and fatalities at automatic level crossings and signalised road intersections: Great Britain 2005.
-
-|                                                                        | Road   | Rail |
-|------------------------------------------------------------------------|--------|------|
-| Estimated number of signalised<br>intersections/automatic<br>crossings | 14,300 | 762  |
-| Number of fatal accidents 2005                                         | 121    | 5.13 |
-| Number of fatalities 2005                                              | 126    | 6.01 |
-| Fatal accidents per 1000<br>intersections/crossings                    | 8.5    | 6.7  |
-| Fatalities per 1000<br>intersections/crossings                         | 8.8    | 7.9  |
-
-than 1 per 1000 crossings per year, the majority being pedestrian accidents. Although their accident rate is low, the large number of passive crossings means that they accounted for a substantial proportion of all accidents in 2009: 43% of fatal accidents and 41% of fatalities.
-
-• The above counteracting trends for the different crossing types mean that, as shown in Fig. 4, the total numbers of fatal accidents at level crossings have remained almost unchanged for about 30 years at a mean of about 11 per year.
-
-It follows that the principal reason why the number of fatal accidents has not declined since the early 1970s is the replacement of railway controlled by automatic crossings. However, it does not follow that this was a mistake, because automatic crossings have a number of important advantages over railway-controlled ones, which are generally the main alternative. The main advantages of automatic crossings are the following.
-
-- They need no staff to operate them.
-- They have much shorter cycle times than railway-controlled crossings, and thus cause less delay and capacity loss to road traffic. That is because railway-controlled crossings are interlocked with the signals. If a train is to run under clear signals, the time interval between the closure of the crossing to the road for an approaching train and the arrival of the train at the crossing is much longer for railway-controlled crossings than for automatic crossings. Thus the delay and the road capacity reduction at railway-controlled level crossings may be substantial.
-
-<span id="page-7-0"></span>**Table 9** Statistical results from fitting negative binomial log-linear models to the accident data.
-
-| Variant of model                       | Degrees of freedom | Difference in −2LLa from (a) | Estimate of trend parameter (standard error in brackets)        |
-|----------------------------------------|--------------------|------------------------------|-----------------------------------------------------------------|
-| (a) No trend                           | 66                 | 0                            | Assumed zero for all types of crossing and accidents            |
-| (b) Common trend                       | 65                 | −7.2                         | −0.80% (0.30%) p.a. for all types of crossing and accidents     |
-| (c) Separate trends for accident types | 64                 | −18.2                        | −0.03% (0.35%) p.a. for pedestrian accidents                    |
-|                                        |                    |                              | −2.01% (0.48%) p.a. for non-pedestrian accidents                |
-| (d) Separate trends for LC types       | 63                 | −24.9                        | −2.50% (0.56%) p.a. for railway-controlled crossings            |
-|                                        |                    |                              | +1.55% (0.91%) p.a. for automatic crossings                     |
-|                                        |                    |                              | −0.33% (0.33%) p.a. for passive crossings                       |
-| (e) Separate trends for LC types and   | 60                 | −42.2                        | −1.95% (0.63%) p.a. for pedestrians at controlled crossings     |
-| accident types                         |                    |                              | −3.68% (1.00%) p.a. for non-pedestrians at controlled crossings |
-|                                        |                    |                              | +3.53% (1.43%) p.a. for pedestrians at automatic crossings      |
-|                                        |                    |                              | +0.14% (1.10%) p.a. for non-pedestrians at automatic crossings  |
-|                                        |                    |                              | +0.49% (0.37%) p.a. for pedestrians at passive crossings        |
-|                                        |                    |                              | −1.75% (0.55%) p.a. for non-pedestrians at passive crossings    |
-
-<sup>a</sup> <sup>−</sup>2LL = minus twice the log likelihood.
-
-Thus a balance has to be struck between the benefits of railwaycontrolled crossings and those of automatic ones. Intermediate types of crossing are possible with modern technology. It is possible that the retention of a relative large number of railway-controlled crossings in Britain is a partial explanation for Britain's relatively good safety performance at level crossings.
-
-### **6. Comparison of accidents at automatic level crossings and signalised road junctions**
-
-There is evidence that a high proportion of level crossing accidents is associated with errors or violations by road users (for example, [RSSB, 2009\).](#page-8-0) This is not surprising, given that most level crossing accidents occur at automatic or passive crossings, where the primary responsibility for safe use rests with road users. Railway operators tend to have a poor view of road users' behaviour at level crossings, and this is backed up by some well-known video clips of very dangerous behaviour by road users. Nevertheless, it is not clear whether road user behaviour is worse on at level crossings than on the roads generally.
-
-This section presents a small piece of data to address this by comparing the fatal accident and fatality rates at automatic rail level crossings with their nearest road equivalent, signalised intersections. [Table 8](#page-6-0) gives data on the numbers of intersections/crossings, and fatal accidents and fatalities at these locations in Great Britain in 2005. The rail data are the modelled estimates of the means for 2005 from the fitted model in Section [4. T](#page-3-0)he road fatality data (121 fatal accidents; 126 fatalities) are taken from a special table produced for the writer by the Department of Transport (DfT) from the official road fatality data [\(DfT, annual\).](#page-8-0) The number of signalised road intersections is based on an estimate by the County Surveyors' Society of 12,300 in 2000, increased by 3% per year to 2005 (DfT, personal communication).
-
-The bottom two lines of [Table 8](#page-6-0) show that the estimated fatal accidents and fatalities per intersection or crossing were similar.
-
-#### **7. Conclusions**
-
-This paper investigates fatalities and fatal accidents at level crossings in Great Britain over the 64-year period 1946–2009. The numbers of fatal accidents and fatalities per year fell by about 65% in the first half of that period, but since then have remained more or less constant at about 11 fatal accidents and 12 fatalities per year. At the same time other types of railway fatality have fallen, so level crossings represent a growing proportion of the total. Nevertheless, Britain's level crossing safety performance remains good by international standards.
-
-The paper classifies level crossing types into three groups: railway-controlled, automatic, and passive. At the beginning of the period all crossings were either railway controlled or passive. Automatic crossings were not introduced into Britain until the early 1960s, replacing other types, but many railway-controlled crossings remain. As in most countries, by far the largest numbers of crossings are passive; most of these are not on public roads, but on private roads, farms, and footpaths. The total number of crossings has fallen by about 75% over the period, due partly to rail route closures and partly to closures of individual crossings, with or without replacement by bridges.
-
-The safety performance of the three types of crossings has been very different. Railway-controlled crossings are the best-performing crossing type, with falling fatal accident rates. Automatic crossings have higher accident rates per crossing than railway controlled or passive crossings, and the accident rates have not decreased. They have advantages over railway-controlled crossings on public roads in not needing staff and imposing less delay on road users. Nevertheless, they are less safe, so a balance has to be struck between safety, cost and delay. Passive crossings are by far the most numerous, but many have low usage by road users. Their fatal accident rate has remained remarkably constant over the whole period at about 0.9 fatal accidents per 1000 crossings per year.
-
-A principal reason why fatal accidents and fatalities have not fallen in the second half of the period as they did in the first half is the increase in the number of automatic crossings, replacing the safer railway controlled crossings on some public roads. However, it does not follow that this replacement was a mistake, because of the advantages of automatic crossings above.
-
-Based on the trends for each type of crossing and for pedestrian and non-pedestrian accidents separately, in 2009 means of 5% of fatal accidents were at railway controlled crossings, 52% were at automatic crossings, and 43% were at passive crossings. Fatalities had similar proportions. About 60% of fatalities were to pedestrians.
-
-A simple comparison of automatic railway level crossings and signalised road intersections found that in 2005 the numbers of fatalities per 1000 crossings or intersections were similar.
-
-### **Appendix A.**
-
-The results in Section [4](#page-3-0) of the paper are based on the assumption that the numbers of accidents are Poisson-distributed, even though they are 'overdispersed'. A common alternative to the Poisson distribution is to assume that the data have a negative binomial distribution. Table 9 gives results for the negative binomial distribution corresponding to those for the Poisson distribution in [Table 6. T](#page-5-0)here is no absolute measure of goodness of fit for the negative binomial distribution corresponding to the scaled deviance, but the differences in minus twice the log likelihoods between the model variants have the same properties as the differences
-
-0952 9.
-
-Railways, Paris.
-
-<span id="page-8-0"></span>in the scaled deviances. On this basis model variant (e) remains the best fitting variant, and all significance tests lead to the same conclusions. The right-hand column of [Table 9](#page-7-0) shows that the parameter estimates with the negative binomial are close to those with the Poisson distribution; the standard errors are somewhat larger.
-
-#### **References**
-
-7110 3308 0.
-
-- Austin, R.D., Carson, J.L., 2002. An alternative accident prediction model for highwayrail interfaces. Accident Analysis and Prevention 34 (1), 31–42.
-- British Railways Board, 1963. The reshaping of British Railways. HM Stationery Office, London.
-- Department for Transport (annual). Reported Road Casualties Great Britain. TSO, London.
-- Evans, A.W., 2003. Fatal Railway Accidents in Great Britain. Report to Rail Safety and Standards Board on research project T168. RSSB, London.
-- Evans, A.W., 2007. Rail safety and rail privatisation in Britain. Accident Analysis and
-- Prevention 39 (3), 510–523. European Railway Agency, 2010. Railway Safety Performance in the European Union.
-- ERA, Valenciennes, France, ISBN 978-92-9205-013-9. Hall, S., Van der Mark, P., 2008. Level Crossings. Ian Allan, Hersham, UK, ISBN:10 0
-
-- London.
-- Regulation, London (Earlier years have other titles and other publishers). Ministry of Transport, 1968. Report of the public inquiry into the accident at Hixon level crossing on 6 January 1968. Cmnd 3706, Her Majesty's Stationery Office,
-
-HM Railway Inspectorate, annual. Railway Safety Statistical Report. Office of Rail
-
-Health and Safety Executive, 1996. Railway Safety Principles and Guidance: Part 2, Section E: Guidance on level crossings. HS(G)153/6. HSE Books, ISBN:0 7176
-
-Heavisides, J., Barker, J., 2008. Impact of traffic flow on level crossing risk. In: Paper Presented at the 10th World level Crossing Symposium ,. International Union of
-
-- Mok, S.C., Savage, I., 2005.Why has safety improved at rail-highway grade crossings? Risk Analysis 25 (4), 867–879.
-- Rail Accident Investigation Branch, 2009. Investigation into safety at user worked crossings. Report 13/2009, RAIB, Derby, UK.
-- Rail Safety and Standards Board, 2004. Road vehicle level crossings special topic report. RSSB, London.
-- Rail Safety and Standards Board, 2006. The cost of level crossings—an international benchmarking exercise. Research Project T364, RSSB, London.
-- Rail Safety and Standards Board, 2009. Annual Safety Performance Report 2008. RSSB, London.
-- Rail Safety and Standards Board, 2010. Development of the All Level Crossing Risk Model: a History. Report prepared by A D Little. RSSB, London.
-- Stott, P.F., 1987. Automatic Open Level Crossings: A Review of Safety. Her Majesty's Stationery Office, London, ISBN:0 11 550831 7.

Zotero/001_artiklid/Road Safety Indicators/forumroadsafetyannualreport2023.md

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-category: literaturenote
-citekey: forumroadsafetyannualreport2023
-title: Road Safety Annual Report 2023
-authors: International Transport Forum
-year: 2023
-date: 2023-12-18 2023-12-18
-doi: 10.1787/8654c572-en
-url: "https://www.oecd.org/en/publications/road-safety-annual-report-2023_8654c572-en.html"
-zotero_key: JEBXV66X
-zotero_storage: 3Q9CYK8Y
-collections: doktoritöö / HLO
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: International Transport Forum
-status: converted
----
-![](_page_0_Picture_0.jpeg)
-
-# **Road Safety** Annual Report 2023
-
-![](_page_0_Picture_2.jpeg)
-
-![](_page_1_Picture_0.jpeg)
-
-# **Road Safety** Annual Report 2023
-
-![](_page_2_Picture_1.jpeg)
-
-### **About this publication**
-
-This work is published under the responsibility of the Secretary‑General of the International Transport Forum. The opinions expressed and arguments employed herein do not necessarily reflect the official views of International Transport Forum member countries. This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law. Data in this report have been provided by countries to the database of the International Traffic Safety Data and Analysis Group (IRTAD). Where data in this report has not been independently validated by IRTAD, this is indicated. Additional information on individual countries is provided online at www.itf-oecd.org/irtad.
-
-Cite this work as: ITF (2023), *Road Safety Annual Report 2023*, OECD Publishing, Paris.
-
-## **About the International Transport Forum**
-
-The International Transport Forum (ITF) is an intergovernmental organisation with 66 member countries that organises global dialogue for better transport. It acts as a think tank for transport policy and hosts the Annual Summit of transport ministers. The ITF is the only global body that covers all transport modes. The ITF is administratively integrated with the OECD, yet politically autonomous.
-
-#### **International Transport Forum**
-
-2 rue André Pascal F‑75775 Paris Cedex 16 contact@itf-oecd.org www.itf-oecd.org
-
-### **About IRTAD**
-
-The International Traffic Safety Data and Analysis Group (IRTAD) is the permanent working group for road safety of the International Transport Forum. The IRTAD Group brings together road safety experts from national road administrations, road safety research institutes, international organisations, automobile associations, insurance companies, car manufacturers and others. With 80 members and observers from more than 40 countries, the IRTAD Group is a central force in promoting international co‑operation on road crash data and its analysis.
-
-### **About the IRTAD Database**
-
-The IRTAD Database includes road safety data, aggregated by country and year from 1970 onwards. It provides an empirical basis for international comparisons and more effective road safety policies.
-
-The IRTAD Group validates data for quality before inclusion in the database. At present, the database includes validated data from 35 countries: Argentina, Australia, Austria, Belgium, Canada, Chile, Colombia, Costa Rica, Czechia, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Lithuania, Luxembourg, the Netherlands, New Zealand, Norway, Poland, Portugal, Serbia, Slovenia, Spain, Sweden, Switzerland, the United Kingdom and the United States.
-
-The data in this report are valid as of 3 December 2023. The data are provided in a common format based on definitions developed and agreed by the IRTAD Group. Selected data are available for free; full online access requires IRTAD membership. Access the database via the OECD statistics portal: https://stats.oecd.org/Index.aspx?DataSetCode=IRTAD\_CASUAL\_BY\_AGE.
-
-| Table of contents | Introduction<br>Foreword                                                             | 8<br>8   |
-|-------------------|--------------------------------------------------------------------------------------|----------|
-|                   | Preface                                                                              | 9        |
-|                   | Short‑term road safety trends<br>Preliminary 2023 data                               | 10<br>10 |
-|                   | Mobility and road safety in 2022                                                     | 12       |
-|                   | Traffic volumes in 2022                                                              | 12       |
-|                   | Road deaths in 2022                                                                  | 14       |
-|                   | Mortality rates and fatality risks in 2022                                           | 28       |
-|                   | Longer-term road safety trends<br>Evolution in the number of<br>road deaths, 2012‑22 | 32<br>32 |
-|                   | Road deaths by user group                                                            | 44       |
-|                   | Road deaths by age group                                                             | 49       |
-|                   | Road deaths by road type                                                             | 50       |
-|                   | National road safety strategies                                                      | 51       |
-|                   | References                                                                           | 52       |
-|                   | Data tables                                                                          | 53       |
-|                   | Annex. Road safety strategies and targets in IRTAD countries                         | 62       |
-|                   | Acknowledgements                                                                     | 72       |
-|                   | IRTAD members and observers                                                          | 73       |
-
-### **Figures**
-
-16 Figure 1: Evolution in road deaths by user category, 2022 compared to average for 2017-19
-
-21 Figure 2: Evolution in road deaths by age group, 2022 compared to average for 2017-19
-
-22 Figure 3: Mortality rate by age group, 2022
-
-26 Figure 4: Road deaths by road type, 2022
-
-27 Figure 5: Evolution in road deaths by road type, 2022 compared to average for 2017-19
-
-29 Figure 6: Road fatalities per 100 000 inhabitants, 2022
-
-30 Figure 7: Road fatalities per 10 000 registered vehicles, 2022
-
-31 Figure 8: Road fatalities per billion vehicle-kilometres, 2021
-
-33 Figure 9: Aggregate evolution in the number of road deaths in IRTAD countries, 2012-2022
-
-35 Figure 10: Percentage change in the number of road deaths, 2012-22
-
-38 Figure 11: Road deaths compared to the linear trend since 2012 (excluding 2020 and 2021)
-
-44 Figure 12: Evolution in road deaths by user category, 2022 compared to 2012
-
-45 Figure 13: Percentage change in the number of passenger car occupants killed, 2012-22
-
-46 Figure 14: Percentage change in the number of pedestrians killed, 2012-22
-
-47 Figure 15: Percentage change in the number of cyclists killed, 2012-22
-
-48 Figure 16: Percentage change in the number of PTW killed, 2012-22
-
-49 Figure 17: Evolution in road deaths by age group, 2022 compared to 2012
-
-50 Figure 18: Evolution in road deaths by road type, 2022 compared to 2012
-
-### **Tables**
-
-11 Table 1: Road deaths, first half of 2017‑2023 Provisional data
-
-13 Table 2: Traffic volumes in 2017-22 (millions vehicle-kilometres)
-
-15 Table 3: 2022 Road fatality data compared to the 2017-19 average
-
-19 Table 4: Percentage share of e-bike users in cyclist fatalities
-
-36 Table 5: Road fatality trends, 2012-22
-
-54 Table 6: National speed limits on urban roads, rural roads and motorways, 2023 passenger vehicles (km/h)
-
-56 Table 7: Maximum authorised blood alcohol content levels, 2023, by country
-
-58 Table 8: Seat-belt laws and wearing rates in front and rear seats of passenger cars, 2022 or latest available year
-
-60 Table 9: Helmet laws and wearing rates, 2022 or latest available year
-
-63 Table A1: Road safety strategies in IRTAD countries
-
-68 Table A2: Targets on road deaths and serious injuries in IRTAD countries
-
-#### **Boxes**
-
-13 Box 1 Mobility in France after the Covid‑19 pandemic
-
-17 Box 2 Motorcycle use in Latin America and the Caribbean
-
-28 Box 3 Measuring risk and comparing countries
-
-### **Foreword**
-
-I am pleased to present to you the 2023 ITF Annual Report on Road Safety. For over a decade, this report has been a trusted source of high‑quality traffic‑crash data. This report would not have been possible without the dedicated work of the International Traffic Safety Data and Analysis Group (IRTAD), the International Transport Forum's permanent working group on road safety.
-
-The good news is that most of the 35 IRTAD countries analysed in this report recorded a reduction in road fatalities in 2022 compared to the 2017‑2019 average. That said, more must be done to reach the target of halving road deaths by 2030, which is enshrined in the 2020 UN General Assembly resolution A/RES/74/299 on "Improving Global Road Safety".
-
-Following the ITF's Safe System approach can help countries reach that goal. ITF's IRTAD working group has actively advocated the Safe System approach in various reports and is currently working on transforming it into a tool for road safety assessment, counselling, and benchmarking Safe System implementations or indicators.
-
-Better data collection and analysis are the essential first steps towards improved road safety, as the foundation for setting targets and monitoring road safety progress. This report offers a glance on the current road safety trends across IRTAD countries, highlighting challenges in front of us, from the emergence of new mobility trends to an ageing population. ITF's IRTAD working group is committed to helping policymakers in addressing these challenges and finding solutions to make our roads safer for everyone. This report offers a departing point of this discussion. I hope you will enjoy reading it.
-
-**Young Tae KIM, Secretary-General of ITF**
-
-### **Preface**
-
-At its 2022 conference in Lyon, the International Traffic Safety Data and Analysis Group (IRTAD) adopted the Lyon Declaration. The declaration's 14 recommendations focus on improving the quality and comparability of road‑safety data to inform policies to achieve the ambitious target of halving the number of road deaths and serious injuries by 2030 (IRTAD, 2022a).
-
-Figures for 2022 presented in this report, as well as the first 2023 figures, clearly show that the impact of the Covid‑19 pandemic on traffic, road crashes and deaths has abated. The bad news is that the number of road deaths between 2020‑21 and 2022 increased. The good news is that in many countries (among the 35 for which figures are presented here), the number of fatalities in 2022 continued to fall compared with 2019 and previous years. However, the ten‑year trends also show that this decrease is sometimes very small. Particular attention must therefore be paid to this issue when devising policies to reduce fatalities and serious injuries.
-
-Two sub‑groups have been set up within IRTAD. The first will focus on data harmonisation and regional road safety observatories, as reliable data are not enough to make relevant comparisons between countries. The second group will focus on national road safety strategies, in co‑operation with the European Commission initiative to develop a tool for countries to monitor the implementation of national strategies. The first results of these two groups will be reported in 2024.
-
-Data reviews have also been carried out by IRTAD members in Cameroon in 2023. Again, it is important to stress the value of these management reviews in enabling the countries concerned to support effective policies to improve their data collection.
-
-Finally, there is cause for optimism in the adoption of low emission zones, traffic limited zones and the development of 30 km/h zones by many cities. Although the decision to introduce a 30 km/h zone is often taken for environmental reasons (low‑emission mobility zones), the impact in terms of improving road safety is obvious.
-
-This shows that road safety is an integral part of mobility management policies and a contributor to sustainable territorial development. The ITF and IRTAD will continue to work for safe and sustainable mobility.
-
-### **Dominique MIGNOT, Chair, IRTAD**
-
-This section presents data on short‑term trends in road safety. It includes preliminary data for the year 2023 and mobility and road safety data for the year 2022.
-
-### **Preliminary 2023 data**
-
-Preliminary data for the first half of 2023 shows an improved situation compared to the beginning of 2022. The analysis is restricted to the 24 countries with available data.
-
-In the first half of 2023, road deaths decreased in 17 countries and increased in only seven countries compared to 2022: Colombia, Denmark, Ireland, Japan, Lithuania, Portugal and Sweden. However, in these countries, apart from Colombia and Ireland, road deaths decreased if the first six months of 2023 are compared with the 2017‑19 average for the first half of the year (see Table 1).
-
-In the first half of 2023, road deaths increased in the Netherlands (4.4%), Norway (8.5%) and the United States<sup>1</sup> (11.9%) when compared to the average 2017‑19.
-
-At the same time, 2023 data for 11 IRTAD members, including several with large populations, were unavailable at the time of writing. Including this data in future reports will influence the overall trend.
-
-1 2022 and 2023 data for the United States are statistical projections.
-
-Road deaths decreased by 2.8% in 2023 compared to 2022.
-
-**Road deaths, first half of 2017‑2023** Provisional data
-
-| Country       | Average<br>2017‑19 | 2022   | 2023   | % change in<br>2023 compared<br>to av. 2017‑19 | % change in<br>2023 compared<br>to 2022 |
-|---------------|--------------------|--------|--------|------------------------------------------------|-----------------------------------------|
-| Austria       | 189                | 191    | 179    | -5.3                                           | -6.3                                    |
-| Colombia      | 3 153              | 3 813  | 4 002  | 26.9                                           | 5.0                                     |
-| Czechia       | 265                | 252    | 241    | -9.1                                           | -4.4                                    |
-| Denmark       | 84                 | 64     | 77     | -8.3                                           | 20.3                                    |
-| Finland       | 107                | 85     | 75     | -29.9                                          | -11.8                                   |
-| France        | 1 557              | 1 536  | 1 380  | -11.4                                          | -10.2                                   |
-| Germany       | 1 502              | 1 271  | 1 264  | -15.8                                          | -0.6                                    |
-| Greece        | 309                | 288    | 281    | -9.1                                           | -2.4                                    |
-| Hungary       | 267                | 233    | 213    | -20.2                                          | -8.6                                    |
-| Iceland       | 7                  | 4      | 1      | -85.7                                          | -75.0                                   |
-| Ireland       | 72                 | 77     | 84     | 16.7                                           | 9.1                                     |
-| Italy         | 1 544              | 1 419  | 1 384  | -10.4                                          | -2.5                                    |
-| Japan         | 1 869              | 1 422  | 1 441  | -22.9                                          | 1.3                                     |
-| Lithuania     | 80                 | 49     | 73     | -8.8                                           | 49.0                                    |
-| Luxembourg    | 13                 | 13     | 13     | 0.0                                            | 0.0                                     |
-| Netherlands   | 273                | 324    | 285    | 4.4                                            | -12.0                                   |
-| New Zealand   | 192                | 182    | 171    | -10.9                                          | -6.0                                    |
-| Norway        | 47                 | 54     | 51     | 8.5                                            | -5.6                                    |
-| Poland        | 1 225              | 893    | 824    | -32.7                                          | -7.7                                    |
-| Portugal      | 299                | 280    | 286    | -4.3                                           | 2.1                                     |
-| Serbia        | 228                | 240    | 220    | -3.5                                           | -8.3                                    |
-| Slovenia      | 51                 | 51     | 39     | -23.5                                          | -23.5                                   |
-| Sweden        | 117                | 95     | 110    | -6.0                                           | 15.8                                    |
-| United States | 17 437             | 20 190 | 19 515 | 11.9                                           | -3.3                                    |
-
-### **Mobility and road safety in 2022**
-
-Data on mobility and road safety in 2022 relate to traffic volumes, road deaths, mortality rates and fatality risks.
-
-### **Traffic volumes in 2022**
-
-This report expresses the traffic volume in individual countries as the total distance travelled in vehicle‑kilometres (vkm).
-
-In 2022, traffic volumes, measured in millions of vkm, increased compared to 2020 but were not yet back to the levels of 2017‑19, before the Covid‑19 pandemic (see Table 2). This was the case for all 15 countries which provided these data, except Canada, Czechia, Denmark, Hungary and Iceland, where traffic volumes in 2022 were back to pre‑Covid 19 levels.
-
-2022 can no longer be considered an "abnormal" year. In some countries, traffic volumes did not recover to pre‑Covid 19 levels, but this was mainly due to a change in mobility behaviour.
-
-In Denmark, for example, the Danish National Travel Survey data indicate that the reduction in travel by car is due to the increase in gasoline prices and a more widespread habit of working from home.
-
-Also, in Germany, working from home could be an explanation for reduced traffic. Between 2017 and 2022, the percentage of employees teleworking some days of the week increased from 13% to 28%. In addition, new mobility routines have emerged in recent years. Walking has increased, while cycling remains stable. Cars and public transport are used less frequently but for longer distances. Box 1 presents the situation in France.
-
-Traffic volumes in 2022 had not returned to pre‑Covid levels, reflecting changes in mobility behaviour.
-
-### **Traffic volumes in 2017-22 (millions vehicle-kilometres)**
-
-| Country       | Average<br>2017-19 | 2020    | 2021    | 2022    | % change<br>in 2022<br>compared to<br>av. 2017-19 | % change<br>in 2022<br>compared to<br>2020 |
-|---------------|--------------------|---------|---------|---------|---------------------------------------------------|--------------------------------------------|
-| Australia     | 256 626            | 242 880 | 244 787 | 240 011 | -6.5                                              | -1.2                                       |
-| Canada        | 398 337            | 378 046 | 409 029 | 410 000 | 2.9                                               | 8.5                                        |
-| Czechia       | 56 240             | 52 280  | 53 742  | 58 818  | 4.6                                               | 12.5                                       |
-| Denmark       | 54 540             | 51 527  | 53538   | 54913   | 0.7                                               | 6.6                                        |
-| Finland       | 50 349             | 48 543  | 48 305  | 47 695  | -5.3                                              | -1.7                                       |
-| France        | 641 000            | 531 911 | 577 044 | 629 380 | -1.8                                              | 18.3                                       |
-| Germany       | 751 900            | 681 749 | 690 000 | 721 000 | -4.1                                              | 5.8                                        |
-| Great Britain | 539 298            | 427 914 | 478 874 | 521 093 | -3.4                                              | 21.8                                       |
-| Hungary       | 45 374             | 41 854  | 46 611  | 49 531  | 9.2                                               | 18.3                                       |
-| Iceland       | 3 981              | 3 800   | 3 942   | 4 010   | 0.7                                               | 5.5                                        |
-| Netherlands   | 135 057            | 117 853 | 123 105 | 131 510 | -2.6                                              | 11.6                                       |
-| New Zealand   | 47 482             | 45 905  | 46 550  | 47 251  | -0.5                                              | 2.9                                        |
-| Norway        | 45 836             | 43 406  | 44 968  | 45 404  | -0.9                                              | 4.6                                        |
-| Slovenia      | 21 903             | 17 612  | 19 449  | 20 508  | -6.4                                              | 16.4                                       |
-| Sweden        | 84 036             | 77 813  | 80 119  | 81 823  | -2.6                                              | 5.2                                        |
-
-#### **Mobility in France after the Covid‑19 pandemic**
-
-In France, there is no recent national survey on mobility behaviour. However, some data are available for major cities. Research lead by LVMT Laboratory (2022) shows that the pandemic increased the attractiveness of cycling and consolidated existing cyling practices due to the construction of new bike lanes. Telework reduced traffic volumes, especially for passenger cars and public transportation. The decrease is more pronounced on Fridays, Mondays and Wednesdays. The impact of telework on car use and average distance remains unclear. However, some studies show that telework is associated with more trips around home on teleworking days but does not necessarily reduce car use.
-
-According to a survey carried out by Île de France Mobilité (2022), new mobility behaviours can be identified in the Île‑de‑France region. In 2022, for all modes of transport combined, Paris region residents made 10% fewer journeys than in 2018. Teleworking and videoconferencing have developed strongly, reducing the number of home‑to‑work journeys, as well as journeys for meetings or lunch breaks". In addition, while road traffic has generally returned to pre‑Covid levels, this is not the case for public transport use, which reached a plateau in 2022, with passenger numbers at between 80% and 85% of pre‑Covid levels. This is also confirmed by public transport operators.
-
-Another research project has been launched, led by LVMT Laboratory (Dablanc et al., 2022a, 2022b), looking at accidental journeys made by delivery drivers on major platforms such as Ubereats and Deliveroo. The data are based on annual surveys conducted by the Logistics City Chair in Paris and a survey in Nantes in 2021, involving fieldwork with 600 delivery drivers. The results reveal a high crash rate reported by drivers themselves. Delivery personnel report 26‑29% of crashes on bicycles or scooters, depending on the year, half of which require a trip to the emergency room and 33% of which require medical attention.
-
-### **Road deaths in 2022**
-
-After the shock of the Covid-19 pandemic in 2020 and 2021, when mobility was restricted everywhere and road deaths showed a general decrease, 2022 was a "normal" year. There were no particular restrictions on mobility in IRTAD countries.
-
-To avoid biased results, this report compares road deaths in 2022 to the average for 2017‑19 for short‑term comparisons.
-
-On average, for the 35 IRTAD countries with validated data, road deaths increased by 3.2% in 2022 compared to the average for 2017‑19.
-
-However, the United States, the most populous country in the analysis, significantly impacts the result. When US data are not included, in 2022, road deaths decreased by 6.4% compared to the average for 2017‑19.
-
-**Evolution by country**
-
-The picture is quite varied when looking at the data for each country. Among the 35 IRTAD countries, 23 countries recorded a reduction in road deaths in 2022, compared to the average for 2017‑19. In 15 countries, the decline was greater than 10% (see Table 3).
-
-The strongest decreases were in Lithuania (-34.4%), Poland (-33.9%), followed by Iceland (-30.8%), Korea (-27.5%), Japan (-22.9%), Finland (-17.5%), Denmark (-15.4%), Sweden (-14.7%), Czechia (-14.5%), Argentina (-14.4%), Slovenia (-14.1%), Hungary (-13.7%), Belgium (-12.8%), Germany (-12%) and Austria (-10.4%).
-
-Five countries recorded an increase in road fatalities of more than 10% in 2022 compared to the average for 2017‑19: Luxembourg (28.6%), Colombia (22.2%), the United States (16%), the Netherlands (14.4%) and Switzerland (11.1%).
-
-In the Netherlands, the number of road deaths in 2022 (745) was the highest in more than ten years, while in Switzerland the number of road deaths in 2022 was higher than in any year since 2015.
-
-In the United States, even if there was an increase of 16% compared to the average for 2017‑19, the number of road deaths slightly decreased in 2022 when compared to 2021.
-
-If the US data are excluded, overall deaths in IRTAD member countries fell by 6.4%.
-
-Five countries recorded an increase of more than 10% in 2022.
-
-### **2022 Road fatality data compared to the 2017-19 average**
-
-| Country                               | 2022 road deaths | Data status | 2017-19 road deaths | % change |  |  |  |  |
-|---------------------------------------|------------------|-------------|---------------------|----------|--|--|--|--|
-| Countries with validated data         |                  |             |                     |          |  |  |  |  |
-| Argentina                             | 4 567            | provisional | 5 334               | -14.4    |  |  |  |  |
-| Australia                             | 1 188            | provisional | 1 182               | 0.5      |  |  |  |  |
-| Austria                               | 370              | final       | 413                 | -10.4    |  |  |  |  |
-| Belgium                               | 540              | final       | 619                 | -12.8    |  |  |  |  |
-| Canada                                | 1 934            | provisional | 1 852               | 4.4      |  |  |  |  |
-| Chile                                 | 2 137            | final       | 1 951               | 9.5      |  |  |  |  |
-| Colombia                              | 8 030            | final       | 6 570               | 22.2     |  |  |  |  |
-| Costa Rica                            | 786              | provisional | 820                 | -4.1     |  |  |  |  |
-| Czechia                               | 527              | final       | 617                 | -14.6    |  |  |  |  |
-| Denmark                               | 154              | final       | 182                 | -15.4    |  |  |  |  |
-| Finland                               | 189              | provisional | 229                 | -17.5    |  |  |  |  |
-| France                                | 3 267            | final       | 3 313               | -1.4     |  |  |  |  |
-| Germany                               | 2 788            | final       | 3 167               | -12.0    |  |  |  |  |
-| Greece                                | 641              | provisional | 706                 | -9.2     |  |  |  |  |
-| Hungary                               | 535              | final       | 620                 | -13.7    |  |  |  |  |
-| Iceland                               | 9                | final       | 13                  | -30.8    |  |  |  |  |
-| Ireland                               | 155              | provisional | 143                 | 8.4      |  |  |  |  |
-| Israel                                | 351              | final       | 345                 | 1.7      |  |  |  |  |
-| Italy                                 | 3 159            | final       | 3 295               | -4.1     |  |  |  |  |
-| Japan                                 | 3 216            | final       | 4 172               | -22.9    |  |  |  |  |
-| Korea                                 | 2 735            | final       | 3 772               | -27.5    |  |  |  |  |
-| Lithuania                             | 120              | final       | 183                 | -34.4    |  |  |  |  |
-| Luxembourg                            | 36               | final       | 28                  | 28.6     |  |  |  |  |
-| Netherlands                           | 745              | final       | 651                 | 14.4     |  |  |  |  |
-| New Zealand                           | 375              | provisional | 369                 | 1.6      |  |  |  |  |
-| Norway                                | 116              | final       | 107                 | 8.4      |  |  |  |  |
-| Poland                                | 1 896            | final       | 2 867               | -33.9    |  |  |  |  |
-| Portugal                              | 618              | final       | 663                 | -6.8     |  |  |  |  |
-| Serbia                                | 553              | final       | 554                 | -0.2     |  |  |  |  |
-| Slovenia                              | 85               | final       | 99                  | -14.1    |  |  |  |  |
-| Spain                                 | 1 746            | final       | 1 797               | -2.8     |  |  |  |  |
-| Sweden                                | 227              | final       | 266                 | -14.7    |  |  |  |  |
-| Switzerland                           | 241              | final       | 217                 | 11.1     |  |  |  |  |
-| United Kingdom                        | 1 766            | final       | 1 834               | -3.7     |  |  |  |  |
-| United States                         | 42 795           | provisional | 36 888              | 16.0     |  |  |  |  |
-| Observers and accession countries (a) |                  |             |                     |          |  |  |  |  |
-| Mexico                                | 15 979           | provisional | 15 371              | 4.0      |  |  |  |  |
-| Morocco                               | 3 499            | final       | 3 695               | -5.3     |  |  |  |  |
-| Uruguay                               | 431              | final       | 473                 | -8.9     |  |  |  |  |
-
-(a) Data as provided by the countries and not validated by IRTAD.
-
-### **Short‑term evolution by user group**
-
-When looking at data by road user group, data are available for 32 countries (see Figure 1). Australia, Greece, and the United States have not yet published detailed 2022 data.
-
-For the countries analysed, road fatalities decreased in 2022 compared to the average 2017‑19 for all road user types except for powered two‑wheelers (PTWs), which increased by 6.7%. Pedestrian fatalities recorded the biggest decrease (‑17.7%), followed by passenger car occupants (-8%) and cyclists (-3.3%).
-
-The increase in the number of PTW users was due mainly to the very sharp increase in Colombia (+41.1% in 2022 compared to the average for 2017‑19). When excluding Colombia, the number of PTW users killed decreased by 6.9% in 2022 compared to the average for 2017‑19.
-
-The safety of PTW users is a growing issue in Colombia as in many Latin American countries. In 2022, over 800 000 new motorcycles were registered in the country, representing the highest number of registrations of new motorcycles over the last decade (see Box 2).
-
-Road deaths decreased for all user groups in 2022, except for users of powered-two wheelers.
-
-**Evolution in road deaths by user category, 2022 compared to average for 2017-19**
-
-![](_page_15_Figure_7.jpeg)
-
-Note: Data include Argentina, Austria, Belgium, Canada, Chile, Colombia, Costa Rica, Czechia, Denmark, Finland, France, Germany, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Lithuania, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Serbia, Slovenia, Spain, Sweden, Switzerland, United Kingdom.
-
-### **Motorcycle use in Latin America and the Caribbean**
-
-In Latin American and Caribbean countries, the number of motorcycle users killed exceeds the world average. According to the International Development Bank (IDB), In some countries, such as Colombia, the Dominican Republic and Uruguay, motorcycles represent more than half of road traffic (IDB, 2022). In these countries, motorcycles accounted for 73%, 56%, and 51% of the vehicle fleet, respectively (IDB, 2022).
-
-Several factors contribute to the increased use of motorcycles in the region. First, in cities where the supply of public transport does not satisfy the demand, motorcycles are considered a valid alternative (Rodríguez et al., 2015). Other contributing factors include rising income levels, lower manufacturing costs, access to financing facilities, tax incentives, ease of maintenance and lower fuel consumption. Additionally, motorcycles provide agility in congested traffic conditions, which are common in large Latin American cities.
-
-There is also a growing trend in using motorcycles as a means of work. The demand for courier, cab or delivery services aboard motorcycles offers lower fares and shorter travel times compared to other modes of transportation. People who use their motorcycles for work tend to have low education and income levels, which is a barrier to accessing employment with better working conditions. The commercial use of motorcycles should be formalised under adequate labour regulations, thus improving the quality‑of‑life standards for people who provide a service without decent conditions.
-
-The IDB report provides a set of guidelines and best practices to enhance road safety for motorcycle users in the region that should be implemented across three key dimensions: 1) drivers and passengers, 2) motorcycle safety and 3) infrastructure and operation.
-
-First, regarding the driver and passenger, the IDB recommends governments should establish
-
-minimum age requirements for motorcycle operation, considering engine power and riding proficiency, and introduce mandatory training programmes for individuals applying for a motorcycle license, starting with a basic category, and progressively advancing based on years of experience. Furthermore, personal safety elements for both the driver and passenger must be enforced.
-
-Second, concerning motorcycles, automatic braking systems (ABS) and appropriate day and night lights should be required and enforced. In addition, compulsory vehicle safety inspections and mandatory insurance coverage are essential to enhance safety.
-
-Third, regarding infrastructure and operation, it is essential to implement speed management measures and the installation of side barriers adapted to accommodate motorcycles.
-
-Source: Inter‑American Development Bank (2022).
-
-While cyclist fatalities in 2022 decreased by 3.3% compared to the average for 2017‑19, there are growing concerns regarding the safety of e‑bikes. Their use is becoming more and more common in the IRTAD countries. This results in increased trips performed by e‑bikes and consequently increased share in the fatalities among cyclists, generating new safety challenges.
-
-Data from the ten countries which reported these data for 2022 confirm this trend (see Table 4). In Israel and Switzerland, more than half of the cyclists killed in road crashes were using an e‑bike. The percentage was quite high also for Germany (44%), Denmark (39%) and Belgium (38%).
-
-This trend is growing. For example, in Switzerland in 2017 only 19% of cyclists killed were riding e‑bikes. This phenomenon particularly affected older people, attracted by the possibility of continuing to do some physical exercise. For example, in Japan in 2022 60% of e‑bikers killed were over 75, while the equivalent figures were around 40% in Belgium and Germany.
-
-Few countries record data concerning new micro‑mobility vehicles, such as e‑scooters. In the countries where these data are collected, there has been an increase in road fatalities in recent years, due mainly to the increasing use. For example, in France, the number of people killed while using these vehicles tripled between 2019 and 2022. However, they still represent a small share of total road fatalities.
-
-Recently, some countries have made efforts to collect road crash data about new mobility modes. But the same cannot be said for exposure (I.e. how many kilometres cyclists and e‑scooter users travel). Only the Netherlands has national data on cycling, based on annual ad hoc surveys. In other countries, data can be obtained at the city level from self‑service bike or scooter operators.
-
-However, even at the local level, there is little data available on user trips outside self‑service fleets. Initial work has been carried out using mobile phone data to measure trips using new mobility modes but at this stage, it has not been shared or validated. This is a major knowledge challenge for the coming years. The ITF Statistics Group has set up a task force on emerging mobility patterns data, including walking and cycling. IRTAD is a member of this group, which is due to report in 2024.
-
-There are growing concerns about e-bikes.
-
-**Percentage share of e-bike users in cyclist fatalities**
-
-| Country     | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
-|-------------|------|------|------|------|------|------|
-| Belgium     | 28   | 23   | 27   | 35   | 47   | 38   |
-| Denmark     |      | 21   | 30   | 30   | 12   | 39   |
-| France      |      |      | 8    | 9    | 11   | 18   |
-| Germany     | 18   | 20   | 27   | 33   | 35   | 44   |
-| Israel      |      |      | 47   | 81   | 52   | 55   |
-| Italy       |      |      |      |      | 6    | 10   |
-| Japan       | 7    | 9    | 12   | 12   | 13   | 15   |
-| Netherlands | 28   | 25   | 32   | 32   | 39   | 34   |
-| Portugal    | 4    | 0    |      |      |      | 26   |
-| Slovenia    | 25   | 0    | 0    | 0    | 0    | 13   |
-| Switzerland | 19   | 29   | 38   | 31   | 41   | 55   |
-
-### **Short-term evolution by age group**
-
-When looking at data by age group, data are available for 30 countries (see Figure 2). Australia, Canada, Costa Rica, Greece, and the United States do not yet have detailed 2022 data.
-
-In 2022, road deaths decreased for all age groups compared to the 2017‑19 average. The biggest reductions were for the youngest generation. The decrease among children under 14 amounted to 12.9%, while for teenagers between 15 and 17 years old, the decrease was 16.4%.
-
-In contrast with previous years, the senior population recorded an improvement in road safety. In 2022, road deaths decreased by 5.2% for the 65‑74 age group and 11.6% among people over 75. People aged between 21 and 24 derived fewer benefits from better road safety. Road deaths for this group decreased by only 1.7%.
-
-Road deaths in 2022 decreased for all age groups.
-
-**Evolution in road deaths by age group, 2022 compared to average for 2017-19**
-
-![](_page_20_Figure_1.jpeg)
-
-Note: Data include Argentina, Austria, Belgium, Chile, Colombia, Czechia, Denmark, Finland, France, Germany, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Lithuania, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Serbia, Slovenia, Spain, Sweden, Switzerland, United Kingdom.
-
-Historically, the 18‑20 and 21‑24 age groups were the most at risk in traffic. In recent years, in many IRTAD countries, seniors aged 75 and over have become more and more at risk in traffic. In 2022, in almost two‑thirds of the countries with available data, the mortality rates of people aged 75 and over were higher than those of people aged 18‑20 or 21‑24 (see Figure 3).
-
-The are several reasons for this shift. First of all, the population is ageing in most of the developed countries. At the same time, the population is more mobile than before since the average health conditions are better and new transport modes, such as e‑bikes, suit the elderly population.
-
-Second, in the last few years, successful measures have been taken to improve the safety of young people. In addition, younger generations tend to drive less and later.
-
-While fewer seniors are dying in road crashes, they remain the most‑at‑risk age group.
-
-#### **Mortality rate by age group, 2022**
-
-Age group Total
-
-![](_page_21_Figure_6.jpeg)
-
-![](_page_21_Figure_7.jpeg)
-
-![](_page_21_Figure_8.jpeg)
-
-![](_page_21_Figure_9.jpeg)
-
-![](_page_22_Figure_0.jpeg)
-
-![](_page_22_Figure_1.jpeg)
-
-![](_page_23_Figure_0.jpeg)
-
-![](_page_23_Figure_1.jpeg)
-
-![](_page_23_Figure_2.jpeg)
-
-![](_page_23_Figure_3.jpeg)
-
-![](_page_23_Figure_4.jpeg)
-
-![](_page_23_Figure_5.jpeg)
-
-![](_page_23_Figure_6.jpeg)
-
-![](_page_23_Figure_7.jpeg)
-
-![](_page_23_Figure_8.jpeg)
-
-![](_page_24_Figure_0.jpeg)
-
-![](_page_24_Figure_1.jpeg)
-
-![](_page_24_Figure_2.jpeg)
-
-![](_page_24_Figure_3.jpeg)
-
-![](_page_24_Figure_4.jpeg)
-
-![](_page_24_Figure_5.jpeg)
-
-![](_page_24_Figure_6.jpeg)
-
-![](_page_24_Figure_7.jpeg)
-
-![](_page_24_Figure_8.jpeg)
-
-### **Short-term evolution by road type**
-
-Data disaggregated by road type are available for 25 countries in 2022 (see Figure 4). Rural roads are the deadliest roads in almost all countries. In 17 countries, more than half of the road deaths occurred on rural roads. In Finland, Ireland and New Zealand, two‑thirds of road deaths occurred in this type of road. Only in Korea, the Netherlands, Japan, and Portugal are urban roads deadlier than other road types.
-
-The reasons for the dangerousness of rural roads relate mainly to road infrastructure and inappropriate speed. Rural roads often lack physical separation of lanes, have numerous intersections and are sometimes poorly maintained. In addition, drivers tend to speed on rural roads, mainly because of a lack of enforcement.
-
-Rural roads remain the deadliest road type.
-
-### **Road deaths by road type, 2022**
-
-![](_page_25_Figure_6.jpeg)
-
-In 2022, for the 25 countries with available data, road deaths decreased by 13.5% compared to the average 2017‑19 (see Figure 5). The biggest decrease was in the number of people killed on urban roads (‑15.5%), followed by rural roads (‑7.5%) and motorways (‑6.2%).
-
-**Evolution in road deaths by road type, 2022 compared to average for 2017-19** 0% -2% -4% -6% -8% -10% -12% -14% -16% -18% Urban roads Rural roads Motorways All road types **-15.5% -7.5% -6.2% -13.5%**
-
-Note: Data include Argentina, Austria, Belgium, Czechia, Denmark, Finland, France, Germany, Great Britain, Hungary, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, Netherlands, New Zealand, Poland, Portugal, Serbia, Slovenia, Spain, Sweden, Switzerland.
-
-## **Mortality rates and fatality risks in 2022**
-
-Three common indicators are used to measure road safety performance and compare safety levels across countries: 1) the number of road deaths per population, 2) the number of road deaths per motorised vehicle, and 3) the number of road deaths per distance travelled (see Box 3). This section explores the 2022 data for the first two indicators and the 2021 data for the latter.
-
-In 2022, the mortality rate ranged from 2.1 to 15.5 fatalities per 100 000 inhabitants (see Figure 6). Norway recorded the lowest mortality rate, with 2.1 fatalities per 100 000 inhabitants. Norway also registered the lowest mortality rate from road crashes in 2021. A total of 22 IRTAD countries had a mortality rate between 3 and 9 in 2022. Four countries had a mortality rate higher than 10 fatalities per 100 000 inhabitants: Chile (10.8), the United States (12.8), Costa Rica (15.2), and Colombia (15.5).
-
-Seven countries recorded per capita mortality rate below 3 in 2022.
-
-### **Measuring risk and comparing countries**
-
-Three common indicators measure road safety performance and compare safety levels across countries. Each has pros and cons; in all cases, interpret country comparisons with great care, especially between countries with different levels of motorisation.
-
-First, the number of **fatalities per head of population** measures the mortality rate. The number of inhabitants (per 100 000 or million) is the most often‑used denominator as this figure is readily available in most countries. This rate expresses the average citizen's overall risk of being killed in traffic. It is comparable to other causes of death (e.g. coronary diseases or HIV/AIDS). It is also useful when comparing risk in countries with similar levels of motorisation. It is not very
-
-meaningful to compare safety levels between highly motorised countries and countries where the level of motorisation is low.
-
-Second, the number of **fatalities per number of registered motorised vehicles** is an alternative to measuring fatalities per distance travelled, although it does not consider actual traffic volume. It is only useful for comparing the safety performance of countries with similar traffic and vehicle‑use characteristics. It also requires reliable statistics on the number of vehicles. In some countries, scrapped vehicles are not systematically removed from registration databases, undermining the accuracy of this indicator. Equally, this indicator does not consider non‑motorised vehicles (e.g. bicycles), which represent a large part of the
-
-vehicle fleet (and fatality figures) in some countries. This indicator is usually expressed as the number of fatalities per 10 000 registered motorised vehicles.
-
-Third, the number of **fatalities per distance travelled** by motorised vehicles measures fatality risk. This indicator describes the safety quality of road traffic. Theoretically, it is the best indicator to assess the level of risk of the road network. However, it does not take into account non‑motorised vehicles (e.g. bicycles). In some countries, non‑motorised vehicles represent a large part of the vehicle fleet and of road fatalities. Furthermore, only a limited number of countries collect data on distance travelled. Fatality risk is usually expressed in road deaths per billion vehicle‑kilometres.
-
-Other six countries recorded less than 3 fatalities from road crashes per 100 000 inhabitants in 2022: Sweden (2.2), Iceland (2.4), Japan, Denmark, and the United Kingdom (2.6), and Switzerland (2.8).
-
-In the case of Switzerland, while it recorded an increase in road fatalities of 11.1% in 2022 compared to the average 2017‑19, the country nevertheless registered a relatively low mortality rate. It is also worth noting that the number of road deaths in 2019 in Switzerland was extremely low.
-
-![](_page_28_Figure_2.jpeg)
-
-Note: (a) Real data (actual numbers instead of reported numbers by the police).
-
-Fatality rates, measured against the number of motorised vehicles, ranged from 0.2 to 4.5 deaths per 10 000 motorised vehicles (see Figure 7) in 2022. Ten countries registered a mortality rate of less than 0.5: Iceland, Norway, Sweden, Japan, Switzerland, Finland, United Kingdom, Denmark, Spain, and Germany.
-
-The fatality risk was highest in Chile and Colombia, with a rate of 3.5 and 4.5 fatalities per 10 000 motorised vehicles, respectively.
-
-**Road fatalities per 10 000 registered vehicles, 2022**
-
-Ten countries recorded a mortality rate per registered vehicle below 0.5.
-
-> New Zealand Hungary (b) Argentina
-
-![](_page_29_Figure_3.jpeg)
-
-Australia Netherlands (a) Lithuania Czech Republic Luxembourg Belgium (b) Greece (b) Canada Portugal (b) Israel
-
-Note: (a) Real data (actual numbers instead of reported numbers by the police). (b) Mopeds are not included in the registered vehicles.
-
-Iceland Norway Sweden Japan Switzerland Finland United Kingdom Denmark (b) Spain Germany (b) Austria Slovenia Ireland Poland
-
-Colombia
-
-The fatality risk calculated by distance travelled is available for 22 countries in 2021 (but only 11 countries available in 2022). In 2021, the fatality risk ranged from 1.8 to 9.9 fatalities per billion vkm (see Figure 9).
-
-Four countries reported less than 3 deaths per billion vkm: Norway (1.8), Iceland (2.3), Denmark (2.4), and Sweden (2.6). Three countries registered more than 8 deaths per billion vkm: Korea (8.2), the United States (8.5) and Czechia (9.9).
-
-Four countries recorded a mortality rate per distance travelled below 3.
-
-### **Road fatalities per billion vehicle-kilometres, 2021**
-
-![](_page_30_Figure_5.jpeg)
-
-Note: (a) Real data (actual numbers instead of reported numbers by the police). (b) Data only for Great Britain.
-
-This section discusses the longer‑term evolution of specific road safety indicators for the period 2012‑22. This section discusses two broad sets of data: data on the total number of road deaths; and data on road deaths disaggregated by user group, age group and road type. The IRTAD database also covers serious injuries; details are provided in the accompanying country profiles.
-
-## **Evolution in the number of road deaths, 2012‑22**
-
-Between 2012 and 2022, road deaths increased by 1.5% in the 35 countries with validated data.
-
-When the United States's data are not included, road deaths decreased by 14.4%. This decrease needs to be accelerated to meet the target under the second Decade of Action for Road Safety 2021‑30 (WHO, 2021).
-
-Figure 9 shows the evolution in the number of road deaths between 2012 and 2022, with and without US data.
-
-In 2021, road deaths increased compared to 2020 but stayed below the pre‑Covid 19 level. In 2022, the total number of road deaths increased a further but stayed below the pre‑Covid 19 level.
-
-If US data are excluded, overall road deaths in IRTAD countries fell by 14%.
-
-#### **Aggregate evolution in the number of road deaths in IRTAD countries, 2012-2022**
-
-![](_page_32_Figure_2.jpeg)
-
-![](_page_32_Figure_3.jpeg)
-
-### **Number of road deaths (excluding the US)**
-
-![](_page_32_Figure_5.jpeg)
-
-Between 2012 and 2022, road deaths decreased in 27 of the 35 IRTAD countries (see Figure 10 and Table 5). Fatalities decreased the most in Lithuania (‑60.1%), Korea (‑49.2%) and Poland (‑46.9%). Five other countries recorded a reduction of more than 30% in 2022 compared to 2012: Japan (‑38.9%), Greece (‑35.1%), Belgium (‑34.7%), Slovenia (‑34.6%), and Austria (‑30.3%).
-
-Road deaths increased in 8 IRTAD countries and by more than 20% in five countries: Colombia (50.9%), the United States (26.7%), New Zealand (21.8%), Israel (21%) and Costa Rica (20%).
-
-Lithuania, Korea and Poland have achieved large drops in road fatalities since 2012.
-
-![](_page_34_Figure_0.jpeg)
-
-Note: (a) Real data (actual numbers instead of reported numbers by the police).
-
-#### **Road fatality trends, 2012-22**
-
-| Country                               |        |        |        |        |        |        |        |
-|---------------------------------------|--------|--------|--------|--------|--------|--------|--------|
-|                                       | 2012   | 2013   | 2014   | 2015   | 2016   | 2017   | 2018   |
-| Countries with validated data         |        |        |        |        |        |        |        |
-| Argentina                             | 5 074  | 5 209  | 5 279  |        | 5 550  | 5 611  | 5 493  |
-| Australia                             | 1 299  | 1 185  | 1 151  | 1 205  | 1 295  | 1 223  | 1 135  |
-| Austria                               | 531    | 455    | 430    | 479    | 432    | 414    | 409    |
-| Belgium                               | 827    | 764    | 745    | 762    | 670    | 609    | 604    |
-| Canada                                | 2 075  | 1 951  | 1 841  | 1 887  | 1 900  | 1 861  | 1 939  |
-| Chile                                 | 1 979  | 2 103  | 2 116  | 2 136  | 2 178  | 1 925  | 1 955  |
-| Colombia                              | 5 320  | 5 757  | 6 118  | 6 406  | 6 936  | 6 505  | 6 629  |
-| Costa Rica                            | 655    | 625    | 662    |        |        | 862    | 811    |
-| Czechia                               | 742    | 654    | 688    | 734    | 611    | 577    | 658    |
-| Denmark                               | 167    | 191    | 182    | 178    | 211    | 175    | 171    |
-| Finland                               | 255    | 258    | 229    | 270    | 258    | 238    | 239    |
-| France                                | 3 653  | 3 268  | 3 384  | 3 461  | 3 477  | 3 448  | 3 248  |
-| Germany                               | 3 600  | 3 339  | 3 377  | 3 459  | 3 206  | 3 180  | 3 275  |
-| Greece                                | 988    | 879    | 795    | 793    | 824    | 731    | 700    |
-| Hungary                               | 605    | 591    | 626    | 644    | 607    | 625    | 633    |
-| Iceland                               | 9      | 15     | 4      | 16     | 18     | 16     | 18     |
-| Ireland                               | 163    | 188    | 192    | 162    | 182    | 154    | 134    |
-| Israel                                | 290    | 309    | 319    | 356    | 377    | 364    | 316    |
-| Italy                                 | 3 753  | 3 401  | 3 381  | 3 428  | 3 283  | 3 378  | 3 334  |
-| Japan                                 | 5 261  | 5 165  | 4 838  | 4 885  | 4 698  | 4 431  | 4 166  |
-| Korea                                 | 5 392  | 5 092  | 4 762  | 4 621  | 4 292  | 4 185  | 3 781  |
-| Lithuania                             | 301    | 258    | 267    | 239    | 188    | 191    | 173    |
-| Luxembourg                            | 34     | 45     | 35     | 36     | 32     | 25     | 36     |
-| Netherlands (b)                       | 650    | 570    | 570    | 621    | 629    | 613    | 678    |
-| New Zealand                           | 308    | 252    | 292    | 317    | 326    | 377    | 379    |
-| Norway                                | 145    | 187    | 147    | 117    | 135    | 106    | 108    |
-|                                       |        |        |        |        |        |        |        |
-| Poland                                | 3 571  | 3 357  | 3 202  | 2 938  | 3 026  | 2 831  | 2 862  |
-| Portugal                              | 718    | 637    | 638    | 593    | 563    | 602    | 700    |
-| Serbia                                | 688    | 650    | 536    | 599    | 607    | 579    | 548    |
-| Slovenia                              | 130    | 125    | 108    | 120    | 130    | 104    | 91     |
-| Spain                                 | 1 903  | 1 680  | 1 688  | 1 689  | 1 810  | 1 830  | 1 806  |
-| Sweden                                | 285    | 260    | 270    | 259    | 270    | 252    | 324    |
-| Switzerland                           | 339    | 269    | 243    | 253    | 216    | 230    | 233    |
-| United Kingdom                        | 1 802  | 1 770  | 1 854  | 1 804  | 1 860  | 1 856  | 1 839  |
-| United States                         | 33 782 | 32 893 | 32 744 | 35 484 | 37 806 | 37 473 | 36 835 |
-| Observers and accession countries (a) |        |        |        |        |        |        |        |
-| Mexico                                | 17 102 | 15 853 | 15 886 | 16 039 | 16 185 | 15 866 | 15 574 |
-| Morocco                               | 4 167  | 3 832  | 3 489  | 3 776  | 3 785  | 3 726  | 3 736  |
-| Uruguay                               | 510    | 567    | 538    | 506    | 446    | 470    | 528    |
-|                                       |        |        |        |        |        |        |        |
-
-<sup>(</sup>a) Data as provided by the countries and not validated by IRTAD. (b) Real data (actual numbers instead of reported numbers by the police).
-
-|                 |                 |                 |                 | 2022 % change from |               | Annual average change |
-|-----------------|-----------------|-----------------|-----------------|--------------------|---------------|-----------------------|
-| 2019            | 2020            | 2021            | 2022            | av. 2017-19        | 2012          | 2012-22               |
-|                 |                 |                 |                 |                    |               |                       |
-| 4 898           | 3 513           | 4 481           | 4 567           | -14.4              | -10.0         | -1.0                  |
-| 1 187           | 1 097           | 1 116           | 1 188           | 0.5                | -8.5          | -0.9                  |
-| 416             | 344             | 362             | 370             | -10.4              | -30.3         | -3.5                  |
-| 644             | 499             | 516             | 540             | -12.8              | -34.7         | -4.2                  |
-| 1 756           | 1 746           | 1 768           | 1 934           | 4.4                | -6.8          | -0.7                  |
-| 1 973           | 1 794           | 2 052           | 2 137           | 9.5                | 8.0           | 0.8                   |
-| 6 577           | 5 447           | 7 238           | 8 030           | 22.2               | 50.9          | 4.2                   |
-| 787             | 570             | 707             | 786             | -4.1               | 20.0          | 1.8                   |
-| 617             | 517             | 531             | 527             | -14.6              | -29.0         | -3.4                  |
-| 199             | 163             | 130             | 154             | -15.2              | -7.8          | -0.8                  |
-| 211             | 223             | 225             | 189             | -17.6              | -25.9         | -3.0                  |
-| 3 244           | 2 541           | 2 944           | 3 267           | -1.4               | -10.6         | -1.1                  |
-| 3 046           | 2 719           | 2 562           | 2 788           | -12.0              | -22.6         | -2.5                  |
-| 688             | 584             | 624             | 641             | -9.2               | -35.1         | -4.2                  |
-| 602             | 460             | 544             | 535             | -13.7              | -11.6         | -1.2                  |
-| 6               | 8               | 9               | 9               | -32.5              | 0.0           | 0.0                   |
-| 140             | 146             | 136             | 155             | 8.6                | -4.9          | -0.5                  |
-| 355             | 305             | 364             | 351             | 1.7                | 21.0          | 1.9                   |
-| 3 173           | 2 395           | 2 875           | 3 159           | -4.1               | -15.8         | -1.7                  |
-| 3 920           | 3 416           | 3 205           | 3 216           | -22.9              | -38.9         | -4.8                  |
-| 3 349           | 3 081           | 2 916           | 2 735           | -27.5              | -49.3         | -6.6                  |
-| 186             | 175             | 148             | 120             | -34.5              | -60.1         | -8.8                  |
-| 22              | 26              | 24              | 36              | 30.1               | 5.9           | 0.6                   |
-| 661             | 610             | 582             | 745             | 14.5               | 14.6          | 1.4                   |
-| 350             | 317             | 318             | 375             | 1.7                | 21.8          | 2.0                   |
-| 108             | 93              | 80              | 116             | 8.1                | -20.0         | -2.2                  |
-| 2 909           | 2 491           | 2 245           | 1 896           | -33.9              | -46.9         | -6.1                  |
-| 688             | 536             | 561             | 618             | -6.8               | -13.9         | -1.5                  |
-| 534             | 492             | 521             | 553             | -0.1               | -19.6         | -2.2                  |
-| 102             | 80              | 114             | 85              | -14.1              | -34.6         | -4.2                  |
-| 1 755           | 1 370           | 1 533           | 1 759           | -2.1               | -7.6          | -0.8                  |
-| 221             | 204             | 210             | 227             | -14.6              | -20.4         | -2.2                  |
-| 187             | 227             | 200             | 241             | 11.2               | -28.9         | -3.4                  |
-| 1 808           | 1 516           | 1 608           | 1 766           | -3.7               | -2.0          | -0.2                  |
-| 36 355          | 39 007          | 42 939          | 42 795          | 16.0               | 26.7          | 2.4                   |
-|                 |                 |                 |                 |                    |               |                       |
-| 14 673<br>3 622 | 13 630<br>3 005 | 14 715<br>3 685 | 15 979<br>3 499 | 4.0<br>-5.3        | -6.6<br>-16.0 | -0.7<br>-1.7          |
-| 422             | 391             | 434             | 431             | -8.9               | -15.5         | -1.7                  |
-|                 |                 |                 |                 |                    |               |                       |
-
-Figure 11 illustrates the trends in road fatalities in IRTAD countries since 2012. It includes expected values for 2020 and 2021 had the trend continued without the Covid‑19 pandemic.
-
-For most of the countries there is some evidence that 2020 and 2021 were exceptional years, with values much lower than the expected trend. For a more in‑depth analysis of the reasons for this variation at the country level, see the individual country reports on the ITF website.
-
-2022 data confirm that 2020 and 2021 were exceptional years.
-
-**Road deaths compared to the linear trend since 2012 (excluding 2020 and 2021)**
-
-![](_page_37_Figure_4.jpeg)
-
-Road deaths Trend
-
-2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
-
-![](_page_37_Figure_5.jpeg)
-
-![](_page_37_Figure_6.jpeg)
-
-![](_page_37_Figure_7.jpeg)
-
-![](_page_38_Figure_0.jpeg)
-
-![](_page_38_Figure_1.jpeg)
-
-![](_page_38_Figure_2.jpeg)
-
-![](_page_38_Figure_3.jpeg)
-
-![](_page_38_Figure_4.jpeg)
-
-![](_page_38_Figure_5.jpeg)
-
-![](_page_38_Figure_6.jpeg)
-
-![](_page_38_Figure_7.jpeg)
-
-![](_page_38_Figure_8.jpeg)
-
-![](_page_38_Figure_9.jpeg)
-
-### Road deaths Trend
-
-![](_page_39_Figure_1.jpeg)
-
-![](_page_39_Figure_2.jpeg)
-
-![](_page_39_Figure_3.jpeg)
-
-![](_page_39_Figure_4.jpeg)
-
-![](_page_39_Figure_5.jpeg)
-
-![](_page_39_Figure_6.jpeg)
-
-![](_page_39_Figure_7.jpeg)
-
-![](_page_39_Figure_8.jpeg)
-
-![](_page_40_Figure_0.jpeg)
-
-![](_page_40_Figure_2.jpeg)
-
-![](_page_40_Figure_3.jpeg)
-
-![](_page_40_Figure_4.jpeg)
-
-![](_page_40_Figure_5.jpeg)
-
-![](_page_40_Figure_6.jpeg)
-
-![](_page_40_Figure_7.jpeg)
-
-![](_page_40_Figure_8.jpeg)
-
-![](_page_40_Figure_9.jpeg)
-
-### Road deaths Trend
-
-![](_page_41_Figure_1.jpeg)
-
-![](_page_41_Figure_2.jpeg)
-
-![](_page_41_Figure_3.jpeg)
-
-![](_page_41_Figure_4.jpeg)
-
-![](_page_41_Figure_5.jpeg)
-
-![](_page_41_Figure_6.jpeg)
-
-![](_page_41_Figure_7.jpeg)
-
-When looking at the trends for 2012‑22, without including 2020 and 2021, the trend in road deaths is going upward in six countries: Colombia, Costa Rica, Israel, the Netherlands, New Zealand and the United States.
-
-In some countries, such as Chile, Spain and the United Kingdom, the trend is plateauing, and except for the two exceptional years in 2020 and 2021, the number of road deaths did not show a clear decrease.
-
-The trend is downward in the rest of the countries, even if few countries reached the target of halving road deaths in the last decade.
-
-In six countries the long‑term trend in road deaths is ascending.
-
-### **Road deaths by user group**
-
-Disaggregated data by user category are available for 32 IRTAD countries (see Figure 12). For these countries, in 2022, road fatalities decreased by 14.1% compared to 2012. When looking at the evolution by user category, passenger car occupants and pedestrians recorded reductions of 26.1% and 27.3%, respectively. Cyclist fatalities decreased by 8.3% in 2022 compared to 2012, while motorcyclist fatalities recorded a strong increase of 19.9%.
-
-In the last 10 years safety for pedestrians improved significantly.
-
-![](_page_43_Figure_4.jpeg)
-
-Note: Data include Argentina, Austria, Belgium, Canada, Chile, Colombia, Costa Rica, Czechia, Denmark, Finland, France, Germany, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Lithuania, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Serbia, Slovenia, Spain, Sweden, Switzerland, United Kingdom.
-
-#### **Passenger car occupants**
-
-When considering all reporting countries, the number of passenger car occupants killed in road crashes decreased by 26.1% between 2012 and 2022. In four countries, such fatalities decreased by more than 50%: Korea (‑63.8%), Costa Rica (‑59.8%), Slovenia (‑56.5%) and Lithuania (‑52.8%) (see Figure 13). However, in five countries, road deaths among passenger car occupants increased. The biggest increase was recorded in Chile (+46.5%), where the safety risk for this group has not improved in recent years.
-
-![](_page_44_Figure_2.jpeg)
-
-Note: (a) Real data (actual numbers instead of reported numbers by the police).
-
-### **Pedestrians**
-
-The number of pedestrians killed in traffic decreased by 27.3% between 2012 and 2022 for the 32 countries with available data. Four countries recorded a reduction of more than 50%: Lithuania (‑71.3%), Norway (‑60.9%), Poland (‑60.2%) and Korea (‑54%) (Figure 14). Pedestrian fatalities increased in four countries, with Ireland recording the largest increase (48.3%), not taking in consideration Luxembourg for which small variations result in big growth rates.
-
-![](_page_45_Figure_2.jpeg)
-
-Note: (a) Real data (actual numbers instead of reported numbers by the police). Data from Iceland are not included in this figure, as percentage changes in small numbers distort trends.
-
-### **Cyclists**
-
-Overall, the number of cyclists killed in traffic decreased by 8.3% in 2022 compared to 2012. In New Zealand, however, fatalities among cyclists more than doubled, from 8 to 19 people killed.
-
-In Colombia, Argentina and Israel, cyclist fatalities increased by more than 50%. In France and the Netherlands, they increased by more than 40%.
-
-The situation is particularly worrying in the Netherlands, where cycling is common. In 2022, cyclists represented 40% of all road fatalities in this country.
-
-Fatalities among cyclists decreased in 19 out of 31 countries. Robust reductions were recorded in Lithuania (‑84.4%), Hungary (‑50.6%) and Norway (‑50%) (see Figure 15).
-
-![](_page_46_Figure_5.jpeg)
-
-Note: (a) Real data (actual numbers instead of reported numbers by the police). Data from Iceland and Luxembourg are not included in this figure, as percentage changes in small numbers distort trends.
-
-#### **Motorcyclists**
-
-Motorcyclists are the only category of road users which recorded an increase in fatalities between 2012 and 2022. Fatalities among motorcyclists more than doubled in Israel and Colombia.
-
-The data are very concerning, especially in Colombia, where the number of new registrations of motorcycles is constantly increasing. In two other Latin American countries, Costa Rica and Chile, motorcyclist fatalities increased by more than 50%.
-
-The reductions in road fatalities among motorcyclists were smaller than for the other categories. Five countries recorded a decrease of more than 30% between 2012 and 2022: Japan (‑44.7%), Poland (‑38.5%), Slovenia (‑31.8%), Serbia and Switzerland (‑30.8%) (see Figure 16).
-
-![](_page_47_Figure_4.jpeg)
-
-Note: (a) Real data (actual numbers instead of reported numbers by the police).
-
-### **Road deaths by age group**
-
-Disaggregated data by age group are available in 29 IRTAD counties. In 2022, road fatalities decreased by 15.5% compared to 2012 (see Figure 17). When looking at age cohorts, road fatalities decreased for all age groups, although at different paces.
-
-The biggest reductions were recorded for children and young people (‑36.8% and ‑29.7%, respectively). Road deaths decreased less for the senior population. People aged between 65 and 74 recorded a decrease of 10.1% between 2012 and 2022, while people over 75 recorded a decrease of 9.9% in road deaths.
-
-In the last ten 10 years, 36% less children died in road crashes.
-
-**Evolution in road deaths by age group, 2022 compared to 2012**
-
-![](_page_48_Figure_5.jpeg)
-
-Note: Data include Austria, Belgium, Chile, Colombia, Czechia, Denmark, Finland, France, Germany, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Lithuania, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Serbia, Slovenia, Spain, Sweden, Switzerland, United Kingdom.
-
-### **Road deaths by road type**
-
-Data disaggregated by road type are available in 23 IRTAD countries. Overall, in 2022, road fatalities decreased by 27% compared to 2012 (see Figure 18). Deaths on urban and rural roads decreased by 27.8% and 27.7%, respectively. The data confirm that rural roads are still the least‑safe roads in absolute number. In 2022, road deaths on motorways decreased by 15.5% compared to 2012.
-
-In the last 10 years, deaths on rural roads have decreased, but they are still the most dangerous road type.
-
-### **Evolution in road deaths by road type, 2022 compared to 2012**
-
-![](_page_49_Figure_5.jpeg)
-
-Note: Data include Austria, Belgium, Czechia, Denmark, Finland, France, Germany, Great Britain, Hungary, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, Netherlands, New Zealand, Poland, Portugal, Serbia, Slovenia, Sweden, Switzerland.
-
-Several countries have recently released new road safety strategies in response to the Global Plan for the Decade of Action for Road Safety 2021‑30. The Annex presents the current road safety strategies and targets.
-
-27 countries have either adopted a new strategy for 2030 or are in the process of preparing one. The texts of the national road safety strategies of 29 of the 34 countries surveyed explicitly mention the Safe System approach or Vision Zero.
-
-The ITF has long promoted the Safe System approach as the best way to improve road safety. This approach is based on the ethical perspective that no one should be killed or seriously injured in road traffic. The Safe System approach includes four principles (ITF, 2016), to which a recent ITF Working Group added a fifth (ITF, 2022b). The Safe System approach is also at the core of the Global Plan.
-
-Most countries have adopted targets to reduce the number of road deaths. Among the 34 countries surveyed, 20 have a target aligned with the UN goal to reduce by 50% the number of road deaths by 2030.
-
-The baseline for this target varies. The year 2020 would have been the natural baseline for the 2021‑2030 decade. However, due to the Covid‑19 pandemic, most countries' road deaths in 2020 were exceptionally low. Using 2020 data as the baseline would therefore make the 2030 target even more challenging.
-
-Most IRTAD countries have chosen either 2019 or the average for 2017‑19 as a baseline for their 2030 targets. Several countries have set specific targets for specific road users (focusing, for example, on children, pedestrians or cyclists).
-
-Eight countries (Australia, Belgium, Finland, Ireland, the Netherlands, New Zealand, Norway, and Spain) have explicitly referred to the long‑term target of zero road deaths by 2050 in their strategies. In 2020 the European Union adopted its "Road Safety Policy Framework 2021‑30" (EC, 2020), which aims to halve the number of fatalities and serious injuries on European roads by 2030. This aim acts as a milestone on the path towards zero fatalities and serious injuries by 2050.
-
-Reducing the number of people seriously injured in road traffic is at the core of the Safe System approach. Among the 34 countries surveyed, 22 have also set a target to reduce the number of people seriously injured in road crashes. This represents significant progress compared to the period coinciding with the First Decade of Action for Road Safety (2011‑20), when very few countries addressed the issue of serious injuries.
-
-A total of 14 of the countries surveyed have adopted a target of halving the number of serious injuries by 2030. Four countries have a slightly less ambitious reduction target of 20‑40%. Four countries set their targets in absolute numbers.
-
-Azzato, F. et al (2022), "Motorcycles in Latin America: current and recommended best practices for the protection of its users", Inter‑American Development Bank, https://publications.iadb.org/en/motorcycles-latinamerica-current-and-recommended-best-practices-protection-its-users.
-
-Dablanc, L. et al. (2022a), « Etude 2022 sur les livreurs des plateformes à Paris et en petite couronne » [2022 study on platform delivery people in Paris], Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux, https://hal.science/hal-03903591.
-
-Dablanc, L. et al. (2022b), « Enquête sur les travailleurs nantais des plateformes de livraison instantanée » [Survey of Nantes workers on instant delivery platforms], Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux, https://hal.science/hal-03897757.
-
-EC (2020), "European Union Road Safety Policy Framework 2021‑30 – Next steps towards 'Vision Zero'", European Commission, Directorate‑General for Mobility and Transport, Brussels, https://data.europa.eu/doi/10.2832/391271.
-
-Institut Paris Région (2022), « Le mass transit à l'heure du télétravail et de la sobriété énergétique » [Mass transit in the era of teleworking and energy sobriety], Note rapide Mobilité, n° 958, 9 October 2022, https://www. institutparisregion.fr/fileadmin/NewEtudes/000pack3/Etude\_2844/NR\_958\_ web.pdf.
-
-IRTAD (2023), IRTAD Road Safety Database, OECD Stats, https://stats.oecd. org/Index.aspx?DataSetCode=IRTAD\_CASUAL\_BY\_AGE.
-
-ITF (2022a), Declaration from the 7th IRTAD Conference, "Better Road Safety Data for Better Safety Outcomes", Lyon, 27‑28 September 2022, www.itf-oecd. org/7th-irtad-conference-better-road-safety-data-better-safety-outcomes.
-
-ITF (2022b), The Safe System Approach in Action, ITF Research Report, OECD Publishing, Paris, https://doi.org/10.1787/ad5d82f0-en.
-
-ITF (2016), Zero Road Deaths and Serious Injuries: Leading a Paradigm Shift to a Safe System, OECD Publishing, Paris, https://doi. org/10.1787/9789282108055-en.
-
-Rodríguez, D.A., M. Santana and C.F. Pardo (2015), "La motocicleta en América Latina: caracterización de su uso e impactos en la movilidad en cinco ciudades de la región" [The motorcycle in Latin America: Characterisation of its use and impacts on mobility in five cities in the region], Development Bank of Latin America and the Caribbean, Bogotá, https://scioteca.caf.com/ handle/123456789/754.
-
-WHO (2021), Global Plan for the Decade of Action for Road Safety 2021‑2030, 20 October 2021, www.who.int/publications/m/item/global-plan-for-thedecade-of-action-for-road-safety-2021-2030.
-
-This section summarises the national data on prevailing speed limits for passenger cars, maximum authorised blood alcohol content levels, and legislation regarding seat belt and helmet use, as well as statistics on their usage. Detailed country profiles with data on deaths and injuries, crash risk exposure and road safety policies are available at www.itf-oecd.org/irtad.
-
-### **National speed limits on urban roads, rural roads and motorways, 2023 passenger vehicles (km/h)**
-
-| Country                   | Urban areas                                                                                                                             | Rural roads                                                                         | Motorways                                                                                                                                                    |
-|---------------------------|-----------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Argentina                 | 40‑60 (Buenos Aires City has a range of 20<br>to 70 km/h)                                                                               | 110                                                                                 | 120–130                                                                                                                                                      |
-| Australia                 | 50 (default)<br>60‑80 (arterial roads - increasing use of 40<br>km/h or lower limits in urban areas with<br>high pedestrian activities) | 100, 110                                                                            | 100 km/h default although<br>often set to 110 km/h<br>(130 km/h in the Northern<br>Territory)                                                                |
-| Austria                   | 50                                                                                                                                      | 100                                                                                 | 130                                                                                                                                                          |
-| Belgium                   | 30‑50<br>20 for the "living streets" regime                                                                                             | 70‑90                                                                               | 120                                                                                                                                                          |
-| Bosnia and<br>Herzegovina | 50                                                                                                                                      | 80, 100                                                                             | 130                                                                                                                                                          |
-| Cambodia                  | 30‑40 (motorcycles, tricycles)<br>40 (passenger cars, trucks)                                                                           | 60‑70 (motorcycles)<br>90                                                           | No motorways                                                                                                                                                 |
-| Canada                    | 40‑70                                                                                                                                   | 80–90                                                                               | 100–110                                                                                                                                                      |
-| Chile                     | 50 (maximum default limit but can vary<br>according to the type of road)<br>30 (school zones)                                           | 90 (rural buses, trucks and school<br>transport)<br>100 (cars and interurban buses) | 120 (maximum default<br>speed limit but can vary in<br>some sections of the road,<br>according to the type of road<br>can be lowered to 100)                 |
-| Colombia                  | 50                                                                                                                                      | 90                                                                                  | 120                                                                                                                                                          |
-| Costa Rica                | 40 (except when there is a 50 sign)                                                                                                     | 40‑100 (60 when there is no signs)                                                  | No motorways                                                                                                                                                 |
-| Czechia                   | 50                                                                                                                                      | 90                                                                                  | 130                                                                                                                                                          |
-| Denmark                   | 50 (sections with 30, 40 or 60)                                                                                                         | 80 (sections with 60, 70 or 90)                                                     | 130 (110 for a large part of<br>the motorway network)                                                                                                        |
-| Finland                   | 30‑60                                                                                                                                   | 80, 100                                                                             | 100, 120                                                                                                                                                     |
-| France                    | 50 by default<br>30 (some urban areas)<br>70 (exceptionally and under certain<br>conditions)                                            | 80 or 90 (90 on dedicated passing slots),<br>110 on dual carriageways               | 130 (110 in wet weather and<br>for novice drivers)                                                                                                           |
-| Germany                   | 50                                                                                                                                      | 100                                                                                 | None (130 recommended)                                                                                                                                       |
-| Greece                    | 50                                                                                                                                      | 90                                                                                  | 130                                                                                                                                                          |
-| Hungary                   | 50 (sections with 30, 40, 60 and 70)                                                                                                    | 90                                                                                  | 130 (110 on "motor roads")                                                                                                                                   |
-| Iceland                   | 50                                                                                                                                      | 90 (paved roads)<br>80 (gravel roads)                                               | n.a.                                                                                                                                                         |
-| Ireland                   | <=60 (can be 60 on arterial roads, 30 in<br>built up areas)                                                                             | 80, 100                                                                             | 120                                                                                                                                                          |
-| Israel                    | 30‑ 50<br>70 (arterial roads)                                                                                                           | 80, 90                                                                              | 100, 110, 120                                                                                                                                                |
-| Italy                     | 50                                                                                                                                      | 70‑90 (110 on some main dual<br>carriageways)                                       | 130 (110 km/h in wet weather,<br>100 for novice drivers.<br>Motorway operator may<br>increase speed limit up to<br>150 if stringent requirements<br>are met) |
-
-| Country        | Urban areas                                                                                                                                                                                                                 | Rural roads                                                                                                                                                    | Motorways                                                                                                              |
-|----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|
-| Japan          | 40, 50, 60                                                                                                                                                                                                                  | 50, 60                                                                                                                                                         | 100                                                                                                                    |
-| Korea          | 50                                                                                                                                                                                                                          | 60‑80                                                                                                                                                          | 110 (100 in urban areas)                                                                                               |
-| Lithuania      | 50                                                                                                                                                                                                                          | 90 (70 on gravel roads and for novice<br>drivers)                                                                                                              | 120,130 (110 in winter, 90 for<br>novice drivers)                                                                      |
-| Luxembourg     | 50                                                                                                                                                                                                                          | 90                                                                                                                                                             | 130 (110 in wet weather)                                                                                               |
-| Mexico         | 10‑80 (20 in school zones, 30 on secondary<br>and tertiary streets, 50 on primary avenues<br>without controlled access, 80 in central<br>lanes of controlled access avenues and 50<br>on state highways within urban areas) | 60‑110 (60 on collector road, 80 on state<br>highways outside urban areas; 50 within<br>urban areas; 110 on roads and motorways<br>under federal jurisdiction) | 110 (110 for car, 95 for buses<br>and 80 for freight transport<br>on roads and highways under<br>federal jurisdiction) |
-| Moldova        | 50<br>30 in school zones, near hospitals, parks<br>and historical centre<br>5 in pedestrian areas                                                                                                                           | 90                                                                                                                                                             | No motorways                                                                                                           |
-| Morocco        | 60 (30 in residential area)                                                                                                                                                                                                 | 70, 80, 90, 100 (depending on vehicle type)                                                                                                                    | 120 (maximum speed, it<br>varies by vehicle type)                                                                      |
-| Netherlands    | 30‑50                                                                                                                                                                                                                       | 60‑80                                                                                                                                                          | 100 between 6:00 and 19:00<br>100, 120, or 130 between<br>19:00 and 06:00                                              |
-| New Zealand    | 50 (sections may have higher or lower<br>limits)                                                                                                                                                                            | 100 (specific sections may have lower<br>limits)                                                                                                               | 100 (specific sections may<br>have limits of 110)                                                                      |
-| Norway         | 50 (30 on residential streets)                                                                                                                                                                                              | 80 (70 on roads with high risk and 90 on<br>roads with very low traffic volumes)                                                                               | 90,100,110                                                                                                             |
-| Poland         | 50                                                                                                                                                                                                                          | 90, 100 (120 on expressways)                                                                                                                                   | 140 (120 on expresways)                                                                                                |
-| Portugal       | 50                                                                                                                                                                                                                          | 90                                                                                                                                                             | 120                                                                                                                    |
-| Serbia         | 50                                                                                                                                                                                                                          | 80, 100                                                                                                                                                        | 130                                                                                                                    |
-| Slovenia       | 50                                                                                                                                                                                                                          | 90                                                                                                                                                             | 130 (110 on expressways)                                                                                               |
-| South Africa   | 60                                                                                                                                                                                                                          | 100                                                                                                                                                            | 120                                                                                                                    |
-| Spain          | 20 (streets with a single carriageway and<br>sidewalk platform)<br>30 (single lane streets in each direction)<br>50 (streets with two or more lanes in each<br>direction)                                                   | 90                                                                                                                                                             | 120                                                                                                                    |
-| Sweden         | 30, 40, 50                                                                                                                                                                                                                  | 60,70,80,90,100                                                                                                                                                | 110,120                                                                                                                |
-| Switzerland    | 50 (sections with 30)                                                                                                                                                                                                       | 80                                                                                                                                                             | 120 (100 on expressways)                                                                                               |
-| United Kingdom | 48 (30 mph) (20 mph in Wales)                                                                                                                                                                                               | 96, 113 (60, 70 mph)                                                                                                                                           | 113 (70 mph)                                                                                                           |
-| United States  | Set by each state                                                                                                                                                                                                           | Set by each state                                                                                                                                              | 88‑129 (55‑80 mph, set by<br>each state)                                                                               |
-
-#### **Maximum authorised blood alcohol content levels, 2023, by country**
-
-| Country                | General BAC level (g/l)                                                                                                   | Differentiated BAC level (g/l)                                                                                                                                                                      |
-|------------------------|---------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Argentina              | 0.0                                                                                                                       | 0.0 for professional drivers<br>0.0 for motorcycle and moped riders                                                                                                                                 |
-| Australia              | 0.5                                                                                                                       | 0.0 for novice drivers<br>0.2 for professional drivers                                                                                                                                              |
-| Austria                | 0.5                                                                                                                       | 0.1 for moped drivers under 20; novice drivers (first three years),<br>truck (>7.5 tons) and bus (>9 seats) drivers                                                                                 |
-| Belgium                | 0.5                                                                                                                       | 0.2 for professional drivers (since January 2015)                                                                                                                                                   |
-| Bosnia and Herzegovina | 0.3                                                                                                                       | 0.0 for professional drivers, novice drivers, drivers who perform<br>public transport, driving instructors, driving candidates, drivers<br>under 21 or with less than 3 years of driving experience |
-| Cambodia               | 0.5                                                                                                                       |                                                                                                                                                                                                     |
-| Canada                 | 0.8                                                                                                                       | administrative maximum level of 0.5 g/l or 0.4 g/l in most<br>provinces<br>0.0 g/l administrative maximum level for novice and young<br>(under 21) drivers in most provinces                        |
-| Chile                  | 0.3                                                                                                                       |                                                                                                                                                                                                     |
-| Colombia               | 0.2                                                                                                                       |                                                                                                                                                                                                     |
-| Costa Rica             | 0.5                                                                                                                       | 0.2 for novice and professional drivers                                                                                                                                                             |
-| Czechia                | 0.0                                                                                                                       |                                                                                                                                                                                                     |
-| Denmark                | 0.5                                                                                                                       |                                                                                                                                                                                                     |
-| Finland                | 0.5                                                                                                                       |                                                                                                                                                                                                     |
-| France                 | 0.5                                                                                                                       | 0.2 for bus/coach drivers, novice drivers                                                                                                                                                           |
-| Germany                | 0.5 (Drivers with a BAC between 0.3‑0.5 g/l<br>can have their licenses suspended if their<br>driving ability is impaired) | 0.0 for drivers under 21 and novice drivers, for professional<br>drivers who transport passengers or hazardous goods                                                                                |
-| Greece                 | 0.5                                                                                                                       | 0.2 for professional drivers, novice drivers, motorcycles and<br>moped riders                                                                                                                       |
-| Hungary                | 0.0                                                                                                                       |                                                                                                                                                                                                     |
-| Iceland                | 0.5                                                                                                                       |                                                                                                                                                                                                     |
-| Ireland                | 0.5                                                                                                                       | 0.2 for learner, novice and professional drivers                                                                                                                                                    |
-| Israel                 | 0.5                                                                                                                       | 0.1 for young (under 24), novice and professional drivers                                                                                                                                           |
-| Italy                  | 0.5                                                                                                                       | 0.0 for young (under 21), novice and professional drivers                                                                                                                                           |
-| Japan                  | 0.3                                                                                                                       |                                                                                                                                                                                                     |
-| Korea                  | 0.3                                                                                                                       |                                                                                                                                                                                                     |
-| Lithuania              | 0.4                                                                                                                       | 0.0 for novice, professional, moped and motorcycle drivers                                                                                                                                          |
-| Luxembourg             | 0.5                                                                                                                       | 0.2 for novice and professional drivers                                                                                                                                                             |
-| Malaysia               | 0.8                                                                                                                       |                                                                                                                                                                                                     |
-| Mexico                 | 0.5                                                                                                                       | 0.0 for professional drivers<br>0.2 for motorcycle drivers                                                                                                                                          |
-
-| Country        | General BAC level (g/l)                | Differentiated BAC level (g/l)                                                        |
-|----------------|----------------------------------------|---------------------------------------------------------------------------------------|
-| Moldova        | 0.3                                    |                                                                                       |
-| Morocco        | 0.2                                    |                                                                                       |
-| Netherlands    | 0.5 (including cyclists)               | 0.2 for novice drivers (first five years) and professional drivers                    |
-| New Zealand    | 0.5                                    | 0.0 for drivers under 20 years                                                        |
-| Nigeria        | 0.5                                    | 0.2 for novice and 0.0 g/l for professional drivers                                   |
-| Norway         | 0.2                                    |                                                                                       |
-| Poland         | 0.2                                    |                                                                                       |
-| Portugal       | 0.5                                    | 0.2 for novice (first three years) and professional drivers (since 1<br>January 2014) |
-| Serbia         | 0.2                                    | 0.0 for novice and professional drivers and for PTW operators                         |
-| Slovenia       | 0.5                                    | 0.0 for novice (first three years) and professional drivers                           |
-| South Africa   | 0.5                                    | 0.2 for professional drivers                                                          |
-| Spain          | 0.5                                    | 0.3 for novice and professional drivers<br>0.0 for drivers under 18                   |
-| Sweden         | 0.2                                    |                                                                                       |
-| Switzerland    | 0.5                                    | 0.0 for novice (first three years) and professional drivers                           |
-| United Kingdom | 0.8 (England, Wales, Northern Ireland) |                                                                                       |
-| United States  | 0.5 (Scotland)                         | 0.4 for professional drivers<br>0.0 to 0.2 for drivers < 21                           |
-
-### **Seat-belt laws and wearing rates in front and rear seats of passenger cars, 2022 or latest available year**
-
-|                        |                                                            | Front seats                                                                    |                                                                                     | Rear seats                  |
-|------------------------|------------------------------------------------------------|--------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|-----------------------------|
-| Country                | Date of<br>application                                     | Wearing rate (%)<br>in 2022                                                    | Date of<br>application                                                              | Wearing rate (%)<br>in 2022 |
-| Argentina              | 1995                                                       | 57 driver (urban areas)                                                        | 1995                                                                                | 13 (urban areas)            |
-| Australia              | 1970s                                                      | 97 (2018)                                                                      | 1970s                                                                               | 96 (2019)                   |
-| Austria                | 1984                                                       | 98 drivers, 99 passengers                                                      | 1990                                                                                | 93                          |
-| Belgium                | 1975                                                       | 94 drivers and 92 passengers                                                   | 1991                                                                                | 79                          |
-| Bosnia and Herzegovina | 2006                                                       |                                                                                | 2006                                                                                |                             |
-| Cambodia               | 2007                                                       | 28 (2016)                                                                      | Law in preparation                                                                  |                             |
-| Canada                 | 1976‑1988                                                  | 97.5 (2017)                                                                    | 1976‑1988                                                                           | 95 (2015)                   |
-| Chile                  | 1985                                                       | 86 drivers, 72 passengers (2021)                                               | 2002 (for vehicles<br>manufactured from<br>2002)                                    | 21 (2021)                   |
-| Colombia               | 2002                                                       | 67 drivers; 49 passengers (2022)                                               | 2004                                                                                | No official data            |
-| Costa Rica             | 2020                                                       | 71 drivers, 63 passengers (2020,<br>national roads)                            | 2020                                                                                | 36 (2020)                   |
-| Czechia                | 1966                                                       | 94.9 (2023)                                                                    | 1975                                                                                | 87.8 (2023)                 |
-| Denmark                | 1970s                                                      | 98                                                                             | 1980s                                                                               | 93                          |
-| Finland                | 1975                                                       | 97                                                                             | 1987                                                                                | 90                          |
-| France                 | 1973 (rural), 1975 (urban<br>by night)<br>1979 (all times) | 99.3 outside built up areas<br>99.4 for small cities,<br>99.7 for major cities | 1991                                                                                | 90                          |
-| Germany                | 1976                                                       | 99 drivers, 98 passengers                                                      | 1984                                                                                | 95                          |
-| Greece                 | 1979                                                       | 72 passengers                                                                  | 1993                                                                                | 56                          |
-| Hungary                | 1976                                                       | 88 drivers, 87 passengers                                                      | 1993 outside built up<br>areas<br>2001 inside built up<br>areas                     | 57                          |
-| Iceland                |                                                            | 97 drivers                                                                     |                                                                                     | 93                          |
-| Ireland                | 1979                                                       | 99 drivers, 98 passengers (2021)                                               | 1992                                                                                | 93 (2021)                   |
-| Israel                 | 1975                                                       | 93 drivers, 91 passengers (2019)                                               | 1995                                                                                | 71 (2019)                   |
-| Italy                  | 1988                                                       | 87.4 drivers, 84 passengers                                                    | 1994                                                                                | 34.5                        |
-| Japan                  | 1985                                                       | 99 drivers, 97 passengers                                                      | 2008                                                                                | 43                          |
-| Korea                  | 1990                                                       | 85 drivers, 86 passengers                                                      | 2008, on motorways<br>only<br>Since September 2018,<br>on the whole road<br>network | 32                          |
-| Lithuania              |                                                            | 98 (2021)                                                                      |                                                                                     | 62 (2021)                   |
-| Luxembourg             | 1975                                                       | 90 (2015)                                                                      | 1992                                                                                | 76 (2015)                   |
-| Malaysia               | 1978                                                       | 87 drivers, 74 pass. (2016)                                                    | 2009                                                                                | 15 (2016)                   |
-
-|                |                                                                                                                   | Front seats                                                                                                                                  | Rear seats                                        |                                |  |
-|----------------|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------|--------------------------------|--|
-| Country        | Date of<br>application                                                                                            | Wearing rate (%)<br>in 2022                                                                                                                  | Date of<br>application                            | Wearing rate (%)<br>in 2022    |  |
-| Mexico         | 2022 (new law)                                                                                                    | 79 drivers, 65 general (2017)                                                                                                                | 2022 (new law)                                    | 46 (2017)                      |  |
-| Moldova        | 2009                                                                                                              | No official data<br>>90 (estimation)                                                                                                         | 2009                                              | No official data               |  |
-| Morocco        | 1977 – rural areas<br>2005 – urban areas                                                                          | 57 passengers                                                                                                                                | 2005 – rural areas                                | 36 (2018)                      |  |
-| Netherlands    | 1975                                                                                                              | 95                                                                                                                                           | 1992                                              |                                |  |
-| New Zealand    | 1972                                                                                                              | 97 drivers, 96 passengers (2016)                                                                                                             | 1979                                              | 92 (2014)                      |  |
-| Nigeria        | 1997 (enforced since<br>2002)                                                                                     | 85 (2017)                                                                                                                                    | 1997 (enforced since<br>2016)                     | 3 (2017)                       |  |
-| Norway         | 1975                                                                                                              | 98.1 drivers in rural areas,<br>97.7 drivers outside rural areas,<br>97 passengers in rural areas,<br>96.6 passengers outside rural<br>areas | 1985                                              | 96 (2014)                      |  |
-| Poland         | 1983                                                                                                              | 96                                                                                                                                           | 1991                                              | 90                             |  |
-| Portugal       | 1978                                                                                                              | 96 drivers and passengers<br>(2017)                                                                                                          | 1994                                              | 77 (2017)                      |  |
-| Serbia         | 1982                                                                                                              | 86 drivers, 81 passengers                                                                                                                    | 2009                                              | 19                             |  |
-| Slovenia       | 1977                                                                                                              | 95 drivers, 96 passengers (2018)                                                                                                             | 1998                                              | 78 adults (2018)               |  |
-| South Africa   | 2005, vehicles registered<br>after 1 January 2006                                                                 | 4.5 drivers, 5 passengers<br>(estimation 2010)                                                                                               | 2005, vehicles registered<br>after 1 January 2006 |                                |  |
-| Spain          | 1974 outside urban<br>areas<br>1992 inside urban areas                                                            | 96 driver, 95.9 passengers (2021)                                                                                                            | 1992                                              | 92.8 (2021)                    |  |
-| Sweden         | 1975                                                                                                              | 96 drivers (2021)                                                                                                                            | 1986; child restraint<br>since 1988               | 94 (2017)                      |  |
-| Switzerland    | 1981                                                                                                              | 95                                                                                                                                           | 1994                                              | 88                             |  |
-| United Kingdom | 1983                                                                                                              | 97 drivers, 97 passengers (2021<br>for Great Britain)                                                                                        | 1989 (children);<br>1991 (adults)                 | 92 (2021 for Great<br>Britain) |  |
-| United States  | Primary law in 34 states<br>and D.C., secondary<br>law in 15 states. Not<br>mandatory for adults in<br>one state. | 92                                                                                                                                           | Varies by State                                   | 76 (25‑69 year‑old)            |  |
-
-### **Helmet laws and wearing rates, 2022 or latest available year**
-
-|                        | Powered two-wheelers                                                                                                                |                                                                         | Cyclists                                                                                                         |                                                                                                              |  |
-|------------------------|-------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|--|
-| Country                | Helmet law                                                                                                                          | Wearing rate (%)<br>in 2022                                             | Helmet law                                                                                                       | Wearing rate (%) in<br>2022                                                                                  |  |
-| Argentina              | Yes                                                                                                                                 | 58 riders, 34 first pass.,<br>18 additional passengers<br>(urban areas) | Yes                                                                                                              | 8 (2018)                                                                                                     |  |
-| Australia              | Yes                                                                                                                                 | 99 riders (2018)                                                        | Yes                                                                                                              |                                                                                                              |  |
-| Austria                | Yes                                                                                                                                 | 100                                                                     | Yes, for children to age 12                                                                                      | 40 (87 for children)                                                                                         |  |
-| Belgium                | Yes                                                                                                                                 | 99.7                                                                    | No                                                                                                               | 24.8%                                                                                                        |  |
-| Bosnia and Herzegovina | Yes                                                                                                                                 |                                                                         | Yes                                                                                                              |                                                                                                              |  |
-| Cambodia               | Yes, motorcycles from 50 cc,<br>motorcycles with trailers,<br>motorised tricycles (riders<br>and passengers)                        | Low (no precise data)                                                   | No                                                                                                               |                                                                                                              |  |
-| Canada                 | Yes                                                                                                                                 |                                                                         | In some jurisdictions                                                                                            |                                                                                                              |  |
-| Chile                  | Yes                                                                                                                                 | 95 riders, 87 passengers<br>(2021)                                      | Yes in urban areas                                                                                               | 67.3 (2019)                                                                                                  |  |
-| Colombia               | Yes                                                                                                                                 | 79.2 motorcycle riders,<br>52.7 passengers (urban<br>areas)             | Yes, for children to age 18                                                                                      | 22.4 (urban areas)<br>(2022)                                                                                 |  |
-| Costa Rica             | Yes                                                                                                                                 | 97.2 riders, 90.1<br>passengers (2020)                                  | No                                                                                                               |                                                                                                              |  |
-| Czechia                | Yes                                                                                                                                 | 100 (2023)                                                              | Yes, for children to age 18                                                                                      | 87.8 (2023)                                                                                                  |  |
-| Denmark                | Yes                                                                                                                                 | 100 motorcycles<br>96.6 light mopeds (urban<br>areas)                   | No                                                                                                               | 50 (urban areas)                                                                                             |  |
-| Finland                | Yes                                                                                                                                 | 99.7 (2019)                                                             | No                                                                                                               | 54                                                                                                           |  |
-| France                 | Yes, since 1973 for<br>motorcyclists<br>1976 for moped riders<br>outside built up areas<br>1980 for moped roders in<br>urban areas  | 97 outside built up areas<br>98 in urban areas                          | Yes, for children under 12                                                                                       | Major cities: 34 weekdays,<br>33 weekends (2021)                                                             |  |
-| Germany                | Yes                                                                                                                                 | 98.5 riders, 98.9<br>passengers (inside urban<br>areas)                 | No                                                                                                               | 40.3 (inside urban areas<br>including sport bicycles)<br>34 (inside urban areas<br>excluding sport bicycles) |  |
-| Greece                 | Yes, since 1977                                                                                                                     | 80.3 riders, 65.5<br>passengers                                         | No                                                                                                               |                                                                                                              |  |
-| Hungary                | Yes since 1965 for<br>motorcyclists,<br>1997 for moped riders<br>outside built up areas<br>1998 for moped riders in<br>urban areas. | 99 Budapest area (2019)<br>97 Rural areas (2019)                        | No                                                                                                               | 18 Budapest area (2019)<br>4.5 Rural areas (2019)                                                            |  |
-| Iceland                | Yes                                                                                                                                 | n.a.                                                                    | Yes, for children to age 14                                                                                      |                                                                                                              |  |
-| Ireland                | Yes, since 1978                                                                                                                     | 99.8 (2021)                                                             | No                                                                                                               | 53                                                                                                           |  |
-| Israel                 | Yes                                                                                                                                 | n.a.                                                                    | Yes. Mandatory for all<br>ages in non-urban roads.<br>Mandatory for cyclists<br>under 18 years in urban<br>roads | 21% (2015 observational<br>survey among cyclists on<br>urban roads)                                          |  |
-| Italy                  | Yes, for all since 2000<br>Since 1986 for motorcyclists<br>and riders of moped under 18                                             | 96.5                                                                    | No                                                                                                               |                                                                                                              |  |
-| Japan                  | Yes                                                                                                                                 | 100 (2021)                                                              | Yes, since 2023                                                                                                  | 13.5                                                                                                         |  |
-|                        |                                                                                                                                     |                                                                         |                                                                                                                  |                                                                                                              |  |
-
-|                | Powered two-wheelers                                                                                                                                                                                        |                                                                                                                | Cyclists                                                                                                 |                                                             |  |
-|----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|--|
-| Country        | Helmet law                                                                                                                                                                                                  | Wearing rate (%)<br>in 2022                                                                                    | Helmet law                                                                                               | Wearing rate (%) in<br>2022                                 |  |
-| Korea          | Yes                                                                                                                                                                                                         | 93 (2021)                                                                                                      | No                                                                                                       | 94                                                          |  |
-| Lithuania      | Yes                                                                                                                                                                                                         |                                                                                                                | Yes, for children to age 18                                                                              |                                                             |  |
-| Luxembourg     | Yes, since 1976                                                                                                                                                                                             | 100 (2021e)                                                                                                    | No                                                                                                       |                                                             |  |
-| Malaysia       | Yes, since 1973                                                                                                                                                                                             | c. 77 (2015)                                                                                                   | No                                                                                                       |                                                             |  |
-| Mexico         | Yes                                                                                                                                                                                                         | 89 riders, 82 passengers<br>(2021)                                                                             | Yes on federal roads since<br>2012                                                                       | 11 (2017)                                                   |  |
-| Moldova        | Yes                                                                                                                                                                                                         | No national data                                                                                               | Yes on road sections with a<br>speed limit above 50 km/h                                                 |                                                             |  |
-| Morocco        | Yes, since 1976                                                                                                                                                                                             | 57 riders, 31 passengers                                                                                       | No                                                                                                       |                                                             |  |
-| Netherlands    | Yes, motorcycles since 1972;<br>mopeds since 1975. Not<br>compulsory on slow mopeds<br>(max. 25 km/h) until 2022<br>As of 1 Jan 2023 all riders of<br>slow-mopeds (speed max<br>25 km./h must wear a helmet | 99 mopeds                                                                                                      | No                                                                                                       | 3% bikes, 8% e-bikes                                        |  |
-| New Zealand    | Yes, since 1956 when<br>travelling above 30 mph<br>Since 1973 at all speeds                                                                                                                                 | 100 (2021)                                                                                                     | Yes, since 1994                                                                                          | 94 (2015)                                                   |  |
-| Norway         | Yes                                                                                                                                                                                                         | 100 (2021)                                                                                                     | No                                                                                                       | 67.1 (all age groups)<br>66.7 (above 12)<br>74.5 (below 12) |  |
-| Poland         | Yes, since 1997                                                                                                                                                                                             | 100                                                                                                            | No                                                                                                       | 25                                                          |  |
-| Portugal       | Yes                                                                                                                                                                                                         | Motorcyclists: 97.6 riders,<br>100 passengers<br>Mopeds: 94 riders, 92<br>passengers<br>(2013)                 | No                                                                                                       |                                                             |  |
-| Serbia         | Yes                                                                                                                                                                                                         | Motorcyclists: 87.7 riders,<br>80.3 passengers<br>Mopeds: 69.2 riders, 70.7<br>passengers                      | No                                                                                                       | 3.7                                                         |  |
-| Slovenia       | Yes                                                                                                                                                                                                         | n.a.                                                                                                           | Yes, for children and<br>youngster under 18                                                              | 21<br>67 (children)<br>27 (young)<br>(2022)                 |  |
-| Spain          | Yes                                                                                                                                                                                                         | 99.4 riders, 96.2<br>passengers<br>99.3 in urban roads<br>(2021)<br>100 in motorways and<br>rural roads (2021) | Yes.<br>Mandatory on non-urban<br>roads for all.<br>Mandatory on urban rods<br>only for cyclists under 6 | 33 in urban roads<br>89.8 in rural roads                    |  |
-| Sweden         | Yes                                                                                                                                                                                                         | 98 for mopeds (2021)                                                                                           | Yes, for children to age 15<br>(since 2015)                                                              | 46 for all age groups<br>64 for children<br>42 for adults   |  |
-| Switzerland    | Yes, motorcycles since 1981;<br>mopeds since 1990                                                                                                                                                           | 100 motorcycles<br>95 mopeds                                                                                   | No for regular bicycles<br>Yes for e-bikes > 25km/h                                                      | 56 cyclists<br>68 e-bikes <25km/h<br>91 e-bikes >25km/h     |  |
-| United Kingdom | Yes, motorcycles 1973;<br>mopeds since 1977                                                                                                                                                                 |                                                                                                                | No                                                                                                       |                                                             |  |
-| United States  | No national law.18 states, D.C.<br>and PR require helmet use<br>by all, 29 by specific users, 3<br>have no helmet law.                                                                                      | 65 use of DOT-compliant<br>helmets                                                                             | Age-specific helmet laws in<br>21 states and D.C.                                                        |                                                             |  |
-
-| Annex. Road safety strategies and targets in IRTAD countries |  |  |  |  |  |  |  |  |
-|--------------------------------------------------------------|--|--|--|--|--|--|--|--|
-|--------------------------------------------------------------|--|--|--|--|--|--|--|--|
-
-This Annex details national road safety strategies (Table A1) and national targets on road deaths and serious injuries (Table A2).
-
-#### **Road safety strategies in IRTAD countries**
-
-| Country                | Strategy                                                                                                                                                                                                                                                                                                                                                                         |
-|------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Australia              | The Australian National Road Safety Strategy 2021-30 was adopted in 2021 following consultation and<br>review. The strategy continues Australia's commitment to the Safe System approach.<br>The Australian National Road Safety Action Plan 2023-25 sets out the key actions all governments will undertake<br>to 2025.<br>Link:<br>https://www.roadsafety.gov.au/nrss          |
-| Austria                | The Austrian Road Safety Strategy 2021-2030 refers to the Safe System.<br>Link:<br>https://www.bmk.gv.at/en/topics/transport/roads/safety/vss2030.html                                                                                                                                                                                                                           |
-| Belgium                | Belgium's federal road safety plan, the Plan Fédéral de Sécurité Routière 2021-25, is based on Vision Zero.<br>There are also three regional plans and a federal strategy, known as "All for Zero".<br>Links:<br>https://all-for-zero.be/storage/minisites/plan-federal-securite-routiere.pdf<br>https://all-for-zero.be/fr/all-for-zero/                                        |
-| Bosnia and Herzegovina | The Framework Road Safety Strategy Development for Bosnia and Herzegovina (2024-2028) is under<br>preparation and has not yet been published. The strategy's vision is the Road To Zero.                                                                                                                                                                                         |
-| Canada                 | Canada's Road Safety Strategy 2025 (RSS 2025) was first published in 2016 and adopts the Safe System<br>approach.<br>Canada also has a long-term vision of zero fatalities and serious injuries on the roads (Vision Zero).<br>Link:<br>http://roadsafetystrategy.ca/en/                                                                                                         |
-| Chile                  | Chile's Estrategia Nacional de Seguridad de Tránsito [National Road Safety Strategy] for 2021-30 was<br>published in December 2020. It specifically refers to the Safe System and Vision Zero ("Vision Zero for Chile").<br>Link:<br>https://conaset.cl/wp-content/uploads/2021/05/Estrategia-Nacional-de-Seguridad-de-Tránsito_2021-2030.pdf                                    |
-| Colombia               | Colombia's National Road Safety Strategy 2022-31 was adopted in July 2022. It officially adopted the Safe<br>System approach.<br>Link:<br>https://www.ansv.gov.co                                                                                                                                                                                                                |
-| Czechia                | Czechia's national road safety strategy for 2021-30 is titled Road Safety is Everyone's Right and<br>Responsibility. Both Vision Zero and the Safe System approach are at its core.<br>Link:<br>https://besip.cz/getattachment/Pro-odborniky/Narodni-strategie-BESIP/Aktualni-strategie/Czech-Road-Traffic<br>Safety-Strategy-2021-30_11-11.pdf                                  |
-| Denmark                | Denmark has adopted the 2021-2030 Action Plan. The plan does not refer to Vision Zero or the Safe System.<br>The current plan's vision is "Every accident is one too many", which dates back to earlier action plans created by<br>the Commission.<br>Link:<br>https://www.faerdselssikkerhedskommissionen.dk/media/eymfxr0n/fsk_resume_handlingsplaneng_2021-2030_<br>final.pdf |
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-| Country | Strategy                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Finland | Finland's traffic safety strategy for 2022-2026 was published in March 2022 and was accompanied by a<br>government resolution on traffic safety. It refers to both Vision Zero and the Safe System.<br>Link:<br>https://www.fintraffic.fi/en/fintraffic/strategy-2022-2026                                                                                                                                                                                                                                                                                                                                  |
-| France  | In France, the road safety strategy is announced with each meeting of the Interministerial Road Safety<br>Committee. The last one was held on 17 July 2023.<br>Link:<br>https://www.onisr.securite-routiere.gouv.fr/en/road-safety-policy/interministerial-road-safety-committees                                                                                                                                                                                                                                                                                                                           |
-| Germany | The German road safety strategy (known as the Road Safety Pact) covers the period 2021-30. It refers to the<br>Safe System.<br>Link:<br>https://www.bmvi.de/SharedDocs/DE/Anlage/StV/road-safety-pact-en.pdf?blob=publicationFile                                                                                                                                                                                                                                                                                                                                                                           |
-| Greece  | Greece's National Road Safety Strategic Plan covers the period 2021-2030. It refers to both the Safe System<br>approach and Vision Zero.<br>Links:<br>https://www.nrso.ntua.gr/nrss2030/?lang=en<br>https://www.nrso.ntua.gr/nrss2030/wp-content/uploads/2022/10/NationalRoadSafetyStrategicPlan-eng.pdf                                                                                                                                                                                                                                                                                                    |
-| Hungary | In Hungary, road safety strategies are prepared for three-year periods. The current Road Safety Action Plan<br>covers the period 2023-25. It is built on the concept of Vision Zero and the Safe System approach.<br>Link:<br>https://www.kti.hu                                                                                                                                                                                                                                                                                                                                                            |
-| Ireland | Ireland's national road safety strategy for 2021-2030, Our Journey Towards Vision Zero, refers to both the Safe<br>System and Vision Zero.<br>The 2021-2030 strategy is supported by a Phase 1 Action Plan for 2021-24.<br>The strategy and action plan focus on seven Safe System priority intervention areas, and commit to achieving<br>Vision Zero in Ireland by 2050.<br>Links:<br>https://www.rsa.ie/about/safety-strategy-2021-2030<br>https://www.rsa.ie/docs/default-source/road-safety/action-plans/rsa_safety_strategy_action_<br>plan_2021_2024_13th_jan2022_final_online.pdf?sfvrsn=67518e36_5 |
-| Italy   | In April 2022, Italy's Interministerial Committee for Economic Planning and Sustainable Development approved<br>the National Road Safety Plan 2030.<br>The plan is based on the Safe System approach.<br>Link:<br>https://www.mit.gov.it/nfsmitgov/files/media/progetti/2022-09/20220916_Piano%20Nazionale%20Sicurezza%20<br>Stradale_Def.pdf                                                                                                                                                                                                                                                               |
-| Japan   | The Japanese government released its 11th Traffic Safety Program in March 2021. It covers the period 2021-25.<br>It does not refer to either the Safe System or Vision Zero.<br>Link:<br>https://www8.cao.go.jp/koutu/kihon/keikaku11/index.html                                                                                                                                                                                                                                                                                                                                                            |
-| Korea   | Korea's 9th National Transport Safety Plan 2022-2026 has been approved by the Ministry of Land,<br>Infrastructure and Transport. The Plan is based on Vision Zero.<br>Link:<br>http://molit.go.kr/viewer/skin/doc.html?fn=3f774e661393273f795b8c521c83a539&rs=/viewer/result/20220928                                                                                                                                                                                                                                                                                                                       |
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-| Country     | Strategy                                                                                                                                                                                                                                                                            |
-|-------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Luxembourg  | Luxembourg has put in place a National Road Safety Action Plan for 2019-2023.                                                                                                                                                                                                       |
-|             | The plan aims to reduce the large number of serious injuries and fatalities on the roads in pursuit of the long-term<br>goal of zero deaths and zero serious injuries.                                                                                                              |
-|             | Link:<br>https://gouvernement.lu/dam-assets/documents/actualites/2019/05-mai/Plan-d-action-securite-routiere.pdf                                                                                                                                                                    |
-| Mexico      | Mexico published the new Mobility and Road Safety Strategy (ENAMOV) 2023-2042 in June 2023.                                                                                                                                                                                         |
-|             | A new General Law of Mobility and Road Safety was published in the Official Gazette of the Federation on 17 May<br>2022. Its objective is to establish the basis for guaranteeing the right to safe mobility and inclusive accessibility.<br>The law adopts a Safe System approach. |
-|             | Links:                                                                                                                                                                                                                                                                              |
-|             | https://www.dof.gob.mx/nota_detalle.php?codigo=5596042&fecha=02/07/2020                                                                                                                                                                                                             |
-|             | https://www.diputados.gob.mx/LeyesBiblio/pdf/LGMSV.pdf                                                                                                                                                                                                                              |
-|             | https://www.gob.mx/cms/uploads/attachment/file/848141/ENAMOV_2023-2042.pdf                                                                                                                                                                                                          |
-| Moldova     | Moldova is currently developing a new road safety strategy to replace the previous strategy covering the period<br>2011-20.                                                                                                                                                         |
-|             | In January 2020, the Moldovan government approved a Road Safety Action Plan for the period 2020-21. The plan<br>referred to Vision Zero and the five road safety pillars.                                                                                                           |
-|             | Link:                                                                                                                                                                                                                                                                               |
-|             | https://www.legis.md/cautare/getResults?doc_id=120102⟨=ro                                                                                                                                                                                                                           |
-| Morocco     | Morocco's current national road safety strategy covers the period 2017-2026.                                                                                                                                                                                                        |
-|             | The strategy refers to the Safe System and is based on the five road safety pillars.                                                                                                                                                                                                |
-|             | Link:                                                                                                                                                                                                                                                                               |
-|             | https://www.narsa.ma/fr                                                                                                                                                                                                                                                             |
-| Netherlands | The Netherlands' road safety strategy is called Door to Door Safety (2018-2030). The Road Safety Strategic<br>Plan 2030 is based on a joint vision on the approach to road safety policy.                                                                                           |
-|             | The strategy is based on the Safe System approach (named Sustainable Safety in the Netherlands).                                                                                                                                                                                    |
-|             | Links:                                                                                                                                                                                                                                                                              |
-|             | https://www.kennisnetwerkspv.nl/getmedia/ce0099b7-ce77-4ce2-98c8-a7810662ef10/19-093-RO-SPV-Engels_                                                                                                                                                                                 |
-|             | v2.pdf.aspx<br>https://open.overheid.nl/documenten/ronl-d55ff6bc0b5d564c03906bb54019eb485f83842e/pdf                                                                                                                                                                                |
-|             |                                                                                                                                                                                                                                                                                     |
-| New Zealand | New Zealand's road safety strategy for 2020-30 is titled Road to Zero and is based on Vision Zero and the Safe<br>System approach.                                                                                                                                                  |
-|             | Link:                                                                                                                                                                                                                                                                               |
-|             | https://www.transport.govt.nz/assets/Uploads/Report/Road-to-Zero-strategy_final.pdf                                                                                                                                                                                                 |
-|             |                                                                                                                                                                                                                                                                                     |
-| Norway      | Vision Zero was adopted by the Parliament for the first time in 2001 and is the base for all the following Road<br>Safety Strategies.                                                                                                                                               |
-|             | The existing strategy was adopted by the Parliament in 2021 as part of the National Transport Plan 2022-2033.                                                                                                                                                                       |
-|             | The National Plan of Action for Road Safety 2022-2025 was developed by the Norwegian Public Roads<br>Administration in cooperation with a wide range of other national stakeholders.                                                                                                |
-|             | Links:                                                                                                                                                                                                                                                                              |
-|             | https://www.vegvesen.no/globalassets/fag/fokusomrader/trafikksikkerhet/nasjonal-tiltaksplan-for                                                                                                                                                                                     |
-|             | trafikksikkerhet-pa-vei-2022-2025.pdf<br>https://www.vegvesen.no/globalassets/fag/fokusomrader/trafikksikkerhet/national-plan-of-action-for-road                                                                                                                                    |
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-| Country     | Strategy                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-|-------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Poland      | Poland published its National Road Safety Programme 2021-2030 in December 2021. The document refers to<br>both Vision Zero and the Safe System approach.<br>Link:<br>https://www.krbrd.gov.pl/wp-content/uploads/2021/12/Narodowy-Program-Bezpieczenstwa-Ruchu<br>Drogowego-2021-2030.pdf                                                                                                                                                                                                                                                                                                                                                                                                                           |
-| Portugal    | Portugal's national road safety strategy 2021-30, entitled "Vision Zero to 2030", is currently under development.<br>It refers to Vision Zero and the Safe System approach.<br>Link:<br>https://visaozero2030.pt/en/                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Serbia      | Serbia adopted the National Road Safety Strategy 2023-2030 in September 2023, along with the Action Plan<br>2023-2025.<br>It refers to Vision Zero and the Safe System approach.<br>Link:<br>https://abs.gov.rs/ср/propisi-71/strateski-dokumenti                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Slovenia    | The new national programme for the period 2023-30 was prepared approved by the National Assembly. It will be<br>based on Vision Zero and the Safe System approach.<br>Link:<br>https://www.avp-rs.si/wp-content/uploads/2023/10/novo1_renpvcp23_30-v01-3-10-2023.pdf                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Spain       | Spain's Road Safety Strategy 2030 (Estrategia de Seguridad Vial 2030, ESV 2030) was published and officially<br>presented by the Minister of the Interior on 9 June 2022.<br>The strategy is based on the Safe System approach. The main target is aligned with the WHO Plan for the<br>Decade of Action as well as the European Union Framework 2021-2030, namely: a 50% reduction in deaths and<br>serious injuries for 2030, and a long-term target of Vision Zero by 2050.<br>Links:<br>https://seguridadvial2030.dgt.es/inicio/<br>https://seguridadvial2030.dgt.es/export/sites/sv2030/.galleries/descargas/Road_Safety_Strategy_2030_<br>Summary_EN.pdf                                                      |
-| Sweden      | Sweden released the updated 2022-30 road safety strategy in 2023. The strategy is based on Vision Zero.<br>The Action Plan 2022-2025, developed by the Swedish Transport Administration, also describes commitments<br>from a wide range of stakeholders.<br>Links:<br>http://trafikverket.diva-portal.org/smash/record.jsf?pid=diva2%3A1657137&dswid=2597<br>https://bransch.trafikverket.se/for-dig-i-branschen/samarbete-med-branschen/Samarbeten-for-trafiksakerhet/<br>tillsammans-for-nollvisionen/gemensam-aktionsplan-for-saker-vagtrafik-2022-2025/                                                                                                                                                        |
-| Switzerland | In 2016 the Swiss Federal Roads Office (FEDRO) published a strategy that set targets for fatalities and serious<br>injuries on Swiss roads to be met by 2030.<br>The sub-strategy on road safety, published in 2020, specifies the need for action and concrete measures. It<br>does not refer either to Vision Zero or the Safe System approach.<br>Links:<br>https://www.astra.admin.ch/dam/astra/fr/dokumente/direktion/strategische-ausrichtung.pdf.download.pdf/<br>Orientation%20strat%C3%A9gique%20de%20l'OFROU.pdf<br>https://www.astra.admin.ch/dam/astra/fr/dokumente/direktion/teilstrategie-verkehrssicherheit.pdf.download.<br>pdf/Strat%C3%A9gie%20partielle%20s%C3%A9curit%C3%A9%20routi%C3%A8re.pdf |
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-| Country        | Strategy                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-|----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| United Kingdom | The United Kingdom is the process of developing a new Road Safety Strategic Framework (RSSF) which will be<br>published. It is likely to be based on a Safe System approach.<br>Link:<br>https://www.gov.uk/government/publications/strategic-framework-for-road-safety                                                                                                                                                                                                                                                                                                    |
-| United States  | In January 2022, the US Department of Transportation released a National Roadway Safety Strategy (NRSS).<br>At the core of this strategy is a Department-wide adoption of the Safe System approach. This is the first step in<br>working towards an ambitious long-term goal of reaching zero roadway fatalities.<br>Links:<br>https://www.transportation.gov/NRSS<br>https://www.transportation.gov/sites/dot.gov/files/2022-04/US_DOT_FY2022-26_Strategic_Plan.pdf<br>DOT NRSS Action Tracking Dashboard<br>2023 Progress Report on the National Roadway Safety Strategy |
-
-### **Targets on road deaths and serious injuries in IRTAD countries**
-
-| Country                | Target                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | Baseline year(s)                                                                                                                                             |
-|------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Australia              | • Reduce fatalities by 50% by 2030<br>• Reduce serious injuries by 30% by 2030.<br>As part of demonstrating a commitment to the 2050 Vision Zero target, the<br>strategy will target by 2030:<br>• Zero deaths for children 7 years and under<br>• Zero deaths in city central business district (CBD) areas<br>• Zero deaths on National highways and on high-speed roads covering<br>80% of travel across the network.<br>There are no interim targets, however, the 2030 Target of a 30 per cent<br>reduction in serious injuries by 2030 will be assessed as part of the mid-term<br>review of the Strategy. | Average for 2018-20 for<br>fatalities.<br>3-year average of hospital<br>cases for 2017-18 and 2018-19<br>and estimates for 2019-20, for<br>serious injuries. |
-| Austria                | • Reduce road deaths and serious injuries by 50% by 2030.<br>Austria also has a Vision Zero for child fatalities.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | Average for 2017-19                                                                                                                                          |
-| Belgium                | • Reduce road deaths by 50% by 2030<br>• Reduce serious injuries, as defined by a maximum abbreviated injury<br>score of three or above (MAIS3+), by 50% by 2030<br>• Reduce road deaths by 100% by 2050<br>• Reduce serious injuries (MAIS3+) by 90% by 2050.                                                                                                                                                                                                                                                                                                                                                   | 2019                                                                                                                                                         |
-| Bosnia and Herzegovina | • Reduction of 50% in the number of deaths and serious injuries by 2030.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |                                                                                                                                                              |
-| Canada                 | No hard quantitative targets.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |                                                                                                                                                              |
-| Chile                  | • Reduce road traffic fatalities by 30% by 2030.<br>There are specific additional targets:<br>• Reduce the share of vulnerable road users in road deaths from 49% to<br>35% of all deaths<br>• Reduce the mortality rate of young people (15 29) from 2.2 in 2019 to<br>1.5 deaths per 100 000 inhabitants in 2030.<br>• Reduce the mortality rate of elderly people (+60) from 1.9 in 2019 to<br>1.3 deaths per 100 000 inhabitants in 2030.                                                                                                                                                                    | Average for 2011-19                                                                                                                                          |
-| Colombia               | • Reduce by 50% the road mortality from 14.6 road deaths per<br>100 000 population in 2021 to 7.3 in 2030.<br>The strategy also includes three specific targets:<br>• Reduce by 47% (from 4 526 in 2021 to 2 421 in 2030) the number of<br>motorcyclists killed in road crashes<br>• Reduce by 44% (from 1 590 in 2021 to 891 in 2030) the number of<br>pedestrians killed in road crashes<br>• Reduce by 37% (from 483 in 2021 to 302 in 2030) the number of cyclists<br>killed in road crashes.                                                                                                                | 2021                                                                                                                                                         |
-| Czechia                | • Reduce road deaths and serious injuries by 50% by 2030.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | Average for 2017-19                                                                                                                                          |
-
-| Country | Target                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | Baseline year(s) |
-|---------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|
-| Denmark | • Reduce the number road deaths to 90 or below (data from policy<br>registry)<br>• Reduce the number of serious injuries to 900 or below (data from the<br>police registry)<br>• Reduce the number of slight injures to 10 000 or below (data from the<br>Danish national patient register).<br>These figures correspond to an approximate 50% reduction of the average for<br>2017-19, which is 182 killed and 1 813 seriously injured persons per year.<br>There are no specific targets, but five focus areas have been pointed out<br>and will be monitored: single vehicle crashes, head-on collisions, crashes at<br>intersections, vulnerable road users and young car drivers. |                  |
-| Finland | • Reduce by 50% the number of road deaths and serious injuries by 2030.<br>The long-term vision is zero road deaths in 2050.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | 2020             |
-| France  | France endorsed the road safety targets, decided at the European Union level<br>in Valetta in March 2017, to reduce by 50% the number of fatalities and severe<br>injuries on European roads by 2030.<br>France reiterated its commitment at the February 2020 Global Ministerial<br>Meeting on Road Safety in Stockholm, which concluded that these same targets<br>should be achieved globally by 2030.<br>The baseline year is 2019 since the year 2020 cannot be considered as a<br>reference, due to the Covid-19 pandemic.<br>France has also endorsed the concept of zero fatalities on the roads by 2050.                                                                      | 2019             |
-| Germany | • Reduce by 40 % the number of road deaths by 2030.<br>• "Significantly" reduce the number of serious injuries by 2030.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | 2021             |
-| Greece  | • Reduce by 50% road deaths and serious injuries by 2030.<br>Additional specific targets:<br>• 66% reduction in motorcyclists killed by 2030<br>• 60% reduction in road fatalities on Greek islands by 2030<br>• No deaths on motorways by 2030<br>• 35% reduction in deaths in single vehicles crashes by 2030<br>• Zero fatalities in 49 cities with a population between 50 000 and<br>100 000 inhabitants<br>• Being ranked 13th among EU countries regarding deaths per<br>100 000 population.<br>There is an interim target to reduce by 30% deaths and serious injuries by 2025.                                                                                                | 2019             |
-| Hungary | Long-term targets:<br>• Reduce by 50% the number of road deaths by 2030 from 460 to 230<br>• Reduce by 50% the number of serious injuries by 2030 from 4 655 to<br>2 327<br>Short-term targets:<br>• Reach the EU average in terms of road fatalities per million inhabitants<br>by 2025<br>• Proportional reduction of the number of fatalities and serious injuries<br>to reach the 2030 target (345 fatalities and 3 491 seriously injuries by<br>2025)                                                                                                                                                                                                                             | 2020             |
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-| Country     | Target                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | Baseline year(s)    |
-|-------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------|
-| Ireland     | • Reduce by 50% the number of road deaths by 2030 from 144 to 72 or<br>lower<br>• Reduce by 50% the number of serious injuries by 2030, from 1 259 to<br>630 or lower.<br>The strategy is divided into three phases (Phase 1 = 2021-24, Phase 2 = 2025-27,<br>Phase 3 = 2028-30) and the targets for the end of Phase 1 are to:<br>• Reduce by 15% the number of road deaths by 2020, from 144 to 122 or<br>lower<br>• Reduce by 10% the number of serious injuries from 1 259 to 1 133 or<br>lower.<br>The strategy commits to achieving Vision Zero in Ireland by 2050. | Average for 2017-19 |
-| Italy       | • Reduce by 50 % the number of road deaths and serious injuries by 2030.<br>A linear decrease in both deaths and serious injuries is hypothesised over the<br>decade, with interim monitoring in 2024 and 2027.<br>Specific targets in terms of reduction of the total number of fatalities have<br>been set for some road users: children, young drivers, motorcyclists, cyclists,<br>pedestrians and people over 65.                                                                                                                                                    | 2019                |
-| Japan       | • Fewer than 2 000 road deaths (within 24 hours) by 2025 (corresponding<br>to a reduction by 30% compared to 2020)<br>• Fewer than 22 000 serious injuries by 2025.                                                                                                                                                                                                                                                                                                                                                                                                       |                     |
-| Korea       | • Reach less than 1 800 road deaths, is a 38% reduction from the number<br>in 2021.<br>The target is in line with the United Nations goal to halve road deaths by 2030.                                                                                                                                                                                                                                                                                                                                                                                                   |                     |
-| Luxembourg  | • Reduce road fatalities and serious injuries by 50% by 2030.<br>This target follows the objectives of the European Commission's Decade of<br>Action 2021-2030 as well as the United Nations target for the same period.                                                                                                                                                                                                                                                                                                                                                  |                     |
-| Mexico      | Not yet defined.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |                     |
-| Moldova     | Not yet defined.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |                     |
-| Morocco     | • Reduce by 50% road deaths by 2026.<br>There are specific targets for pedestrians, powered two- and three-wheelers,<br>children, single-vehicle crashes and commercial transport.                                                                                                                                                                                                                                                                                                                                                                                        | 2015                |
-| Netherlands | The 2030 road safety strategy in general aims at zero fatalities and injuries by<br>2050.<br>At this moment politicians are debating an intermediate goal of a reduction of<br>50% in serious injuries and fatalities by 2030 as well as the reference year.                                                                                                                                                                                                                                                                                                              |                     |
-| New Zealand | • A 40 % reduction in killed and serious injuries by 2030.<br>The long-term vision of the strategy is to achieve zero deaths and serious<br>injuries on the roads by 2050.                                                                                                                                                                                                                                                                                                                                                                                                | 2018                |
-| Norway      | In 2030, the number of killed or seriously injured in road traffic should be<br>maximum 350, with no more than 50 fatalities.<br>There should be zero fatality from road traffic crashes in 2050.                                                                                                                                                                                                                                                                                                                                                                         |                     |
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-| Country        | Target                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | Baseline year(s)    |
-|----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------|
-| Poland         | • To reduce by 50% the number of road deaths and serious injuries by<br>2030.<br>There are specific targets for vulnerable road users (pedestrians, cyclists,<br>moped and motorcyclists riders) and alcohol-related fatalities.<br>There are also interim targets for each year of the programme.                                                                                                                                                                                                                          | 2019                |
-| Portugal       | • Reduce by 50% the number of road deaths by 2030.<br>• Reduce by 50% the number of MAIS3+ serious injuries by 2030                                                                                                                                                                                                                                                                                                                                                                                                         | 2019                |
-| Serbia         | • Reduce by 50% the number of road deaths and serious injuries by 2030.<br>• 0 children killed in traffic from 2030.<br>There are specific targets per pillar, as well as interim targets for specific year<br>before 2030.                                                                                                                                                                                                                                                                                                 | 2019                |
-| Slovenia       | • Reduce by 50% the number of road deaths and serious injuries by 2030.                                                                                                                                                                                                                                                                                                                                                                                                                                                     |                     |
-| Spain          | • Reduce by 50% the number of road deaths and serious injuries by 2030.<br>There is a long term target of zero road deaths and serious injuries by 2050.<br>No intermediate targets are explicitly set, but a linear reduction up to the final<br>target is implicitly used as reference value for the year to year decrease in the<br>figures.<br>There are specific targets in terms of reduction of the total number of deaths<br>and serious injuries, for the different road users, types of roads, and age<br>groups. | 2019                |
-| Sweden         | • Reduce by 50% the number of road deaths by 2030, with a maximum of<br>133 road deaths in 2030<br>• Reduce by 25% the number of serious injuries by 2030.<br>There are some more specific targets:<br>• 25% reduction in seriously injured pedestrians falling (single) by 2030<br>• 25% reduction in seriously injured cyclists in single crashes by 2030<br>A quantification of the target to reduce road deaths due to suicides (including<br>jumping from bridges) may come at a later stage.                          | Average for 2017-19 |
-| Switzerland    | • Maximum 100 fatalities and 2 500 seriously injured per year by 2030 on<br>Swiss roads.<br>• Maximum 25 fatalities and 500 seriously injured among human‑powered<br>forms of mobility per year by 2030 on Swiss roads (e.g. pedestrians,<br>bicycles and e-bikes, scooters and e-scooters, inline skates or<br>skateboards).                                                                                                                                                                                               |                     |
-| United Kingdom | Targets not yet defined.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |                     |
-| United States  | The 2022-26 Strategic Plan includes the target to reduce by 66% motor vehicle<br>related fatalities by 2040 to demonstrate progress to achieve zero roadway<br>fatalities.<br>The national strategy includes a summary of the key actions the Department<br>will take over the next three years to work towards the ambitious long-term<br>goal of reaching zero roadway fatalities.<br>Intermediate targets also exist for 2022 and 2023.                                                                                  |                     |
-
-Rachele Poggi (ITF) prepared this report with support from the ITF Secretariat.
-
-David Prater (ITF) copy-edited this report.
-
-Renaud Madignier designed the report. It is based on data and information provided by the IRTAD Group.
-
-The ITF is grateful to all the members of the IRTAD Group for their contributions to this report.
-
-More than 80 institutes worldwide are members or observers of the IRTAD Group, representing an extensive range of public and private organisations with a direct interest in road safety.
-
-IRTAD Group Chair: Dominique MIGNOT (France)
-
-| Argentina              | National Road Safety Agency (ANSV)                                                              |
-|------------------------|-------------------------------------------------------------------------------------------------|
-| Australia              | Department of Infrastructure, Transport, Regional Development, Communications and the Arts      |
-|                        | Australian Road Research Board (ARRB)                                                           |
-| Austria                | AIT Austrian Institute of Technology GmbH                                                       |
-|                        | Kuratorium für Verkehrssicherheit (KfV)                                                         |
-|                        |                                                                                                 |
-| Belgium                | Vias Institute                                                                                  |
-| Bosnia and Herzegovina | Ministry of Communications and Transport                                                        |
-| Cambodia               | National Road Safety Committee                                                                  |
-|                        |                                                                                                 |
-| Canada                 | Transport Canada                                                                                |
-| Chile                  | Ministry of Transport and Telecommunications, Comisión Nacional de Seguridad de Tránsito        |
-| Colombia               | National Road Safety Agency (ANSV)                                                              |
-| Costa Rica             | National Road Safety Council (COSEVI)                                                           |
-| Czechia                | Transport Research Centre (CDV)                                                                 |
-| Denmark                | Road Directorate                                                                                |
-|                        | Technical University of Denmark (DTU)                                                           |
-| Finland                | Finnish Transport and Communications Agency Traficom                                            |
-|                        |                                                                                                 |
-| France                 | National Interministerial Road Safety Observatory (ONISR)                                       |
-|                        |                                                                                                 |
-|                        | Centre for Studies on Expertise and Risks, Mobility, Land Planning and the Environment (Cerema) |
-|                        | Gustave Eiffel University                                                                       |
-|                        | GIE PSA Renault                                                                                 |
-| Germany                | Federal Highway Research Institute (BASt)                                                       |
-|                        | DEKRA e.V                                                                                       |
-|                        | Fraunhofer Institute for Transportation and Infrastructure Systems                              |
-|                        | German Automobile Association (ADAC)                                                            |
-|                        | German Insurance Association(GDV)                                                               |
-|                        | German Road Safety Council (DVR)                                                                |
-|                        | Mercedes Benz AG                                                                                |
-|                        | PTV Group                                                                                       |
-|                        | Robert Bosch GmbH                                                                               |
-|                        | Traffic Accident Research Institute at University of Technology Dresden (VUFO)                  |
-|                        | Volkswagen AG                                                                                   |
-| Greece                 | National Technical University of Athens                                                         |
-|                        | EL.STAT                                                                                         |
-| Hungary                | Institute for Transport Sciences (KTI)                                                          |
-| Iceland                | Icelandic Road and Coastal Administration                                                       |
-|                        | Icelandic Transport Authority (ICETRA)                                                          |
-
-| Ireland      | Road Safety Authority                            |
-|--------------|--------------------------------------------------|
-| Israel       | National Road Safety Authority                   |
-|              | Central Bureau of Statistics                     |
-| Italy        | Centre for Transport and Logistics (CTL)         |
-|              | Italian Automobile Club (ACI)                    |
-|              | Fred Engineering                                 |
-|              | ISTAT                                            |
-|              |                                                  |
-| Japan        | National Police Agency                           |
-|              | Institute for Traffic Research and Data Analysis |
-|              | National Research Institute for Police Science   |
-|              | Kansai University                                |
-| Korea        | Korea Road Traffic Authority (KoROAD)            |
-|              | Korea Expressway Corporation                     |
-|              | Korea Transportation Safety Authority (KOTSA)    |
-|              | Korea Transport Institute (KOTI)                 |
-| Lithuania    | Ministry of Transport and Communications         |
-| Luxembourg   | STATEC                                           |
-| Mexico       | Mexican Institute of Transportation (IMT)        |
-| Moldova      | Technical University of Moldova                  |
-| Morocco      | National Road Safety Agency (NARSA)              |
-| Netherlands  | Ministry of Infrastructure and Water Management  |
-|              | Institute for Road Safety Research (SWOV)        |
-|              | VIA Software                                     |
-| New Zealand  | Ministry of Transport                            |
-| Norway       | Norwegian Public Road Administration             |
-| Poland       | Motor Transport Institute (ITS)                  |
-| Portugal     | National Road Safety Authority (ANSR)            |
-|              | National Laboratory for Civil Engineering (LNEC) |
-| Serbia       | Road Traffic Safety Agency                       |
-|              | Motor Vehicle Centre (AMSS)                      |
-| Slovenia     | Slovenian Traffic Safety Agency                  |
-| South Africa | Road Traffic Management Corporation              |
-| Spain        | General Traffic Directorate                      |
-|              |                                                  |
-
-| Sweden                      | Swedish Transport Agency                                  |
-|-----------------------------|-----------------------------------------------------------|
-|                             | Swedish Transport Administration                          |
-|                             | Swedish Road and Transport Research Institute (VTI)       |
-| Switzerland                 | Federal Roads Office (FEDRO)                              |
-|                             | Swiss Council for Accident Prevention (BfU)               |
-| United Kingdom              | Department for Transport                                  |
-|                             | Agilysis                                                  |
-|                             | Transport Research Laboratory                             |
-| United States               | National Highway Traffic Safety Administration            |
-| Uruguay                     | National Road Safety Unit (UNASEV)                        |
-| International organisations | European Commission                                       |
-|                             | European Transport Safety Council (ETSC)                  |
-|                             | Fédération Internationale de l'Automobile (FIA)           |
-|                             | FIA Foundation                                            |
-|                             | International Motorcycle Manufacturers Association (IMMA) |
-|                             | The Motorcycle Industry in Europe (ACEM)                  |
-|                             | Towards Zero Foundation                                   |
-|                             | World Bank                                                |
-|                             | World Health Organisation (WHO)                           |
-|                             |                                                           |
-
-![](_page_79_Picture_0.jpeg)
-
-# **Road Safety** Annual Report 2023
-
-The Road Safety Annual Report 2023 provides an overview of road safety performance for the 42 countries participating in the International Transport Forum's permanent working group on road safety, known as IRTAD. Based on the latest data, the report describes recent road safety developments in these countries and compares their performance against the main road safety indicators.
-
-Online country profiles complement this report: www.itf-oecd.org/irtad.
-
-![](_page_79_Picture_6.jpeg)
-
-![](_page_79_Picture_7.jpeg)

Zotero/001_artiklid/Road Safety Indicators/fucomparisonthresholddeterminationmethods2021.md

@@ -1,408 +0,0 @@
----
-category: literaturenote
-citekey: fucomparisonthresholddeterminationmethods2021
-title: Comparison of threshold determination methods for the deceleration rate to avoid a crash (DRAC)-based crash estimation
-authors: "Fu, Chuanyun; Sayed, Tarek"
-year: 2021
-date: 2021-00-00 2021
-doi: 10.1016/j.aap.2021.106051
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2021.106051"
-zotero_key: XPJ7HC4U
-zotero_storage: A845NAKI
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Fu, Chuanyun"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-Contents lists available at [ScienceDirect](www.sciencedirect.com/science/journal/00014575)
-
-# Accident Analysis and Prevention
-
-journal homepage: [www.elsevier.com/locate/aap](https://www.elsevier.com/locate/aap)
-
-![](_page_0_Picture_5.jpeg)
-
-# Comparison of threshold determination methods for the deceleration rate to avoid a crash (DRAC)-based crash estimation
-
-![](_page_0_Picture_7.jpeg)
-
-Chuanyun Fu a,b, \*, Tarek Sayed <sup>b</sup>
-
-- <sup>a</sup> *School of Transportation and Logistics, Southwest Jiaotong University, China* <sup>b</sup> *Department of Civil Engineering, The University of British Columbia, Canada*
-- ARTICLE INFO
-
-*Keywords:*  Traffic conflicts Crash estimation DRAC MADR Bayesian hierarchical framework Extreme value theory
-
-## ABSTRACT
-
-There is an increased interest in the use of traffic conflicts as a surrogate safety measure and several traffic conflict indicators have been developed. One of these indicators is the deceleration rate to avoid a crash (DRAC). Generally, the greater the DRAC value, the higher the crash risk and a crash would occur when the DRAC exceeds the maximum available deceleration rate (MADR). It is noted that the MADR varies considerably for individual vehicles and depends on many factors such as the pavement conditions, vehicle weight, tire, and the braking system. Previous studies usually either set a specific value for the MADR or randomly sample values from a truncated normal distribution of MADR. However, little is known about which threshold determination approach is better. Therefore, this study aims to compare the threshold determination approaches for DRAC-based crash estimation applying Bayesian hierarchical extreme value modeling. Using traffic conflict and crash data collected from four signalized intersections in the city of Surrey, several Bayesian hierarchical models are developed for five specific values of MADR and values from two truncated normal distributions of MADR. The crash frequencies estimated from these models were compared with observed crashes. The results show that, in terms of DRACbased crash estimation accuracy, the truncated normal distribution N(8.45, 1.4<sup>2</sup> )I(4.23, 12.68) of MADR outperforms other determination methods of MADR. Moreover, in terms of DRAC-based crash estimation accuracy and precision, the use of multisite Bayesian hierarchical models outperforms the at-site models. The truncated normal distribution N(8.45, 1.42 )I(4.23, 12.68) of MADR is therefore recommended for DRAC-based crash estimation.
-
-#### **1. Introduction**
-
-The problems associated with the use of crash data in road safety analysis are well-documented [\(Sayed and Zein, 1999\)](#page-8-0). There are quality and availability problems associated with crash data. As well, the use of crash data in safety analysis is a reactive approach. Crashes need to be observed over a prolonged time period to identify safety problems and to correct them. Therefore, the development of surrogate safety measures such as traffic conflicts for use in safety analysis has attracted considerable research interest. Compared to traffic crashes, traffic conflicts occur more frequently, can be easily observed, and provide better insight into the collision failure mechanism ([Sayed and Zein, 1999](#page-8-0)).
-
-Over the past decades, a number of traffic conflict indicators have been established and used in traffic safety analysis. Examples of most widely used conflict indicators include temporal and spatial proximity indicators and deceleration-based indicators. Several researchers have explicitly recognized the deceleration rate to avoid a crash (DRAC) as a useful surrogate safety measure [\(Archer, 2005](#page-8-0)). The DRAC is the rate at which a following vehicle must decelerate to avoid a collision with the leading vehicle ([Zheng and Sayed, 2019a](#page-9-0)). It is defined as the differential speed between a following vehicle and its leading vehicle divided by their closing time ([Cooper and Ferguson, 1976;](#page-8-0) [Gettman and Head,](#page-8-0)  [2003\)](#page-8-0). Compared to the other surrogate safety measures, such as time to collision (TTC) and the post encroachment time (PET), the DRAC has some advantages, since it could reflect the evasive action (e.g., required deceleration) of the following vehicle to come to a timely stop or reach the corresponding speed of the leading vehicle and thereby avoid a collision ([Cunto and Saccomanno, 2008](#page-8-0)).
-
-The greater the DRAC value the higher the crash risk, and a crash would occur if the DRAC exceeds the maximum available deceleration rate (MADR). Therefore, the crash risk based on DRAC can be calculated given a MADR. Since the MADR is unique for individual vehicles and
-
-<sup>\*</sup> Corresponding author at: School of Transportation and Logistics, Southwest Jiaotong University, China. *E-mail addresses:* [fcy1128@mail.ubc.ca](mailto:fcy1128@mail.ubc.ca) (C. Fu), [tsayed@civil.ubc.ca](mailto:tsayed@civil.ubc.ca) (T. Sayed).
-
-depends on pavement conditions, vehicle weight, tire and braking system, and other factors, it is usually assumed to follow a truncated normal distribution N(8.45, 1.4<sup>2</sup> ), with an upper limit of 12.68 m/s2 and a lower limit of 4.23 m/s2 ([Cunto and Saccomanno, 2008](#page-8-0)). Several studies have investigated the crash risk based on DRAC by mainly using the extreme value distribution (EVT) and the truncated normal distribution. The former treats the conflict extremes characterized by DRAC as a random variable, while the latter treats the MADR as a random variable. Additionally, a threshold needs to be developed to identify traffic conflict extremes for use in the establishment of the EVT model based on DRAC.
-
-Previous studies used different specific values for MADR to conduct DRAC-based traffic conflict safety evaluation. A value of 8 m/s2 for MADR was considered as a conservative value that all vehicles could reach in one previous study [\(Ceunynck, 2017\)](#page-8-0). [Wang et al. \(2019\)](#page-8-0)  employed the MADR values of 8 m/s2 and 12 m/s2 for crash estimation from the bivariate extreme value models, respectively. In the study by ([Zheng et al., 2019b](#page-9-0)), the mean value (i.e., 8.45 m/s<sup>2</sup> ) of the truncated normal distribution N(8.45, 1.42 )I(4.23, 12.68) of MADR was selected as the threshold for crash occurrence, which was applied to develop the bivariate extreme value models for crash estimation. It is noted that a specific value of MADR may lead to DRAC-based crash underestimation or overestimation at some sites. Therefore, [Zheng and Sayed \(2019a\)](#page-9-0)  used the values of DRAC sampled from the fitted extreme value distribution and the values of MADR sampled from a given truncated normal distribution N(8.45, 1.4<sup>2</sup> )I(4.23, 12.68) to calculate the DRAC-based crash risk. As well, [Fu et al. \(2020\)](#page-8-0) determined the values of MADR for DRAC-based crash estimation by sampling from the truncated normal distribution N(8.45, 1.4<sup>2</sup> )I(4.23, 12.68).
-
-In addition to the extreme value distribution, the truncated normal distribution is also used to estimate the crash risk based on DRAC. In other words, the MADR is regarded as a random variable and is assumed to follow a truncated normal distribution. The crash risk can be calculated as the probability of the MADR being less than the DRAC. [Meng](#page-8-0)  [and Weng \(2011\)](#page-8-0) evaluated the rear-end crash risk measured by DRAC at work zones. They assumed that the MADR of cars and trucks followed truncated normal distributions N(8.45, 1.4<sup>2</sup> )I(1.23, 12.68) and N(6.82, 1.4<sup>2</sup> )I(0.60, 10.05), respectively. This approach was also applied to investigate the work zone rear-end crash risk based on DRAC for different vehicle-following patterns ([Weng et al., 2014](#page-8-0)), assess the performance of two microscopic safety indices (i.e., DRAC and TTC) on safety evaluation for expressways [\(Qu et al., 2014](#page-8-0)), and explore the rear-end crash risk based on DRAC and TTC in work zone merging areas ([Weng et al., 2015](#page-8-0)). With the updated data of the braking performance of various automobiles, a new truncated normal distribution of MADR N (9.7, 1.3<sup>2</sup> )I(4.2, 12.7) was obtained ([Wang and Stamatiadis, 2013](#page-8-0), [2014\)](#page-8-0). The two studies employed the distribution of MADR to calculate the probability of MADR being less than the required braking rate to avoid a crash conditional on the reaction time being less than the TTC.
-
-The review of previous studies using DRAC in road safety analysis shows that limited research has been undertaken on selecting which threshold determination approach for DRAC-based traffic conflict analysis (i.e., setting specific values or distributions of MADR) would yield the best results in terms of accuracy and precision.
-
-Therefore, the objective of this study is to compare the threshold determination approaches for DRAC-based crash estimation in terms of accuracy and precision. For the comparison, a Bayesian hierarchical extreme value model that combines traffic conflict extremes from different sites, incorporates the impact of various covariates, and considers the unobserved site-specific heterogeneity is developed.
-
-The structure of this paper is organized as follows: Section 2 presents the data used in this study. Section 3 provides details of the proposed Bayesian hierarchical extreme value model, including the generalized extreme value model, the Bayesian hierarchical structure, the model estimation method, the model choice, and the crash estimation approach. The model estimation results, model comparisons, and variables interpretation are discussed in Section [4](#page-4-0). The last section, Section [5](#page-8-0), presents the conclusions of this study and the recommended future work.
-
-## **2. Data description**
-
-The data used for this study include video data and crash data. The video data were collected from four signalized intersections in the city of Surrey, British Columbia, Canada. They are the intersections of 72 Ave & 128 St, 72 Ave & 132 St, 64 Ave & King George Blvd, and Fraser Hwy & 168 St (see [Fig. 1](#page-2-0)).
-
-Video data were collected by cameras that focus on the approaches where most of rear-end conflicts occur. Computer vision techniques were applied to identify signal cycles, extract vehicle trajectories, and generate space-time diagram of vehicles from the video footages. The detailed video processing procedures are presented in ([Essa and Sayed,](#page-8-0)  [2018,](#page-8-0) [2019](#page-8-0)). The DRAC was then automatically calculated from the extracted vehicle trajectories. It is noted that the conflict indicator is computed continuously (i.e., frame by frame and one second = 30 frames) over time for an event, and the maximum value is selected for DRAC. For each pair of consecutive vehicles on the through lane, the DRAC could be acquired. Additionally, three traffic parameters at the signal cycle level were extracted from the space-time diagram, including: traffic volume (V, vehicles/lane/cycle) which is the number of vehicles per cycle on the studied lane; shock wave area (A, km s) which is a triangle area where it is the state II of classical four traffic flow states theory ([May, 1990\)](#page-8-0), as illustrated in [Fig. 2](#page-2-0); and platoon ratio (P) which is defined as the proportion of all vehicles arriving during green multiplied by the ratio of the signal cycle length to the effective green time of the subject movement [\(Highway Capacity Manual, 2000\)](#page-8-0).
-
-In this study, only DRAC was utilized to characterize traffic conflicts. It is calculated as ([Cooper and Ferguson, 1976](#page-8-0); [Gettman and Head,](#page-8-0)  [2003\)](#page-8-0):
-
-$$DRAC_{F,I} = \frac{\left(v_{F,I} - v_{L,I}\right)^2}{2\left(x_{L,I} - x_{F,I} - D_L\right)} \tag{1}$$
-
-where, *vF,t*and *vL,t*are speeds of following vehicle and leading vehicle at time *t*, respectively; *xF,t*and *xL,t*are positions of following vehicle and leading vehicle at time *t*, respectively; *DL* is the length of leading vehicle.
-
-It is noted that there may be zero or even no values of DRAC for a pair of consecutive vehicles. Therefore, only events with DRAC *>* 0 m/s2 were considered as candidate traffic conflicts for the following analysis. A total of 2202 events with DRAC values were obtained from 416 signal cycles, which is on average 5.3 events per cycle. The summary statistics of traffic conflict and crash data are shown in [Table 1.](#page-3-0)
-
-Three years of crash data (2013–2015) at the four signalized intersections in the city of Surrey, British Columbia was used. The data, provided by the Insurance Corporation of British Columbia, included the information such as location, date, time, crash type, and crash severity, among others. Since the crash data is used to assess the performance of the proposed model, only rear-end crashes that occurred in the through lanes at the target approaches were selected. Moreover, since rear-end conflicts were observed in the daytime period, only rear-end crashes happened during the daytime were kept. Eventually, a total of 43 rearend crashes remained, as presented in [Table 1.](#page-3-0)
-
-#### **3. Model development**
-
-# *3.1. Generalized extreme value model*
-
-In this study, the generalized extreme value (GEV) distribution is fitted based on the block maxima (BM) extremes. The observations are aggregated into fixed blocks, and the maxima of each block is treated as extreme ([Coles, 2001;](#page-8-0) [Zheng et al., 2018](#page-9-0)). Suppose that *X*1*, X*2*,* ⋯*, Xn* is a sequence of independent random variables having a common distribution function and *Mn* = max{*X*1*, X*2*,* ⋯*, Xn*} represents the block maxima.
-
-<span id="page-2-0"></span>![](_page_2_Picture_2.jpeg)
-
-**Fig. 1.** Study intersections and corresponding approaches.
-
-![](_page_2_Figure_4.jpeg)
-
-**Fig. 2.** Classical four different traffic flow states at signalized intersections.
-
-If there exist sequences of constants {*an >* 0} and {*bn*} such that
-
-$$\Pr\{(M_n - b_n)/a_n \le z\} = G(z) \text{ as } n \to \infty$$
- (2)
-
-where, *G* is a non-degenerate distribution function. It is a member of GEV family [\(Coles, 2001\)](#page-8-0), which is given by:
-
-$$G(z) = exp\left\{ -\left[1 + \xi \frac{(z - \mu)}{\sigma}\right]^{-1/\xi} \right\}$$
-(3)
-
-defined on the set {*z* : 1 + *ξ*(*z* − *μ*)*/σ >* 0}, where *μ* is the location parameter, − ∞ *< μ <* ∞; *σ* is the scale parameter, *σ >* 0; *ξ* is the shape parameter, − ∞ *< ξ <* ∞.
-
-# *3.2. Bayesian hierarchical structure*
-
-A Bayesian hierarchical structure is developed for the univariate extreme value model, which has three layers: data layer, process layer, and prior layer [\(Zheng and Sayed, 2019b; Zheng et al., 2019a;](#page-9-0) [Fu et al.,](#page-8-0)  [2020;](#page-8-0) [Zheng and Sayed, 2020](#page-9-0)).
-
-## *3.2.1. Data layer*
-
-A GEV distribution given by Eq. (2) is employed to model the data at the first layer of hierarchy, which is
-
-$$G(z_{ij} < z; \mu_i, \sigma_i, \xi_i) = exp \left\{ -\left[1 + \xi_i \frac{(z - \mu_i)}{\sigma_i}\right]^{-1/\xi_i} \right\}$$
-(4)
-
-where, *zij* is the *j* th recorded block maxima at site *i*, with *i* = 1*,* 2*,* ⋯*, m*  and *j* = 1*,*2*,*⋯*,ni*; (*μ<sup>i</sup> , σi, ξi*) represent the parameters at site *i*. Given that *zij* corresponds to the block maxima, it can be depicted by the GEV distribution whose parameters are dependent on the site. To ensure a positive scale parameter, the *σ* is reparameterized as *ψ* = log*σ*. Therefore, the Eq. (4)
-
-can be rewritten as:
-
-$$G(z_{ij} < z; \mu_i, \sigma_i, \xi_i) = exp \left\{ -\left[1 + \xi_i \frac{(z - \mu_i)}{\exp(\psi_i)}\right]^{-1/\xi_i} \right\}$$
- (5)
-
-<span id="page-3-0"></span>**Table 1**  Summary statistics of data.
-
-| Data information                         |                                 | 72 Ave &<br>128 St                           | 72 Ave &<br>132 St                | 64 Ave &<br>King<br>George<br>Blvd | Fraser<br>Hwy &<br>168 St            |
-|------------------------------------------|---------------------------------|----------------------------------------------|-----------------------------------|------------------------------------|--------------------------------------|
-| Video was recorded in                    |                                 | March 28,<br>2012<br>(10− 12 am;<br>2− 4 pm) | April 3,<br>2012<br>(9− 12<br>am) | June 10,<br>2015 (1− 5<br>pm)      | June 10,<br>2015 (9<br>am − 1<br>pm) |
-| Number of cycles                         |                                 | 130                                          | 72                                | 103                                | 111                                  |
-|                                          |                                 | 928                                          | 303                               | 436                                | 535                                  |
-| Event counts<br>DRAC value<br>(m/s2<br>) | Mean<br>Min<br>Max              | 2.05<br>0.00<br>7.10                         | 1.81<br>0.16<br>6.38              | 2.41<br>0.01<br>5.98               | 2.60<br>0.06<br>6.70                 |
-| V (vehicle                               | Mean                            | 13.52                                        | 13.43                             | 11.62                              | 13.71                                |
-| count per                                | Min                             | 2                                            | 7                                 | 4                                  | 3                                    |
-| cycle)                                   | Max                             | 28                                           | 25                                | 18                                 | 23                                   |
-| A (km⋅s)                                 | Mean                            | 1.38                                         | 0.96                              | 1.19                               | 1.10                                 |
-|                                          | Min                             | 0.00                                         | 0.00                              | 0.00                               | 0.00                                 |
-|                                          | Max                             | 4.45                                         | 2.90                              | 3.03                               | 5.66                                 |
-| P                                        | Mean                            | 1.10                                         | 1.07                              | 1.40                               | 1.12                                 |
-|                                          | Min                             | 0                                            | 0.14                              | 0.00                               | 0.32                                 |
-|                                          | Max                             | 1.88                                         | 1.72                              | 2.11                               | 1.75                                 |
-| Observed<br>crashes                      | 2013<br>2014<br>2015<br>Average | 8<br>3<br>3<br>4.7                           | 0<br>1<br>0<br>0.3                | 9<br>7<br>2<br>6.0                 | 4<br>3<br>3<br>3.3                   |
-
-#### *3.2.2. Process layer*
-
-A latent Gaussian process underlying extremes is characterized through developing a structure that connects the parameters in the data layer with certain covariates. The identity link function is used to build the connection. It is noted that since it is hard to precisely estimate the shape parameter *ξ* of GEV distribution, it is not possible to model *ξ* as a smooth function of certain covariates. Hence, the shape parameter *ξ* is hypothesized to be unknown but not influenced by covariates during the process [\(Coles, 2001](#page-8-0); [Cooley et al., 2006\)](#page-8-0). As such, the GEV distribution parameter can be expressed as:
-
-$$\begin{cases} \mu_i = \beta_{\mu 0} + \beta_{\mu} \mathbf{X} \\ \psi_i = \beta_{\mu 0} + \beta_{\psi} \mathbf{X} \\ \xi_i = \beta_{20} \end{cases}$$
-
-$$\tag{6}$$
-
-where, *βμ*0, *βψ*0, and *βξ*0 are intercepts; **X** is the vector of covariates; **β***<sup>μ</sup>* and **β***ψ*are the vectors of estimated parameters of covariates. In fact, there usually seems to be additional heterogeneity that is not directly addressed by covariates. In order to capture the additional unobserved heterogeneity, random error terms are also introduced as:
-
-$$\begin{cases} \mu_{i} = \beta_{\mu 0} + \beta_{\mu} \mathbf{X} + \varepsilon_{\mu i} \\ \psi_{i} = \beta_{\psi 0} + \beta_{\psi} \mathbf{X} + \varepsilon_{\psi i} \\ \xi_{i} = \beta_{20} + \varepsilon_{\xi i} \end{cases}$$
-(7)
-
-where, *εμi*, *εψi*, and *εξi* are random effects reflecting between-site variation, which are consistent for extremes in the same site but vary between different sites. Therefore, the model for the process layer can be expressed in the following form of a random intercept:
-
-$$\begin{cases} \mu_{i} = \beta_{\mu i} + \beta_{\mu} \mathbf{X} \\ \psi_{i} = \beta_{\psi i} + \beta_{\psi} \mathbf{X} \\ \xi_{i} = \beta_{\xi i} \end{cases}$$
-(8)
-
-where, *βμi*, *βψi*, and *βξi*are site-specific random intercepts.
-
-#### *3.2.3. Prior layer*
-
-In this layer, priors are assigned to site-specific random intercepts and the estimated parameters of covariates for characterizing the latent process. It is hypothesized that each parameter in this layer is independent to the others. Due to no available prior information regarding how the parameters of GEV distribution are associated with covariates, uninformative priors for these parameters, i.e., {*βμi*, *βψi*, **β***μ*, **β***<sup>ψ</sup>* }, are adopted. They are hypothesized to be normally distributed with zero mean and large variance, i.e., *N*(0*,*106). It is worth noting that the GEV model is sensitive to its shape parameter, so an improper defined prior would lead to a convergence issue during the model estimation. According to the previous road safety studies such as [\(Songchitruksa and](#page-8-0)  [Tarko, 2006\)](#page-8-0) and ([Zheng et al., 2014](#page-9-0)), the estimated shape parameters are usually within the range of (-1.0, 1.0). Therefore, the priors for shape parameters are assumed to follow a uniform distribution, i.e., Unif(-1.0, 1.0).
-
-## *3.3. Model estimation method*
-
-In the above-mentioned three-layer hierarchical model, according to the Bayes theorem, the posterior inference for the univariate Bayesian hierarchical extreme value model parameters **θ** given the data **Ω** is
-
-$$p(\mathbf{\theta}|\mathbf{\Omega}) \propto \underbrace{p_1(\mathbf{\Omega}|\mathbf{\theta}_1) p_2(\mathbf{\theta}_1|\mathbf{\theta}_2) p_3(\mathbf{\theta}_2)}_{Data} \underbrace{p_{rocess} p_{rior}}_{Process}$$
-(9)
-
-where, *p*(**θ**|**Ω**) is the posterior distribution of the parameter **θ**; *ps* is the density function associated with layer *s* (*s* = 1*,* 2*,* 3) of the hierarchical model and depends on parameters **θ***v*(*v* = 1*,*2).
-
-The density associated with the data layer is written as:
-
-$$p_{1}(\mathbf{Z}|\boldsymbol{\theta}_{1}) = \prod_{i=1}^{m} \prod_{j=1}^{n_{1}} \left(\frac{1}{\exp\left(\boldsymbol{y}_{i}\right)}\right) \exp \left\{ -\left[1 + \xi_{i} \frac{\left(z_{ij} - \mu_{i}\right)}{\exp\left(\boldsymbol{y}_{i}\right)}\right]^{-1/\xi_{i}} \right\} \left[1 + \xi_{i} \frac{\left(z_{ij} - \mu_{i}\right)}{\exp\left(\boldsymbol{y}_{i}\right)}\right]^{-1-1/\xi_{i}}$$
-
-$$(10)$$
-
-where, **θ1** = [**μ***,* **ψ***,* **ξ**] *<sup>T</sup>*; *m* is the number of study sites (i.e., intersections); *ni* is the number of blocks at site *i*. According to the GEV parameters specified in Eq. (8), the density related to the process layer is obtained as:
-
-$$\begin{split} p_{2}(\boldsymbol{\theta}_{1}|\boldsymbol{\theta}_{2}) &= \frac{1}{\delta_{\mu}\sqrt{2\pi}} \exp\left[-\frac{(\mu-\mu_{i})^{2}}{2\delta_{\mu}^{2}}\right] \times \frac{1}{\delta_{\psi}\sqrt{2\pi}} \exp\left[-\frac{(\psi-\psi_{i})^{2}}{2\delta_{\psi}^{2}}\right] \\ &\times \frac{1}{\delta_{\xi}\sqrt{2\pi}} \exp\left[-\frac{(\xi-\xi_{i})^{2}}{2\delta_{\xi}^{2}}\right] \end{split} \tag{11}$$
-
-where, **θ2** = [**βμ***,* **βψ***,* **βξ**] *<sup>T</sup>*. With the priors specified, the density associated with the prior layer is given by
-
-$$p_3(\mathbf{\theta}_2) = p_{\beta_{\mu}}(\beta_{\mu}) \times p_{\beta_{\psi}}(\beta_{\psi}) \times p_{\beta_{\xi}}(\beta_{\xi})$$
-(12)
-
-The posterior distributions of model parameters can be inferred by virtue of Markov Chain Monte Carlo (MCMC) simulation using Gibbs Sampling.
-
-#### *3.4. Model choice*
-
-When developing the univariate Bayesian hierarchical extreme value model with relating the parameters of GEV distribution to several covariates, there will be many models to choose from. In the Bayesian framework, the Deviance Information Criterion (DIC) is commonly adopted to compare models. The basic principle of DIC is parsimony, obtaining the simplest model that explains as much of the variation in the data as possible ([Spiegelhalter et al., 2002\)](#page-8-0). The DIC is computed as follows:
-
-$$DIC = \overline{D} + n_D \tag{13}$$
-
-where, *D* is the posterior mean deviance that measures the model fitting and *nD* is the effective number of parameters in the model. Generally, a smaller DIC value indicates a better model. It is also suggested that a difference greater than 10 can definitely rule out the model with the higher DIC value, a difference ranging from 5 to 10 is considered sub<span id="page-4-0"></span>stantial, and a difference less than 5 makes the models competitive ([El-Basyouny and Sayed, 2012b; 2012a](#page-8-0)).
-
-## *3.5. Crash estimation method*
-
-When the GEV (*μ, σ, ξ*) is fitted based on the data from individual sites, it is called the at-site model. As well, it is called the multisite model when the GEV (*μ, σ, ξ*) is fitted based on the data from several sites simultaneously. In this study, a traffic conflict is characterized by DRAC. A crash would occur if the DRAC exceeds the MADR [\(Cunto and Sac](#page-8-0)[comanno, 2008](#page-8-0)). As such, with the fitted GEV distribution of maximum DRAC, the risk of crash can be calculated as:
-
-$$R_{i} = \Pr\{Z_{i} \ge MADR\} = 1 - G_{i}(MADR)$$
-
-$$= 1 - \exp\left\{-\left[1 + \xi_{i}^{MADR} - \mu_{i}\right]^{-1/\xi_{i}}\right\}$$
-(14)
-
-where, *Ri* is the risk of crash for block *i*; *Xi* is the maximum DRAC in block *i*; *Gi*(⋅) is the fitted univariate Bayesian hierarchical extreme value distribution for block *i*. Five specific values (i.e., 7 m/s2 , 8 m/s<sup>2</sup> , 8.5 m/ s 2 , 9.7 m/s2 , and 12 m/s2 ) of MADR are employed to calculate the risk of crash, respectively. As well, two truncated normal distributions (i.e., N (8.45, 1.4<sup>2</sup> )I(4.23, 12.68) and N(9.7, 1.3<sup>2</sup> )I(4.23, 12.68)) of MADR are used to generate the sampled values of MADR for crash risk calculation.
-
-#### **4. Results and discussions**
-
-#### *4.1. Model estimation results*
-
-With the maximum values of DRAC from different signal cycles of four signalized intersections, the multisite Bayesian hierarchical extreme value models were developed. The estimations of the proposed models were conducted using WinBUGS ([Lunn et al., 2000](#page-8-0)). Two separate chains for each model parameter with diverse initial values were set to run 50,000 iterations and the first 20,000 iterations were discarded as burn-in samples. The posterior estimates were acquired from the remaining 30,000 iterations. The model convergence was checked by calculating the Gelman-Rubin statistic of two chains for each parameter and visually examining the trace plots of parameter chains ([Gelman and](#page-8-0) 
-
-[Rubin, 1992](#page-8-0); [El-Basyouny and Sayed, 2009; Zheng and Ismail, 2017\)](#page-8-0).
-
-Four multisite Bayesian hierarchical models were established: BHM\_GEV(0,0,0), BHM\_GEV(1,0,0), BHM\_GEV(0,1,0), and BHM\_GEV (1,1,0). All the four models have site-specific random error terms for the location parameter, scale parameter, and shape parameter. BHM\_GEV (0,0,0) is the multisite stationary Bayesian hierarchical model without covariates. BHM\_GEV(1,0,0) and BHM\_GEV(0,1,0) are the multisite nonstationary Bayesian hierarchical models with the three covariates added to the location parameter and scale parameter separately. BHM\_GEV (1,1,0) is the multisite non-stationary Bayesian hierarchical model with covariates added to both location parameter and scale parameter simultaneously, and only significant covariates are included in this model. The estimation results of four multisite Bayesian hierarchical models are presented in Table 2.
-
-As shown in Table 2, the DIC values are 1562.089, 1394.052, 1543.146, and 1384.523 for the multisite BHM\_GEV(0,0,0), multisite BHM\_GEV(1,0,0), multisite BHM\_GEV(0,1,0), and multisite BHM\_GEV (1,1,0), respectively. In general, the stationary model has a greater DIC value than those of non-stationary models. The smallest DIC difference between stationary model and non-stationary models is 18.943, which shows that the non-stationary models considerably outperform the stationary model. This also confirms that the introduced covariates in the non-stationary models explain significant variations in the observed data. Moreover, the multisite BHM\_GEV(1,1,0) has the smallest DIC value, which indicates that it is the best fitted model and better than other models in terms of goodness of fit.
-
-## *4.2. Covariates interpretation*
-
-As discussed in [\(Zheng et al., 2019a](#page-9-0)), it is not straightforward to interpret the influences of covariates on safety, since they directly impact the location parameter and scale parameter of the GEV distribution. The location and scale parameters determine the shape convexity of GEV distribution, which includes the part of the distribution where most crash events are located. Given a specific MADR, an illustration of GEV distributions of DRAC with different location parameters and scale parameters is presented in [Fig. 3](#page-5-0). Given the fixed scale parameter and shape parameter, the GEV distribution with a smaller location parameter is farther away from the crash risk area, implying higher safety level. Similarly, given the fixed location parameter and
-
-**Table 2**  Estimation results of multisite Bayesian hierarchical models.
-
-|                 |        | BHM_GEV(0,0,0) |        | BHM_GEV(1,0,0) |        | BHM_GEV(0,1,0) |        | BHM_GEV(1,1,0) |        |
-|-----------------|--------|----------------|--------|----------------|--------|----------------|--------|----------------|--------|
-| Model parameter |        | Mean           | S.D.   | Mean           | S.D.   | Mean           | S.D.   | Mean           | S.D.   |
-|                 | βμ0[1] | 2.9090**       | 0.1612 | 1.5410**       | 0.3118 | 2.5930**       | 0.1714 | 1.6250**       | 0.2769 |
-| μ               | βμ0[2] | 2.0830**       | 0.1330 | 0.9711**       | 0.3025 | 2.0100**       | 0.1287 | 1.0950**       | 0.2840 |
-|                 | βμ0[3] | 2.7340**       | 0.1910 | 1.7710**       | 0.3607 | 2.4670**       | 0.1963 | 1.6880**       | 0.3364 |
-|                 | βμ0[4] | 2.9670**       | 0.1978 | 1.6910**       | 0.3202 | 2.7480**       | 0.1962 | 1.7450**       | 0.2965 |
-|                 | βμ(V)  | –              | –      | 0.0999**       | 0.0182 | –              | –      | 0.0825**       | 0.0202 |
-|                 | βμ(A)  | –              | –      | 0.6016**       | 0.0878 | –              | –      | 0.7287**       | 0.1088 |
-|                 | βμ(P)  | –              | –      | − 0.6242**     | 0.2183 | –              | –      | − 0.5974**     | 0.2060 |
-|                 | βΨ0[1] | 0.4562**       | 0.0794 | 0.2203**       | 0.0694 | 0.1955         | 0.1457 | − 0.0259       | 0.0999 |
-| ψ =             | βΨ0[2] | 0.0150         | 0.0972 | 0.0362         | 0.0900 | − 0.2311       | 0.1580 | − 0.1767       | 0.1128 |
-| log(σ)          | βΨ0[3] | 0.5244**       | 0.0929 | 0.2391**       | 0.0833 | 0.3245*        | 0.1634 | 0.0179**       | 0.1092 |
-|                 | βΨ0[4] | 0.5208**       | 0.0966 | 0.2511**       | 0.0866 | 0.2907**       | 0.1509 | 0.0651**       | 0.1023 |
-|                 | βΨ(V)  | –              | –      | –              | –      | 0.0167**       | 0.0091 | –              | –      |
-|                 | βΨ(A)  | –              | –      | –              | –      | 0.1309**       | 0.0497 | 0.1619**       | 0.0524 |
-|                 | βΨ(P)  | –              | –      | –              | –      | − 0.0964       | 0.0926 | –              | –      |
-|                 | βξ0[1] | − 0.2369**     | 0.0893 | − 0.2131**     | 0.0521 | − 0.3069**     | 0.0688 | − 0.1450**     | 0.0586 |
-|                 | βξ0[2] | 0.0318         | 0.0851 | − 0.1363*      | 0.0690 | − 0.0458       | 0.0833 | − 0.1611**     | 0.0754 |
-| ξ               | βξ0[3] | − 0.4058**     | 0.1055 | − 0.2970**     | 0.0812 | − 0.4181*      | 0.0838 | − 0.2333**     | 0.0767 |
-|                 | βξ0[4] | − 0.2262**     | 0.1275 | − 0.1135**     | 0.1005 | − 0.3026**     | 0.0954 | − 0.1304       | 0.0946 |
-| D               |        | 1550.000       |        | 1380.000       |        | 1530.000       |        | 1370.000       |        |
-| nD              |        | 12.089         |        | 14.052         |        | 13.146         |        | 14.523         |        |
-| DIC             |        | 1562.089       |        | 1394.052       |        | 1543.146       |        | 1384.523       |        |
-
-Note: "–" means the covariate is not included; S.D. is the standard deviation; "\*" and "\*\*" mean the covariate is significant at 0.1 and 0.05 level, respectively; "V" is the traffic volume; "A" is the shock wave area; and "P" is the platoon ratio.
-
-<span id="page-5-0"></span>![](_page_5_Figure_2.jpeg)
-
-**Fig. 3.** Illustration of GEV distributions with different location and scale parameters.
-
-shape parameter, the GEV distribution with a smaller scale parameter has a skinny shape, and its tail area within the crash risk area becomes narrower, indicating higher safety level as well.
-
-According to the results from the best fitted model, multisite BHM\_GEV(1,1,0), all the three covariates are significantly related to the location parameter of GEV distribution, while only one covariate is significantly associated with the scale parameter of GEV distribution. Specifically, the traffic volume and shock wave area are positively related to the location parameter of GEV distribution, whereas the platoon ratio is negatively associated with the location parameter of GEV distribution. Moreover, the shock wave area is positively related to the scale parameter of GEV distribution. Both the greater traffic volume and shock wave area will increase the location parameter, which yields more expected number of crashes. However, a greater platoon ratio will decrease the location parameter, and thus produce less expected number of crashes. Similarly, a greater shock wave area will increase the scale
-
-parameter, which generates more expected number of crashes.
-
-#### *4.3. Comparison of threshold determination methods for DRAC*
-
-The best fitted model, multisite BHM\_GEV(1,1,0), was used to predict the annual number of crashes for each intersection, where *T* = 365 × 12 = 4380, and t = 4, 3, 4, and 4 for intersections of 72 Ave & 128 St, 72 Ave & 132 St, 64 Ave & King George Blvd, and Fraser Hwy & 168 St, respectively. Five specific values (i.e., 12 m/s<sup>2</sup> , 9.7 m/s<sup>2</sup> , 8.5 m/s<sup>2</sup> , 8 m/s<sup>2</sup> , and 7 m/s2 ) and two truncated normal distributions (i.e., N(9.7, 1.3<sup>2</sup> )I(4.23,12.68) and N(8.45, 1.42 )I(4.23,12.68)) for MADR were used. In addition, the uncertainties of estimated crashes were determined directly in WinBUGS during the model posterior inference via different quantiles, and this is also an advantage of Bayesian inference that can incorporate all uncertainties in the data and model parameters ([El-Basyouny and Sayed, 2012b;](#page-8-0) [Zheng and Sayed, 2020\)](#page-9-0). The crash estimation results from the multisite BHM\_GEV(1,1,0) are shown in Table 3. In the table, the 95% confidence intervals (95% C.I.) of observed crashes are also presented, given that the observed crashes at each intersection are just one observation of the underlying process ([Zheng and Sayed, 2020](#page-9-0)). By hypothesizing that the observed crashes follow the Poisson distribution, the 95% C.I.s were computed as { *λ* : <sup>1</sup> 2*nχ*<sup>2</sup> <sup>2</sup>*y*0*,*0*.*<sup>975</sup> ≤ *λ* ≤ <sup>1</sup> 2*nχ*<sup>2</sup> <sup>2</sup>(*y*0+1)*,*0*.*025} , where *n* = 3 is the number of years for crash data collection, and *y*0 is the total number of crashes in the 3-year period for an intersection [\(Songchitruksa and Tarko, 2006\)](#page-8-0).
-
-As shown in Table 3, there are three intersections where the estimated crashes from multisite BHM\_GEV(1,1,0) using the MADR of 12 m/ s 2 are smaller than the observed crashes and outside the 95% confidence intervals of the observed crashes. Similar results are obtained from multisite BHM\_GEV(1,1,0) using the MADR of 9.7 m/s2 . There are two intersections where the estimated crashes from multisite BHM\_GEV (1,1,0) using the MADR of 8.5 m/s2 are smaller than the observed crashes and outside the 95% confidence intervals of the observed crashes, whereas the estimated crashes of other two intersections are within the 95% confidence intervals of the observed crashes. The multisite BHM\_GEV(1,1,0) using the MADR of 8 m/s2 generates crash underestimation at two intersections with the estimated crashes outside the 95% confidence intervals of the observed crashes, while produces crash overestimation at one intersection with the estimated crashes
-
-**Table 3**  Crash estimation results of multisite Bayesian hierarchical models with different thresholds.
-
-outside the 95% confidence intervals of the observed crashes. Although the multisite BHM\_GEV(1,1,0) using the MADR of 7 m/s2 yields the estimated crashes inside the 95% confidence intervals of the observed crashes at two intersections, it produces serious crash overestimations at other two intersections. The estimated crashes from multisite BHM\_GEV (1,1,0) using the values of MADR sampled from a truncated normal distribution N(9.7, 1.32 )I(4.23, 12.68) are similar to those from multisite BHM\_GEV(1,1,0) using a specific value of 8.5 m/s2 for MADR. However, the estimated crashes from multisite BHM\_GEV(1,1,0) using the values of MADR sampled from a truncated normal distribution N(8.45, 1.42 )I (4.23, 12.68) are all within the 95% confidence intervals of the observed crashes.
-
-The results of crash estimation indicate that in terms of accuracy, the multisite BHM\_GEV(1,1,0) using the sampled values for MADR is better than the one using a specific value for MADR. This is due to the MADR varying with the vehicle, pavement, and environmental conditions at different sites. A larger specific value of MADR would lead to crash underestimation at some sites, while a smaller specific value of MADR would generate crash overestimation at these sites. By using the multisite BHM\_GEV(1,1,0), the comparison of estimated crashes among different MADR values is presented in Fig. 4. In terms of crash estimation accuracy, among 16 different specific values of MADR, the best ones are 7.5 m/s2 , 8.4/8.5/8.6 m/s<sup>2</sup> , 6.8 m/s2 , and 8.7 m/s2 for the four intersections, respectively. According to the 95% confidence interval of observed crashes, the ranges of appropriate MADR are 7.2–7.8 m/s2 , 7.3–8.6 m/s2 , 6.6–7.1 m/s<sup>2</sup> , 8.2–9.5 m/s2 for the four intersections, respectively. Therefore, it is hard to find an appropriate specific value or range of MADR for DRAC-based crash estimation at all these intersections.
-
-Additionally, the multisite BHM\_GEV(1,1,0) using the values of MADR sampled from a truncated normal distribution N(8.45, 1.4<sup>2</sup> )I (4.23, 12.68) outperforms the one with the sampled values of MADR from a truncated normal distribution N(9.7, 1.32 )I(4.23, 12.68). Crash underestimation at two intersections from the multisite BHM\_GEV (1,1,0) using the values of MADR sampled from a truncated normal distribution N(9.7, 1.3<sup>2</sup> )I(4.23, 12.68) may be a result of the inappropriate upper and lower limits of such a truncated normal distribution. Therefore, in terms of crash estimation accuracy, it is recommended to determine the value of MADR by sampling from the truncated normal distribution N(8.45, 1.42 )I(4.23, 12.68).
-
-# *4.4. Comparison of multisite and at-site Bayesian hierarchical models*
-
-The at-site Bayesian hierarchical models were developed for each site by following the same procedure as the multisite Bayesian hierarchical models. All the three covariates were also considered in the model development. Those at-site Bayesian hierarchical models with the smallest DIC values were recognized as the best fitted models, and their estimation results are shown in [Table 4.](#page-7-0) It can be seen that the sign of significant covariates of the at-site models is the same as the multisite BHM\_GEV(1,1,0), but the significance of covariates are varying across the sites.
-
-With the values of MADR sampled from the truncated normal distribution N(8.45, 1.42 )I(4.23, 12.68), the best fitted at-site models were employed to predict the annual crash frequencies, which were then compared to the observed crashes and the estimated crashes from the
-
-![](_page_6_Figure_9.jpeg)
-
-**Fig. 4.** Comparison of estimated crashes among different MADR values (m/s2 ).
-
-<span id="page-7-0"></span>**Table 4**  Estimation results of the best at-site Bayesian hierarchical models.
-
-|                 | 72 Ave & 128 St |                | 72 Ave & 132 St |                | 64 Ave & King George Blvd |                | Fraser Hwy & 168 St |                |        |
-|-----------------|-----------------|----------------|-----------------|----------------|---------------------------|----------------|---------------------|----------------|--------|
-| Model parameter |                 | BHM_GEV(1,1,0) |                 | BHM_GEV(1,1,0) |                           | BHM_GEV(1,0,0) |                     | BHM_GEV(1,1,0) |        |
-|                 |                 | Mean           | S.D.            | Mean           | S.D.                      | Mean           | S.D.                | Mean           | S.D.   |
-| μ               | βμ0             | 1.5760**       | 0.4281          | 1.3260**       | 0.1477                    | 1.2050**       | 0.6346              | 0.6576**       | 0.4479 |
-|                 | βμ(V)           | 0.1327**       | 0.0324          | –              | –                         | 0.0989**       | 0.0453              | 0.0902**       | 0.0354 |
-|                 | βμ(A)           | 0.3402**       | 0.1334          | 1.0380**       | 0.1929                    | 1.0350**       | 0.1997              | 1.0670**       | 0.2185 |
-|                 | βμ(P)           | − 0.7599**     | 0.3658          | –              | –                         | − 0.5886**     | 0.3608              | –              | –      |
-| log(σ)          | βΨ0             | − 0.1577**     | 0.2235          | − 0.5869**     | 0.1421                    | 0.1947**       | 0.0815              | 0.0088         | 0.1445 |
-|                 | βΨ(V)           | 0.0237*        | 0.0141          | –              | –                         | –              | –                   | –              | –      |
-|                 | βΨ(A)           | –              | –               | 0.5125**       | 0.1159                    | –              | –                   | 0.2181**       | 0.1110 |
-|                 | βΨ(P)           | –              | –               | –              | –                         | –              | –                   | –              | –      |
-| ξ               | βξ0             | − 0.1373**     | 0.0673          | − 0.1592*      | 0.0797                    | − 0.2525**     | 0.0748              | − 0.1391       | 0.0946 |
-| D               |                 | 430.000        |                 | 200.000        |                           | 330.000        |                     | 390.000        |        |
-| nD              |                 | 5.868          |                 | 4.451          |                           | 5.153          |                     | 6.082          |        |
-| DIC             |                 | 435.868        |                 | 204.451        |                           | 335.153        |                     | 396.082        |        |
-
-Note: "–" means the covariate is not included; S.D. is the standard deviation; "\*" and "\*\*" mean the covariate is significant at 0.1 and 0.05 level, respectively; "V" is the traffic volume; "A" is the shock wave area; and "P" is the platoon ratio.
-
-multisite BHM\_GEV(1,1,0), respectively. The results of the comparison are presented in Table 5. The estimated crashes of four intersections from the multisite BHM\_GEV(1,1,0) are all within the 95% confidence intervals of the observed crashes, while the estimated crashes of only one intersection from the at-site model are within the 95% confidence intervals of the observed crashes. Moreover, the estimated crashes of three intersections from the multisite BHM\_GEV(1,1,0) are closer to the observed crashes than those from the at-site models. Additionally, the 95% confidence intervals of estimated crashes from the multisite BHM\_GEV(1,1,0) are narrower than those from the at-site Bayesian hierarchical models. This indicates that as compared to the at-site Bayesian hierarchical models, the multisite BHM\_GEV(1,1,0) distinctly improves crash estimation in terms of accuracy and precision. Such significant improvement is probably due to synthesizing the strengths across different sites for model estimation.
-
-However, it is noted that the confidence intervals of estimated crashes from the multisite BHM\_GEV(1,1,0) are still much wider than the 95% Poisson intervals of observed crashes. As discussed in one previous study [\(Zheng et al., 2019a\)](#page-9-0), the large uncertainty in crash estimation based on the multisite Bayesian hierarchical model is mainly a result of the paucity of data. On one hand, the short-term observation period of three-four hours may not completely reflect the daily/weekly/seasonal variation of traffic in one year. On the other hand, any uncertainty in the estimated risk of crashes during the traffic conflict observation period will be magnified by at least *T/t* = 365 × 12*/*4 = 1095 times. From this point of view, the precision issue can be handled with a larger data sample collected from a longer observation period.
-
-**Table 5**  Crash estimate comparison between multisite model and at-site models.
-
-| Site                         | Observed | crashes per year | per year | Multisite model<br>estimated crashes | At-site models<br>estimated crashes<br>per year |             |  |
-|------------------------------|----------|------------------|----------|--------------------------------------|-------------------------------------------------|-------------|--|
-|                              | Mean     | 95%<br>C.I.      | Mean     | 95% C.I.                             | Mean                                            | 95% C.I.    |  |
-| 72 Ave & 128<br>St           | 4.7      | [2.6,<br>7.8]    | 6.7      | [0.0,505.5]                          | 8.3                                             | [0.0,647.6] |  |
-| 72 Ave & 132<br>St           | 0.3      | [0.0,<br>1.9]    | 1.9      | [0.0,174.9]                          | 1.1                                             | [0.0,176.2] |  |
-| 64 Ave & King<br>George Blvd | 6.0      | [3.6,<br>9.5]    | 4.4      | [0.0,440.1]                          | 2.7                                             | [0.0,492.5] |  |
-| Fraser Hwy &<br>168 St       | 3.3      | [1.6,<br>6.1]    | 5.6      | [0.0,479.6]                          | 13.2                                            | [0.0,969.6] |  |
-
-## *4.5. Comparison of extreme value distribution and truncated normal distribution*
-
-Given that the MADR follows a truncated normal distribution N (8.45, 1.402 ), with a lower limit of 4.23 m/s2 and an upper limit of 12.68 m/s2 ([Cunto and Saccomanno, 2008\)](#page-8-0), the risk of crash can also be calculated as:
-
-$$R_{i} = \Pr(MADR \le DRAC_{i}) = \frac{\Phi(\frac{DRAC_{i} - \mu_{MADR}}{\sigma_{MADR}}) - \Phi(\frac{C_{MADR} - \mu_{MADR}}{\sigma_{MADR}})}{\Phi(\frac{U_{MADR} - \mu_{MADR}}{\sigma_{MADR}}) - \Phi(\frac{C_{MADR} - \mu_{MADR}}{\sigma_{MADR}})}$$
-(15)
-
-where, *μMADR* and *σMADR* are the mean and standard deviation of MADR distribution (here refers to a truncated normal distribution); *LMADR* and *UMADR* are the lower and upper limits of MADR; *Φ*(⋅) is the cumulative distribution function of the standard normal distribution.
-
-Based on Eq. (15), the estimated crashes for each site were obtained and compared to the observed crashes and the estimated crashes from the multisite BHM\_GEV(1,1,0) using the values of MADR sampled from the truncated normal distribution N(8.45, 1.4<sup>2</sup> )I(4.23, 12.68), respectively. The comparison results are shown in Table 6. The estimated crashes from the Bayesian hierarchical extreme value distribution are closer to the observed crashes than those from the truncated normal distribution. This confirms that the Bayesian hierarchical extreme value distribution is more suitable to fit the traffic conflict extremes than the truncated normal distribution.
-
-**Table 6**  Crash estimate comparison between Bayesian hierarchical extreme value and truncated normal distributions.
-
-| Site                         | per year | Observed crashes |      | Estimated crashes<br>from extreme value<br>distribution per year | Estimated<br>crashes from<br>truncated<br>normal<br>distribution per<br>year |             |
-|------------------------------|----------|------------------|------|------------------------------------------------------------------|------------------------------------------------------------------------------|-------------|
-|                              | Mean     | 95% C.<br>I.     | Mean | 95% C.I.                                                         | Mean                                                                         | 95%<br>C.I. |
-| 72 Ave & 128 St              | 4.7      | [2.6,<br>7.8]    | 6.7  | [0.0,505.5]                                                      | 9.3                                                                          | –           |
-| 72 Ave & 132 St              | 0.3      | [0.0,<br>1.9]    | 1.9  | [0.0,174.9]                                                      | 4.3                                                                          | –           |
-| 64 Ave & King<br>George Blvd | 6.0      | [3.6,<br>9.5]    | 4.4  | [0.0,440.1]                                                      | 3.8                                                                          | –           |
-| Fraser Hwy & 168<br>St       | 3.3      | [1.6,<br>6.1]    | 5.6  | [0.0,479.6]                                                      | 14.7                                                                         | –           |
-
-## <span id="page-8-0"></span>**5. Conclusions**
-
-This study compared the threshold determination methods (i.e., setting specific values or distributions of MADR) for DRAC-based crash estimation using Bayesian hierarchical extreme value modeling. The crash data and traffic conflicts defined by DRAC were collected from four signalized intersections in the city of Surrey, British Columbia, Canada. Four multisite Bayesian hierarchical models were developed, including a stationary model and three non-stationary models. For comparison purposes, the at-site non-stationary models for four sites were also developed separately. As well, the estimated crashes from a truncated normal distribution of MADR for four sites were calculated. The estimated crashes from the best fitted multisite Bayesian hierarchical model with five specific values (i.e., 7 m/s<sup>2</sup> , 8 m/s<sup>2</sup> , 8.5 m/s<sup>2</sup> , 9.7 m/s<sup>2</sup> , and 12 m/s2 ) of MADR and two truncated normal distributions (i. e., N(8.45, 1.42 )I(4.23, 12.68) and N(9.7, 1.3<sup>2</sup> )I(4.23, 12.68)) of MADR were compared to the observed crashes, respectively. With the best threshold determination approach, the estimated crashes from the best fitted multisite Bayesian hierarchical model were compared to those from the best fitted at-site non-stationary models and the truncated normal distribution, respectively.
-
-The results show that, in terms of DRAC-based crash estimation accuracy, (i) the truncated normal distribution N(8.45, 1.4<sup>2</sup> )I(4.23, 12.68) of MADR outperforms both the truncated normal distribution N(9.7, 1.3<sup>2</sup> )I(4.23, 12.68) of MADR and the specific values (i.e., 7 m/s2 , 8 m/s2 , 8.5 m/s2 , 9.7 m/s<sup>2</sup> , and 12 m/s<sup>2</sup> ) of MADR; (ii) the Bayesian hierarchical extreme value distribution outperforms the truncated normal distribution. In terms of DRAC-based crash estimation accuracy and precision, the multisite Bayesian hierarchical model is better than the at-site Bayesian hierarchical models. Moreover, finding specific appropriate values of MADR to use for DRAC-based crash estimation at all intersections is difficult. Additionally, it is also found that during a signal cycle, more vehicles and greater shock wave area will generate higher crash risks, whereas a greater platoon ratio will produce a lower crash risk.
-
-In conclusion, this study highlights the difference in DRAC-based crash estimation with different thresholds. It also demonstrates the usefulness and advantages of the Bayesian hierarchical extreme value modeling in crash estimation compared to the truncated normal distribution. However, some limitations in this study merit further investigations. Firstly, the traffic conflicts are observed during a short period and from only four signalized intersections. Data from more sites and longer observation periods can be used to confirm the results of this study. Furthermore, only rear-end traffic conflicts and crashes are used. Other types of traffic conflicts and crashes need to be investigated. Secondly, as to a truncated normal distribution of MADR, its parameters (e.g., lower limit, upper limits, mean, and standard error) need to be further investigated, since the MADR is subject to various vehicles, road, and environmental conditions. Third, other distributions of MADR can be investigated. Fourth, to avoid a collision, the driver of the following vehicle may change lane instead of braking heavily. The evasion maneuvers mainly dependent on the severity level of the conflict. Similar to the deceleration to safety time (Hupfer, 1997), the conflict level can be determined based on DRAC which can be investigated in future studies.
-
-# **CRediT authorship contribution statement**
-
-**Chuanyun Fu:** Conceptualization, Methodology, Investigation, Validation, Writing - original draft. **Tarek Sayed:** Conceptualization, Methodology, Data curation, Supervision, Writing - review & editing.
-
-# **Declaration of Competing Interest**
-
-None.
-
-## **Acknowledgments**
-
-This study was jointly supported by the China Scholarship Council (CSC) and the National Natural Science Foundation of China (Grant No. 71801182).
-
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Zotero/001_artiklid/Road Safety Indicators/garnowskifactorsrelatedcaraccidents2011.md

@@ -1,413 +0,0 @@
----
-category: literaturenote
-citekey: garnowskifactorsrelatedcaraccidents2011
-title: On factors related to car accidents on German Autobahn connectors
-authors: "Garnowski, Martin; Manner, Hans"
-year: 2011
-date: 2011-00-00 2011
-doi: 10.1016/j.aap.2011.04.026
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2011.04.026"
-zotero_key: DFHFPG6A
-zotero_storage: RENKHK6A
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Garnowski, Martin"
-status: converted
----
-Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/00014575)
-
-# Accident Analysis and Prevention
-
-journal homepage: [www.elsevier.com/locate/aap](http://www.elsevier.com/locate/aap)
-
-![](_page_0_Picture_5.jpeg)
-
-# On factors related to car accidents on German Autobahn connectors
-
-Martin Garnowski a, Hans Manner <sup>b</sup>,<sup>∗</sup>
-
-- <sup>a</sup> FedEx Express Europe, Middle East, Indian Subcontinent and Africa, Cologne, Germany
-- <sup>b</sup> Department of Economic and Social Statistics, University of Cologne, Meister-Ekkehart-Str. 9, 50937 Cologne, Germany
-
-#### article info
-
-Article history: Received 7 January 2011 Received in revised form 15 April 2011 Accepted 24 April 2011
-
-Keywords: Highway connectors German Autobahn Accident causes Negative binomial regression Random parameters
-
-#### abstract
-
-We make an attempt to identify factors that explain accidents on German Autobahn connectors. To find these factors we perform an empirical study making use of count data models with fixed and random coefficients. The findings are based on a set of 197 ramps, which we classify into three distinct types of ramps. For these ramps, accident data is available for a period of 3 years (January 2003 until December 2005). The negative binomial model with some random coefficients proved to be an appropriate model in our cross-sectional setting for detecting factors that are related to accidents. The most significant variable is a measure of the average daily traffic. For geometric variables, not only continuous effects were found to be significant, but also threshold effects indicating the exceedance of certain values.
-
-© 2011 Elsevier Ltd. All rights reserved.
-
-## **1. Introduction**
-
-The traffic on German highways, the so-called "Autobahn", has been increasing drastically over the past years and is expected to grow further in the future, due to Germany's central geographical position in Europe. The increase in traffic surpasses not only the economic growth, but also the speed of construction of roads. If the road network is not expanded significantly, the increasing number of vehicles on German Autobahns will certainly lead to an increasing number of accidents. Due to limitations in the potential expansions of the Autobahn, particularly in the short run, an important task is to identify accident factors and their influence on accident probabilities. This information could give suggestions for low-cost, short-term improvements for the prevention of accidents on existing Autobahn segments. One of the most dangerous situations for car drivers on Autobahns is the weaving out of the flow of traffic via a road connector. In the years 2003–2005 nearly 8000 accidents happened on road connectors on Autobahns in the administrative district Düsseldorf, which has an extremely dense Autobahn network and is the region we focus on in this study. Due to the safety-standards on Autobahns "only" 10 of these accidents were fatal, however, the economic damage caused by accidents is remarkable.
-
-Several studies found that about 90% of all accidents are at least partially caused by human failure, see, e.g. [Treat et al. \(1977\).](#page-7-0) As driver behavior is influenced by the whole environment, the aim of road construction should be to construct road sites that prevent or forgive human errors. The problem with road connectors is that each of them is constructed differently according to distinct traffic volumes or geographical constraints. The key question is which factors cause drivers to make mistakes. The aim of our study is to find a model that explains the number or the probability of accidents at various types of Autobahn connectors. This is a statistical problem. However, due to the nature of the problem, the use of standard linear regression models is inappropriate, as argued by [Jovanis and Chang \(1986\)](#page-7-0) and [Miaou and Lum \(1993\). T](#page-7-0)he variable of interest, namely the number of accidents during a given time interval, suggests the use of count data models.
-
-[Miaou and Lum \(1993\),](#page-7-0) who investigated the relationship between truck accidents and roadway geometries, and [Pickering](#page-7-0) [et al. \(1986\)](#page-7-0) used the Poisson regression model to study accident data. [Hauer et al. \(1988\), o](#page-7-0)n the other hand, introduced the more appropriate negative binomial model to find that traffic flow and various road characteristics have a significant effect on the number of accidents on signalized intersections in Toronto. Another study applying the negative binomial model to determine the causes of car accidents is [Shankar et al. \(1995\), w](#page-7-0)ho analyzed accidents on a section of the Interstate 90 near Seattle. Both Poisson and negative binomial models require a cross sectional setting. [Chin and](#page-7-0) [Quddus \(2003\)fo](#page-7-0)und that panel count data models have the advantage that they are able to deal with spatial or temporal effects in contrast to cross sectional count data models. They analyzed different types of accidents on signalized intersections in Singapore using a set of variables containing geometric variables, traffic volume variables and regulatory controls. Another paper applying panel data techniques to study accident data is [Shankar et al. \(1998\).](#page-7-0) As accident data typically tends to have more zero-observations
-
-<sup>∗</sup> Corresponding author. Tel.: +49 221 470 4130; fax: +49 221 470 5084. E-mail addresses: [mgarnowski@fedex.com](mailto:mgarnowski@fedex.com) (M. Garnowski), [manner@statistik.uni-koeln.de](mailto:manner@statistik.uni-koeln.de) (H. Manner).
-
-<span id="page-1-0"></span>than are predicted by standard count data models, zero-inflated models have been introduced into traffic accident research. For example, [Shankar et al. \(1997\)](#page-7-0) investigated accidents on arterials in Washington with two years of accident data and concluded that zero-inflated models have a great flexibility in uncovering processes affecting accident frequencies on roadway sections. For run-off-roadway accidents on a section of a highway in Washington State [Lee and Mannering \(2002\)](#page-7-0) got promising results applying zero-inflated models in contrast to standard models. However, [Washington et al. \(2003\)](#page-7-0) and [Lord et al. \(2005\)](#page-7-0) provide arguments against the use of zero-inflated models in the analysis of accident data. Recently, [Anastasopoulos and Mannering \(2009\)](#page-7-0) and [El-Basyouny and Sayed \(2009\)](#page-7-0) introduced count data models with random parameters to account for unobserved heterogeneity and found that these models perform very well. For more on regression models with count data we refer to, among others, [Cameron and](#page-7-0) [Trivedi \(1986\); Lord et al. \(2005\); Lord and Mannering \(2010\)](#page-7-0) and [Kibria \(2006\).](#page-7-0)
-
-None of the above-mentioned studies analyzes data on highway connectors, but the statistical techniques and explanatory variables that they use are similar to the ones used here.We make an attempt to find an appropriate model for our dataset of 3 years of accidents on Autobahn connectors in the administrative district Düsseldorf (approximately a fifth of the area of North Rhine-Westphalia). In our analysis, we consider more than 60 Autobahn connectors with 197 ramps in an area of approximately 2300 km2, using traffic data and geometric variables both in continuous form and allowing for threshold effects, which are represented by dummies indicating the exceedance of certain threshold values.
-
-The remainder of the paper is organized as follows. In the next section we describe ourmethodology. In Section[3](#page-2-0) we introduce and explain our dataset, followed by the presentation of the empirical results in Section [4. F](#page-4-0)inally, Section [5](#page-6-0) concludes our paper.
-
-#### **2. Methodology**
-
-As our variable of interest, the number of accidents on highway connectors, is a count variable, linear regression models are not an appropriate tool for our analysis. Instead, we make use of count data regressionmodels that have been designed for the specific purpose of modeling discrete count variables. The benchmark model for count data is the Poisson regression model, which is derived from the Poisson distribution. We assume a cross sectional setting with n independent observations, the ith of which being (yi, **x**i), where yi is the number of occurrences of the event of interest and **x**<sup>i</sup> is a vector of regressors that determine the number of accidents yi. The Poisson regression model is defined by
-
-$$f(y_i|\mathbf{x}_i,\beta) = \frac{e^{-\lambda_i}\lambda_i^{y_i}}{y_i!}, \qquad y_i = 0, 1, 2, \dots$$
- (2.1)
-
-where<sup>i</sup> > 0 is the intensity or rate parameter of observationi. Equation (2.1) measures the probability of yi occurrences of an event during a unit of time. In this model, the mean and the variance are the same, which is called the equidispersion-property of the Poisson distribution. The intensity parameter <sup>i</sup> is assumed to depend on the regressors through
-
-$$\lambda_i = \exp(\mathbf{x}_i'\beta),\tag{2.2}$$
-
-where the log-linear dependence of <sup>i</sup> on **x**<sup>i</sup> assures that the intensity parameter is always positive. It is crucial that the conditional mean equation is correctly specified and that the assumption of equidispersion is satisfied. In the case of overdispersion, maximum likelihood estimation (MLE) t-statistics are inflated, which can lead to too optimistic conclusions about the statistical significance of regressors. The assumption that yi is Poisson distributed can be relaxed considerably as studied in [Gourieroux et al. \(1984a,b\).](#page-7-0) Given a correctly specifiedmean, the pseudo MLEbased on a density from the linear exponential family (LEF) is consistent. This allows the assumption of equidispersion to be relaxed either by allowing for specific variance functions or by leaving the form of the variance unspecified. In the latter case standard errors can be obtained by a robust sandwich or bootstrap estimator.
-
-As the assumption of equidispersion is unlikely to hold in reality, a natural extension of the model is to allow for unobserved heterogeneity. Unobserved heterogeneity arises when the covariates do not account for the full amount of individual heterogeneity. An extension of the Poisson model that allows for unobserved heterogeneity and overdispersion is the negative binomial regression (NB) model. The NB model can be obtained by writing
-
-$$\lambda_i = \exp(\mathbf{x}_i'\boldsymbol{\beta} + \varepsilon_i), \tag{2.3}$$
-
-where exp (εi) follows a gamma distribution with mean 1 and variance ˛. For this reason it is also called the Poisson-gamma model. The density of the NB distribution is given by
-
-$$f(y_i|\mathbf{x}_i,\beta,\alpha) = \frac{\Gamma(\alpha^{-1}+y_i)}{\Gamma(\alpha^{-1})y_i!} \left(\frac{\alpha^{-1}}{\alpha^{-1}+\lambda_i}\right)^{\alpha^{-1}} \left(\frac{\lambda_i}{\alpha^{-1}+\lambda_i}\right)^{y_i}.$$
- (2.4)
-
-The variance of this distribution is:
-
-$$V[y_i|\lambda_i,\alpha] = \lambda_i(1+\alpha\lambda_i) > \lambda_i.$$
-
-i
-
-Thus, for ˛> 0, this model allows for overdispersion. The NB regression is also estimated by MLE and, as it is also a member of LEF, it is robust to distributional misspecifications. However, if the model is misspecified the maximum likelihood standard errors are in general inconsistent and either robust sandwich or bootstrap standard errors should be used.
-
-One possibility to allow for heterogeneity across observations (possibly caused by unobserved factors) is to let all or some of the parameters be random. Random parameter count data models for accident data have been proposed by [Anastasopoulos and](#page-7-0) [Mannering \(2009\)](#page-7-0) and [El-Basyouny and Sayed \(2009\). T](#page-7-0)he random parameters are written as
-
-$$\beta_i = \beta + \varphi_i, \tag{2.5}$$
-
-where ϕ<sup>i</sup> is a random variable with density g(·). The most popular choice is the normal distribution with mean 0 and variance 2, which we also use in this paper. Conditional on the random components, the intensity parameters are given by <sup>i</sup>|ϕi = exp(**x** i ˇ) and <sup>i</sup>|ϕi = exp(**x** ˇ + εi) for the Poisson and negative binomial regression, respectively. The log-likelihood of the random parameter model can be obtained by integrating out ϕ<sup>i</sup> from the joint density of yi and ϕi:
-
-$$\ln L = \sum_{i=1}^{n} \ln \int_{\varphi_i} g(\varphi_i) f(y_i | \varphi_i) d\varphi_i.$$
- (2.6)
-
-As this integral cannot be evaluated analytically and numerical integration is computational infeasible when the number of random parameters goes beyond one or two, [Anastasopoulos and](#page-7-0) [Mannering \(2009\)](#page-7-0) suggest to evaluate it by simulation. However, instead of using pseudo random numbers the integral above is evaluated using so called scrambled Halton sequences. Halton sequences are non-random sequences that cover the domain of integration more uniformly than random numbers and lead to a more precise evaluation of the integral with fewer draws. We refer to [Train \(1999\)](#page-7-0) and [Bhat \(2001, 2003\)](#page-7-0) for details on Halton sequences and their use in simulated maximum likelihood. Note that we treat a parameter as random when its estimated variance is significantly different from zero.
-
-In order to decide between the competing models, it is important to test for overdispersion in the data. Besides comparing the <span id="page-2-0"></span>sample mean and variance, a simple formal test can be performed by noting that the NB model reduces to the Poisson model when ˛= 0. Thus, the null hypothesis of equidispersion can be tested by estimating the NB and Poisson models and performing a likelihood ratio (LR) test for H<sup>0</sup> :˛= 0. Since ˛ is restricted to be positive the LR statistic asymptotically has probability mass of a half at zero and a half 2(1) distribution above 0. The critical value is then <sup>2</sup> <sup>1</sup>−2ı(1) when testing at level ı.
-
-The fit of competing models can be measured by the (McFadden) pseudo-R<sup>2</sup> given by
-
-$$1 - \frac{\ln \mathsf{L}_1}{\ln \mathsf{L}_0} \tag{2.7}$$
-
-where ln L<sup>1</sup> is the log-likelihood of the full model and ln L<sup>0</sup> is the log-likelihood of the model without any regressors. Additionally, potentially non-nestedmodels can be compared by looking at information criteria. The most popular ones are the Akaike Information Criterion (AIC) proposed by [Akaike \(1973\)](#page-7-0) and defined as
-
-$$AIC = -2 \ln L + 2k, \tag{2.8}$$
-
-and the Bayesian Information criterion (BIC) proposed by [Schwarz](#page-7-0) [\(1978\)](#page-7-0) and given by
-
-$$BIC = -2 \ln L + (\ln n)k, \tag{2.9}$$
-
-where k is the number of parameters in the model. The BIC places a larger penalty on additional regressors and thus leads to the selection of more parsimonious models.
-
-The estimated coefficients of the above models can be interpreted as semi-elasticities and, in contrast to linear regression coefficients, the response does not stay constant with varying regressors. For dummy variables the conditional mean of the dependant variable is exp(ˇˆ <sup>j</sup>) times larger if the dummy variable is one. The marginal effect is different for each observation i, but we consider both the average marginal effect ("Avg") and the marginal effect evaluated at the average ("At Avg"), which are calculated as
-
-$$\frac{1}{n}\sum_{i=1}^{n}\frac{\partial \mathsf{E}[y_{i}|\mathbf{x}_{i}]}{\partial x_{ij}} = \frac{1}{n}\sum_{i=1}^{n}\beta_{j}\exp(\mathbf{x}_{i}'\beta) \tag{2.10}$$
-
-and
-
-$$\frac{\partial \mathbf{E}[\mathbf{y}_i|\mathbf{x}]}{\partial \mathbf{x}_j}\bigg|_{\tilde{\mathbf{x}}} = \beta_j \exp(\tilde{\mathbf{x}}'\beta),$$
- (2.11)
-
-respectively, where **x**¯ denotes the vector of sample means of the regressors.
-
-# **3. Data**
-
-In this section we describe important terms and introduce our dataset. Further details about the data are available from the authors upon request.
-
-#### 3.1. Clarification of terms
-
-We distinguish between the terms connector, ramp and curve. Fig. 1 shows a schematic picture of a connector for two Autobahns. This connector consists of 8 ramps and each ramp consists of at least one curve. One has to distinguish between different types of connectors, namely clover leaves which connect two Autobahns as in Fig. 1 and diamond interchanges which connect an Autobahn with a minor road as in[Fig. 2. T](#page-3-0)he schematic pictures present only two possible shapes of connectors as their form varies due to construction constraints.
-
-The ramps can be categorized as tangents, loops, egress-ramps and drive-up ramps. Drive-up ramps are not considered here, because many variables that can be gathered for the other types
-
-![](_page_2_Picture_20.jpeg)
-
-**Fig. 1.** Clover leaf.
-
-of ramps cannot be found for this type. Furthermore, significantly fewer accidents occur on these ramps due to a lower permitted driving speed. Thus, we only consider ramps on which cars leave a specific Autobahn and change onto another Autobahn or a minor road. For the sake of simplicity tangent ramps, loop-ramps and egress-ramps will be called T-ramps, O-ramps and E-ramps, respectively.
-
-## 3.2. Data description
-
-Our data contains the following information: details on individual accidents, traffic flow data and geometrical properties of the ramps. We describe each type in turn.
-
-Accident data: The raw data on accidents was provided by the "Autobahnpolizei Düsseldorf", the highway police for the district Düsseldorf. The dataset contains detailed information for all reported accidents on Autobahns in the administrative district in the time period from January 2003 to December 2005. This amounts to a total of 39,032 accidents (12,887 in 2003, 13,433 in 2004 and 12,712 in 2005). This detailed information includes the exact time of the accident, its location, type of vehicle, number of vehicles involved, the severity and type of the accident, information on the driver, sight conditions, and road sleekness. Out of nearly 40,000 reported accidents, we filtered out the accidents that happened on ramps of the various connectors. Given the constraints imposed by the datasets we had a total of 197 ramps under investigation of which 95 were E-ramps, 33 were O-ramps and 69 were T-ramps. After the filtering process a total of 3048 accidents were analyzed. Due to the fact that we are interested in modeling the aggregated counts over a certain period of time at a particular location, the detailed information on the accidents is disregarded in this study.
-
-[Table 1](#page-3-0) shows some descriptive statistics for accidents on the different types of ramps. The significant difference in the mean of accident numbers for the different types of ramps is eye-catching and suggests the existence of heterogeneity across ramp types.
-
-A phenomenon that is often present in accident data analysis is the predominance of zero-observations, which calls for the use of zero inflated models, i.e. count data models that explicitly account for the presence of a large number of zero-observations. [Lord et al.](#page-7-0)
-
-<span id="page-3-0"></span>![](_page_3_Figure_2.jpeg)
-
-**Fig. 2.** Diamond interchange.
-
-**Table 1** Descriptive statistics, accidents, whole sample period.
-
-| Variable                          | All ramps | E-ramps | O-ramps | T-ramps |
-|-----------------------------------|-----------|---------|---------|---------|
-| Min number of accidents on a ramp | 0         | 0       | 0       | 0       |
-| Max number of accidents on a ramp | 103       | 76      | 99      | 103     |
-| Mean number of accidents per ramp | 15.32     | 10.97   | 15.86   | 21.03   |
-| Mode                              | 4         | 4       | 2       | 4       |
-| Median                            | 9         | 7       | 11      | 12      |
-| Standard deviation                | 18.53     | 12.01   | 18.56   | 23.84   |
-| Variance                          | 343.33    | 144.20  | 344.48  | 568.44  |
-| Inter quartile range              | 15        | 12      | 18      | 25      |
-| Total number of accidents         | 3048      | 1042    | 555     | 1451    |
-| Number of ramps                   | 197       | 95      | 33      | 69      |
-| Number of zero-accident ramps     | 10        | 5       | 1       | 4       |
-
-[\(2005\)](#page-7-0) concluded that excess zeros indicate an inappropriate choice of time scale. Referring to Table 1, for neither ramp-type the mode is zero. We have only 10 zero-observations out of the 197 ramps (5 E-ramps, 1 O-ramp and 4 T-ramps). On the other hand, on 35 ramps, 25 ormore accidents happened in our sample period of three years. Furthermore, [Cheng andWashington \(2005\)fo](#page-7-0)und that three years of crash-history data provides an appropriate crash history duration, which initially motivated our choice of the data period. Therefore we decided against the use of zero inflated models in our analysis.
-
-Traffic flow data: The traffic flow data was provided by the "Landesbetrieb Straßenbau Nordrhein-Westfalen", the institution responsible for the planning, construction and maintenance of the Autobahn in the area of North Rhine-Westphalia.
-
-The traffic volume is counted automatically by induction loops that additionally recognize the length of passing cars and categorizes them into the groups of cars smaller or larger than 7.50 m. We will use the term passenger cars for the former and trucks for the latter group.
-
-The raw dataset contained daily data for all induction loops on Autobahns in the administrative district Düsseldorf for the period from the 4th of March 2005 to the 7th of March 2006, from which the traffic volume on the connectors of interest was extracted. Note that the sample period is not exactly the same as that of the accident data. However, we believe that the available data captures the essential information on the amount of traffic and can be used without any reservations.
-
-For our cross sectional analysis we calculated the average daily traffic (abbreviated ADT) for each ramp. For ramp i the ADT is defined as:
-
-$$ADT_{i} = \frac{1}{T_{i}} \sum_{t=1}^{T_{i}} TV_{i,t},$$
-(3.1)
-
-where TVi,<sup>t</sup> is the traffic volume on ramp i on day t and Ti is the number of daily observations available for ramp i. We also calculated the percentage of trucks on each ramp. Table 2 shows some descriptive statistics for the variable ADT. It is imminent that there is a large spread in the distribution of traffic volume on the various ramps.
-
-Geometry data: The geometry data was collected manually by using aerial photos of the ramps of interest. Details on how the variables were constructed are available from the authors upon request. [Table 3](#page-4-0) presents a list of the geometry variables along with their descriptive statistics.
-
-Next, a number of dummy variables were constructed that are shown in [Table 4.](#page-4-0)
-
-Finally, we have information on the surface types on the ramps. Three different types of surfaces have to be distinguished, namely, melted or mastic asphalt (MA), found on 57 ramps, mastic asphalt using chipping (MAC), found on 136 ramps, and asphaltic concrete
-
-**Table 2** Descriptive statistics ADT, whole sample period.
-
-| Average daily traffic (ADT) |           |
-|-----------------------------|-----------|
-| All vehicles (ADTTotal)     |           |
-| Mean                        | 6630.8    |
-| Maximum                     | 28,178.47 |
-| Minimum                     | 579.26    |
-| Standard deviation          | 4579.36   |
-| Passenger cars (ADTPC)      |           |
-| Mean                        | 5173.95   |
-| Maximum                     | 25,094.72 |
-| Minimum                     | 472.57    |
-| Standard deviation          | 3582.56   |
-| Trucks (ADTTrucks)          |           |
-| Mean                        | 1456.85   |
-| Maximum                     | 15,844.4  |
-| Minimum                     | 51.94     |
-| Standard deviation          | 2051.49   |
-
-<span id="page-4-0"></span>**Table 3** Descriptive statistics of geometry variables.
-
-| Variable                                   | Minimum | Maximum | Mean   | Stand.Dev. |
-|--------------------------------------------|---------|---------|--------|------------|
-| Length of the ramp (m)                     | 49.56   | 1868.15 | 335.12 | 243.95     |
-| Length of the deceleration lane (m)        | 0.00    | 902.24  | 258.00 | 158.08     |
-| Total width of the lanes on the ramp (m)   | 3.30    | 8.19    | 4.93   | 1.13       |
-| Width per lane (m)                         | 3.02    | 7.16    | 4.33   | 0.73       |
-| Width of the shoulder lane (m)             | 0.00    | 4.81    | 2.14   | 1.40       |
-| Radius steepest curve (m)                  | 28.43   | 1428.47 | 164.78 | 195.56     |
-| Total deflection angle (◦)                 | 0.00    | 305.15  | 115.78 | 92.75      |
-| Absolute total deflection angle (◦)        | 4.32    | 305.15  | 148.29 | 83.97      |
-| Angle of the steepest curve (◦)            | 3.76    | 302.00  | 119.86 | 86.47      |
-| Length of the steepest curve (m)           | 30.05   | 1259.25 | 205.49 | 155.90     |
-| Number of lanes on the ramp                | 1       | 2       | 1.17   | 0.38       |
-| Number of inflection points on the ramp    | 0       | 4       | 0.59   | 0.86       |
-| Position of the steepest curve on the ramp | 1       | 4       | 1.45   | 0.72       |
-| Number of curves on the ramp               | 1       | 6       | 1.72   | 0.93       |
-| Number of lanes on the autobahn leaving    | 1       | 3       | 2.48   | 0.51       |
-
-**Table 4** Descriptive statistics dummy variables.
-
-| Variable                                | Mean | Stand.Dev. |
-|-----------------------------------------|------|------------|
-| Right Autobahn lane becomes decel. lane | 0.06 | 0.24       |
-| A curve gets steeper                    | 0.24 | 0.43       |
-| A curve gets less steep                 | 0.13 | 0.34       |
-| Incline on the ramp                     | 0.50 | 0.50       |
-| Decline on the ramp                     | 0.54 | 0.50       |
-| Trees inside                            | 0.87 | 0.33       |
-| Trees outside                           | 0.78 | 0.42       |
-| Crossing lane at the access of the ramp | 0.22 | 0.42       |
-| Crossing lane at the exit of the ramp   | 0.22 | 0.41       |
-| Median between Autobahn and decel. lane | 0.38 | 0.49       |
-
-(AC) found on 4 ramps. Without going into details, according to [Richter and Heindel \(2004\)](#page-7-0) the main advantages of the different surface types are: MA is known to have a good grip, with the tradeoff of being a loud surface type. AC has the worst grip, but it is the cheapest of the three surface types. Roads build out of MAC are very durable and thus MAC is perfect for roads with high traffic density.
-
-# 3.3. Missing information
-
-There are a number of potential variables that may have been useful for the analysis but could not be collected. First of all, we did not have any information on traffic signs located on the connectors. On German Autobahns as well as on ramps there is no mandatory speed limit. However, on several ramps there are speed limit signs. "Slippery road"-signs can also be found on several ramps.
-
-Next, the aerial photos we used for the collection of our geometry variables did not give us any information on standing guardrails on the ramps. However, the effect of guardrails on accidents is a complex issue since guardrails may increase the frequency of property damage, but reduce the severity of accidents.1
-
-Another shortcoming of our dataset was the short period of our traffic flow variable, not allowing us for a substantiated panel data approach and hence making us ignore information on seasonality. Since a panel approach was not conducted, weather variables have not been used in our research. As the area of the connectors investigated is quite small (around 2300 km2), we think there are no significant differences in weather conditions for aggregated data. Nevertheless, in a panel approach it might have been interesting to take weather into consideration, not only to explain cross-sectional differences, but also to explain seasonality.
-
-Finally, the information we had on the road surface was also quite rudimentary. Without doubt, not only the type of the surface, but also the age of the surface has an impact on the grip of the road and thus an influence on the accident frequency.
-
-# **4. Empirical results**
-
-In this section we present the results of our empirical analysis. Section [4.1](#page-5-0) presents the estimation result. Their interpretation can be found in Section [4.2.](#page-6-0)
-
-Before we present our results we would like to note the following. First, Table 5 presents all variables we use along with their abbreviations. Second, for the regressions, the unit of measurement was changed to thousands of cars for all ADT-variables and to hundreds of meters for the radius in order to avoid unreadably small regression results.
-
-**Table 5** Abbreviations of variables in the dataset.
-
-| Variable                                         | Abbrevation             |
-|--------------------------------------------------|-------------------------|
-| Number of accidents                              | nb acc                  |
-| Length of the ramp                               | length ramp             |
-| Length of the deceleration lane                  | length decel            |
-| Total width of the lanes on the ramp             | width lanes             |
-| Width per lane                                   | width per lane          |
-| Width of the shoulder lane                       | width should            |
-| Radius steepest curve                            | radius                  |
-| Total deflection angle                           | angle tot               |
-| Absolute total deflection angle                  | angle abs               |
-| Angle of the steepest curve                      | angle steepest          |
-| Length of the steepest curve                     | length steepest         |
-| Number of lanes on the ramp                      | nb lanes                |
-| Number of inflection points on the ramp          | nb infl                 |
-| Position of the steepest curve on the ramp       | pos steepest            |
-| Number of curves on the ramp                     | nb curves               |
-| Number of lanes on the Autobahn leaving          | nb autob lanes          |
-| Dummy – Right Autobahn lane becomes decel. lane  | D autob decel           |
-| Dummy – A curve gets steeper                     | D steeper               |
-| Dummy – A curve gets less steep                  | D less steep            |
-| Dummy – Incline on the ramp                      | D incline               |
-| Dummy – Decline on the ramp                      | D decline               |
-| Dummy – Trees inside                             | D trees in              |
-| Dummy – Trees outside                            | D trees out             |
-| Dummy – Crossing lane at the access of the ramp  | D cross access          |
-| Dummy – Crossing lane at the exit of the ramp    | D cross exit            |
-| Dummy – Median between Autobahn and decel. lane  | D median                |
-| Average daily traffic passenger cars             | ADT pc                  |
-| Average daily traffic trucks                     | ADT trucks              |
-| Truck percentage                                 | truck perc              |
-| Threshold Dummy – Length deceleration lane       | T length decelthreshold |
-| Threshold Dummy – Width of the lanes             | T width lanesthreshold  |
-| Threshold Dummy – Position of the steepest curve | T pos steepestthreshold |
-| Threshold Dummy – Radius of the steepest curve   | T radiusthreshold       |
-
-<sup>1</sup> We would like to thank an anonymous referee for pointing this out.
-
-<span id="page-5-0"></span>**Table 6** Estimation results.
-
-| Variable             | Fixed parameters model |             | Random parameters model |             |  |
-|----------------------|------------------------|-------------|-------------------------|-------------|--|
-|                      | Coefficient            | Std. Errors | Coefficient             | Std. Errors |  |
-| Constant             | −0.9091                | 0.4791      | −1.2067                 | 0.4690      |  |
-| ln(ADT pc)           | 0.7363                 | 0.0988      | 0.8130                  | 0.1030      |  |
-| truck perc           | 0.8954                 | 0.4194      | 1.2078                  | 0.4114      |  |
-| ln(angle abs)        | 0.2685                 | 0.0840      | 0.2838                  | 0.0780      |  |
-| D steeper            | 0.3435                 | 0.1661      | 0.2364                  | 0.1776      |  |
-| RC                   |                        |             | 0.5989                  | 0.1925      |  |
-| T length decel180    | 0.4831                 | 0.1409      | 0.4352                  | 0.1382      |  |
-| RC                   |                        |             | 0.5071                  | 0.1381      |  |
-| T width lanes3.90    | 0.4333                 | 0.1824      | 0.4057                  | 0.1942      |  |
-| T pos steepest1      | 0.3115                 | 0.1470      | 0.2246                  | 0.1527      |  |
-| RC                   |                        |             | 0.5012                  | 0.1765      |  |
-| Dispersion parameter | 0.6267                 | 0.0719      | 0.2909                  | 0.1160      |  |
-| ln L                 | −689.86                |             |                         | −685.99     |  |
-| AIC                  | 1397.7                 |             | 1395.9                  |             |  |
-| BIC                  | 1427.3                 |             | 1435.4                  |             |  |
-| Pseudo-R2            |                        | 0.0672      |                         | 0.0725      |  |
-
-#### 4.1. Estimation results
-
-We consider the negative binomial (NB) regression both with fixed and with (possibly) random coefficients (RC). A coefficient is treated as random when its estimated standard deviation RC is significantly different from 0. Since the negative binomial model nests the Poisson regression model when ˛= 0 the null hypothesis of a Poisson Model, and hence the null hypothesis of equidispersion, can be tested using the LR test as described in Section [2. W](#page-1-0)e computed this LR statistic for all estimated models and it turned out that the test has p-values of virtually zero for all the model specifications we considered. An exception is the RC model with random intercept, in which case the fit of the Poisson regression is almost identical to the fit of the NB model. This is not surprising as the NB model and the Poisson model with random intercept are very similar differing only in the error distributions.
-
-Preliminary estimates of the NB regression reveal that from our initial set of variables only ADT pc, ln(angle abs), truck perc and D steeper are statistically significant. Alternatively, the radius can be used instead of ln(angle abs), as these two variables have a correlation of about −0.76. In the next step of our analysis, we test for the different functional forms of the regressor ADT pc in order to allow for nonlinearities. We consider two variations, first by adding a quadratic term to the initial model and second by working with the natural logarithm of ADT pc. Both choices clearly improve the model fit as indicated by the pseudo-R<sup>2</sup> and information criteria. However, there is no clear evidence whether the squared form is to be preferred over the logarithmic form. In order to get an additional evaluation criterion we conducted the LR-test for non-nested models proposed by [Vuong \(1989\). T](#page-7-0)he null hypothesis of equivalence of the models cannot be rejected in favor of any of the specifications. However, as it has one parameter less to estimate we continue working with the logarithmic form.
-
-Next, we want to investigate the heterogeneity across the different ramp types suggested by the descriptive statistics. Ideally, a separate regression should be estimated for each ramp type, but given the small number of observations for the individual ramp types the results would not be reliable. Therefore we consider dummy variables for each ramp type (skipping the intercept to avoid multicollinearity) and perform a likelihood ratio test to see if the model fit improved. The improvement in fit is not significant so we dropped the dummies from the model.
-
-As none of the remaining variables were found to have additional significant effects in explaining accident frequencies we investigate whether there are possible threshold effects. By this we mean the effect when variables are larger than some (predetermined) value and we consider these to capture potential nonlinearities. To identify the thresholds we create dummy variables that for values exceeding the supposed threshold value and zero otherwise. By varying the threshold value and comparing the information criteria and the pseudo-R<sup>2</sup> we try to determine the actual value of the threshold. The notation of the threshold variables is as follows: A "T" in front of the variable indicates that this is a dummy measuring the threshold and the index-number shows the value of the threshold. This means that the variable is 1 for values larger than the threshold and zero otherwise. Three variables seem to have a threshold effect, namely the length of the deceleration lane (length decel), the total width of the lane(s) (width lanes) and the position of the steepest curve (pos steepest).
-
-We would like to note two things concerning our final model: (i) the variable width lanes measures the total width of the officially accessible lanes without accounting for the width of a possible shoulder lane. We also investigated a possible threshold effect of the total width of the official road together with the shoulder lane (i.e. the whole possibly accessible road). However no significant effect could be found. (ii) the variable T pos steepest1 takes on the value one if the first curve is not the steepest curve on the ramp.
-
-The following step in the analysis is to allow for random coefficients. We estimate the model using 500 Halton draws to simulate the log-likelihood function. Initially, all model parameters are allowed to vary randomly, but we treat parameters as fixed when the estimated variance of the random coefficient RC is not significantly different from 0. Only the variables D steeper, T length decel180 and T pos steepest1 have random coefficients. Furthermore, in the random coefficient model no additional variables from our dataset have a significant (fixed or random) effect on the number of accidents.
-
-Table 6 presents the estimation results of our model with fixed and random coefficients. First of all, it is noticeable that the random coefficient specification only leads to a small improvement in the model fit, which is in contrast to the findings of [Anastasopoulos](#page-7-0) [andMannering \(2009\). N](#page-7-0)evertheless, a likelihood ratio test suggests that the improvement in fit is significant with a p-value of about 0.05. The BIC, on the other hand, indicates that the simpler version of the model with fixed parameters is more suitable. Furthermore, the estimated dispersion parameter˛of the binomial distribution is much smaller for the random coefficient model, which is likely due to the fact that the heterogeneity of the random coefficients picks up some of the heterogeneity originally captured by the dispersion parameter. The parameter estimates are mostly very similar across the models, with the exception that the parameter of the variable truck perc changes when allowing for random coefficients. How-
-
-<span id="page-6-0"></span>**Table 7** Marginal effects.
-
-| Variable          |             | Fixed parameters model |        |             | Random parameters model |        |  |
-|-------------------|-------------|------------------------|--------|-------------|-------------------------|--------|--|
-|                   | Coefficient | Avg                    | At Avg | Coefficient | Avg                     | At Avg |  |
-| ln(ADT pc)        | 0.74        | 11.14                  | 8.89   | 0.81        | 10.53                   | 8.32   |  |
-| truck perc        | 0.90        | 13.55                  | 10.82  | 1.21        | 15.64                   | 12.36  |  |
-| ln(angle abs)     | 0.27        | 4.06                   | 3.24   | 0.28        | 3.67                    | 2.90   |  |
-| D steeper         | 0.34        | 5.20                   | 4.15   | 0.24        | 3.06                    | 2.42   |  |
-| T length decel180 | 0.48        | 7.31                   | 5.84   | 0.44        | 5.64                    | 4.45   |  |
-| T width lanes3.90 | 0.43        | 6.56                   | 5.23   | 0.41        | 5.25                    | 4.15   |  |
-| T pos steepest1   | 0.31        | 4.71                   | 3.76   | 0.22        | 2.91                    | 2.30   |  |
-
-ever, for the variables that have random coefficients, the estimates are a bit lower compared to their fixed counterparts.
-
-#### 4.2. Interpretation
-
-In Table 7 we report both the average marginal effect, as well as the marginal effect evaluated at the averages of the regressors. We only interpret the model with random coefficients, since the overall model fit is better and the parameter estimates are quite close for most of the variables.
-
-The variable that has the strongest effect on the number of accidents is the measure of average daily traffic. Since the variable ADT pc enters in logarithmic form it means that a 1% increase in average daily traffic on average leads to a 0.81% increase in the expected number of accidents. An increase of truck perc by 0.1 units, hence an increase of the truck ratio by 10 percentage points leads to an increase in the expected number of accidents by 12.08%. The mean effect "Avg" suggests that the average effect of a 10 percentage point increase in the ratio of trucks yields 1.56 more expected accidents per ramp in a time period of three years. The "Avg" estimates for this model are about 26% higher than those of the "representative" ramp given in "At Avg", which is due to the convex exponential mean function. The same phenomenon can therefore be observed for the other variables. The remaining variables are more interesting from an engineering perspective. The estimated coefficient of the variable ln(angle abs) implies that a 1% increase in the absolute total deflection angle leads to a 0.27% increase in the expected number of accidents. Next, a ramp on which a curve gets steeper on average has an exp (0.24) = 1.27 times higher expected number of accidents than a ramp on which no curve is getting steeper. In absolute terms you expect on average 3.06 more accidents over a period of three years when a curve on a ramp gets steeper. The distribution of the random coefficient implies that in 35% of the cases the effect of a curve getting steeper actually reduces the number of accidents, but for the majority of cases this has a positive effect.
-
-The estimates of the remaining variables are rather counterintuitive. The estimate of 0.4352 for the coefficient of the variable T length decel180 suggests that exp (0.43) = 1.54 times more accidents occur on ramps with a deceleration lane larger than 180 m in contrast to ramps with a deceleration lane that is smaller than 180 m. However, 20% of the parameter distribution is less than 0 and thus of the expected sign. Similarly, the expected number of accidents on ramps with lane widths exceeding 3.9 m is 1.5 times higher than on ramps with lane widths of less than 3.9 m. Finally, the estimate of the threshold variable T pos steepest1 suggests that we can expect the number of accidents to be 1.25 times higher, if the first curve is not the steepest on the ramp. In this case, about 33% of the distribution is smaller than zero. One reason for these three counterintuitive results might be that an unsafe looking ramp causes the driver to be more aware. Another explanation is an omitted variable bias, since it is likely that there are stricter speed restrictions (or warning sings) on these unsafe ramps, which in would turn decrease the number of accidents. However, part of the possible problem of omitted variable bias is handled by allowing for random parameters.
-
-## **5. Conclusion**
-
-The aim of this paper was to find an appropriate statistical model that helps to explain accident frequencies on Autobahn connectors in Germany. The negative binomial regression model with random coefficients turned out to be the most appropriate tool. Our dataset contains detailed information on accidents on 197 connectors, data on traffic flow of passenger cars and trucks, and a set of nearly 30 geometry variables. Although the three types of ramps, egressramps (n =95), tangent-ramps (n =69) and loop-ramps (n =33), have quite distinct characteristics, dummy variables indicating ramp types are not significant and separate models cannot be estimated due to the small number of observations in the sub-populations.
-
-The most important variable explaining accident frequencies is the average daily traffic. The fraction of trucks is also an important factor. Beyond that, measures of the curvature, like the radius of the steepest curve or a dummy indicating that a curve gets steeper, turned out to be relevant. The variables measuring the length of the deceleration lane, the width of the lanes and the position of the steepest curve only have an effect when exceeding certain thresholds.
-
-The final question is whether and how our findings can be used to improve the safety of existing Autobahn connectors and to give recommendations in the construction of new ones. The accident factors identified in this study cannot be used for simple short term improvements, which is not surprising, as the easiest and cheapest solution one can think of is putting up additional warning signs and speed restrictions. Since we had no data available on these, and it is likely that these measures were already taken on relatively dangerous connectors, we have to leave the recommendation of simple solutions to future research that includes such information. However, if connectors are built from scratch, our results might be helpful. The finding that curves getting steeper yield higher accident frequencies is definitely a result that future planning should not disregard. The radius effect might also be interesting for the design of ramps. This can be simplified as: the steeper a curve, the more accidents can be observed. The significant positive parameter of the variable absolute total deflection angle can be interpreted as: the simpler a ramp is constructed, the less accidents can be expected.
-
-In future research we need to deal with the data limitations encountered. Information on speed limitations, vertical grades on the ramps or exit approaches, warning signs and pavement friction is likely to add significant explanatory power to the model. Furthermore, one may try to expand the dataset and consider temporal and dynamic effects, possibly in a panel framework. Finally, the small number of observations is another limitation of the current research, as it was not possible to estimate models for the different ramp types separately.
-
-### <span id="page-7-0"></span>**Acknowledgments**
-
-The authors would like to thank Jean-Pierre Urbain, Frowin Schulz and three anonymous referees for valuable comments. The authors would also like to thank the Landesbetrieb Straßenbau Nordrhein-Westfalen and the Autobahnpolizei Düsseldorf for providing the raw data.
-
-#### **References**
-
-- Akaike, H., 1973. Information theory and an extension of the maximum likelihood principle. In: Petrov, B., Csaki, F. (Eds.), Second International Symposium on Information Theory. , pp. 267–281.
-- Anastasopoulos, P.C., Mannering, F.L., 2009. A note on modeling vehicle accident frequencies with random-parameters count models. Accident Analysis and Prevention 41, 153–159.
-- Bhat, C., 2001. Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model. Transportation Research Part B 17, 677–693.
-- Bhat, C., 2003. Simulation estimation of mixed discrete choice models using randomized and scrambled halton sequences. Transportation Research Part B 37, 837–855.
-- Cameron, A.C., Trivedi, P.K., 1986. Econometric models based on count data: comparisons and applications of some estimators. Journal of Applied Econometrics 1, 29–53.
-- Cheng,W.,Washington, S.P., 2005. Experimental evaluation of hotspot identification methods. Accident Analysis and Prevention 37, 870–881.
-- Chin, H., Quddus, M., 2003. Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections. Accident Analysis and Prevention 35, 253–259.
-- El-Basyouny, K., Sayed, T., 2009. Accident prediction models with random corridor parameters. Accident Analysis and Prevention 41, 1118–1123.
-- Gourieroux, C., Monfort, A., Trognon, A., 1984a. Pseudo maximum likelihood methods: applications to poisson models. Econometrica 52, 701–720.
-- Gourieroux, C., Monfort, A., Trognon, A., 1984b. Pseudo maximum likelihood methods: theory. Econometrica 52, 681–700.
-- Hauer, E., Ng, J., Lovell, J., 1988. Estimation of safety at signalized intersections. Transportation Research Record 1185, 48–61.
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-- Jovanis, P.P., Chang, H.-L., 1986. Modeling the relationship of accidents to miles traveled. Transportation Research Record 1068, 42–51.
-- Kibria, B.M.G., 2006. Applications of some discrete regression models for count data. Pakistan Journal of Statistics and Operation Research 2, 1–16.
-- Lee, J., Mannering, F., 2002. Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. Accident Analysis and Prevention 34, 149–161.
-- Lord, D., Mannering, F.L., 2010. The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives. Transportation Research Part A 44, 291–305.
-- Lord, D., Washington, S., Ivan, J., 2005. Poisson, poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory. Accident Analysis and Prevention 37, 35–46.
-- Miaou, S., Lum, H., 1993. Modeling vehicle accidents and highway geometric design relationships. Accident Analysis and Prevention 25, 689–709.
-- Pickering, D., Hall, R., Grimmer, M., 1986. Accidents on rural t-junctions. Tech. rep., Research Report 65. Transportation and Research Laboratory, Department for Transport, Crowthrone, Berkshire, United Kingdom.
-- Richter, D., Heindel, M., 2004. Straßen- und Tiefbau. Mit lernfeldorientierten Projekten. Teubner.
-- Schwarz, G., 1978. Estimating the dimension of a model. Annals of Statistics 6, 461–464.
-- Shankar, V., Albin, R., Milton, J., Mannering, F., 1998. Evaluating median cross-over likelihoods with clustered accident counts: an empirical inquiry using the random effects negative binomial model. Transportation Research Record, 1635.
-- Shankar, V., Mannering, F., Barfield, W., 1995. Effect of roadway geometrics and environmental factors on rural freeway accident frequencies. Accident Analysis and Prevention 27, 371–389.
-- Shankar, V., Milton, J., Mannering, F., 1997. Modeling accident frequencies as zero-altered probability processes: an empirical inquiry. Accident Analysis and Prevention 29, 829–837.
-- Train, K., (1999). Halton sequences for mixed logit. Working paper. University of California, Department of Economics, Berkeley.
-- Treat, J., Tumbas, N., McDonald, S., Shinar, D., Hume, R., Mayer, R., Stanisfer, R., Castellan, N., 1977. Tri-level study of the causes of traffic accidents. Report No. DOT-HS-034–3-535–77 (TAC).
-- Vuong, Q., 1989. Likelihood ratio tests for model selection and non-nested hypothesis. Econometrica 57, 307–334.
-- Washington, S., Karlaftis, M.G., Mannering, F.L., 2003. Statistical and Econometric Methods for Transportation Data Analysis. CRC, Chapman and Hall.

Zotero/001_artiklid/Road Safety Indicators/gitelmandesigningcompositeindicatorroad2010.md

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----
-category: literaturenote
-citekey: gitelmandesigningcompositeindicatorroad2010
-title: Designing a composite indicator for road safety
-authors: "Gitelman, Victoria; Doveh, Etti; Hakkert, Shalom"
-year: 2010
-date: 2010-00-00 2010
-doi: 10.1016/j.ssci.2010.01.011
-publication: Safety Science
-url: "https://doi.org/10.1016/j.ssci.2010.01.011"
-zotero_key: B4W7HUXZ
-zotero_storage: 9DKWFUXN
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Gitelman, Victoria"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-## Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/09257535)
-
-# Safety Science
-
-journal homepage: [www.elsevier.com/locate/ssci](http://www.elsevier.com/locate/ssci)
-
-![](_page_0_Picture_5.jpeg)
-
-# Designing a composite indicator for road safety
-
-Victoria Gitelman a,\*, Etti Doveh <sup>b</sup> , Shalom Hakkert <sup>a</sup>
-
-- <sup>a</sup> Transportation Research Institute, Technion City, Haifa 32000, Israel
-- <sup>b</sup> The Technion Statistical Laboratory, Technion City, Haifa 32000, Israel
-
-### article info
-
-#### Article history: Received 1 September 2009 Received in revised form 12 January 2010 Accepted 14 January 2010
-
-Keywords: Road safety Performance Composite indicator Statistical weighting
-
-### abstract
-
-This study set out to develop a composite road safety indicator for benchmarking countries' road safety performance, which would combine the main layers of the road safety pyramid which describes the complex nature of road safety activities, performance and outcomes. Four groups of basic safety indicators were considered, which refer to: policy performance (road safety programmes), final road safety outcomes (fatality rates, scope of traffic injury), intermediate outcomes (wearing rates of seat belts, crashworthiness and composition of vehicle fleet, alcohol-impaired driving), and background characteristics of countries (motorization level, population density). The analysis used the data collected for 27 European countries. Weights based on statistical models were used to combine the basic indicators into a composite one. Principal Component Analysis and Common Factor Analysis weighting were examined. The composite indicators, estimated by several methods, enabled us to rank and group the countries according to their safety performance.
-
-The analysis revealed that the countries' ranking based on the composite indicators is not necessarily similar to the traditional ranking of countries based on fatality rates only. Furthermore, it was observed that the indicators belonging to the final outcomes and intermediate outcomes are not uniform in their behaviour. Indicators which were found to be more consistent and influential and termed a 'core set of basic indicators' are recommended for future uses. The general conclusion is that the design of a composite road safety indicator in which relevant information from the different components of the road safety pyramid has been captured and weighted is realistic and meaningful. Such an indicator gives a more enriched picture of road safety than a ranking based only on fatality rates, which is the common practice at present. Grouping countries in this process is promising and seems to be preferable to simply ranking countries.
-
-> -2010 Elsevier Ltd. All rights reserved.
-
-# 1. Introduction
-
-The number of indicators suggested for use in the field of road safety has been growing rapidly, especially over the last decade ([ETSC, 2001; Wegman et al., 2005; Al Haji, 2005; Hakkert et al.,](#page-12-0) [2007; Hermans et al., 2008\)](#page-12-0). The purposes of road safety indicators are to enable meaningful national or sub-national (e.g. regional, local, etc.) comparisons and monitoring over time, of road safety developments. Recognising the complex character of the road safety phenomenon, today more and more indicators are intended to measure the factors contributing to accidents, to identify conditions which are associated with increased accident/injury risks, and to detail the structure of traffic injury patterns. In contrast, the traditional approach mainly considered the safety outcomes in terms of fatalities per population, vehicle fleet or exposure.
-
-Moreover, road safety is steadily developing as a major policy area [\(Peden et al., 2004](#page-12-0)), where safety performance indicators could and should serve as supportive tools for policymakers. In comparing the safety achievements of countries there is a need to reduce the dimensions of the problem and to be able to work with a composite indicator which can describe all the relevant components in a concise and comprehensive way. Examples of such indicators are known in other domains such as: the Human Development Index which reflects life expectancy, education level and living standards in each country, and is used by the United Nations for the estimation of progress and annual country comparisons; the Environmental Sustainability Index which is used by the World Economic Forum; the Overall Health System Index used by the World Health Organisation (WHO), and others ([Al Haji,](#page-12-0) [2005; Nardo et al., 2005\)](#page-12-0).
-
-A number of studies were recently carried out that were aimed at the development of a composite road safety index. [Al Haji \(2005\)](#page-12-0) suggested a Road Safety Development Index (RSDI) and used it for a comparison of road safety progress in ten Asian countries plus Sweden. The RSDI development started with the definition of eight
-
-<sup>\*</sup> Corresponding author. Tel.: +972 4 8295132; fax: +972 4 8225716. E-mail addresses: [trivica@tx.technion.ac.il](mailto:trivica@tx.technion.ac.il) (V. Gitelman), [ierde01@ie.technion.](mailto:ierde01@ie.technion.ac.il) [ac.il](mailto:ierde01@ie.technion.ac.il) (E. Doveh), [hakkert@tx.technion.ac.il](mailto:hakkert@tx.technion.ac.il) (S. Hakkert).
-
-dimensions of the road safety domain, which are traffic risk, personal risk, vehicle safety, road situation, road user behaviour, socio-economic background, road safety organisation and enforcement. For each dimension, one or several quantitative indicators were suggested and their applicability was analysed based on available data. Finally, each country was characterized by eleven separate indicators which were combined into one composite indicator. To construct a composite index, [Al Haji \(2005\)](#page-12-0) applied three ways of weighting, which were the simple equal average, the use of theoretical weights, and the Principal Component Analysis. The results of the different methods were consistent and enabled a robust classification of countries into three groups of high, medium or low safety development.
-
-[Hermans et al. \(2008\)](#page-12-0) studied the issue of assigning weights to individual indicators, to provide a combined road safety index. The researchers considered the seven safety domains which were defined by the SafetyNet project [\(Hakkert et al., 2007](#page-12-0)): alcohol and drugs, speed, protective systems, visibility (daytime running lights), vehicles, infrastructure, and trauma care. They suggested one indicator for each domain. Based on the data available in the international databases, from the WHO, and from the SARTRE project, the indicators were estimated for 21 European countries. Five weighting techniques as suggested by [Nardo et al. \(2005\)](#page-12-0) were used to combine the separate indicators into one index: factor analysis, analytical hierarchy process, budget allocation, data envelopment analysis, and equal weighting. The rankings resulting from these weighting methods were further compared with the countries' rankings according to the personal risk (the number of traffic fatalities per million inhabitants). It was found that different weighting methods agreed most on the ranking of countries with a low road safety ranking. Moreover, of the five methods, the data envelopment analysis method resulted in a ranking which best approaches the countries' ranking based on the personal risk.
-
-Both studies have clearly demonstrated the possibilities for creating composite road safety indices. However, they both considered a relatively small number of basic indicators, for some of which the quality of the data for the quantitative measures selected can be questioned.
-
-In this study, we aimed to create and explore a comprehensive composite road safety indicator, based on the recent concepts of the road safety domain developed by the SUNflower ([Wegman](#page-12-0) [et al., 2005\)](#page-12-0) and SafetyNet ([Hakkert et al., 2007](#page-12-0)) projects. In the SafetyNet project safety performance indicators were developed, but no attempt was made to combine the results from different indicators into one composite value, in order to compare countries. Somewhat earlier the SUNflower project tried to develop a methodology to compare countries on their safety level. This resulted in the footprint methodology. It was felt that, on the basis of these prior methods and the available data, a new composite indicator could be developed, what has been carried out in the present study.
-
-In this process, the types of benchmarking discussed in the literature were considered, and data for the European countries were used that were collected in the SafetyNet project and which were available from international databases. This study was a component of a comprehensive project aimed at developing a knowledge-based framework for the benchmarking of road safety performances and developments for national and sub-national jurisdictions [\(Wegman et al., 2008\)](#page-12-0).
-
-### 2. The model
-
-### 2.1. The road safety pyramid
-
-The SUNflower approach ([Koornstra et al., 2002; Wegman et al.,](#page-12-0) [2005](#page-12-0)) described the road safety domain as a pyramid consisting of several layers, from bottom to top: safety measures and programmes (as the road safety policy performance); safety performance indicators (as intermediate outcomes); the numbers of accident fatalities/injuries (as the final outcomes) and the social costs of accidents/injuries at the very top. An additional 'Structure and culture' layer has been added at the bottom of the pyramid to include the background conditions of the system or the policy context [\(Koornstra et al., 2002\)](#page-12-0).
-
-The layer of safety performance indicators (SPIs) was originally explored by [ETSC \(2001\).](#page-12-0) The reason for the SPIs development was the assumption that accidents and injuries are only the tip of the iceberg, because they occur as the 'worst case' result of unsafe operational conditions in the road traffic system. Thus, SPIs can be defined as measures that are causally related to accidents or injuries and are used in addition to the figures about accidents or injuries, in order to indicate safety performance or understand the processes that lead to accidents [\(ETSC, 2001\)](#page-12-0).
-
-The SafetyNet project [\(Hakkert et al., 2007\)](#page-12-0) provided a further methodological basis for the SPIs' development. Based on the potential of different road safety areas for increasing road safety as well as on the experiences and data available, seven problem areas were designated as central to road safety activities in Europe. They are: (1) alcohol and drug-use; (2) speeds; (3) protective systems; (4) daytime running lights; (5) vehicles (passive safety); (6) roads and (7) trauma management.
-
-For each one of these areas, SPIs were developed and, then, using the data provided by national representatives of the European countries, SPIs were estimated and country comparisons were performed – see [Vis and Van Gent \(2007\)](#page-12-0).
-
-### 2.2. Basic indicators
-
-This study set out to develop a composite road safety indicator for benchmarking purposes, which would combine the main layers of the road safety pyramid.
-
-The layers considered were the road safety policy performance (safety measures and programmes), accident fatalities/injuries (as the final outcomes) and safety performance indicators (as intermediate outcomes). These layers will be further described by A-, Band C-groups of indicators, respectively. A fourth group of indicators (D) was added trying to present some background variables for each country as a first attempt to identify components of the lowest level of the pyramid ('structure and culture').
-
-To develop a composite indicator, basic indicators should first be defined, for each layer of the pyramid that needs to be considered. As stated earlier, the present study builds upon the theory and evidence developed and collected in two earlier related projects: the SUNflower [\(Wegman et al., 2005](#page-12-0)) and SafetyNet ([Hakkert](#page-12-0) [et al., 2007](#page-12-0)) studies. In these studies a theory was developed linking the characteristics of four basic layers related to safety, especially concerning the final outcomes' and intermediate outcomes' layers. In the SafetyNet project a large amount of evidence was collected and described linking the indicators of intermediate outcomes to safety. On the other hand, the final selection of basic indicators was limited by the data currently available for the European countries from international databases and from the Safety-Net project.
-
-As for the policy indicators, results have been reported in the literature that countries with comprehensive safety programs and quantitative targets usually performed better than others. The main principles of successful road safety management are well summarized by [OECD \(2002\), ETSC \(2006\), Elvik \(2008\).](#page-12-0) Based on these principles, for the A-group of indicators a number of characteristics were suggested which would reflect the quality of national road safety programmes. The five indicators introduced for the Agroup related to the level of national safety targets, the approach applied for selection of interventions, the scope of economic evaluation which preceded the programme's composition, the way of monitoring and the level of responsibility for the programme's performance. For each indicator, the categories of values were defined as presented in Table 1.
-
-For each country, the value of each indicator should be selected based on the published national road safety programmes, other background papers and available follow-up reports, including those prepared by the European Transport Safety Council (ETSC), Organisation for Economic Co-operation and Development (OECD) or other international working groups. The values applied in the current study should be treated as preliminary as those are mostly based on two sources: country reports from [OECD/ECMT \(2008\)](#page-12-0) and surveys provided by [OECD/ITF \(2008\),](#page-12-0) and on some other frequently cited road safety publications.
-
-Giving the values according to Table 1, the national safety programme was marked as having an ''ambitious" target when it was in line with the European policy of 50% reduction in fatalities by 2010 or when it implied an average annual reduction of fatalities by 5% or more; such a mark (value ''a") was given for many countries. On the other hand, some countries (e.g. Italy) stated that they have no national targets (value ''c"); the remainder of countries received value ''b".
-
-Considering the selection of safety interventions for the national programme, the best mark (value ''a") was given for the programmes where a comprehensive categorization of road safety problems and estimation of their magnitude was performed as it was done, for example, for Sweden by [Elvik and Amundsen](#page-12-0) [\(2000\)](#page-12-0) and for Switzerland by [VESIPO \(2002\).](#page-12-0) Value ''b" of this indicator reflects the emphasis on evidence-based measures selected by the programme, where the ''c" value indicates a case where detailed analysis of accident statistics is performed but no estimation of safety effects associated with the measures is provided.
-
-Considering the economic evaluation, the best mark (value ''a") is given for the programme, when a background study estimated the safety benefits expected. For example, in Switzerland, the development of a road traffic safety policy was based on a comprehensive study which assessed the efficiency of 77 road safety measures [\(VESIPO, 2002](#page-12-0)); [Elvik \(2001\)](#page-12-0) demonstrated cost-effective analyses of road safety policies for Norway and Sweden, which enveloped 132 and 139 road safety measures, respectively. Usually, a general economic evaluation is performed where the numbers of fatalities and injuries to be saved are compared with the investments required for the programme's performance (value ''b").
-
-For the monitoring indicator, value ''a" is assigned when the monitoring analysis reports are available, e.g. [Broughton and](#page-12-0) [Buckle \(2006\)](#page-12-0) for the UK. As to the stakeholders' indicator, the level of adoption and responsibility for the programme's performance can be learnt from the OECD country reports where the governmental responsibility (value ''a"), the responsibility of national road safety authority (value ''b"), shared responsibility of several bodies (value ''c") or no clear responsibility (value ''d") are indicated.
-
-In spite of the thorough effort to provide the data, it should be realised that it is difficult to obtain quantitative and unbiased estimates on the actual implementation of road safety policies and programs.
-
-The B-group of indicators deals with the final outcomes of the system, i.e. the numbers of road accident fatalities and injuries, which should be presented in a form suitable for comparisons. The present study focused on four issues: personal risk, traffic risk, the scope of traffic injury and the scope of the problem of vulnerable road users. For each issue one or several indicators were defined – [Table 2.](#page-3-0)
-
-Concerning this group of indicators, we can note that personal risk and traffic risk indicators are common in international comparisons; the indicators of traffic injury, in addition to fatalities, receive more attention over the last years both in international comparisons and in setting national safety targets (e.g. [OECD/ITF,](#page-12-0) [2008\)](#page-12-0). As to the share of pedestrians/two-wheelers out of the total fatalities, those are compared among the countries where higher than average values frequently lead to more concerted safety efforts and interventions related to the subject (e.g. pedestrian safety issue in the national safety programme in the UK).
-
-The C-group of indicators captures the intermediate outcomes, containing the safety performance indicators which characterize the safety quality of the road traffic system. Having analysed the data available in the seven pre-defined SPI areas ([Vis and Van Gent,](#page-12-0) [2007\)](#page-12-0) as well as the summaries on safety performance indicators (PIN Flashes) recently published by the ETSC, it was decided to limit the present project to only those safety areas for which the estimates are available for a significant number of EU Member States. The remaining safety areas are: alcohol-impaired driving, use of protective systems in cars, the crashworthiness of the passenger car fleet, and the vehicle fleet composition. For each area one or two indicators have been defined – [Table 3.](#page-3-0)
-
-Table 1 Definition of basic indicators for the A-group: characteristics of national safety programmes.
-
-| Indicators                    | Possible values                                                                                                                                                                   |
-|-------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| A1 Safety targets             | a. Ambitious                                                                                                                                                                      |
-|                               | b. Available but not ambitious                                                                                                                                                    |
-|                               | c. Not available                                                                                                                                                                  |
-| A2 Selection of interventions | a. Sound analysis and diagnosis of road safety problems preceded the programme's development, and evidence-based                                                                  |
-|                               | interventions were selected                                                                                                                                                       |
-|                               | b. Some analysis was performed, and evidence-based interventions were selected                                                                                                    |
-|                               | c. Detailed analysis of road safety problems was performed, however, the selection of interventions was arbitrary                                                                 |
-|                               | d. The diagnosis of road safety problems was poor and the selection of interventions was arbitrary                                                                                |
-| A3 Economic evaluation        | a. Detailed economic evaluation preceded the programme's composition                                                                                                              |
-|                               | b. Some economic evaluation was performed                                                                                                                                         |
-|                               | c. Economic evaluation was not performed                                                                                                                                          |
-| A4 Monitoring the programme's | a. Systematic monitoring takes place                                                                                                                                              |
-| performance                   | b. A need for monitoring is stated but monitoring reports are not found                                                                                                           |
-|                               | c. No evidence of monitoring activities                                                                                                                                           |
-| A5 Programme's stakeholders   | a. Commitment was stated on the governmental level, and the programme is supervised by a central authority which is<br>empowered to coordinate the activities of all other bodies |
-|                               | b. No commitment from the government, however, a central authority was commissioned for the programme's performance                                                               |
-|                               | c. A number of authorities share the responsibility for the programme's performance                                                                                               |
-|                               | d. No authority has a clear responsibility for the programme's performance                                                                                                        |
-
-<span id="page-3-0"></span>Table 2 Definition of basic indicators for the B-group: final outcomes.
-
-| Issues considered                             | Indicators defineda                                             |
-|-----------------------------------------------|-----------------------------------------------------------------|
-| Personal risk                                 | B1 Fatalities per million inhabitants                           |
-| Traffic risk                                  | B2 Fatalities per million passenger cars                        |
-|                                               | B3 Fatalities per 10 billion passenger-km travelled             |
-| Scope of traffic injury                       | B4 Injury accidents per fatalityb                               |
-| Scope of the problem of vulnerable road users | B5 Share of pedestrian fatalities out of the total fatalities   |
-|                                               | B6 Share of bicyclist fatalities out of the total fatalities    |
-|                                               | B7 Share of motorcyclist fatalities out of the total fatalities |
-
-<sup>a</sup> Note: Obviously, various definitions are possible. B2 can for instance be defined per total vehicle fleet instead of passenger cars, B3 per vehicle kilometer travelled instead of passenger kilometer, etc. The selection was based on the estimates available to the EU Member States.
-
-Table 3 Definition of basic indicators for the C-group: intermediate outcomes, SPIs.
-
-| Safety areas considered                              | Indicators defined                                                                                    |
-|------------------------------------------------------|-------------------------------------------------------------------------------------------------------|
-| Alcohol-impaired driving                             | C1 Share of total for fatalities in drink-driving accidents                                           |
-| Use of protective systems in cars                    | C2 Daytime wearing rates of seat belts in the front seats (aggregated for driver and front passenger) |
-|                                                      | C3 Daytime wearing rates of seat belts in the rear seats                                              |
-| Vehicles: crashworthiness of the passenger car fleet | C4 Average EuroNCAP score of passenger car fleet                                                      |
-|                                                      | C5 Median age of the passenger car fleet                                                              |
-| Vehicle fleet composition                            | C6 Share of motorcycles in the vehicle fleet                                                          |
-|                                                      | C7 Share of heavy goods vehicles (HGV) in the vehicle fleet                                           |
-
-Finally, two background indicators were added (the D-group) which characterize the motorization level and the population density of the country – Table 4. Both indicators are common in international and other comparisons as context variables reflecting the development and structural differences among the countries or other entities compared [\(Wegman et al., 2008](#page-12-0)).
-
-The choice of indicators used was influenced by the immediate availability of data and, therefore, their definitions have a preliminary character, supporting the main purpose of the study, to demonstrate along which lines composite indicators can be designed.
-
-# 2.3. Data collection
-
-In total, 21 basic safety indicators were defined for consideration. The estimates of the indicators and/or data for their calculation were taken from a wide range of international databases and recent publications of international working groups, including:
-
-For group A – [OECD/ITF \(2008\), OECD/ECMT \(2008\)](#page-12-0); For group B – [EC \(2007\), ERSO \(2008\);](#page-12-0) For C1 – [ETSC \(2007a\);](#page-12-0) for C2, C3 – [ETSC \(2007b\);](#page-12-0) for C4 – [Vis](#page-12-0) [and Van Gent \(2007\)](#page-12-0); for C5 – [UNECE \(2008\)](#page-12-0); for C6, C7 – [EC](#page-12-0) [\(2007\)](#page-12-0) and OECD country reports (2008).
-
-The basic indicators were estimated for 27 countries: 25 Member States of the European Union plus Norway and Switzerland – [Table 5](#page-4-0). (The majority of indicators are not yet available for the new Member States Bulgaria and Romania.)
-
-### 2.4. Method of analysis
-
-The purpose of this analysis is to create a composite road safety indicator and to explore the behaviour of basic indicators. The
-
-Table 4 Definition of basic indicators for the D-group: background characteristics.
-
-| Characteristic<br>considered | Indicators defined                               |
-|------------------------------|--------------------------------------------------|
-| Motorization level           | D1 Number of passenger cars per 1000 inhabitants |
-| Population density           | D2 Population per 1 km2 of country's territory   |
-
-composite indicator will enable ranking countries in accordance with their safety performance or, at least, to define several groups of countries with different levels of safety performance.
-
-As can be seen in [Table 5](#page-4-0), only six countries had values for the whole set of basic indicators A–D. Therefore, prior to the analysis, missing data imputations were performed. The SAS 9.2 MI procedure was applied using the MCMC method for imputation. This imputation assumes multivariate normality of variables. We used SAS macro % bctrans to find a Box–Cox transformation in order to achieve marginal normality of each of the continuous variables ([LaLonde, 2005\)](#page-12-0). Due to the relatively small number of observations with respect to the number of basic indicators, the imputations were carried out separately for the groups of indicators. The final data set, with initial and imputed values of basic indicators, is presented in Appendix A.
-
-To combine the basic indicators into a composite one, weights based on statistical models were applied. We examined both Principal Component Analysis (PCA) and Common Factor Analysis (FA) weighting [\(Tabachnick and Fidell, 2006\)](#page-12-0). Both methods group collinear indices to form a composite indicator that captures as much common information among sub-indicators as possible. The idea under PCA/FA is to account for the highest possible variation in the set of indicators using the smallest possible number of factors ([Nardo et al., 2005\)](#page-12-0). If there is no correlation between indicators these methods can not be used to obtain the weights.
-
-The first step in the FA is to check the correlation structure of the data, and the second step is to identify a certain number of latent factors, smaller than the number of indicators, representing the data. In the PCA, we retain those factors that account for the largest amount of variance. This implies that in both methods the number of factors fitted to the data were data-dependent and not dictated by an external reason.
-
-We used Kaiser–Meyer–Olkin (KMO) statistics to predict and demonstrate if the data are likely to factor well, based on their correlations and partial correlations. There is a KMO statistic for each individual variable, and an overall KMO statistic. It is customary that the KMO overall should be 0.60 or higher, to proceed with the factor analysis. If this condition is not satisfied, one should drop the indicator variables with the lowest individual KMO statistic values, until the KMO overall rises above 0.60.
-
-<sup>b</sup> Considering the reporting problems and definition's differences among the countries, a preferable indicator would be the 'number of hospitalized injuries per one fatality', which is unavailable as yet.
-
-<span id="page-4-0"></span>Table 5Basic indicators estimated for 27 European countries (n/a <sup>=</sup> not available).
-
-| Cou<br>ntry              | Cod      | e B<br>1                                                                        | B2                                                                                 | B3                                                                     | B4                                                                       | B5                                                                                      | B6                                                                                     | B7                                                                                                            | D1                                                                                                       | D2                                                                    | C1                                                                            | C2                                                                                       | C3                                                               | C4                                                           | C5                                                                    | C6                                                                   | C7                                                 | A1                        | A2                                                    | A3                                     | A4                 | A5                           |
-|--------------------------|----------|---------------------------------------------------------------------------------|------------------------------------------------------------------------------------|------------------------------------------------------------------------|--------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------|-------------------------------------------------------------------------------|------------------------------------------------------------------------------------------|------------------------------------------------------------------|--------------------------------------------------------------|-----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------|---------------------------|-------------------------------------------------------|----------------------------------------|--------------------|------------------------------|
-|                          |          | Fata<br>litie<br>s<br>mil<br>lion<br>per<br>inh<br>abit<br>ant<br>s<br>200<br>6 | Fata<br>litie<br>s<br>per<br>mil<br>lion<br>pas<br>sen<br>ger<br>s-2<br>006<br>car | Fata<br>litie<br>s<br>per<br>10 b<br>illio<br>n<br>pkm<br><br>200<br>6 | Inju<br>ry<br>Acc<br>ide<br>nts<br>per<br>fata<br>litie<br>s<br>200<br>6 | Ped<br>ian<br>estr<br>fata<br>litie<br>s<br>as %<br>of<br>l<br>tota<br>6 (%<br>)<br>200 | Bicy<br>clis<br>t<br>fata<br>litie<br>s<br>as %<br>of<br>l<br>tota<br>6 (%<br>)<br>200 | Mo<br>ycli<br>torc<br>st +<br>ped<br>mo<br>fata<br>litie<br>s<br>of t<br>l<br>as %<br>ota<br>6 (%<br>)<br>200 | Num<br>ber<br>of<br>pas<br>sen<br>ger<br>cars<br>per<br>100<br>0<br>inh<br>abit<br>ant<br>s,<br>200<br>6 | Pop<br>ulat<br>ion<br>m2<br>1 k<br>per<br>of a<br>rea<br><br>200<br>6 | Sha<br>f<br>re o<br>drin<br>k<br>driv<br>ing<br>iden<br>ts<br>acc<br>200<br>5 | We<br>arin<br>g<br>-Fro<br>rate<br>nt<br>ts<br>sea<br>d<br>ate<br>agg<br>reg<br>200<br>5 | We<br>arin<br>g<br>rate<br><br>Rea<br>r<br>ts<br>sea<br>200<br>5 | Ave<br>rag<br>e<br>Eur<br>oNC<br>AP<br>sco<br>re<br>200<br>3 | Me<br>dia<br>n<br>of<br>age<br>pas<br>sen<br>ger<br>s-2<br>003<br>car | %<br>Mo<br>ycle<br>torc<br>s<br>in fl<br>eet<br><br>6 (%<br>)<br>200 | %<br>HG<br>V<br>in<br>flee<br>t<br>200<br>6<br>(%) | Safe<br>ty<br>targ<br>ets | Sele<br>ctio<br>f<br>n o<br>inte<br>ntio<br>rve<br>ns | Eco<br>ic<br>nom<br>luat<br>ion<br>eva | Mo<br>nito<br>ring | Sta<br>ke<br>hol<br>der<br>s |
-| Aus<br>tria              | AT       | 88                                                                              | 175                                                                                | 100                                                                    | 54.<br>6                                                                 | 15.<br>1                                                                                | 6.6                                                                                    | 18.<br>4                                                                                                      | 507                                                                                                      | 98.<br>9                                                              | 6.0<br>%                                                                      | 83                                                                                       | 52                                                               | n/a                                                          | 7.5                                                                   | 12.<br>4                                                             | 7.0                                                | a                         | c                                                     | b                                      | b                  | a                            |
-| Belg<br>ium              | BE       | 101                                                                             | 216                                                                                | 96                                                                     | 38.<br>5                                                                 | 11.<br>4                                                                                | 8.6                                                                                    | 15.<br>5                                                                                                      | 470                                                                                                      | 347<br>.0                                                             | 4.4<br>%                                                                      | 71                                                                                       | n/a                                                              | 21.<br>3                                                     | 6.7                                                                   | 6.0                                                                  | 11.<br>1                                           | b                         | b                                                     | b                                      | b                  | b                            |
-| lan<br>Swi<br>tzer       | d C<br>H | 50                                                                              | 95                                                                                 | n/a                                                                    | 58.<br>1                                                                 | 20.<br>5                                                                                | 9.5                                                                                    | 21.<br>6                                                                                                      | 519                                                                                                      | 181<br>.8                                                             | 19.<br>3%                                                                     | 82                                                                                       | 53                                                               | n/a                                                          | 7.3                                                                   | 12.<br>5                                                             | 6.4                                                | a                         | a                                                     | a                                      | b                  | a                            |
-| Cyp<br>rus               | CY       | 111                                                                             | 236                                                                                | 167                                                                    | 31.<br>1                                                                 | 22.<br>1                                                                                | 2.3                                                                                    | 29.<br>1                                                                                                      | 479                                                                                                      | 83.<br>8                                                              | 22.<br>5%                                                                     | 80                                                                                       | n/a                                                              | n/a                                                          | 9.3                                                                   | 7.6                                                                  | 21.<br>7                                           | n/a                       | n/a                                                   | n/a                                    | n/a                | n/a                          |
-| Cze<br>ch                | CZ       | 104                                                                             | 264                                                                                | 146                                                                    | 20.<br>8                                                                 | 19.<br>0                                                                                | 10.<br>3                                                                               | 10.<br>9                                                                                                      | 399                                                                                                      | 130<br>.4                                                             | 5.5<br>%                                                                      | 72                                                                                       | 13                                                               | 20.<br>7                                                     | 11                                                                    | 15.<br>1                                                             | 9.0                                                | b                         | c                                                     | b                                      | b                  | b                            |
-| Rep<br>ubl<br>ic         |          |                                                                                 |                                                                                    |                                                                        |                                                                          |                                                                                         |                                                                                        |                                                                                                               |                                                                                                          |                                                                       |                                                                               |                                                                                          |                                                                  |                                                              |                                                                       |                                                                      |                                                    |                           |                                                       |                                        |                    |                              |
-| Ger<br>ma<br>ny          | DE       | 62                                                                              | 110                                                                                | 57                                                                     | 64.<br>4                                                                 | 14.<br>0                                                                                | 9.5                                                                                    | 17.<br>7                                                                                                      | 566                                                                                                      | 230<br>.6                                                             | 5.1<br>%                                                                      | 96                                                                                       | 89                                                               | 20.<br>1                                                     | 6                                                                     | 10.<br>5                                                             | 5.1                                                | a                         | b                                                     | b                                      | a                  | b                            |
-| rk<br>Den<br>ma          | DK       | 56                                                                              | 154                                                                                | 56                                                                     | 17.<br>7                                                                 | 19.<br>6                                                                                | 10.<br>1                                                                               | 14.<br>7                                                                                                      | 371                                                                                                      | 126<br>.4                                                             | 23.<br>0%                                                                     | 85                                                                                       | 63                                                               | n/a                                                          | 7.3                                                                   | 6.7                                                                  | 18.<br>7                                           | a                         | b                                                     | b                                      | b                  | b                            |
-| nia<br>Esto              | EE       | 152                                                                             | 389                                                                                | 175                                                                    | 12.<br>7                                                                 | 27.<br>2                                                                                | 4.1                                                                                    | 4.1                                                                                                           | 413                                                                                                      | 29.<br>7                                                              | 28.<br>4%                                                                     | 74                                                                                       | 30                                                               | n/a                                                          | 12.<br>7                                                              | 1.9                                                                  | 14.<br>0                                           | b                         | c                                                     | b                                      | b                  | a                            |
-| Gre<br>ece               | EL       | 149                                                                             | 375                                                                                | 174                                                                    | 9.8                                                                      | 16.<br>1                                                                                | 1.3                                                                                    | 30.<br>0                                                                                                      | 407                                                                                                      | 84.<br>6                                                              | 10.<br>7%                                                                     | n/a                                                                                      | n/a                                                              | 20.<br>7                                                     | 8                                                                     | 17.<br>2                                                             | 17.<br>4                                           | a                         | b                                                     | b                                      | b                  | b                            |
-| Spa<br>in                | ES       | 93                                                                              | 201                                                                                | 117                                                                    | 24.<br>3                                                                 | 15.<br>3                                                                                | 1.8                                                                                    | 17.<br>6                                                                                                      | 464                                                                                                      | 87.<br>9                                                              | n/a                                                                           | 74                                                                                       | 51                                                               | n/a                                                          | 7.6                                                                   | 14.<br>6                                                             | 16.<br>7                                           | a                         | c                                                     | b                                      | b                  | b                            |
-| Fin<br>land              | FI       | 64                                                                              | 136                                                                                | 53                                                                     | 20.<br>1                                                                 | 11.<br>9                                                                                | 11.<br>3                                                                               | 9.5                                                                                                           | 475                                                                                                      | 15.<br>6                                                              | 23.<br>5%                                                                     | 88                                                                                       | 78                                                               | n/a                                                          | 9.1                                                                   | 10.<br>5                                                             | 11.<br>6                                           | a                         | b                                                     | b                                      | b                  | b                            |
-| Fra<br>nce               | FR       | 77                                                                              | 153                                                                                | 64                                                                     | 17.<br>1                                                                 | 11.<br>4                                                                                | 3.8                                                                                    | 23.<br>5                                                                                                      | 504                                                                                                      | 113<br>.1                                                             | 28.<br>8%                                                                     | 97                                                                                       | 70                                                               | n/a                                                          | 8.3                                                                   | 6.4                                                                  | 13.<br>7                                           | a                         | a                                                     | b                                      | a                  | a                            |
-| Hun<br>gar<br>y          | HU       | 129                                                                             | 446                                                                                | 271                                                                    | 16.<br>1                                                                 | 22.<br>7                                                                                | 11.<br>7                                                                               | 10.<br>1                                                                                                      | 293                                                                                                      | 108<br>.2                                                             | 8.8<br>%                                                                      | 67                                                                                       | 34                                                               | 20.<br>6                                                     | 8                                                                     | 3.7                                                                  | 12.<br>5                                           | a                         | c                                                     | b                                      | b                  | b                            |
-| Irel<br>and              | IE       | 86                                                                              | 211                                                                                | 130                                                                    | 18.<br>6                                                                 | 19.<br>0                                                                                | 3.0                                                                                    | 16.<br>3                                                                                                      | 418                                                                                                      | 61.<br>4                                                              | n/a                                                                           | 86                                                                                       | 46                                                               | n/a                                                          | 5.1                                                                   | 1.6                                                                  | 14.<br>7                                           | b                         | b                                                     | b                                      | a                  | b                            |
-| Ital<br>y                | IT       | 96                                                                              | 162                                                                                | 74                                                                     | 42.<br>0                                                                 | 12.<br>6                                                                                | 4.8                                                                                    | 23.<br>8                                                                                                      | 597                                                                                                      | 196<br>.3                                                             | n/a                                                                           | 71                                                                                       | n/a                                                              | n/a                                                          | 7.9                                                                   | 20.<br>5                                                             | 8.7                                                | c                         | d                                                     | c                                      | c                  | d                            |
-| Lith<br>ia<br>uan        | LT       | 224                                                                             | 498                                                                                | 191                                                                    | 8.9                                                                      | 34.<br>0                                                                                | n/a                                                                                    | n/a                                                                                                           | 470                                                                                                      | 51.<br>9                                                              | 11.<br>8%                                                                     | 60                                                                                       | n/a                                                              | n/a                                                          | 14.<br>5                                                              | 1.4                                                                  | 7.7                                                | b                         | c                                                     | b                                      | b                  | b                            |
-| Lux<br>bou<br>em<br>rg   | LU       | 76                                                                              | 116                                                                                | 54                                                                     | 21.<br>2                                                                 | 9.7                                                                                     | 1.6                                                                                    | 0.0                                                                                                           | 661                                                                                                      | 183<br>.1                                                             | 0.0<br>%                                                                      | 80                                                                                       | 60                                                               | n/a                                                          | 4.4                                                                   | 10.<br>0                                                             | 8.0                                                | n/a                       | n/a                                                   | n/a                                    | n/a                | n/a                          |
-| Latv<br>ia               | LV       | 178                                                                             | 520                                                                                | 256                                                                    | 10.<br>6                                                                 | 38.<br>0                                                                                | 6.0                                                                                    | 4.0                                                                                                           | 360                                                                                                      | 35.<br>3                                                              | 21.<br>7%                                                                     | 77                                                                                       | n/a                                                              | 21.<br>3                                                     | 10                                                                    | 3.7                                                                  | 12.<br>2                                           | b                         | c                                                     | b                                      | b                  | b                            |
-| Ma<br>lta                | MT       | 25                                                                              | 46                                                                                 | 49                                                                     | 84.<br>1                                                                 | 35.<br>3                                                                                | 0.0                                                                                    | 17.<br>6                                                                                                      | 535                                                                                                      | 136<br>0.0                                                            | n/a                                                                           | 96                                                                                       | 28                                                               | 18.<br>6                                                     | 9                                                                     | 4.4                                                                  | 16.<br>4                                           | a                         | c                                                     | b                                      | b                  | b                            |
-| her<br>lan<br>ds<br>Net  | NL       | 45                                                                              | 102                                                                                | 48                                                                     | 33.<br>6                                                                 | 9.4                                                                                     | 18.<br>3                                                                               | 18.<br>4                                                                                                      | 442                                                                                                      | 394<br>.2                                                             | 15.<br>3%                                                                     | 90                                                                                       | 64                                                               | 20.<br>2                                                     | 6                                                                     | 6.5                                                                  | 11.<br>3                                           | a                         | a                                                     | b                                      | a                  | a                            |
-| Nor<br>way               | NO       | 52                                                                              | 116                                                                                | n/a                                                                    | 32.<br>7                                                                 | 14.<br>5                                                                                | 3.3                                                                                    | 15.<br>3                                                                                                      | 445                                                                                                      | 14.<br>4                                                              | n/a                                                                           | 91                                                                                       | 83                                                               | 21                                                           | 8                                                                     | 9.4                                                                  | 17.<br>0                                           | b                         | a                                                     | a                                      | b                  | b                            |
-| Pol<br>and               | PL       | 137                                                                             | 408                                                                                | 235                                                                    | 8.9                                                                      | 32.<br>3                                                                                | 11.<br>1                                                                               | 3.9                                                                                                           | 351                                                                                                      | 121<br>.9                                                             | 9.8<br>%                                                                      | 78                                                                                       | n/a                                                              | n/a                                                          | 11.<br>8                                                              | 4.7                                                                  | 14.<br>4                                           | b                         | c                                                     | b                                      | b                  | b                            |
-| al<br>Por<br>tug         | PT       | 92                                                                              | 228                                                                                | 131                                                                    | 36.<br>8                                                                 | 16.<br>0                                                                                | 4.1                                                                                    | 24.<br>1                                                                                                      | 405                                                                                                      | 115<br>.3                                                             | n/a                                                                           | 86                                                                                       | 45                                                               | n/a                                                          | 9.7                                                                   | 9.0                                                                  | 21.<br>3                                           | b                         | c                                                     | b                                      | b                  | b                            |
-| Swe<br>den               | SE       | 49                                                                              | 107                                                                                | 45                                                                     | 40.<br>7                                                                 | 12.<br>4                                                                                | 5.8                                                                                    | 15.<br>7                                                                                                      | 461                                                                                                      | 20.<br>3                                                              | 34.<br>0%                                                                     | 92                                                                                       | 73                                                               | 22.<br>6                                                     | 7                                                                     | 9.6                                                                  | 9.2                                                | a                         | a                                                     | a                                      | a                  | a                            |
-| Slov<br>eni<br>a         | SI       | 131                                                                             | 270                                                                                | 113                                                                    | 44.<br>4                                                                 | 13.<br>7                                                                                | 5.3                                                                                    | 20.<br>5                                                                                                      | 488                                                                                                      | 99.<br>0                                                              | 32.<br>2%                                                                     | 87                                                                                       | 30                                                               | n/a                                                          | 5.0                                                                   | 4.8                                                                  | 6.3                                                | b                         | c                                                     | b                                      | b                  | b                            |
-| Slov<br>akia             | SK       | 107                                                                             | 439                                                                                | 215                                                                    | 13.<br>8                                                                 | 9.3                                                                                     | 5.5                                                                                    | 7.4                                                                                                           | 247                                                                                                      | 110<br>.5                                                             | 12.<br>0%                                                                     | n/a                                                                                      | n/a                                                              | n/a                                                          | n/a                                                                   | 3.7                                                                  | 11.<br>9                                           | b                         | c                                                     | b                                      | b                  | b                            |
-| Gre<br>at<br>Brit<br>ain | UK       | 54                                                                              | 116                                                                                | 48                                                                     | 58.<br>9                                                                 | 21.<br>0                                                                                | 4.6                                                                                    | 17.<br>5                                                                                                      | 471                                                                                                      | 249<br>.3                                                             | 5%<br>17.                                                                     | 90                                                                                       | 84                                                               | 20.<br>6                                                     | 5                                                                     | 3.7                                                                  | 10.<br>7                                           | a                         | b                                                     | b                                      | a                  | a                            |
-
-In both methods (PCA/FA), the combination of factors into a composite indicator is done by their weighted sum, where the weights are taken in accordance with the variance explained by each factor. At this point, different estimation procedures are possible. For both PCA and FA, we used orthogonal rotation of factors and, hence, 'the variance explained by each factor' was applied for estimating the weights.
-
-Both PCA and FA are suitable for the creation of a composite indicator. Accounting for the data behaviour, five trials of creating a composite indicator were performed. They are:
-
-- (1) PCA-all A PCA in which all variables (basic indicators) were analysed together.
-- (2) PCA-groups A PCA in which each group of basic indicators (A, B, C, D) was first analysed separately to create the group factors. The group factors were then analysed together to provide the final composite indicator.
-- (3) FA-4factors A common factor analysis with all variables considered and four factors' solution accepted.
-- (4) FA-2factors-noC4 A common factor analysis with two factors' solution, where C4 (average EuroNCAP score) is excluded from the analysis, due to statistical reasons.
-- (5) FA-2factors A common factor analysis with two factors' solution, where C4 is retained in the analysis.
-
-Concerning the FA analyses, a preliminary examination of all variables together revealed a low value of the overall KMO statistics (0.45), and the lowest values of the KMO statistics for such variables as B6 (0.131), B7 (0.309), C7 (0.136). Once the indicators B6, B7, C7 were excluded, the overall KMO statistics increased to 0.63, enabling to proceed with the FA. Therefore, the three trials of creating composite indicator by means of FA considered 18 variables (basic indicators), without B6 (share of bicyclist fatalities), B7 (share of motorcyclist fatalities) and C7 (percentage of HGV in fleet).
-
-To determine the number of factors required, the variance explained criterion was applied. At this point, two approaches are possible. Some researchers use the rule of keeping enough factors to account for a certain share (e.g. 90% or 80%) of the variation. Conversely, if the researcher's goal emphasizes parsimony (explaining variance with as few factors as possible), the criterion could be as low as 50%. In our case, the variation was taken to be the sum of the Eigenvalues of the reduced correlation matrix. It was found, for example, that four factors explain more than 80% of the variation.
-
-Further, examining the solution for four factors demonstrated that the C4 variable (average EuroNCAP score) is nearly identical to one of the factors, where in such cases it is customary to exclude such a variable from the analysis. The exclusion of C4 enabled fitting two factors to the rest of the variables (indicators), but with a significantly lower value of the total explained variance than in the four factors' analysis (71% versus 84%). A further consideration revealed that keeping C4 inside the set and limiting the number of factors to two yielded a comparable value of the explained variance (about 69%). Hence, we decided to explore the results of all three trials as mentioned above.
-
-Each of the five analyses produced a rotated factor pattern showing which variables (basic indicators) are more heavily loaded by each one of the factors built, i.e. the correlations between the variables and the factors. Considering the 'safety-desirable' behaviour of major variables which composed the factors and their coefficients, it was stated whether higher or lower values of each factor are associated with better safety performance. An insight into similarities and dissimilarities in the behaviour of basic indicators was also provided. In addition, each trial produced the tools for the estimation of each country's score, including: scoring coefficients, factors' weights, means and variances of the variables to estimate the standardized values. Appendix B provides an example of a rotated factor pattern and of tools for the estimation of country scores produced by the 'FA-4factors' analysis.
-
-Once the factors are created, the following procedure is applied to calculate a composite indicator, for each country:
-
-- a. Standardize the data, for each country, i.e. subtract mean and divide by standard deviation, for each variable.
-- b. Multiply these standardized variables by their respective standardized scoring coefficients.
-- c. Sum up these products over all the relevant variables. The sum is the value of the new variable Factor 1.
-- d. Repeat steps a–c for Factors 2, 3, etc. (if relevant). This creates the data set scores.
-- e. The composite indicator is a weighted sum of the Factors (called WF – weighted factor).
-
-Using the WF and a WARD clustering procedure, in each trial a classification tree was produced, i.e. the countries were classified into similar groups (clusters). Inside the group the WF values are close, but there are distances between the groups.
-
-### 3. Results and discussion
-
-Detailed results of each of the five analyses can be found in [Wegman et al. \(2008\).](#page-12-0) Section 3.1 gives a summary of the results of each trial. Furthermore, in Section 3.2, the composite indicators and clusters of countries are compared among the trials, providing considerations of their meanings and practical implications for the development of composite safety indicator.
-
-### 3.1. Results of separate trials
-
-# 3.1.1. PCA-all
-
-In the 'PCA-all' analysis, five factors were fitted to the data (explaining 76.3% of the total variation), where
-
-Factor 1 – Mainly reflects the road safety outcomes (the Bgroup), car fleet's age and seatbelt use;
-
-Factor 2 – The policy performance indicators (the A-group) but also includes a negative correlation with C1 (share of drinkdriving accidents);
-
-Factor 3 – Reflects the share of bicyclist fatalities, EuroNCAP scores for cars and population density;
-
-Factor 4 – The share of motorcycles in the fleet and the share of motorcyclist fatalities;
-
-Factor 5 – The share of HGV in the fleet, the number of injury accidents per fatality and the motorization level of a country.
-
-Similar behaviour was noted for variable groups such as: B1-B2- B3 (number of fatalities per population, vehicles, passenger kilometers travelled), A1–A4 (safety targets, monitoring the programme performance), A2-A3-A5 (other characteristics of safety programmes) and C2–C3 (safety belt wearing rates in front and rear seats).
-
-[Fig. 1](#page-6-0) presents the countries' subdivision into six clusters based on the WF values, where the country positions are plotted using the combined indicator (WF) and Factor 1 values. In this model, better safety position is associated with lower WF and Factor 1 values. According to this classification, the countries with the best safety level are Sweden, Great Britain and France; the second group includes Luxembourg, Norway, Germany and Ireland; the third group consists of Slovenia, Finland and the Netherlands, and so on.
-
-### <span id="page-6-0"></span>3.1.2. PCA-groups
-
-The first step in the 'PCA-groups' analysis was to fit one combined factor for each group of indicators separately, namely for the A-, B-, C- and D- groups of basic indicators. The four combined factors obtained are then subjected to another PCA, where the final combined safety index is generated.
-
-For the A-group indicators ('policy performance'), one factor was fitted to all basic (A1–A5) indicators in the first step of this analysis (explaining 64.4% of the total variation).
-
-For the B-group indicators ('final outcomes'), two factors were chosen, explaining 74.5% of the total variation, where variables B1-B2-B3-B4-B5 (number of fatalities per population, vehicles, passenger kilometers travelled, injury accidents per fatality, and share of pedestrian fatalities) were mainly loaded by Factor 1 and B6–B7 (shares of bicyclist and motorcyclist fatalities) – by Factor 2. The results demonstrated similarities in the behaviour of B1- B2-B3-B5 indicators and very different behaviours of B4, B6, B7.
-
-Exploring the C-group indicators ('intermediate outcomes'), low communalities with other indicators were observed for C6, C7 indicators (the percentages of motorcycles and HGV in the vehicle fleet, respectively), which, consequently, were excluded from the analysis. For the retained indicators, two factors were chosen, explaining 80.1% of the total variation, where variables C2–C3 (seat belt wearing rates) and C5 (median age of passenger cars) were mainly loaded by Factor 1, while C1 (share of drink-driving accidents) and C4 (average EuroNCAP score) were mainly loaded by Factor 2.
-
-For the D-group indicators ('background characteristics'), one factor was chosen, which reflected both basic variables (explaining 63.2% of the total variation).
-
-In the second step of this analysis two factors were chosen (explaining 86.3% of the total variation), where the combined factors of A- and C-groups were mainly loaded by Factor 1, and the combined factors of B- and D-groups – by Factor 2. Based on the factors' scoring coefficients and components, in this analysis, lower values of A- and B-group factors and higher values of C-group factors are 'safety-preferable', leading to lower values of Factor 1 and higher values of Factor 2 as associated with a better safety position.
-
-Plotting the positions of countries on the dimensions of these two combined factors, one general 'cloud' can be noted, with several outliers (e.g. Italy, Malta) – Fig. 2. Inside the 'cloud', the countries with better safety-related positions are: France, Great Britain, Sweden, Switzerland, the Netherlands, Germany and Norway. Besides, in this analysis a lack of similarity in the behaviour of the group factors was recognized.
-
-![](_page_6_Figure_10.jpeg)
-
-Fig. 1. Countries plotted using the composite indicator (WF) and Factor 1 values (PCA-all analysis).
-
-### 3.1.3. FA with four factors
-
-In the 'FA-4factors' analysis (see a rotated factor pattern in Appendix B) the basic indicators B1–B3 (number of fatalities per population, vehicles, passenger kilometers travelled), B5 (share of pedestrian fatalities), C5 (median age of passenger cars), C2–C3 (safety belt wearing rates in front and rear seats), B4 (injury accidents per fatality), and D1 (number of passenger cars per population) were mainly loaded by Factor 1, whereas Factor 2 reflected mostly the behaviour of policy performance indicators A1, A2, A4, A5 and C6 (percentage of motorcycles in vehicle fleet). Factor 3 mainly loaded the C4 variable only (average EuroNCAP score), whereas Factor 4 reflected mostly the behaviour of D2 (population density), A3 (quality of economic basis of safety programmes) and C1 (share of drink-driving accidents). The four factors explained 84% of the total variance.
-
-The countries were subdivided into five groups ([Fig. 3](#page-7-0)), where the countries with the best safety performance (first group) are Sweden, Norway, Switzerland, Great Britain, France, the Netherlands and Germany; the second group includes Finland, Denmark, Malta, Austria, Luxembourg and Ireland; and so on. Malta, Luxembourg and, especially, Italy, are 'outliers' of their groups, having much lower values of Factor 1 in comparison with the countries with a similar level of the combined safety indicator. (In this model, similar to the 'PCA-all', better safety position is associated with lower WF and Factor 1 values.)
-
-### 3.1.4. FA with two factors
-
-In the FA with two factors, two alternatives were considered: excluding and including the C4 indicator (average EuroNCAP score). Those were termed 'FA-2factors-noC4' and 'FA-2factors', respectively, and provided 71% and 69% of the explained variance.
-
-In both trials, Factor 1 reflected mostly the behaviour of B1–B5 indicators (final outcomes), D1–D2 (background characteristics), C5 (median age of passenger cars), C6 (percentage of motorcycles in vehicle fleet) and C4 (if included in the analysis); whereas Factor 2 reflected mostly the behaviour of policy performance indicators A1–A5, C2–C3 (safety belt use) and C1 (share of drink-driving accidents).
-
-In both cases, similar behaviour was observed for basic indicators: B1-B2-B3-B5-C5 (final outcomes and median age of cars), A1-A2-A3-A4-A5 (policy performance), C2–C3 (use of seat belts) and D1–D2 (background characteristics). Moreover, C4 indicator is relatively close to the group of 'final outcomes' plus 'median age of cars', whereas C1 (share of drink-driving accidents) behaves
-
-![](_page_6_Figure_19.jpeg)
-
-Fig. 2. Countries' positions on the dimensions of the combined factors (PCA-group analysis).
-
-<span id="page-7-0"></span>![](_page_7_Figure_2.jpeg)
-
-Fig. 3. Countries plotted using the composite indicator (WF) and Factor 1 values (FA-4factors analysis).
-
-differently from all the other indicators. Besides, B4 (injury accidents per fatality) and C6 (percentage of motorcycles in fleet) are relatively close to the D1–D2-group.
-
-In both cases the groups of countries with similar values of combined indicators were as shown in Fig. 4, where the countries with the best safety level (first group) are Malta, the Netherlands, Sweden, Great Britain, Germany, Switzerland, France and Norway; the second group includes Finland, Denmark, Austria, Luxembourg and Ireland; and so on. Similar to the results of the 'FA-4factors' analysis, Malta, Italy and Luxembourg behave as 'outliers' of their groups, and better safety position of a country is associated with lower values of WF and Factor 1.
-
-### 3.2. Discussion: comparisons among the trials
-
-The discussion is focused on two major issues. The first is an analysis of the consistency of the results of the various trials and identification of final groups of countries with similar safety performance – Sections 3.2.1 and [3.2.2](#page-8-0). The second issue relates to the summing up of the behaviour of the basic indicators in the various trials, to recognize the existence of a stable group of indicators
-
-![](_page_7_Figure_8.jpeg)
-
-Fig. 4. Countries plotted using the composite indicator (WF) and Factor 1 values (FA-2factors analysis).
-
-– Section [3.2.3.](#page-9-0) Both issues are essential for further development of a composite road safety indicator.
-
-### 3.2.1. Comparisons of countries' rankings
-
-The composite indicators obtained for each country with each analysis (WF values) were compared graphically in [Fig. 5](#page-8-0). The values obtained for each country were plotted against the countries' ranking according to their final scores from the 'PCA-all'.
-
-It can be seen that:
-
-- The results of the 'PCA-groups' analysis are very different from all other results.
-- The results of the 'FA-2factors' and the 'FA-2factors-noC4' analyses are very close.
-- In general, a certain similarity exists between the results of 'PCA-all', 'FA-4factors', 'FA-2factors' and 'FA-2factors-noC4' analyses.
-
-The different character of the results obtained with the 'PCAgroups' method probably stems from the different approach taken in this case, where the indicators were initially analysed in the predefined groups, not accounting for the inter-group correlations. Such a consideration enables a deeper insight into the behaviour of indicators in each group, in comparison to other analyses, but can provide a different final picture when a combined indicator is created.
-
-Accounting for their character, it was interesting to compare the intermediate results of the 'PCA-groups' analysis, i.e. the factors built for the A-, B-, C-groups of indicators, with the results of other analyses. Due to scaling reasons, prior to this step, we passed from the WF values to country ranks, where a country with the best value of the combined indicator receives rank '1', the next country – rank '2' and so on.
-
-For example, [Fig. 6](#page-8-0) presents a comparison of country ranks according to the 'PCA-all' analysis and the group factor analyses: FB (final B-group factor), FC (final C-group factor) and FB1 – Factor 1 fitted in B-group analysis, which reflects five B-indicators together (fatalities per population, vehicles, passenger km-travelled as well as injury accidents per fatality and share of pedestrian fatalities). The basic countries' ranking is built using FB1 results, which actually represents a substitute to the traditional approach of countries' comparison in terms of fatality rates (per population, vehicles, etc.). To note, a similar comparison was performed by [Hermans et al. \(2008\),](#page-12-0) who used the countries' ranking based on personal risk as a basis for judging the results of other rankings. The FB1 ranks in [Fig. 6](#page-8-0) are accompanied by ±5 rank deviation bars (vertical grey lines).
-
-It can be seen from [Fig. 6](#page-8-0) that:
-
-- For the majority of countries, the total FB ranks are close to the FB1 ranks, which confirms the leading role of FB1 indicators (fatalities per population, vehicles, passenger kilometers travelled, etc.) in countries' estimation by 'final safety outcomes'.
-- Both 'PCA-all' ranks and FC ranks create deviations from the countries' ranks based on final outcomes only. The countries with the widest deviations in ranks and, therefore, with the most unstable answers in this context are Malta and Italy. Essential deviations from the FB1 ranking are also observed for France, Luxembourg, Slovenia, Ireland, Estonia and Latvia.
-- At the same time, we can note a number of countries for which the ranks are relatively consistent across the different considerations. These are: Sweden, Great Britain, Norway, Portugal, Spain, Slovakia, Greece, Hungary, Poland and Lithuania.
-
-<span id="page-8-0"></span>![](_page_8_Figure_2.jpeg)
-
-Fig. 5. Final composite safety indicators (WF values) as a result of five analyses.
-
-![](_page_8_Figure_4.jpeg)
-
-Fig. 6. Countries' ranks resulting from the FB1 ranks versus PCA-all, FC and FB ranks.
-
-Finally, to compare the countries' ranks based on the combined indicators, we decided to present the results of four analyses, i.e. 'PCA-all', 'FA-4factors', 'FA-2factors', and 'FA-2factors-noC4' (where the results of the 'PCA-groups' analysis are left out due to their different nature) – [Fig. 7](#page-9-0). As a basic ranking we chose the one that provided the minimum sum of squared deviations from other rankings, i.e. the 'FA-2factors-noC4' ranking.
-
-[Fig. 7](#page-9-0) shows that:
-
-- Similar to countries' ranking based on the combined indicators WF (see Fig. 5), the results of 'FA-4factors', 'FA-2factors' and 'FA-2factors-noC4' analyses are reasonably close, whereas 'PCA-all' ranks are associated with large deviations.
-- Essential differences between all rankings are observed for Malta.
-- Relatively large deviations between some rankings are observed for the Netherlands, Switzerland, Norway, Luxembourg, Ireland, Slovenia, the Czech Republic, Italy and Estonia.
-
-In general, the level of consistency between the different rankings seems average, whereas the most similar results were provided by a similar type of analysis (e.g. the FA variations).
-
-## 3.2.2. Identification of groups of countries
-
-Due to various reasons (e.g. data quality, method of analysis, random variations in data), the country rankings from different analyses will probably never be identical. Therefore, a more reasonable comparison of the results of several analyses could be made by forming groups of countries with similar safety performance. Such a comparison is possible in our case, because each analysis produced clusters of countries with very similar values of the composite indicator.
-
-Using the results of countries' clustering observed in separate trials, the countries' classifications into five groups were compared. [Table 6](#page-9-0) summarizes the findings. The left part of [Table 6](#page-9-0) illustrates the country groupings obtained from the four analyses with more consistent results, i.e. 'FA-4factors', 'FA-2factors', 'FA-2factorsnoC4' and 'PCA-all', whereas the right part of the table illustrates the country groupings obtained from the initial and final steps of the 'PCA-groups' analysis. The two central columns give the final classifications of the countries.
-
-[Table 6](#page-9-0) clearly shows that the countries' classifications in the left part are much more consistent than those in the right part of the table. Based on the left part of the table (results of four analyses), five groups of countries with different levels of safety performance can be defined as follows:
-
-- 1. Countries with the highest level of safety performance: Sweden, Norway, France, Great Britain, Germany.
-- 2. Countries with a relatively high level of safety performance: Switzerland, the Netherlands, Finland, Denmark, Ireland, Austria, Luxembourg, Malta.
-- 3. Countries with a medium level of safety performance: Cyprus, Slovenia, Portugal, Belgium, Spain.
-
-<span id="page-9-0"></span>![](_page_9_Figure_2.jpeg)
-
-Fig. 7. Final countries' ranks resulting from four analyses.
-
-Table 6 Groups of countries with similar safety performance, based on the results of various analyses.
-
-- 4. Countries with a relatively low level of safety performance: Estonia, Slovakia, Greece, Czech Republic.
-- 5. Countries with a low level of safety performance: Latvia, Hungary, Poland, Lithuania, Italy.
-
-# 3.2.3. Behaviour of basic indicators
-
-In the analyses performed, similarities in behaviour were observed for the following groups of indicators:
-
-B1-B2-B3-B5-C5 (final safety outcomes – the fatality rates per population, per vehicles, per passenger kilometers travelled and share of pedestrian fatalities, and the median age of passenger cars),
-
-A1-A2-A3-A4-A5 (indicators of the quality of national safety programmes),
-
-C2–C3 (wearing rates of safety belts in front and rear seats) and D1–D2 (background characteristics).
-
-Moreover, the C4 indicator (average EuroNCAP score) was relatively close to the group containing final outcomes, where B4 (injury accidents per fatality) and C6 (percentage of motorcycles in fleet) were relatively close to the D1–D2 group. On the other hand, C1 (share of drink-driving accidents) behaved differently from all the other indicators, whereas indicators B6 (share of bicyclists fatalities), B7 (share of motorcyclist fatalities) and C7 (percentage of HGV in fleet) were excluded from several analyses due to low correlation with other variables.
-
-We should mention here that the values of the indicators relating to safety policy have a rather arbitrary and subjective element, which could partly explain the homogeneity of their behaviour. As <span id="page-10-0"></span>far as possible, the indicators were based on quantifiable facts, collected from published country reports. However, at this stage it is difficult to objectively quantify elements of a safety policy, and further research is required.
-
-Using the scoring coefficients and factors' weights produced by each of the five analyses (see example in App. B), final weights of the basic indicators can be estimated and compared among the trials. Such weights may illustrate the contribution of various indicators to the composite safety indicator. Table 7 presents these final weights, which for a comparison are given in a normalized form (a sum in column is equal to 1). It can be seen that among the basic indicators considered (except for B6, B7 and C7, which were excluded from several analyses):
-
-- The largest differences between the trials are observed for B2 (fatalities per vehicles) and both background characteristics.
-- On average, higher weights were observed for B2, C1–C5 (intermediate outcomes), D2 (population density) and A2–A5 (policy indicators), where the lowest weights are associated with B4 (injury accidents per fatality) and B5 (share of pedestrian fatalities).
-- More consistent weights between the various trials are found for C1–C5 and A1–A5 groups of indicators.
-
-However, as to the contribution to the composite indicator, the interpretation of the weights is not straightforward, since for the estimation of composite indicator for each country the original
-
-Table 7 Final weights of the basic indicators based on the results of five analyses.
-
-| Basic indicator | PCA-all   | PCA-groups | FA-4factors | FA-2factors-noC4 | FA-2factors | Average value | Standard deviation |
-|-----------------|-----------|------------|-------------|------------------|-------------|---------------|--------------------|
-| B1              | 0.091     | <br>0.039  | <br>0.039   | 0.081            | 0.013       | 0.021         | 0.063              |
-| B2              | 0.122     | 0.049<br>  | 0.897       | 0.531            | 0.555       | 0.411         | 0.376              |
-| B3              | 0.166     | <br>0.047  | 0.107       | 0.133            | <br>0.008   | 0.070         | 0.093              |
-| B4              | <br>0.022 | 0.042      | <br>0.035   | <br>0.061        | 0.008       | <br>0.013     | 0.040              |
-| B5              | 0.089     | <br>0.042  | 0.003       | 0.039            | <br>0.033   | 0.011         | 0.054              |
-| B6              | 0.239     | 0.026<br>  | n/a         | n/a              | n/a         | 0.107         | 0.187              |
-| B7              | 0.209     | 0.049      | n/a         | n/a              | n/a         | 0.129         | 0.113              |
-| C1              | <br>0.191 | <br>0.211  | <br>0.047   | <br>0.003        | <br>0.123   | <br>0.115     | 0.090              |
-| C2              | 0.243<br> | 0.157<br>  | 0.417<br>   | 0.197<br>        | 0.111<br>   | 0.225<br>     | 0.118              |
-| C3              | <br>0.256 | <br>0.139  | <br>0.250   | <br>0.221        | <br>0.159   | <br>0.205     | 0.053              |
-| C4              | <br>0.155 | <br>0.073  | <br>0.155   | n/a              | <br>0.063   | <br>0.111     | 0.051              |
-| C5              | 0.216     | 0.023      | 0.160       | 0.171            | 0.013       | 0.117         | 0.093              |
-| C6              | 0.257     | n/a        | 0.011<br>   | 0.005<br>        | 0.075       | 0.079         | 0.125              |
-| C7              | 0.120     | n/a        | n/a         | n/a              | n/a         | 0.120         | n/a                |
-| D1              | <br>0.181 | 0.479      | 0.035       | <br>0.012        | 0.082       | 0.080         | 0.244              |
-| D2              | 0.116     | 0.479      | 0.002       | 0.046<br>        | 0.062       | 0.123         | 0.208              |
-| A1              | 0.016     | 0.135      | 0.147       | 0.099            | 0.062       | 0.092         | 0.054              |
-| A2              | 0.021     | 0.154      | 0.244       | 0.195            | 0.196       | 0.162         | 0.085              |
-| A3              | 0.004     | 0.131      | 0.194       | 0.075            | 0.130       | 0.107         | 0.071              |
-| A4              | 0.270     | 0.140      | 0.008<br>   | 0.067            | 0.107       | 0.115         | 0.103              |
-| A5              | 0.113     | 0.153      | 0.173       | 0.152            | 0.195       | 0.157         | 0.031              |
-
-Table A1 The final set of basic safety indicators for the 27 countries.
-
-| Country | A1 | A2 | A3 | A4 | A5 | B1    | B2    | B3    | B4   | B5   | B6   | B7   | C1   | C2 | C3 | C4   | C5   | C6   | C7   | D1  | D2   |
-|---------|----|----|----|----|----|-------|-------|-------|------|------|------|------|------|----|----|------|------|------|------|-----|------|
-| AT      | 1  | 3  | 2  | 2  | 1  | 88.1  | 174.6 | 99.7  | 54.6 | 0.15 | 0.07 | 0.18 | 0.06 | 83 | 52 | 20.5 | 7.5  | 0.12 | 0.07 | 507 | 99   |
-| BE      | 2  | 2  | 2  | 2  | 2  | 101.3 | 216.1 | 96.2  | 38.5 | 0.11 | 0.09 | 0.16 | 0.04 | 71 | 24 | 21.3 | 6.7  | 0.06 | 0.11 | 470 | 347  |
-| CY      | 1  | 2  | 2  | 2  | 1  | 111.3 | 236.2 | 167.5 | 31.1 | 0.22 | 0.02 | 0.29 | 0.23 | 80 | 36 | 20.7 | 9.3  | 0.08 | 0.22 | 479 | 84   |
-| CZ      | 2  | 3  | 2  | 2  | 2  | 103.5 | 263.5 | 145.8 | 20.8 | 0.19 | 0.10 | 0.11 | 0.06 | 72 | 13 | 20.7 | 11.0 | 0.15 | 0.09 | 399 | 130  |
-| DK      | 1  | 2  | 2  | 2  | 2  | 56.3  | 153.6 | 55.8  | 17.7 | 0.20 | 0.10 | 0.15 | 0.23 | 85 | 63 | 20.7 | 7.3  | 0.07 | 0.19 | 371 | 126  |
-| EE      | 2  | 3  | 2  | 2  | 1  | 151.8 | 389.4 | 175.3 | 12.7 | 0.27 | 0.04 | 0.04 | 0.28 | 74 | 30 | 21.7 | 12.7 | 0.02 | 0.14 | 413 | 30   |
-| FI      | 1  | 2  | 2  | 2  | 2  | 63.8  | 136.1 | 53.0  | 20.1 | 0.12 | 0.11 | 0.09 | 0.23 | 88 | 78 | 20.5 | 9.1  | 0.10 | 0.12 | 475 | 16   |
-| FR      | 1  | 1  | 2  | 1  | 1  | 76.8  | 153.1 | 63.9  | 17.1 | 0.11 | 0.04 | 0.23 | 0.29 | 97 | 70 | 20.7 | 8.3  | 0.06 | 0.14 | 504 | 113  |
-| DE      | 1  | 2  | 2  | 1  | 2  | 61.8  | 109.9 | 57.3  | 64.4 | 0.14 | 0.10 | 0.18 | 0.05 | 96 | 89 | 20.1 | 6.0  | 0.10 | 0.05 | 566 | 231  |
-| UK      | 1  | 2  | 2  | 1  | 1  | 54.4  | 115.7 | 47.6  | 58.9 | 0.21 | 0.05 | 0.18 | 0.17 | 90 | 84 | 20.6 | 5.0  | 0.04 | 0.11 | 471 | 249  |
-| EL      | 1  | 2  | 2  | 2  | 2  | 148.6 | 374.6 | 174.4 | 9.8  | 0.16 | 0.01 | 0.30 | 0.11 | 79 | 28 | 20.7 | 8.0  | 0.17 | 0.17 | 407 | 85   |
-| HU      | 1  | 3  | 2  | 2  | 2  | 129.0 | 446.0 | 271.2 | 16.1 | 0.23 | 0.12 | 0.10 | 0.09 | 67 | 34 | 20.6 | 8.0  | 0.04 | 0.13 | 293 | 108  |
-| IE      | 2  | 2  | 2  | 1  | 2  | 86.3  | 211.1 | 129.6 | 18.6 | 0.19 | 0.03 | 0.16 | 0.15 | 86 | 46 | 20.6 | 5.1  | 0.02 | 0.15 | 418 | 61   |
-| IT      | 3  | 4  | 3  | 3  | 4  | 96.2  | 162.1 | 73.8  | 42.0 | 0.13 | 0.05 | 0.24 | 0.02 | 71 | 21 | 21.0 | 7.9  | 0.20 | 0.09 | 597 | 196  |
-| LV      | 2  | 3  | 2  | 2  | 2  | 177.9 | 520.3 | 256.5 | 10.6 | 0.38 | 0.06 | 0.04 | 0.22 | 77 | 23 | 21.3 | 10.0 | 0.04 | 0.12 | 360 | 35   |
-| LT      | 2  | 3  | 2  | 2  | 2  | 223.6 | 498.1 | 190.6 | 8.9  | 0.34 | 0.05 | 0.12 | 0.12 | 60 | 13 | 22.6 | 14.5 | 0.01 | 0.08 | 470 | 52   |
-| LU      | 1  | 4  | 2  | 2  | 2  | 76.2  | 115.8 | 54.1  | 21.2 | 0.10 | 0.02 | 0    | 0    | 80 | 60 | 20.8 | 4.4  | 0.10 | 0.08 | 661 | 183  |
-| MT      | 1  | 3  | 2  | 2  | 2  | 24.6  | 46.4  | 48.8  | 84.1 | 0.35 | 0    | 0.18 | 0.25 | 96 | 28 | 18.6 | 9.0  | 0.04 | 0.16 | 535 | 1360 |
-| NL      | 1  | 1  | 2  | 1  | 1  | 44.7  | 101.9 | 48.4  | 33.6 | 0.09 | 0.18 | 0.18 | 0.15 | 90 | 64 | 20.2 | 6.0  | 0.06 | 0.11 | 442 | 394  |
-| NO      | 2  | 1  | 1  | 2  | 2  | 52.2  | 116.0 | 62.8  | 32.7 | 0.14 | 0.03 | 0.15 | 0.31 | 91 | 83 | 21.0 | 8.0  | 0.09 | 0.17 | 445 | 14   |
-| PL      | 2  | 3  | 2  | 2  | 2  | 137.5 | 407.6 | 234.9 | 8.9  | 0.32 | 0.11 | 0.04 | 0.10 | 78 | 23 | 20.6 | 11.8 | 0.05 | 0.14 | 351 | 122  |
-| PT      | 2  | 3  | 2  | 2  | 2  | 91.6  | 228.3 | 130.8 | 36.8 | 0.16 | 0.04 | 0.24 | 0.11 | 86 | 45 | 20.7 | 9.7  | 0.09 | 0.21 | 405 | 115  |
-| SK      | 2  | 3  | 2  | 2  | 2  | 107.4 | 439.1 | 215.0 | 13.8 | 0.09 | 0.06 | 0.07 | 0.12 | 76 | 24 | 21.0 | 7.0  | 0.04 | 0.12 | 247 | 111  |
-| SI      | 2  | 3  | 2  | 2  | 2  | 130.6 | 270.0 | 112.7 | 44.4 | 0.14 | 0.05 | 0.21 | 0.32 | 87 | 30 | 21.1 | 5.0  | 0.05 | 0.06 | 488 | 99   |
-| ES      | 1  | 3  | 2  | 2  | 2  | 93.0  | 200.7 | 117.2 | 24.3 | 0.15 | 0.02 | 0.18 | 0.12 | 74 | 51 | 20.6 | 7.6  | 0.15 | 0.17 | 464 | 88   |
-| SE      | 1  | 1  | 1  | 1  | 1  | 49.0  | 106.5 | 44.7  | 40.7 | 0.12 | 0.06 | 0.16 | 0.34 | 92 | 73 | 22.6 | 7.0  | 0.10 | 0.09 | 461 | 20   |
-| CH      | 1  | 1  | 1  | 2  | 1  | 49.6  | 95.0  | 42.1  | 58.1 | 0.21 | 0.09 | 0.22 | 0.19 | 82 | 53 | 20.5 | 7.3  | 0.12 | 0.06 | 519 | 182  |
-
-Note: Prior to the calculations, the nominal values of the A-group indicators were converted into ordinal scale values. In our calculations we used the usual Pearson correlation for all the measures. This correlation treats all variable as scale variables. Using the ordinal measures as scales is equivalent in our case to the idea of using rank correlation. <span id="page-11-0"></span>indicators should be standardized, i.e. mean centred and divided by their standard deviation (see Section [2.4](#page-3-0)), which may influence the sign and the value of the contribution.
-
-Based on all the analyses performed in the study, basic safety indicators with consistent behaviour and a clearer contribution to the final composite safety indicator can be suggested. Such indicators are: B1-B2-B3 (fatality rates), A1-A2-A3-A4-A5 (quality of national safety programmes), C2–C3 (wearing rates of safety belts) and C5 (median age of cars). These indicators can serve as a core set of basic indicators for the characteristic of a country's safety performance.
-
-#### 4. Conclusions
-
-In this study, we examined Principal Component Analysis and Common Factor Analysis weighting to create a composite safety indicator, based on four groups of basic road safety indicators of countries. The basic indicators considered referred to the three types of road safety benchmarking: policy performance indicators, final road safety outcomes and intermediate outcomes, and also included background characteristics of the countries (motorization level, population density). The analysis used the data collected for 27 European countries. Five trials of creating a composite indicator were performed, where each trial produced a composite road safety indicator for each country, and groups of countries with similar values of the composite indicator.
-
-The purpose of our analysis was to explore the ways for creating a composite road safety indicator and the behaviour of basic indicators involved. It has been demonstrated that both tasks can be realized by the statistical weighting methods applied. The composite indicators, estimated by several methods, enabled us to rank the countries according to their safety performance.
-
-Similar to other studies ([Hermans et al., 2008](#page-12-0)) differences were observed in the countries' rankings obtained by the different methods used. It appears that a more reasonable comparison can be made by using groups of countries with similar levels of safety performance, where the countries are grouped based on the values of composite indicators received. In particular, among the 27 European countries considered, five groups with different safety performance were recognized, although, of course, a particular country could move from one group to another (neighbouring) group, depending on the method used. With the methodology used and with the available data, the countries with the highest level of safety performance are: Sweden, Norway, France, Great Britain and Germany. This group remained fairly consistent among the different methods used.
-
-The analysis revealed that the results of countries' rankings based on the composite indicators are not necessarily identical to the traditional ranking they receive based on fatality rates only. Moreover, it was observed that the indicators belonging to the final outcomes (fatality and injury rates) and intermediate outcomes (wearing rates of seat belts, crashworthiness and composition of vehicle fleet, share of drink-driving) are not uniform in their behaviour in the analyses. Indicators which were found to be more consistent and influential and termed the 'core set of basic indicators' are recommended for future use.
-
-The overall conclusion is that it is realistic and meaningful to design a composite road safety indicator in which relevant information from the different components of the road safety pyramid has been captured and weighted. Such an indicator gives a more
-
-Table B2 Standardized scoring coefficients.
-
-| Basic indicator (variables) | Factor1   | Factor2   | Factor3   | Factor4   |
-|-----------------------------|-----------|-----------|-----------|-----------|
-| B3                          | 0.283     | 0.039     | <br>0.474 | <br>0.046 |
-| B2                          | 0.832     | <br>0.191 | <br>0.348 | 0.202     |
-| B1                          | 0.433<br> | 0.071<br> | 0.970     | 0.030     |
-| B5                          | 0.017     | <br>0.050 | 0.055     | <br>0.011 |
-| C5                          | 0.091     | 0.060     | 0.046     | <br>0.103 |
-| C2                          | 0.085<br> | 0.045     | 0.332<br> | 0.203<br> |
-| C3                          | 0.061<br> | 0.163<br> | 0.087     | 0.090<br> |
-| B4                          | <br>0.049 | <br>0.051 | 0.041     | 0.102     |
-| D1                          | <br>0.107 | 0.028     | 0.124     | 0.133     |
-| A4                          | 0.041<br> | 0.371     | 0.240<br> | 0.282<br> |
-| A5                          | <br>0.027 | 0.330     | <br>0.136 | <br>0.056 |
-| A2                          | <br>0.122 | 0.143     | 0.263     | 0.168     |
-| C6                          | 0.063<br> | 0.223     | 0.089<br> | 0.146<br> |
-| A1                          | 0.088<br> | 0.039     | 0.344     | 0.013     |
-| C4                          | <br>0.148 | 0.010     | 0.219     | <br>0.160 |
-| D2                          | <br>0.109 | <br>0.180 | 0.143     | 0.413     |
-| A3                          | 0.133     | 0.042     | 0.281<br> | 0.243     |
-| C1                          | 0.030     | <br>0.044 | 0.149     | <br>0.241 |
-|                             |           |           |           |           |
-
-Table B3 Factors' weights.
-
-| w1    | w2    | w3    | w4    |
-|-------|-------|-------|-------|
-| 0.388 | 0.273 | 0.178 | 0.161 |
-
-Table B1 Rotated factor pattern.
-
-|    | Basic indicator and its ''safety-desired" behaviour | Factor1   | Factor2   | Factor3   | Factor4   |
-|----|-----------------------------------------------------|-----------|-----------|-----------|-----------|
-| B3 | ;                                                   | 0.931     | 0.162     | 0.164     | 0.099     |
-| B2 | ;                                                   | 0.901     | 0.158     | 0.343     | 0.072     |
-| B1 | ;                                                   | 0.686     | 0.247     | 0.594     | 0.114     |
-| B5 | ;                                                   | 0.604     | <br>0.039 | 0.454     | <br>0.064 |
-| C5 | ;                                                   | 0.527     | 0.208     | 0.463     | <br>0.235 |
-| C2 | "                                                   | 0.501<br> | 0.490<br> | 0.433<br> | 0.288<br> |
-| C3 | "                                                   | 0.588<br> | 0.472<br> | 0.412<br> | 0.255<br> |
-| B4 | ;                                                   | <br>0.704 | <br>0.170 | <br>0.094 | 0.140     |
-| D1 | "                                                   | <br>0.821 | 0.139     | 0.195     | 0.174     |
-| A4 | ;                                                   | 0.187     | 0.850     | 0.139     | 0.019<br> |
-| A5 | ;                                                   | 0.028     | 0.819     | <br>0.008 | 0.196     |
-| A2 | ;                                                   | 0.318     | 0.656     | 0.221     | 0.414     |
-| C6 | ;                                                   | 0.456<br> | 0.587     | 0.307<br> | 0.055<br> |
-| A1 | ;                                                   | 0.160     | 0.560     | 0.446     | 0.108     |
-| C4 | "                                                   | 0.096     | <br>0.002 | 0.773     | <br>0.325 |
-| D2 | ;                                                   | <br>0.361 | <br>0.193 | <br>0.165 | 0.715     |
-| A3 | ;                                                   | 0.096     | 0.482     | 0.013<br> | 0.659     |
-| C1 | ;                                                   | <br>0.024 | <br>0.401 | 0.131     | <br>0.739 |
-
-<span id="page-12-0"></span>Table B4 Means and standard deviations of variables.
-
-|                            | B1             | B2             | B3             | B4             | B5             | C1             | C2            | C3           | C4          | C5          | C6             |
-|----------------------------|----------------|----------------|----------------|----------------|----------------|----------------|---------------|--------------|-------------|-------------|----------------|
-| Mean<br>Standard deviation | 98.6<br>44.1   | 240.1<br>135.3 | 120.0<br>71.1  | 29.1<br>16.9   | 0.180<br>0.077 | 0.158<br>0.098 | 81.2<br>9.1   | 46.6<br>23.5 | 20.9<br>0.6 | 8.08<br>2.4 | 0.082<br>0.050 |
-|                            | N_A1           | N_A2           | N_A3           | N_A4           | N_A5           | D1             | D2            |              |             |             |                |
-| Mean<br>Standard deviation | 1.530<br>0.566 | 2.381<br>0.871 | 1.937<br>0.405 | 1.795<br>0.489 | 1.784<br>0.638 | 449.3<br>88.0  | 126.6<br>95.6 |              |             |             |                |
-
-enriched picture of road safety than a ranking based only on data on fatality rates, which is normal practice today. Grouping countries in this process is promising and seems to be preferable to simply ranking countries. Before defining the composite road safety indicator and actually using the results for policy making, two improvements are recommended: firstly, to design the final set of basic safety performance indicators and, secondly, to develop procedures for collecting high-quality and comparable data for the indicators for all the countries compared.
-
-### Acknowledgements
-
-The authors would like to acknowledge the SUNflowerNext team (Wegman et al., 2008) for the inspiration, multiple fruitful discussions and assistance in data collection for the study's performance.
-
-### Appendix A
-
-See [Table A1.](#page-10-0)
-
-### Appendix B
-
-Results of the 'FA-4factors' analysis See [Tables B1–B4.](#page-11-0)
-
-## References
-
-- Al Haji, G., 2005. Towards a road safety development index (RSDI). Development of an international index to measure road safety performance. Linkoping studies in Science and Technology, Licentiate Thesis No. 1174. Department of Science and Technology, Linkoping University.
-- Broughton, J. Buckle, G, 2006. Monitoring progress towards the 2010 casualty reduction target – 2004 data. TRL Report 653. TRL Limited, Wokingham.
-- European Commission, 2007. Energy and Transport in Figures. Part 3: Transport. Directorate-General for Energy and Transport in co-operation with Eurostat. (Chapter: 3.6 Means of Transport; Chapter 3.7 Safety).
-- Elvik, R., 2001. Improving road safety in Norway and Sweden: analyzing the efficiency of policy priorities. Traffic Engineering and Control January, 9–16.
-- Elvik, R., 2008. Road safety management by objectives: a critical analysis of the Norwegian approach. Accident Analysis and Prevention 40, 1115–1122.
-- Elvik, R., Amundsen, A.H., 2000. Improving road safety in Sweden. Report 490. Institute of Transport Economics, Oslo.
-- European Road Safety Observatory, 2008. Annual statistical Report 2007. [<http://](http://www.erso.eu) [www.erso.eu>](http://www.erso.eu)
-- European Transport Safety Council, 2001. Transport Safety Performance Indicators, Brussels.
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-- European Transport Safety Council, 2006. A Methodological Approach to National Road Safety Policies, Brussels.
-- European Transport Safety Council, 2007a. Reducing Deaths From Drink Driving. PIN Flash 5 Background data.
-- European Transport Safety Council, 2007b. Increasing Seat Belt Use. PIN Flash 4 Background data.
-- Hakkert, A.S., Gitelman, V., Vis, M.A. (Eds.), 2007. Road Safety Performance Indicators: Theory. Deliverable D3.6 of the EU FP6 Project SafetyNet.
-- Hermans, E., Van der Bossche, F., Wets, G., 2008. Combining road safety information in a performance index. Accident Analysis and Prevention 40, 1337–1344.
-- Koornstra, M., Lynam, D., Nilsson, G., Noordzij, P., Pettersson, H.-E., Wegman, F., Wouters, P., 2002. SUNflower; A Comparative Study of the Development of Road Safety in Sweden, the United Kingdom, and the Netherlands. SWOV Institute for Road Safety Research, Leidschendam, the Netherlands.
-- LaLonde, S.M., 2005. Transforming variables for normality and linearity When, How, Why and Why Not's. In: SAS Conference Proceedings NESUG 2005, 11–14 September, Portland, Maine.
-- Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., 2005. Handbook on Constructing Composite Indicators: Methodology and User Guide. OECD Statistics Working Papers, STD/DOC(2005)3. Organisation for Economic Co-operation and Development.
-- Organisation for Economic Co-operation and Development, 2002. Safety on Roads What's the Vision, Paris.
-- Organisation for Economic Co-operation and Development/European Conference of Ministers of Transport, 2008. Country Reports on Road Safety Performance. OECD/ECMT working group on achieving ambitious road safety targets, Joint OECD/ECMT Transport Research Centre, Paris. [<http://www.](http://www.internationaltransportforum.org/jtrc/safety/targets/Performance/performance.html) [internationaltransportforum.org/jtrc/safety/targets/Performance/performance.](http://www.internationaltransportforum.org/jtrc/safety/targets/Performance/performance.html) [html>.](http://www.internationaltransportforum.org/jtrc/safety/targets/Performance/performance.html)
-- Organisation for Economic Co-operation and Development/International Transport Forum, 2008. Towards Zero – Ambitious Road Safety Targets and the Safe System Approach. Joint Transport Research Centre of OECD/ITF, Paris.
-- Peden, M., Scurfield, R., Sleet, D., Mohan, D., Hyder, A., Jarawan, E., Mathers, C. (Eds.), 2004. World Report on Road Traffic Injury Prevention. World Health Organisation, Geneva.
-- Tabachnick, B.G., Fidell, L.S., 2006. Using Multivariate Statistics. Pearson/Allyn & Bacon, Boston.
-- UNECE Database, 2008. [<http://w3.unece.org/pxweb/Dialog/varval.asp?ma=01\\_](http://w3.unece.org/pxweb/Dialog/varval.asp?ma=01_TRRoadtypVeh_r&ti=Road+vehicle+fleet+at+31+December+by+Type+of+vehicle%2C+Age+group%2C+Country+and+Year&path=./DATABASE/Stat/40-TRTRANS/02-TRRoadFleet/&lang=1) [TRRoadtypVeh\\_r&ti=Road+vehicle+fleet+at+31+December+by+Type+of+vehicle%](http://w3.unece.org/pxweb/Dialog/varval.asp?ma=01_TRRoadtypVeh_r&ti=Road+vehicle+fleet+at+31+December+by+Type+of+vehicle%2C+Age+group%2C+Country+and+Year&path=./DATABASE/Stat/40-TRTRANS/02-TRRoadFleet/&lang=1) [2C+Age+group%2C+Country+and+Year&path=./DATABASE/Stat/40-TRTRANS/02-](http://w3.unece.org/pxweb/Dialog/varval.asp?ma=01_TRRoadtypVeh_r&ti=Road+vehicle+fleet+at+31+December+by+Type+of+vehicle%2C+Age+group%2C+Country+and+Year&path=./DATABASE/Stat/40-TRTRANS/02-TRRoadFleet/&lang=1) [TRRoadFleet/&lang=1>.](http://w3.unece.org/pxweb/Dialog/varval.asp?ma=01_TRRoadtypVeh_r&ti=Road+vehicle+fleet+at+31+December+by+Type+of+vehicle%2C+Age+group%2C+Country+and+Year&path=./DATABASE/Stat/40-TRTRANS/02-TRRoadFleet/&lang=1)
-- VESIPO, 2002. Erarbeitung der Grundlagen fur die Strassenverkehrssicherheitspolitik des Bundes u.a. Basler & Hofman, Eckhardt, Perrin, Schonenberger, Fierz.
-- Vis, M.A., Van Gent, A.L. (Eds.), 2007. Road Safety Performance Indicators: Country Comparisons. Deliverable D3.7a of the EU FP6 Project SafetyNet.
-- Wegman, F., Eksler, V., Hayes, S., Lynam, D., Morsink, P., Oppe, S., 2005. SUNflower+6: A Comparative Study of the Development of Road Safety in the SUNflower+6 Countries: Final Report. SWOV Institute for Road Safety Research, Leidschendam, The Netherlands.
-- Wegman, F., Commandeur, J., Doveh, E., Eksler, V., Gitelman, V., Hakkert, S., Lynam, D., Oppe, S., 2008. SUNflowerNext: Towards a Composite Road Safety Performance Index. SWOV Institute for Road Safety Research, Leidschendam, the Netherlands.

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-category: literaturenote
-citekey: globalstatusreportroad2023
-title: Global Status Report on Road Safety 2023
-authors: ""
-year: 2023
-date: 2023-00-00 2023
-zotero_key: PSHYMNZB
-zotero_storage: T22HEPXX
-collections: doktoritöö / HLO
-folder: 001_artiklid/Road Safety Indicators
-status: converted
----
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-# Global status report on road safety 2023
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-# Global status report on road safety 2023
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-Made possible by funding from **Bloomberg Philanthropies**
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-Global status report on road safety 2023
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-**Suggested citation**. Global status report on road safety 2023. Geneva: World Health Organization; 2023. Licence: [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/igo/) IGO.
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-# Contents
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-| Acknowledgements<br>List of contributors<br>Executive summary<br>Introduction<br>Section 1. The global burden of road traffic deaths<br>Fatalities by road user type<br>Fatality counts and rates, by region and country-income level<br>Section 2. How the burden and context have evolved<br>Progress toward the target of a 50% reduction in deaths<br>Section 3. Measures to mitigate the risk of death and injury<br>Multimodal transport<br>Safe road infrastructure<br>Safe vehicles<br>26<br>Road user behaviours<br>29<br>Speed management<br>30<br>Impaired and distracted driving<br>31<br>Motorcycle helmet use<br>32<br>Seat-belt use<br>Child restraint systems use<br>Post-crash response<br>Progress towards safe road use: summary<br>Section 4. Measures to strengthen road safety governance<br>Institutional management<br>Monitoring, evaluation, and data management<br>Section 5. The way forward<br>References<br>Annexes<br>Annex 1. Methodology<br>59<br>Annex 2. Progress towards the voluntary UN Performance Targets<br>Annex 3. Population covered by selected road safety-related measures<br>Annex 4. National vehicle and infrastructure laws and international<br>conventions or regulations<br>73<br>Annex 5. Example Country and territory profile template<br>74<br>Annex 6. Guide to Country and territory profiles<br>75 | Foreword | iv   |
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-## <span id="page-5-0"></span>Foreword
-
-![](_page_5_Picture_1.jpeg)
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-*Our transport systems open the world to us, but they come with a tragic price.* 
-
-**Dr Tedros Adhanom Ghebreyesus** Director-General World Health Organization
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-By the time you have read this page, at least five people will have died in road traffic crashes.
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-Road crashes are the leading killer of children and youth, and they typically strike during our most productive years, causing huge health, social and economic harm throughout society.
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-Yet this report offers hope, and points to a way forward.
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-Since 2010, deaths from road crashes have fallen slightly to 1.19 million per year. More than half of all UN Member States, including some of the worst-affected countries, report a decline in deaths.
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-These hard-won gains were made while much of the world was heading in the wrong direction.
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-As motor vehicles proliferate, countries are doubling down on transport systems built for cars, not people, and not with safety at their core.
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-This holds back efforts to save lives, protect the vulnerable and secure a sustainable future.
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-Some of the greatest progress has been made where the safe system approach to road safety has been applied. This holistic approach to mobility puts people and safety front and centre.
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-With a rapidly growing and increasingly urban population, it calls for a safe, efficient and sustainable mix of transport types, including mass public transport, while ensuring the safety of pedestrians, cyclists and other vulnerable road users, who account for half of all deaths. Building safe systems also benefits many more areas of health and development. By encouraging walking and cycling for example, we help reduce the burden of noncommunicable diseases, boost physical activity, strengthen access to jobs and education and help fight climate change.
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-We know what works; political will must match the scale and urgency of this crisis.
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-The Global Plan for the United Nations Decade of Action for Road Safety charts the way forward, and everyone has a role in making safe, clean, affordable and green mobility a reality.
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-Governments must lead mobility strategies that are rooted in good data, backed by strong laws and funds, and that include all sectors of society. Businesses must put safety and sustainability at the core of their value chains. Academia and civil society must generate evidence and hold leaders to account. Youth can demand action and help take it.
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-Safe mobility is a crucial aspect of the universal right to health, a fundamental right of every human. Mobility must not, and need not, come with a tragic cost in human lives.
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-The decline in deaths shown in this report falls far short of what is needed to halve road traffic fatalities by 2030, which means the need for action is urgent, to realise the promise of safe and sustainable mobility, and a safer, healthier and better future.
-
-# Foreword
-
-![](_page_6_Picture_1.jpeg)
-
-Diseases and Injuries
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-*It is encouraging to see more countries saving lives through safer street design, police enforcement, and paid media campaigns. Still, the scope of the problem is not receiving enough public attention.* 
-
-**Michael R. Bloomberg** Founder, Bloomberg L.P. & Bloomberg Philanthropies WHO Global Ambassador for Noncommunicable
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-Our mission at Bloomberg Philanthropies is straightforward: to save and improve as many lives as possible. One of the most significant opportunities to do that is making more of the world's roads safer for drivers, passengers, cyclists, and pedestrians alike.
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-This new report highlights how, over the course of more than a decade, we have made encouraging progress together with the World Health Organization and our road safety partners. Our focus is on low- and middleincome countries and cities, where 90 percent of traffic deaths occur. Since just 2018, 23 national governments have strengthened their laws to align with WHO's best practices.
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-It is encouraging to see more countries saving lives through safer street design, police enforcement, and paid media campaigns. Still, the scope of the problem is not receiving enough public attention.
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-Any life lost in a traffic crash is one too many – and, in 2021, more than one million people died on the world's roads. Road crashes are also the leading cause of death for children and young people 5 to 29 years old. As the world's population grows, and the demand for vehicles grows alongside it, ensuring that our roads are safe for all is becoming even more important.
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-To meet our goal of cutting road deaths in half by the end of this decade, more governments need to take action. Only six countries have laws that meet WHO best practice criteria for addressing road safety's key risk factors. Fewer than 50 countries have policies that promote walking, cycling, and public transport – a glaring lack of investment in safe, sustainable mobility options. More countries also need to step up regulation of vehicle safety standards, which can protect everyone involved in a collision. Right now, nearly 80 countries have no laws at all on vehicle safety standards.
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-As this report makes clear, faster progress on road safety requires stronger commitments from governments worldwide. Persuading more leaders of the urgent need for action will continue to be a major priority for us at Bloomberg Philanthropies – and we thank Dr. Tedros, WHO, and our global network of allies for their continued partnership in this lifesaving work.
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-# <span id="page-7-0"></span>Acknowledgements
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-The *Global status report on road safety 2023* benefited from the substantial intellectual contribution of many WHO staff and collaborators. At WHO headquarters´ Safety and Mobility Unit, Maria Segui-Gomez coordinated the technical concept, study design, data collection, data analysis, writing and review of the report. Fangfang Luo and Evelyn Murphy led and performed the legislation review. Kacem Iaych led the mortality estimation data analyses in collaboration with Bochen Cao, Doris Ma Fat and Wayno Retno Mahanani from the WHO Division of Data, Analytics and Delivery for Impact. Overall supervision was provided by Nhan Tran and Etienne Krug.
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-Advisory Board members actively participated in the study design and the review of the report: Shrikant Bangdiwala, McMaster University, Canada; Said Dahdah, Global Road Safety Facility at the World Bank, Washington (DC), United States of America (USA); Jennifer Ellis, Bloomberg Philanthropies, New York, United States of America (USA); Eduard Fernandez, CITA – the International Motor Vehicle Inspection Committee, Brussels, Belgium; Floor Lieshout, Youth for Road Safety (YOURS), Culemborg, the Netherlands; John Milton, Road Safety Committee World Road Association (Permanent International Association of Road Congresses -PIARC), Paris, France; Fernanda Rivera, Road Safety and Sustainable Urban Mobility, Mexico City's Mobility Ministry; David Studdert, Stanford University, California, United States of America (USA) ; Claes Tingvall, Chalmers University of Technology, Gothenburg, Sweden, and Monash University Accident Centre in Melbourne, Australia; Geetham Tiwari, Transportation Research and Injury Prevention Centre, Delhi, India; and Nancy Vandyke, Infrastructure Global Practice, Transport at the World Bank Group, Washington (DC), USA.
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-Data collection relied heavily on the collaboration of Member States and WHO regional personnel. Member State government officials supported the project and provided official clearance of data gathered and compiled by designated National Data Focal Points (names of National Data Focal Points are presented in the List of contributors) and approximately 800 or more collaborators. Country1 data delivery involved the use of a data entry platform and securing consensus among participants.
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-Country-level participation was facilitated through the WHO designated Regional Data Focal Points who were responsible for training and supervision of country collaborators, and who also reviewed the report: Eunice Chomi and Idrissa Talla (African Region), Alessandra Senisse (Region of the Americas), Rania Saad (South-East Asia Region and Eastern Mediterranean Region), and Roseanne Vandermeer (Western Pacific Region). Further support at WHO regional level was provided by Binta Sako (African Region), Ricardo Pérez-Núñez (Region of the Americas), Tashi Tobgay (South-East Asia Region), Jonathon Passmore – who also acted as Regional Data Focal Point (European Region), Hala Sakr Ali (Eastern Mediterranean Region), and Fang Dan (Western Pacific Region).
-
-Additional data came from publicly available data sources and collaboration with others. WHO thanks Robert McInerney (iRoad Assessment Program London, United Kingdom of Great Britain and Northen Ireland); Uta Meesmann (VIAS Institute, Brussels, Belgium); and Susanna Zamataro (International Road Federation, Geneva, Switzerland). Walter Nissler (United Nations Economic Commission for Europe, Geneva, Switzerland) provided advice during the collection and interpreting of UN conventions and regulations.
-
-WHO also thanks the following consultants whose expert contributions made this document possible: Afef Ben Ghenaya, Aida Kaffel, and Maria Teresa Martin-Nájera for their support in reviewing and analysing legislative documents; Manuel Valdano (Universidad Pontificia de Comillas, Spain) for his assistance in data management. Angela Burton provided technical writing; Adappt provided the data entry platform and country and territory profile generation that made this document possible.
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-WHO thanks Bloomberg Philanthropies for its generous financial support for the development of this report.
-
-<sup>1</sup> The terms "country" and "national" as used in the text of this publication should be understood to refer to countries, territories, and areas as well as national and local institutions, data, and information.
-
-# <span id="page-8-0"></span>List of contributors
-
-With thanks to the following contributors from across all WHO regions who acted as National Data Focal Points to coordinate country-level data entry to the *Global status report on road safety 2023* questionnaire:
-
-**African Region**: Alexis Abouna, Fatima Adamou, Gabriel Adu-Sarpong, Kahsay Araya, Ali Atouli, Irene Bagahirwa, Matos Celso, Ovost Chooye, Jean de Dieu Havyarimana, Fetiya Dedgba, Jean Dikiyi, Serge Elanda, Kedou Eyasema, Malick Fall, Gladwell Gathecha, Michael Gaye, Hassan Guire, Ismaël Hoteyi, Sydney Ibeanusi, Amadou Kamagate, David Kasembe, Mamoudou Keita, Abdoulaye Kone, Yankho Luwe, George Madeleine, Cristovao Manjuba, Celso Matos, Euridice Matsiengouni, Bethino Mbirimujo, Amos Motshegwe, Abdelhakim Nacef, Lee Nkala, Maria Nkalubo, Aimé Nkunku, Onwar Nyibong, Tsembayena Phakathi, Emmanuel Phasha, Maphahamiso Ralienyane, Sandra Rodrigues, Gaye Sulayman, Napoleão Sumbane, Harvindradas Sungker, Ousseynou Tall, Ambrose Tucker, Marie Venerozia, and Nina Yameogo.
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-**Region of the Americas:** Valdimy Adolphe, Shalauddin Ahmed, Klever Almeida, Jorge Arce-Araya, Donna Bannister, Donna Barker, Paul Boase, Norberto Borba, Zane Castillo, Jesus Chaicoj, Joel Collazos-Carhuay, Jasson Cruz-Villamil, Ramona Doorgen, Erick Duron, Lloyd Hodson, Deidrie Hudson-Sinclair, Johanna Lakhisaran, Phil Leon, Kishon Leslie, Claudia Maldonado-Núñez, Carla Medina, Luiz Miranda, Silvia Moran, Paola Olmos-Rojas, Juan Palacios, Sergio Peralta-Medina, Adande Piggott, Yania Plá-Ramírez, Pablo Rojas, Mateo Terreo, Valarie Williams, Merissa Yellman.
-
-**South-East Asia Region**: Robed Amid, Dail Asri, Ramya Bandaranayaka, Sonam Choden, Nusaer Chowdhury, Heitor da Pereira, Akhmad Fais Fauzi, Shri Dinesh Kumar, Bhim Prasad Sapkota, Fathimath Shabana, Nongnuch Thanthithum, and Maung Maung Toe.
-
-**European Region**: Bayonzoda Jamalidin Bayoni, Adrian Bedaj, Sinead Bracken, Ivana Brkic, Patrick Cachia, Rute Isabel Calheiros, Tamar Chachava, Vladislav Cojuhari, Peter Csizmadia, Miroslav Djeric, Charis Evripidou, Tomas Fredlund, Álvaro Gómez-Méndez, Anders Møller Gaardbo, Stratos Georgiopoulos, Yuri Gnedko, Gunnar Geir Gunnarsson, Tolga Hakan, Bakhish Hasanli, Akmal Ikromov, Kjell Jacobs, Jānis Kalniņš, Slavomir Kral, Maria Giuseppina Lecce, Peter Mak, Milen Markov, Nurian Moldaliev, Andraz Murkovic, Artemis Olavesen, Maria Pashkevich, Riikka Rajamäki, Sergey Alexandrovich Ryzhov, Manuelle Salathe, Arman Sargsyan, Karin Schranz, Yanik Scolastici, Boranova Aktoty Sermakhankyzy, Assaf Sharon, Przemyslaw Skoczynski, Renata Slaba, Thomas Spillmann, Svetlana Stojanovic, Milan Tesic, Fimka Tozija, Matthew Tranter, Raschid Urmeew, Harry Winkler, and Ingrida Zurlyte.
-
-**Eastern Mediterranean Region**: Hayat Ahmed Mohammed Abdullah, Hameed Akhtar, Mooath Mohammed Aldossari Tasneem Alfadil, Haya Alhammad, Sana Alkhawada, , Mansour Hassan Ali, Ahmed Bardan, Henda Chebbi, Okasneem Elfadil, Amani Elkhatim, Ziad Khalid Gabar, Taufik Hasaba, Ibrahim Jabeal, Fuad Al Maaytah, Suliman Al Mahrazi, Loubana Maazouri, Mohieldin Hassan Haj Hamad Mohammed, Noha Moussa, Mohammed Qais Niazai, Farah Nuh, Hamid Qahil Al Otiby, Wafa Said, Ramzi Salamé, and Hormoz Zakeri.
-
-**Western Pacific Region**: Josephine Afuamua, Enkhbold Anjim, Jarizza Mae Biscante, Leilei Duan, Mark Ellis, Paul Graham, Joonbum Lim, Lay Tin Ong, Noor Raihan Khamal, Ean Sokoeu, Daolueang Syhaphon, Mweritonga Temareti, Huu Minh Tran, Nuhisifa Williams, Timothy Wilson, and Yukiko Yamaguchi.
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-National Data Focal Points for the participating territories were Kishon Leslie and Imad Masri.
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-<span id="page-9-0"></span>![](_page_9_Picture_0.jpeg)
-
-# Executive summary
-
-There were an estimated 1.19 million road traffic deaths in 2021 – a 5% drop when compared to the 1.25 million deaths in 2010. More than half of all United Nations Member States reduced road traffic deaths between 2010 and 2021. The slight overall reduction in deaths occurred despite the global motor vehicle fleet more than doubling, road networks significantly expanding, and the global population rising by nearly a billion. This shows that efforts to improve road safety are working but fall far short of what is needed to meet the target of the United Nations Decade of Action for Road Safety 2021–2030 to halve deaths by 2030.
-
-Road traffic deaths and injuries remain a major global health and development challenge. As of 2019, road traffic crashes are the leading killer of children and youth aged 5 to 29 years and are the 12th leading cause of death when all ages are considered. Two-thirds of deaths occur among people of working age (18– 59 years), causing huge health, social and economic harm throughout society.
-
-More than half of fatalities are among pedestrians, motorcyclists and cyclists. Occupants of 4-wheel vehicles account for almost one-third of fatalities. Occupants of vehicles carrying more than 10 people, heavy goods vehicles and "other" users constitute one-fifth of all deaths. Micro-mobility modes such as e-scooters account for 3% of deaths.
-
-Vulnerable road users such as pedestrians, cyclists and motorcyclists remain dangerously exposed. Nearly 80% of all roads assessed do not meet a minimum 3-star rating for pedestrian safety, and as cyclist fatalities increase, just 0.2% of all roads assessed have cycle lanes.
-
-Nine in 10 deaths occur in low- and middleincome countries, while people in low-income countries continue to face the highest risk of death per population. Globally, 28% of all fatalities occur in the WHO South-East Asia Region, 25% in the Western Pacific Region, 19% in the African Region, 12% in the Region of the Americas, 11% in the Eastern Mediterranean Region, and 5% in the European Region.
-
-The European Region reports the largest drop in deaths since 2010 – a 36% decline. The Western Pacific Region reports a 16% decline, the South-East Asia Region a 2% decline and the number of deaths has remained constant in the Region of the Americas. Reductions in the number of deaths were observed in 108 countries, including 10 where the 50% was achieved by 2021. However, in 66 countries there was a rise; 28 of these countries are in the African Region, which has seen a 17% rise in the number of deaths since 2010.
-
-Measures to mitigate the risk of death and injury, including enacting laws that meet WHO best practices, have advanced modestly. Policy-makers have known of the key risk factors that contribute to road crashes for decades, yet only six countries have reached WHO best practice legislation on five risk factors – speeding, drink driving, motorcycle helmet use, and seat-belts and child restraint systems.
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-With a growing and increasingly urban global population, the rising demand for mobility is set to overwhelm transport systems, particularly those that rely heavily on private vehicles. Yet many countries continue to design and build their mobility systems for motor vehicles, not for people, and not with safety as the main concern. This slows efforts to save lives and to protect vulnerable road users.
-
-Some of the greatest gains have been made where the safe system approach to road safety – which puts people and safety at the core of mobility systems – is most widely applied. The European Region has the greatest concentration of countries with policies and legislation that align with this approach and reports the largest drop in deaths. The Western Pacific Region is second, both in the number of countries adopting aspects of the safe system approach and in reducing fatalities. These examples show that fatality reduction targets can be met, given a level of political will, investment and capacity that matches the scale of the road death and injury crisis.
-
-## <span id="page-12-0"></span>Introduction
-
-This *Global status report on road safety 2023* (the fifth edition since 2009) *[\(1](#page-64-1)[–4\)](#page-64-2)* presents findings from a unique vantage point on the road to safe mobility: it provides the first complete overview of progress made during the Decade of Action for Road Safety 2011–2020 *([5\)](#page-64-3)* and sets a baseline for the Decade of Action for Road Safety 2021–2030 *([6](#page-64-4))*. This report looks at how and where the burden is changing and at how we are responding. Its specific objectives are to:
-
-- describe the road safety situation in United Nations (UN) Member States and assess changes since the publication of previous versions of this report, with a particular focus on the evolution of the burden and responses since 2010;
-- evaluate gaps in road safety nationally to stimulate action;
-- inspire research on road safety implementation decision-making; and
-- strengthen the network of individuals working on road safety around the world.
-
-This report addresses the UN General Assembly Resolutions to monitor progress in the reduction of deaths and nonfatal injuries in countries *[\(2,](#page-64-5) [6\)](#page-64-4)*. Individual Country or territory profiles for all 194 countries and two territories that volunteered their data are available in a companion report to this publication *([7\)](#page-64-6)*.
-
-## **A note on methodology**
-
-The findings of this report are based mainly on a survey and review of legislation in which 170 UN Member States and two territories participated. For the 24 Member States not participating in this report, the most recent data from previous surveys are used in their Country Profiles. The methodology used is described in Annex 1. Progress against the voluntary UN Performance Targets is set out in Annex 2. The percentage of the world population covered by selected road safety laws (in 2022) is set out in Annex 3. The relationship between national legislation and adherence to related UN conventions or regulations is set out in Annex 4. Annex 5 presents the template used in the Country and Territory Profiles and Annex 6 presents the operational definitions used to produce them. This report is accompanied by a summary version *[\(8](#page-64-7))*; a Country and territory profile report; and the World Health Organization (WHO) Road Safety Data mobile application *([9\)](#page-64-8).* Documents, individual country and territory profiles, and data can be accessed in multiple languages [ht t p s : //w w w.w h o. i nt / t e a m s /s o c i a l](https://www.who.int/teams/social-determinants-of-health/safety-and-mobility/global-status-report-on-road-safety-2023)  [determinants-of-health/safety-and-mobility/](https://www.who.int/teams/social-determinants-of-health/safety-and-mobility/global-status-report-on-road-safety-2023) [global-status-report-on-road-safety-2023.](https://www.who.int/teams/social-determinants-of-health/safety-and-mobility/global-status-report-on-road-safety-2023)
-
-The report incorporates WHO generated mortality estimates; a legislation review conducted by WHO legal experts based on the survey, augmented by a review of original legislation to assess whether legislation meets WHO best practice criteria.
-
-Mortality estimates for all causes of death are updated periodically by the WHO Division of Data, Analytics and Delivery for Impact (WHO DDI) *[\(10\)](#page-64-9)*. As more data are submitted by countries and territories to WHO DDI, mortality estimates for all causes are updated retrospectively. Thus, total fatality numbers set out by year in Fig. 5 do not necessarily correspond to the estimates published in previous reports. The revised mortality estimates for the previous years are as follows: 1.21 million for 2016, 1.25 million for 2013, 1.26 million for 2011, and 1.26 million for 2007.
-
-![](_page_13_Picture_0.jpeg)
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-<span id="page-14-0"></span>![](_page_14_Picture_0.jpeg)
-
-# The global burden of road traffic deaths
-
-![](_page_14_Picture_2.jpeg)
-
-There were an estimated 1.19 million road traffic deaths in 2021; this corresponds to a rate of 15 road traffic deaths per 100 000 population.
-
-![](_page_14_Picture_4.jpeg)
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-As of 2019, road traffic injury remains the leading cause of death for children and young people aged 5–29 years and is the 12th leading cause of death when all ages are considered.
-
-![](_page_14_Picture_6.jpeg)
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-Globally, 4-wheel vehicle occupants represent 30% of fatalities; followed by pedestrians who make up 23% of fatalities; and powered two- and three-wheeler users who make up 21% of fatalities.
-
-![](_page_14_Picture_8.jpeg)
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-Cyclists account for 6% of fatalities while 3% of deaths are among users of micro-mobility devices such as e-scooters.
-
-![](_page_14_Picture_10.jpeg)
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-92% of deaths occur in low- and middle-income
-
-![](_page_14_Picture_12.jpeg)
-
-The risk of death is three times higher in low-income countries than high-income countries despite these countries having less than 1% of all motor vehicles.
-
-## There were an estimated 1.19 million road traffic deaths in 2021; this corresponds to a rate of 15 road traffic deaths per 100 000 population.
-
-There were an estimated 1.19 million road traffic deaths in 2021; this corresponds to a rate of 15 road traffic deaths per 100 000 population. Based on 2019 data on the age distribution of all-cause mortality, road traffic injury remains the leading cause of death for children and young people aged 5–29 years and is the 12th leading cause of death when all ages are considered *[\(11\)](#page-64-10)* (Table 1).
-
-As a leading cause of death and major contributor to disability, road traffic injuries also impose an enormous economic cost on societies. Some estimates put the global macroeconomic cost of road traffic injuries as high as US\$ \$1.8 trillion<sup>2</sup> , roughly equivalent to 10–12% of global gross domestic product (GDP) *([12](#page-64-11))*. As such, road traffic injuries are an important health and development challenge.
-
-Table 1. Leading causes of death, all ages, and ages 5–29 years, 2019
-
-| Rank | All ages                                | Ages 5–29 years              |
-|------|-----------------------------------------|------------------------------|
-| 1    | Ischaemic heart disease                 | Road Injury                  |
-| 2    | Stroke                                  | Tuberculosis                 |
-| 3    | Chronic obstructive pulmonary disease   | Diarrhoeal diseases          |
-| 4    | Lower respiratory infections            | Interpersonal violence       |
-| 5    | Neonatal conditions                     | Self-harm                    |
-| 6    | Trachea, bronchus, lung cancers         | HIV/AIDS                     |
-| 7    | Alzheimer´s disease and other dementias | Lower respiratory infections |
-| 8    | Diarrhoeal diseases                     | Maternal conditions          |
-| 9    | Diabetes mellitus                       | Drowning                     |
-| 10   | Kidney diseases                         | Cirrhosis of the liver       |
-| 11   | Cirrhosis of the liver                  | Malaria                      |
-| 12   | Road injury                             | Meningitis                   |
-
-*Source:* Adapted from: *[11](#page-64-10))*
-
-<sup>2</sup> For the period 2015–2030 using 2010 constant USD\$
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-<span id="page-16-0"></span>In addition to being the leading killer for children and young adults, road traffic deaths impact people during their most productive years. Approximately 66% of fatalities are among people aged 18–59 years and 19% are aged 60 years or above. Road traffic deaths continue to disproportionately impact men, with an overall female-to-male fatality ratio of 1 to 3.
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-The importance of deaths among people of working ages and the disproportionate impact on males is also reflected in data on work-related driver deaths available from 21 countries. In these countries, approximately 23% of driver deaths relate to trips to or from work (e.g., commuters); 16% of deaths result from work-related driving (e.g., deliveries and appointments); and an additional 12% of deaths are among (disproportionately male) professional drivers "at work" (e.g., bus drivers).
-
-## **Fatalities by road user type**
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-Globally, occupants of 4-wheel vehicles represent 30% of fatalities; followed by pedestrians who represent 23% of fatalities; and powered two- and three-wheeler users make up 21% of fatalities. Cyclists account for 6% of fatalities. Occupants of vehicles carrying more than 10 people, heavy goods vehicles, "other" users and "unknown" user types comprise the remaining 20% of deaths. Given the rise in powered personal micromobility modes such as e-scooters, questions on these modes of transport were newly included in the survey for this report, and reveal that globally, 3% of deaths are among users of these modes (which are included in the "other" road user category).
-
-The distribution of deaths among road users changes significantly, however, when data are disaggregated by region. As shown in Fig. 1, except for the European Region and Eastern Mediterranean Region (where occupants of 4-wheel vehicles comprise the largest share of the deaths at 49% and 33% respectively), in most regions, it is pedestrians and powered two- and three-wheelers users that make up the majority of deaths. In the Western Pacific Region, pedestrians comprise the largest share of fatalities while in the South-East Asia Region, cyclists account for 12% of all deaths. This is especially concerning given that pedestrians and cyclists tend to be the most vulnerable road users and, in most countries, represent the economically most disadvantaged *([13](#page-64-12))*.
-
-**Fig. 1. Percentage distribution of country-reported deaths by road user type and WHO region, 2021** 
-
-![](_page_17_Figure_1.jpeg)
-
-## <span id="page-18-0"></span>**Fatality counts and rates, by region and country-income level**
-
-The vast majority of road traffic deaths, 92%, occur in upper-middle, lower-middle, and low-income countries combined. Seventynine percent of road traffic deaths occur in lower-middle-income countries and uppermiddle-income countries combined (44% and 35% respectively), with low-income countries accounting for 13%, and high-income countries accounting for the remaining 8%.
-
-Relative to the size of countries' motor vehicle fleets and road networks, there is a disproportionately high number of fatalities in low- and middle-income countries compared to high-income countries. For example, highincome countries have 16% of the world's population, 28% of the world's vehicle fleet, 88% of all paved inter-urban roads, and 8% of fatalities; by contrast, low-income countries have 9% of the world's population, less than 1% of the world's powered vehicle fleet and paved inter-urban roads, yet 13% of fatalities. (Fig. 2).
-
-In terms of absolute numbers, the highest number of fatalities occur in the South-East Asia Region (330 222 deaths, or 28% of the global burden), followed by the Western Pacific Region (297 733 deaths, or 25% of the global burden); the African Region (225 482 deaths, or 19% of the global burden); the Region of the Americas (144 090 deaths, or 12% of the global burden); the Eastern Mediterranean Region (125 781 deaths, or 11% of the global burden); and the European Region (62 670 deaths, or 5% of the global burden) (Fig. 3).
-
-Fig. 2. Share of global population, road traffic deaths, paved inter-urban roads, and registered motor vehicles, by country income level, 2021
-
-![](_page_18_Figure_6.jpeg)
-
-Excludes expressways
-
-![](_page_19_Figure_0.jpeg)
-
-![](_page_19_Figure_1.jpeg)
-
-Fatality rates are highest among low-income countries, at 21 deaths per 100 000 population, and lowest in high-income countries, at eight deaths per 100 000 population (Fig. 4). Uppermiddle-income and lower-middle-income countries both have fatality rates of 16 per 100 000 population.
-
-The African Region has the highest fatality rate at 19 deaths per 100 000 population, and the European Region has the lowest fatality rate at seven deaths per 100 000 population. For the other WHO regions, fatality rates per 100 000 population are 16 in both the Eastern Mediterranean Region and in the South-East Asia Region, 15 in the Western Pacific Region, and 14 in the Region of the Americas.
-
-Within regions, the same correlation between income level and fatality rates can be observed, with fatality rates highest in lowincome countries and lowest in high-income countries in all regions. In some regions, such as in the Region of the Americas, the differences between the rates are not as significant, but in others, such as the Eastern Mediterranean Region, fatality rates in the region's low-income countries are nearly double those of its high-income countries (Fig. 4).
-
-**Fig. 4. Road traffic fatality rate per 100 000 population by WHO region and country income level, 2021**
-
-![](_page_20_Figure_1.jpeg)
-
-![](_page_21_Picture_0.jpeg)
-
-## **The voice of youth:**
-
-Raquel Barrios, Executive Director, YOURS – Youth for Road Safety
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-Road traffic crashes have been the leading killer of youth for over a decade, and despite being the largest generation of youth in history, young people's voices are rarely heard when it comes to designing road safety policies. So it should be no surprise that many young people do not trust policy-makers.
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-Meaningful youth participation requires a shift in mindset. Moving beyond the idea that youth are beneficiaries, we need a big change in how we work to ensure that young people's experiences, ideas, expertise and perspectives are fully and systematically integrated into all programmatic, policy-, and decision-making processes.
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-Here are just some of the ways youth can boost road safety:
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-As active road users, youth can ensure that their behaviours are supportive of safety as well as bring their needs to the table and support the design of effective policies around urban planning and sustainable cities.
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-As out-of-the-box thinkers, they bring creativity and innovation to tackle the most pressing issues.
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-As millennials and centennials, they are leading the digitalization movement and the adoption of technology for greater efficiency.
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-As change-makers, youth bring energy and flexibility and put their time, skills, and resources into community development initiatives, which could strengthen road safety.
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-As fearless advocates, youth can push for road safety to find a place on many agendas and be vocal about the crucial role of road safety in achieving all of the Sustainable Development Goals.
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-Road safety is key to building a healthy, inclusive, sustainable and safe environment for everyone, so we must promote road safety in all areas of development to bring more action, more investment and more resources to help reduce road crash deaths and injuries.
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-Youth must always be at the table; their involvement in the entire cycle of road safety policies, from design to implementation and monitoring and evaluation is essential. Governments must realize the potential of intergenerational collaboration and its dynamism to save more lives on the roads.
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-![](_page_23_Picture_0.jpeg)
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-<span id="page-24-0"></span>![](_page_24_Picture_0.jpeg)
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-# How the burden and context have evolved
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-![](_page_24_Picture_2.jpeg)
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-Globally, the number of road traffic deaths has fallen 5% since 2010.
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-![](_page_24_Picture_4.jpeg)
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-The global fatality rate per 100 000 population has fallen 16% since 2010 when set against the 13% rise in global population.
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-![](_page_24_Picture_6.jpeg)
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-The global fatality rate per 100 000 vehicles has fallen 41% since 2010 when set against the 160% increase in the global motor vehicle fleet.
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-![](_page_24_Picture_8.jpeg)
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-The global share of fatalities has fallen 1% among 4-wheel vehicle users and 2% among two- and three-wheeler users since 2010 but has risen from 5% to 6% among cyclists.
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-![](_page_24_Picture_10.jpeg)
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-In 108 countries, reductions in fatality counts between 2010 and 2021 were observed, including, for the first time, low-income countries.
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-![](_page_24_Picture_12.jpeg)
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-10 countries in four regions achieved the target of a 50% reduction in road traffic deaths between 2010 and 2021.
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-## When compared to the estimated 1.25 million road traffic deaths in 2010, the current figure of 1.19 million for 2021 represents a reduction of 5%.
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-Since the start of the Decade of Action for Road Safety 2011–2020 there have been significant rises in the global population *[\(14](#page-64-13))*, the number of powered vehicles *[\(15\)](#page-64-14)*, and the size of the world's road networks *[\(16\)](#page-64-15)*. Rapidly evolving technology, increasing population density and growth in urban areas, along with the emergence and growing presence of micro-mobility and use of mobility services, are some of the challenges affecting the burden of road traffic injuries in the past decade.
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-When compared to the estimated 1.25 million road traffic deaths in 2010, the current figure of 1.19 million for 2021 represents a reduction of 5%. After the start of the Decade of Action for Road Safety 2011–2020, the number of road traffic deaths peaked in 2012 (at 1.26 million). This was followed by a gradual decline that started in 2013 and continues until 2021. The predominantly steady downward trend since 2010 contrasts with the gradual upward trend during the 10-year period prior to the Decade of Action for Road Safety 2011–2020 (Fig. 5). The current estimates are now close to those in 2000, at the start of the upward trend.
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-The only notable exception to these gradual shifts can be seen in 2020, when the Coronavirus disease of 2019 (COVID-19) related confinement policies restricted mobility, and fatalities significantly, if temporarily, declined.
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-Fig. 5. WHO estimated number of road traffic fatalities, 2000–2021
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-![](_page_25_Figure_6.jpeg)
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-As discussed, the period 2010–2021 saw a 5% reduction in absolute numbers of road traffic fatalities, and it also saw the global population grow by nearly 1 billion *([14](#page-64-13))* or roughly 13%. When this growth in population is considered, the road traffic fatality rate has also declined – from nearly 18 per 100 000 people in 2010 to the current estimate of 15 per 100 000 people in 2021. This represents a 16% fall in the death rate since 2010 (Fig. 6).
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-Similarly, the period 2011–2020 saw the global motor vehicle fleet burgeon, with countries reporting a 160% increase since 2010. Four wheel vehicles comprise 85% of the world's motor vehicle fleet, with powered twoand three-wheelers accounting for the next largest share at 12%. Powered two- and three-wheelers have Fig.6. WHO-estimated global fatality rates relative to population and powered vehicle eet, by year, 2010–2021
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-nearly tripled in number, with a 175% increase since 2011. The global increase is driven by the South-East Asia Region with a 273% increase, the Region of the Americas with a 217% increase, the Western Pacific Region with a 155% increase and the European Region with a 142% increase. This growth corresponds to data from the International Road Federation which shows an increase in road density worldwide, but especially in the African and the Western Pacific Region *([17\)](#page-64-16).*
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-Against this backdrop, a substantial decline can be seen in annual fatality rates per 100 000 vehicles, from 79 deaths per 100 000 vehicles in 2010 to 47 deaths per 100 000 vehicles in 2021 – a 41% reduction (Fig. 7).
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-Fig. 6. WHO estimated global road traffic fatality rates per 100 00 population, 2010–2021
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-![](_page_26_Figure_5.jpeg)
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-Fig. 7. WHO estimated global road traffic fatality rates per 100 000 vehicles, 2010–2021
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-![](_page_26_Figure_7.jpeg)
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-<span id="page-27-0"></span>The impact of increased levels of motorization are reflected in changes in the relative shares of deaths among user types. Comparisons with the *Global status report on road safety 2013* reveal modest overall changes in the total share of road traffic deaths by user type, with reductions of 1% for 4-wheel vehicle occupants and 2% for users of powered two- and three-wheelers. These overall changes hide more significant changes by WHO region. For example, fatalities among powered two- and three-wheeler users have reduced their share of total deaths by 5% in the Eastern Mediterranean Region and 20% in the Western Pacific Region but have increased their share by 4% in the European Region; 11% in the African Region; 13% in the Region of the Americas; and 15% in the South-East Asia Region.
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-And compared to the *Global status report on road safety 2013*, the number of cyclist deaths has risen from 5% of all fatalities in 2010 to a current estimate of 6%, representing a 20% rise. This increase is particularly prominent in the South-East Asia Region and the European Region where the proportion of cyclist road traffic deaths rose from a reported 4% in each region in the *Global status report on road safety 2013*, to current shares of 12% and 9% – an increase of 200% and 125% respectively.
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-Emerging evidence from some countries suggests this increase in cyclist fatalities is due in part to the electrification of bicycles which has resulted in increased ridership in cities that often lack adequate cycling infrastructure. In some countries, there has been an increase in e-bike use among older populations who are especially vulnerable to serious injury and death in the event of road crashes. These examples illustrate the importance of monitoring and conducting research on how new technological innovations are adopted within the transport system and their impact on safety.
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-## **Progress toward the target of a 50% reduction in deaths**
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-While the global target to halve road traffic deaths from the baseline set by the Decade of Action for Road safety 2011–2020 was not met globally, at the end of 2021,3 10 countries from four different regions achieved the target reduction of at least 50% in their fatality numbers: Belarus, Brunei Darussalam, Denmark, Japan, Lithuania, Norway, Russian Federation, Trinidad and Tobago, United Arab Emirates, and Venezuela (Bolivarian Republic of) *([5\)](#page-64-3).* (For more on the impact of the COVID-19 pandemic on fatalities in Europe, see Box 1).
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-In addition to the 10 countries where the target of a 50% reduction in deaths was met, reductions of 40–49% were observed in 15 countries, of 30–39% in 20 countries, of 20–29% in 33 countries, and of 10–19% in 19 countries. An additional 11 countries achieved reductions of 2–9%.
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-Overall, during this period, reductions larger than 2% were observed in 108 countries – nearly half of which are high income. Reductions have been observed in eight lowincome countries (Fig. 8).
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-<sup>3</sup> Due to the restrictions on mobility as a result of the COVID-19 pandemic, there was a temporary reduction in the number of deaths in 2020; for this reason, 2021 is the year used to assess progress towards the Decade of Action for Road Safety 2011–2020.
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-Fig. 8. Number of countries where a change in total road traffic deaths has been observed, by region and country income level, 2010–2021<sup>4</sup>
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-![](_page_28_Figure_1.jpeg)
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-<sup>4</sup> This excludes small countries with a population of less than 200 000 that have 1–2 deaths annually; this small number makes it impossible to assess meaningful reductions over time.
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-![](_page_29_Figure_0.jpeg)
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-![](_page_29_Figure_1.jpeg)
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-These reductions have been observed across four regions, ranging from a 36% decrease in the European Region to a 0.1% decrease in the Region of the Americas, a 2% reduction in the South-East Asia Region (even though the South-East Asia Region has the highest death rates and numbers overall); and a decrease of 16% in the Western Pacific Region. In contrast, the number of deaths rose in 66 countries, of which 28 are in the African Region (where there was an overall increase of 17% in the number of fatalities); 20 in the Region of the Americas; eight in the Eastern Mediterranean Region (where there was an overall increase of less than 1% in the number of fatalities); four in each of the European Region and the South-East Asia Region; and two in the Western Pacific Region (Fig. 9).
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-Some of the greatest gains were made where the safe system approach to road safety – which puts people and safety at the core of mobility systems – was most widely applied. The results of this report demonstrate that the European Region saw the greatest concentration of countries with policies and legislation that align with this approach and reported the largest drop in deaths. The Western Pacific Region came second, both in the number of countries adopting aspects of the safe system approach and reducing fatalities. These examples show that fatalityreduction targets can be met, given a level of political will, investment and capacity that matches the scale of the road death and injury crisis.
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-### Box 1: COVID-19, mobility and political decision-making
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-## Road traffic deaths fell 13% in European Region in 2020 as consequence of rapid government COVID-19 restrictions.
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-![](_page_30_Picture_2.jpeg)
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-Research shows that the COVID-19 pandemic response had a significant impact on mobility *([18\)](#page-64-17).* Pandemic related restrictions on movement cut exposure to road crash risks, and this was reflected in reduced rates of road trauma. More significantly for policy-making, mobility patterns also responded to restrictions (and to fears of exposure to infection) more flexibly than might have been expected. Active mobility replaced motorized transport, with cycling in particular substituting for public transport and cars. Many local governments were quick to accommodate the change, reallocating road space to accommodate safe cycling. Many of the temporary protected cycle lanes have been retained and the stimulus to investment in infrastructure for active mobility has radically changed mobility patterns in many cities towards more sustainable mobility.
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-The strictness of COVID-19 restrictions was monitored by the United Kingdom's Oxford University Governmental Response tracker. Its stringency index was highest for most countries near the beginning of the pandemic and fluctuated thereafter according to the number of COVID-19 cases and casualties. All countries saw a fall in traffic volumes from March 2020. In Europe, April saw traffic fall by over a third in countries reporting monthly vehicle-kilometre data.
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-Overall for the year, the 17 countries<sup>5</sup> with consistent data recorded a 13% decrease in traffic volume compared to the average for 2017–2019. These countries recorded an overall reduction in road deaths of 16% compared to the 2017–2019 baseline. Twenty-five members of the WHO European Region also report validated data to the International Transport Forum's International Traffic Safety Data and Analysis (IRTAD) group. For these countries6 the number of road deaths decreased by an average of 18% in 2020 compared to the 2017–19 baseline. There are substantial differences between countries, but the majority saw a reduction in fatalities of around 20%.
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-Young people and older people over the age of 75 years were the two age-groups recording the largest reductions in road deaths, with falls of 25% and 19% respectively, on average (for the 25 countries minus the Netherlands (Kingdom of the)). This relates to the closing of educational institutions for the young and particularly restricted mobility for older people who were among the most vulnerable to COVID-19.
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-All transport modes saw reduced fatalities, 24% for pedestrians, 16% for powered two-wheelers and 20% for car occupants. The shift to cycling in lockdown periods made the reduction in the number of cyclist fatalities less than for other road users, with a decrease of only 2% for the 25 countries in 2020 compared to baseline years. Changes in fatalities should also be viewed in light of exposure to crash risks, for example in relation to kilometres driven or walked. Available data are insufficiently differentiated between modes to draw conclusions.
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-<sup>5</sup> Austria, Czechia, Denmark, Finland, France, Germany, Hungary, Iceland, Ireland, Israel, Netherlands (Kingdom of the), Norway, Poland, Slovenia, Sweden, Switzerland, the United Kingdom.
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-<sup>6</sup> Austria, Belgium, Czechia, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Lithuania, Luxembourg, Netherlands (Kingdom of the), Norway, Poland, Portugal, Serbia, Slovenia, Spain, Sweden, Switzerland, the United Kingdom.
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-![](_page_31_Picture_0.jpeg)
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-<span id="page-32-0"></span>![](_page_32_Picture_0.jpeg)
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-# Measures to mitigate the risk of death and injury
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-![](_page_32_Picture_2.jpeg)
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-Most people identify as pedestrians and public transport users, yet only 47 countries have policies to promote walking, cycling, and public transport.
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-![](_page_32_Picture_4.jpeg)
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-Nearly 80% of the roads assessed do not meet a minimum 3-star rating for pedestrian safety and just 0.2% of the roads assessed have cycle lanes.
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-![](_page_32_Picture_6.jpeg)
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-Only 35 countries have legislation mandating all five core areas of vehicle safety equipment while 79 countries have no legislation on vehicle safety standards.
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-![](_page_32_Picture_8.jpeg)
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-As of 2022, 140 countries have legislation meeting WHO best practice for at least one of the five key risk factors,<sup>7</sup> although only six countries have legislation on all five that meet WHO best practice criteria.
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-![](_page_32_Picture_10.jpeg)
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-Since the *Global status report on road safety 2018,* 23 countries have modified their laws to align with WHO best practice: speeding *[\(8](#page-64-7));* drink driving *([3\)](#page-64-18),* motorcycle helmets *([5\)](#page-64-3);* seat-belts *[\(11](#page-64-10));* and child restraint systems *[\(4\)](#page-64-2).*
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-![](_page_32_Picture_12.jpeg)
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-131 countries have national legislation mandating thirdparty liability insurance for vehicles.
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-![](_page_32_Picture_14.jpeg)
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-Only 25 countries mandate provision of psychological assistance to road traffic crash victims and their families.
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-<sup>7</sup> Speeding, drink driving, motorcycle helmet use, seat-belts, and child restraint systems.
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-## <span id="page-33-0"></span>**Multimodal transport**
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-About 60% of the global population is expected to live in urban settings by 2030 *[\(19\)](#page-64-19)*, meaning that increased demand for mobility will exceed the capacity of most current systems in these areas. Mobility needs and the systems to fulfil them must continue to evolve in response to opportunities created by technological innovations as well as challenges such as the impact of transport on climate change as well as road traffic death and injury (see Box 2 for an example on mobility as a right in Mexico).
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-Information on transport mode use in a recent survey of 48 countries *[\(20\)](#page-65-0)* shows that most people in these countries see themselves, at one time or another, as pedestrians (with percentages close to 95% in all regions except the Region of the Americas, where 85% of the population recognized themselves as pedestrians). The next most common user category is public transport user (between 68% and 96%, depending on region). Around 93% of people across all regions see themselves, at one time or another, as a car passenger, while identifying as a car driver is reported by between 67% and 81% of individuals, depending on region. Identifying as a motorcycle rider or passenger is reported by between 41% and 72% of people. While these findings underscore the importance of active modes of transport, particularly considering the mental and physical health benefits associated with their use *([21\)](#page-65-1)*, the increasing proportion of deaths among pedestrians and cyclists observed over the past decade is cause for concern.
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-Despite the potential benefits of multimodal transport and the need to ensure that vulnerable road users are equally protected as other road users (including those in passenger vehicles), few countries to date have systematically assessed multimodal transport planning as part of their road safety strategies. To assess the status of data collection on multimodal transport, countries were asked if they tracked the frequency and distribution of trips by mode of transport. About a quarter of all countries report collecting data on transport modes. Forty-two countries have data on the use of passenger (4-wheel) vehicles; 30 countries report collecting data on walking or cycling, and 10 collect data on the use of powered two- and three-wheelers and other personal mobility devices. Data on publicly operated transport is available in 54 countries.
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-## **Legislation, policies, plans and strategies related to multimodal transport use**
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-While no countries report legislation related to multimodal transport use, 87 report national strategies to promote access to, and use of, public transport. Forty-seven countries report national policies and strategies to promote walking and cycling. According to the 2022 WHO *Global status report on physical activity [\(21\)](#page-65-1),* approximately three quarters of all countries conduct national surveillance of physical activity – including walking and cycling among adults, adolescents and children.
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-### <span id="page-34-0"></span>Box 2: Safe mobility as a right, Mexico
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-In 2020 Mexico established a fundamental constitutional right: the right of everyone to safe, accessible, efficient, sustainable, inclusive and equitable mobility. This marked an important milestone in the country's approach to road safety and sustainable mobility.
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-The General Law on Mobility and Road Safety, which sets out this new constitutional right, recognizes the need to protect vulnerable road users such as pedestrians, cyclists and motorcyclists, and defines which sectors are involved in the promotion of road safety and sustainable mobility. Reflecting this comprehensive and collaborative approach, in October 2022 the National Mobility and Road Safety System was established to coordinate related activity across Mexico's government and civil society.
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-And in October 2023 the government published its National Strategy for Mobility and Road Safety 2023–2042, presenting a long-term vision for
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-![](_page_34_Picture_4.jpeg)
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-the development of mobility and road safety that embraces all of the values enshrined in the law, from accessibility to equity. The new constitutional right was made possible partly by the active and constant participation of civil society, which helped push safe mobility up the political agenda.
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-However, there are hurdles to realizing the law, such as adequate resource allocation and the inclusion of mandatory vehicle insurance; vehicle safety (an essential aspect that was not comprehensively addressed in the Law), and the need for continuous leadership and accountability. To achieve this, the participation of all sectors and institutions linked to the issue is necessary – especially civil society, which has an essential role in the process.
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-Finally, there is the challenge of guaranteeing state level regulations and standards, since the General Law covers all recommendations at the federal level. Municipal and state level competencies require special attention to ensure effective and homogeneous implementation of road safety policies.
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-## **Safe road infrastructure**
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-Safe road infrastructure is key for safety. Road infrastructure should be designed and operated to eliminate or reduce risks for all road users (see Box 3 for an example in Indonesia). In addition to improving safety, road infrastructure can enhance accessibility, including for persons with disabilities, and facilitate transfers from one transport mode to another. Infrastructure safety can be maximized for new roads as well as existing roads.
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-Despite this, the results of the survey for this report indicate that (where audited) most roads continue to be built for the growing motor vehicle fleet. Of particular concern, many new roads being built in low- and middleincome countries fail to meet recognized safety standards. In total, reporting countries collectively account for nearly 68 million km of roads, of which 4.5 million km are paved expressway; 47 million km are paved interurban roads; and 10 million km are unpaved inter-urban roads. Only 35 countries report on the availability of cycle lanes, which account for a total length of 140 000 km, or roughly 0.2% of the total length of roads reported. This deficit in infrastructure for cyclists provides some insight as to why more cyclists are dying in recent years.
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-Fig. 10. Proportion of paved roads with a 3-star<sup>a</sup> or higher safety rating, by user group (500 000 kms evaluated, globally), 2021
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-![](_page_35_Figure_1.jpeg)
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-*Source:* International Road Assessment Programme *[\(22](#page-65-2)).*
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-### **Road safety inspections or audits**
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-A non-representative sample of nearly 500 000 km of paved road evaluated in 82 countries across all regions and income levels was conducted using the star-based road safety scoring system developed by the International Road Assessment Programme (iRAP) which rates roads from 0 to 5 *([22\)](#page-65-2)*.
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-A 3-star rating is widely accepted as the minimum acceptable rating for new and old roads *[\(23](#page-65-3))*. 8 Using this approach, the results of this assessment reveal that only 21% of roads meet a 3-star or higher rating in relation to pedestrians and powered two- and threewheelers; 23% for cyclists; and 40% for passenger vehicles (Fig. 10).
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-Additionally, formal road safety evaluations9 are reported by 93 countries in the survey for this report, of which nearly 50 report the percentage of their national road network evaluated. Of these countries, most declare evaluating 20–50% of their national road network.10
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-## **Legislation, policies, plans and standards related to safe road infrastructure**
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-Ninety-four countries report having national legislation requiring a formal road safety inspection or assessment for existing roads. However, the legal review of documents confirms the presence of this law in only 66 countries. Of these, 55 laws contain a requirement for periodic checks (maintenance or inspection), and 51 require that the needs of all road users considered.
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-*Out of a possible 5-star rating*
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-<sup>8</sup> This includes voluntary UN Performance Target (4b) that calls for 75% of all travel done in roads 3 stars or more for all road users.
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-<sup>9</sup> This corresponds to UN Voluntary Performance Target 3.
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-<sup>10</sup> Most of these evaluations were done using unspecified methods, but six countries reported using the Global Street Design guidelines and 15 other countries used the star-rating system.
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-#### Box 3: Inclusive school zones, Indonesia
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-Many public spaces in Indonesia are inaccessible to vulnerable groups such as children and persons with disabilities, partly due to a lack of infrastructure and limited awareness on the part of citizens and government agencies.
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-The Inclusive Banjarmasin Initiative, led by the Transformative Urban Mobility Initiative and Kota Kita, with support from the Asian Development Bank, aimed to improve urban mobility and accessibility in Banjarmasin, Indonesia. A central focus of the project was the creation of safe and inclusive school zones to enhance road safety.
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-The Safe School Zone project was implemented from 2019 to 2021 in two schools in the Gadang neighborhood. The project was inclusive and involved school administrators, teachers, students, parents, and other stakeholders in co-designing and implementing road safety measures. This approach ensured that the improvements are communitydriven and cater to local needs. The results of the project related to road safety are significant.
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-![](_page_36_Picture_4.jpeg)
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-Firstly, the project improved the safety and accessibility of sidewalks, parking areas, and drop-off/pick-up zones for all students. Dedicated sidewalks, crossroads, and curb ramps were constructed to ensure safe and convenient movement for pedestrians, including people with disabilities. Rumble strips and guiding blocks were installed to help people with visual impairments to navigate the sidewalks. These measures created a safer environment for all pedestrians, particularly vulnerable road users.
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-Secondly, the project focused on traffic management to enhance road safety. Speed limit signs and Safe School Zone signs were installed within a 100 metre radius of the schools to raise awareness and encourage responsible driving. This helped to mitigate speeding issues and improve traffic management around the schools.
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-The success of the Safe School Zone project in Gadang has inspired the development of five more such zones in Banjarmasin. This success underscores the scalability and replicability of the initiative, offering a model for other urban areas seeking to simultaneously enhance mobility, road safety and accessibility.
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-One hundred and twenty countries report using technical standards for the development of new roads that account for the safety of all road users,11 and 61 of these countries report using UN or other international conventions to inform these standards.12 In addition, 92 countries report having a systematic programme to target investments and upgrade higher-risk locations, identifying road-crash hotspots as their most common mechanism for allocating available funds.
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-While information was not available in most countries about the investments being made in road infrastructure, according to a report
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-<sup>11</sup> This is the first proposed sub-indicator for UN Voluntary Performance Target 3 (Annex 2).
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-<sup>12</sup> Sixty-one Member States adhere to at least one of three existing international road conventions. The 1950 Traffic Arteries Convention, 1975 European Agreement on Main International Traffic Arteries, and the 2003 Interstate Asian Highway Convention. We have chosen this as one of two UN voluntary Performance Target 3 indicators (Annex 2). See Annex 4 for more detail on adherence to conventions and existing national legislation.
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-<span id="page-37-0"></span>by the World Bank in 2022, more than US\$ 800 billion is being spent annually on road infrastructure development by public and private investors *([24](#page-65-4))*. Similarly, data from the International Transport Forum show that spending by some countries on transport infrastructure ranged between less than 1% to as high as 5% of GDP between 2019 and 2021 *([25](#page-65-5))*. These investments represent an enormous opportunity to build infrastructure that supports multimodal transport and ensures the safety of all road users.
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-## **Safe vehicles**
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-The world's motor vehicle fleet – currently exceeding one billion vehicles – is likely to double between now and 2030 *([26](#page-65-6)).* Despite this growth, many new vehicles are being produced and sold that do not meet minimum safety standards. Where legislation requiring these standards are lacking, manufacturers can "de-specify" life-saving technologies in newer models sold in countries where regulations are weak or non-existent in order to reduce costs *([27](#page-65-7)).*
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-## **Legislation, strategies, policies and plans related to safe vehicles**
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-Two types of laws are essential for ensuring vehicle safety: the first is legislation that specifies requirements and standards for equipment; the second is legislation on inspections or assessments.
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-As summarized in Table 2, legislation specifying requirements and standards for core safety equipment in vehicles is absent in most countries, most notably in low- and middle-income countries. Just over half of countries (88) currently have legislation that specifies the requirements and standards for seat-belts and seat-belt anchorages and only around a third of all countries have legislation on other vehicle core safety elements, including front and side impact protection, electronic stability control, pedestrian protection, and braking systems.13
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-Table 2. Number of countries with legislation on "core" vehicle safety standards, by income, 2022
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-|                                                               |       | Income levelsa |                           |                           |               |
-|---------------------------------------------------------------|-------|----------------|---------------------------|---------------------------|---------------|
-|                                                               | Total | High<br>income | Upper<br>middle<br>income | Lower<br>middle<br>income | Low<br>income |
-|                                                               | N=170 | N=51           | N=43                      | N=46                      | N=27          |
-| Vehicle safety equipment:                                     |       |                |                           |                           |               |
-| National law on front and side impact protection              | 52    | 39             | 9                         | 4                         | 0             |
-| National law on seat-belt and seat-belt anchorages            | 88    | 44             | 21                        | 16                        | 7             |
-| National law on electronic stability control                  | 49    | 39             | 8                         | 2                         | 0             |
-| National law on pedestrian protection                         | 44    | 35             | 7                         | 2                         | 0             |
-| National law on braking systems                               | 56    | 38             | 11                        | 6                         | 1             |
-| National law requiring periodic vehicle inspection/assessment | 134   | 46             | 30                        | 35                        | 20            |
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-*a Not shown by income level counted in totals are the three countries for which there is no information on income level.*
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-<sup>13</sup> Additionally, 29 countries report legislation mandating the availability of eCall or Accident Emergency Call Systems in all vehicles to trigger an emergency response through a vehicle sensor.
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-Currently 35 countries have legislation mandating all five core areas of safety equipment; 10 have legislation for four core areas, nine have legislation for three core areas; eight have legislation for two core areas, and 29 countries have legislation for only one of the five core areas. Seventy-nine countries report no legislation on vehicle safety at all (Fig. 11).
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-In contrast, the number of countries requiring periodic vehicle inspections is much higher. While the presence of a national law was confirmed, few of these laws specified how the inspections should be done. Only 38 of the 134 countries specify the use of international standards for vehicle inspections, as set out in international conventions (for more on the UN road safety conventions see Box 4 and Annex 4).
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-As well as legislation on safety standards, vehicle safety can be assured through consumer oriented safety tests. Eightyseven countries report having consumer oriented crash test programmes, such as the New Car Assessment Programme (NCAP) tests, but only 25 of these countries report disseminating the safety-rating results. Fewer than 20 countries report having requirements to ensure that customers are informed on whether the vehicles they purchase meet minimum safety standards.
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-Restrictions on used-vehicle imports are reported by 150 countries.14 Of these, 61 indicate a vehicle safety inspection criterion with or without an additional vehicle age limit, though age limit alone is also used in several countries. That only a third of countries require vehicle safety inspections for used vehicles is especially of concern considering that the African Region accounts for the largest share of the used-vehicle market – the region where the rates of road traffic deaths are highest. According to a report published by the UN Environment Programme, between 2015 and 2020 roughly 23 million used passenger vehicles were exported, of which 66% went to low- and middle-income countries. Without regulations to ensure the safety of these vehicles, these exports at present pose a significant threat to road safety *([28](#page-65-8))*.
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-Fig. 11. Countries with legislation on "core" vehicle safety standards, 2022
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-![](_page_38_Figure_6.jpeg)
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-<sup>14</sup> This relates to UN Voluntary Performance Target 5.
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-### Box 4: UN road safety conventions
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-United Nations (UN) road safety legal instruments provide a strong foundation upon which countries can build domestic legal frameworks and transport systems in order to contribute to road safety and facilitate international road traffic. To fully realize their benefits, countries must not only accede to the conventions that provide these instruments, but also transpose the conventions into national or regional legislation. In this way they can ensure the effective application of the conventions, and thereafter enforce them through traffic police and inspection bodies.
-
-#### Number of countries adhering to UN road safety conventions
-
-| Convention                                                                                                                                                                                                                                                                                                              | Countries |
-|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------|
-| 1957 Agreement concerning the International Carriage of Dangerous Goods by Road (ADR)                                                                                                                                                                                                                                   | 53        |
-| 1958 Agreement concerning the Adoption of Harmonized Technical United Nations<br>Regulations for Wheeled Vehicles, Equipment and Parts which can be Fitted and/or be<br>Used on Wheeled Vehicles and the Conditions for Reciprocal Recognition of Approvals<br>Granted on the Basis of these United Nations Regulations | 56        |
-| 1968 Convention on Road Traffic                                                                                                                                                                                                                                                                                         | 86        |
-| 1968 Convention on Road Signs and Signals                                                                                                                                                                                                                                                                               | 71        |
-| 1970 European Agreement concerning the Work of Crews of Vehicles Engage in<br>International Road Transport (AETR)                                                                                                                                                                                                       | 51        |
-| 1997 Agreement Concerning the Adoption of Uniform Conditions for Period Technical<br>Inspection of Wheeled Vehicles and Reciprocal Recognition of Such Inspections                                                                                                                                                      | 38a       |
-| 1998 Agreement concerning the Establishing of Global Technical Regulations for Wheeled<br>Vehicles, Equipment and Parts                                                                                                                                                                                                 | 50        |
-
-*Including EU members mandated by EU Directive 2014/45/EU*
-
-To date, 120 countries report accession to one or more of the core road safety-related UN standards (see map), although 16 countries only adhere to all seven of them *[\(29](#page-65-9)).* See Annex 4 for the relationship between adherence to conventions and national legislation.
-
-#### Countries adhering to core road safety UN conventions, 2022
-
-![](_page_39_Figure_9.jpeg)
-
-## <span id="page-40-0"></span>**Road user behaviours**
-
-While the safe system approach emphasizes the importance of system designs that facilitate safe road use, laws governing road user behaviours are essential to the prevention of crashes, injuries, and deaths.
-
-Data collected for this report indicate that approximately 10% of road traffic deaths are related to drink driving; this corresponds to self-reported rates of 16–21% of people admitting to drink driving in a survey conducted by the European Survey Research Association (ESRA). The same self-reports reveal that nearly 50% of drivers across 48 countries report exceeding the speed limit
-
-outside built-up areas, with the perceived likelihood of being penalized for such violations ranging from 30% to 46%*.* Non-use of helmets among motorcyclists was reported as 20% for drivers and 30% for passengers; this corresponds to self-reports of 26–47% motorcyclists admitting to not using a helmet despite it being the law.
-
-Similar trends exist for non-use of seat-belts, with countries reporting 20% for drivers while self-reports show between 12–47% of drivers admitting to not using seat-belts. And 11–47% of people report not using a child restraint system while more than half of people surveyed admit to using communication devices while driving (Table 3).
-
-Table 3. Self-reported road user behaviours
-
-|                                                | Global status report on road safety<br>2023 survey (114 countries reporting<br>on at least one)                     | ESRA Survey (48 countries)                            |
-|------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------|
-| Exceeding speed limit                          | 1–66%                                                                                                               | 50%                                                   |
-| Drink driving                                  | 10% of fatalities (77 countries)                                                                                    | 16–21%                                                |
-| Non-use of helmet (adult)                      | 20% drivers (out of 44 countries);<br>30% passengers (out of 39 Member states)                                      | 26–47%                                                |
-| Non-use of seat-belts                          | 20% drivers (57 countries);<br>30% front seat passengers (50 countries);<br>50% rear seat passengers (42 countries) | 12–47% drivers<br>36–71% passengers                   |
-| Non-use of child restraint<br>system           | Information not included                                                                                            | 11–47%a                                               |
-| Distracted driving<br>(i.e., mobile phone use) | Information not included                                                                                            | 29–52% handheld phone use<br>vs 48–65% hands-free use |
-
-*Sources:* Survey for *Global status report on road safety 2023*; and *[\(20](#page-65-0), [30\)](#page-65-10).*
-
-These risks can be mitigated through the adoption and implementation of appropriate legislation. Although many countries have such laws, those laws do not always meet WHO best practice and are not consistently implemented through regulations or enforced. In the subsequent section, existing legislation is assessed against WHO best practice criteria (Table 4).15 In the colour-coded maps, those countries with laws that meet all criteria are coloured green; those with laws meeting some but not all are coloured yellow; and those with laws that do not conform with any best practice criteria or without legislation at all are coloured red.
-
-Transport of children under 150 cm in height without using a child restraint system.
-
-<sup>15</sup> WHO best practice criteria do not exist for laws on drug driving, distracted driving, and professional driver time limits
-
-## <span id="page-41-0"></span>**Speed management**
-
-Speed management remains one of the biggest challenges facing road safety practitioners around the world and calls for a concerted, long-term, multidisciplinary response. The speed at which a vehicle travels directly influences the risk of a crash as well as the severity of injuries sustained, and the likelihood of death resulting from that crash. Reducing vehicle speeds in areas where the road user mix includes a high volume of vulnerable road users, such as pedestrians and cyclists, is especially important.
-
-Among countries surveyed for this report, 163 report having laws on speeding, of which 57 meet WHO best practice – meaning they include a national speed limit; an urban speed limit of 50 km/h or lower; and the ability of local authorities to adapt speed limits to local contexts *[\(31](#page-65-11))* (Fig. 12).16 Of these 57 countries, three have laws mandating national speed limits in urban areas of 30 km/h where there is a frequent mix of road users, as recommended in the Global Plan of Action for the Decade for Road Safety *([27\)](#page-65-7)*. This represents an additional 8 countries meeting WHO best practice since the *Global status report on road safety 2018 [\(4](#page-64-2))*.
-
-Data on enforcement levels were not collected through the survey for this report, but information on self-reported behaviour and "enforcement perception" gathered by ESRA reveals that nearly 50% of drivers (across 48 countries where the survey was implemented) admit to exceeding the speed limit outside built-up areas, with the perceived likelihood of being penalized for violation ranging from 30% to 46% *[\(20\)](#page-65-0).*
-
-Fig. 12. Status of speed laws in countries, 2022
-
-![](_page_41_Figure_6.jpeg)
-
-<sup>16</sup> This corresponds to UN Voluntary Performance Target 6.
-
-<span id="page-42-0"></span>The use of speed cameras is mentioned in the speed laws of 81 countries to help enforce speed limits while penalties in the form of fines are reported as the main means of enforcement in 154 countries. Since 2018, six countries have increased their penalties for speeding.
-
-## **Impaired and distracted driving**
-
-Drinking alcohol significantly increases the risk and severity of a crash and therefore the chance it will result in death and serious injury. In high-income countries it is estimated that about 20% of fatally injured drivers have blood alcohol concentration (BAC) levels above the legal limit. And studies in low- and middleincome countries show that between 33% and 69% of fatally injured drivers and between 8% and 29% of nonfatally injured drivers had consumed alcohol before their crash *([32\)](#page-65-12).* Drink driving legislation that is evidencedriven, context relevant, consistently enforced and well understood by enforcement officials and the public has been effective in saving lives in many jurisdictions.
-
-## **Legislation on drink driving**
-
-Among countries surveyed for this report, 18 prohibit alcohol consumption among the general population. Specific legislation on drink driving is reported by 166 countries, of which 48 meet WHO best practice – which means that the law specifies a BAC limit of ≤0.05 g/dl for the general driving population and ≤0.02 g/dl for novice drivers *([32\)](#page-65-12).* This represents an increase of three countries meeting WHO best practice since the *Global status report on road safety 2018 [\(4\)](#page-64-2)* (Fig. 13).
-
-Of note, fatally injured drivers are tested for the presence of alcohol routinely in 61 countries, whereas nonfatally injured drivers involved in a fatal road crash are tested for the presence of alcohol in 51 countries.
-
-![](_page_42_Figure_7.jpeg)
-
-![](_page_42_Figure_8.jpeg)
-
-## <span id="page-43-0"></span>**Legislation on drug driving**
-
-Among those surveyed in this report, 167 countries have legislation that prohibits driving under the influence of drugs and other psychoactive substances. There are currently no WHO best practice criteria against which to assess these laws *([33](#page-65-13))*.
-
-## **Legislation on distracted driving**
-
-Among those surveyed in this report, 162 countries have legislation that prohibits distracted driving in general, but this mostly relates to mobile phone use – 144 countries prohibit the use of hand-held phones and 35 also prohibit the use of hands-free phones. There are currently no WHO best practice criteria against which to assess these laws *([34\)](#page-65-14)*.
-
-## **Legislation on professional driving times**
-
-Given the high level of exposure to traffic among professional drivers, there is a need to ensure regulation of commercial practices, including regulating driving times and conditions. Eighty-two countries have legislation on rest periods for professional drivers.
-
-Only 30 countries report a maximum number of driving hours (most typically 4–5 hours) while 23 countries report having minimum rest periods, most frequently reported as either a 30 minute rest after maximum driving time or a minimum number of daily hours. Fifty-six of these countries are signatories of the corresponding UN Convention.17 Whether this legislation matches other international recommendations such as International Labour Organization (ILO) R616 or the EU rules for working in road transport18 was not assessed at this time.
-
-## **Motorcycle helmet use**
-
-Nearly 21% of all road traffic fatalities reported in the survey involve powered two- and threewheelers, such as motorcycles, mopeds, or scooters. Yet as the use of powered twoand three-wheelers increases, particularly in developing countries, the use of lifesaving helmets often lags far behind. Head injuries are the main cause of death in most motorcycle crashes*.* Quality helmets reduce the risk of death by over six times and reduce the risk of brain injury by up to 74% *([35](#page-65-15))*.
-
-Despite this, several challenges slow the uptake and proper use of quality helmets, particularly in developing countries. These challenges include availability and affordability of quality helmets, improperly fastened helmets, a lack of available helmets for children, hot weather, and even misinformation.
-
-Among countries surveyed for this report, 160 report having legislation on helmet use, of which 54 meet WHO best practice (Fig. 14) – meaning that the law applies to both drivers and passengers; to all roads and all engine types; specifies a particular helmet standard;<sup>19</sup> and requires that the helmet be appropriately fastened *[\(35\)](#page-65-15)*. This represents an increase of five countries meeting WHO best practice since the *Global status report on road safety 2018 ([4](#page-64-2))*.
-
-Official reports by 35 countries indicate a correct helmet use rate of approximately 80% among drivers and riders, while self-reported rates of motorcycle riders correctly using helmets range from 53% to 74% *[\(20](#page-65-0)).*
-
-<sup>17</sup> 1970 European Agreement concerning the Work of Crews of Vehicles Engaged in International Road Transport (AETR).
-
-<sup>18</sup> International Labour Organization R161, 1979 or EU Reg 561/2006.
-
-<sup>19</sup> Whether countries adhere to international helmet standards is shown in Annex 4.
-
-![](_page_44_Figure_0.jpeg)
-
-![](_page_44_Figure_1.jpeg)
-
-Table 4. WHO best practice criteria for legislation on the five key risk factors
-
-| Risk Factor |                                  | WHO Best Practice Criteria                                                                                                                                                                                                                                         |
-|-------------|----------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-|             | Speeding                         | National law exists, urban limits are set at 50 km/h or lower, and<br>local authorities can further modify this limit                                                                                                                                              |
-|             | Drink<br>driving                 | National law exists, alcohol levels are defined by BAC, alcohol limits<br>per general driving population are ≤0.05 g/dl and for novice drivers<br>≤0.02 g/dl                                                                                                       |
-|             | Motorcycle<br>helmet use         | National law exists and it covers all riders, on all road types, and<br>all engine types, and the helmet must be fastened and meet a<br>standard                                                                                                                   |
-|             | Seat-belt<br>use                 | National law exists and it applies to all seating positions in<br>vehicles                                                                                                                                                                                         |
-|             | Child<br>restraint<br>system use | National law exists, children up to the age of 10 years, or 135 cm<br>in height, must use a child restraint system meeting a standard<br>in addition to the prohibition of children of a particular age/height<br>being prohibited from sitting in the front seats |
-
-![](_page_45_Picture_0.jpeg)
-
-## **The voice of civil society:**
-
-Lotte Brondum, Executive Director, Global Alliance of NGOs for Road Safety
-
-The *Global status report on road safety 2023* provides new and crucial evidence of the performance and progress of countries in reducing road crash deaths. We, the road safety NGOs, urge all to read and act on it urgently.
-
-We cannot accept the preventable tragedies that are devastating families and communities every day. We cannot accept that our children and youth are dying on the roads. We cannot accept the unfairness – that the majority of all road traffic deaths occur in low- and middleincome countries. We need transformation on our roads and communities to put people and their rights at the centre of our mobility system. We want safe mobility to be our guaranteed right.
-
-Immediate action is required to achieve a 50% reduction in road deaths by 2030 and have a transport system that is safe for people and sustainable for our planet. Civil society is an essential partner in achieving all this, and NGOs represent civil society. We are the voice of our communities. We show the reality of people's everyday journeys; we spotlight the challenges people face to reach work or school and bring them to those who have the power to act; we hold governments to account for the safety and protection of all road users.
-
-We must proceed with evidence-based and accountable measures. NGOs, we ask you to take the lead to collect data at intersections in your cities from the perspective of a pedestrian; this will provide a reality check from the ground and help to show the experiences of communities. We ask you to obtain public acceptance of the enforcement and promotion of helmet law to reduce deaths and injuries; we ask you to foster the importance of 30 km/h where people walk, bike, live, and play to save lives. We have the knowledge, expertise, and persistence necessary to make a real impact. As politicians come and go, we remain steadfast in our mission.
-
-Governments, we are the people. We urge you to rely on our experiences, take this information, and turn it into targeted interventions to prioritize safe mobility in decision-making. We are the voice of those who use the transport systems. Use this evidence; it will save lives and is a building block to achieve the SDG agenda for peace and prosperity for people and planet. Invest in safety: it has a high return on investment economically and socially. Involve your NGOs to bridge the gap between data and experience, policy and reality.
-
-The evidence for what works to transform our vision into reality is clear and abundant. There are proven actions that can save lives. We cannot afford to wait any longer. We must act now to save lives on the road.
-
-The Decade of Action's goal is to reduce road traffic deaths and injuries by 50%. Use this report to take action with us. Together, we can make a difference, leaving no one behind.
-
-## <span id="page-47-0"></span>**Seat-belt use**
-
-Failure to use seat-belts is a major risk factor for road traffic deaths and injuries among vehicle occupants. Passengers not wearing a seat-belt at the time of a collision accounts for most occupant road traffic fatalities. The most frequent and most serious injuries to occupants that occur as a result of frontal impacts are to the head, chest and abdomen*.* Disabling injuries to the legs and neck also occur*.* A systematic review and meta-analysis found unequivocally that the risk of major trauma among seat-belt-using passengers was much lower than that among those not using one; facial, abdominal and spinal injuries were significantly reduced among seat-beltwearing passengers *[\(36\)](#page-66-0)*.
-
-Among countries surveyed for report, 170 have mandatory seat-belt use laws, of which 117 meet WHO best practice – meaning that they require all front- and rear-seat occupants to use seat-belts. This is an increase of 11 countries meeting WHO best practice since the *Global status report on road safety 2018 [\(4](#page-64-2))* (Fig. 15.) Whether countries adhere to seatbelt international standards is described in Annex 4.
-
-Sixty-three countries report having national data systems to measure appropriate use of seat-belts. Among these, 12 report a compliance level of at least 80% for all occupants in both front and rear seating positions. In contrast, self-reported seat-belt use among passenger car occupants ranges from 30% to 60%, with the rate among drivers ranging from 50% to 80% *[\(20\)](#page-65-0)*.
-
-![](_page_47_Figure_4.jpeg)
-
-![](_page_47_Figure_5.jpeg)
-
-## <span id="page-48-0"></span>**Child restraint systems use**
-
-Child restraint systems (CRS) are highly effective in reducing injury and death among child occupants – the use of CRS can lead to at least a 60% reduction in deaths *[\(36\)](#page-66-0)*. The benefits of child restraints have been shown to be greatest for younger children, particularly those under the age of 4 years. For children aged 8–12 years, booster seats are associated with a 19% reduction in the likelihood of injury compared to solely using a seat-belt. The position of children in either front or rear seats is also important, as a higher risk for injury is associated with sitting in the front *[\(36\)](#page-66-0).*
-
-Among countries surveyed for this report, 128 report having laws on the use of CRS, of which 36 meet WHO best practice – meaning that they include provisions for the minimum age and height of the child requiring a CRS (set for children under the age of 11 years and equal to or less than 135 cm tall); the presence of a CRS standard;20 and prohibition on sitting in the front seats *[\(20\)](#page-65-0)*. This represents an increase of four countries meeting WHO best practice since the *Global status report on road safety 2018 [\(4](#page-64-2))* (Fig. 16).
-
-While the survey for this report did not collect data on the use of CRS, self-reported use from the ESRA2 survey indicates that the use of CRS ranges between 11% and 47% *[\(20,](#page-65-0) [30](#page-65-10))*.
-
-**Fig. 16. Status of child restraint system laws in countries, 2022**
-
-![](_page_48_Figure_5.jpeg)
-
-<sup>20</sup> Whether countries adhere to international CRS standards is shown in Annex 4.
-
-## <span id="page-49-0"></span>**Post-crash response**
-
-Increasing the likelihood of survival following a crash requires coordination across multiple sectors including police, health, justice and finance (see Box 5 for an example from Thailand). Post-crash care and survival is extremely time sensitive: delays of minutes can make the difference between life and death. Proper and timely rehabilitation service can be critical for mitigating the long-term impact of road traffic injuries and prevent lifelong disability *([37\)](#page-66-1).* In addition to postcrash care, the justice and legal system also has an important role in ensuring financial and psychosocial protection for victims.
-
-## **Legislation, policies, plans and strategies related to post-crash care**
-
-Ensuring timely access to care is critical following a crash event, and as such, one of the most important elements of the postcrash response is an emergency number that can be used to activate the response, whether it is by an individual or through an automated system like the e-call. Of those surveyed, 97 countries report having single or multiple emergency care service numbers that guarantee total country coverage, while 18 countries have a single number but without national coverage. Additionally, 118 countries report having an agency that effectively coordinates pre-hospital and facility-based emergency medical services (EMS).
-
-In addition to having a means to activate the emergency response, there is often a need for immediate care to be provided by lay bystanders whose interventions can sometimes be lifesaving. Encouraging them to do so requires protection from civil liability. While 59 countries have national legislation requiring lay bystanders to help anyone involved in a vehicle crash, only eight of them have national laws providing protection from civil liability to these lay bystanders (i.e. Good Samaritan laws).
-
-In relation to actions to ensure the quality of care provided by health facilities, 35 countries have national laws requiring training, licencing, or other certification processes for first health responders, while 104 Member states have these certifications in place for emergency medicine physicians and 95 have them for trauma surgeons. Seventy-six countries report having a trauma registry where facility-based trauma data are aggregated nationally, while 28 countries report these systems at a subnational level and 23 additional countries report trauma registries only in selected facilities.
-
-In terms of access to rehabilitative medical care for all injured persons regardless of their ability to pay, 46 countries report having laws that mandate rehabilitative medical care. While the coverage of rehabilitative care was reported at a level of 75% for road traffic injuries by only 25 countries, the actual availability and coverage of these services is largely unknown in most countries.
-
-In relation to protection and support for victims (including the families of road traffic fatalities), 131 countries have national legislation mandating third-party liability insurance for vehicles. An even lower number – 25 countries – mandate the provision of psychological assistance to road victims and their families regardless their ability to pay.
-
-#### Box 5: Better post-crash care management, Thailand
-
-In Thailand, road crashes exact a high human and economic toll. In 2022 alone, 17 000 people died as a result of road injury and 15 000 people were left with disabilities. The economic losses totalled around 500 000 million Baht (approximately US\$12.5 million). Against this backdrop, a multidisciplinary coalition including the government, NGOs, the private sector and the media has collaborated to reduce the death toll from road traffic injury. In Khon Kaen, northeast of Bangkok, progress is being made, with the province seeing a 2% decline in preventable road fatalities.
-
-Led by the Ministry of Interior's National Directing Center for Road Safety, alongside the Ministry of Public Health and numerous road safety foundations, the coalition identified and deployed three key activities to reduce the country's fatality burden: leadership and networking; data integration and policy advocacy; and strengthening post-crash response. Consequently, a range of road safety activities, including legislation, policy advocacy, and political negotiations, were conducted nationally and implemented effectively in Khon Kaen.
-
-Khon Kaen hospital is a centre of excellence within Thailand's Emergency Care System (ECS), a system that has been systematically developed to include pre-hospital care, hospital-based emergency care, referral systems, and mass-casualty management. Health care providers have been seamlessly integrated into the ECS – from sub-district primary care clinicians through to emergency physicians in Khon Kaen hospital.
-
-Key to success in Khon Kaen was starting with small-scale implementation and building over the long-term with a consistent and coherent strategic approach to strengthen post-crash care. Khon Kaen
-
-![](_page_50_Figure_5.jpeg)
-
-hospital itself now has a robust ambulance system and a strong provincial dispatch centre for the national health emergency number, 1669.
-
-Integrating emergency care has allowed for the successful implementation of EMS medical decision-making to get patients to the right location for care, aeromedical transport, facility-based triage, and telemedicine support for both pre-hospital and referral systems. This, alongside rigorous quality control and improvement processes, has significantly reduced preventable trauma deaths by more than half – from 4% to 1%
-
-In addition to a focus on post-crash care to reduce road traffic deaths, data integration has also been prioritized. Thailand's Injury Surveillance system has been pivotal in monitoring and enhancing trauma care quality but has also been instrumental in injury prevention since 2011. Integration of data from the injury surveillance system with that of the Police and the Road Accident Victims Protection Company provides more accurate insights into mortality and injuries stemming from road traffic crashes. Accurate data has allowed clinicians in Khon Kaen to undertake effective policy advocacy and to gain trust among policy-makers and politicians.
-
-This success has benefited from the continued and concerted efforts of dedicated individuals and organizations who have monitored and championed the cause of road safety in Thailand over recent decades.
-
-## <span id="page-51-0"></span>**Progress towards safe road use: summary**
-
-Improvements have been made to legislation in 23 countries to align with existing WHO best practice criteria in relation to the five key risk factors. Since the *Global status report on road safety 2018 [\(4\)](#page-64-2)*, 29 laws in 23 countries have been updated to meet WHO best practice. Specific laws improved are described in Figure 17.
-
-As of 2022, only six countries have laws addressing all five key risk factors that meet WHO best practice. Twenty-one countries have laws that meet WHO best practice on four of the five risk factors, 25 have laws on three of the five risk factors; 35 have laws on two of the five risk factors; 53 have laws on one of them, and 54 countries have no laws meeting best practice criteria for any of the key risk factors (Fig. 18).
-
-Fig. 17. Number of countries with laws meeting WHO best practice criteria on the five key risk factors, 2022
-
-![](_page_51_Figure_4.jpeg)
-
-Fig.18. Countries with laws meeting WHO best practice on one or more of the five key risk factors, 2022
-
-![](_page_52_Figure_1.jpeg)
-
-![](_page_53_Picture_0.jpeg)
-
-<span id="page-54-0"></span>![](_page_54_Picture_0.jpeg)
-
-# Measures to strengthen road safety governance
-
-![](_page_54_Picture_2.jpeg)
-
-84 countries have a dedicated road safety agency.
-
-![](_page_54_Picture_4.jpeg)
-
-117 countries report having a national road safety strategy, while just 16 of these strategies are fully funded.
-
-![](_page_54_Picture_6.jpeg)
-
-More than half of countries use general taxes on vehicle purchases, insurance, fuel and alcoholic beverages among others, to finance road safety activities.
-
-![](_page_54_Picture_8.jpeg)
-
-About half of all countries use dedicated taxes and fines from traffic violations to finance road safety activities.
-
-![](_page_54_Picture_10.jpeg)
-
-Differences between reported counts of road traffic deaths and WHO estimated counts exist in 120 countries. In some cases the WHO estimates are 10 times higher, and in one case, 49 times higher, than self-reported figures.
-
-![](_page_54_Picture_12.jpeg)
-
-Only 114 countries report having a specific definition for injuries that result from a road traffic crash.
-
-<span id="page-55-0"></span>Experience from the Decade of Action for Road Safety 2011–2020 highlights the importance of addressing the challenge of implementation – and its complexity – through effective road safety governance. This goes beyond purely managing road safety strategies and actions: it includes coordination across sectors (including health, transport, urban planning and police, etc.), and managing the social and commercial factors that influence sustainable development and other societal practices that ultimately impact road safety.
-
-## **Institutional management**
-
-Management should not be pursued as a standalone goal but as a means to govern – through coordination, legislation, funding and resource allocation, promotion, monitoring and evaluation, research and development, and knowledge transfer. How this function is organized is each country's own decision, but it is necessary to ensure shared multisectoral responsibility for results through an integrated road safety approach.
-
-The existence of a national agency responsible for road safety is reported by 84 countries, of which 81 report that the agency has funding. A national road safety strategy is reported by 117 countries – proof that the presence of a dedicated road safety agency is not essential to the development of a national strategy. However, when assessed if active during 2021, and if the strategies align with the criteria21 of having regular updates and time-bound targets for reductions in fatalities and injuries, the actual number of countries with an up-todate national road safety strategy is 17.
-
-Sixteen countries report having full funding for their plan, while 65 others report partial funding. Most countries could not answer this question. When asked about the source of funding, most countries report not knowing.
-
-In contrast to the smaller number of countries reporting on the origin of road safety funding for their strategies, a much larger number of countries (more than half) report on whether fiscal interventions (such as taxation) are applied to aspects of road transport. More than half of all countries report having taxes on vehicle purchases, vehicle insurance, fuel, or alcoholic beverages. Similarly, half of all countries report using fines from traffic violations to finance road safety activities.
-
-## **Monitoring, evaluation, and data management**
-
-Achieving the goal of 50% reduction in road traffic deaths and injuries by 2030 requires countries to assess their road safety situations, prepare or revise their road safety action plans and implement the solutions highlighted in this document. It is important to monitor and evaluate, at the global level, the progress and outcomes of country implementation of solutions. This includes monitoring and collecting data to measure impact as well as to assess implementation progress (see Box 6 for an example from Zambia).
-
-### **Evaluating implementation**
-
-In order to assess progress towards the implementation of the recommended safe system approach for road safety, a set of voluntary performance targets and indicators was identified at the request of a World Health Assembly Resolution in 2016 *[\(38\)](#page-66-2)*. The 12 performance targets were agreed upon by consensus in 2017 and corresponding indicators were agreed upon in 2018 *([23\)](#page-65-3)*. This report is the first time an assessment has been carried out to measure progress towards these performance targets. Annex 2 summarizes progress as reported by countries for these indicators and shows that globally there remain significant gaps in achieving these targets; moreover, there are nine indicators (out of 34) for which data cannot be obtained. Nonetheless, the fact that most countries are able to respond to the majority of the indicators agreed upon is encouraging and an indication of the increased coordination across sectors working to improve road safety.
-
-<sup>21</sup> As prescribed in UN Voluntary Performance Target 1.
-
-## **Evaluating impact**
-
-While the primary indicator for the Decade of Action for Road Safety 2011–2020 is the number of road traffic deaths, not all countries have this data, and the reported fatality data from different countries are not necessarily comparable, as different definitions and timeframes have been used. For this reason, WHO estimates (both absolute numbers and rates per 100 000 population) allow for comparisons between countries.
-
-Moreover, there remain significant differences in fatality numbers reported by countries and WHO estimates. Differences between estimated and reported fatalities have been highlighted in all previous editions of the *Global status report on road safety*, with average ratios of 1.8 (2013, 2015 and 2023) and 1.7 (2018) when data for countries participating in all reports to date are evaluated (Fig. 19). For 2021, differences between reported and estimated mortality figures are observed in 120 countries. In some cases, the estimated figures are 10 times higher, and in one case, 49 times higher.
-
-While the causes of these differences vary, major contributing factors are the data sources and definitions used. WHO estimates are based on civil registration and vital statistics that consolidate data from multiple sources and include all deaths resulting from road traffic crashes in a given year, regardless of the length of time between the crash date and the death (see Annex 1). Many countries report data from only one source and only include deaths that occur at the scene, or within a limited time period from the date of the crash.
-
-Producing a global morbidity figure for road traffic crashes is challenging, because around a third of countries report no measure for nonfatal cases, while the other two thirds report using a variety of operational definitions. Only 114 countries report having a specific definition for injuries that result from a road traffic crash. More than half of these countries (57%) use either the need for hospitalization as the operational definition (or hospitalization plus another condition) or require three or more days of leave from work. The next most common definition used by more than 10% of countries relates to standardized injury definitions such as the Maximum Abbreviated Injury Scale (MAIS) *[\(39\)](#page-66-3)*, the Revised Trauma Score (RTS) *([40](#page-66-4)),* or the Mechanism/Glasgow Coma Score (Age/ Pressure (MGAP) *[\(41](#page-66-5))*. The remaining countries report using a variety of definitions.
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-Fig. 19. Road traffic deaths reported by 146 countries collaborating in all *Global status report on road safety* surveys to date, compared to WHO estimated fatalities
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-![](_page_56_Figure_7.jpeg)
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-### Box 6: Harnessing fatality data through capacity building, Zambia
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-In Zambia, three key authorities handle road traffic crash fatality data: the police, health care facilities, and the Civil Registration and Vital Statistics (CRVS) system. Together these authorities face challenges around a paper-based data collection system; lack of harmonization; non-standardized coding practices and definitions of road traffic deaths; and low CRVS registration in rural areas. So, while Zambia's official records indicate an average of just under 2000 road traffic deaths annually, WHO's global health estimates suggest that this figure is 3600 – implying an approximate 50% underestimation of road traffic fatalities in official records.
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-### **Zambia's approach to improve data collection**
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-*Collaboration and partnerships:* Zambia established robust partnerships with key stakeholders in road safety including the Zambia Police Service, the Road Transport and Safety Agency, the Department of National Registration, Passports and Citizenship under the Ministry of Home Affairs, University Teaching Hospitals, and the Lusaka Provincial Health Office. In addition, the Bloomberg Data for Health Initiative, various road safety NGOs, and academic institutions were also part of this collaborative effort.
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-*Capacity building:* A series of meetings and workshops with key stakeholders served as dynamic platforms for knowledge sharing, skill enhancement, and strategic planning, sparking innovation and paving the way for the development of new policies and intervention strategies.
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-*Business process mapping:*A comprehensive review and analysis of the processes and procedures for road traffic crash mortality data collection in Zambia was conducted. A process map was developed that helped identify bottlenecks in data collection which, if adequately addressed, would lead to enhanced efficiency in the system.
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-*Source: [\(42\)](#page-66-6)*
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-![](_page_57_Figure_7.jpeg)
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-*Data collection, record-linking and estimation of completeness***:** To estimate the extent of traffic mortality underreporting in Zambia, data were gathered from police records, hospitals, and the civil registration and vital statistics databases for a one-year period from 1 January to December 30, 2020, focusing on crashes in Lusaka Province. A meticulous method was employed to link these records using specific identifiers.
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-The initiative has led to transformative outcomes*.* A multidisciplinary team for road crash data analysis and reporting is being set up, and there has been a significant uptick in data sharing and collection, broadening the utility of road traffic crash data for policy formulation.
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-And statistical methods have been used to estimate how complete data collection was from different sources. Specifically, data from police records were about 19% complete, hospital records were 12% complete, and vital statistics from the CRVS system were estimated to be 14% complete. Importantly, when data from all these sources were combined, completeness improved to 37%. The Zambian experience serves as both a template and a testament to the transformative impact of quality data on public health initiatives.
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-|  | Section 4. Measures to strengthen road safety governance | 47 |
-|--|----------------------------------------------------------|----|
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-![](_page_59_Picture_0.jpeg)
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-<span id="page-60-0"></span>![](_page_60_Picture_0.jpeg)
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-# The way forward
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-Road traffic deaths fell slightly to 1.19 million in 2021 – a 5% drop since 2010. More than half of all UN Member States, including some of the worst-affected countries, reported a decline in fatalities. The slight reduction in deaths occurred despite the global motor vehicle fleet more than doubling, road networks significantly expanding, and the global population increasing by more than one billion. Though the decline in deaths falls far short of what is needed to meet the UN Decade of Action for Road Safety 2021–2030 target of halving deaths by 2030, it shows how to accelerate progress. Ten countries managed to reduce road deaths by 50% since 2010, showing that such a reduction over a 10-year period is possible.
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-Some of the greatest gains were made where the safe system approach – which puts people and safety at the core of mobility systems – was applied. The European Region has the greatest concentration of countries with policies and legislation that align with this approach and reported the largest drop in deaths – a 36% drop since 2010. Belarus, Norway, and the United Arab Emirates, for example, adopted elements of the safe system and were among the small number of countries that reduced fatalities by 50% by 2020.
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-With a rapidly growing and increasingly urban global population, the safe system calls for an efficient and sustainable mix of transport modalities – including mass public transport – while upholding the safety of pedestrians, cyclists and other vulnerable road users, who account for half of all deaths.
-
-Yet as motor vehicle fleets and road networks built for these vehicles expand, vulnerable road users are left dangerously exposed. Just one fifth of the world's roads meet the basic safety requirements needed for cyclists and pedestrians, and just 0.2% of the world's roads have cycle lanes. The 20% increase in deaths among cyclists is worrying.
-
-Political will must match the scale and urgency of this crisis. Road crashes are the leading killer of children and youth aged 5–29 years. There are more than 3 200 road traffic deaths each day, and nine in ten deaths occur in Iow- and middle-income countries. Two thirds of all deaths occur among people of working age, causing huge health, social and economic harm throughout societies.
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-Measures to mitigate the risk of death an injury, including enacting laws the meet WHO best practice, have advanced modestly. Policymakers have known of the key risk factors that contribute to road crashes for decades, yet only six countries have legislation on all five – speeding, drink driving, motorcycle helmet use, and seat-belts and child restraint systems – that meet WHO best practice.
-
-The Global Plan for the United Nations Decade of Action for Road Safety 2021–2030 charts the way forward, and everyone has a role to play in making safe, inclusive, and sustainable mobility a reality. Governments must lead strategies that are rooted in good data, backed by strong laws and funds, and involve all relevant sectors. Businesses must put safety and sustainability at the core of their value chains. Academia and civil society must generate evidence and hold leaders to account and young people can demand action and help take it.
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-This more holistic approach to mobility will bring benefits in tackling many other crucial issues. By encouraging walking and cycling for example, we can reduce the burden of noncommunicable diseases, reduce pollution and combat climate change. By prioritizing the safety of vulnerable road users, we can help reduce poverty and tackle inequalities, including access to jobs and education.
-
-The increase in motor vehicles and motor vehicle based transport systems poses serious questions around sustainability. As the global and increasingly urban population grows, the demand for mobility will outstrip the capacity of systems that rely heavily on private vehicles. With rising greenhouse gas emissions, this also poses a challenge to efforts to meet global climate targets.
-
-Greater coordination with leaders from related fields could help strengthen impact through better coordination, help raise awareness of the road safety crisis among key decisionmakers and leverage greater investment into mobility systems that are designed for people, with safety front and centre.
-
-The Global Plan of Action for the United Nations Decade of Road Safety 2021–2030 calls for a holistic, safe system approach to halve road traffic deaths by 2030. This report shows that it is possible if the right decisions are taken and measures are put in place.
-
-## **The way ahead – reflections from Jean Todt, UN Special Envoy for Road Safety**
-
-The latest *Global status report on road safety 2023*  tells two competing stories.
-
-One is a tale of hope, where the tragic tally of road crash deaths is finally falling. Where major gains are made in countries that adopt the safe system approach to road safety, and governments, businesses, civil society, citizens, and communities, come together around a crucial global plan to rethink mobility and to halve road crash deaths by 2030.
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-The other story is a troubling tale of a world barrelling towards 'carmageddon.' Where the number of motor vehicles expands exponentially and pedestrians, cyclists and other vulnerable road users are left dangerously exposed. In this narrative, more money is invested on roads with all their dangers, rather than in safe public transport or more sustainable means of mobility; and safety is an afterthought, not a goal and guiding light.
-
-The decisions that we make now will determine how many lives are saved and will have an impact on many more areas of our future, including our fragile natural environment.
-
-Motor vehicles are set to double in number by 2030 from our starting point at the first Decade of Action. This could stretch transport systems built for private vehicles to breaking point, especially in low- and middle-income countries. It means more congestion, more pollution, and spiralling health, social and economic costs for us all to bear.
-
-With the rapid expansion of urban populations – 68 percent of humanity is projected to live in cities by 2050, compared to 54 percent in 2016 – and private vehiclebased systems becoming an inefficient waste of space, the safe system approach to road safety calls for a mix of different types of mobility, including efficient and affordable public transport.
-
-And then there is climate change. The transport sector is responsible for about one quarter of the world's greenhouse gas emissions, even as we strive to reduce these emissions in a final effort to stave off climate catastrophe and protect our planet for future generations.
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-Electric vehicles and more energy efficient solutions are good, but they are just one part of the answer. Fossil fuelled motor vehicles are also expanding much faster than e-vehicles.
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-The Global Plan for the second Decade of Action for Road Safety (2021–2030) calls on the world to move from drab, dirty and dangerous streets to safe, green, and vibrant spaces designed for people. With safe mobility touching on many areas of sustainable development, we must work with all relevant sectors to ensure the best possible results. We must all understand that improved sustainability leads to improved safety, while unsafe transport is unsustainable.
-
-As we work towards meeting the goal of halving road crash deaths by 2030, we need a paradigm shift in leadership, commitment, investment, and action from governments everywhere, and including everyone in society, from road users to those who design and build our infrastructure.
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-The treasure trove of data in this report should help us refine and redouble our efforts. The United Nations is fully committed to accelerating action to save lives on the roads, reflected in the establishment of a UN Road Safety Fund, UN road safety conventions, and a Global Plan of Action for Road Safety.
-
-The future is teetering on the brink, and only we ourselves will determine which of the two stories gets told.
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-| 52 | Global status report on road safety 2023 |  |  |
-|----|------------------------------------------|--|--|
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-- <span id="page-66-5"></span>41. Baghi I, Shokrgozar L, Herfatkar MR. Mechanism of injury, Glasgow Coma Scale, age and systolic Blood pressure: A new trauma scoring system to predict mortality in trauma patients. Trauma Monthy. 2015;20(3):e24473 ([https://www.ncbi.nlm.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4630599/) [nih.gov/pmc/articles/PMC4630599/,](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4630599/) accessed 15 June 2023).
-- <span id="page-66-6"></span>42. Mwale M, Mwangilwa K, Kakoma E, Iaych K. Estimation of completeness of road traffic mortality data in Zambia using a three source capture recapture method. Accid Anal & Prev. 2023(186);107048 ( [https://doi.org/10.1016/j.](https://doi.org/10.1016/j.aap.2023.107048) [aap.2023.107048,](https://doi.org/10.1016/j.aap.2023.107048) accessed 2 November 2023).
-
-![](_page_67_Picture_0.jpeg)
-
-<span id="page-68-0"></span>![](_page_68_Picture_0.jpeg)
-
-## Annexes
-
-## <span id="page-70-0"></span>Annex 1. Methodology
-
-Since its inception in 2009, WHO's *Global status report on road safety* series has evolved through an iterative and consultative process with participating countries and other territories, while integrating data from a variety of sources.
-
-For this report, the fifth in the series, a consultative Advisory Board was established to evaluate the structure of the four previous reports *[\(1](#page-76-0)[–4\)](#page-76-1)* and outcomes, and to discuss the focus for this report. Following the advice of the Advisory Board, the objectives for this report were broadened to better reflect the Decade of Action for Road Safety 2021–2030 framework.
-
-WHO Regional Advisors established regional networks through their respective WHO Regional Data Focal Points (RDFP) and government-designated National Data Focal Points (NDFP). In turn, NDFPs were invited to seek help from up to 10 National Data Collaborators (NDCs) to foster country-based networks of experts from a variety of backgrounds to arrive at consensual responses to the survey developed for this project and its subsequent legal review. WHO headquarters coordinated the management and data collection processes using online tools, some of which were specifically built for this report. Data validation required frequent consultation with collaborators. For the most part, all processes were handled remotely.
-
-Once the report (including the country and territory profiles) and the summary report were available, feedback was solicited from Advisory Board members, WHO Regional Advisors and Regional Data Focal Points (RDFPs). Estimated mortality figures were shared with countries and territories to enable them to respond to any changes resulting from the verification and validation process. This consultation provided countries with an opportunity to comment on WHO's estimates for road traffic fatalities, which is often much higher than countries' official statistics (see Introduction).
-
-## **Data sources**
-
-This report uses several data sources. These include:
-
-### **a) WHO latest data on modelled deaths by country.**
-
-The statistical models developed were informed by the survey data collected for this report (see point b) below), particularly in relation to the number and distribution of reported fatalities by age and sex, as well as with the vehicle fleet information. However, reported figures are not sufficient, as explained previous reports in this series, and independent publications *[\(5](#page-76-2)[–6](#page-76-3))*. A comprehensive review on mortality estimation methods used by WHO and other institutions is forthcoming.1 WHO DDI produces updated estimates periodically, the latest of which on road traffic deaths, referring to year 2021, are presented in this report. whereas the latest comprehensive estimates (2019) are accessible on the [WHO website Global Health Estimates](https://www.who.int/data/global-health-estimates) [\(who.int\)](http://who.int).
-
-To estimate road traffic deaths (all ages, both sexes), WHO uses an improved regression model as a function of a set of covariates that include measures of economic development, road transport factors and legislation, road use and safety governance/ enforcement, and health system access. In addition, the regression model uses death registration data for the period 2000–2021 that were 80% or more complete for a given year or where the average completeness for last decade was greater or equal to 80%. Death registration cause of death data are submitted to WHO regularly by the national statistics offices or Ministries of Health from around the world, and mostly coded using the 10th revision of the International Classification of Diseases (ICD-10) [\(https://www.who.int/data/data-collection-tools/](https://www.who.int/data/data-collection-tools/who-mortality-database) [who-mortality-database](https://www.who.int/data/data-collection-tools/who-mortality-database)). The regression model produces estimates using ICD-10 *([7\)](#page-76-4)* criteria, which counts all deaths within a calendar year that result from a road traffic crash, regardless of the time period in which they occur (unlike many official road traffic surveillance data sources, where road traffic death data are based on a 30-day definition following a road traffic crash).
-
-<sup>1</sup> An article on the evolution in methods used to produce this series of reports is currently under review for a forthcoming edition of *Injury Prevention*.
-
-- **b) Data collected for this report via the online survey.** Following the advice of the Advisory Board, the survey used for the last report was updated to better reflect the Decade of Action for Road Safety 2021–2030 framework. This required a broadening of the scope of questions which was partly offset by removing redundant questions or questions leading to unreliable answers. The new survey was reviewed by Advisory Board members and WHO Headquarters and Regional Advisors on Injury. The survey was machine-translated into all six official UN languages and distributed by RDFPs to NDFPs (and, where requested, to NDCs). Data collection ran from September 2022 to August 2023. A copy of the original survey in English is available.2 Training on the use of the platform was done via online meetings in all six UN official languages during the third quarter of 2022. Data validation involved verifying data against source documents where available and checking for logical inconsistencies. Using an online platform allowed for controls at several levels, from data entry limitations to higher-level approval of submitted data. Discrepancies were referred, where possible, to the NDFPs for resolution. The platform allowed for the uploading of support documents as needed. Data in the survey was requested in relation to 2021 or as close to it as possible.
-- **c) WHO review of legislation and related information collected for the** *Global status report on road safety 2023***.** The WHOgenerated survey included question on several legislation matters encompassing post-crash care, infrastructure, vehicles, and road user behaviour, and totalling 25 specific legislation areas. Using the answers to the survey (see point b) above) and support legislation documentation provided by country contributors or identified in legal libraries and/or via the Internet, a team of experienced and trained lawyers validated the existence of 17 of these 25 legislative areas. In-depth evaluation of five of the 25 was done (Annex 3 lists all 25 and whether they were: only reported, validated, or evaluated). The legal reviewers were native speakers of English, French, Spanish, Arabic and Chinese. They used translations into English of legislation written in other languages. The review lasted from January to September 2023. Legislation had to be active by December 31, 2022, to be included. The legal analysis was then shared with NDFPs, and a validation process was undertaken to resolve any data conflicts through discussion and/ or submission of new legal documents. The WHO evaluation process has evolved over time as the review itself has led to refinements to the criteria to better reflect evidence and practice, as well to the review of additional legislation. Table A1.1 presents whether there is comparability of seven legislation areas reviewed with earlier editions of the *Global status report on road safety.*
-
-Table A1.1 Comparability between all editions of the Global status report on road safety for selected legislation best practice
-
-| 2023                        | 2018 | 2015 | 2013 | 2009 |
-|-----------------------------|------|------|------|------|
-| Speed                       | Yes  | Yes  | No   | No   |
-| Seat-belts use              | Yes  | Yes  | No   | No   |
-| Child restraint systems use | Yes  | No   | No   | No   |
-| Helmets use                 | Yes  | Yes  | No   | No   |
-| Drink & drive               | Yes  | Yes  | No   | No   |
-| Drug & drive                | Yes  | No   | No   | No   |
-| Distraction                 | Yes  | No   | No   | No   |
-|                             |      |      |      |      |
-
-<sup>2</sup> For a copy of the survey in English with operational instructions, please contact [sam@who.int](mailto:sam@who.int).
-
-- **d) Data from all previous editions of WHO's** *Global status report on road safety* (2009, 2013, 2015 and 2018), including data on participation; gaps between reported and estimated road traffic fatalities; and all variables presented in these reports' statistical annexes. These data were originally gathered by government designated representatives in participating countries or territories and had undergone government clearance prior to publication, as described in original reports. In depth legal reviews for the seven legislation areas described in Table A1.1 were also compiled.
-- **e)** Data on country populations (including persons under the age of 11 years) as of 1 July 2021 and 2010 were drawn from the UN Population Division *[\(8\)](#page-77-0).*
-- **f)** Data on income level per country clustered into high-, upper-middle, lower-middle, and low- income categories were extracted from the World Bank *([9\)](#page-77-1).*
-- **g)** Data on 2010 and 2021 UN or equivalent conventions and regulations on vehicles, roads and drivers, extracted from UN Economic Commission for Europe (UNECE) *[\(10](#page-77-2))* and the UN Treaty Collection *[\(11](#page-77-3))*.
-- **h)** Data on mobility patterns, self-reported behaviours, and perceived enforcement on selected behavioural related aspects (speeding, alcohol or drug consumption, mobile phone use, use of seat-belts, child restraint systems or helmets) were drawn from the E-survey of Road users' Attitudes (ESRA) initiative coordinated by Vias institute *[\(12\)](#page-77-4)* (these data are available for 48 of countries and territories).
-- **i)** Data on road density and safety score ratings were obtained from the International Road Federation's World Road Statistics *[\(13](#page-77-5))* while data on safety scoring or roads by user type were obtained from iRAP *[\(14\)](#page-77-6)*.
-- **j)** Case studies and testimonials were collected to address areas of the Global Plan for the Decade of Action for Road Safety 2021–2030 not sufficiently covered in all other data sources. A call for case studies on targeted areas was shared with NDFPs via the Regional Advisers.
-
-## **Data management**
-
-Integrating these data sources allowed the development of a selection of indicators used in the country and territory profiles as well as an update of the mobile application. For a description of indicators used in the text of this report or the Country and territory profiles, please see Annexes 5 and 6. The next paragraphs provide more detailed explanations on the development of two selected data elements: road fatalities and the legal reviews.
-
-**Road fatalities.** Government reported fatalities and their distribution by sex, age, user type and work connection were collected through the report-specific survey. Some of these values are included in the Country and territory profiles. The reported deaths are also used in the mathematical models to estimate the number of road traffic for the reasons described above. The regression models used to estimate road traffic deaths are the same as those of the previous reports with updated Civil Registration and Vital Statistics (CRVS) data for the period 2000–2021. A time series for each covariate was used for this period for each country.
-
-As in the previous reports, countries are classified based on their CRVS data into: Group 1 (countries with death registration data), Group 2, Group 3 (countries with population less than 150 000 population), and Group 4 (countries without eligible death registration data). The novelty in this report is that former Group 2 has been subdivided into groups 2A and 2b based on the status of their data systems improvements.
-
-Group 2A: Countries that have death registration data shared with WHO but face certain limitations. These limitations could be related to the number of data observations being insufficient (not equal to or less than 5), or the data quality not being high enough to classify them in Group 1, which likely represents countries with the best data quality. In such cases, WHO has supported these countries in improving their data collection and estimation methods. Instead of relying on a single data source for the entire population, these countries have conducted a linkage of data from various stakeholders. The capture-recapture method used to estimate the number of road traffic deaths for a specific year.
-
-Group 2B: Countries that are still in the process of enhancing their systems for recording road traffic deaths. The completeness of death registration data, particularly for the causes of death related to road traffic incidents, is relatively low, at around 30%. These countries conducted a linkage of data from sources other than just police records with the support of WHO. The focus of these efforts is limited to specific geographical areas, such as the capital or a district within the capital. This may be due to resource constraints or a phased approach to improving data collection and reporting.
-
-Whether a Member State belongs to a Group, or another is shown in the Country and territory profile. The specific methodology to derive estimated road fatalities and their 95% Confidence Interval varies by Group, but the methods are those used in the previous report *([5](#page-76-2)).* As in previous reports, countries or territories in Group 4 were handled using three separate negative binominal regression models. One of two peculiarities for this report lies on the fact that due to the disruptive impact of the COVID-19 pandemic on typical trends, the estimates derived from the negative binomial regression model for the years 2020 and 2021 were not used. The second peculiarity is that in the case of China and India, the estimated road traffic deaths data from Global Health Estimates (GHE) 2020 were used to account for road traffic deaths from 2000 to 2019. Subsequently, the rate of change between reported deaths in 2020 and 2019 was used as correction coefficient to estimate the deaths for 2020 and using the rate of change between reported deaths in 2021 and 2019 as correction coefficient to estimate the deaths for 2021.
-
-**Legislation.** This report presents reviewed national legislation on 22 road-related topics. For five of these topics (urban speed control, drinking and driving, motorcycle helmet use, seat-belts and child restraint restraints), equivalent information was gathered in a comparable format in previous reports as shown in Table A1.1, and best criteria are described in corresponding sections of the report and in Annex 6. These criteria are used to qualify the legislation into one of several categories, although we acknowledge that these are, de facto "minimal criteria" for the laws to have a significant safety impact. For two other topics (drugs and driving, and distracted driving), comparable data are available from all reports since the *Global status report on road safety 2015*. However, no evaluation criteria are available for these areas. Seventeen other legislative areas are evaluated and presented for the first time in this report including professional drivers' rest periods, vehicle safety (five specific aspects),3 vehicle registration and inspection, third-party vehicle insurance, road and infrastructure, access to emergency assistance, rehabilitation assistance or psychological assistance for road traffic victims, and good Samaritan laws. Three other legislative areas were not validated or evaluated but reported answers by participated countries are presented in the report. Annex 3 presents all legislative areas and the level of validation and evaluation they underwent. There are three countries whose legislation had to be reviewed at subnational level for all or some of these legislation areas because the topics are delegated to and within the jurisdiction of the subnational authorities. These countries are Australia, Canada, and the United States of America. These countries are classified as having a law at national level if 80% of their subnational entities meet the selected criteria for best practise or the existence of legislation on the topic.
-
-The population covered by these legistlations is shown in Annex 3. Calculations use general population country figures for all legislative areas except child restraint systems, whose impact is calculated in relation to the population aged below 11 years in each country.
-
-## **Data analysis**
-
-This is a descriptive report and the primary unit of analyses are Member States themselves, which we refer to as "countries" in the text, however, in Annex 3, some indicators are based on population. The analyses are kept at global level and variations by region and income level are presented. Compared with other editions of the *Global status report on road safety*, we present more income-based analyses in following with the Plan of Action recommendation to focus on low- and middleincome countries.
-
-In addition, as part of its evolutionary focus, comparison between the findings of the fifth report and the earliest available data within the Decade of Action for Road Safety 2011–2020 are presented throughout the report and in the Country and territory profiles. This implies using revised WHO mortality estimates since 2010, other 2010 information such as population, income level, adherence to conventions or regulations, etc., and the country-generated data via the *Global status report on road safety 2013.* As stated above, for selected legislative
-
-<sup>3</sup> In previous reports, vehicle safety has been addressed through the accession to UN or equivalent safety regulations. On this occasion, national-level legislation was also reviewed.
-
-comparisons we had to rely on the Global status report on road safety 2015 or 2018 for comparability criteria, although changes since the 2018 one prioritized through the report.
-
-Where change can be documented, a quantitative value of the magnitude of change and a qualitative value have been created. Differences larger than 2% are presented as "increases" or "decreases" (depending on direction). Differences of 2% or less are presented as "no change". In other instances, change is not quantifiable, although it can be a change that implies a step forward towards "better" situations. In those cases, "change" or "advancement" are used to describe the evolution. Changes in fatality counts on rates for countries with populations less than 200 000 population are not reported.
-
-For countries not participating in the *Global status report on road safety 2023*, no evolutionary analyses are shown, and their profiles contain their latest information, including the 2021 mortality estimates.
-
-## **Participation results**
-
-All 194 countries were formally invited to collaborate in this report. In addition, two territories requested to participate.4
-
-The 194 WHO countries represent 98% of the world's population, whereas the 170 countries participating in the survey represent 97% of the world's population. Participation by WHO region and income level was even, as shown in Table A1.2.
-
-<sup>4</sup> Participating territories are: British Virgin Islands (high income, Region of the Americas) and occupied Palestinian territory, including east Jerusalem (lowermiddle-income, Eastern Mediterranean Region).
-
-Table A1.2 Number of participating countries by WHO region and income level, 2021
-
-| Participating / Member states | High-income | Upper-middle<br>income | Lower-middle<br>income | Low-income | Total        |
-|-------------------------------|-------------|------------------------|------------------------|------------|--------------|
-| African Region                | 1/1         | 5/6                    | 17/18                  | 22/22      | 45/47        |
-| Region of the Americas        | 8/9         | 17/19                  | 5/5                    | 0/0        | 32/35        |
-| South-East Asia Region        | 0/0         | 2/2                    | 8/8                    | 0/1        | 10/11        |
-| European Region               | 30/34       | 14/15                  | 3/4                    | 0/0        | 47/53        |
-| Eastern Mediterranean Region  | 6/6         | 3/3                    | 6/7                    | 5/5        | 20/21        |
-| Western Pacific Region        | 6/8         | 1/7                    | 7/11                   | 0/0        | 16/27        |
-| Total                         | 51/58       | 43/52                  | 46/53                  | 27/28      | 170a<br>/194 |
-
-*a Cook Islands, Niue and Venezuela (Bolivarian Republic of) are only listed under regional totals as their income level is unknown.*
-
-NDFPs and NDCs for the 172 participating states or territories total nearly 1000 individuals. It is worth pointing out that in 50 participating countries there was one collaborator who was also their country representative in a Regional Road Safety Observatory, as suggested in UN GA74/299 *[\(15](#page-77-7)).* Additional 100 professionals have participated in the production of this report, including WHO headquarters staff, regional advisors, RDFPs and consultants participating in data management, communications, and coordination.
-
-#### **Other**
-
-Data from their participation in *the Global status report on road safety 2018* was used for 135 of the 24 not participating in *the Global status report on road safety 2023*. Six other non-participating countries had last contributed to *the Global status report on road safety 2015*. <sup>6</sup> Among the remaining five non-participating countries, three last did in *the Global status report on road safety 2013*<sup>7</sup> and only for two did we use *the Global status report on road safety 2009* (which was their first and last participation).8 Mortality estimates for 2021 for the 24 non-participating countries are, on average 16 per 100 000 population, a figure very close to the average rate for participating countries (15 per 100 000 population).
-
-Notably, all countries have participated at least once in the *Global status report on road safety* since it was first published. One hundred and forty-six countries (plus one territory) have participated in all five of them (Fig. A1.1).
-
-<sup>5</sup> Angola, Equatorial Guinea, Fiji, Grenada, Micronesia (Federated States of), Papua New Guinea, Romania, San Marino, Solomon Islands, Tonga, Turkmenistan, Ukraine, Vanuatu.
-
-<sup>6</sup> Andorra, Djibouti, Marshall Islands, Monaco, Palau (Republic of), Saint Vincent and the Grenadines.
-
-<sup>7</sup> Brunei Darussalam, Democratic People's Republic of Korea, Saint Kitts and Nevis.
-
-<sup>8</sup> Nauru, Tuvalu.
-
-![](_page_76_Figure_0.jpeg)
-
-![](_page_76_Figure_1.jpeg)
-
-Overall, completion rates for the report survey exceeds 70% of the requested information, with more gaps occurring in the areas of work relationship of the crash, road density and financing arrangements for road safety
-
-Supporting documents, including legal documents, are in custody at WHO headquarters. All data used in the Country and territory profiles are publicly available except those of the International Road Federation.
-
-## **Annex 1 references**
-
-- <span id="page-76-0"></span>1. Global status report on road safety: time for action. Geneva: World Health Organization; 2009 [\(https://apps.who.int/iris/](https://apps.who.int/iris/handle/10665/44122?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [handle/10665/44122?search-result=true&query](https://apps.who.int/iris/handle/10665/44122?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [=global+status+report+on+road+safety&scope](https://apps.who.int/iris/handle/10665/44122?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [=&rpp=10&sort\\_by=score&order=desc&page=1,](https://apps.who.int/iris/handle/10665/44122?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) accessed 15 June 2023).
-- 2. Global status report on road safety 2013: supporting a decade of action. Geneva: World Health Organization; 2013 [\(https://apps.who.int/](https://apps.who.int/iris/handle/10665/78256?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [iris/handle/10665/78256?search-result=true&qu](https://apps.who.int/iris/handle/10665/78256?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [ery=global+status+report+on+road+safety&scop](https://apps.who.int/iris/handle/10665/78256?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [e=&rpp=10&sort\\_by=score&order=desc&page=1](https://apps.who.int/iris/handle/10665/78256?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1), accessed 15 June 2023).
-- 3. Global status report on road safety 2015. Geneva: World Health Organization; 2015 [\(https://apps.](https://apps.who.int/iris/handle/10665/189242?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [who.int/iris/handle/10665/189242?search-res](https://apps.who.int/iris/handle/10665/189242?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1)
-
-- [ult=true&query=global+status+report+o](https://apps.who.int/iris/handle/10665/189242?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [n+road+safety&scope=&rpp=10&sort\\_](https://apps.who.int/iris/handle/10665/189242?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [by=score&order=desc&page=1,](https://apps.who.int/iris/handle/10665/189242?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) accessed 15 June 2023).
-- <span id="page-76-1"></span>4. Global status report on road safety 2018. Geneva: World Health Organization; 2018 [\(https://apps.](https://apps.who.int/iris/handle/10665/276462?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [who.int/iris/handle/10665/276462?search](https://apps.who.int/iris/handle/10665/276462?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1)[result=true&query=global+status+report](https://apps.who.int/iris/handle/10665/276462?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [+on+road+safety&scope=&rpp=10&sort\\_](https://apps.who.int/iris/handle/10665/276462?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [by=score&order=desc&page=1,](https://apps.who.int/iris/handle/10665/276462?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) accessed 15 June 2023).
-- <span id="page-76-2"></span>5. Global status report on road safety 2018, explanatory note 3: Estimation of total road traffic deaths. Geneva: WHO; 2018 [https://apps.who.int/](https://apps.who.int/iris/handle/10665/276462?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [iris/handle/10665/276462?search-result=true&qu](https://apps.who.int/iris/handle/10665/276462?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [ery=global+status+report+on+road+safety&scop](https://apps.who.int/iris/handle/10665/276462?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) [e=&rpp=10&sort\\_by=score&order=desc&page=1,](https://apps.who.int/iris/handle/10665/276462?search-result=true&query=global+status+report+on+road+safety&scope=&rpp=10&sort_by=score&order=desc&page=1) accessed 15 June 2023).
-- <span id="page-76-3"></span>6. Papadimitriou E, Iaych K, Adamantiadis M. Understanding and bridging the differences between country-reported and WHO estimated road traffic fatality data. Brussels: WHO and EuroMed TSP; 2019 [http://etsp.](http://etsp.eu/?page_id=24985&mdocs-cat=mdocs-cat-74&mdocs-att=nul) [eu/?page\\_id=24985&mdocs-cat=mdocs-cat-](http://etsp.eu/?page_id=24985&mdocs-cat=mdocs-cat-74&mdocs-att=nul)[74&mdocs-att=nul,](http://etsp.eu/?page_id=24985&mdocs-cat=mdocs-cat-74&mdocs-att=nul) accessed 15 June 2023).
-- <span id="page-76-4"></span>7. International Classification of Diseases – 10th Revision. Geneva: World Health Organization; 2019 (https://icd.who.int/browse10/2019/en#/, accessed 15 June 2023).
-
-- <span id="page-77-0"></span>8. UN Population Division [online database]. New York (NY): United Nations; 2022 [\(https://](https://population.un.org/wpp/) [population.un.org/wpp/](https://population.un.org/wpp/), accessed 15 June 2023).
-- <span id="page-77-1"></span>9. World Bank Country and Lending groups [online database]. Washington (DC): World Bank; 2022 ([https://datahelpdesk.worldbank.org/](https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups) [knowledgebase/articles/906519-world-bank](https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups)[country-and-lending-groups,](https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups) accessed 15 June 2023).
-- <span id="page-77-2"></span>10. United Nations Economic Commission for Europe. United Nations Road Safety Conventions, Geneva, 2020 [\(https://unece.org/transport/publications/](https://unece.org/transport/publications/united-nations-road-safety-conventions) [united-nations-road-safety-conventions,](https://unece.org/transport/publications/united-nations-road-safety-conventions) accessed 15 June 2023).
-- <span id="page-77-3"></span>11. United Nations. Treaty Collection [online database]. New York (NY): United Nations; [no date] [\(https://treaties.un.org/Pages/](https://treaties.un.org/Pages/ViewDetails.aspx?src=TREATY&mtdsg_no=XI-B-34&chapter=11&clang=_en) [ViewDetails.aspx?src=TREATY&mtdsg\\_no=XI-B-](https://treaties.un.org/Pages/ViewDetails.aspx?src=TREATY&mtdsg_no=XI-B-34&chapter=11&clang=_en)[34&chapter=11&clang=\\_en](https://treaties.un.org/Pages/ViewDetails.aspx?src=TREATY&mtdsg_no=XI-B-34&chapter=11&clang=_en), accessed 15 February 2023).
-- <span id="page-77-4"></span>12. Meesmann U, Wardenier N, Torfs K, Pires C, Delannoy S, Van den Berghe W. A global look at road safety. Synthesis from the ESRA2 survey in 48 countries. ESRA project 2022 (E-Survey of Road users' Attitudes). Brussels: Vias institute; 2022 [\(https://www.esranet.eu/storage/minisites/](https://www.esranet.eu/storage/minisites/esra2-main-report-def.pdf) [esra2-main-report-def.pdf](https://www.esranet.eu/storage/minisites/esra2-main-report-def.pdf), accessed 15 June 2023).
-- <span id="page-77-5"></span>13. World road statistics [online database]. Geneva: International Road Federation; 2023 ([www.irfnet.](http://www.irfnet.ch) [ch](http://www.irfnet.ch) and [www.worldroadstatistics.org](http://www.worldroadstatistics.org/), accessed 28 September 2023).
-- <span id="page-77-6"></span>14. iRAP Safety Insights Explorer [Internet]. London: iRAP, 2021 [\(https://irap.org/safety-insights](https://irap.org/safety-insights-explorer/)[explorer/](https://irap.org/safety-insights-explorer/), accessed 7 November 2023).
-- <span id="page-77-7"></span>15. UN General Assembly resolution 74/299. Improving global road safety. United Nations: New York (NY); 2020 [\(https://digitallibrary.un.org/](https://digitallibrary.un.org/record/3879711) [record/3879711](https://digitallibrary.un.org/record/3879711), accessed 15 June 2023).
-
-## <span id="page-78-0"></span>Annex 2. Progress towards the voluntary UN Performance Targets
-
-Twelve Voluntary Performance Targets were set out to help direct efforts to achieve the 50% fatality and non-fatal reduction in road traffic victims worldwide. In addition, a series of indicators totalling 34 were proposed to allow monitoring progress towards the targets. Table A2.1 shows their first global assessment. Performance at country level for several of them are shown in the Country and territory profiles. Adaptation of the proposed indicators to existing data (and results) resulted in some deviations, for example that instead of counting Member states reaching 100% objectives, we count for countries reaching at least 80% objectives. Similarly, "effective enforcement" was not able to be assessed for several indicators. Albeit imperfect, the emerging picture is that there are big gaps in covering these, even among high-income member states. There nine proposed indicators that could not be assessed at this time.
-
-Table A2.1. Status of UN Voluntary Target Performance indicators, Global and by income level, 2021
-
-| Target                                                                                                                            | Indicators                                                                                                                              | Number of countries      |                |                           |                           |               |  |
-|-----------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------|--------------------------|----------------|---------------------------|---------------------------|---------------|--|
-|                                                                                                                                   |                                                                                                                                         |                          |                |                           | Income levelsa            |               |  |
-|                                                                                                                                   |                                                                                                                                         | All<br>N=(170)           | High<br>(N=51) | Upper<br>middle<br>(N=43) | Lower<br>middle<br>(N=46) | Low<br>(N=27) |  |
-| Target 1                                                                                                                          | Published national action plan                                                                                                          | 17                       | 9              | 5                         | 3                         | 0             |  |
-| By 2020, all countries establish<br>a comprehensive multisectoral<br>national road safety action<br>plan with time-bound targets. | that provides for regularly<br>updated, time-bound targets for<br>reductions in fatalities and injuries                                 |                          |                |                           |                           |               |  |
-|                                                                                                                                   | Presence of national lead agency<br>to coordinate, monitor, evaluate<br>and implement multisectoral<br>national road safety action plan | 84                       | 23             | 26                        | 24                        | 11            |  |
-| Target 2                                                                                                                          | Ratification or accession, and                                                                                                          | 128                      | 50             | 33                        | 31                        | 14            |  |
-| By 2030, all countries accede<br>to one or more of the core<br>road safety-related UN<br>legal instruments.                       | adhesion, to one or more core<br>road safety-related UN legal<br>instruments (out of seven)                                             | (only 7<br>MS<br>have 7) |                |                           |                           |               |  |
-
-| Target                                                                                                                                                                              | Indicators                                                                                                                                                                   |                          | Number of countries                                                       |                                                                          |                                                                           |                                                                          |  |
-|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------|---------------------------------------------------------------------------|--------------------------------------------------------------------------|---------------------------------------------------------------------------|--------------------------------------------------------------------------|--|
-|                                                                                                                                                                                     |                                                                                                                                                                              |                          | Income levelsa                                                            |                                                                          |                                                                           |                                                                          |  |
-|                                                                                                                                                                                     |                                                                                                                                                                              | All<br>N=(170)           | High<br>(N=51)                                                            | Upper<br>middle<br>(N=43)                                                | Lower<br>middle<br>(N=46)                                                 | Low<br>(N=27)                                                            |  |
-| Target 3<br>By 2030, all new roads achieve<br>technical standards for all road<br>users that take account of<br>road safety or meet a three                                         | Presence of technical standards<br>for new roads that take account<br>of all road-user safety, or align<br>with relevant UN Conventions and<br>regulate compliance with them | 61                       | 27                                                                        | 16                                                                       | 17                                                                        | 1                                                                        |  |
-| star rating or better.                                                                                                                                                              | Use of systematic approaches to<br>assess/audit new roads                                                                                                                    | 120                      | 38                                                                        | 31                                                                       | 33                                                                        | 18                                                                       |  |
-| Target 4<br>By 2030, more than 75% of<br>travel on existing roads is on<br>roads that meet technical                                                                                | Plan for improvement of existing<br>roads that take account of the<br>safety of all road users developed<br>and implemented                                                  | Cannot be calculated yet |                                                                           |                                                                          |                                                                           |                                                                          |  |
-| standards for all road<br>users that take account of<br>road safety.                                                                                                                | Use of systematic approaches to<br>assess/audit existing roads                                                                                                               | Cannot be calculated yet |                                                                           |                                                                          |                                                                           |                                                                          |  |
-| Target 5<br>By 2030, 100% of new (defined                                                                                                                                           | Presence of high-quality safety<br>standards for new vehicles                                                                                                                | 93                       | 38                                                                        | 22                                                                       | 25                                                                        | 8                                                                        |  |
-| as produced, sold or imported)<br>and used vehicles meet high                                                                                                                       | Use of systematic approaches for<br>vehicle assessments                                                                                                                      | 134                      | 48                                                                        | 30                                                                       | 36                                                                        | 20                                                                       |  |
-| quality safety standards,<br>such as the recommended<br>priority UN Regulations, Global<br>Technical Regulations, or<br>equivalent recognized national<br>performance requirements. | Presence of high-quality safety<br>standards for used-vehicle<br>exports                                                                                                     | 113                      | 35                                                                        | 31                                                                       | 32                                                                        | 15                                                                       |  |
-| Target 6<br>By 2030, halve the proportion<br>of vehicles travelling over the<br>posted speed limit and achieve<br>a reduction in speeding<br>related injuries and fatalities.       | Speed-control legislation been<br>strengthened since last reporting                                                                                                          |                          | 26; 3<br>since<br>Global<br>status<br>report<br>on road<br>safety<br>2018 | 9; 7<br>since<br>Global<br>status<br>report<br>on road<br>safety<br>2018 | 10; 0<br>since<br>Global<br>status<br>report<br>on road<br>safety<br>2018 | 8; 0<br>since<br>Global<br>status<br>report<br>on road<br>safety<br>2018 |  |
-|                                                                                                                                                                                     | Proportion of vehicles travelling<br>over the posted speed limit have<br>reduced by half                                                                                     | Cannot be calculated yet |                                                                           |                                                                          |                                                                           |                                                                          |  |
-|                                                                                                                                                                                     | Presence of national and, where<br>applicable, subnational data<br>systems on speeding violations<br>and speeding-related injuries and<br>fatalities                         | 154                      | 47                                                                        | 41                                                                       | 42                                                                        | 24                                                                       |  |
-|                                                                                                                                                                                     | Reductions in speeding-related<br>injuries and fatalities have been<br>achieved                                                                                              | Cannot be calculated yet |                                                                           |                                                                          |                                                                           |                                                                          |  |
-
-| Target                                                                                                                        | Indicators                                                                                                                                                 | Number of countries                                  |                        |                           |                           |                        |  |
-|-------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------|------------------------|---------------------------|---------------------------|------------------------|--|
-|                                                                                                                               |                                                                                                                                                            |                                                      | Income levelsa         |                           |                           |                        |  |
-|                                                                                                                               |                                                                                                                                                            | All<br>N=(170)                                       | High<br>(N=51)         | Upper<br>middle<br>(N=43) | Lower<br>middle<br>(N=46) | Low<br>(N=27)          |  |
-| Target 7<br>By 2030, increase the<br>proportion of motorcycle<br>riders correctly using standard<br>helmets to close to 100%. | Presence of legislation requiring<br>ADULT motorcycle riders to wear<br>a helmet properly fastened and<br>meeting appropriate standards2<br>for protection | 49                                                   | 21                     | 12                        | 14                        | 2                      |  |
-|                                                                                                                               | Effective enforcementb of helmet<br>use legislation                                                                                                        | 85                                                   | 27                     | 22                        | 23                        | 13                     |  |
-|                                                                                                                               | Regulations on safety for (child<br>and) adult helmets sold                                                                                                | 93                                                   | 37                     | 23                        | 25                        | 8                      |  |
-|                                                                                                                               | Presence of national and, where<br>applicable, subnational data<br>systems on helmet use                                                                   | 105                                                  | 40                     | 29                        | 23                        | 13                     |  |
-|                                                                                                                               | Proportion of motorcycle riders<br>correctly using helmets is close<br>to 100%c                                                                            | 32                                                   | 20                     | 7                         | 4                         | 1                      |  |
-| Target 8<br>By 2030, increase the<br>proportion of motor vehicle<br>occupants using safety belts                              | Legislative improvements made<br>since last reporting towards best<br>practice standards                                                                   | 108<br>(improvement<br>in 10 Seat-belt<br>and 4 CRS) | 46<br>(2 SB:<br>0 CRS) | 33<br>(2 SB:<br>3 CRS)    | 18<br>(3 SB:<br>1 CRS)    | 11<br>(3 SB:<br>0 CRS) |  |
-| or standard child restraint<br>systems to close to 100%.                                                                      | Presence of effectively enforced<br>b legislation requiring the use of<br>child restraint systems that meet<br>appropriate standards                       | Cannot be calculated yet                             |                        |                           |                           |                        |  |
-|                                                                                                                               | Proportion of all motor vehicle<br>occupants using safety belts is<br>close to 100% c                                                                      | 12                                                   | 12                     | 0                         | 0                         | 0                      |  |
-|                                                                                                                               | Proportion of all child motor<br>vehicle occupants using standard<br>child restraints systems is close<br>to 100% c                                        | Cannot be calculated yet                             |                        |                           |                           |                        |  |
-|                                                                                                                               | Effective enforcement b of safety<br>regulations for child restraint<br>systems sold                                                                       | cannot be calculated yet                             |                        |                           |                           |                        |  |
-|                                                                                                                               | Presence of national and, where<br>applicable, subnational data on<br>use of safety belts, as well as the<br>appropriate use of child restraint<br>systems | 63                                                   | 32                     | 16                        | 8                         | 7                      |  |
-
-| Target                                                                                                                                                                                                           | Indicators                                                                                                                                                                                                        | Number of countries                                             |                    |                           |                           |                    |  |
-|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------|--------------------|---------------------------|---------------------------|--------------------|--|
-|                                                                                                                                                                                                                  |                                                                                                                                                                                                                   |                                                                 | Income levelsa     |                           |                           |                    |  |
-|                                                                                                                                                                                                                  |                                                                                                                                                                                                                   | All<br>N=(170)                                                  | High<br>(N=51)     | Upper<br>middle<br>(N=43) | Lower<br>middle<br>(N=46) | Low<br>(N=27)      |  |
-| Target 9<br>By 2030, halve the number<br>of road traffic injuries<br>and fatalities related to<br>drivers using alcohol, and/<br>or achieve a reduction<br>in those related to other<br>psychoactive substances. | Presence of legislation (and<br>effective enforcement) on driving<br>under the influence of alcohol and/<br>or other psychoactive substances<br>(based on alcohol laws)                                           | 47                                                              | 28                 | 12                        | 6                         | 1                  |  |
-|                                                                                                                                                                                                                  | Availability of national and, where<br>applicable, subnational data on<br>driving under the influence of<br>alcohol and/or psychoactive<br>substances and related road<br>traffic-related fatalities and injuries | 114                                                             | 41                 | 32                        | 27                        | 14                 |  |
-|                                                                                                                                                                                                                  | Road traffic injuries and fatalities<br>related to driving under the<br>influence of alcohol and/or other<br>psychoactive substances have<br>reduced by half                                                      | Cannot be calculated yet                                        |                    |                           |                           |                    |  |
-| Target 10<br>By 2030, all countries have<br>national laws to restrict or                                                                                                                                         | Effectively enforcedb legislation on<br>restricting or prohibiting the use<br>of mobile phones while driving                                                                                                      | 153<br>(8 since Global<br>status report on<br>road safety 2018) | 50<br>before<br>31 | 41<br>before<br>32        | 41<br>before<br>30        | 21<br>before<br>19 |  |
-| prohibit the use of mobile<br>phones while driving.                                                                                                                                                              | Presence of national and, where<br>applicable, subnational data<br>systems on the use of mobile<br>phones while driving                                                                                           | 91                                                              | 34                 | 24                        | 22                        | 11                 |  |
-| Target 11<br>By 2030, all countries to enact<br>regulation for driving time and<br>rest periods for professional                                                                                                 | Presence of international/regional<br>regulation on driving time and<br>rest periods for professional<br>drivers                                                                                                  | 45                                                              | 28                 | 14                        | 3                         | 0                  |  |
-| drivers, and/or accede<br>to international/regional<br>regulation in this area.                                                                                                                                  | Professional drivers' driving time<br>and rest periods are regulated,<br>effectively enforced, and audited                                                                                                        | 83                                                              | 40                 | 20                        | 18                        | 5                  |  |
-| Target 12<br>By 2030, all countries<br>establish and achieve national<br>targets in order to minimize                                                                                                            | National target met for time<br>between serious crash-related<br>injury and initial provision of<br>professional emergency care                                                                                   | Cannot be calculated yet                                        |                    |                           |                           |                    |  |
-| the time interval between<br>road traffic crash and the<br>provision of first professional<br>emergency care.                                                                                                    | Presence of agencies that<br>effectively coordinate pre<br>hospital and facility-based<br>emergency medical services                                                                                              | 118                                                             | 35                 | 32                        | 33                        | 18                 |  |
-
-- a Total does not add to 170 because of the three Member States with no information on income.
-- b No assessment on effectivity of enforcement is available.
-- c Use equal or higher than 80% was considered sufficient.
-
-## **Annex 2 reference**
-
-1. Voluntary global performance targets for road safety risk factors and service delivery mechanisms and corresponding indicators. Geneva: World Health Organization; 2018 [\(https://cdn.who.int/media/](https://cdn.who.int/media/docs/default-source/documents/un-road-safety-collaboration/targets-and-indicators-visual-clean.pdf?sfvrsn=29627bde_5)
-
-[docs/default-source/documents/un-road-safety](https://cdn.who.int/media/docs/default-source/documents/un-road-safety-collaboration/targets-and-indicators-visual-clean.pdf?sfvrsn=29627bde_5)[collaboration/targets-and-indicators-visual-clean.](https://cdn.who.int/media/docs/default-source/documents/un-road-safety-collaboration/targets-and-indicators-visual-clean.pdf?sfvrsn=29627bde_5) [pdf?sfvrsn=29627bde\\_5,](https://cdn.who.int/media/docs/default-source/documents/un-road-safety-collaboration/targets-and-indicators-visual-clean.pdf?sfvrsn=29627bde_5) accessed 15 June 2023).
-
-## <span id="page-82-0"></span>Annex 3. Population covered by selected road safetyrelated measures
-
-The report focuses on Member State-based data. Table A3.1 presents the percentage of the world population that is covered by selected road safety interventions.
-
-Table A3.1 Percentage of population living in countries with selected road safety laws, by income level, 2022
-
-|                                                                                                                                                                              |        | Income levela |                 |                 |     |  |
-|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|---------------|-----------------|-----------------|-----|--|
-|                                                                                                                                                                              | Global | High          | Upper<br>middle | Lower<br>middle | Low |  |
-| Countries                                                                                                                                                                    | 170    | 51            | 43              | 46              | 27  |  |
-| Population (Million)                                                                                                                                                         | 7 874  | 1 233         | 2 526           | 3 397           | 718 |  |
-| Legislation reported, validated and evaluated (thus percentages reflect the percentage of the population whose<br>countries have these laws meeting best practice criteria): |        |               |                 |                 |     |  |
-| Legislation setting appropriate urban speed limits for 4-wheel and<br>powered 2/3 wheelers                                                                                   | 42     | 48            | 69              | 22              | 27  |  |
-| Legislation on drink driving                                                                                                                                                 | 33     | 34            | 72              | 12              | 3   |  |
-| Legislation requiring adult motorcycle riders to wear a helmet                                                                                                               | 39     | 30            | 22              | 62              | 4   |  |
-| Legislation on the use of seat-belts                                                                                                                                         | 72     | 68            | 89              | 65              | 46  |  |
-| Legislation requiring the use of child restraint systemsb                                                                                                                    | 10     | 42            | 21              | 2               | 0   |  |
-| Legislation reported and validated, but not evaluated (Thus % reflect population whose countries have these laws<br>regardless of details):                                  |        |               |                 |                 |     |  |
-| Legislation on drug driving                                                                                                                                                  | 94     | 100           | 100             | 92              | 76  |  |
-| Legislation on distracted driving (mobile phones)                                                                                                                            | 93     | 100           | 100             | 90              | 75  |  |
-| Legislation on driver licensing requirements                                                                                                                                 | 93     | 85            | 98              | 91              | 96  |  |
-| National law on vehicle registration                                                                                                                                         | 91     | 84            | 93              | 91              | 96  |  |
-| National law requiring a formal road safety inspection/assessment                                                                                                            | 58     | 67            | 66              | 59              | 14  |  |
-| National law requiring periodic vehicle inspection/assessment                                                                                                                | 84     | 59            | 92              | 85              | 86  |  |
-| National laws on front and side impact protection                                                                                                                            | 60     | 96            | 74              | 50              | 0   |  |
-| National laws on seat-belt and seat-belt anchorages                                                                                                                          | 74     | 87            | 93              | 68              | 17  |  |
-| National law on electronic stability control                                                                                                                                 | 57     | 89            | 79              | 43              | 0   |  |
-| National law on pedestrian protection                                                                                                                                        | 53     | 63            | 77              | 43              | 0   |  |
-| National law on braking systems                                                                                                                                              | 44     | 92            | 78              | 11              | 3   |  |
-| National law on universal access to emergency care                                                                                                                           | 76     | 60            | 97              | 77              | 23  |  |
-| National law guaranteeing free-of-charge access to rehabilitative care<br>for all injured                                                                                    | 51     | 43            | 77              | 44              | 3   |  |
-
-|                                                                                                                                           |        | Income levela |                 |                 |     |
-|-------------------------------------------------------------------------------------------------------------------------------------------|--------|---------------|-----------------|-----------------|-----|
-|                                                                                                                                           | Global | High          | Upper<br>middle | Lower<br>middle | Low |
-| National law guaranteeing free-of-charge access to psychological<br>services to road traffic crash victims and their families             |        | 23            | 78              | 2               | 3   |
-| National good Samaritan law                                                                                                               | 43     | 46            | 57              | 42              | 0   |
-| National legislation mandating third-party liability insurance for powered<br>vehicles                                                    | 87     | 95            | 89              | 81              | 92  |
-| National law on driving time and rest periods for professional drivers                                                                    | 74     | 81            | 89              | 72              | 16  |
-| Legislation reported but not validated nor evaluated (Thus, % reflects population living in countries that report to<br>have these laws): |        |               |                 |                 |     |
-| National law on eCall                                                                                                                     | 13     | 32            | 13              | 8               | 6   |
-| Legislation on restrictions to import vehicles                                                                                            | 32     | 32            | 26              | 35              | 40  |
-| National prohibition on alcohol consumption                                                                                               | 10     | 4             | 3               | 17              | 15  |
-
-a Not shown in income categories but counted in Totals are the three Member states with no information on income level
-
-b Population denominator is population less than 11 years old
-
-## <span id="page-84-0"></span>Annex 4. National vehicle and infrastructure laws and international conventions or regulations
-
-Table A4.1 illustrates the relationship between national legislation and adhesion or related international conventions or regulations. It shows a number of convention- or regulation-signatory countries that do not have national legislation in place on those vehicle topics. In contrast, a number of other countries have national legislation in place, although whether these match international standards is not know as they have not signed the corresponding international standards. This report does not assess whether national legislation matches the specifications of the international regulations.
-
-Table A4.1. Member States´ vehicle national laws and/or international regulation and convention adhesion for selected safety laws, 2022
-
-|                                                                                                                           | National legislation             |                                     |                                  |                                     |                         |
-|---------------------------------------------------------------------------------------------------------------------------|----------------------------------|-------------------------------------|----------------------------------|-------------------------------------|-------------------------|
-|                                                                                                                           | Yes                              |                                     |                                  | No                                  |                         |
-|                                                                                                                           | Also<br>adheres to<br>regulation | Does not<br>adhere to<br>regulation | Also<br>adheres to<br>regulation | Does not<br>adhere to<br>regulation | Cannot be<br>determined |
-| Infrastructure                                                                                                            |                                  |                                     |                                  |                                     |                         |
-| Requirement for formal road safety inspection/<br>assessment and/or any of the 3 road conventions<br>(1950, 1975 or 2003) | 30                               | 64                                  | 7                                | 62                                  | 7                       |
-| Vehicle                                                                                                                   |                                  |                                     |                                  |                                     |                         |
-| Periodic vehicle technical inspection and/or 1997<br>Periodic Inspections Conv.                                           | 35                               | 118                                 | 0                                | 14                                  | 3                       |
-| Front and side impact protection) and/or 1958<br>Regs 94 and 1958 Regs95                                                  | 31                               | 21                                  | 6                                | 70                                  | 42                      |
-| Seat-belt and seat-belt anchorages and/or 1958<br>Reg 14 and 1958 Reg 16                                                  | 37                               | 51                                  | 2                                | 41                                  | 39                      |
-| Electronic stability control and/or 1958 Reg 140, or<br>1998 Reg 8                                                        | 38                               | 11                                  | 9                                | 71                                  | 41                      |
-| Pedestrian protection and/or 1958 Reg 127 or 1998<br>Reg 9 Pedestrians                                                    | 37                               | 7                                   | 9                                | 75                                  | 42                      |
-| Braking systems and/or 1958 Reg 13H                                                                                       | 35                               | 21                                  | 6                                | 69                                  | 39                      |
-| Helmet legislation referring to and/or specifies<br>standard  and /or 1958 Reg 22                                         | 31                               | 62                                  | 15                               | 60                                  | 2                       |
-| Child restraint law specifies standard and/or 1958<br>Reg 44                                                              | 32                               | 24                                  | 12                               | 102                                 | 0                       |
-
-## <span id="page-85-0"></span>Annex 5. Example Country and territory profile template
-
-![](_page_85_Figure_1.jpeg)
-
-## <span id="page-86-0"></span>Annex 6. Guide to Country and territory profiles
-
-This annex explains the terms used through the report and the indicators included in the country and territory9 profiles. Wherever relevant, It also connects these terms with the wording of the UN Voluntary Performance Targets indicators (UNVTI).
-
-Concepts presented in the order in which they appear in the country or territory profile (top to bottom, left to right).
-
-#### **Country name, Population:** Self explanatory
-
-**Income group**: The source of this information is the World Bank estimated 2021 Gross Domestic Product and the following are the cut off points: less or equal than US\$1 085 low-income; US\$ 1 086 to US\$ 4 355 lower-middle-income; US\$ 4 256 to US\$ 13 205 uppermiddle-income; US\$ 13 205 or more, high-income. More information in [https://databankfiles.worldbank.org/](https://databankfiles.worldbank.org/public/ddpext_download/GDP.pdf) [public/ddpext\\_download/GDP.pdf.](https://databankfiles.worldbank.org/public/ddpext_download/GDP.pdf)
-
-**WHO Region:** WHO clusters countries into six regions: the African Region, the Region of the Americas, the South-East Asia Region, the Eastern Mediterranean Region, the European Region, and the Western Pacific Region. Unless stated otherwise, countries are WHO Member States.
-
-**GSRRS participation** *(participation in the Global status report on road safety series):*The *Global status report on road safety* has been published in 2009, 2013, 2015, 2018, and 202310.
-
-**Reported fatalities (year):** Country-reported number of road traffic deaths and calendar10 year to which reported figure belongs.
-
-**Reported fatalities by sex distribution (Males; Females):** Country-reported data by sex may be from a different source to the reported fatalities (above). The proportion of deaths where sex was unknown are not show. As a result, proportions may not add up to 100. Proportions may also not add up to 100% due to rounding off.
-
-**Reported fatalities by user distribution:** Countryreported data by user type may be from a different source to those used for the indicators above. User types shown are motorized-4-wheel occupants, powered 2/3 wheel occupants, pedestrians, cyclists, and others. The proportion of deaths where user type was unknown are not show. As a result, proportions may not add up to 100. Proportions may also not add up to 100% due to rounding off.
-
-**WHO estimated road traffic fatalities (and 95% Confidence Interval (CI)) (year):** The estimated number of road traffic deaths is based on methodology described in Annex 1. Where this number is based on a negative binomial regression model, a 95% CI is also shown. Estimates are all for year 2021.
-
-## **WHO estimated rate per 100 000 population (year):**
-
-The estimated rate per 100 000 population is based on the estimated number of road traffic deaths referred to above and the July 1 population in the country as described in UN Population Division.
-
-**Total paved kilometres (year):** Country-reported number of paved kilometres, and calendar year to which reported figure belongs.
-
-**Presence of technical standards for new roads that take account of all road-user safety, or align with relevant UN Conventions and regulate compliance with them:** Country-reported information on audits or star ranting on new road infrastructure projects is reported as "yes", "no" or "partial" and contrasted (as indicated by optional footnote) with country adherence to at least one of the following international road conventions: The 1950 Traffic Arteries Convention; 1975 European Agreement on Main International Traffic Arteries; and the 2003 Interstate Asian Highway Convention.
-
-**Presence of systematic approaches to assess/ audit new roads:** Information on inspections/star ratings of existing road infrastructure projects is
-
-<sup>9</sup> In the two territory profiles the terms national/subnational are not used.
-
-<sup>10</sup> Gregorian calendar year is used through the report. Years reported in other calendars have been translated into the closest full Gregorian calendar year.
-
-reported as "yes" or "no". "Yes" responses were those where respondents answered "yes" for the existence of formal road safety inspections and/or existence of star rating assessments. Those countries for which respondents answered "yes" only for the existence of maintenance safety inspections are reported as "No". This information is treated as equivalent to "Use of systematic approaches to assess/audit new roads", UN Voluntary Global Road Safety Performance Target (UNVTI3b).
-
-**National law requiring a formal road safety inspection/assessment:** Country-reported. Its WHO validation (not evaluation) is shown in optional footnote.
-
-**Target for roads to meet technical safety standards for all users (year):** Country-reported.
-
-**Investments to upgrade high-risk locations:**  Country-reported.
-
-**Total registered vehicles [rate per 100 000 pop] (year):** Country-reported information about the total number of vehicles in the country includes only registered vehicles, and various categories of such vehicles. This is the cumulative number of vehicles in circulation in 2021 (or the most recent year for which data were available). The year is also documented. Note this is not the number of vehicles brought into circulation that year. In a few countries the number of vehicles in subcategories did not add up to the total number provided. Rate is calculated using same denominator as fatality rate described earlier in this Annex.
-
-**4-wheel vehicles:** Breakdown of total vehicle figure as reported by countries: includes cars and light vehicles (e.g., vans, sport utility vehicles (SUVs), pick-up trucks) carrying no more than nine occupants.
-
-**Powered 2- and 3-wheelers:** Breakdown of total vehicle figure; includes powered 2-wheel mobility devices.
-
-**Heavy trucks:** Breakdown of total vehicle figure: ≥3500 kg).
-
-**Buses:** Breakdown of total vehicle figure: carrying more than nine occupants.
-
-**Other:** Breakdown of total vehicle figure: excludes unknown.
-
-**Legislation on periodic vehicle technical inspection:** Country-reported and WHO validated (but not evaluated) legislation. This is contrasted (and indicated with footnote) with the country's adherence to international conventions: 1997 Periodic Technical Inspection as described by UNECE or 2014 European Union (EU) Directive 45. This information is treated as equivalent to "Use of systematic approaches for vehicle assessments", UN Voluntary Global Road Safety Performance Target (UNVTI5b)
-
-**National laws on front and side impact protection:**  Country-reported and WHO validated (but not evaluated) legislation. In addition, the footnote informs on whether the country adheres to international vehicle standards such as Frontal impact standard (UN Regulation 94 and 95 or equivalent).
-
-**National laws on seat-belts and seat-belt anchorages:** "Seat-belt anchorages" are the parts of the vehicle structure or the seat structure or any other part of the vehicle to which the safety-belt assemblies are to be secured.11 This item corresponds to countryreported and WHO validated (but not evaluated) legislation. In addition, the footnote says whether the country adheres to international vehicle standards such as Frontal impact standard (UN regulation 14 and 16, or equivalent).
-
-**National law on electronic stability control:**  Electronic stability control (ESC) is an active safety system that can be fitted to cars, buses, coaches and trucks. It is an extension of antilock brake technology, which has speed sensors and independent braking for each wheel. It aims to stabilize the vehicle and prevent skidding under all driving conditions and situations, within physical limits. It does so by identifying a critical driving situation and applying specific brake pressure on one or more wheels, as required.This item corresponds to country-reported and WHO validated (but not evaluated) legislation. In addition, the footnote says whether the country adheres to international vehicle standards such as ESC (Regulation 13H or GTR8). More information in [https://road-safety.transport.ec.europa.eu/.](https://road-safety.transport.ec.europa.eu/)
-
-**National law on pedestrian protection:** Pedestrian protection systems are in-vehicle technology systems that detect pedestrians and cyclists in close proximity to the vehicle and may give a signal when collision is imminent. For more information, visit [https://unece.org/](https://unece.org/sustainable-development/press/two-new-un-vehicle-regulations-will-increase-protection-pedestrians) [sustainable-development/press/two-new-un-vehicle](https://unece.org/sustainable-development/press/two-new-un-vehicle-regulations-will-increase-protection-pedestrians)[regulations-will-increase-protection-pedestrians](https://unece.org/sustainable-development/press/two-new-un-vehicle-regulations-will-increase-protection-pedestrians). This item corresponds to country-reported and WHO validated (but not evaluated) legislation. In addition, the footnote says whether the country adheres to international vehicle standards such as Regulation 127 or GTR9.
-
-<sup>11</sup> Adapted from the UN regulation on seatbelts and anchorages
-
-**National law on braking systems:** Anti-lock braking systems aim to prevent the locking of wheels during braking when under emergency conditions, thereby preventing the motorcyclist from falling from their vehicle. For more information, visit [https://road-safety.](https://road-safety.transport.ec.europa.eu/) [transport.ec.europa.eu/.](https://road-safety.transport.ec.europa.eu/) This item corresponds to country-reported and WHO validated (but not evaluated) legislation. In addition, the footnote says whether the country adheres to international vehicle standards such as Regulation 13H (not to be confused with newly proposed Regulation 152).
-
-**Government vehicle procurement practices include safety prerequisites:** Country-reported policies regarding the purchase of vehicles.
-
-**Presence of high-quality safety standards for used-vehicle imports:** Country-reported. This information is treated as equivalent to UN Voluntary Global Road Safety Performance Target, "Presence of high-quality safety standards for used-vehicle exports" (UNVTI5c).
-
-**National law on universal access to emergency care:** Refers to specific legislation to ensure access to care regardless of the ability to pay (i.e., payment cannot be required as a pre-requisite for receiving care). This information is country-reported and WHO validated (but not evaluated).
-
-**National law guaranteeing free-of-charge access to rehabilitative care for all injured:** Reference to laws that do not require payment as a pre-requisite to accessing rehabilitation care. This information is country-reported and WHO validated (but not evaluated).
-
-**National law guaranteeing free-of-charge access to psychological services for road-crash victims and their families:** Reference to laws that do not require payment as a pre-requisite to accessing psychological care. This information is country-reported and WHO validated (but not evaluated).
-
-**National good Samaritan law:** Good Samaritan/ bystander protection laws seeks to protect good Samaritans/bystanders from some legal claims when a recipient of the bystander emergency care is harmed in the process. This information is country-reported and WHO validated (but not evaluated).
-
-**National emergency care access number:** National emergency care access number is reported as "national, single number", "national multiple number" and "partial coverage". Countries with a "national, single number" comprise those that had one single emergency care services number with total country coverage and also those having additional numbers with partial coverage. Countries with "national, multiple numbers" comprise those that had multiple emergency care services access numbers that, taken together, provide total country coverage. Countries with "partial coverage" comprise those that had one or more emergency care services access numbers with partial country coverage overall with areas of the country remaining uncovered: this information is country-reported.
-
-**National target for time between serious crash and initial provision of professional emergency care (year):** Country-reported information
-
-**Presence of strategies to promote alternatives to individuals' use of powered vehicles:** This information is country-reported.
-
-**National road safety strategy:** Country-reported information that is combined into an indicator to characterize it as "yes" if a published national action plan exists that provides for regularly updated, time‐ bound targets for reductions in fatalities and injuries. In addition, the strategy needs to be active at the calendar year 2021. This indicator is used to inform UN Voluntary Global Road Safety Performance Target (UNVTI1a).
-
-**Fatality reduction target (year):** Country-reported percentage reduction target (if any) and calendar year in which it is to be secured.
-
-**Nonfatal reduction target (year):** Country-reported percentage reduction target (if any) and calendar year in which it is to be secured.
-
-**Funding to implement strategy:** Country-reported.
-
-**National legislation mandating third-party liability insurance for powered vehicles:** Refers to legislation requiring drivers to carry insurance that covers the driver in the event of a crash for which they are responsible for injury to a person or damage to property. This information is country-reported and WHO validated (but not evaluated).
-
-**National law on driving time and rest periods for professional drivers:** Country-reported and WHO validated (but not evaluated) legislation. In addition, the footnote says whether the country adheres to international vehicle standards such as the 1979 AETR Convention. This indicator is used to inform whether Professional drivers' driving time and rest periods are regulated, effectively enforced, and audited – this corresponds to UN Voluntary Global Road Safety Performance Target UNVTI11b.
-
-**Adherence to one or more of the seven UN road safety conventions:** External sources (see Annex 1) facilitate information on adhesion to any of the following regulations: 1949 Convention on Road Traffic, 1968 Convention on Road Traffic, 1968 Convention on Road Signs and Signals, 1958 Agreement concerning the Adoption of Harmonized Technical United Nations Regulations for Wheeled Vehicles, Equipment and Parts which can be Fitted and/or be Used on Wheeled Vehicles and the Conditions for Reciprocal Recognition of Approvals Granted on the Basis of these United Nations Regulations, 1997 Agreement concerning the Adoption of Uniform Conditions for Periodical Technical Inspections of Wheeled Vehicles, 1998 Agreement concerning the Establishing of Global Technical Regulations for Wheeled Vehicles, Equipment and Parts, or 1957 Agreement concerning the International Carriage of Dangerous Goods by Road (ADR). An indicator summarizing whether a country has adhered to at least one of these seven conventions is the basis for UN Voluntary Global Road Safety Performance Target (UNVTI12).
-
-**Presence of national lead agency to implement national road safety strategy:** Country-reported information combined into one indicator to summarize "Presence of national lead agency to coordinate, monitor, evaluate and implement multisectoral national road safety action plan", UN Voluntary Global Road Safety Performance Target (UNVTI1b).
-
-**Presence of agencies that coordinate pre hospital and emergency medical services:** Country-reported information combined to confirm the "Presence of agencies that effectively coordinate pre hospital and facility based emergency medical services" which doubles as UN Voluntary Global Road Safety Performance Target (UNVTI12b).
-
-**Legislation on urban speed limits for passenger cars and motorcycles:** Country reported and WHO validated and evaluated information. Evaluation results of below explained items allow characterization of countries into four levels (strongest to weakest): level 3) law exists, urban limits are set at 50 km/h or lower, and local authorities can further modify this limit; level 2) ) law exists, urban limits are set at 50 km/h or lower but limits cannot be lowered locally; level 1) law exists but urban limits are higher than 50 km/h or no legislation exists; and level 0) legislation was not available for validation. Whether a country has changed its legislation to meet level 3 defines a country's "Speed-control legislation been strengthened since last reporting", UN Voluntary Global Road Safety Performance Target (UNVTI6a).
-
-**National law setting a speed limit:** Speed limits are the default speed limits on urban roads, rural roads and motorways for private passenger cars. The speed limits have been, where needed, converted in kilometres per hour. "Default speed limit" was interpreted as the maximum speed limit applied in normal circumstances (regardless of weather, roadworks, special events, etc.) on the road type considered. As road classifications vary greatly from country to country, special attention was paid to confirm or correct speed limits reported in the legal analysis for the different types of roads according to the definitions used in the country concerned. In some countries, the legislation does not articulate speed limits by road type but only by vehicle type. In these countries, the speed limits provided for private passenger cars is reported in the country and territory profiles for all road types.
-
-**Maximum urban speed limit:** See above.
-
-**Maximum rural speed limit:** See above.
-
-**Maximum motorway speed limit:** See above.
-
-**Local authorities can modify limits:** The criterion "local authorities able to modify speed limit" corresponds to country answering "Yes" to whether the speed limit can be altered at a local level in any way (decreased and/or increased). The definition of local authorities is interpreted broadly as any entity that is not from the central system of government (i.e., not form a national ministry) having jurisdiction over a local area whether the local area is a region, a province, a district, a department or a city. This criterion is automatically answered "Yes" for countries in which laws are set at subnational level if at least 80% of subnational entities of the country have set their own speed limits.
-
-**Presence of targets to reduce speeds nationally (year):** Country-reported information.
-
-**Available types of enforcement:** Countryreported information.
-
-**Legislation on drink driving:** Country-reported and WHO validated and evaluated information. Evaluation of results on topics explained items allow characterization of countries into 4 levels (strongest to weakest): level 3) national legislation on drink driving exists, alcohol levels are defined by BAC, alcohol limits per general driving population are ≤0.05 g/dl and for novice drivers ≤0.02 g/dl; level 2) national legislation on drink driving exists, alcohol levels are defined by BAC, alcohol limits per general driving population BAC is between 0.05 and 0.08 g/dl or the novice/professional drivers are allowed >0.02 g/dl; level 1) legislation is not based on BAC or legal limits >0.08 g/dl or no legislation exists; and level 0) legislation was not available for validation. Whether a country has changed its legislation to meet level 3 defines a country's "Presence of legislation (and effective enforcement) on driving under the influence of alcohol substances (based on alcohol laws)", UN Voluntary Global Road Safety Performance Target (a modification, as it excludes "psychoactive substance" and "effective enforcement" from the definition of UNVTI9a).
-
-If the country has national legislation to prohibit alcohol in general population, this is noted both in the Country and territory profiles as well as the corresponding map.
-
-**National law on drink driving:** As explained above.
-
-**BAC limit – general population:** Blood alcohol concertation (BAC) limits (or breath alcohol limits (BrAC) converted to BAC limits) refer to the maximum amount of alcohol legally acceptable in the blood of a driver on the road (i.e., the blood alcohol level above which a driver may be punished by law). This figure is provided for the general population and for young/novice drivers in grams per decilitre (g/dl). This survey gathered information on drink driving laws regardless of the legal status of alcohol in the country. Where alcohol consumption was legally prohibited in a country, this is indicated by a footnote. BAC limits are reported with a dash "–" for countries that have a drink driving law that is not based on blood (or equivalent breath) alcohol concentration.
-
-**BAC limit – young or novice drivers:** As explained above.
-
-**Random breath testing carried out:** The use of random breath testing is indicated based on countries' reports on whether or not such testing is carried out. It refers to the ability or statutory authority of an enforcement officer to stop a vehicle and test the driver at random, without need to establish that the driver committed another offence, or that the driver showed any signs of impairment prior to being stopped. For more information, see [https://www.grsproadsafety.](https://www.grsproadsafety.org/wp-content/uploads/2023/09/3094-IFRC-Drink-Driving-Management-manual-revision-Sept-2023.pdf) [org/wp-content/uploads/2023/09/3094-IFRC-Drink-](https://www.grsproadsafety.org/wp-content/uploads/2023/09/3094-IFRC-Drink-Driving-Management-manual-revision-Sept-2023.pdf)[Driving-Management-manual-revision-Sept-2023.pdf](https://www.grsproadsafety.org/wp-content/uploads/2023/09/3094-IFRC-Drink-Driving-Management-manual-revision-Sept-2023.pdf).
-
-**Presence of national targets to reduce drink driving (year):** Country-reported information.
-
-**Testing carried out in case of fatal crashes:** Countryreported information.
-
-**Legislation on drug-driving:** Country-reported and WHO validated (but not evaluated) legislation. This information is used to define the "Availability of national and, where applicable, subnational data on driving under the influence of […] psychoactive substances and related road traffic‐related fatalities and injuries" a modification – as it excludes alcohol of UNVTI9a. This modification we label UNVTI\_drug.
-
-**Legislation on distracted driving (mobile phones):**  Country-reported and WHO validated (but not evaluated) legislation. This information is used to define the "Effectively enforced legislation on restricting or prohibiting the use of mobile phones while driving", which doubles as UN Voluntary Global Road Safety Performance Target (UNVTI10a).
-
-**Ban on mobile phone use:** Self explanatory.
-
-**Presence of national targets to reduce distracted driving nationally (year):** Countryreported information.
-
-**Legislation on helmets for motorcycle riders:**  Country-reported and WHO validated and evaluated information. Evaluation results of below explained items allow characterization of countries into four levels (strongest to weakest): level 3) law exists and it covers all riders, on all road types, and all engine types, and the helmet must be fastened and the helmet must meet a standard; level 2) law exists and it covers all riders, on all road types, and all engines types but fastening or standard are not required; level 1) law applies only to certain types of riders, roads or engine types or no legislation exists; and level 0) legislation was not available for validation. Meeting the level 3 definition is used to define the presence of legislation requiring adult motorcycle riders "to wear a helmet properly fastened and meeting appropriate standards for protection" which doubles as UN Voluntary Global Road Safety Performance Target (UNVTI7a).
-
-**National motorcycle helmet law:** Self explanatory.
-
-**Legislation requires helmet fastening:** Self explanatory.
-
-**Legislation applies to drivers and passengers:** A reference to "riders" in the law is understood to include both drivers and adult passengers.
-
-**Legislation applies to all road types:** Self explanatory.
-
-**Legislation applies to all engine types:** All powered 2- and 3-wheelers are covered by the law, regardless of engine power.
-
-**Legislation refers to and/or specifies helmet standard:** The criteria "law refers to and/or specifies a helmet standard" is answered "Yes" if the law refers to a specific standard (such as ECE 22 or a national standard) or an authority in charge of setting such a standard, or regulations or rules to specify or develop a standard. Information on the actual adoption of the regulations prescribing a helmet standard was not always available. Whether the country has adhered to the international standard itself is in an optional footnote.
-
-**Presence of targets to increase helmet use (year):**  Country-reported information.
-
-**Helmet wearing rate (driver; passenger):** Countryreported information. The most disaggregated data are represented here (i.e., separate figures for drivers and passengers). Note the information for drivers and passengers does not necessarily represent the same year, nor come from the same source. The data on passenger rates refer to adult passengers unless otherwise indicated. Whether rates for helmet use for drivers and passengers are above 80% is used as indicative of the "Proportion of motorcycle riders correctly using helmets close to 100%, which corresponds to UN Voluntary Global Road Safety Performance Target (UNVTI7e).
-
-**Minimum age/height children are allowed as passengers:** Country-reported and WHO validated information on whether the country restricts children as passengers on motorcycles and if "yes", for what age group.
-
-**Legislation on seat-belts for motor vehicle occupants:** Country-reported and WHO validated and evaluated information. Evaluation results of the topics explained below allow characterization of countries into four levels (strongest to weakest): level 3) law exists and it applies to all seating positions in vehicles always; level 2) law only applies to front-seat occupants; level 1) law only applies to the driver or no legislation exist; and level 0) legislation was not available for validation. Reaching the level 3 definition is used to define "Legislative improvements made since last reporting towards best practice standards" which corresponds to UN Voluntary Global Road Safety Performance Target ( UNVTI8a).
-
-**National seat-belt law:** Law applies to all roads and at all times.
-
-**Legislation applies to front and rear-seat occupants:** Self explanatory.
-
-**Presence of targets to increase seat-belt use (year):** Country-reported information.
-
-**Seat-belt wearing rate (drivers; front seat occupants; rear seat occupants):** Country-reported information. Where available, information on wearing rates disaggregated by driver, front- and rear-seat occupants were used. Note that the information provided for front-seat and rear-seat occupants does not necessarily represent the same year, nor come from the same source, Whether rates for driver, frontseat occupant and rear-seat occupants are above 80% is used as indicative of the "Proportion of all motor vehicle occupants using safety belt close to 100%, which corresponds to UN Voluntary Global Road Safety Performance Target (UNVTI8c).
-
-**Legislation on child restraint systems:** Countryreported and WHO validated and evaluated information. Evaluation results of the topics below allows characterization of countries into 4 levels (strongest to weakest): level 3) law exists, children up to 10 years of age or 135 cm of height must use a child restraint system matching a standard in addition to the prohibition of children of a particular age/height prohibited from sitting in the front seats; level 2) same as previous level except that either the age of the child is set at 4 years or there is no requirement for a standard; level 1) law not based on age/heigh criteria and no standard or no legislation exist; and level 0) legislation was not available for validation. A country is interpreted as having a child restraint law where the country requires the mandatory use of child restraint systems for an identified group of children based on either their height and/or their age and/or their weight. Countries whose laws require that children within a certain age group/ weight use either a seat-belt or a child restraint were reported as not having a child restraint law for this age group/height. Countries that referred to child restraint use for children sitting in the front only (and not in the rear) were reported as not having a child restraint law. Reaching the level 3 definition is used to define "Presence of effectively enforced legislation requiring the use of child restraint systems that meet appropriate standards" which doubles as UN Voluntary Global Road Safety Performance Target (UNVTI8b).
-
-**National child restraints use law:** Child restraints include rear-facing child restraints, forward-facing child restraints, as well as booster seats. Regular (adult) seat-belts, on their own, are not counted as appropriate child restraints.
-
-**Children seated in front seat:** The criterion "children seated in front" sets out whether a country restricts children as passengers in front seats, and if so, what the restrictions are (e.g. a complete ban, or subject to placing the child in a safety restraint system and for which age group).
-
-**Age or height specified for children requiring child restraint:** The age and/or height reported for the criteria "child restraint required" corresponds to the range of years of age for which only child restraint systems are allowed to restrain children (i.e. no other form of restraint is allowed such as seat-belts only, "other means", etc.)
-
-**Child restraint standard referred to and/or specified:** Countries that referred to either child restraint use or "other means" were considered as not meeting the "standard" criteria. The criterion "law refers to and/or specifies a standards" is answered "Yes" if the law refers to a specific standard (such as ECE 44 or ECE 120) or an authority in charge of setting such a standard, or regulations or rules to specify or develop a standard. Information on the actual adoption of the regulations prescribing a standard was not always available; in cases where the country indicated that the standard had not yet been set, a corresponding footnote was included in the Country and territory profile. Whether the country adheres to any international standard on child restraint systems is noted with a footnote.
-
-**Presence of targets to increase child restraint use (year):** Country-reported information.
-
-**Civil Registration and Vital Statistics 2021:** Source is WHO. See Annex 1 (Methods).
-
-**Frequency and distribution of journeys by modal type:** Country-reported information.
-
-**Speeding violations and speedingg related injuries and fatalities:** Country-reported information. This information is meant to capture "Presence of national and, where applicable, subnational data systems on speeding violations and speeding‐related injuries and fatalities", which is UN Voluntary Global Road Safety Performance Target (UNVTI6c).
-
-### **Driving under the influence of alcohol or drugs and related road traffic‐related fatalities and injuries:**
-
-Country-reported information. This information is meant to capture "Availability of national and, where applicable, subnational data on driving under the influence of alcohol and/or psychoactive substances and related road traffic‐related fatalities and injuries" which is UN Voluntary Global Road Safety Performance Target (UNVTI9b).
-
-**Seat-belt and child restraint systems use:** Countryreported information. This information is meant to capture "Presence of national and, where applicable, subnational data on use of safety belts, as well as the appropriate use of a-restraint systems" which is UN Voluntary Global Road Safety Performance Target (UNVTI8f).
-
-**Powered 2- and 3-wheeler helmet use:** Countryreported information. This information is meant to capture "Presence of national and, where applicable, subnational data systems on helmet use" which is UN Voluntary Global Road Safety Performance Target (UNVTI7d).
-
-**Mobile phone use while driving :**Country-reported information. This information is meant to capture "Presence of national and, where applicable, subnational data systems on the use of mobile phones while driving" which is corresponds to UN Voluntary Global Road Safety Performance Target (UNVTI10b).
-
-![](_page_95_Picture_0.jpeg)
-
-#### **World Health Organization**
-
-The Department of Social Determinants of Health, Safety and Mobility Unit
-
-20 Avenue Appia 1211 Geneva 27 Switzerland Phone: +41227912881
-
-[https://www.who.int/teams/social-determinants-of-health/](https://www.who.int/teams/social-determinants-of-health/safety-and-mobility/global-status-report-on-road-safety-2023) [safety-and-mobility/global-status-report-on-road-safety-2023](https://www.who.int/teams/social-determinants-of-health/safety-and-mobility/global-status-report-on-road-safety-2023)
-
-![](_page_95_Figure_5.jpeg)

Zotero/001_artiklid/Road Safety Indicators/goelcorrelatesfatalityriskvulnerable2018.md

@@ -1,350 +0,0 @@
----
-category: literaturenote
-citekey: goelcorrelatesfatalityriskvulnerable2018
-title: Correlates of fatality risk of vulnerable road users in Delhi
-authors: "Goel, Rahul; Jain, Parth; Tiwari, Geetam"
-year: 2018
-date: 2018-00-00 2018
-doi: 10.1016/j.aap.2017.11.023
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2017.11.023"
-zotero_key: EZZJUVJ5
-zotero_storage: 8ZZ9QJ53
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Goel, Rahul"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/00014575)
-
-# Accident Analysis and Prevention
-
-journal homepage: [www.elsevier.com/locate/aap](https://www.elsevier.com/locate/aap)
-
-![](_page_0_Picture_5.jpeg)
-
-# Correlates of fatality risk of vulnerable road users in Delhi
-
-Rahul Goel[a,](#page-0-0)[⁎](#page-0-1) , Parth Jain[b](#page-0-2) , Geetam Tiwari[c](#page-0-3)
-
-- <span id="page-0-0"></span><sup>a</sup> MRC Epidemiology Unit, University of Cambridge, United Kingdom, UK
-- <span id="page-0-2"></span><sup>b</sup> Civil Engineering, Shiv Nadar University, Gautam Budh Nagar District, India
-- <span id="page-0-3"></span>c Transportation Research and Injury Prevention Programme (TRIPP), Indian Institute of Technology Delhi, New Delhi, India
-
-![](_page_0_Picture_11.jpeg)
-
-## ARTICLE INFO
-
-Keywords: Delhi Vulnerable road users Road deaths India Population Density Grade separators
-
-# ABSTRACT
-
-Pedestrians, cyclists, and users of motorised two-wheelers account for more than 85% of all the road fatality victims in Delhi. The three categories are often referred to as vulnerable road users (VRUs). Using Bayesian hierarchical approach with a Poisson-lognormal regression model, we present spatial analysis of road fatalities of VRUs with wards as areal units. The model accounts for spatially uncorrelated as well as correlated error. The explanatory variables include demographic factors, traffic characteristics, as well as built environment features. We found that fatality risk has a negative association with socio-economic status (literacy rate), population density, and number of roundabouts, and has a positive association with percentage of population as workers, number of bus stops, number of flyovers (grade separators), and vehicle kilometers travelled. The negative effect of roundabouts, though statistically insignificant, is in accordance with their speed calming effects for which they have been used to replace signalised junctions in various parts of the world. Fatality risk is 80% higher at the density of 50 persons per hectare (pph) than at overall city-wide density of 250 pph. The presence of a flyover increases the relative risk by 15% compared to no flyover. Future studies should investigate the causal mechanism through which denser neighborhoods become safer. Given the risk posed by flyovers, their use as congestion mitigation measure should be discontinued within urban areas.
-
-# 1. Introduction
-
-Indian cities have witnessed an exponential growth of vehicles during the previous two decades or so, contributed largely by motorised two-wheelers (MTW) [\(Pucher et al., 2007](#page-7-0); [MoRTH, 2012\)](#page-7-1). Coincident to this, burden from road traffic injuries in India has also been rising, and the number of deaths have more than doubled from 1991 through 2011 ([Mohan et al., 2015\)](#page-7-2). According to the official sources, there were more than 140,000 road deaths in year 2013-14 ([NCRB, 2015](#page-7-3)). When expressed as the number of road deaths per 100,000 persons, fatality risk in India is 2–4 times higher than high-income settings such as the UK, Germany, France and Canada [\(MoRTH, 2012](#page-7-1)).
-
-A majority of the victims are men in age-group 15–59 years ([Gururaj, 2008; Mohan et al., 2009; Hsiao et al., 2013\)](#page-7-4). Pedestrians, cyclists, and MTW riders have the largest share. The three road-user categories, with no rigid barrier protecting them against traumatic forces, are often termed as vulnerable road users (VRU) [\(Peden et al.,](#page-7-5) [2004\)](#page-7-5). Globally, VRUs account for around 46% of all road deaths ([WHO, 2015\)](#page-7-6), while in India this share is much higher.
-
-According to Million Death Study, a national-level mortality survey in India, VRUs accounted for 68% of all road deaths during the period 2001–2003 [\(Hsiao et al., 2013](#page-7-7)). A study conducted in six Indian cities with population ranging between 1–2 million reported that the proportion of VRU fatalities for years 2008 through 2011 varied from 84% to 93% ([Mohan et al., 2016](#page-7-8)). This proportion is much lower in high– income countries and is as low as 22% in the Americas [\(WHO, 2015](#page-7-6)). There are multiple factors contributing to these differences, such as road design, provision of safe infrastructure for pedestrians and cyclists, traffic management, and the enforcement of speed and alcohol limits. Apart from these, the major underlying difference is how people travel in these settings.
-
-According to Census 2011, close to one-third of the workers (30%) in Indian cities walk to work, 17% cycle, a quarter (25%) use some form of public transport (bus, autorickshaw or train), more than one-fifth (22%) use MTW and only 5% use cars [\(Census-India, 2016\)](#page-6-0). As a result, 69% of the workers can be categorised as VRUs during their commute trips. If we consider walking involved in either ends of a public transport trip, the proportion of work trips involving VRU reach up to 94%.
-
-When trips of all purposes are considered, data from various cities in India show that the share of non-motorised modes is even higher ([Arora](#page-6-1) [et al., 2014](#page-6-1); [RITES, 2008](#page-7-9); [Goel, 2017\)](#page-7-10). As a result, a large proportion of road users are exposed to high injury risk through collisions with high-
-
-E-mail addresses: [rahulatiitd@gmail.com,](mailto:rahulatiitd@gmail.com) [rg574@medschl.cam.ac.uk](mailto:rg574@medschl.cam.ac.uk) (R. Goel).
-
-0001-4575/ © 2017 Elsevier Ltd. All rights reserved.
-
-<span id="page-0-1"></span><sup>⁎</sup> Corresponding author.
-
-powered motorised vehicles such as cars, buses, and trucks. This is in complete contrast with high-income settings where a large proportion of trips are carried out in cars. For instance, 86% of the work trips in the US (2009; [McKenzie and Rapino, 2011](#page-7-11)), 64% in the UK ([2011; Gower,](#page-7-12) [2013\)](#page-7-12) and 62% in the Netherlands (2007; [MOT, 2009\)](#page-7-13) were carried out using cars. As a result, in case of a crash, the road users in these settings have much higher protection.
-
-Road transport in India also differs in the form of motorisation from their western counterparts. Increasing motorisation is not resulting in reduction of VRUs on roads, as MTW remains a preferred mode of private transport. While MTW in India account for more than two-thirds of private motorised fleet [\(MoRTH, 2012\)](#page-7-1), their share in western settings such as the USA, UK, Germany and France, is only 3–10% ([EEA,](#page-7-14) [2003;](#page-7-14) [USDOT, 2015](#page-7-15)).
-
-A large number of crash-level epidemiological studies have been carried out in India to understand the causal mechanism of crashes or the injury severity [\(Garg and Hyder, 2006](#page-7-16)). However, epidemiology of crashes using ecological models is lacking. In this study, we present a spatial analysis of VRU fatalities in Delhi to assess their geographic variation with respect to built environment, demographic factors, and traffic characteristics. We restricted our analysis to fatal crashes as number of injury crashes reported by police are highly underreported in India ([Gururaj, 2008; Mohan et al., 2009; Mohan et al., 2015\)](#page-7-4). Delhi being the capital of India and the seat of federal government has an active police department and is a dense urban area. Therefore, underreporting of traffic deaths in a setting like Delhi is highly unlikely.
-
-#### 2. Literature review
-
-Crash rates have been established to have a positive association with the speed of vehicles [\(Nilsson, 1981; Cameron and Elvik, 2010](#page-7-17)). In addition to the probability of a crash, speed of vehicles is also a determinant of severity level of injuries ([OECD/ECMT, 2006; Aarts and](#page-7-18) [Van Schagen, 2006](#page-7-18)). How fast vehicles travel on road is a function of built environment ([Ewing and Dumbaugh, 2009\)](#page-7-19) and road design features ([Török, 2011; Fitzpatrick et al., 2001\)](#page-7-20), among other factors such as speed limit ([Fitzpatrick et al., 2001](#page-7-21)), or traffic conditions ([Török,](#page-7-20) [2011\)](#page-7-20). Given these links of crashes with speed and that of speed with built environment, many studies have found association between crash rates and built environment ([Ewing et al., 2003](#page-7-22)).
-
-There are other factors which result in higher number of crashes such as through increasing the exposure to risk, increasing the chances of a crash, or increasing the severity of injury. Higher exposure to risk is a function of economic and demographic factors and mode of travel. Higher crash occurrence is associated with lack of law enforcement by police and lack of safe infrastructure for pedestrians and cyclists, and higher severity level can result from lack of forgiving vehicle front to protect pedestrians in a collision, use of seat belts by cars occupants and helmets by MTW riders and cyclists ([Peden et al., 2004](#page-7-5)).
-
-A large number of studies have carried out area-level crash modelling to quantify the association of road traffic injuries with built environment and traffic characteristics as well as population characteristics. Such models, after accounting for confounding variables, estimate the independent effects of different built environment variables, such as, type of junctions, intersection density, type of roads, speed limit, road widths, and road curvature. With this knowledge, built environment can be modified in ways which can increase the safety of road users. The sensitivity of safety to those modifications is given by the coefficients of the regression models.
-
-Most of the area-level modelling studies have been carried out in settings from highly motorised developed countries—US, Canada, UK, and Australia. For instance, studies from cities/states in the US include San Francisco, California ([LaScala et al., 2000; Wier et al., 2009](#page-7-23)), Tucson, Arizona [\(Ladron de Guevara et al., 2004](#page-7-24)), Pennsylvania ([Aguero-Valverde and Jovanis, 2006](#page-6-2)), Hawaii [\(Kim et al., 2006](#page-7-25)), Charlotte, North Carolina [\(Pulugurtha et al., 2013\)](#page-7-26), California ([Chakravarthy et al., 2010](#page-6-3)), San Antonio, Texas [\(Dumbaugh et al.,](#page-7-27) [2013\)](#page-7-27), New York city ([DiMaggio, 2015](#page-7-28)), Manhattan ([Narayanamoorthy](#page-7-29) [et al., 2013\)](#page-7-29), New Jersey [\(Demiroluk and Ozbay, 2014](#page-7-30)), and Hillsborough and Pinellas counties of Florida [\(Siddiqui et al., 2012; Xu et al.,](#page-7-31) [2017\)](#page-7-31), from those in Canada include Toronto [\(Hadayeghi et al., 2003](#page-7-32)), Greater Vancouver region [\(Lovegrove and Sayed, 2006](#page-7-33)) and British Columbia [\(MacNab, 2004](#page-7-34)), those in UK, London [\(Quddus, 2008](#page-7-35)), England [\(Graham and Glaister, 2003; Noland and Quddus, 2004\)](#page-7-36), and England and Wales [\(Jones et al., 2008](#page-7-37)), and in Australia, Melbourne ([Amoh-Gyimah et al., 2016\)](#page-6-4). Among low-and middle-income countries (LMICs), the only study reported is by [Wang et al. \(2016\)](#page-7-38) in which they modeled pedestrian crashes in Shanghai city.
-
-The areal unit of analyses used by various studies also varied and included counties [\(Aguero-Valverde and Jovanis, 2006; Demiroluk and](#page-6-2) [Ozbay, 2014\)](#page-6-2), census tracts ([LaScala et al., 2000; Chakravarthy et al.,](#page-7-23) [2010; Narayanamoorthy et al., 2013; DiMaggio, 2015](#page-7-23)), census statistical area levels [\(Amoh-Gyimah et al., 2016\)](#page-6-4), wards ([Graham and](#page-7-36) [Glaister, 2003; Noland and Quddus, 2004; Quddus, 2008\)](#page-7-36), traffic analysis zones (TAZ) [\(Hadayeghi et al., 2003; Pulugurtha et al., 2013;](#page-7-32) [Siddiqui et al., 2012; Wang et al., 2016; Xu et al., 2017\)](#page-7-32), city blocks ([Dumbaugh et al., 2013\)](#page-7-27) or grids [\(Kim et al., 2006](#page-7-25)).
-
-The modeling has been carried out using non-spatial models (Hadayeghi [et al., 2003; Graham and Glaister, 2003; Noland and](#page-7-32) [Quddus, 2004; Kim et al., 2006; Pulugurtha et al., 2013; Lovegrove and](#page-7-32) [Sayed, 2006; Wier et al., 2009; Chakravarthy et al., 2010; Dumbaugh](#page-7-32) [et al., 2013\)](#page-7-32), spatial models [\(LaScala et al., 2000; MacNab, 2004;](#page-7-23) [Narayanamoorthy et al., 2013; Demiroluk and Ozbay, 2014; DiMaggio,](#page-7-23) [2015; Wang et al., 2016](#page-7-23)), as well as both [\(Quddus, 2008; Aguero-](#page-7-35)[Valverde and Jovanis, 2006; Siddiqui et al., 2012; Amoh-Gyimah et al.,](#page-7-35) [2016; Xu et al., 2017](#page-7-35)). Spatial models have accounted for spatial correlation using traditional econometric models, such as spatial autoregressive models ([Quddus, 2008; LaScala et al., 2000\)](#page-7-35) or spatial error models [\(Quddus, 2008](#page-7-35)) or using more recently developed hierarchical Bayesian modelling which include specifications of error terms for uncorrelated heterogeneity as well as spatial heterogeneity [\(MacNab,](#page-7-34) [2004; Aguero-Valverde and Jovanis, 2006; Quddus, 2008; Siddiqui](#page-7-34) [et al., 2012; Wang et al., 2016; Amoh-Gyimah et al., 2016; Xu et al.,](#page-7-34) [2017\)](#page-7-34).
-
-It is noteworthy that even though a major share of global road traffic injury burden is contributed by LMICs, their representation in such studies is almost absent. In Indian cities, most roads do not have posted speed limits, and when they do, police rarely enforces those. As a result, speed chosen by drivers is likely to be much more associated with traffic conditions, road design features and other built environment factors. This underscores the importance of built environment as risk factor for crashes in Indian cities. Other factors which set Indian cities apart from their high-income counterparts are lack of safe infrastructure for non-motorised modes, heterogeneous mix of traffic, low level of car-based travel and a high share of MTW. The contrasting contexts of on-road traffic mix, built environment, demographics, and level of traffic enforcement between India and high-income countries warrant an area-level crash study in an Indian city.
-
-## 3. Case study city—Delhi
-
-Delhi is the capital city of India and one of the most heavily motorised large cities in India. Among the cities with population more than 10 million, it has the highest ownership of cars, with more than one in every 5 households owning a car ([Guttikunda et al., 2014\)](#page-7-39). Delhi along with its contiguous cities have grown rapidly over the last two decades. The population of the region more than doubled from 10 million in 1991–22 million in 2011, with Delhi contributing 16.7 million to the latter. Over the same period, the number of registered vehicles have increased by more than 300%. Public transport (PT) is served through a combination of road– and rail–based modes. These include buses, intermediate public transportation such as cycle rickshaws, electric rickshaws, auto rickshaws or tuktuks, and mini buses, and rail-based systems including metro rail and commuter rail [\(Goel and Guttikunda,](#page-7-40) [2015; Goel and Tiwari, 2016\)](#page-7-40).
-
-According to Census 2011, among all the work trips in Delhi, up to a quarter of trips are walked (26%), one-tenth are cycled (11%), onethird use some form of public transport (32%), 17% use MTW and 13% use cars ([Census-India, 2016](#page-6-0)). A large number of grade-separated intersections have been built in Delhi as a measure to reduce congestion as well as to reduce vehicular idling. Cycle lanes have been built as a part of 5.8-km long bus rapid transit corridor, while almost no other road in Delhi has cycle lanes. Though small isolated sections of cycle lanes have been built in various parts of the city. There is no helmet use among bicycle users in Delhi.
-
-#### 4. Data
-
-In this study we model road deaths corresponding to the 3-year period: 2010–2012. The year 2011 corresponds to the latest Census. The inclusion of fatalities for three years brings stability in the fatality counts for disaggregated spatial units within the city. We used casespecific fatal crashes reported in First Information Reports (FIRs) compiled by Delhi Traffic Police for the years 2010 through 2012. FIRs are the first set of information documented by police department as reported by those involved in the crash or anyone who knows about the crash or by a police official [\(Mohan et al., 2015\)](#page-7-2).
-
-The case-specific details consist of date, time, location, police station of the crash location, striking vehicle type, and victim road-user type. Age and gender of crash victims were available for year 2010 only. The three-year period includes a total of 5972 fatalities, which amounts to 1991 fatalities per year, and 11.9 fatalities per 100,000 persons, assuming 2011 census population as average of the 3-year period. In comparison, New York has a fatality rate of 3 per 100,000 persons ([NYDMV, 2014\)](#page-7-41), Greater London, 1.6, [\(TFL, 2014](#page-7-42)), and Amsterdam, 2 ([Iamsterdam, 2014](#page-7-43)). The three VRU categories, pedestrians (45.5%), cyclists (5.9%), and motorised two-wheeler (MTW) riders (34.5%), contribute 86% (5138) of all the fatalities.
-
-The location of the crashes mentioned in the FIR data consisted of the name of the road where the crash occurred along with a landmark. Using this information, geographical coordinates of the crash locations were identified using Google Maps as well as Wikimapia [\(http://](http://wikimapia.org/country/India/Delhi/) [wikimapia.org/country/India/Delhi/](http://wikimapia.org/country/India/Delhi/)). The latter has information regarding informal landmarks known among local population and collected through crowdsourcing, which are often missing in Google Maps. In addition, we referred to jurisdiction map of police stations. Landmarks of some of the crash locations were reported using serial number of pillars of elevated metro corridors, and were also not available on Wikimapia. For these, we visited those road sections and geo-located those pillars using GPS.
-
-We use wards as areal units which are administrative units in the city for the purpose of municipal corporations. In 2011, Delhi was divided in to 282 wards with an average size of 4.9 km<sup>2</sup> with more than half (54%) of all the wards having an area of less than 2 km<sup>2</sup> . The average number of VRU fatalities across the wards is 18 varying from a minimum of zero to maximum of 183. We used ward-specific demographic and socio-economic statistics from Primary Census Abstract (PCA) reported by Census 2011.
-
-From PCA, we used population, literacy rate, and percent of population who are workers. The population of wards also vary from ∼14,000 to ∼146,000 with an average of ∼58,000. Literacy rate is defined as the percentage of population above 6 years who are literate. Workers have been classified based on the length of employment during the past one year—main worker: 6 months or more, marginal worker: less than 6 months, and non-worker: no employment. For our analysis, we only used the main worker category.
-
-In the absence of city-wide traffic counts, modelled vehicle kilometers travelled (VKT) were used from Travel Demand Forecast Study (TDFS) commissioned by Transport Department of Delhi ([RITES, 2008](#page-7-9)). The study carried out traffic assignment model for 2007 which consisted of volume of vehicles in each link (road segments), expressed as Passenger Car Units. TDFS also included validation of assignment model with the observed traffic counts at various locations. We used model output for 2007 and estimated ward-specific VKT using the sum total of product of length of each link and its corresponding volume. While the traffic deaths in our model refer to 2010–2012 period, we assume that 2007 traffic volume is sufficient for assessing relative variation across the wards. Even if the growth in traffic volume occurred, we assume that growth rate was consistent across the wards.
-
-The model of road deaths presented in this study also accounts for exposure for each ward. We calculated exposure as the sum of population of the ward and the total number of daily person trips destined to the ward. This was then multiplied by 3 since the fatality counts correspond to a three-year period. Thus, exposure accounts for population residing in the ward as well those visiting the ward during the course of a day. For instance, in case of a ward with offices and other commercial land use, while the residing population could be small, it will still attract a large number of people during the day. For estimating the number of external trips to wards we used TDFS study.
-
-From TDFS, origin-destination (OD) matrices of person trips estimated for year 2011 were available for motorised modes and classified among four categories—car, MTW, intermediate public transport (IPT) which includes auto rickshaws (or tuk-tuks), and public transport (PT) including bus and train. We used OD matrices for year 2011 as these need to be consistent with the population which corresponds to 2011. We used sum total of all modes to estimate total trips destined to each zone. The OD units in TDFS are traffic analysis zones (TAZs) which were formed using wards. In cases where ward size was much bigger, TAZs were formed by dividing the ward into two or more units. By overlaying the TAZ over wards in a GIS platform, TAZs were mapped to their corresponding wards. Using this correspondence, ward-specific VKT and exposure were calculated using zonal data. The total number of external trips to each ward are shown in [Table 1](#page-2-0) as Person trips destined to ward.
-
-For built environment variables we included grade separators (overpass/flyovers), roundabouts, bus stops, and built-up population density. Built up area was identified using Google Earth for 2013 ([Goel](#page-7-40) [and Guttikunda, 2015\)](#page-7-40), using which ward-specific population density were estimated. The average built-up population density of wards is 490 persons per hectare (pph), with 60% of the wards within 500 pph and 85% within 800 pph. Other built environment variables were also identified using Google Earth for year 2012. In case of grade-separated intersections, we used the corresponding intersection as a point location to represent grade separator. Most flyovers in Delhi connect two
-
-<span id="page-2-0"></span>Table 1 Descriptive statistics.
-
-|                                         | Mean   | Standard<br>Deviation | Min    | Median | Max     |
-|-----------------------------------------|--------|-----------------------|--------|--------|---------|
-| Population                              | 58,046 | 19,205                | 14,217 | 54,404 | 145,715 |
-| Population Density<br>(persons per km2) | 49,359 | 38,216                | 1808   | 40,796 | 279,200 |
-| Person trips destined to<br>ward        | 38,786 | 31,192                | 5287   | 30,450 | 300,213 |
-| VKT                                     | 2581   | 3396                  | 20     | 1631   | 36,326  |
-| # Bus stops                             | 12     | 13                    | 0      | 9      | 67      |
-| # Flyovers                              | 0      | 1                     | 0      | 0      | 7       |
-| # Roundabouts                           | 0      | 1                     | 0      | 0      | 12      |
-| Area                                    | 4.9    | 10.7                  | 0.3    | 1.9    | 80.0    |
-| # VRU fatalities                        | 18     | 20                    | 0      | 13     | 183     |
-| % Population main<br>workers            | 32.3   | 4.0                   | 23.7   | 32.3   | 46.1    |
-| % Population (> 6 years)<br>literate    | 86.6   | 5.5                   | 72.0   | 87.5   | 97.1    |
-
-parallel legs of a major intersection to facilitate the uninterrupted movement of through moving traffic. Few flyovers span across more than one intersection and are often referred to as elevated roads. For those flyovers, we denoted locations at their beginnings and at their ends. [Table 1](#page-2-0) presents descriptive statistics of all the variables.
-
-## 5. Method
-
-The objective of this study is to explore the effect of built environment and demographic and socioeconomic characteristics of the population on the fatality risk of VRUs. For this, we used the Bayesian hierarchical modelling framework as proposed by Besag, York and Mollié (BYM) [\(Besag et al., 1991](#page-6-5)). The model has been implemented widely such as for cancer mapping by [Cramb et al. \(2011\)](#page-6-6) and injury modelling by [Quddus \(2008\)](#page-7-35) and [DiMaggio \(2015\)](#page-7-28). The model is described as follows:
-
-<span id="page-3-1"></span><span id="page-3-0"></span>
-$$y_i = Poisson(\theta_i) \tag{1}$$
-
-$$\log(\theta_i) = \log(e_i) + \beta_0 + \beta_i X_i + \delta_i + \nu_i$$
-(2)
-
-where, yi are the observed VRU fatality counts in each ward i, θ<sup>i</sup> are the expected count of fatalities, Xi represents a vector of explanatory variables, or covariates for each ward, ei is the exposure, β<sup>0</sup> is the intercept, β is a vector of fixed effect parameters, δ*<sup>i</sup>* is the uncorrelated heterogeneity or unstructured error, and ν<sup>i</sup> is the spatially correlated heterogeneity. The random error components represent the effects of unmeasured/unknown risk factors. Here, δ*<sup>i</sup>* represents overdispersion and accounts for variation in the expected fatality risk of wards after controlling for the independent variables, and ν<sup>i</sup> represents spatial patterns affecting fatality risk and not accounted for by the independent variables.
-
-The first level of the hierarchical modeling framework presented in the Eq. [\(1\)](#page-3-0) represents ward-level observed crash counts (yi) generated from a Poisson distribution with an expected θ<sup>i</sup> count of
-
-The second level, presented in Eq. [\(2\)](#page-3-1), includes the linear relationship between log of expected counts and independent variables. Here, exposure (ei) is an offset (a covariate with coefficient value 1) and, therefore, effectively acts as a denominator for left-hand side of the equation. This in turn expresses the dependent variables as risk (log (λi) =log( / ) *θ e i i* ). Therefore, this modelling framework accounts for exposed population explicitly, rather than treating it as a covariate. Note that exposure is the sum total of population and the number of external trips destined to the ward.
-
-The Bayesian modelling was done using R-INLA [\(Rue et al., 2009\)](#page-7-44) which is an R package and employs Integrated Nested Laplace Approximations to estimate the posterior distributions. R-INLA has been recently developed as a computationally efficient alternative to Monte Carlo Markov Chain (MCMC). Unlike MCMC methods which rely on simulation methods to trace posterior distribution, INLA estimates parameters using a closed-form deterministic method and is much faster. It has been applied in injury modeling by [DiMaggio \(2015\).](#page-7-28)
-
-R-INLA includes a latent model for uncorrelated random effects (δ*i*), in which these effects are modelled as δ (0, 1/ *<sup>i</sup>* ∼ *N τ*δ), where *τ*<sup>δ</sup> refers to the precision of the Normal distribution and is inverse of the variance. log(*τ*δ) is assigned a prior of log-gamma distribution with mean and precision of 1 and 0.0005, respectively. Using log(*τ*δ) instead of simply *τ*<sup>δ</sup> provides some advantages as log(*τ*δ) is not constrained to be positive. Fixed effects, including the intercept, have a Gaussian prior with fixed mean and precision (*N* (0,0.001)).
-
-For spatial dependence we use the intrinsic conditional autoregressive (CAR) specification as proposed by [Besag et al. \(1991\).](#page-6-5) According to this specification, the spatial random effects ν<sup>i</sup> are distributed as:
-
-$$\nu_i \left| \nu_j, \; \tau_\nu \sim N \left( \frac{1}{n_i} \sum_{i \sim j} \; \; \nu_j, \; \frac{1}{\tau_\nu n_i} \right) i \neq j \right.$$
-
-where, j refers to the indices of all wards which are neighbours of a given ward i, and ni is the total number of neighbours of ward i. To determine the number of neighbours and to identify the pairs of wards as neighbours, a contiguous neighbor-adjacency matrix was created using the poly2 nb function in the spdep R package [\(Bivand et al., 2011](#page-6-7)). To define neighbours, we used queen adjacency method according to which two wards are neighbours if they share a common boundary or a point.
-
-The above specification implies that spatial component of error at any ward (νi) has a normal distribution. That distribution is centered around the mean of the spatial error components of all its neighbouring wards and the variation around the mean is inversely proportional to the number of its neighbours. As the number of neighbouring wards increase, the spread of the distribution around the mean value also reduces.
-
-Similar to log(*τ*δ), log(τν) is also assigned a prior of log-gamma distribution with mean and precision of 1 and 0.0005. The parameters describing the priors are often referred to as hyper-parameters, which in the current specifications are *τ*<sup>δ</sup> and τν, for uncorrelated and spatially correlated error terms, respectively. Their respective distributions are called hyperprior distributions. Fixed effects, on the other hand, have no hyperparameters. Note that all the priors are defined with very large variances (inverse of variance varies from 0 for intercept, to 0.0005 for hyperparameters, to 0.001 for other fixed effects), and therefore, these priors are uninformative, signifying lack of our prior understanding of these effects.
-
-Note that while *τ*<sup>δ</sup> is an indicator of uncorrelated heterogeneity across all wards, τν represents the variation of the conditional autoregressive specification, therefore the two cannot be interpreted in the similar manner. Using R-INLA output, we obtained the posterior distributions of spatial error components of each of the ward. To estimate variance of spatial components, we simulated 1000 random values of spatial components of each of the ward using their corresponding posterior distributions. For each of those 1000 runs, we estimated variance of spatial error across all wards, and the mean of 1000 variance values was estimated as the variance of spatial error component.
-
-To compare the performance of Bayesian models, Deviance information criterion (DIC) is estimated which is a Bayesian version of Akaike information criterion (AIC). DIC is calculated as:
-
-$$DIC = D(\hat{\theta}_{Bayes}) + 2p_{DIC}$$
-
-where, the first term in right-hand side is the deviance calculated for the posterior mean of the estimated parameters, and second term is the effective number of parameters in the model. Compared to maximum likelihood method, in Bayesian hierarchical modeling, deviance is evaluated at mean of posterior distributions rather than maximum likelihood estimate of parameters and the number of effective parameters tend to be less [\(Gelman et al., 2014,](#page-7-45) p. 172). Similar to AIC, lower value of DIC implies higher predictive accuracy.
-
-# 5.1. Selection of variables
-
-Before progressing to development of the regression model, we investigate the Pearson correlation between various variables in order to avoid multicollinearity between the independent variables. VKT has high positive correlation with number of bus stops and high negative correlation with population density. Population density and number of bus stops are also highly negatively correlated. We found that adding the three variables together did not significantly affect the standard deviations of their coefficients compared to when they are added individually. In addition, magnitude of the coefficients also changed by a maximum of 25% in case of population density. At the same time, DIC
-
-<span id="page-4-0"></span>Table 2 Results of intercept-only and full model using Bayesian Hierarchical modelling.
-
-| Variable               | Intercept-only model |         |         | Full model     |         |        |
-|------------------------|----------------------|---------|---------|----------------|---------|--------|
-|                        | mean (sd)            | P2.5    | P97.5   | mean (sd)      | P2.5    | P97.5  |
-| Intercept              | −10.108 (0.042)      | −10.192 | −10.024 | −8.049 (1.480) | −10.988 | −5.170 |
-| % Literate             |                      |         |         | −0.024 (0.009) | −0.042  | −0.006 |
-| % Main workers         |                      |         |         | 0.039 (0.016)  | 0.008   | 0.071  |
-| ln(Population density) |                      |         |         | −0.355 (0.090) | −0.532  | −0.177 |
-| ln(VKT)                |                      |         |         | 0.317 (0.064)  | 0.192   | 0.445  |
-| # Bus stops            |                      |         |         | 0.012 (0.004)  | 0.004   | 0.021  |
-| # Flyovers             |                      |         |         | 0.137 (0.056)  | 0.028   | 0.247  |
-| # Roundabouts          |                      |         |         | −0.042 (0.038) | −0.117  | 0.034  |
-| τδ (iid component)     | 3.069 (1.103)        | 1.503   | 5.772   | 3.434 (0.651)  | 2.304   | 4.85   |
-| τv (spatial component) | 0.467 (0.136)        | 0.251   | 0.779   | 9.706 (11.461) | 1.548   | 38.67  |
-| DIC                    | 1737.91              |         |         | 1705.71        |         |        |
-
-reduced significantly by 5 units compared to the model with only VKT among the three variables. Therefore, in the final model, all the three variables were retained.
-
-#### 6. Results
-
-We obtained results for an intercept-only as well as a full model, as shown in [Table 2.](#page-4-0) The table shows mean and 2.5th and 97.5th percentiles of the posterior distributions of all coefficients as well as error components, and also presented are the DIC values. The percentiles represent the 95% confidence interval (CI). We found that for the frailty or intercept-only model, 66% of the variance is due to spatial component, while the rest is due to unstructured heterogeneity of ward. Full model explained 89% of the variation of spatial error, however, it explained less than 20% of the variation in uncorrelated heterogeneity. In the intercept-only model, exponential of intercept term, exp(β0), represents the background fatality risk across the wards and exponential of sum of two error components, exp(δ ν ) + , *i i* represents the relative risk of each ward, and the latter is presented in [Fig. 1.](#page-5-0)
-
-On the basis of 95% CI of posterior distributions, all the coefficients are significantly different from zero, except number of roundabouts. Percentage of literate population, number of roundabouts and population density have a negative association with fatality risk and percentage of population as workers, number of bus stops, number of flyovers, and VKT have positive association. Here, a positive association indicates that with an increase in a variable, the fatality risk increases.
-
-#### 7. Discussion
-
-# 7.1. Socio-economics and demographics
-
-An increase in literacy rate, which is an indicator of socio-economic status (SES) of the ward, is associated with lower risk of fatalities. This is possible because population with low SES are more likely to be VRUs as they walk, cycle, use PT or ride MTW for their daily travel. In Delhi, only one-fifth of all households own a car [\(Census-India, 2012](#page-6-8)). With low level of car ownership, whether an individual is VRU or not is highly sensitive to their income level. Million Death Study [\(Hsiao et al.,](#page-7-7) [2013](#page-7-7)) also reported pedestrian deaths to be positively associated with living in poorer neighborhoods. A large number of studies have shown similar results linking higher risk of fatalities, or number of road crashes in general, with lower SES ([Aguero-Valverde and Jovanis, 2006;](#page-6-2) [Wier et al., 2009; DiMaggio, 2015; Xu et al., 2017\)](#page-6-2).
-
-The percentage of population as main workers is positively associated with the fatality risk. According to Census 2011, 65% of the main workers in Delhi are in the age group 30–59, and 86% of them are males. Therefore, workers represent a specific demographic group, which is predominantly male in the age group 30–59. This is also reflected in the age and sex distribution of injuries. For the three-year fatality data (2010–2012) reported in the current study, sex of victims was reported for year 2010, according to which males accounted for 91% of all fatality victims, while their share in overall population is 54% [\(Census-India, 2012](#page-6-8)). The disproportionate share of men in the age group 15–59 years was also reported by the Million Death Study ([Hsiao](#page-7-7) [et al., 2013\)](#page-7-7). This explains a positive association of main workers with fatality risk.
-
-It is interesting to note that even though Pearson correlation between percentage of main workers and percentage of literate population is positive, the coefficients of the two variables are opposite in signs. This means that SES (indicated by literacy) and demographics (indicated by workers) have their independent effects which are opposite in directions.
-
-#### 7.2. Traffic volume and roundabouts
-
-Positive effect of VKT is expected and has been consistently reported by all studies which considered it as one of the covariates [\(Amoh-](#page-6-4)[Gyimah et al., 2016; Demiroluk and Ozbay, 2014; DiMaggio, 2015;](#page-6-4) [Quddus, 2008; Xu et al., 2017; Aguero-Valverde and Jovanis, 2006;](#page-6-4) [Huang et al., 2010; Wier et al., 2009\)](#page-6-4). According to the posterior distribution of coefficient of number of roundabouts, up to 85th percentile value is a negative. One of the benefits of Bayesian method over frequentist method is that while the latter reports coefficients as single values, the former reports them as distributions of values. Thus it can be said that, given the data, there is more evidence in favour of a negative association of roundabouts with fatality risk than a positive or no effect.
-
-The negative association of roundabouts with fatality risk is also expected from international experience. Roundabouts have been adopted globally as a traffic calming measure because of their effectiveness to reduce road crashes. According to a meta-analysis of 28 studies in non-US locations, conversion of intersections to roundabouts resulted in 50–70% reduction of the fatal crashes [\(Elvik, 2003](#page-7-46)). In Holland, before-and-after studies of the construction of about 200 roundabouts showed a significant drop of 89% of pedestrian fatalities ([Schoon and van Minnen, 1994\)](#page-7-47).
-
-# 7.3. Flyovers and bus stops
-
-Apart from roundabouts, flyovers and bus stops are two other variables representing road infrastructure, and we will discuss the two together because of their related features. Flyovers have a strong positive association with fatality risk, with one flyover increasing the relative risk by 15% compared to no flyover. Bus stops are also positively related to fatality risk. These effects are independent of the volume of traffic. The coefficients of the two variables may not be isolated effects of the two infrastructure elements and could also be indicating the effect of other built environment features which occur simultaneously.
-
-Flyovers in Delhi have been built along major arterial roads (for
-
-Fig. 1. Relative risk of VRU fatality risk in wards across Delhi.
-
-<span id="page-5-0"></span>![](_page_5_Figure_3.jpeg)
-
-instance the two ring roads) as well as highways. Bus stops in Delhi are also located on most major roads, of which arterials and highways are subsets. Most residential and commercial areas do not have enough carriageway width for movement of the bus. Therefore, both bus stops and flyovers are likely to represent road types with heavy vehicular movement. The roads with flyovers also have 40–50% higher average speed than other major roads [\(Mohan et al., 2017](#page-7-48)).
-
-A study conducted in Delhi ([Khatoon et al., 2013\)](#page-7-49) studied traffic characteristics before and after the replacement of signalised junction with a flyover. The study reported that the average speed travelled by trucks and buses as well as the variability of the speed of all vehicle types increased after the construction of flyover. Another study from Delhi also found presence of flyovers as a significant factor affecting the number of pedestrian crashes ([Rankavat and Tiwari, 2015](#page-7-50)). Thus there is a strong evidence suggesting that construction of flyovers results in high increase in the risk of injuries.
-
-One of the major confounding variables which has been excluded in this analysis is the volume of trucks, which may bring endogeneity in the model results. A network of national highways pass through the city in multiple directions making it a natural route for long–distance trucks as well as a hub for goods exchange. A large proportion of goods movement occurs in Delhi through road-based freight modes. High volume of trucks is also a major source of pollution in Delhi ([Goel and](#page-7-40) [Guttikunda, 2015](#page-7-40)). It is possible that the model may have introduced an upward bias in the effect of number of flyovers and number of bus stops. However, given the magnitude of association for both bus stops and flyovers as well as high statistical significance indicated by their posterior distributions, the addition of any other risk factor is unlikely to change the direction of association.
-
-Population density is log transformed therefore its coefficient
-
-cannot be interpreted in the similar manner as other independent variables. Since the relative risk is an exponent of product of the variable and its coefficient (exp (βiXi)), the relative risk (RR) of population density can be expressed as power functions, as[1](#page-5-1)
-
-= <sup>−</sup> *RR Populationdensity* ( ) 0.355
-
-In order to understand the effect of density, we expressed the relative risk with respect to the overall average population density (total population/total built-up area) of 250 pph. [Fig. 2](#page-6-9) indicates that relative risk of fatalities is more than 1.8 times higher at density of 50 pph compared to city-level average. The non-linear curve shows that at higher density levels, the effect of density flattens off and the most reduction in relative risk is up to a density of 850 pph. There are various factors which could result in this association of density with risk and we discuss those in the following text.
-
-High density locations are more likely to have higher number of pedestrians. In the absence of dedicated facilities for pedestrians and cyclists, the two slow-moving road users occupy the curb-side lane of the roads. This effectively slows down the traffic and makes roads safer. With an increase in the volume of pedestrian, their risk reduces, and this phenomenon is referred to as safety-in-numbers [\(Jacobsen, 2003;](#page-7-51) [Elvik and Bjørnskau, 2017\)](#page-7-51). Thus the negative association of relative risk with density may likely be an indicator of safety-in-numbers.
-
-High density also attracts higher number of IPT, such as cycle rickshaws, auto rickshaws, and e-rickshaws. These modes are demand responsive and are operated by private operators. Therefore, their volumes are proportional to density or the demand. Since buses do not
-
-<span id="page-5-1"></span><sup>1</sup> <sup>e</sup>β.ln(X) = X<sup>β</sup>
-
-<span id="page-6-9"></span>![](_page_6_Figure_2.jpeg)
-
-Fig. 2. Relative risk at different density levels compared to city-level average (250 pph).
-
-operate through streets in residential areas, IPT is also used for last-mile connectivity of a bus or metro trip [\(Goel and Tiwari, 2016\)](#page-7-52). In the absence of dedicated parking bays or stops, these vehicles idle along the curb-side lane for passenger boarding and alighting, leading to further congestion. On-street parking/idling effectively narrows the roads, and driver tend to be more cautious while driving through those sections ([Gattis, 2000](#page-7-53)).
-
-In Delhi, as well as in most Indian cities, most informal neighborhoods or commercial areas have high built-up density and narrow roads. Informality implies that most growth in built-up is in-situ (as opposed to Greenfield development). Also, the street design is not according to municipal bye-laws which ensure wide-enough streets. Formally designed high-income neighborhoods often have wider streets, but due to on-street car parking by the residents, road widths are effectively reduced.
-
-As a result, most through movement of motorised traffic occurs on major roads, and those driving through the narrow streets tend to drive slow. In addition to slower and low volume of traffic, trucks and buses are almost absent in these locations. While trucks are restricted by police, buses do not ply due to lack of space. This can also be seen through a negative correlation of population density with both, number of bus stops as well as VKT, which in turn are proxies of major roads. Therefore, high density should also be interpreted as a proxy of residential/commercial land-use and street design, and these correlates of high density act as speed calming measures.
-
-The relationship of crash risk with population density has been inconsistent across the studies. While [Graham and Glaister \(2003\)](#page-7-36) and [Noland and Quddus \(2004\)](#page-7-54) reported a negative association between density and crash risk, [Lovegrove and Sayed \(2006\),](#page-7-33) [Huang et al.](#page-7-55) [\(2010\),](#page-7-55) [Dumbaugh and Li \(2010\),](#page-7-56) [Chakravarthy et al. \(2010\),](#page-6-3) [Siddiqui](#page-7-31) [et al. \(2012\)](#page-7-31), and [Narayanamoorthy et al. \(2013\)](#page-7-29) reported a positive association between the two. Both the studies showing negative relationship were based on country-wide analysis in the UK using wards as areal units, and all the studies showing positive relationship were based in either US or Canada— Florida, San Antonio, California, Manhattan, and Vancouver.
-
-The cities in the US have higher car ownership and lower population density than the UK ([Giuliano and Narayan, 2003](#page-7-57)). Compared to both US and UK, Delhi's density is an order of magnitude higher and car ownership a magnitude of order lower. In a setting with high car ownership, higher density may imply higher number of cars against a smaller number of pedestrians. In contrast, in a setting such as Delhi, it implies much higher number of pedestrians in conflict with comparatively smaller number of motorised modes. Thus, density can imply different mechanisms in place in different settings.
-
-## 8. Conclusion
-
-Pedestrians, cyclists and MTW users constitute the largest group of fatality victims in Delhi. In Delhi as well as in most Indian cities, overall traffic enforcement is weak, especially in terms of speed as well as alcohol limit. In addition, the infrastructure facilities for pedestrians are poor, for cyclists almost absent, and MTW use the same lanes as other motorised modes. The mixing of VRUs with vehicles of much larger weight and speed results in greater injury risk. In this context, improving safety through design of built environment can prove to be highly effective. Therefore, it is important to understand built environment factors which affect fatality risk. In this study we assessed the risk resulting from roundabouts, bus stops, flyovers and population density while controlling for traffic volumes and population characteristics.
-
-With higher emphasis on smooth traffic flow and higher speed, a large number of flyovers have been built within populated areas in Delhi as well as many Indian cities. We found that an addition of a flyover increases the fatality risk in a ward by up to 15%, and this effect is independent of traffic volume. While the construction of flyovers pose a challenge of lock-in, their effect on speed of vehicles can be controlled by using speed enforcement by the police or using passive measures such as installment of rumble strips. Given the high risk posed by addition of flyovers, their use as congestion mitigation measures within urban areas should be discontinued.
-
-In addition, cities in India need to consider the use of roundabouts as an alternative of traffic junctions to minimise the number of road crashes. Many cities in India are doing exactly the reverse by replacing roundabouts with traffic junctions. For traffic planners to willingly adopt roundabouts, it is important that their designs are based on latest international experience which result in increased safety as well as efficient traffic movement.
-
-There is a positive association with fatality risk and social deprivation, thus indicating socio-economic inequity of injury risk. Given a negative relationship of risk and population density, future studies should investigate the street design and built environment features of high density locations in Delhi to understand the causal mechanism behind this relationship. These factors can then be incorporated in future city designs.
-
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Zotero/001_artiklid/Road Safety Indicators/greibeaccidentpredictionmodelsurban2003.md

@@ -1,565 +0,0 @@
----
-category: literaturenote
-citekey: greibeaccidentpredictionmodelsurban2003
-title: Accident prediction models for urban roads
-authors: "Greibe, Poul"
-year: 2003
-date: 2003-00-00 2003
-doi: 10.1016/s0001-4575(02)00005-2
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/s0001-4575(02)00005-2"
-zotero_key: QVSGTIXD
-zotero_storage: JCVAIJBJ
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Greibe, Poul"
-status: converted
----
-![](_page_0_Picture_0.jpeg)
-
-Accident Analysis and Prevention 35 (2003) 273–285
-
-![](_page_0_Picture_2.jpeg)
-
-# Accident prediction models for urban roads
-
-# Poul Greibe
-
-*Danish Transport Research Institute, Knuth Winterfeldts Allé, DK-2800 Kgs. Lyngby, Denmark* Received 23 May 2001; received in revised form 16 November 2001; accepted 4 December 2001
-
-#### **Abstract**
-
-This paper describes some of the main findings from two separate studies on accident prediction models for urban junctions and urban road links described in [Uheldsmodel for bygader-Del1: Modeller for 3-og 4-benede kryds. Notat 22, The Danish Road Directorate, 1995; Uheldsmodel for bygader- Del2: Modeller for strækninger. Notat 59, The Danish Road Directorate, 1998] (Greibe and Hemdorff, 1995, 1998).
-
-The main objective for the studies was to establish simple, practicable accident models that can predict the expected number of accidents at urban junctions and road links as accurately as possible. The models can be used to identify factors affecting road safety and in relation to 'black spot' identification and network safety analysis undertaken by local road authorities.
-
-The accident prediction models are based on data from 1036 junctions and 142 km road links in urban areas. Generalised linear modelling techniques were used to relate accident frequencies to explanatory variables.
-
-The estimated accident prediction models for road links were capable of describing more than 60% of the systematic variation ('percentage-explained' value) while the models for junctions had lower values. This indicates that modelling accidents for road links is less complicated than for junctions, probably due to a more uniform accident pattern and a simpler traffic flow exposure or due to lack of adequate explanatory variables for junctions.
-
-Explanatory variables describing road design and road geometry proved to be significant for road link models but less important in junction models. The most powerful variable for all models was motor vehicle traffic flow. © 2002 Elsevier Science Ltd. All rights reserved.
-
-*Keywords:* Accident prediction models; Generalised linear modelling; Road accidents; Junction; Road links
-
-#### **1. Background**
-
-Traffic planners and engineers in Denmark have used accident prediction models since the early 1980s. The models used were based on data from state and county roads, which are generally located in rural areas. An increasing need for models that could also be used for urban roads stimulated research on accident prediction models for urban junctions and road links beginning in the early 1990s.
-
-The main objective of this study was to formulate practicable accident prediction models which would describe the expected number of accidents at junctions and road links in urban areas as accurately as possible. Furthermore, the models were to be used to identify factors affecting safety, e.g. geometry, land use, etc. Unlike the used models for rural roads, which were based only on traffic flows, this study was to include additional explanatory variables in the modelling.
-
-*E-mail address:* Poul@greibe.dk (P. Greibe).
-
-URL: <http://www.dtf.dk>
-
-#### **2. Description of data**
-
-In order to develop the models, detailed information on accident data, traffic flow and road design was required. Since the official Danish national road database only holds data on state and county roads (mainly rural), a close co-operation was established with a number Danish municipalities in order to collect data from urban roads.
-
-Accident data were collected from the official accident statistics database covering all police recorded accidents. The data included all possible data on the accident, including a small text description of the nature and location of each accident. All accidents were related to the specific road link or road junction by use of the road ID number. A 5 years accident period was used (1987–1991 for junctions and 1990–1994 for road links).
-
-Traffic flow counts were collected from the municipalities and converted to AADT counts. Unfortunately, the traffic counts did not always include heavy truck volumes and volumes of vulnerable road users, so these data fields were left blank.
-
-Data on junction and road link geometry were collected partly by visual inspections and video recordings, and partly by use of local road databases at the municipalities.
-
-The road network had to be divided into junctions and road links, since the objective was to make separate models for junctions and road links. Junctions with an incoming traffic flow on secondary arms of less than 250 and 500 AADT for 3- and 4-arm junctions, respectively were considered as part of the road link. These limits were chosen since most traffic count databases used had these as a minimum level for traffic flows. As a result of this definition, road links in this study may have a number of minor junctions or accesses along the road section. Variables describing the number of minor junctions and accesses are therefore included in the road link models.
-
-#### *2.1. Road link data*
-
-Data from 142 km urban road were collected and separated into 314 individual homogeneous road sections, giving an average road link length of approximately 450 m (S.D. = 285 m).
-
-Data on the following variables were collected for road links:
-
-- (a) traffic flow (motor vehicles, heavy vehicles and vulnerable road users);
-- (b) length of road section;
-- (c) speed limit;
-- (d) one/two-way traffic;
-- (e) number of lanes;
-- (f) road width;
-- (g) speed reducing measures;
-- (h) number of minor crossings/exits/side roads;
-
-- (i) cyclist facilities;
-- ( j) footway;
-- (k) central island;
-- (l) parking facilities;
-- (m) bus stop;
-- (n) land use.
-
-The traffic flow data were considered as a continuous variable while all other data were converted into class variables with two–six levels. See Appendix A for a complete list of variables.
-
-A total of 1058 police recorded accidents can be related to the 314 road links, 523 of which were personal injury accidents and 535 damage only accidents. The personal injury accidents resulted in 30 deaths and 554 injured persons.
-
-Even though the road links only have small access or exit roads, more than 40% of the accidents can be related to crossing manoeuvres.
-
-The accident distribution for road links can be found in Appendix A.
-
-#### *2.2. Junction data*
-
-The study included data from 1036 urban junctions. The number of each junction type is as follows: 85 (3-arm, signalised), 547 (3-arm, non-signalised), 250 (4-arm, signalised), 154 (4-arm, non-signalised).
-
-Other junction types, e.g. roundabouts, have not been treated since there was an insufficient number of them.
-
-Data on the following variables had been collected for junctions:
-
-(a) traffic flows (motor vehicles, heavy vehicles and vulnerable road users);
-
-![](_page_1_Figure_30.jpeg)
-
-Fig. 1. Illustration of correlation matrix for road link data. Thick lines indicate 'strong' correlation (ρ > 0.6) and thin or no lines indicate 'weaker' correlation among variables. For example, the number of lanes and the presence of a central island correlate strongly.
-
-- (b) number of lanes;
-- (c) traffic island;
-- (d) turning lane;
-- (e) bicycle facilities;
-- (f) signalised/non-signalised;
-- (g) number of arms.
-
-The traffic flow data were considered as a continuous variable while all other data were converted to class variables with two–seven levels. See Appendix A for a complete list of variables.
-
-A total of 2534 police recorded accidents can be related to the 1036 junctions. The majority of accidents occurred in 4-arm signalised junctions and more than 50% of the 3-arm non-signalised junctions had zero observed accidents.
-
-The accident distribution for junctions can be found in Appendix A.
-
-In general, data on traffic flows for vulnerable road users and heavy vehicles were difficult to obtain, since these are rarely counted. This unfortunately also applies to junctions and for road links.
-
-#### *2.3. Correlation among data*
-
-Analysis of the data collected from junctions and road links showed some correlation among the explanatory variables.
-
-Fig. 1 illustrates some general correlations within the data for road links based on the correlation matrix for the data-set. The motor vehicle traffic flow variable (AADT) correlated strongly (ρ > 0.6) with other explanatory variables, e.g. the presence of a central island, the number of lanes, road width, speed limit etc. The same conditions were found for data on junctions.
-
-Due to the strong correlation among variables in the data-sets, it was not expected to be possible to estimate the true safety effect from a single road geometry variable.
-
-#### **3. Model structure**
-
-The models relate the number of observed accidents to traffic flow and road design. Generalised linear modelling techniques were used to fit the model, and the distributions of accident counts were assumed to follow a Poisson distribution. The regression analyses were performed by use of the GENMOD procedure in SAS.
-
-Whether it is reasonable to assume that accident counts are Poisson distributed is a recurrent issue. The main advantage of the Poisson distribution is its simplicity, e.g. the variance is equal to its mean. However, difficulty arises concerning the phenomenon of "overdispersion" when the observed variance is actually greater than the mean. Overdispersion does not affect the coefficient estimates but does cause their standard errors to be underestimated (Miaou et al., 1992). Recent studies have proved that the negative binomial distribution might be more appropriate because it allows greater variance in the data and thereby deals with the overdispersion.
-
-In this study, the Poisson distribution was chosen over the negative binomial simply because it is easier to use and, as the study was planned and initiated in the early 1990s, the Poisson was the only practicable solution with the statistical software available at that time. However, the final estimations were performed by use of the GENMOD procedure, which does support a number of different distributions, but subsequent estimations proved, as in other studies (Kulmala, 1995), that the values of the model parameters were almost the same regardless of the distributional assumption. It was therefore decided to stick to the pure Poisson model. It should be mentioned that no corrections were made for any possible overdispersion.
-
-#### *3.1. Model structure*
-
-Different ways to relate accident frequencies to traffic flows have been investigated in a number of previous studies, e.g. Satterthwaite (1981), Hauer et al. (1988), Bélanger (1994), Brüde and Larson (1993), Golias (1992), Hall (1986), Taylor et al. (1996), Maher and Summersgill (1996).
-
-For road links the general opinion is that accident frequencies can be described by a flow function raised to a power. Often the flow function consists simply of the motor vehicle traffic flow along the link (AADT), but some studies (e.g. Summersgill et al., 1996) also include flows for pedestrians along or across the link.
-
-The flow function for junctions are more complex since there is a number of individual traffic flows, especially when turning flows and flows for pedestrians and cyclists are included. In most cases, as in this study, the only flow data available will be the incoming motor vehicle traffic from each arm of the junctions, leaving limited possibilities to use complicated flow functions.
-
-A number of TRL studies (e.g. Layfield et al., 1996) on junctions proved that models with more complicated flow functions based on data from individual traffic flow (including pedestrian flows) predict the accidents frequencies quite well. However, such models require data on turning flows, etc. which put a strain on practicality.
-
-Recent studies (Mountain et al., 1998) include variables that allow for changes in risk over time in order to take any possible trend into consideration. This would ensure that the models do not become rapidly outdated. They found a 6% annual decrease in risk per year for junctions. However, to estimate annual changes in risk, large time series are required.
-
-Previous Danish studies on accident prediction models (Wass et al., 1983; Kronbak and Greibe, 1994) have investigated models based on different flow functions of some with flows for cyclists and moped drivers as well. The general conclusion was that the models shown below are suitable for Danish conditions, and since these new models for urban roads were to be used along with existing models for rural roads, it was decided to use the same model structure here as well.
-
-The model structure for road links
-
-$$E(\mu) = aN^p \exp \sum \beta_j x_{ij}$$
-
-where *E*(µ) is the expected number of accidents (accidents per year per km), *N* the motor vehicle traffic flow (AADT), *x* variables describing road geometry or environment of the road *a*, *p*, βj are estimated parameters and for junctions
-
-$$E(\mu) = aN_{\text{pri}}^{p1}N_{\text{sec}}^{p2}\exp\sum\beta_{j}x_{ij}$$
-
-where *E*(µ) is the expected number of accidents (per year), *N*pri the incoming motor vehicle traffic flow (AADT) from the primary direction, *N*sec the incoming motor vehicle traffic flow (AADT) from the secondary direction, *x* variables describing road geometry, *a*, *p*1, *p*2, βj are estimated parameters.
-
-For non-signalised junctions, the primary direction represents the two arms where traffic has the right of way. For signalised junctions, the primary direction at the junction represents the two arms with the highest traffic volumes. It can not be rejected that the primary direction in signalised junctions represents two arms which are not opposite in the junction. This will be very seldom though.
-
-#### *3.2. Modelling procedure*
-
-At first, all variables were included in the model for the regression analysis. Insignificant variables were excluded one by one, starting with the least significant variables. Insignificant variables were identified based on likelihood ratio statistics and standard errors of the estimated parameter values. At the same time, the correlation matrix was studied and if two variables were correlated strongly with each other, one variable was excluded from the model on the condition that the model fit did not suffer significantly. If a variable turned out to be significant but did not contribute significantly to the decrease in deviance, the variable was excluded from the model.
-
-Some explanatory variables were simplified, e.g. if a model showed that the parameter values for speed limits of 60 and 70 km/h did not differ much, the two categories were combined. All variables were also tested solely along with the traffic volume in order to identify any 'hidden' effects that did not show when other variables were included.
-
-Separate models were estimated for: all accidents, all injury accidents, certain types of accidents (single accidents, rear-end accidents, crossing accidents, turning accidents, etc.). The purpose of modelling certain types of accidents was to examine the effect of road design factors on different accident types.
-
-Individual models for accidents involving two-wheelers were also examined. Some of these are presented in this paper.
-
-#### *3.3. Goodness-of-fit*
-
-The total variation in the accident count consists of a random part (presumed Poisson distributed) and a systematic part. The model's 'goodness-of-fit' was measured by how much of the systematic variation the model could explain and will be referred to as "percentage explained". The method is proposed by (Kulmala, 1995) as being suitable for Poisson models. The percentage explained value are estimated on the basis of the scaled deviance of the studied model, the zero model (a model with only one constant parameter) and the expected scaled deviance of a model describing the total systematic variation.
-
-Some problems arise in using this method when the average number of accidents is low (<0.5). Even though this study had a number of sites having zero accidents, we did not have mean values below 0.5. However, when modelling certain types of accidents, e.g. single accidents, mean values for some junction groups were below 0.5.
-
-In general, the results (as described below) showed 'percentage explained' values for the models in the area of 30–80%. The best models were produced for road links.
-
-#### **4. Results**
-
-#### *4.1. Junctions*
-
-Since there are considerable differences in the accident pattern and risk at different junction types, individual models were produced for four individual types of junctions, 3- and 4-arm, signalised and non-signalised, respectively.
-
-Individual models for each junction type were estimated with all explanatory variables included. There was difficulty in achieving stable and significant explanatory variables in the junction models. This might be due to the fact that the safety effect of the individual explanatory variables is relatively small, and the models allow only for small variations in the estimated coefficients without becoming insignificant.
-
-For all junction models, the variable for motor vehicle traffic proved to be the strongest variable, representing more than 90% of the systematic variation described by the entire model. Only few other explanatory variables were significant.
-
-As previously mentioned, strong correlation within the data-sets caused interpretation difficulties of the explanatory variables. The "true" safety effect of the explanatory variables could therefore not be estimated properly.
-
-To test whether the number of junctions in the data set was sufficient, models based on 40, 80, 120, 160 and 200 junctions were estimated. Serious estimation problems arose when the models were based on less than 80 junctions. The estimated parameter values did not change considerably when more than 100 junction were used. The standard error decreased as more junctions were added but the geometry variables were still not significant and it was estimated that
-
-Table 1 Estimated parameter values for 4-arm signalised junctions<sup>a</sup>
-
-| Parameter              |     | Estimated value         |
-|------------------------|-----|-------------------------|
-| a                      |     | 1.13 × 10−4 [0.41;4.04] |
-| p1                     |     | 0.56 [0.44;0.68]        |
-| p2                     |     | 0.47 [0.37;0.56]        |
-| β1: Number of lanes in | 2   | 1.29 [1.08;1.56]        |
-| primary direction      | >2  | 1                       |
-| β2: Number of lanes in | 2–3 | 0.83 [0.72;0.96]        |
-| secondary direction    | >3  | 1                       |
-
-<sup>a</sup> Values of 95% confidence limits are shown in brackets.
-
-the number of junctions would have to be increased by three times in order to see any effect. Since it was only practicable to increase the number of junctions by a few hundred, the idea of adding more junctions to the data was rejected, as this would not help to make geometry variables significant.
-
-An example of an accident prediction model for 4-arm signalised junctions with variables describing motor vehicle flow and other explanatory variables are presented in Table 1. The only significant variables, besides traffic flow were β<sup>1</sup> and β<sup>2</sup> describing the number of lanes.
-
-The model results shown in Table 1 has a percentage explained value of 60%, which is only 1% better than a similar model that excludes explanatory variables describing road geometry. Equivalent results were found for other junction types. By far the most powerful variable was that of motor vehicle volumes. In general, variables describing junction design improved the models by only 1–4%.
-
-The results for junctions indicate that simple models (only motor vehicle flow-functions) are almost as good as more complicated models. The reason for this must be the complicated internal correlations in junction design data or lack of good descriptive variables on road design.
-
-Models with explanatory variables describing traffic volumes for vulnerable road users were only 4–5% better than models without these variables. The reason for this might also be strong correlation between motor vehicle traffic flows and traffic flows for vulnerable road users, or the fact that less than 5% of accidents involving vulnerable road users are reported to the police.
-
-Results from the simple flow function models (only holding motor vehicle traffic flows) are presented in Table 2.
-
-The best models were estimated for 4-arm signalised junctions, probably because this junction type had the highest number of observed accidents.
-
-*4.1.1. Safety effect of signal control*
-
-To evaluate the influence of signal control (traffic light) on the total number of observed accidents, a model was created with a two-level variable with or without signal control. The effect is given by the estimated coefficients.
-
-The signal control variable was not significant in the model, which indicates that the expected total number of accidents is very similar for signalised and non-signalised junctions with the same flow function. In other words, non-signalised junctions are in general just as safe as signalised junctions with the same amount of incoming motor vehicles. This applies for an incoming motor vehicle traffic flow between 5,000–18,000 and 5,000–22,000 for 3- and 4-arm junctions, respectively.
-
-With respect to different accident types, the figures for single and rear-end accidents are significantly higher in signalised junctions than in non-signalised junctions. The number of crossing accidents is, however, lower in signalised junctions. The estimated safety effects are shown in Fig. 2.
-
-It was expected that signalised junctions would have more rear-end accidents and fewer accidents with crossing road users but also the number of single accidents are higher at signalised junction. The reason for this is unknown but one reason could be the fact that signalised junctions are more complex with more obstacles and other equipment that drivers can run into.
-
-Other effect studies on signal control (Elvik et al., 1997) finds by use of meta-analysis on a number of before–after studies a 15% reduction in accidents for 3-arm junction and 30% reduction for 4-arm junctions.
-
-These figures can not be confirmed in this study. However, considering only personal injury accidents (not shown in Fig. 2), a non-significant reduction of 9 and 12% were found for 3- and 4-arm junctions, respectively.
-
-## *4.1.2. Cyclist safety at junctions*
-
-As 32% of the accidents in junctions involve cyclists or moped riders, special models to predict these accidents were formulated. Unfortunately, only 4-arm signalised junctions had sufficient traffic flow data to permit regression analysis.
-
-The model below is estimated on a subgroup of junctions comprising only 41 4-arm signalised junctions. The results were
-
-$$E(\mu_{\text{cyc/mop}}) = 8.01 \times 10^{-8} N_{\text{tot}}^{0.91} C_{\text{tot}}^{0.86}$$
-
-Table 2 Estimated parameter values for junction models<sup>a</sup>
-
-| Junction type         | a                       | p1               | p2               | 'Percentage explained' |
-|-----------------------|-------------------------|------------------|------------------|------------------------|
-| 3-Arm, non-signalised | 1.04 × 10−5 [0.41;3.43] | 0.69 [0.57;0.82] | 0.60 [0.48;0.72] | 47                     |
-| 3-Arm, signalised     | 1.34 × 10−5 [0.07;23.4] | 0.88 [0.58;1.19] | 0.33 [0.14;0.50] | 32                     |
-| 4-Arm, non-signalised | 7.12 × 10−4 [1.16;43.7] | 0.30 [0.15;0.44] | 0.55 [0.34;0.76] | 29                     |
-| 4-Arm, signalised     | 1.08 × 10−4 [0.51;3.08] | 0.53 [0.42;0.64] | 0.52 [0.44;0.60] | 59                     |
-
-<sup>a</sup> Values of 95% confidence limits are shown in brackets.
-
-![](_page_5_Figure_2.jpeg)
-
-Fig. 2. Safety effect for the number of accidents at signalised junctions compared to non-signalised junctions. 1.00: no effect. (e.g. signalised 3-arm junctions have 2.85 times more rear-end accidents than non-signalised 3-arm junctions with the same traffic flows). Figures marked with an ∗ are not significant.
-
-where *E*(µcyc/mop) is the expected number of accident involving cyclists or moped riders per year, *N*tot the incoming motor vehicle traffic flow (total sum, AADT), *C*tot the incoming cyclist/moped traffic flow (total sum, AADT).
-
-Explanatory variables describing facilities for cyclist (cycle track/path) were not significant and stable in the model. The variable for traffic flows for cyclists and moped riders improved the 'percentage explained' value by 5% from 70 to 75%. It seems that although the explanatory variable for cyclist and moped traffic flows are statistically significant, they do not improve the models substantially. In view of the difficulty of obtaining flow data for cyclists and moped riders, it can be questioned whether it is worth using explanatory flow data variables for cyclists and moped riders in the models.
-
-#### *4.2. Road links*
-
-Road links with a speed limit of 30, 40 or 80 km/h were excluded from the data sets since these road sections were represented by a total of less than 20 km, and furthermore differed considerably from other road sections. Only sections with a speed limit of 50, 60 or 70 km/h are represented in the following models.
-
-As mentioned earlier, generalised linear modelling techniques were used to fit the models and the variation in accidents was assumed to follow a Poisson distribution.
-
-Separate models were estimated for:
-
-- (a) all accidents (injury and damage only accidents);
-- (b) injury accidents;
-- (c) certain types of accidents (single accidents, rear-end accidents, head-on accidents, accidents involving turning traffic, accidents involving pedestrians, accidents involving parked vehicles, etc.).
-
-The purpose of modelling certain types of accident, was to examine the effect of road design factors on different accident types. For reasons of space, only results for models based on all accidents (injury and damage only accidents) will be presented in the following sections.
-
-#### *4.2.1. Simple flow function model*
-
-First a simple model containing only variables for motor vehicle traffic flow was tested. The result was
-
-$$E(\mu) = 2.44 \times 10^{-3} N^{0.75}$$
-
-where *E*(µ) is the expected number of accidents (per km per year), and *N* the motor vehicle traffic flow measured as AADT.
-
-This model had a 'percentage explained' value of 30%. As found in other studies (Hemdorff, 1996; Summersgill and Layfield, 1996) the traffic volume is raised to a power of less than 1, in this case 0.75.
-
-#### *4.2.2. Models with explanatory variables*
-
-A model with other explanatory variables included was tried and the results are given below. Note that variables for vulnerable road user traffic flows were not included since data on this were insufficient.
-
-The model below describes the total number of accidents (injury and damage only accidents). Only variables that proved to be significant are included. The model (simplified) has the following structure:
-
-$$E(\mu) = aN^{p}\beta_{1,i}\beta_{2,i}\beta_{3,i}\beta_{4,i}\beta_{5,i}\beta_{6,i}$$
-
-where *E*(µ) is the expected number of accidents (per km per year), *N* the motor traffic flow (AADT), β1−<sup>6</sup> class variables-represents certain road design or road layout parameters, *a*, *p*, βn,i are estimated parameters.
-
-The estimated parameters are shown in Table 3.
-
-As an example, the estimated accident frequency for a road link with an AADT of 4000, speed limit of 50 km/h, road width of 5.0–7.5 m, 5–40 exits per km, 0–5 minor crossings per km, limited parking, situated in a residential neighbourhood would be
-
-$$\begin{split} E(\mu) &= 6.09 \times 10^{-4} \times 4000^{0.80} \times 2.25 \times 0.83 \times 1.00 \\ &\times 0.75 \times 1.00 \times 1.58 \\ &= 1.03 \text{ accidents per km per year} \end{split}$$
-
-Table 3 Estimated parameter values<sup>a</sup>
-
-An interpretation of the variable values, e.g. β<sup>4</sup> (number of minor side roads), indicates that the accident risk rises as the number of minor side roads per km increases. A road link with >10 side roads per km (value 1.25) has a 1.7 times higher accident risk than a road link with 0–5 side roads per km (value 0.75). Furthermore, road sections in shopping districts have approximately 2.4 times higher risk than roads in areas with scattered housing.
-
-The 'percentage explained' value for the model above is 69%, which is considerable higher than a model without the other explanatory variables (30%).
-
-Similar models have been estimated for different accident types. In general, variables describing traffic flow, land use, number of minor crossings, parking facilities and speed limits proved to be the most important variables in the models.
-
-Since the models did not include exposure data on vulnerable road users, it must be expected that other variables, e.g. land use and speed limit, to a certain extend explain the level of exposure for vulnerable road users. This relates to the fact that land use and speed limit turn out to be among the most descriptive variables in the data-set.
-
-#### *4.2.3. Cyclist safety on road links*
-
-A simple flow model describing the number of accidents involving cyclists and moped riders as a function of the traffic flows for motor vehicles, cyclists and mopeds were tested. The results are given below
-
-$$E(\mu_{\rm cyc/mop}) = 4.44 \times 10^{-5} N_{\rm car}^{0.68} N_{\rm cyc/mop}^{0.49}$$
-
-where *E*(µcyc/mop) is the expected number of accident involving cyclists and moped riders (per km per year), *N*car
-
-| Parameter                             |                                                                                       | Estimated value                                               |
-|---------------------------------------|---------------------------------------------------------------------------------------|---------------------------------------------------------------|
-| a                                     |                                                                                       | 6.09 × 10−4 [1.90;19.5]                                       |
-| p                                     |                                                                                       | 0.80 [0.68;0.92]                                              |
-| β1: Speed limit                       | 50 km/h<br>60 km/h<br>70 km/h                                                         | 2.25 [1.66;3.05]<br>2.85 [2.07;3.90]<br>1                     |
-| β2: Road width                        | 5.0–7.5 m<br>8.0–8.5 m<br>9.0–15.0 m                                                  | 0.83 [0.70;0.99]<br>0.68 [0.57;0.81]<br>0.80 [0.67;0.95]      |
-| β3: Number of exits per km            | 0 and >40<br>5–40                                                                     | 1<br>1                                                        |
-| β4: Number of minor side roads per km | 0<br>0–5<br>5–10<br>>10                                                               | 0.72 [0.58;0.89]<br>0.75 [0.64;0.88]<br>1<br>1.25 [1.05;1.48] |
-| β5: Parking                           | Prohibited<br>Rarely<br>Bays at kerb                                                  | 1.19 [1.48;2.11]<br>1<br>1.77 [1.48;2.11]                     |
-| β6: Land use                          | Shops<br>Blocks of flats<br>Industrial/residential/neighbourhood<br>Scattered housing | 2.44 [1.81;3.28]<br>1.56 [1.21;2.03]<br>1.58 [1.32;1.89]<br>1 |
-
-<sup>a</sup> Values of 95% confidence limits are shown in brackets.
-
-![](_page_7_Figure_2.jpeg)
-
-Fig. 3. Percentage of systematic variation explained by individual variables in the road link model.
-
-the motor vehicle traffic flow (AADT), *N*cyc/mop the traffic flow (AADT) for cyclists and moped riders.
-
-The model was estimated on a data sub-set comprising only 26 road sections.
-
-#### *4.2.4. Percentage explained by variables in road link models*
-
-A model with all variables included gives a 'percentage explained' value of 72%. Fig. 3 illustrates how much the individual variables contribute to the overall 'goodness-of-fit'.
-
-As mentioned earlier, the 'percentage explained' value indicates the level of systematic variation explained by the model. The AADT variable is the most powerful variable (describing approximately 30%), followed by variables for land use, number of side roads, etc.
-
-Even though all variables are represented in the models, 30% of the systematic variation remains unexplained. A likely explanatory variable that could increase the 'percentage explained' value might be the actual speed of the motor vehicles. We know from other studies that speed is one of the most significant factors in traffic safety. Another likely explanatory variable that could increase the 'percentage explained' value could be better exposure data for vulnerable road users and better exposure data for vehicles entering the road link. As mentioned earlier a high proportion of accidents at road links involved vehicles entering the link from side roads or accesses and/or accidents with vulnerable road users.
-
-The figure also illustrates that it is possible to explain more than 50% of the systematic variation by use of the four–five most important variables, and that additional explanatory variables do not improve the models very much.
-
-#### *4.2.5. Explanatory variables and their safety effect*
-
-One of the objectives of this study was to evaluate the safety effect of different explanatory variables, e.g. road layout, etc. Despite the strong correlation among the variables in the data-set, which may lead to incorrect conclusions, the effects derived by the models are given in the following sections. The list below is based on Table 3 and results on different types of accidents, which are not presented elsewhere in the article. Only variables that proved to be significant are shown.
-
-*4.2.5.1. AADT.* The motor vehicle traffic flow is the most important variable in the models. The results showed that accident frequencies are related to the AADT raised to power of 0.8–1.0. However, for some accident types, e.g. rear-end and single accidents, the parameter value were 1.23 and 0.52, respectively. These deviations correspond with results found by (Summersgill and Layfield, 1996).
-
-*4.2.5.2. Speed limits.* The modelling showed that road links with high speed limits tend to have lower accident risk. This does not mean that high speeds in general are safer, rather the results for this variable illustrate the correlation
-
-Table 4 Description of road types
-
-| Road type | Land use                                     | Minor side road per km | Speed limit (km/h) |
-|-----------|----------------------------------------------|------------------------|--------------------|
-| A–D       | Shopping/city centre                         | 0–10                   | 50–60              |
-| E         | Blocks of flats/residential housing/industry | 0–10                   | 50–60              |
-| F         | Blocks of flats/residential housing/industry | 0–10                   | 70                 |
-| G         | Blocks/residential housings/industry         | >10                    | 50–60              |
-| H         | Scattered                                    | –                      | 50–60              |
-| I         | Scattered                                    | –                      | 70                 |
-
-problems within the data set. High speed roads tend to have few vulnerable road users and to be situated in sparsely built-up areas.
-
-*4.2.5.3. Number of lanes.* Road links with only one lane (no marked centre line) have more accidents involving motor vehicles going in the same direction than road links with two or more lanes.
-
-*4.2.5.4. Road width.* Apparently, road links with a road width (from kerb to kerb) of 8–8.5 m have the lowest accident risks for most accident types.
-
-*4.2.5.5. Speed reducing measures.* Road links with speed reducing measures have a higher risk of single accidents. Even though it is well-known that speed reducing measures usually improve safety, the explanatory variable describing
-
-![](_page_8_Figure_9.jpeg)
-
-Fig. 4. Accidents per km per year as a function of the AADT for six urban road types.
-
-the presence of speed reducing measures in the models was not significant in most cases. It should be noted that only a few km of road with speed reducing measures were represented in the data material.
-
-*4.2.5.6. Number of accesses.* The relation between accident risk and the number of accesses (exits from private properties, parking places, etc.) seems to be an inverted 'U-shape'. Roads with no accesses and roads with a large number of accesses have the lowest accident risk, while roads with a medium number of accesses have the highest accident risk.
-
-*4.2.5.7. Number of minor side roads.* The explanatory variable for the number of minor side roads proved to be very important in the models. In general, the more side roads, the higher the accident risk.
-
-*4.2.5.8. Parking conditions.* Road links with parked motor vehicles along the roadside (at kerb) or in marked parking bays have the highest accident risk, particularly for accidents involving pedestrians, accidents involving motor vehicles from access roads or minor side roads, and for accidents involving parked vehicles. Other studies, (Elvik et al., 1997) also find that marked parking bays increases risk.
-
-*4.2.5.9. Land use.* The road environment (type and function of buildings along the road) has a considerable influence on the accident risk. Shopping streets and city centre roads have significantly higher accident risk than, for example, residential roads in less densely built-up areas. In general, the lower the building density, the lower the accident risk. Since exposure data for vulnerable road users were not included in the models, it must be presumed that the variable 'land use' to some extent also represents the level of pedestrian and cyclist activity.
-
-*4.2.5.10. Urban road types.* Based on the collected data, six urban road link types have been defined, and accident prediction models have been estimated for each road type. For practical reasons only six road types were defined, based on the most important variables found from the models. The road type definitions are therefore based on information on land use, number of minor side roads and on the speed limit.
-
-The six road types are shown in Table 4.
-
-Fig. 4 illustrates the expected average accident frequency for the six urban road types based on accident prediction models.
-
-The six road types were produced by starting from 23 road types, and then grouping the roads step by step until all individual road types had significant parameter estimates, while at the same time attempting to keep an overall logical and functional classification of the road type. From a statistical viewpoint road type F and H could possibly be grouped as one road type, but since these roads are very different in geometry, etc. it would not be consistent with the overall logic to put these roads in the same group.
-
-#### **5. Conclusion and discussion**
-
-The purpose of the study was to formulate accident prediction models which would describe the expected number of accidents at junctions and on road links in urban areas as accurately and practicably as possible. Furthermore, the objective was to identify factors affecting safety at junctions and on road links by use of the explanatory variables in the models.
-
-Based on data from 1024 junctions and 142 km of road links, a number of models were estimated in two separate studies.
-
-In general, the most powerful variables in the models were those describing the motor vehicle traffic flow, particularly for junctions. Additional explanatory variables in the junction models did not improve the 'percentage explained' value considerably. For road links, however, additional explanatory variables describing the road environment, number of minor side roads, parking facilities and speed limit proved to be significant and important variables for predicting the number of accidents.
-
-The models produced had a 'percentage explained' value in the area of 40–80%, with junction models seems to be at the lower end. This indicates that modelling accidents is less complex for road links than for junctions.
-
-The safety effect from signal control in 3- and 4-arm junctions was examined and the results showed that nonsignalised junctions in general are just as safe as signalised junction with the same traffic flows. The accident distribution differs though: for example, signalised junctions have fewer crossing accidents but more rear-end accidents.
-
-A high proportion of accidents in urban areas involve cyclists or moped riders. Models estimating this accident group were tested including explanatory variables describing exposure data for cyclists and moped riders. Models with exposure data for cyclists and moped riders were only slightly better than models without.
-
-A major problem was strong internal correlation within the data. Variables describing traffic flow tend to correlate strongly with other variables like road width, number of lanes, etc. Hence, the safety effects from a single explanatory variable were difficult to estimate since it may be affected by other variables in the model. Variables describing traffic flows for vulnerable road users (cyclist and mopeds) were also included in some of the models. They were significant but did not contribute considerably to the 'percentage explained' values, mainly due to strong correlation with motor vehicle traffic flow variables.
-
-Another well-known problem in safety analysis is the relatively small number of observed accidents in the data, which may cause problems in the statistical studies. In the study on junctions for example, less than 50% of the 3-armed non-signalised junctions had any police reported accidents, which limited modelling possibilities. Efforts were made to estimate accidents involving cyclists or moped riders, but since less than 10% of these accidents are reported by the police, the reliability of the data is limited, which complicates the modelling.
-
-In general, the safety effect from factors like road geometry, road environment, etc. can be estimated in various ways. The most reliable way is by use of controlled 'before–after' studies. However, 'before–after' studies require a large number of sites and long study periods. An alternative is to make multidimensional cross-tabulation of accident rates by different safety factors, a so-called 'with-or-without' study, e.g. accident rates for junctions with or without signal control. However, it is only possible to cross-tabulate for a limited number of variables/factors and these variables cannot be continuous. Furthermore, comparing accident rates at different sites can be complicated since differences in geometry, etc. can rarely be explained by a few variables.
-
-The use of models has some advantages over the abovementioned study methods. Models relate the number of accidents to selected factors that can be explained by either continuous or class variables. In addition, in the models, the accident number is assumed to follow a certain statistical distribution, e.g. the Poisson or negative binomial distribution.
-
-However, the safety effects from various factors found in this study were not always absolutely comparable to the safety effects found in other studies, e.g. traditional before– after studies. The reason for this could be the internal correlation problems within the data sets as mentioned earlier. It is therefore recommended to interpret the safety effect of a single variable with caution. Nevertheless, the accuracy of safety effects found by modelling must be considered better than that of 'with-or-without' studies, but worse than that of 'before–after' studies.
-
-Currently, the practical use of the developed accident prediction models (black spot identification, safety effects from changes in traffic flow, etc.) described in this paper are being tested and demonstrated in a number of Danish municipalities. In relation to this, a handbook on the use of accident prediction models has been published (Greibe and Hemdorff, 2001).
-
-**Appendix A** Number of junctions and road links according to the number of accidents
-
-| Number of accidents | Road links | Junctions  |                |            |                |  |  |  |
-|---------------------|------------|------------|----------------|------------|----------------|--|--|--|
-|                     |            | 3-Arm      |                | 4-Arm      |                |  |  |  |
-|                     |            | Signalised | Non-signalised | Signalised | Non-signalised |  |  |  |
-| 0                   | 70         | 19         | 278            | 25         | 40             |  |  |  |
-| 1                   | 62         | 13         | 132            | 22         | 34             |  |  |  |
-| 2                   | 41         | 8          | 61             | 28         | 25             |  |  |  |
-| 3                   | 28         | 13         | 29             | 30         | 17             |  |  |  |
-| 4                   | 21         | 11         | 19             | 21         | 11             |  |  |  |
-| 5                   | 18         | 6          | 12             | 27         | 17             |  |  |  |
-| 6                   | 18         | 4          | 9              | 18         | 3              |  |  |  |
-| 7                   | 15         | 4          | 4              | 15         | 4              |  |  |  |
-| 8                   | 11         | 0          | 2              | 13         | 2              |  |  |  |
-| 9                   | 10         | 2          | 1              | 14         | 1              |  |  |  |
-| 10                  | 3          | 1          | 0              | 7          | 0              |  |  |  |
-| 11                  | 3          | 2          | 0              | 7          | 0              |  |  |  |
-| 12                  | 4          | 1          | 0              | 3          | 0              |  |  |  |
-| 13                  | 3          | 0          | 0              | 3          | 0              |  |  |  |
-| 14                  | 1          | 1          | 0              | 2          | 0              |  |  |  |
-| 15                  | 3          | 0          | 0              | 4          | 0              |  |  |  |
-| 16                  | 1          | 0          | 0              | 3          | 0              |  |  |  |
-| 17                  | 0          | 0          | 0              | 2          | 0              |  |  |  |
-| 18                  | 1          | 0          | 0              | 1          | 0              |  |  |  |
-| 19                  | 0          | 0          | 0              | 1          | 0              |  |  |  |
-| 20                  | 0          | 0          | 0              | 2          | 0              |  |  |  |
-| 21                  | 0          | 0          | 0              | 0          | 0              |  |  |  |
-| 22                  | 1          | 0          | 0              | 1          | 0              |  |  |  |
-| 23                  | 0          | 0          | 0              | 0          | 0              |  |  |  |
-| 24                  | 0          | 0          | 0              | 1          | 0              |  |  |  |
-
-Class variable list for road link models
-
-| Explanatory variable    | Value              | Road data              |                                      | Accident data      |                                |                                  |  |
-|-------------------------|--------------------|------------------------|--------------------------------------|--------------------|--------------------------------|----------------------------------|--|
-|                         |                    | Road<br>length<br>(km) | Travelled<br>vehicle<br>(million km) | Total<br>(5 years) | Frequency<br>(per km per year) | Rate (per million<br>vehicle km) |  |
-| Speed limit             | 30–40 km/t         | 4.5                    | 4.0                                  | 15                 | 0.67                           | 0.74                             |  |
-|                         | 50 km/t            | 97.5                   | 185.5                                | 767                | 1.57                           | 0.83                             |  |
-|                         | 60 km/t            | 20.8                   | 53.1                                 | 204                | 1.96                           | 0.77                             |  |
-|                         | 70 km/t            | 13.2                   | 47.8                                 | 54                 | 0.82                           | 0.23                             |  |
-| One-way street          | 80 km/t            | 6.3                    | 37.5                                 | 18                 | 0.57                           | 0.10                             |  |
-|                         | Yes                | 3.3                    | 5.8                                  | 15                 | 0.91                           | 0.52                             |  |
-|                         | No                 | 139.0                  | 322.2                                | 1043               | 1.50                           | 0.65                             |  |
-| Number of lanes         | 1                  | 35.3                   | 42.5                                 | 188                | 1.06                           | 0.89                             |  |
-|                         | 2                  | 97.2                   | 233.9                                | 805                | 1.66                           | 0.69                             |  |
-|                         | 4                  | 9.8                    | 51.6                                 | 65                 | 1.33                           | 0.25                             |  |
-| Road width              | 5.0–6.0 m          | 33.6                   | 49.8                                 | 203                | 1.21                           | 0.82                             |  |
-|                         | 6.5–7.5 m          | 37.2                   | 68.7                                 | 275                | 1.48                           | 0.80                             |  |
-|                         | 8. 0–8. 5m         | 38.1                   | 82.0                                 | 249                | 1.31                           | 0.61                             |  |
-|                         | 9.0–15.0 m         | 33.3                   | 127.5                                | 331                | 1.99                           | 0.52                             |  |
-| Speed reducing measures | Yes                | 16.3                   | 16.8                                 | 81                 | 1.00                           | 0.96                             |  |
-|                         | No                 | 126.0                  | 311.1                                | 977                | 1.55                           | 0.63                             |  |
-| Accesses per km         | 0                  | 27.4                   | 83.3                                 | 130                | 0.95                           | 0.31                             |  |
-|                         | 0–10               | 29.2                   | 84.0                                 | 245                | 1.68                           | 0.58                             |  |
-|                         | 10–20              | 35.0                   | 82.0                                 | 320                | 1.83                           | 0.78                             |  |
-|                         | 20–30              | 18.0                   | 29.5                                 | 174                | 1.93                           | 1.18                             |  |
-|                         | 30–40              | 12.7                   | 24.6                                 | 102                | 1.60                           | 0.83                             |  |
-|                         | >40                | 20.0                   | 24.6                                 | 87                 | 0.87                           | 0.71                             |  |
-| Side roads per km       | 0                  | 26.2                   | 96.6                                 | 140                | 1.07                           | 0.29                             |  |
-|                         | 0–5                | 47.2                   | 102.0                                | 285                | 1.21                           | 0.56                             |  |
-|                         | 5–10               | 48.1                   | 93.6                                 | 425                | 1.77                           | 0.91                             |  |
-|                         | >10                | 20.8                   | 35.7                                 | 208                | 2.00                           | 1.16                             |  |
-| Cyclist facilities      | None               | 58.5                   | 81.2                                 | 314                | 1.07                           | 0.77                             |  |
-|                         | Lane               | 17.2                   | 31.0                                 | 106                | 1.23                           | 0.68                             |  |
-|                         | Track              | 66.6                   | 215.8                                | 638                | 1.92                           | 0.59                             |  |
-| Footway                 | Yes                | 124.6                  | 282.6                                | 1010               | 1.62                           | 0.71                             |  |
-|                         | No                 | 17.6                   | 45.4                                 | 48                 | 0.54                           | 0.21                             |  |
-| Median divider          | Yes                | 12.4                   | 63.8                                 | 149                | 2.41                           | 0.47                             |  |
-|                         | No                 | 129.9                  | 264.1                                | 909                | 1.40                           | 0.69                             |  |
-| Parking                 | Bays at kerb       | 14.8                   | 38.2                                 | 240                | 3.25                           | 1.26                             |  |
-|                         | Prohibited         | 54.0                   | 144.9                                | 384                | 1.42                           | 0.53                             |  |
-|                         | Rarely             | 73.5                   | 144.8                                | 434                | 1.18                           | 0.60                             |  |
-| Bus stop                | Yes                | 98.2                   | 234.4                                | 833                | 1.70                           | 0.71                             |  |
-|                         | No                 | 44.0                   | 93.5                                 | 225                | 1.02                           | 0.48                             |  |
-| Land use                | Shopping           | 4.7                    | 10.2                                 | 85                 | 3.65                           | 1.66                             |  |
-|                         | Block of flats     | 9.3                    | 26.6                                 | 139                | 2.99                           | 1.04                             |  |
-|                         | Residential        | 72.5                   | 132.2                                | 548                | 1.51                           | 0.83                             |  |
-|                         | Scattered housings | 47.7                   | 139.0                                | 218                | 0.91                           | 0.31                             |  |
-|                         | Industry           | 8.0                    | 19.9                                 | 68                 | 1.69                           | 0.68                             |  |
-| Total                   |                    | 142.3                  | 328.0                                | 1058               | 1.49                           | 0.65                             |  |
-
-| Explanatory variable          | Value | 3-Arm          |                  |            |                  | 4-Arm          |                  |            |                  |
-|-------------------------------|-------|----------------|------------------|------------|------------------|----------------|------------------|------------|------------------|
-|                               |       | Non-signalised |                  | Signalised |                  | Non-signalised |                  | Signalised |                  |
-|                               |       | Number         | Accident<br>rate | Number     | Accident<br>rate | Number         | Accident<br>rate | Number     | Accident<br>rate |
-| Lanes in primary direction    | 1     | 68             | 0.08             | 0          | –                | 35             | 0.27             | 0          | –                |
-|                               | 2     | 376            | 0.08             | 14         | 0.09             | 91             | 0.18             | 40         | 0.25             |
-|                               | 3     | 75             | 0.08             | 35         | 0.12             | 19             | 0.15             | 107        | 0.19             |
-|                               | 4     | 17             | 0.08             | 8          | 0.13             | 5              | 0.08             | 33         | 0.18             |
-|                               | 5     | 9              | 0.09             | 3225       | 0.12             | 4              | 0.14             | 39         | 0.21             |
-|                               | 6     | 1              | 0.22             | 5          | 0.11             | 0              | –                | 27         | 0.18             |
-|                               | 7     | 1              | 0.09             | 1          | 0.25             | 0              | –                | 4          | 0.24             |
-| Lanes in secondary direction  | 1     | 100            | 0.06             | 2          | 0.06             | 15             | 0.20             | 3          | 0.22             |
-|                               | 2     | 436            | 0.09             | 38         | 0.10             | 134            | 0.18             | 112        | 0.18             |
-|                               | 3     | 10             | 0.13             | 38         | 0.14             | 4              | 0.24             | 93         | 0.18             |
-|                               | 4     | 1              | 0.00             | 5          | 0.11             | 0              | –                | 24         | 0.24             |
-|                               | 5     | 0              | –                | 2          | 0.13             | 1              | 0.29             | 13         | 0.25             |
-|                               | 6     | 0              | –                | 0          | –                | 0              | –                | 4          | 0.22             |
-|                               | 7     | 0              | –                | 0          | –                | 0              | –                | 1          | 0.34             |
-| Turning lanes in primary      | Yes   | 97             | 0.09             | 71         | 0.12             | 26             | 0.15             | 214        | 0.20             |
-| direction                     | No    | 450            | 0.08             | 14         | 0.10             | 128            | 0.20             | 36         | 0.19             |
-| Turning lanes in              | Yes   | 14             | 0.10             | 48         | 0.13             | 4              | 0.25             | 139        | 0.20             |
-| secondary direction           | No    | 533            | 0.08             | 37         | 0.09             | 150            | 0.18             | 111        | 0.18             |
-| Island at primary direction   | Yes   | 113            | 0.10             | 72         | 0.12             | 43             | 0.16             | 191        | 0.20             |
-|                               | No    | 434            | 0.08             | 13         | 0.09             | 111            | 0.20             | 59         | 0.18             |
-| Island at secondary direction | Yes   | 71             | 0.10             | 45         | 0.12             | 44             | 0.21             | 139        | 0.21             |
-|                               | No    | 476            | 0.08             | 40         | 0.12             | 110            | 0.17             | 111        | 0.18             |
-| Total number                  |       | 547            | 0.08             | 85         | 0.12             | 154            | 0.18             | 250        | 0.20             |
-
-### **References**
-
-- Bélanger, C., 1994. An estimation of the safety of four legged unsignalized intersections. In: Proceedings of the 73rd Annual Meeting of Transportation Research Board, Washington, DC.
-- Brüde, U., Larson, J., 1993. Models for predicting accidents at junctions where pedestrians and cyclists were involved. Anal. Prevention 25 (5), 499–509.
-- Elvik, R., Mysen, A.B., Vaa, T., 1997. Trafikksikkerhetshånbok-3. udgave. TØI.
-- Golias, J.C., 1992. Establishing relationships between accidents. Anal. Prevention 24 (6), 689–694.
-- Greibe, P., Hemdorff, S., 1995. Uheldsmodel for bygader-Del1: Modeller for 3-og 4-benede kryds. Notat 22, The Danish Road Directorate.
-- Greibe, P., Hemdorff, S., 1998. Uheldsmodel for bygader- Del2: Modeller for strækninger. Notat 59, The Danish Road Directorate.
-- Greibe, P., Hemdorff, S., 2001. Håndbog i trafiksikkerhedsberegninger-Brug af uheldsmodeller og andre vurderingsmetoder. Rapport 220, The Danish Road Directorate.
-- Hall, R.D., 1986. Accidents at 4-arm single carriageway urban traffic signals. TRRL, Contractor Report 65.
-- Hauer, E., Jerry, C.N., Lovell, J., 1988. Estimation of Safety at Signalized Intersections, Transportation Research Record 1185.
-- Hemdorff, S., 1996. Introduktion til VISplet. Notat 29. The Danish Road Directorate.
-
-- Kronbak, J., Greibe, P., 1994. Uheldsmodeller for signalregulerede vejkryds. Technical University of Denmark.
-- Kulmala, R., 1995. Safety at rural 3- and 4-arm junctions. Development and application of accident prediction models. VTT Publications 233.
-- Layfield, R.E., Summersgill, I., Hall, R.D., Chatterjee, K., 1996. Accidents at urban priority crossroads and staggered junctions. TRL report 185.
-- Maher, M.J., Summersgill, I., 1996. A comprehensive methodology for the fitting of predictive accidents models. Acci. Anal. Prevention, Vol. 28 (3).
-- Miaou, S.-P., Hu, P.S., Wright, T., Rathi, A.K., Davis, S.C., 1992. Relationship between truck accidents and highway geometric design: a poisson regression approach. Transport Research Record 1376.
-- Mountain, L., Maher, M., Fawaz, B. 1998. The influence of trend on estimates of accident at junctions. Acci. Anal. Prevention 30 (5).
-- Satterthwaite, S.P. 1981. A survey of research into relationships between traffic accidents and traffic volumes. TRRL Supplementary Report 692.
-- Summersgill, I., Kennedy, Janet V., Baynes. D., 1996. Accidents at three-arm priority junctions on urban single-carriageway roads. TRL report 184.
-- Summersgill, I., Layfield, R.E., 1996. Non-junction accidents on urban single-carriageway roads. TRL report 183.
-- Taylor, M.C., Hall, R.D., Chatterjee, K., 1996. Accidents at 3-arm traffic signals on urban single-carriageway roads. TRL report 135.
-- Wass, C., Jørgensen, N.O., Jørgensen, E., 1983. Model for uheld i vejkryds. Vejdatalaboratoriet, Danmarks Tekniske Højskole.

Zotero/001_artiklid/Road Safety Indicators/gundogduapplyinglinearanalysismethods2010.md

@@ -1,254 +0,0 @@
----
-category: literaturenote
-citekey: gundogduapplyinglinearanalysismethods2010
-title: "Applying linear analysis methods to GIS-supported procedures for preventing traffic accidents: Case study of Konya"
-authors: "Gundogdu, Ismail Bulent"
-year: 2010
-date: 2010-00-00 2010
-doi: 10.1016/j.ssci.2010.02.016
-publication: Safety Science
-url: "https://doi.org/10.1016/j.ssci.2010.02.016"
-zotero_key: NN23P7ZA
-zotero_storage: 5D9PX62X
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Gundogdu, Ismail Bulent"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-## Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/09257535)
-
-# Safety Science
-
-journal homepage: [www.elsevier.com/locate/ssci](http://www.elsevier.com/locate/ssci)
-
-![](_page_0_Picture_5.jpeg)
-
-# Applying linear analysis methods to GIS-supported procedures for preventing traffic accidents: Case study of Konya
-
-Ismail Bulent Gundogdu \*
-
-Department of Geomatic Engineering, Selcuk University, Konya, Turkey
-
-#### article info
-
-Article history: Received 26 August 2009 Received in revised form 10 February 2010 Accepted 20 February 2010
-
-Keywords: Linear analysis GIS Traffic accident Highest Probable Hot Spot Hot Pieces
-
-### abstract
-
-This study develops methods to obtain maps to determine traffic Hot Spots in Konya, Turkey, by applying linear analysis supported by Geographical Information Systems (GIS).
-
-Hot Spot analysis is known method but the study differs from former researches at the point of determining of risky zones, classification and illustration of them on the maps with the different accident parameters. The aim is not to contribute another Hot Spot analysis using a number of statistical methods, but to determine the Highest Potential Hot Spots (HPHS), which are inter-sectional clusters, and to use different parameters, such as number of accidents, number of fatalities and injured, and number of accidents with only financial loss.
-
-In addition, apart from classical illustrational techniques, Hot Pieces (HPCS) on roads divided into 1 km segments are shown by their grading according to their numerical values. Hence, thematic illustration distinguishes them from others.
-
-Another aspect of this study is that, besides investigation of Hot Spots by means of data of accidents of previous years', Probable Hot Spots (PRHS) were illustrated and highly potential Hot Spots were determined. These latter are candidates for Hot Spots in the near future. Thus, premature accidents can be anticipated easily.
-
-The main intention of this study is to emphasize the importance of using criteria, other than total accident number, to illustrate intersection Hot Spots and to constitute a model of accident severity and variety. It is anticipated that the results obtained from highway accidents data will guide improvement of the route segments.
-
--2010 Elsevier Ltd. All rights reserved.
-
-### 1. Introduction
-
-Traffic accidents have been recognized as one of the major causes of human and economic losses, both in developed and developing countries. This problem is of great concern in developing countries, because of its seriousness and the limited resources available to develop feasible countermeasures for reducing this ever-growing challenge [\(Berhanu, 2004](#page-6-0)).
-
-A report from the [World Health Organization and the World](#page-6-0) [Bank \(2004\)](#page-6-0) on road traffic accidents and injuries estimated that 1.2 million people are killed in road crashes each year and as many as 50 million are injured worldwide [\(Rathinam et al., 2007; Rutter](#page-6-0) [and Quine, 1996; Hilakivi et al., 1989\)](#page-6-0). According to estimations of the World Bank, traffic accidents will be the third most frequent cause of deaths in 2020.
-
-Statistics from the Turkish Statistical Institute (TUIK) reveal that, in 2006, there were 665, 618 traffic accidents in Turkey, 2629 of which resulted in mortality, 77,644 resulted in injuries and 585,345 in economical damage. The damage was estimated as \$870 million.
-
-According to a questionnaire from [Arslan \(2007\)](#page-6-0) among 800 people whose relatives lost their lives in traffic accidents in European countries revealed that 37% of those had a tendency to commit suicide and 64% of those had depression in the subsequent 3 years. Another questionnaire applied in Turkey revealed that 50% of 240 people whose relatives lost their lives in traffic accidents suffered from insomnia and 39.2% suffered from hysteria, in addition to depression.
-
-Several precautionary procedures have been conducted to prevent or minimise the destructive problem of traffic accidents. Plotting, which needs to be conducted by analysing causes of accidents through a large amount of data, is one of the major precautionary measures that might prevent accidents.
-
-According to a report from the [Turkish Ministry of Public Works](#page-6-0) [and Settlement \(2007\)](#page-6-0), there was, between 2003 and 2005, a 64% decrease in the number of accidents, a 94% decrease in road fatalities and a 65% decrease in the number of people injured on rehabilitated segments of roads.
-
-<sup>\*</sup> Tel.: +90 555 7214769; fax: +90 332 2410635. E-mail address: [bgundogdu@selcuk.edu.tr](mailto:bgundogdu@selcuk.edu.tr)
-
-<span id="page-1-0"></span>The most frequent method in defining a Hot Spot is how they are determined. A Hot Spot can be defined as a point where there is a particularly frequent recurrence of an event. These spots are determined statistically and have various visual methods and usage fields that can be described as follows ([Eck and Weisburd,](#page-6-0) [1995\)](#page-6-0). Areas of concentrated crime are often referred to as Hot Spots. Researchers and police use the term in many different ways. Some refer to Hot Spot addresses [\(Eck and Weisburd, 1995; Sher](#page-6-0)[man et al., 1989\)](#page-6-0), others refer to Hot Spot blocks ([Weisburd and](#page-6-0) [Green, 1994; Taylor et al., 1984\)](#page-6-0), and others examine clusters of blocks [\(Block and Block, 1995](#page-6-0)).
-
-The number of accidents on a given road section during a certain period of time is probabilistic in nature and is a non-negative integer. Despite the fact that accidents are random and unpredictable at micro-level, statistical models can predict reliable estimates of expected accidents by relating aggregates of accidents to various explanatory measures of flow, site characteristics, and road geometry at macro-level. Numerous empirical relationships between vehicle accidents and these explanatory variables have been established in several previous studies (e.g. [Maher and Summersgill,](#page-6-0) [1996; Miaou, 1994; Miaou and Lum, 1993](#page-6-0)). Such accident predicting models are useful in identifying the most critical variables of safety, assessing design and management alternatives, and improving safety standards for new roads.
-
-The significantly different areas detected by the applied statistics (usually termed LISA: Local Indicators of Spatial Autocorrelation) are, traditionally, termed Hot Spots, hotpoints or, occasionally, hotbeds [\(Ratcliffe and McCullagh, 1999](#page-6-0)). The statistics used to define the hotbed coverage may result in circular, elliptical or amoeboid shapes depending on the method or application used. There is a wide choice of approaches available ([Mccullagh, 2006\)](#page-6-0). The numerical analysis of spatial point distributions has interested academics for many years. A very early work that resulted is the famous Moran's I statistic by [Moran](#page-6-0) [\(1948\).](#page-6-0) An early important collection of work on general spatial analysis is that edited by [Berry and Marble \(1968\).](#page-6-0) The development of spatial point analysis theory is illustrated by later works by [Cliff and Ord \(1973, 1981\), Ord and Getis \(1995\), Anselin](#page-6-0) [\(1995\) and Gatrell et al. \(1996\).](#page-6-0) The overview by [Getis and Ord](#page-6-0) [\(1996\)](#page-6-0) has led to large number of recent studies, such as [Cho](#page-6-0) [\(2003\),](#page-6-0) using the techniques that have been developed over the last 50 years ([Mccullagh, 2006](#page-6-0)).
-
-The Linear Referencing Model (LRM) was used to determine Hot Spots in this study.
-
-An LRM can be defined as a mechanism for finding and stating the location of an unknown point along a network by referring it to a known point [\(Vonderohe and Chou, 1997\)](#page-6-0). More specifically, an LRM is a process for determining a previously unknown location based on a defined path along the underlying transportation network; a distance along that path measured from a known datum location; and, optionally, an offset from the path. There are several types of LRMs, which differ based on the parts of the network used for referencing and the ways in which measures and offsets are calculated [\(Nyerges, 1990](#page-6-0)).
-
-The following tasks must be undertaken in a study which has the same aim:
-
-- - Determining the locations where an increased number of accidents occur.
-- - Detailed functional analysis of a location where an accident has occurred for the purpose of determining the factors causing accidents.
-- - Developing methods used for determining elements, such as traffic notices, and lights controlling traffic flow that cause danger.
-- - Inferring the effect of physical features of a road such as curve, pavement material, and turning directions on accident occurrence.
-
-![](_page_1_Figure_13.jpeg)
-
-Fig. 1. General map of roads in Konya Province and location of the related highways.
-
-- Handling traffic problems of the province in a comprehensive context by assessing all highways.
-
-It is not adequate processing just the total number of accidents in carrying out these tasks. All criteria must be evaluated separately. For this purpose, there must be an acceptable and detailed accident report for the long term. It may be possible to explain the effects of different accident information details by comparing four base criteria. These main criteria, detailed below, are the total number of accidents, fatalities, injured and with only financial loss. In particular, result base maps related to base criteria have been produced for this purpose. In addition, the question of how many and which segments are problematic? Will be explained. That the problems are either changing according to period of year or structure of road will be determined.
-
-## 2. Study area and accident data
-
-# 2.1. Study area
-
-Highway transport is very important in Turkey. Konya is the biggest city of the country, and has 2957 km of highway, out of a total 61,939 km of roads.
-
-According to statistics in 2006, the city is eighth in the rank of total accident rates, third in the rank of mortality rates, fifth in the rank of injuries. The region has been one of the most important transport connection points for some time. The city is in the centre of the country and connects crowded cities and tourist regions. A large amount of agricultural crops are carried from east to west by trucks with the help of seven routes of the city.
-
-Having a smooth topography and straight routes enables high– speed traffic. Thus, in addition accidents are inevitable, and the severity of accidents and losses may be at a very disturbing level. [Fig. 1](#page-1-0) shows digitized routes on the map.
-
-#### 2.2. Traffic accident data
-
-The most consuming phase of the study is detailed registration of accident data. In this study, 3856 accidents data for the last 10 years, obtained from the Konya Police Department. All detailed data have been prepared by using MS Access to evaluate by Arc GIS 9.2 program. Seven highways totally 724 km have been digitized with the scale of 1/25,000 maps and connected with coordinates of accident points and other details by the program.
-
-Because of the changing transport density in certain seasons, it will be possible to evaluate old data with new data or a different period of year and different purposes for the future.
-
-## 3. Methodology
-
-It can be easily understood, through statistical tests, whether or not the inputs are meaningful in determining Hot Spots. The Poisson distribution technique is commonly used for this purpose. Recent studies (e.g. [Meel and Safr, 2008](#page-6-0)) have looked at on application of the Poisson distribution technique to traffic accidents, analysing high-crash locations ([Kristen and Mark, 2002\)](#page-6-0) black spots and Poisson distribution [\(Ding and Pei, 2007\)](#page-6-0) explaining dangerous locations with four categories [\(Hu et al., 2007\)](#page-6-0), crash counts and prediction modelling [\(Ma et al., 2008; Caliendo et al.,](#page-6-0) [2007; Mitra and Washington, 2007; Ng et al., 1995](#page-6-0)). Poisson assumptions for the crash densities ([Li et al., 2008\)](#page-6-0) and time series ([Quddus, 2008](#page-6-0)) can be arranged.
-
-The main features of Poisson distribution are that it was developed for specific circumstances of randomly occurring events over a time and for repetitive experiments of events with low potential for occurrence. When formation of Hot Spots is analysed, it is seen that independently occurring individual accidents form Hot Spots by assembling in clusters. This is why Poisson distribution is the most fitting statistical distribution for determining Hot Spots by handling randomly occurring traffic accidents.
-
-In order to use Poisson distribution for the purpose of determining Hot Spots along a road, the road must be divided into certain segments. The area where an accident happened is recorded officially to the nearest kilometre. It is, therefore, sensible to divide the road into 1 km segments. After accident probability for each individual segment is calculated, 1 km segments exceeding the threshold value are characterized as Hot Spots. In this case, the number of accidents is calculated for each part using the formula below (n: number of accidents; k: number of segments).
-
-$$\lambda = \frac{n}{k}$$
-
-Accident potential for each segment is estimated using the formula
-
-$$f(k;\lambda) = \frac{\lambda^k e^{-\lambda}}{k!}$$
-
-With a probability rate of 0.05, where e is the base of the natural logarithm (e = 2.71828...), k is the number of occurrences of an event – the probability of which is given by the function, k! is the factorial of k, and k is a positive real number, equal to the expected number of occurrences that occur during the given interval.
-
-In order to determine which pieces, whose accident potential has previously been estimated with Poisson distribution, have the character of Hot Spots, the Getis–Ord Gi analysis must be applied. The statistics Gi and Gi, introduced by [Getis and Ord](#page-6-0) [\(1996\)](#page-6-0) for the study of local patterns in spatial data, were extended and re-written (below) in 1995 to redefine Gi as a standard variate and to allow for non-binary classifications of the distance d ([Ord](#page-6-0) [and Getis, 1995](#page-6-0)). In this analysis, the Gi(d) statistic is used as follows ([Khan et al., 2005](#page-6-0)):
-
-$$G_{i}(d) = \frac{\sum_{j=1}^{N} w_{ij}(d)x_{j} - \bar{x}_{i}\sum_{j=1}^{N} w_{ij}(d)}{S(i)\sqrt{\frac{\left[\binom{N-1}{j-1}w_{ij}^{N}(d) - \left(\sum_{j=1}^{N} w_{ij}(d)\right)^{2}\right]}{\frac{N-2}{N-2}}}, j \neq i$$
-
-where
-
-$$\bar{x}_i = \frac{\sum_{j=1, i \neq j}^{N} X_j}{N-1} \quad \text{and} \quad S(i) = \sqrt{\frac{\sum_{j=1, i \neq j}^{N} X_j^2}{N-1} - \left(\bar{x}_i\right)^2}$$
-
-$$G_{i}^{*}(d) = \frac{\sum_{j=1}^{N} w_{ij}(d)x_{j} - \bar{x}\sum_{j=1}^{N} w_{ij}(d)}{S\sqrt{\frac{\left[\sum_{j=1}^{N} w_{ij}^{2}(d) - \left(\sum_{j=1}^{N} w_{ij}(d)\right)^{2}\right]}{N-1}}}$$
-
-where
-
-$$\bar{x} = \frac{\sum_{j=1}^{N} x_j}{N}$$
- and  $S = \sqrt{\frac{\sum_{j=1}^{N} x_j^2}{N} - (\bar{x})^2}$ 
-
-where Gi is the Getis–Ord Gi z-score value excluding the value at segment i, G <sup>i</sup> is Getis–Ord G <sup>i</sup> z-score value including the value at segment i, xi is relative crash rate of segment i, xj is relative crash rate of neighboring locations to segment i, d is fixed band radius around segment i, wij is spatial weight matrix for all segment j within distance d, and N is the number of weighted points.
-
-Getis and Ord's Gi analysis calculates the 'z' value for each segment. If the number of accidents at a certain point significantly differs from the z values calculated from the median, z values must be
-
-<span id="page-3-0"></span>![](_page_3_Figure_2.jpeg)
-
-Fig. 2. Hot Spots are shown according to: (a) number of accidents, (b) rate of mortality, (c) number of people injured, (d) quantity of damage, and (e) the inter-sectional cluster of all showing the highest risk points.
-
-over the threshold value of 1.645. Thus, Hot Spots can be easily determined by an enquiry based on z values.
-
-## 3.1. Analysis of Hot Spots with unusual criteria and the Highest Potential Hot Spots
-
-By means of this method, at which point and according to which criterion rehabilitation of roads must be performed can be predetermined, [Fig. 2](#page-3-0) shows Hot Spots according to different criteria. Hot Spots have been determined by the number of accidents so far. The upper limit of analysis (1.645) has been processed in the accident number column. It must be mentioned that other criteria are as important as accident numbers. These are the number of fatalities, number of injured and amount of damage. In this study, Hot Spots were determined on the basis of not only the number of accidents, but also the number of people killed/injured and the amount of damage. This is so that a more realistic analysis can be suggested. [Fig. 2](#page-3-0)a shows the classical method used with accident numbers, but the numbers and sizes of Hot Spots vary with other criteria, as seen in [Fig. 2b](#page-3-0)–d. It must not be forgotten that different maps enable different analyses to take into consideration different criteria. Another important point relates to the intersection segments of the four criteria, as seen in [Fig. 2](#page-3-0)e. The results of this study indicate that, between 10–14 km and 55–65 km of route 3, 25–29 km of route 4 and 37–45 km of route 6 are intersection segments and they must be considered as priority areas.
-
-#### 3.2. Illustration of hazardous segments as Hot Pieces
-
-Maps are indispensable products of numerical data to illustrate or analyse various subjects. According to the type of map reader, or subject, different maps are produced for different purposes. Use of correct illustration techniques allows correct analysis. Occasionally, spot representation may suit any subject, but linear or area may not be sufficient. So, in this study, various maps have been produced by different illustrating types for future investigations.
-
-Hot Spots have been shown in speckles so far. When involved in traffic accidents, it is seen that there is not just one event at any one point. For this reason, it would be wrong to be concerned with only one point in determining Hot Spots. A lot of factors that have caused an accident may have been effective before the accident occurred. If any roads are improved with the help of the maps, segments (±500 m) of accident points must be shown on the maps. Graduated segments, in particular, can show the way at the beginning and end of points of improving of risky segments by degrees. So, in this study, linear clustering, as a new technique, was used to determine the hazardous segments to prevent of traffic accidents and take precautions on the highways. Specifically, the results of risk assessment of 1 km segments are illustrated in linear clusters.
-
-Assessment of Hot Pieces (HPCS) would be a more precise guideline for the study of risk factors and accident regions together in linear clusters, as seen in Fig. 3. By the same method, different maps, which show deaths, injuries and financial damage distributions, can be produced.
-
-# 3.3. Illustration of potential Hot Spots (Probable Hot Spots)
-
-Up to now, dangerous points have been shown as Hot Spots by points which exceed the upper limit. But when accident data are investigated, there are some points which will be on the Hot Spot limit after 1–2 accidents. Investigating these points is as important as for normal Hot Spot points. There are some points which are not Hot Spots but they will be Hot Spots after just one accident.
-
-In this study, another important subject is taking preventative measures before a point becomes a Hot Spot.
-
-Points that are not Hot Spots yet but have high-potential for being Hot Spots have not been mentioned in any applications so far. This study, it must be emphasized, includes segments with high-potential (95%) for being Hot Spots. In [Fig. 4,](#page-5-0) Probable Hot Spots (PRHS) were determined and illustrated for the region approaching 1.645 value, at a rate of 95%. These probable Hot Spots can be compared with [Fig. 2a](#page-3-0)–d. On routes 5 and 6 particularly,
-
-![](_page_4_Figure_13.jpeg)
-
-Fig. 3. HPCS illustrations in linear clusters.
-
-<span id="page-5-0"></span>![](_page_5_Figure_2.jpeg)
-
-Fig. 4. Probable Hot Spots (PRHS).
-
-some points are not Hot Spots but they will be soon. Urgent precautions must be taken for these points, therefore.
-
-# 4. Conclusions and future work
-
-Interpretation of results on a map is one of the fundamental guides. In this study, the map user is supplied with different visualizations of linear analysis illustrated with different techniques.
-
-As a consequence of the application, however the hotspots have been evaluated by the number of accidents so far, studying with only accident numbers and mapping of them alone are not sufficient. Thematic maps show graduated accident parameters are the most important products for the study. It is important to determine which types of traffic accidents occur at which segments. The maps show the user not only kilometre values of accident points but also hazardous road segments. The number of people killed or injured, and the number of accidents with only financial damage, must be taken as information clusters individually and then HPHSs must be assessed through inter-sectional clusters of individual information clusters.
-
-When route maps producing different criteria are investigated, as seen in [Fig. 2](#page-3-0)a–d, Hot Spots appear at different points on the same routes. For instance, Hot Spots determined by mortality criteria are seen on straight routes because of the high speed on those sections. If there were an accident on this route, someone would normally be killed. Although the number of accidents with injuries is fewer than mortalities, the factors are more or less the same. Maps of damage Hot Spots show them appearing on international roads, which attract expensive vehicles. There is less mortality but more injured people and more accidental damage on international roads. These facts may give an idea about using goals and type of the routes and kinds of vehicles. However, the idea is not acceptable for the entire route. Nevertheless, the method should be applicable in other similar road networks especially using different accident parameters and considering all segments of roads.
-
-In pieces maps, some segments do not have any Hot Spots, but some segments meet all the criteria. In this case, Highest Potential Hot Spots, which are determined by the intersection of all criteria must be considered first. Hot Spot maps producing different criteria are the most important tools for road improvement.
-
-When we consider that the place where the accident happened is recorded with 1 km proximity, and the factors affecting the accident do not start just at the accident point, it is obvious that analysis of HPCS is critical. Table 1 shows the number of Hot Spots and priority status for all points, as another useful result for improving route planning.
-
-When the results of the study compared with existing accident data, some useful suggestions can be mentioned. HPHS and PRHS numbers and locations which are determined by this method are different from normal Hot Spot numbers or locations. Naturally priorities of precaution types will be various at different points or road segments.
-
-It can be seen that 22 high-potential Hot Spots could be real Hot Spots in the near future. These segments ought to be dealt with as a priority, in terms of preventative measures. All dangerous zones and the severity of accidents at the point of different criteria can be shown numerically from Table 1. So, both values and kilometres from maps must be used to improve current routes and planning new routes. The priority of the plan is reduction of mortality. It is also structurally problematic segments or points on routes that can be determined easily with the help of result values and terrestrial research. As an example, although route 5 does not have any
-
-Table 1 Critical pieces and their values (for seven routes).
-
-| Reference           | Hot Spot<br>numbers | Priority status                                                      |
-|---------------------|---------------------|----------------------------------------------------------------------|
-| Number of accidents | 38                  | Hot Spots estimated from data                                        |
-| Number of deaths    | 37                  | 1st priority spots                                                   |
-| Number of injuries  | 27                  | 2nd priority spots                                                   |
-| Financial damage    | 27                  | 3rd priority spots                                                   |
-| HPHS                | 04                  | Most dangerous spots (to be analysed<br>initially)                   |
-| HPCS                | 38                  | Accident zones                                                       |
-| PRHS                | 22                  | Highly potential hotspots<br>necessitating precautionary<br>measures |
-
-<span id="page-6-0"></span>Hot Spots determined for damage, it has the most number of deaths, since it is so straight and crowded.
-
-Investigating the dangerous spots and the road segments illustrated in the analysis maps prepared here, studying the contributory factors and taking precautions are the responsibility of the related administration.
-
-# References
-
-- Anselin, L., 1995. The local indicators of spatial association-LISA. Geographical Analysis 27 (2), 93–115.
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-- Li, W., Carriquiry, A., Pawlovich, M., Welch, T., 2008. The choice of statistical models in road safety countermeasure effectiveness studies in Iowa. Accident Analysis and Prevention 40 (4), 1531–1542.
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-- Ma, J., Kockelman, K.M., Damıen, P.A., 2008. Multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods. Accident Analysis and Prevention 40 (3), 964–975.
-- Maher, M.J., Summersgill, J., 1996. A comprehensive methodology for the fitting of predictive accident models. Accident Analysis and Prevention 28 (3), 281– 296.
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-- Miaou, S.P., 1994. The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accident Analysis and Prevention 26 (4), 471–482.
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-- Mitra, S., Washington, S., 2007. On the nature of over-dispersion in motor vehicle crash prediction models. Accident Analysis and Prevention 39 (3), 459–468.
-- Moran, P., 1948. The interpretation of statistical maps. Journal of Royal Statistical Society 1 (2), 243–251.
-- Ng, C.H., Wong, Y.D., Lum, K.M., 1995. The applicability of the Poisson distribution in traffic accident count analysis – the Singapore experience. IES Journal 32 (2).
-- Nyerges, T.L., 1990. Locational referencing and highway segmentation in a geographic information system. ITE Journal 60 (3), 27–31.
-- Ord, J.K., Getis, A., 1995. Local spatial autocorrelation statistics: distribution issues and an application. Geographical Analysis 27 (4), 286–306.
-- Quddus, M.A., 2008. Time series count data models: an empirical application to traffic accidents. Accident Analysis and Prevention 40 (5), 1732–1741.
-- Ratcliffe, J.H., Mccullagh, M.J., 1999. Hotbeds of crime and the search for spatial accuracy. Journal of Geographical Systems 1 (4), 385–398.
-- Rathinam, C., Nair, N., Gupta, A., Joshi, S., Bansal, S., 2007. Self-reported motorcycle riding behaviour among school children in India. Accident Analysis and Prevention 39 (2), 334–339.
-- Rutter, D.R., Quine, L., 1996. Age and experience in motorcycling safety. Accident Analysis and Prevention 28 (1), 15–21.
-- Sherman, L.W., Gartin, P.R., Buerger, M.E., 1989. Hot Spots of predatory crime: routine activities and the criminology of place. Criminology 27 (1), 27–55.
-- Taylor, R.B., Gottfredson, S.D., Brower, S., 1984. Block crime and fear: defensible space, local social ties, and territorial functioning. Journal of Research in Crime and Delinquency 21 (4), 303–310.
-- Turkish Ministry of PublicWorks and settlement, 2007. [<http://www.bayindirlik.](http://www.bayindirlik.gov.tr/turkce/dosya/2003_2006faaliyetvehedefler.pdf) [gov.tr/turkce/dosya/2003\\_2006faaliyetvehedefler.pdf](http://www.bayindirlik.gov.tr/turkce/dosya/2003_2006faaliyetvehedefler.pdf)> (accessed October 2009).
-- Vonderohe, A., Chou, C., 1997. A Generic Data Model for Linear Referencing Systems. Transportation Research Board, Washington (DC).
-- Weisburd, D., Green, L., 1994. Defining the street level drug market. In: Mackenzie, D.L., UchidA, C. (Eds.), Drugs and Crime: Evaluating Public Policy Initiatives. Sage, Thousand Oaks, CA.
-- World Health Organisation World report on road traffic injury prevention: summary, 2004. <[http://www.who.int/world-health-day/2004/infomaterials/](http://www.who.int/world-health-day/2004/infomaterials/worldreport/en/summary_en_rev.pdf) [worldreport/en/summary\\_en\\_rev.pdf>](http://www.who.int/world-health-day/2004/infomaterials/worldreport/en/summary_en_rev.pdf) (accessed May 2008).

Zotero/001_artiklid/Road Safety Indicators/hadayeghitemporaltransferabilityupdatingzonal2006.md

@@ -1,397 +0,0 @@
----
-category: literaturenote
-citekey: hadayeghitemporaltransferabilityupdatingzonal2006
-title: Temporal transferability and updating of zonal level accident prediction models
-authors: "Hadayeghi, Alireza; Shalaby, Amer S.; Persaud, Bhagwant N.; Cheung, Carl"
-year: 2006
-date: 2006-00-00 2006
-doi: 10.1016/j.aap.2005.12.003
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2005.12.003"
-zotero_key: ZUN7M7TU
-zotero_storage: B8H9E7F4
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Hadayeghi, Alireza"
-status: converted
----
-![](_page_0_Picture_0.jpeg)
-
-Accident Analysis and Prevention 38 (2006) 579–589
-
-![](_page_0_Picture_2.jpeg)
-
-# Temporal transferability and updating of zonal level accident prediction models
-
-Alireza Hadayeghi <sup>a</sup>,∗, Amer S. Shalaby <sup>b</sup>,1, Bhagwant N. Persaud <sup>c</sup>,2, Carl Cheung <sup>c</sup>,<sup>3</sup>
-
-<sup>a</sup> *Department of Civil Engineering, University of Toronto and Synectics Transportation Consultants Inc., 36 Hiscott Street, St. Catharines, Ont., Canada L2R 1C8* <sup>b</sup> *University of Toronto, 35 St. George Street, Toronto, Ont., Canada M5S 1A4* <sup>c</sup> *Ryerson University, 350 Victoria Street, Toronto, Ont., Canada M5B 2K3*
-
-Received 3 March 2005; received in revised form 22 September 2005; accepted 5 December 2005
-
-#### **Abstract**
-
-This paper examines the temporal transferability of the zonal accident prediction models by using appropriate evaluation measures of predictive performance to assess whether the relationship between the dependent and independent variables holds reasonably well across time. The two temporal contexts are the years 1996 and 2001, with updated 1996 models being used to predict 2001 accidents in each traffic zone of the City of Toronto. The paper examines alternative updating methods for temporal transfer by imagining that only a sample of 2001 data is available. The sensitivity of the performance of the updated models to the 2001 sample size is explored. The updating procedures examined include the Bayesian updating approach and the application of calibration factors to the 1996 models. Models calibrated for the 2001 samples were also explored, but were found to be inadequate. The results show that the models are not transferable in a strict statistical sense. However, relative measures of transferability indicate that the transferred models yield useful information in the application context. Also, it is concluded that the updated accident models using the calibration factors produce better results for predicting the number of accidents in the year 2001 than using the Bayesian approach.
-
-© 2005 Elsevier Ltd. All rights reserved.
-
-*Keywords:* Temporal transferability; Model calibration; Bayesian approach; Safety planning models
-
-#### **1. Introduction**
-
-Accident prediction models are used to predict the number of accidents at intersections, on arterial road sections, on highways or on transportation networks. Of late, there is considerable interest in transportation planning applications, for which these models (usually developed at the planning zone level) are aimed at establishing a relationship between accident frequency and a number of explanatory variables such as traffic volume, road network characteristics and socioeconomic and demographic features. It is important for safety planning models to enable transportation planners to assess the safety implications of alternative transport planning options that are considered for medium to long-range implementation, in a way similar to how road designers make use of tools such as the Interactive Highway Safety Design Model (IHSDM)<sup>4</sup> to assess the safety implications of design decisions. For this reason and others, it is desirable to obtain reasonably accurate safety planning models that are transferable across time and have good predictive performance.
-
-In general, the importance of accident prediction models makes it crucial that they be properly calibrated ([Persaud et al.,](#page-10-0) [2002\).](#page-10-0) The quantity and quality of the data used to calibrate accident prediction models is very important to the success or failure of these models. Models calibrated with limited data will not perform adequately in terms of predictive accuracy. Therefore, having a reasonable dataset is very important in the development of accident prediction models. Moreover, the specification of an
-
-<sup>∗</sup> Corresponding author. Tel.: +1 905 704 0763; fax: +1 905 682 4495. *E-mail addresses:* ahadayeghi@synectics-inc.net (A. Hadayeghi), amer@ecf.utoronto.ca (A.S. Shalaby), bpersaud@ryerson.ca (B.N. Persaud), c22cheun@ryerson.ca (C. Cheung).
-
-<sup>1</sup> Tel.: +1 416 978 5907; fax: +1 416 978 5054.
-
-<sup>2</sup> Tel.: +1 416 979 5000x6464; fax: +1 416 979 5122.
-
-<sup>3</sup> Tel.: +1 416 979 5000; fax: +1 416 979 5122.
-
-<sup>4</sup> Interactive Highway Safety Design Model (IHSDM), Federal Highway Administration, US Department of Transportation.
-
-appropriate functional model form for calibration of accident prediction models is a complex task. It gets even more complicated for safety planning models because of the paucity of these.
-
-Data for transportation planning variables are usually obtained from surveys held periodically (for example, the Transportation Tomorrow Survey is conducted once every 5 years in the Greater Toronto Area). These surveys usually cover a comprehensive area and are therefore expensive in terms of the time and cost associated with data collection and analysis. As such, once a model is well specified to capture the decision process in one context, it is cost effective to transfer such a model to other contexts that have limited calibration data as long as the basic nature of the decision-making process remains the same [\(Badoe](#page-9-0) [and Miller, 1995a\).](#page-9-0)
-
-There are two aspects to model transferability—temporal and spatial. The first, results from application of a model estimated at one point of time for prediction at another point of time for the same spatial environment. This occurs whenever estimated models are applied in a forecasting framework. The second aspect – spatial transferability – involves the application of a model estimated in a specific geographic region for prediction in a different one for the same time period. Of course, there can be a combination of temporal and spatial transfers.
-
-Several research efforts have been conducted to address the temporal and spatial transferability of transportation planning models. Most of this research was on trip generation models and disaggregate and aggregate mode choice models [\(Ben-Akiva,](#page-9-0) [1981; Koppelman and Wilmot, 1982; Atherton and Ben-Akiva,](#page-9-0) [1976; Gunn et al., 1985; Gunn, 2001; Badoe and Miller, 1995b;](#page-9-0) [Ben-Akiva and Bolduc, 1987; Parody, 1977\).](#page-9-0)
-
-However, no similar efforts have been directed at safety planning models. This is likely in reflection of the reality that few safety planning models have been calibrated to date. A literature search indicated that only few studies have been undertaken to evaluate and quantify traffic safety impacts in the planning process [\(Hadayeghi et al., 2003; Lovegrove and Sayed, 2005;](#page-9-0) [Ladron de Guevara et al., 2004; NCHRP 8-44, in press\).](#page-9-0)
-
-Of most relevance to the current effort was some work by [Oh et al. \(2003\)](#page-9-0) that validated intersection accident prediction models intended for use in the IHSDM from two perspectives—internal validity and external validity. Internal model validity examines the strength of the original models in explaining the underlying phenomenon. External validity examines the ability of the models to predict crashes over space and time. The authors recommended that the validation over time could suffer from omitted variables that influence safety, poorly measured and surrogate variables, and model functional forms. On the other hand, the authors argue that internal validity and validation over space could suffer from omitted variables, site selection and countermeasure selection bias, poorly measured and surrogate variables, and misspecification of model functional form.
-
-This paper has two basic objectives. First, it examines the temporal transferability of the accident prediction models by using appropriate evaluation measures of predictive performance to assess whether the relationship between the dependent and independent variables holds reasonably well across time.
-
-The two temporal contexts are the years 1996 and 2001, with models estimated from 1996 data being used to predict 2001 accidents. A series of accident prediction models based on 1996 database were calibrated in the previous study ([Hadayeghi et al.,](#page-9-0) [2003\)](#page-9-0) to estimate the number of accidents in planning zones in the City of Toronto as a function of planning variables. It should be stressed that the spatial transferability of accident prediction models is outside the scope of this paper. However, some insights on this issue have been provided in some other papers [\(Persaud](#page-10-0) [et al., 2002\).](#page-10-0)
-
-The second objective of the paper is to compare alternative updating methods of temporal transfer and to estimate effects of the size of the application context data on model transferability. The updating procedures examined are the Bayesian updating and updating based on recalibration factors. Models calibrated for the 2001 samples were also explored, but were found to be inadequate, so were not tested in the 2001 application context. In addition, this study investigates the transfer bias caused by sample size. This issue was not addressed in previous studies which focused mainly on using only one method and one sample size. This is important since it is believed a large portion of differences in coefficients and predictions is caused by random variation due to selection of data. Also, the goodness of fit measures resulting from updated models are compared to the models estimated using the entire dataset in the application context. A key contribution of this paper is the variety in the validation methods and measures used, tested and compared.
-
-#### **2. Data**
-
-The data used in this study are comprised of accident, socioeconomic, demographic and road network characteristics, and traffic flow for the City of Toronto's 463 traffic zones for 1996 and 481 traffic zones for 2001. The zone definitions for 1996 and 2001 are not identical. For the purpose of this study, the 2001 zone definition is adopted for both 1996 and 2001.
-
-The data for this study were obtained from three sources. The accident data for 1996 and 2001, the first dataset, was provided electronically by the Traffic Data Centre (TDC) of the City of Toronto. The accident data are geocoded, which allows the grouping of accidents by traffic zone using GIS tools. The relevant information for each accident record includes severity (fatal or non-fatal injury and property damage only (PDO)) and period of day. The latter allows for separate models to be developed for the morning peak period (6:00–9:00 am) to which traffic intensity data pertained.
-
-The second dataset, consisting of socioeconomic and demographic data, was obtained from the 1996 and 2001 Transportation Tomorrow Survey (TTS), a 1-day travel survey conducted in the Greater Toronto and surrounding areas (GTA) on approximately 5% of its population. The survey is conducted to provide detailed demographic and travel information of GTA household members to local, regional, provincial road authorities and to transit operating agencies. The traffic demand data were obtained from the results of a traffic assignment of the morning <span id="page-2-0"></span>peak-period vehicle trips to the road network using the [EMME/2](#page-9-0) [\(1998\)s](#page-9-0)oftware package to estimate vehicle kilometers travelled (VKT) in each zone. This approximation is necessary because cordon counts, the only available source of traffic demand data collected consistently across the city, are made at a limited number of locations and therefore cannot be used directly and sufficiently to provide traffic flow estimates for each zone in the city. It is worth mentioning that it is a common practice in building travel demand and planning models to use traffic assignment model outputs.
-
-In addition to the above data, 1996 GIS road files were obtained from the Integrated Land Use, Transport and Environment (ILUTE) group at the University of Toronto, and the corresponding 2001 database, CanMap Street Files, was attained from University of Toronto Map Library.
-
-#### **3. Comparison of 1996 and 2001 conditions**
-
-Table 1 presents the magnitude of changes in various characteristics in the City of Toronto between 1996 and 2001. The number of households grew by 3.68%, from 908,499 in 1996 to 943,252 in 2001. The population of the City of Toronto grew by 2.7% in the 5-year period, while the total population of the Greater Toronto Area (GTA) increased by 9.3%. The difference in percent growth between the GTA and City of Toronto is indicative of the reality that more people lived in the suburbs in 2001 as a result of urban sprawl. During this period, the total employment in the City of Toronto increased by more than 7%.
-
-Total vehicle kilometers travelled (VKT) per person increased by 15%, from 0.75 in 1996 to 0.88 in 2001. However, there was little change in total major road kilometers in the same period, suggesting that traffic conditions should have worsened from 1996 to 2001. This observation is confirmed by the average volume to capacity (*V*/*C*) ratios in Table 1, which increased by almost 30% from 1996 to 2001. The percent average changes in zonal traffic volume from 1996 to 2001 in the City of Toronto, based on the 1996 zonal definitions, are presented in [Fig. 1](#page-3-0) . As seen, most of the zones experienced an increase in traffic volume in the 5-year period.
-
-Table 1 also shows that the total number of accidents increased by 27% from 1996 to 2001, an average annual increase of 5.41%. On the other hand, the number of severe (i.e., fatal plus injury) accidents increased only by a total of 5.64% in the 5-year period.
-
-[Fig. 2](#page-3-0) presents the zonal percentage change in total number of accidents for the City of Toronto. As can be seen, most of the zones outside of downtown have experienced a higher percentage change in total number of accidents than that for the downtown zones.
-
-## **4. Model specification**
-
-A series of macro-level, zonal accident prediction models that estimate the number of zonal accidents in the City of Toronto for 1996 was developed in the work described in [Hadayeghi et al.](#page-9-0) [\(2003\). A](#page-9-0) generalized linear modelling approach was employed, in which negative binomial regression models were developed separately for total accidents and for severe (fatal and non fatal injury) accidents as a function of socioeconomic/demographic, traffic demand and network data variables. A total of 22 possible explanatory variables describing zonal characteristics were explored. The definition of variables was based on (a) previous research for similar studies and (b) an iterative process in which various definitions were tried and the ones that gave the best results were ultimately selected.
-
-The following model form was used
-
-$$E(A) = a_0 \text{ VKT}^{b_0} \exp \sum_{i=1}^{n} b_i X_i$$
- (1)
-
-Table 1 Change between 1996 and 2001 data in the City of Toronto
-
-| Attribute for independent variables                                               | 1996<br>2001 |         | Difference | Percent change (%) |  |
-|-----------------------------------------------------------------------------------|--------------|---------|------------|--------------------|--|
-| (a) Changes in urban structure, travel behavior and socioeconomic and demographic |              |         |            |                    |  |
-| Vehicle kilometer travelled (VKT)                                                 | 1728775      | 2089257 | 360482     | 17.25              |  |
-| Average volume capacity (V/C)                                                     | 0.22         | 0.31    | 0.09       | 30.18              |  |
-| Average posted speed                                                              | 42.08        | 42.48   | 0.40       | 0.95               |  |
-| Total population                                                                  | 2305600      | 2368700 | 63100      | 2.66               |  |
-| Number of households                                                              | 908499       | 943252  | 34753      | 3.68               |  |
-| Number of part-time employees                                                     | 181224       | 187890  | 6666       | 3.55               |  |
-| Number of full-time employees                                                     | 875225       | 943373  | 68148      | 7.22               |  |
-| Total number of employees                                                         | 1056449      | 1131263 | 74814      | 6.61               |  |
-| Total minor road kilometer                                                        | 4172.66      | 4406.24 | 233.58     | 5.30               |  |
-| Total major road kilometer                                                        | 859.83       | 900.73  | 4.90       | 0.54               |  |
-| Total number of intersections                                                     | 15498        | 16649   | 1151       | 6.91               |  |
-| Attribute for dependent variables                                                 | 1996         | 2001    | Difference | Percent change (%) |  |
-| (b) Change in Number of Accidents                                                 |              |         |            |                    |  |
-| Total accidents                                                                   | 53286        | 73043   | 19757      | 27.1               |  |
-| Severe (fatal and injury) accidents                                               | 15818        | 16764   | 946        | 5.64               |  |
-| Total accidents, morning peak period                                              | 5317         | 8691    | 3374       | 38.82              |  |
-| Severe accidents, morning peak period                                             | 1664         | 1971    | 307        | 15.58              |  |
-
-<span id="page-3-0"></span>![](_page_3_Figure_2.jpeg)
-
-Fig. 1. Percent change in average traffic volume from 1996 to 2001 in the City of Toronto based on 1996 zonal definitions.
-
-![](_page_3_Figure_4.jpeg)
-
-Fig. 2. Percent change in total number of accidents from 1996 to 2001 in the City of Toronto based on 1996 zonal definitions.
-
-<span id="page-4-0"></span>Table 2 Results of negative binomial regression for models I and II
-
-| Parameter                     | Yearly total accident model (Model I) |                        | Yearly severe accident model (Model II) |                                                                                                                                                         |  |
-|-------------------------------|---------------------------------------|------------------------|-----------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|--|
-|                               | 1996 Model                            | 2001 Model             | 1996 Model                              | 2001 Model                                                                                                                                              |  |
-| Intercept (log of α0)         | 4.1767 (0.2957, 199.47)               | 2.8065 (0.3572, 61.75) | 2.6618 (0.3239, 67.55)                  | 1.9593 (0.3736, 27.50)                                                                                                                                  |  |
-| Log of VKT                    | 0.2656 (0.0347, 58.76)                | 0.2845 (0.0540, 27.81) | 0.2415 (0.039, 38.41)                   | 0.1739 (0.0561, 9.60)<br>0.2423 (0.0208, 136.31)<br>0.0650 (0.0162, 16.03)<br>−0.0096 (0.0067, 2.07)<br>−0.3963 (0.3193, 1.54)<br>0.0013 (0.0012, 1.03) |  |
-| Total major road in kilometer | 0.1175 (0.0186, 40.05)                | 0.1960 (0.0197, 93.98) | 0.1225 (0.0202, 36.77)                  |                                                                                                                                                         |  |
-| Number of households (× 10−3) | 0.0864 (0.015, 33.27)                 | 0.0582 (0.0156, 13.89) | 0.0858 (0.0164, 27.38)                  |                                                                                                                                                         |  |
-| Speed                         | −0.0431 (0.0071, 36.61)               | −0.0124 (0.0065, 3.64) | −0.0312 (0.0078, 16.05)                 |                                                                                                                                                         |  |
-| Volume/capacity (V/C)         | −1.3678 (0.2766, 24.45)               | −0.6472 (0.3112, 4.33) | −1.2232 (0.3042, 16.18)                 |                                                                                                                                                         |  |
-| Intersection density          | 0.0052 (0.0012, 19.87)                | 0.0024 (0.0012, 3.98)  | 0.0038 (0.0013, 9.14)                   |                                                                                                                                                         |  |
-| Goodness of fit measures      |                                       |                        |                                         |                                                                                                                                                         |  |
-| Mean Pearson χ2/d.f.          | 1.0313                                | 1.1559                 | 1.0346                                  | 1.0110                                                                                                                                                  |  |
-| Pearson R2                    | 37.48%                                | 37.24%                 | 31.90%                                  | 43.06%<br>0.2296                                                                                                                                        |  |
-| α                             | 0.1941                                | 0.2355                 | 0.2092                                  |                                                                                                                                                         |  |
-
-*Note*: Refer to Eq. [\(1\). T](#page-2-0)he dependent variable is the natural logarithm of yearly total accidents. Parameter estimate (S.E., χ2).
-
-where *E*(*A*) is the predicted accident frequency per zone per year, VKT the zonal vehicle kilometers travelled during peak hours (6:00–9:00 am), *Xi* the other explanatory variables, *a*0, *b*<sup>0</sup> and *bi* is the model parameters
-
-The above model form is consistent with the non-linear relationship between accidents and traffic intensity (measured in this case by VKT) suggested by several other researchers. Separate models are calibrated for total and severe (fatal plus non-fatal injury) accidents per year and, for each, models were estimated for the entire day and for the morning peak period (6:00–9:00 am) to which the VKT and other traffic variable estimates from EMME/2 pertain. The final model parameters and corresponding χ<sup>2</sup> and goodness-of-fit criteria are shown in Tables 2 and 3. This Table is taken from [Hadayeghi et al. \(2003\)](#page-9-0) which discusses the modelling approach and related issues.
-
-The models developed in this study for both 1996 and 2001 include major arterial and minor road data in each zone, but exclude freeway data since the presence of a freeway in a zone was found after exploratory analysis to be generally unrelated to characteristics of the zone.
-
-# **5. Measures of model transferability**
-
-The transferability of a model relates to the application of a model estimated in one context based on adequate data to another context with limited data. Model transferability is based on the idea that the estimated model from a previous study in a different context may provide useful information for estimating parameters appropriate for the application context. "Local" or "estimation" context refers to the context for which the adequate dataset was used in the model estimation. The "transfer" or "application" context refers to the context for which predictions are required, and to which the model from the local context is transferred for application.
-
-Transportation planning models are not perfectly transferable between contexts since the models do not capture the behavior of individuals completely and the data available for the estimation of the unknown parameters are not error free due to a variety of sources such as sampling errors ([Lerman, 1981\).](#page-9-0) As [Lerman \(1981\)](#page-9-0) and [Ben-Akiva \(1981\)](#page-9-0) discuss, it is appropriate to describe the measure of transfer effectiveness on a continuous
-
-Table 3 Results of negative binomial regression for models III and IV
-
-| Parameter                       | Yearly total accident model for<br>morning period (Model III) |                         | Yearly severe accident model for<br>morning period (Model IV) |                        |  |
-|---------------------------------|---------------------------------------------------------------|-------------------------|---------------------------------------------------------------|------------------------|--|
-|                                 | 1996 Model                                                    | 2001 Model              | 1996 Model                                                    | 2001 Model             |  |
-| Intercept (log of α0)           | 1.7851 (0.2909, 37.66)                                        | 0.4328 (0.3127, 1.92)   | 0.2079 (0.0403, 2.7)                                          | 1.0527 (0.4562, 5.32)  |  |
-| Log of VKT                      | 0.2225 (0.0430, 26.73)                                        | 0.3268 (0.0574, 32.47)  | 0.2171 (0.0606, 12.85)                                        | 0.3163 (0.0803, 15.53) |  |
-| Total major road in kilometer   | 0.1171 (0.0201, 34.07)                                        | 0.2127 (0.0202, 111.39) | 0.1316 (0.0264, 24.8)                                         | 0.2528 (0.0267, 89.51) |  |
-| Number of households (× 10−3)   | –                                                             | –                       | 0.0892 (0.0220, 16.43)                                        | 0.0701 (0.0216, 10.51) |  |
-| Number of employements (× 10−3) | 0.0692 (0.0162, 18.18)                                        | 0.0533 (0.0155, 11.80)  | –                                                             | –                      |  |
-| Speed                           | −0.0324 (0.0073, 19.59)                                       | −0.0139 (0.0063, 4.91)  | −0.0213 (0.0102, 4.34)                                        | −0.0141 (0.0087, 2.61) |  |
-| Volume/capacity (V/C)           | −1.1156 (0.3296, 11.46)                                       | −0.7236 (0.3234, 5.01)  | −0.9074 (0.4500, 4.07)                                        | −0.8137 (0.4412, 3.40) |  |
-| Intersection density            | –                                                             | –                       | –                                                             | –                      |  |
-| Total minor road in kilometer   | 0.0108 (0.0051, 4.52)                                         | 0.0006 (0.0049, 0.02)   | –                                                             | –                      |  |
-| Goodness of fit measures        |                                                               |                         |                                                               |                        |  |
-| Mean Pearson χ2/d.f.            | 1.0805                                                        | 1.0802                  | 1.0791                                                        | 0.9659                 |  |
-| Pearson R2                      | 36.56%                                                        | 48.46%                  | 31.57%                                                        | 42.17%                 |  |
-| α                               | 0.1586                                                        | 0.1959                  | 0.1961                                                        | 0.2290                 |  |
-
-*Note*: Refer to Eq. [\(1\). T](#page-2-0)he dependent variable is the natural logarithm of yearly total accidents. Parameter estimate (S.E., χ2).
-
-scale rather to describe transferability as a dichotomous property. Transferability is a property that may exist to a greater or lesser degree depending on the formation of the model and the characteristics of the estimation and application contexts [\(Lerman,](#page-9-0) [1981; Ben-Akiva, 1981\).](#page-9-0)
-
-Measures of model transferability were used to assess the effectiveness of models when they are transferred for use in an application context (2001 in this study) that is different from their calibration context (1996). The indices and test-statistics are all based on log-likelihood measure. These measures, defined below, are largely similar to those used by [Koppelman and Rose](#page-9-0) [\(1983\)](#page-9-0) and [Badoe \(1994\).](#page-9-0)
-
-#### *5.1. Nested likelihood ratio test (LR)*
-
-This measure indicates the statistical similarity of the estimation and application model coefficients. In this test, the null hypothesis is that the application and estimation context can be presented by a single model. Basically this test examines whether the model parameters have remained stable over time and is given by
-
-$$LR = -2[LL_{i \cup j}(\hat{\theta}_{i \cup j}) - LL_{i}(\hat{\theta}_{i}) - LL_{j}(\hat{\theta}_{j})]$$
-(2)
-
-The above equation is χ<sup>2</sup> distributed with degrees of freedom equal to the number of model parameters. In the event of rejection of the null hypothesis, an asymptotic *t*-test can be performed to test which parameter(s) were responsible for the rejection. For this, the *t*-statistic is given as
-
-$$t = (\hat{\theta}_{ki} - \hat{\theta}_{kj}) / \sqrt{(\text{var}(\hat{\theta}_{ki}) + \text{var}(\hat{\theta}_{kj}))}$$
-(3)
-
-where ˆ θik is the estimate of parameter *k* in context *I* and var(ˆ θik) is the variance of the estimate of parameter *k* in context *i*.
-
-#### *5.2. Transfer index (TI)*
-
-Transfer index (TI) is a relative measure that indicates how well a transferred model performs in predicting the application dataset relative to a locally estimated model in the application context and of similar specification. For this particular research, this index compares performance of the transferred and application context models relative to the performance of a 2001 "constant" model that has no independent variables. The index has an upper bound, 1.0, which is attained when the transferred model performs as well as an estimated model on the application data. It has no lower bound. Negative values imply that the transferred model is worse than the local constant model. It is expressed as
-
-$$TI = [LL_j(\hat{\theta}_i) - LL_j(\hat{c})]/[LL_j(\hat{\theta}_j) - LL_j(\hat{c})]$$
-(4)
-
-where LLj(ˆ θi) is the log-likelihood value in context *j* using model parameters estimated from year *i*, LLj(ˆ θj) the loglikelihood value given by application context model *j* and LLj(ˆc) is the log-likelihood value given by constant model estimated in application context *j*.
-
-## **6. Updating 1996 models**
-
-This section provides an approach for updating local models into an application context in a situation when only limited data are available in the application context for calibrating a meaningful model. The updating technique is helpful in applying an "old" estimated model to an application context to predict present conditions with minimum data requirements, consequently cutting the cost of data collection and time. Also, it seems desirable, when transferring a model, to update it by making adjustments based on locally available information. To illustrate such a situation, and to investigate the feasibility of this approach, random subsets of zonal information were selected from the 2001 database. To explore the impact of sample size on updating performance, 10 samples of each of 10, 20, 25, and 35 zones and their corresponding information were randomly selected from the 2001 dataset. However, before using updating procedures, a set of models were estimated using different sample sizes. These models were used to examine whether the coefficients in the estimation and application contexts are similar when limited data are available.
-
-#### *6.1. Updating using a calibration factor*
-
-The updating procedure was recommended by [Harwood et](#page-9-0) [al., 2000](#page-9-0) and a recent paper ([Oh et al., 2003\),](#page-9-0) for application in the Interactive Highway Safety Design Model (IHSDM) for transferring accident prediction models calibrated for one geographic region to apply to another region. In this procedure, [Harwood et al. \(2000\)](#page-9-0) in essence obtained a calibration factor as the total number of accidents observed in a sample from one jurisdiction divided by the sum of the predicted number of accidents in the sample using the model from another jurisdiction. The calibration factor was calculated as follows:
-
-calibration factor = 
-$$\sum_{i=1}^{n} Y_i / \sum_{i=1}^{n} \hat{Y}_i$$
- (5)
-
-where *Yi* is the number of observed accidents in zone *i* in 2001 for a given sample size and Yˆ<sup>i</sup> is the predicted number of accidents in zone *i* by applying values of the 2001 independent variables (for the same sample size of 2001) to the 1996 model.
-
-Then, the calibration factors estimated for each sample size were simply multiplied by the base models (1996 models) in order to "transfer" the 1996 models for application to 2001 conditions.
-
-### *6.2. Bayesian updating*
-
-Bayesian updating, which was introduced by [Atherton and](#page-9-0) [Ben-Akiva \(1976\),](#page-9-0) is another approach that is adopted for updating the 1996 models using the 2001 sample data. This methodology combines sample information with prior information in order to achieve more accurate updated information. [Atherton and Ben-Akiva \(1976\)](#page-9-0) assumed both prior and posterior (updated) distributions of the parameters to be normally distributed. The updated parameters are expressed as
-
-$$\beta_{\text{updated}} = [(\beta_{96}/\sigma_{96}^2) + (\beta_{01\text{-sample}}/\sigma_{01\text{-sample}}^2)]/[(1/\sigma_{96}^2) + (1/\sigma_{01\text{-sample}}^2)]$$
-(6)
-
-$$\sigma_{\text{updated}} = 1/[(1/\sigma_{96}^2) + (1/\sigma_{01\text{-sample}}^2)]^{0.5}$$
- (7)
-
-where βupdated is the 2001 updated coefficient, β<sup>96</sup> the coefficient estimated from 1996 model, β01-sample the coefficient estimated from 2001 sample model, σupdated the standard deviation of 2001 updated coefficient, σ<sup>96</sup> the standard deviation of coefficient estimated from 1996 model and σ01-sample is the standard deviation of coefficient estimated from 2001 sample model.
-
-#### *6.3. Measures of goodness of fit*
-
-Measures of goodness of fit are used to assess the performance of the updated models relative to the 1996 models. These measures are similar to those used by [Oh et al. \(2003\).](#page-9-0)
-
-#### *6.3.1. Mean prediction bias (MPB)*
-
-This measure describes the magnitude and direction of the average bias in the subject model. The smaller the absolute value of average prediction bias is, the better the model predicts the observed data. Positive values of MPB indicate that the model over-predicts the observed accident data, and vice versa:
-
-MPB = 
-$$\sum_{i=1}^{n} (\hat{Y}_i - Y_i)/n$$
- (8)
-
-where Yˆ<sup>i</sup> is the predicted number of accidents at zone *i*, *Yi* the observed number of accidents at zone *i* and *n* is the data sample size.
-
-#### *6.3.2. Mean absolute deviation (MAD)*
-
-This measure provides a measure of the average misprediction of the model. A value close to zero suggests that the model on average predicts the observed data well. It is defined as
-
-$$MAD = \sum_{i=1}^{n} |(\hat{Y}_i - Y_i)|/n$$
- (9)
-
-# *6.3.3. Mean squared prediction error and mean squared error*
-
-The mean squared prediction error (MSPE) is the sum of squared differences between observed and predicted accidents divided by the sample size. It is defined as
-
-MSPE = 
-$$\sum_{i=1}^{n} (\hat{Y}_i - Y_i)^2 / n$$
- (10)
-
-The mean squared error (MSE) is the sum of squared differences between observed and predicted accidents divided by the sample size minus the number of model parameters. It is defined as
-
-$$MSE = \sum_{i=1}^{n} (\hat{Y}_i - Y_i)^2 / (n - p)$$
- (11)
-
-where *p* is the number of model parameters.
-
-A comparison of MSPE and MSE shows potential over or under fitting of the models to the estimation data. A MSPE that is higher than MSE may indicate that models may have been over fit to the estimation data and that some of the observed relationships may have not been correctly shown in the model.
-
-#### **7. Results and discussion**
-
-#### *7.1. Transferability results*
-
-The dependent variable of each developed model is the number of zonal accidents per year. The regression parameters were estimated based on the maximum likelihood method for negative binomial (NB) regression and using PROCGENMOD in SAS statistical software ([SAS Institute Inc., 1999\).](#page-10-0)
-
-# *7.1.1. Yearly model for total accidents*
-
-[Table 2](#page-4-0) presents the parameter estimates and corresponding χ2's for the 1996 model and the 2001 model for total yearly accidents (Model I). A comparison of the parameter estimates in [Table 2](#page-4-0) indicates that the speed becomes insignificant at the 5% level in the 2001 model. The parameter estimates of each variable have identical signs in both models. The dispersion parameters (α) are in the range of 0.19 and 0.23 for the 1996 model and the 2001 model, respectively, which indicates that the data are quite overdispersed relative to the Poisson distribution and that the negative binomial error structure is therefore justified. The goodness of fit measures for each model, also presented in [Table 2,](#page-4-0) show the mean Pearson χ<sup>2</sup> ratios to be close to 1, an indication that the NB regression model fits the data very well. The Pearson *R*<sup>2</sup> values for the 1996 model and the 2001 model are 37.48% and 37.24%, respectively, which indicate that the developed models explain the variation in the dependent variable to a reasonable degree. In addition, a test was done to examine whether keeping the dispersion parameter in the 2001 model the same as the one in the 1996 model would affect the 2001 model performance considerably. It was found that the model parameters and corresponding parameters' signs were almost the same as those of Model I. The total predicted number of accidents from this model for the whole study area is different by less than 1% from the total from Model I, which is quite insignificant in comparison. Therefore, no further investigation was conducted.
-
-Results of tests of parameter equality, which were done by the nested likelihood ratio test, are shown in [Table 4.](#page-7-0) These indicate that 1996 and 2001 model parameters are not statistically equal. Therefore, the null hypothesis can be rejected. The errors in the modelling calibration procedures are most likely to affect transferability of the explanatory variables and the estimated parameters. To investigate whether the rejection of the null hypothesis is because of an individual coefficient or sub-set of coefficients, the asymptotic *t*-test is used to test equality of 1996 and 2001 variable coefficient estimates. Part (b) of [Table 4](#page-7-0) presents the results of this test for the yearly model for total accidents. As it can be seen, only two explanatory variables of VKT and number of households cannot be rejected at the 5% significance level under the null hypothesis of equality in the two contexts. Theoretically, it can be concluded that the
-
-<span id="page-7-0"></span>Table 4 Results from transferring 1996 Models to 2001 dataset
-
-| Transfer measures                                         | Model I | Model II | Model III | Model IV |  |
-|-----------------------------------------------------------|---------|----------|-----------|----------|--|
-| (a) Absolute and relative transfer measures               |         |          |           |          |  |
-| Nested likelihood ratio                                   | 128.6   | 52.4     | 177.4     | 30.84    |  |
-| a (× 105)<br>LL01(θ96)                                    | 3.087   | 0.460    | 0.179     | 0.0116   |  |
-| Transfer index                                            | −0.966  | 0.528    | −0.881    | 0.640    |  |
-| Parameter                                                 | Model I | Model II | Model III | Model IV |  |
-| (b) Statistical test of parameter equality in two context |         |          |           |          |  |
-| Log of VKT                                                | −0.29   | 0.99     | −1.45     | −0.99    |  |
-| Total major road in kilometer                             | −2.71   | −4.13    | −3.35     | −3.23    |  |
-| Number of households (× 10−3)                             | 1.30    | 0.90     | –         | 0.62     |  |
-| Number of employements (× 10−3)                           | –       | –        | 0.71      | –        |  |
-| Speed                                                     | −3.19   | −2.10    | −0.06     | −0.54    |  |
-| Volume/capacity (V/C)                                     | −1.73   | −1.88    | −1.21     | −0.15    |  |
-| Intersection density                                      | 1.65    | 1.41     | –         | –        |  |
-| Total minor road in kilometer                             | –       | –        | 1.44      | –        |  |
-
-<sup>a</sup> LL01(θ96) is the 2001 context log-likelihood using θ<sup>96</sup> from year 1996.
-
-roadway parameters such as major road kilometers and posted speed did not remain stable over time. The value of transfer index, which describes the degree to which the log likelihood of the transferred model exceeds the reference model relative to improvement provided by a model developed in the application context, is found to be −0.966. The negative sign implies that the transferred model is worse than the local reference model.
-
-## *7.1.2. Yearly model for severe accidents*
-
-The model parameters and corresponding χ<sup>2</sup> values for the yearly model of severe accidents (Model II) are shown in[Table 2.](#page-4-0) All the coefficients are close in magnitude and share the same sign in the both the 1996 and 2001 models. The intersection density and *V*/*C* variables are insignificant at the 5% level in the 2001 model but are significant in the 1996 model.
-
-The dispersion parameters (α) of 0.2092, and 0.2226 for the 1996 model and the 2001 model, respectively, again confirm the appropriateness of using negative binomial as opposed to Poisson regression. The mean Pearson χ<sup>2</sup> ratios for the two models are within the acceptable range of 0.8–1.2, another indication that the choice of the negative binomial error structure appears to be appropriate. The Pearson *R*<sup>2</sup> values for the 1996 model and the 2001 model are 31.90% and 43.3%, respectively, which indicate that the developed models explain the variation in the dependent variable to a reasonable degree.
-
-Results of the test of parameter equality in Table 4 indicate that the 1996 and 2001 model parameters are not statistically equal. *V*/*C*, speed, and major road kilometers are the parameters most responsible for the statistical inequality, as shown in part (b) of Table 4. The value of transfer index of 0.528, suggests that the transferred model provides some portion of the information obtained from the 2001 model, irrespective of model specification.
-
-#### *7.1.3. Morning peak period accident models*
-
-For the morning peak period models, accidents between 6:00 and 9:00 am were extracted from the dataset by running a query in Microsoft Access. [Table 3](#page-4-0) shows the parameter estimates for the total accident models (Model III) and severe accident models (Model IV) for the morning peak period. For both Models III and IV, the signs of all variables and constant terms are the same for the 1996 and 2001 models. The coefficients in the 2001 models are largely different from those of the 1996 models, in particular, those with low levels of significance such as total minor road kilometers in the 2001 model. For both Models III and IV, the coefficient of the total major road kilometers variable for the 2001 models is three times as large as that for the 1996 models.
-
-The goodness of fit statistics show mean Pearson χ<sup>2</sup> values close to one, again indicating that the models fit the data reasonably well. The dispersion parameters (α) for all the models are significantly different from zero, again confirming the appropriateness of negative binomial regression. The Pearson *R*<sup>2</sup> values are reasonable for all the models.
-
-The statistical tests of equality of model parameters for the Models III and IV reject the null hypothesis of equality, indicating that some or all model parameters have not remained stable over time. Both Models III and IV exhibit statistically significant equality at the 5% significance level in all the parameters, except major road kilometers. The values of Transfer index are found to be −0.881 and 0.640 for Models III and IV, respectively, suggest that the transferred model provides some portion of the information obtained from the 2001 model, irrespective of model specification. However, the negative value, as explained previously, implies that the transferred model is worse than the local reference model.
-
-# *7.2. Updating results*
-
-As expected, none of the models calibrated with limited data performed reasonably in terms of the magnitude of the coefficient parameters or their level of significance in the application context regardless of the sample size. The magnitude of coefficient parameters for some of the models were the same as the 1996 models but either the parameters were insignificant at the 5% confidence level or the goodness of fit measures were not
-
-<span id="page-8-0"></span>Table 5Goodness of fit measures for predicting 2001 accidents from 1996, 2001 and updated models
-
-| del<br>Mo<br>typ<br>es | GO<br>F             | 20<br>01<br>del<br>Mo | 6<br>199<br>del<br>Mo | dat<br>ing<br>wi<br>th<br>le o<br>f<br>Up<br>sam<br>p<br>10<br>in<br>20<br>01<br>zon<br>es |                                           | dat<br>ing<br>wi<br>th<br>le o<br>f<br>Up<br>sam<br>p<br>20<br>in<br>20<br>01<br>zon<br>es |                                           | dat<br>ing<br>wi<br>th<br>le o<br>f<br>Up<br>sam<br>p<br>25<br>in<br>20<br>01<br>zon<br>es |                                                 | dat<br>ing<br>wi<br>th<br>le o<br>f<br>Up<br>sam<br>p<br>35<br>in<br>20<br>01<br>zon<br>es |                                           |
-|------------------------|---------------------|-----------------------|-----------------------|--------------------------------------------------------------------------------------------|-------------------------------------------|--------------------------------------------------------------------------------------------|-------------------------------------------|--------------------------------------------------------------------------------------------|-------------------------------------------------|--------------------------------------------------------------------------------------------|-------------------------------------------|
-|                        |                     |                       |                       | Re<br>cal<br>ibr<br>d<br>ate<br>199<br>6 m<br>ode<br>l                                     | Ba<br>ian<br>dat<br>yes<br>up<br>e        | Re<br>cal<br>ibr<br>d<br>ate<br>199<br>6 m<br>ode<br>l                                     | Ba<br>ian<br>dat<br>yes<br>up<br>e        | Re<br>cal<br>ibr<br>d<br>ate<br>199<br>6 m<br>ode<br>l                                     | Ba<br>ian<br>dat<br>yes<br>up<br>e              | Re<br>cal<br>ibr<br>d<br>ate<br>199<br>6 m<br>ode<br>l                                     | Ba<br>ian<br>dat<br>yes<br>up<br>e        |
-| Mo<br>del<br>I         | MP<br>B             | 1.1<br>7              | −3<br>7.8<br>0        | −1<br>0.5<br>7 (<br>27<br>.39<br>)                                                         | −3<br>0.0<br>5 (<br>39<br>.78<br>)        | −6<br>.87<br>(<br>15.<br>49<br>)                                                           | −2<br>8.0<br>4 (<br>18.<br>50<br>)        | 1.3<br>1 (<br>17.<br>59<br>)                                                               | −3<br>4.1<br>5 (<br>26<br>.86<br>)              | −2<br>.42<br>(<br>21<br>.94<br>)                                                           | −4<br>0.6<br>4 (<br>23<br>.72<br>)        |
-|                        | MA<br>D<br>MS<br>E  | 59<br>.20<br>67<br>80 | 65<br>.17             | 71<br>.62<br>(<br>4.8<br>6)                                                                | 61<br>.95<br>(<br>16.<br>97<br>)          | 69<br>.73<br>(<br>2.6<br>1)                                                                | 58<br>.85<br>(<br>16.<br>14)              | 71<br>.51<br>(<br>5.3<br>8)                                                                | 65<br>.19<br>(<br>18.<br>26<br>)                | 71<br>.33<br>(<br>5.6<br>1)                                                                | 65<br>.77<br>(<br>16.<br>39)              |
-|                        | MS<br>PE            |                       | 92<br>94              | 95<br>169<br>04<br>(<br>0)                                                                 | 58<br>89<br>93<br>(<br>23<br>)            | 56<br>89<br>(<br>294<br>)                                                                  | 96<br>85<br>350<br>6)<br>(                | 66<br>68<br>90<br>(<br>8)                                                                  | 46<br>83<br>92<br>(<br>00<br>)                  | 05<br>67<br>92<br>(<br>3)                                                                  | 62<br>63)<br>87<br>(<br>44                |
-| del<br>Mo<br>II        | MP<br>B             | 2.3<br>1              | −0<br>.51             | −0<br>.66<br>(<br>8.5<br>9)                                                                | 7.4<br>9 (<br>20<br>.33<br>)              | 0.5<br>4 (<br>3.0<br>5)                                                                    | −2<br>.69<br>(<br>5.4<br>1)               | 0.6<br>2 (<br>2.1<br>2)                                                                    | 0.5<br>3 (<br>1.8<br>0)                         | −1<br>.17<br>(<br>4.7<br>1)                                                                | 3.2<br>1 (<br>6.0<br>0)                   |
-|                        | MA<br>D<br>MS<br>E  | 14<br>.89<br>44<br>9  | 15<br>.36             | 17<br>.68<br>(<br>2.3<br>8)                                                                | 21<br>.45<br>(<br>13.<br>33)              | 16<br>.97<br>(<br>0.5<br>6)                                                                | 15<br>.69<br>(<br>2.1<br>4)               | 16<br>.78<br>(<br>0.4<br>6)                                                                | 13<br>.99<br>(<br>1.8<br>7)                     | 16<br>.85<br>(<br>0.7<br>3)                                                                | 16<br>.58<br>(<br>2.5<br>8)               |
-|                        | MS<br>PE            |                       | 41<br>9               | 54<br>5 (<br>123<br>)                                                                      | 80<br>8 (<br>90<br>2)                     | 48<br>2 (<br>12)                                                                           | 44<br>5 (<br>127<br>)                     | 47<br>6 (<br>4)                                                                            | 51<br>3 (<br>90<br>)                            | 49<br>4 (<br>25<br>)                                                                       | 44<br>1 (<br>180<br>)                     |
-| Mo<br>del<br>III       | MP<br>B<br>MA<br>D  | 2.2<br>8<br>8.1<br>1  | −6<br>.10<br>8.5<br>1 | −1<br>.42<br>(<br>3.5<br>6)<br>8.4<br>0.7                                                  | −6<br>.09<br>(<br>3.5<br>2)<br>7.4<br>3.2 | −0<br>.53<br>(<br>1.7<br>7)<br>8.2<br>0.3                                                  | −6<br>.54<br>(<br>1.7<br>8)<br>8.7<br>1.6 | 1.1<br>6 (<br>1.9<br>0)<br>8.6<br>0.6                                                      | −8<br>.24<br>(<br>2.3<br>8)<br>10<br>.16<br>1.5 | 0.2<br>6 (<br>2.5<br>1)<br>8.4<br>0.7                                                      | −5<br>.87<br>(<br>3.1<br>3)<br>8.2<br>2.1 |
-|                        | MS<br>E             | 13<br>2               |                       | 7 (<br>7)                                                                                  | 9 (<br>1)                                 | 4 (<br>6)                                                                                  | 4 (<br>3)                                 | 2 (<br>3)                                                                                  | (<br>7)                                         | 8 (<br>0)                                                                                  | 1 (<br>0)                                 |
-|                        | MS<br>PE            |                       | 16<br>8               | 15<br>2 (<br>12)                                                                           | 13<br>8 (<br>155<br>)                     | 14<br>0 (<br>4)                                                                            | 14<br>3 (<br>50<br>)                      | 14<br>2 (<br>9)                                                                            | 27<br>8 (<br>149<br>)                           | 14<br>3 (<br>11)                                                                           | 17<br>8 (<br>149<br>)                     |
-| Mo<br>del<br>IV        | MP<br>B             | 0.3<br>9              | −0<br>.52             | 0.2<br>3 (<br>1.1<br>1)                                                                    | −0<br>.76<br>(<br>1.0<br>8)               | −0<br>.04<br>(<br>0.8<br>5)                                                                | −0<br>.28<br>(<br>0.6<br>9)               | 0.1<br>0 (<br>0.6<br>6)                                                                    | −0<br>.48<br>(<br>1.0<br>6)                     | −0<br>.25<br>(<br>0.5<br>8)                                                                | −0<br>.29<br>(<br>0.7<br>9)               |
-|                        | MA<br>D<br>MS<br>PE | 2.3<br>9<br>10<br>.15 | 2.3<br>4              | 2.6<br>5 (<br>0.1<br>6)                                                                    | 2.6<br>6 (<br>0.6<br>2)                   | 2.5<br>4 (<br>0.1<br>8)                                                                    | 2.3<br>2 (<br>0.7<br>5)                   | 2.5<br>3 (<br>0.1<br>3)                                                                    | 2.5<br>0 (<br>0.3<br>8)                         | 2.4<br>7 (<br>0.1<br>0)                                                                    | 2.2<br>3 (<br>0.3<br>9)                   |
-|                        | MS<br>E             |                       | 10<br>.90             | 12<br>.58<br>(<br>1.0<br>9)                                                                | 12<br>.89<br>(<br>7.0<br>6)               | 12<br>.02<br>(<br>0.6<br>5)                                                                | 11<br>.16<br>(<br>7.3<br>5)               | 11<br>.72<br>(<br>0.4<br>2)                                                                | 10<br>.46<br>(<br>3.9<br>8)                     | 11<br>.73<br>(<br>0.4<br>6)                                                                | 9.2<br>2 (<br>3.7<br>9)                   |
-
-*Note*: MPB <sup>=</sup> mean prediction bias, MAD <sup>=</sup> mean absolute deviation, MSPE <sup>=</sup> mean squared prediction error, MSE <sup>=</sup> mean squared error (S.D.).
-
-<span id="page-9-0"></span>satisfactory. This was expected since the sample sizes selected for this study were too small (less than 8% of the total data) to result in a meaningful conclusion. Therefore, it was concluded that the updating procedure is the best approach when limited data are available and no further investigation was conducted regarding developing models with limited data.
-
-[Table 5](#page-8-0) presents the relative goodness of fit measures for predicting the number of accidents in 2001 using the 1996 models, the 2001 models, models that apply a calibration factor to the 1996 model and the Bayesian approach. For the latter two, various sample sizes are used to estimate the "updated" models that are applied.
-
-As expected, the measures indicate that 2001 model fits the 2001 data better than other models. The real value of these results is in the comparison of the performance of the "updated" models among themselves for different sample sizes and with the 1996 model. This comparison shows that in general the recalibrated "updated" models perform better than the updated Bayesian models and the1996 models. However, no clear-cut conclusions can be drawn regarding the performance of updated models in respect to the sample size used for estimating them.
-
-#### **8. Conclusions and summary**
-
-This paper examined the temporal transferability of zonal accident prediction models for the City of Toronto. In so doing, model calibration and related transferability issues were also addressed. The coefficient estimates for the 2001 models are consistent with the 1996 models. Most of the coefficient parameters and constant terms are significant for the 1996 and the 2001 models across all four accident type models (Models I–IV).
-
-The results of nested likelihood ratio test indicate that the model parameters from two urban contexts are not statistically equal and have not remained stable over time. Therefore, from a theoretical perspective, the transfer of macro-level accident prediction models is undesirable. However, relative measures of transferability indicate that the transferred models yield useful information in the application context. Transfer index values show that transferred models capture at least 52% of the variance provided by estimating all the parameters in the 2001 data. The results of the *t*-test for all the models indicate that the VKT and socioeconomic and demographic parameters are stable over time.
-
-In the absence of a complete dataset for calibrating the 2001 model, it looks like the "updated" models, which are developed by applying calibration factors to the 1996 models, perform reasonably and better than the 1996 models and the Bayesian updated models regardless of the sample size used for estimating these factors. The Bayesian approach has not yielded good results in general. The possible problem for this method is that in this method the coefficients in model transferring are heavily affected by the estimation context data since the number of observations is larger in this context. Also, this method assumes that the prior and posterior (updated) distributions of the parameters are normally distributed, which may not be a good assumption.
-
-This research looked only at temporal transferability of zonal accident prediction models. A natural next step for further research would be the investigation of spatial transferability and the combination of temporal and spatial transferability. That research should aim to identify the conditions that make two study areas "similar enough" to justify transferring a model from one to the other.
-
-#### **Acknowledgment**
-
-The authors acknowledge the Natural Sciences and Engineering Research Council of Canada (NSERC) for its support of some of the fundamental research on which this work is based.
-
-# **References**
-
-- Atherton, T.J., Ben-Akiva, M.E., 1976. Transferability and updating of disaggregation travel demand models. Transport. Res. Rec. 610, 12–18.
-- Badoe, D.A., 1994. An investigation into the long range transferability of work-trip discrete mode choice models. PhD Dissertation. Department of Civil Engineering, University of Toronto, Ont., Canada.
-- Badoe, D.A., Miller, E.J., 1995a. Analysis of the temporal transferability of disaggregate work trip mode choice models. Transport. Res. Rec. 1493, 1–11.
-- Badoe, D.A., Miller, E.J., 1995b. Comparison of alternative methods for updating disaggregated logit mode choice models. Transport. Res. Rec. 1493, 90–100.
-- Ben-Akiva, M., 1981. Issues in transferring and updating travel-behaviour models. In: Stopher, P.R., et al. (Eds.), New Horizons in Travel Behaviour. Lexington Books, Lexington, MA, pp. 665–686.
-- Ben-Akiva, M., Bolduc, D., 1987. Approaches to model transferability and updating: the combined transfer estimator. Transport. Res. Rec. 1139, 1–7.
-- EMME/2, 1998. User's Guide Manual Release 9. INRO Consultants Inc.
-- Gunn, H.F., 2001. Spatial and temporal transferability of relationships between travel demand, trip cost and travel time. Transport. Res. Part E: Logist. Transport. Rev. 37 (2/3), 163–189.
-- Gunn, H.F., Ben-Akiva, M.E., Bradley, M.A., 1985. Tests of the scaling approach to transferring disaggregate travel demand models. Transport. Res. Rec. 1037, 21–30.
-- Hadayeghi, A., Shalaby, A.S., Persaud, B., 2003. Macro-level accident prediction models for evaluating safety of urban transportation systems. Transport. Res. Rec. 1840, 87–95.
-- Harwood, D.W., Council, M., Hauer, E., Hughes, W. E., Vogt, A., 2000. Prediction of the expected safety performance of rural two-lane highways. Report FHWA-RD-99-207, FHWA, Department of Transportation.
-- Koppelman, F.S., Rose, G., 1983. Geographic transfer of travel choice models: evaluation and procedure. In: Optimization and Discrete Choice in Urban Systems: Proceedings of the International Symposium on New Directions in Urban Systems Modeling, pp. 272–309.
-- Koppelman, F.S., Wilmot, C.G., 1982. Transferability analysis of dissagregate choice models. Transport. Res. Rec. 895, 18–24.
-- Ladron de Guevara, F., Washington, S.P., Oh, J., 2004. Forecasting crashes at the planning level: simultaneous negative binomial crash model applied in Tucson, Arizona. Transport. Res. Rec. 1897, 191–199.
-- Lerman, S.R., 1981. A comment on interspatial, intraspatial, and temporal transferability. In: Stopher, P.R., et al. (Eds.), New Horizons in Travel Behaviour. Lexington Books, Lexington, MA, pp. 628–632.
-- Lovegrove, G.R., Sayed, T., 2005. Macro-level collision prediction models for evaluating neighbourhood traffic safety. In: Proceedings of the Sixth Transportation Specialty Conference, Canadian Society of Civil Engineering, Toronto, Canada.
-- Oh, J., Lyon, C., Washington, S., Persaud, B., Bared, J., 2003. Validation of FHWA crash models for rural intersection lessons learned. Transport. Res. Rec. 1840, 41–49.
-
-- <span id="page-10-0"></span>Parody, T.E., 1977. An analysis of disaggregate mode choice models in prediction. Transport. Res. Rec. 649, 23–31.
-- National Cooperative Highway Research Program (NCHRP), in press. Incorporating Safety into Long-range Transportation Planning, Project 8-44. National Cooperative Highway Research Program.
-- Persaud, B., Lord, D., Palmisano, J., 2002. Calibration and transferability of accident prediction models for urban intersections. Transport. Res. Rec. 1784, 57–64.
-- SAS Institute Inc., 1999. SAS/STAT® Users Guide, Version 8. SAS Institute Inc., Cary, NC.

Zotero/001_artiklid/Road Safety Indicators/hakkertusesexposureriskroad2002.md

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----
-category: literaturenote
-citekey: hakkertusesexposureriskroad2002
-title: The uses of exposure and risk in road safety studies
-authors: "Hakkert, A.S.; Braimaister, L."
-year: 2002
-date: 2002-00-00 2002
-url: "https://swov.nl/en/publication/uses-exposure-and-risk-road-safety-studies"
-zotero_key: TE6DS2K6
-zotero_storage: C779GGFD
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Hakkert, A.S."
-status: converted
----
-# **The uses of exposure and risk in road safety studies**
-
-Prof. dr. A.S. Hakkert & dr. L. Braimaister
-
-R-2002-12
-
-![](_page_2_Picture_0.jpeg)
-
-# **The uses of exposure and risk in road safety studies**
-
-## **Report documentation**
-
-Number: R-2002-12
-
-Title: The uses of exposure and risk in road safety studies
-
-Author(s): Prof. dr. A.S. Hakkert & dr. L. Braimaister
-
-Research theme: Road users: the relation between behaviour, surroundings and
-
-accidents
-
-Theme leader: I.N.L.G. van Schagen
-
-Project number SWOV: 37.230
-
-Keywords: Exposure (human), vehicle mile, application (use), risk, accident,
-
-theory, statistics.
-
-Contents of the project: This report explores the theoretical possibilities of defining
-
-exposure and risk, discusses the problems associated with the use of exposure and risk and gives examples of various safety studies in which use is made of exposure and risk indicators.
-
-Number of pages: 53
-
-Price: L 11,25
-
-Published by: SWOV, Leidschendam, 2002
-
-SWOV Institute for Road Safety Research P.O. Box 1090 2260 BB Leidschendam The Netherlands Telephone +31-703173333 Telefax +31-703201261
-
-## **Summary**
-
-This report explores the theoretical possibilities of defining exposure and risk, discusses the problems associated with the use of exposure and risk and gives examples of various safety studies in which use is made of exposure and risk indicators.
-
-The report sets out with a definition of the three central terms used: accidents, exposure and risk. Each of the three terms is defined and definition problems and problems concerning accuracy of measurement and reporting are described. The conclusion reached is that there are general definitions of exposure and of risk, as used in the field of health prevention and risk analysis, but that in road safety practice, these terms should be defined within the context of the issue studied. For each application, the correct exposure measure should be used. This is sometimes impossible on account of the unavailability of the required information, or the necessity to collect it at great cost. Generally, the more aggregate the exposure measure, the more indirect the variables that are introduced, which casts shadows over the resulting risk calculations.
-
-In the case of transport, the most widely used measure of exposure is the number of kilometres travelled for each travel mode. In some cases, useful additional insight is provided by taking into account the speed of travel, in which case exposure is expressed as the amount of time spent in the traffic system. One of the developments in recent years has been the installation of electronic and telecommunication equipment inside vehicles and along roads. Another development is the increasingly widespread use of mobile telephones. As a result it is becoming easier to collect up-to-date and reliable information on a variety of parameters that could be of importance in the calculation of vehicle exposure and risk. Additional information on the distribution of speeds, types of vehicles and following distances also seems to be a future possibility.
-
-One of the contexts in which the term risk is used, is in comparing risks between different parts of the transport system, different transport modes or even different activities outside the field of transport. The desire is to have various activities exposed to equal risks, so as to establish a fair distribution of risks. After discussing the various problems and pitfalls in using this approach, it is concluded that the desire for equal risks in various segments of the transportation system is not practical. It is more useful to search for ways to make each segment of the transport system as safe as possible, keeping cost-effectiveness considerations into account.
-
-## **Contents**
-
-| 1.   | Introduction                                                       | 7  |
-|------|--------------------------------------------------------------------|----|
-| 2.   | Definitions of exposure and risk                                   | 8  |
-| 2.1. | Definitions of exposure                                            | 8  |
-| 2.2. | Definitions of risk                                                | 8  |
-| 2.3. | The size of the safety problem in terms of risk and exposure       | 9  |
-| 3.   | Quantifying exposure and risk                                      | 11 |
-| 3.1. | Exposure measurement                                               | 11 |
-| 3.2. | Quantifying risk by hours of exposure                              | 11 |
-| 3.3. | The relevant measures of exposure and risk                         | 14 |
-|      | 3.3.1.<br>Aggregate measures of exposure and risk                  | 15 |
-|      | 3.3.2.<br>Traffic volume as a measure of exposure                  | 16 |
-|      | 3.3.3.<br>Problems with quantifying exposure                       | 16 |
-|      | 3.3.4.<br>The possibilities with new information technology (IT)   | 17 |
-|      | 3.3.5.<br>Traffic exposure at intersections                        | 18 |
-|      | 3.3.6.<br>Pedestrian exposure and risk                             | 19 |
-| 3.4. | The use of fN diagrams                                             | 19 |
-| 4.   | Some issues associated with the use of exposure and risk           | 22 |
-| 4.1. | The desire to assess risk levels and to strive for equal risks     | 22 |
-| 4.2. | Over-representation and risk                                       | 23 |
-| 4.3. | Doing without information on risk and exposure - an example        | 24 |
-| 4.4. | National safety levels and targets                                 | 26 |
-| 4.5. | What is an acceptable level of risk?                               | 26 |
-| 4.6. | Risk versus effectiveness                                          | 28 |
-| 5.   | Some examples of the use of risk calculations in a number          |    |
-|      | of road safety applications                                        | 31 |
-| 5.1. | The involvement of lorries in fatal accidents                      | 31 |
-| 5.2. | Drivers involved in accidents while under the influence of alcohol | 33 |
-| 5.3. | The effect of speed on the risk of accident involvement            | 35 |
-| 5.4. | A preliminary study to explore directions for elaborating on risks | 36 |
-| 5.5. | Predictions of traffic safety in 2010 in the Netherlands           | 36 |
-| 5.6. | The involvement of elderly drivers in accidents                    | 37 |
-| 5.7. | The involvement of young drivers in accidents                      | 38 |
-| 5.8. | Road safety in the Netherlands up to 1999                          | 40 |
-| 6.   | A more detailed discussion on risk and exposure in the             |    |
-|      | context of infrastructure                                          | 41 |
-| 6.1. | The relationship between accidents and traffic volumes             | 41 |
-|      | 6.1.1.<br>Safety Performance Functions (SPF)                       | 43 |
-|      | 6.1.2.<br>The relation between aggregate number of collisions      |    |
-|      | and distance travelled                                             | 43 |
-| 6.2. | Hazardous locations                                                | 44 |
-| 7.   | Summary and conclusions                                            | 47 |
-|      | References                                                         | 50 |
-
-## 1. **Introduction**
-
-Many safety studies involve the use of the terms accidents, exposure and risk. One sometimes gets the impression that the use and definition of the terms exposure and risk differ from study to study and are chosen in an adhoc fashion. This report explores the theoretical possibilities of defining exposure and risk, discusses the problems associated with the use of exposure and risk and gives examples of various safety studies in which use is made of exposure and risk indicators. It will be shown that there is no general definition of exposure and of risk and that these terms should be defined within the context of the issue studied. The report also deals with many of the limitations connected with the use of exposure and points out the many pitfalls that lie on the path of the correct use of the term risk in safety studies.
-
-The report sets out the generally used definitions of the terms exposure and risk in chapter two. This is followed by a chapter on the quantification of exposure on both an aggregate level and as applying to the study of road infrastructure. Chapter 4 presents some of the general issues associated with the use of risk and exposure, over-representation, the setting of national targets and risk versus effectiveness. Chapter 5 presents and discusses a variety of examples of safety studies that use risk definitions and some safety studies that were conducted without the use of risk as a denominator to evaluate safety effects. Chapter 6 discusses, in more detail, the relationship between accidents, exposure and risk as they are applied in studies that are concerned with the safety of road infrastructure, including the relationship between accidents and traffic volumes and the issue of risk as applied to the identification of hazardous locations. Chapter 7 presents the conclusions of the report.
-
-# 2. **Definitions of exposure and risk**
-
-Whereas the definition of a road accident is generally well-understood in road safety literature, the definitions of the concepts exposure and risk are much less well-defined. An accident is generally defined, in this context, as an event in which at least one motor-vehicle was involved, that occurred on a public road and which resulted in injury. This definition is used because in most countries computerised systems exist for the recording of such accidents. This is also the definition mostly used by police forces to record an accident. Other road accidents are registered in other databases or are unrecorded. National differences exist in the definitions of accidents that are recorded by the police, and in the definition of accidents with casualties that are to be entered into statistical databases and definitions of the various degrees of severity of injury. International data bases do not always account for such differences in definitions. The international accident data base IRTAD, maintained by the German Federal Road Research Institute (BASt), makes an effort to report on these differences and account for them where possible.
-
-Although many shortcomings can be identified regarding this definition of an accident, at least we are talking about a recordable event which is to a certain degree exact. As to exposure and risk, on the other hand, we first have to agree on a definition and then it remains to be seen if the accepted notions are quantifiable and reasonably exact.
-
-#### 2.1. **Definitions of exposure**
-
-In the context of this report, exposure is meant as exposure to risk. To what extent are certain segments of the population likely to be involved in an accident? The measure of exposure is generally defined as some form of the amount of travel, either by vehicle or on foot. Once the amount of travel is known for certain activities, or road users, and if we know the number of crashes that are associated with that activity or population, the associated risk can be calculated. Risk assessments can be used to improve transport safety and determine public health priorities. …."The various ways of measuring the amount of travel are referred to collectively as 'exposure data' because they measure traveller's exposure to the risk of death or injury." (European Transport Safety Council (ETSC), 1999)
-
-### 2.2. **Definitions of risk**
-
-According to the Oxford Dictionary, risk is a "hazard, chance of bad consequences, loss etc., exposure to mischance". Collins Dictionary describes risk as "a possibility that something unpleasant or undesirable might happen; something that you do which might have unpleasant or undesirable results." In the field of road safety the concept of risk is used as a way to quantify the level of road safety relative to the amount of exposure, as opposed to the absolute level of safety as measured by the absolute number of accidents or casualties. The literature differentiates between various kinds of risk such as personal risk, societal risk, individual risk, group risk. Other differentiations can be added such as objective risk, subjective risk, voluntary risk and compulsory risk. A treatise on the various definitions of risk in the literature, their definitions, meanings and relevance is outside the scope of this document. For our purposes, risk will be used to mean the probability of an accident occurring. Such a definition was proposed in Hauer (1982).
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-One element which might usefully be added to the definition of risk as a probability is taking into account the severity of the outcome of an event. This distinction is proposed in Haight (1986). Most people would agree that, whereas the probability of death in one game of 'Russian roulette' (onesixth) is considerably less than the probability of heads in the toss of a coin (one-half), Russian roulette is 'riskier' than coin-tossing. This distinction can be achieved when adding to the probability the concept of expected costs. When the cost of the outcome of each probability is multiplied by the probability of each event, risk can be defined as an expectation. Van Poortvliet (1999) quotes a definition of risk that takes both aspects into account, i.e both the probabilistic nature of risk and the severity of the consequences: "A combination of the probability, or frequency, of occurrence of a defined hazard and the magnitude of the consequences of the occurrence."
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-Reliability engineers use the term risk to express the probability of a welldefined, hazardous event occurring. In many industrial applications the consequences of such events can be estimated by using models that cover the chemical, physical and biological effects of such a hazardous event. Thus risk analysis typically yields a set of likelihoods, accident scenarios and consequences (Van Poortvliet, 1999). The quantitative approach makes it possible to calculate the expected losses during a given period of time, usually expressed in terms of fatalities, casualties and/or material damage. In the field of road safety a similar approach is generally used. This approach is generally preferred over the qualitative approach.
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-Popular perception associates risk with both the probability of a hazardous event for someone involved in a certain activity and with the severity of the outcome. If an accident occurs, problems generally arise when one tries to compare situations with both different probabilities and different consequences. Therefore, it is often preferable to talk about accident rates (or casualty rates) rather than risk. When comparing accident rates, it is assumed we are talking about situations with outcomes of similar severity, otherwise the situations should not be compared.
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-#### 2.3. **The size of the safety problem in terms of risk and exposure**
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-Having defined risk and exposure, it is now possible to define safety and the size of the safety problem in those terms. Figure 1 (Rumar,1999) defines the safety problem - I as a function of exposure, accident risk and injury risk:
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-I = E x A/E x I/A, where I is the number of people injured, E is exposure, A/E is the probability of an accident (accident risk), I/A is the probability of being injured in an accident (injury risk).
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-![](_page_11_Figure_0.jpeg)
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-Figure 1. The safety problem (human injury) illustrated by the volume of the box. From: Rumar (1999).
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-From this definition it becomes clear that countermeasures to improve road safety can come from activities along any one of the three axes. One can think of measures that reduce exposure, measures that reduce the risk of an accident and measures that reduce the risk of injury. It is also possible to take the time trend element into consideration. The absolute size of the safety problem, expressed in either the number of accidents or the number of casualties (SAFETY) of a certain severity results from multiplying the degree of risk, which has a trend with the exposure, which also has a trend element expressed as:
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-SAFETY (severity) = RISK (trend) x EXPOSURE (trend).
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-# 3. **Quantifying exposure and risk**
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-#### 3.1. **Exposure measurement**
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-Exposure is generally expressed in a form related to the amount of travel. In most countries traffic data (volumes) are counted for traffic engineering purposes but few countries have systems of counting traffic volumes that enable comprehensive measurement of volumes on various parts of the road system, segregated by type of road user. Some countries have such systems for parts of the road system - motorways, main rural roads, major intersections, etc. Still other countries have only approximate estimates of the amount of travel, conducted on the basis of the known fuel consumption in the country.
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-Some countries conduct systematic counts at a series of counting sites, chosen to be nationally representative, covering all types of roads and different regions, and classifying counts according to type of vehicle and time of day. Such counts can be combined with detailed information about the road network to provide national and regional estimates of traffic volumes. Even in those countries, such estimates are generally not available on a per-city basis. Also in many cases local roads and minor roads are not included in the counting systems and the amount of travel on such roads cannot be estimated.
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-For the calculation of exposure by different groups of road users: car drivers, car passengers, public transport passengers, cyclists and pedestrians the information from traffic counts does not suffice. Additional information is generally obtained from national travel surveys, which are conducted in many countries with a frequency of between 5-10 years.
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-Data from different sources can be combined to calculate disaggregate exposure estimates for different population subgroups.
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-At this stage in the report, it can be said that for each application, the correct exposure measure should be used. This is sometimes impossible because the required information is not available, or has to be collected at great cost. Generally, the more aggregate the exposure measure, the more intervening variables are introduced that cast shadows over the resulting risk calculations.
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-#### 3.2. **Quantifying risk by hours of exposure**
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-A number of authors have suggested that it is useful to calculate risk by hours of exposure to a certain activity (Evans, 1993; ETSC, 1999; Wesemann et al., 1998). In the case of transport, the most widely used measure of exposure is distance travelled, but it is obvious that even for transport the speed at which such travel is conducted might influence the risk. Speeds for various transport modes (walking, cycling, motorised transport) are widely different and therefore it has been suggested to normalise exposure (vehicle kms travelled) by multiplying by speed,
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-thereby obtaining a risk measure which is expressed as accidents or casualties per hour of exposure.
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-Collins (1990) provides details on accident fatality rates for various activities in transport and other fields of activity per passenger-hour and per passenger-km travelled (Table 1).
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-| Transport and other activities | Fatalities per 100 million<br>passenger-hours | Fatalities per 100 million<br>passenger-km |
-|--------------------------------|-----------------------------------------------|--------------------------------------------|
-| Passenger travel by            |                                               |                                            |
-| Bus or coach                   | 1.4                                           | 0.06                                       |
-| Rail                           | 6.0                                           | 0.1                                        |
-| Car                            | 12.4                                          | 0.4                                        |
-| Water                          | 16.0                                          | 0.8                                        |
-| Air                            | 20.0                                          | 0.04                                       |
-| Foot                           | 27.0                                          | 7.0                                        |
-| Pedal cycle                    | 64.0                                          | 4.6                                        |
-| Two wheel motor vehicle        | 342.0                                         | 11.4                                       |
-| Employment                     |                                               |                                            |
-| All work                       | 0.9                                           |                                            |
-| Banking and financing          | 0.17                                          |                                            |
-| Chemical industry              | 1.1                                           |                                            |
-| Construction work              | 4.9                                           |                                            |
-| All railway work               | 5.6                                           |                                            |
-| Extraction of ores             | 13.0                                          |                                            |
-| Being at home                  |                                               |                                            |
-| All ages                       | 2.6                                           |                                            |
-| People under 65                | 1.0                                           |                                            |
-| People 65 and over             | 11.5                                          |                                            |
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-Table 1. Accidental fatality rates for transport and other activities. In Evans (1993), from Collins (1990).
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-It can be seen that travel is a relatively high risk activity compared with such other activities as being at work (most kinds of work) and being at home. Large differences can be observed in the risk associated with the various travel modes, with bus and rail travel being relatively safe both in terms of vehicle kilometres travelled and hours exposed, air travel being relatively safe in terms of kilometres travelled but not in terms of hours of travel, due to the high speeds of travel. Travel by vulnerable road users, on foot, pedal cycle and motor-cycle being relatively unsafe both in terms of hours and kilometres of exposure.
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-Koornstra (in: ETSC,1999) also presents a risk comparison for various modes of travel aggregated for the whole of the European Union (Table 2).
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-| Travel mode | 108<br>person km |            | 108      | person hours |
-|-------------|------------------|------------|----------|--------------|
-|             | absolute         | normalised | absolute | normalised   |
-| ROAD        |                  |            |          |              |
-| total       | 1.1              | 13.8       | 3.3      | 16.5         |
-| bus/coach   | 0.08             | 1          | 2        | 1            |
-| car         | 0.8              | 10         | 30       | 15           |
-| foot        | 7.5              | 93.8       | 30       | 15           |
-| cycle       | 6.3              | 78.8       | 90       | 45           |
-| mot.c/moped | 16.0             | 200        | 500      | 250          |
-| TRAINS      | 0.04             | 0.5        | 2        | 1            |
-| FERRIES     | 0.33             | 4.2        | 10.5     | 5.3          |
-| PLANES      | 0.08             | 1.0        | 36.5     | 18.3         |
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-Table 2. Fatality risks over distance and time for travel modes in the EU. Source: ETSC (1999). Normalised rates calculated and added by the authors.
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-Taking bus and coach travel as a normative unity, it can be seen that risk expressed in terms of person-kilometres travelled is 10 times greater for cars and 100-200 times greater for the vulnerable modes (foot, cycle and motor-cycle/moped), with motor-cycles clearly being the most hazardous. Train and plane travel are also relatively safe. Looking at the same data with risk expressed in terms of person-hours of travel, the picture does not change a great deal. Travel by foot or bicycle becomes slightly less hazardous, because of the low speeds of these modes, whereas air travel becomes significantly more hazardous because of the high speeds of travel (hence the small number of hours per km of travel. Chapter 5 of the report will discuss the usefulness of making such comparisons in more detail.
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-Wesemann et al. (1998) suggested the use of risk per hour of exposure involved in various activities as a measure of comparison. Having collected data on accidents and fatalities at home, at work, in sports and in traffic from a variety of sources, it might be possible to calculate the risk of injury associated with these activities in terms of exposure in hours. However, for their study the detailed data needed to present such a comparison was not available. Instead, a much coarser comparison was conducted in terms of the number of individuals exposed to each activity (traffic and home). For the case of traffic accidents a calculation was presented comparing the risk of injury and fatality per distance and per hour of travel for various age groups, male and female (Tables 3 and 4).
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-Comparing results of risk in each age group per kilometre travelled and per hour exposed does not change the picture drastically. The two groups that have the higher risks, by both measures of exposure, are the young (15-24) and the old (65+). When looking at risk per hour of exposure, the risk of the young group increases and the risk of the old group decreases somewhat. This seems to indicate that the young are exposed to the risks at higher speeds.
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-| Age              | Male   | Female | Total  |
-|------------------|--------|--------|--------|
-| Fatalities       |        |        |        |
-| 15-24            | 0.1520 | 0.0512 | 0.1055 |
-| 25-39            | 0.0676 | 0.0297 | 0.0530 |
-| 40-49            | 0.0478 | 0.0271 | 0.0403 |
-| 50-59            | 0.0577 | 0.0359 | 0.0493 |
-| 60-64            | 0.0821 | 0.0594 | 0.0725 |
-| 65+              | 0.2445 | 0.1675 | 0.2059 |
-| Total            | 0.0884 | 0.0504 | 0.0730 |
-| Hospitalisations |        |        |        |
-| 15-24            | 2.1633 | 1.2094 | 1.7232 |
-| 25-39            | 0.8189 | 0.6016 | 0.7354 |
-| 40-49            | 0.6037 | 0.6072 | 0.6050 |
-| 50-59            | 0.7185 | 0.9057 | 0.7907 |
-| 60-64            | 0.7948 | 1.2733 | 0.9965 |
-| 65+              | 2.0844 | 2.5745 | 2.3300 |
-| Total            | 1.0559 | 1.0023 | 1.0342 |
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-Table 3. Risk of fatality and hospitalisation per 10<sup>7</sup> traveller kilometres in the Netherlands, 1994-1996. From: Wesemann et al. (1998).
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-| Age              | Male   | Female | Total  |
-|------------------|--------|--------|--------|
-| Fatalities       |        |        |        |
-| 15-24            | 0.3462 | 0.1077 | 0.2313 |
-| 25-39            | 0.2037 | 0.0620 | 0.1365 |
-| 40-49            | 0.1366 | 0.0493 | 0.0951 |
-| 50-59            | 0.1415 | 0.0603 | 0.1026 |
-| 60-64            | 0.1549 | 0.0734 | 0.1120 |
-| 65+              | 0.3395 | 0.1775 | 0.2474 |
-| Total            | 0.2210 | 0.0880 | 0.1553 |
-| Hospitalisations |        |        |        |
-| 15-24            | 4.9201 | 2.5474 | 3.7801 |
-| 25-39            | 2.4648 | 1.2577 | 1.8934 |
-| 40-49            | 1.7259 | 1.1049 | 1.4310 |
-| 50-59            | 1.7604 | 1.5216 | 1.6462 |
-| 60-64            | 1.4965 | 1.5780 | 1.5393 |
-| 65+              | 2.9030 | 2.7290 | 2.8040 |
-| Total            | 2.6389 | 1.7539 | 2.2021 |
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-Table 4. Risk of fatality and hospitalisation per 10<sup>6</sup> hours of travel in the Netherlands, 1994-1996. From: Wesemann et al. (1998).
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-#### 3.3. **The relevant measures of exposure and risk**
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-In the context of risk calculations it is important to define and select the correct measure of exposure. The selection of what should serve as a 'correct' measure of exposure will depend mainly on the intended use. A large number of exposure variables can be selected, each suitable to a specific problem. In many cases, because of the lack of sufficiently detailed data, or the lack of accurate data, it is not possible to select the correct measure of exposure to suit a particular issue.
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-#### 3.3.1. Aggregate measures of exposure and risk
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-In international comparisons and for trend studies on a national level, in many cases, the number of inhabitants or the number of vehicles is selected as the available exposure measure. Risk calculated as the number of fatalities divided by the number of inhabitants can be considered as a measure of mortality, i.e. what is the chance of death per population unit? The advantage of this risk measure is that it uses fairly reliable data which are generally widely available. It is therefore possible to conduct such international comparisons. The same cannot be said when attempting to calculate the risk of injury. Between countries large differences exist in the reporting procedures of road casualties. Various levels of under-reporting of accidents and casualties exist in different countries (Nilsson, 1997). Defining risk as the number of fatalities (or casualties) per number of vehicles can be seen as a proxy to the risk of travel. In this context the number of vehicles is selected as a proxy to the number of vehiclekilometres travelled which is a variable that is much more difficult to obtain reliably in many countries. Obviously, if a certain type of vehicle is considered, one should use only relevant accident figures for the type of vehicle under consideration.
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-One of the complications associated with this type of comparison is that the two risk measures- fatality per population unit and fatality per vehicle unitbehave very differently over time when comparing different countries. It should also be remembered that both measures are very comprehensive, not differentiating between different segments of the population (by age group, sex, urban or rural, etc.) and different types of vehicles. It is generally found that trends relating to the number of vehicles are a reasonable proxy to the number of vehicle kilometres travelled. In the literature, many studies exist reporting on risk comparisons conducted with these measures of exposure.
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-Such comparisons are useful in obtaining a very coarse picture of the safety situation, without contributing much to the understanding of the differences found between countries or changes over the years. For such an understanding risk calculations should be conducted on a much more detailed level.
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-Table 5 presents some examples of risk indicators on an aggregate level and their availability in international databases as well as the US database on fatal accidents (FARS).
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-|                                             | WHO | IRF | ECMT | IRTAD | EURO<br>STAT | FARS |
-|---------------------------------------------|-----|-----|------|-------|--------------|------|
-| Accidents/inhabitants                       |     |     |      | +     |              |      |
-| Accidents/VehKm                             |     | +   |      | +     |              |      |
-| Accidents/VehKm I/O built-up areas          |     |     |      | +     |              |      |
-| Accidents/VehKm by road class               |     |     |      | +     |              |      |
-| Killed/inhabitants                          | +   |     | +    | +     | +            | +    |
-| Killed/inhabitants by age group             | +   |     |      | +     | +            | +    |
-| Killed/inhabitants by age group & sex       | +   |     |      |       | +            | +    |
-| Killed/licensed drivers                     |     |     |      |       |              | +    |
-| Killed/million vehicles                     |     |     | +    |       |              | +    |
-| Killed/million road users by road user type |     |     | +    |       |              |      |
-| Killed/VehKm                                |     | +   |      | +     |              | +    |
-| Killed/VehKm I/O built-up areas             |     |     |      | +     |              |      |
-| Killed/VehKm by road class                  |     |     |      | +     |              |      |
-| Injured/inhabitants                         |     |     |      | +     | +            | +    |
-| Injured/inhabitants by age group            |     |     |      | +     | +            | +    |
-| Injured/inhabitants by age group & sex      |     |     |      |       | +            | +    |
-| Injured/ licensed drivers                   |     |     |      |       |              | +    |
-| Injured/million vehicles                    |     |     |      |       |              | +    |
-| Injured/Vehkm                               |     |     |      | +     |              |      |
-| Injured/VehKm I/O built-up areas            |     | +   |      | +     |              |      |
-| Injured/VehKm by road class                 |     |     |      | +     |              |      |
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-Table 5. Available risk indicators. From: DUMAS (1998).
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-#### 3.3.2. Traffic volume as a measure of exposure
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-Some form of traffic flow is generally taken as the measure of exposure when calculating risk on a certain part of the road network or for a certain type of vehicle. The most frequently used measure of exposure is the number of vehicle kilometres travelled. Details on the collection of data on vehicle kilometres travelled were given in Section 3.1 of the report. A more detailed discussion on the relations between accidents and traffic volumes will be given in Section 6.1 of the report. Risk levels on various parts of the road network or at various types of intersections are deduced from the ratio between accidents, casualties or fatalities and the relevant exposure measure related to traffic flow.
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-#### 3.3.3. Problems with quantifying exposure
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-Most countries collect data on traffic volumes in a systematic way. These would almost certainly include traffic count data that are needed for decisions on building a new road, widening an existing one or re-designing an intersection. Some countries also have aggregated traffic counts for some parts of the road network (generally the major roads). A few countries have aggregated traffic counts which can be converted to reliable numbers of vehicle kilometres travelled, but these are subdivided for various parts of
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-the urban and non-urban network. When such traffic censuses are available, they can give us the necessary information on exposure, by type of road and type of vehicle, from which detailed risks can be calculated. In many other countries the alternative to a traffic census, is to measure fuel consumption on a national scale and to calculate the amount of travel on the basis of this measurement. Such calculations are open to many different error sources and generally lead to only rough estimates of the number of vehicle kilometres travelled. Such estimates can also not be subdivided into types of road. These are the major difficulties in obtaining exposure measures on a national scale. For more detailed studies, relating to a specific research question, it is generally required to collect special and dedicated exposure data. This is an expensive process and is not always feasible.
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-Further information can be obtained from travel surveys. Such surveys, conducted either at home or along the road, can supply further information on the number of trips people make, trip purposes, travel modes and details about the travellers. Some countries have such travel surveys and conduct them periodically every 5-10 years. Even in those countries, travel surveys generally only apply to the adult population and generally do not include trips on foot.
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-The lack of detailed and high quality exposure data is one of the reasons that in so many cases international comparisons are conducted on a percapita or per-vehicle basis.
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-A recent report by the US Bureau of Transportation Statistics (BTS) discusses transportation data and risk measures (BTS, 2001). It states that:
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-"More accurate, comprehensive and consistent measures of risk exposure could help our understanding of the relative importance of factors contributing to transportation crashes and improve our analysis of safety trends. Better exposure measures require data not only on the numbers of fatalities, injuries and accidents, but also data that indicate the overall level of transportation activity. Analyses of safety trends for non-motorised modes- bicycling and walking- suffer from the absence of exposure measures (such as hours of exposure to traffic). Moreover, bicyclists and walkers often take trips too short in length to be counted in national travel surveys. Furthermore, trips that begin and end at a residence, without an intermediate stop, are typically not counted, thus excluding much recreational bicycling and walking. Yet, more than 6,400 pedestrians and bicyclists died in 1995 in crashes involving motor-vehicles (15 per cent of all transportation fatalities)" "There is also a need for exposure information on specific populations (e.g children or elderly drivers). For example, inadequate exposure data on children under five years of age makes the evaluation of some transportation risks difficult. Moreover, better exposure measures and incident data are needed for evaluating risks associated with the transportation of hazardous materials…." (BTS, 2001).
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-#### 3.3.4. The possibilities with new information technology (IT)
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-### Traffic volumes
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-One of the developments in recent years is the installation of electronics and telecommunications inside vehicles and along the roads. The increasingly widespread use of mobile phones also presents many new possibilities. As a result it is becoming easier to collect up-to-date and reliable information on a variety of parameters that could be of importance in the calculation of vehicle exposure and risk. The amount of traffic flow can be determined by a number of IT technologies which are becoming cheaper each year. Above-ground vehicle detectors will in time become more prevalent and will become part of an ever-expanding system of traffic monitoring and control. Such systems are already well on their way along
-
-motorways and will probably also be introduced on other types of roads and at intersections. Eventually it should be possible to develop a reliable system of traffic volume measurement on a road network-wide basis. Additional information on the distribution of speeds, types of vehicles and following distances also seems to be a future possibility.
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-### Traffic conflicts
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-The study of traffic conflicts or critical situations has been carried out since the 1960's and is sometimes proposed as an intermediate estimator of risk. The argument provided is that at most locations the number of accidents is (fortunately) very small and subject to large chance variations. To be able to form an opinion on the level of safety of such a location, or the assessment of the effect of a safety improvement in a short period of time, it is important to have a method for the establishment of risk. The measurement of conflicts (which can be regarded as a proxy to accidents) is one practical method in this context. Flows can thus be related to conflicts, which are measured over a short period of several days, instead of being related to accidents, for which a number of years of data are needed. One of the drawbacks of the conflict methods (Brundell-Freij & Ekman, 1990) is that conflict measurement is a highly complicated and expensive method of data collection. With the advances in video observation, pattern recognition and automatic image processing it is becoming increasingly feasible to use automatic means of traffic observation to detect critical situations, such as conflicts or even accidents. These techniques are already being applied in systems of incidence detection on some motorways around the world.
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-#### 3.3.5. Traffic exposure at intersections
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-On road sections, the number of vehicle kilometres is generally accepted as the relevant measure of exposure. At intersections, the correct measure of exposure is not that obvious, and has been the subject of many discussions.
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-It seems reasonable to suggest that some combination of the traffic flows on the intersecting roads is needed as the measure of exposure. The simplest combined measure is the sum of the daily traffic volumes. This is also a measure which is generally available, if traffic volumes are measured at all. But many other combinations are possible. The product of the daily flows, the sum of the products of the hourly flows, the sum of the products of the hourly flows- but subdivided into separate turning movements, etc. How does one decide on the correct measure of exposure, assuming that the right data can be made available. From logic, a multiplicative measure of flows seems more logical than an additive one. 999 Vehicles on one road interacting with one vehicle crossing on the other road would result in 1000 interactions according to the additive rule, as would 500 vehicles interacting with 500 crossing vehicles. According to the multiplicative rule, however, the first situation would result in 999 interactions, and the second one in 25,000 interactions, which seems a more reasonable result.
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-One way of establishing what is reasonable, is to calculate various indices of exposure and rank them for different intersections. The average number of accidents at those intersections could be recorded (over a period of some years) and also ranked. The measure of exposure that produced the highest correlation could be accepted for our purposes- being accident investigations (Mahalel & Hakkert,1976). The product of the daily crossing flows
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-and the sum of the products of the hourly crossing flows have been used as exposure measures in many accident studies.
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-This topic becomes even more complicated when one has to consider the correct exposure measure at roundabouts and at signalised intersections, where for obvious reason the logic described above does not hold.
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-#### 3.3.6. Pedestrian exposure and risk
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-In a way which is fairly similar to the discussion of risk and exposure at intersections one can consider the definition of risk to pedestrians. First one has to differentiate between pedestrians moving along the sidewalk (pavement) and pedestrians crossing the road. For pedestrians moving along the sidewalk a measure of exposure similar to vehicle kilometres on a road section - but in this case pedestrian kilometres - could be adopted. For pedestrians crossing the road, obviously the measure of exposure is linked to the number of pedestrians crossing the road. However it should also be some measure of the volume of vehicular traffic along the road. In this manner one obtains a measure of exposure which is the product of the traffic flow and the number of crossing pedestrians. Older & Grayson (1976) used PxV as a measure of exposure, P and V being the hourly numbers of pedestrians and vehicles on a certain road section. The risk was then defined by dividing the annual number of pedestrian accidents by the exposure. The reason accidents are counted annually and volumes hourly, is that the exposure has to be measured especially for such risk studies and is generally measured for a few hours only. Hakkert & Bar Ziv (1976) calculated various risk functions for pedestrians crossing under different road conditions- on zebra crossings, near zebra crossings, away from the crossing, at signalised crossings and near intersections. The proposed method seems to be reasonable for comparative purposes, i.e comparing the risk of crossing under different road conditions, but it remains to be proven that this technique by linking annual accident data with hourly volumes is robust enough. It also immediately becomes obvious that such calculations require special data collection efforts because the data needed are not generally available.
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-Another way of looking at pedestrian injury rates is proposed by Roberts, Norton & Taua (1996). In their study the number of streets crossed per child per day was selected as measure of exposure. Data from 2873 completed questionnaires were classified by number of cars in the family and by socioeconomic background. It was found that children from poor families and children from families without a car crossed a considerably greater number of street, compared with families with one car and even more so, compared with children from families with two cars. The values ranged between 7.4 street crossings per day to 1.7 crossings. As has been discussed above, it seems that this exposure measure is not perfect, not taking into account the pedestrian and vehicular volumes at each crossing, but it certainly provides for a better indication of risk than just calculating the number of accidents per child.
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-#### 3.4. **The use of fN diagrams**
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-The results of calculations on risk, especially in high-risk technologies, are often expressed in fN diagrams, which show the expected frequency f of an event that has at least N fatalities as a function of this number of fatalities. Figure 2 shows calculated fN curves for a number of high-risk technologies. This, in principle, enables a comparison of risks for society as a whole.
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-The graph starts with events with ten or more fatalities per year. Aviation appears highest on the curve with ten events with ten fatalities or more per year in the US. Rail transport comes lower down the scale with one event per two years (0.5). Lower still appear various types of transport of hazardous materials (LPG, chlorine, LNG). Except for rail transport, the order of the curves does not change when taking events with greater numbers of fatalities.
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-![](_page_21_Figure_3.jpeg)
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-Figure 2. Calculated fN curves for high risks in the US. In: Van Poortvliet (1999) from Cappola and Hall (1981).
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-For the various transport modes (road, rail, civil aviation and merchant shipping) the graph from Evans (1994) is illuminating. Figure 3 presents data for Great Britain for the years 1963-1992.
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-![](_page_22_Figure_0.jpeg)
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-Figure 3. fN-curves for British transport accidents: 1963-1992. From Evans (1994).
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-The total number of casualties and fatalities accumulated through the occurrences of a large number of events with only one or two fatalities is much higher for road transport than for the other modes of transport.
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-Because of the distribution of the number of fatalities per event in road transport in particular, the use of fN curves is not that helpful. The large majority of fatal road accidents incur one fatality or two. Events with many fatalities are rare and almost invariably capture the media's and politicians' attention with the result that actions are considered and taken which would not have occurred, had the same number of fatalities been spread over a large number of events. This points to a lack of rationality in terms of decision-making on the basis of cost-benefit considerations. In the other transport modes the frequency of events with small numbers of fatalities is much lower. Only rail industry has a considerable number of events with one death or more (around one hundred), compared with over five thousand in road transport. The number of large scale disasters with tens to hundreds of fatalities is reversed for the different modes. Merchant shipping had an event with 189 fatalities once in a hundred years, civil aviation had an event with 146 fatalities once in a hundred years. The corresponding figure for rail was 49, and for road transport 32.
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-# 4. **Some issues associated with the use of exposure and risk**
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-#### 4.1. **The desire to assess risk levels and to strive for equal risks**
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-According to a recent ETSC report it is important to collect exposure data for risk assessments that can be used to improve transport safety. ETSC defines the following ways to conduct such assessments:
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-- "monitoring casualty trends and evaluating policies which have been introduced to improve safety, in order to provide a sound basis for developing new policies
-- comparing the levels of risk of different types of travel ( for example by transport environment or transport mode)
-- setting priorities, that is identifying those transport situations with high levels of risk in order to formulate policies and concentrate resources to reduce risk levels, especially for high severity crashes (ETSC,1999)."
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-The safety policy for each mode of transport should be similar: research, develop and apply cost-effective countermeasures that have the potential to reduce crash risk and/or severity.
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-A basic assumption is that it is desirable to strive for equal risk to various categories of road users, including the vulnerable road user, in order to achieve equal risk on various categories of roads. Unequal risks on different categories of roads and for different categories of road users do, however, exist and it is almost impossible to envisage that such risks can be made equal at reasonable economic costs. Similarly, equal risks in various parts of the country, municipalities, regions, and provinces are almost certainly unachievable at reasonable economic costs. Even the calculation of such risks is almost impossible because of the lack of reliable and detailed exposure data on the provincial and municipal levels. If good data is not available, one resorts to the usual levels of aggregation-
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-national, provincial or municipal- per inhabitant, per kilometre of road, etc.
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-At the regional and municipal levels there is also the problem of interpretation. Some authorities have high levels of risk but have done a lot of work on road safety, others have done little. What is the correct measure of risk? At a high level of safety, the additional effort required to achieve more safety is much greater (more costly) than at lower levels of safety, and so positive results are more difficult to achieve. A further complication arises from the fact that equal levels of risk are unrealistic if conditions are not equal. Large municipalities generally also have higher population densities and have more congestion than smaller towns. As a result they will experience lower numbers of fatalities and seriously injured victims per capita. Similarly, provinces or regions that are highly urbanised will have higher population densities and experience lower levels of fatalities and serious injuries. This issue will be discussed in further detail in Section 4.6 which considers the cost-benefit considerations of safety measures and in section 6.1 which discusses accident rates and traffic flows.
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-In this context one should ask whether it is correct to strive for equal risk or for highest benefit-cost returns on safety improvements. Striving for equal risks on various parts of the road network or equal risks to various groups of road users sounds politically correct, but is, in all likelihood, impossible to achieve at reasonable economic costs. Urban roads will have fewer fatalities per kilometre travelled than rural roads, because of the lower speeds of travel. Motorways will have fewer fatalities per vehicle kilometre travelled than other types of rural roads, because of the level of segregation between different modes of transport (no slow vehicles) and the segregation of movements (no at-grade intersections). It would be more fruitful to try and find ways in which each category of road and road user is made safer by applying cost-effective countermeasures.
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-More generally, and along similar lines of thought, when dealing with risk comparisons between different activities- is it really worthwhile to strive for equal risk levels for citizens in a variety of occupations? Is it reasonable to expect a factory worker to be exposed to the same level of risk as the bank employee? Looking at the risk and exposure data collected in Evans (1993), this clearly does not seem to be the case. In banking and financial employment there are 0.17 fatalities per hundred-million hours of work, compared with a rate of 4.9 in construction work and 13.0 in mining. In transport modes (under certain rough assumptions of travel speed) the risk of a fatality per hundred-million passenger hours is 1.4 in a bus or coach, 12.4 in a car, 27.0 on foot and 342.0 on a motor-cycle. There is no realistic way in which it will be possible to achieve equal risks for these modes. This concept is considered 'unfair' by some. Others on the other hand do not think there is any unfairness (Ekman, 1996). No person who considers riding a motor-cycle would be doing so under the assumption that it will be as safe as riding a bus, or as safe as travelling in a car. What could be asked however, in such circumstances, is what safety measures could be taken that would improve the level of safety of motor-cyclists and would still be considered cost-effective. Some of these measures would also apply to other types of road users. An example of such a measure is making car fronts safer, which would lead to lesser injury severity when crashes with motor-cyclists and other road users occur.
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-#### 4.2. **Over-representation and risk**
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-Much of the work on risk in safety studies seems to stem from accepted and established procedures taken from the field of epidemiology as practiced in medical and health related studies. From epidemiology a logic is adopted which seems to be the basis for the use of exposure and the calculation of risk (Hauer, 1995a). It is the search for over-representation in various populations under study that lies at the basis of this approach. Searching for over-representation seems to suggest the following line of thought and action:
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-[deviation-from-normal] leads to [clue-to-cause] leads to [clue-toremedy]
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-Many classical epidemiological-medical studies have successfully taken this approach and in some cases led to major achievements.
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-One underlying requirement in this chain of thought is a further assumption which is the "presumption-of-sameness-except-for-the-suspected-cause" (Hauer,1995a; Ekman, 1996). In medical studies, this might well be a legitimate assumption in many cases. Hauer (1995a) presents the example of John Snow, who established in 1854 that people who live in the same districts, but whose water is supplied by different companies, had vastly different incidences of cholera, many years before the discovery of the vibrio of cholera. From this Snow could infer what the cause of the cholera epidemic was and what the remedy might be.
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-In transportation safety studies, the presumption of sameness hardly ever holds good. In the comparison of accident and fatality rates for different modes of transport (air travel vs. road travel), rates for different modes of road transport (lorries, cars, motor-cycles, etc.) it is definitely wrong to assume sameness. By itself, over-representation in accidents or fatalities has no discernable logical link to funding, programming, decision or action. The decision-making process should be concerned about establishing the relevant facts, adopt a set of proven countermeasures and base the decision on the cost effectiveness of the preferred solution.
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-A subject that has received a lot of attention in recent years is the issue of older drivers and their 'over-representation' in accidents or their higherthan-average risk (Hauer,1995a). The motivation behind this seems to be related to the fact that in many countries society is ageing, and that, therefore, something should be done about this over-representation. Indeed, when the rate is calculated 'per mile driven', drivers in the 75+ group have trice the rate of drivers in the 35-40-year-old age group. Yet, when looking at the numbers, 1486 drivers who were 75-80 years old died in the US in 1986, whereas 3203 drivers in the 30-35-year-old age group died in the same year. This indicates that in terms of absolute numbers, the focus should be on the 30-35-year-old group. It seems perfectly clear that the quest for countermeasures should be driven by improving the safety record for the older driver. This could be done by adapting the highway infrastructure to better suit the older driver, or by other means that could be implemented in a cost-effective manner. The knowledge about the higher involvement rate does not help us further in this matter.
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-#### 4.3. **Doing without information on risk and exposure - an example**
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-The following study on the effect of seat belts can serve as an example that demonstrates that it is not always necessary to use exposure data and calculate risks to measure the effect of a safety measure. Yet, the example also shows that omission of exposure data may result in inexplicable outcomes or mistaken conclusions.
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-In 1983 a law requiring the use of safety belts in cars came into force in Great Britain. Subsequently, Professors Harvey and Durbin from the London School of Economics were asked to evaluate the safety effect of this law. Their results are described in Harvey & Durbin (1986). The authors looked at a time series of fatal and seriously injured car drivers and passengers over a period of fifteen years starting in 1969 and continuing till December 1984, two years after the introduction of the law (Figure 4).
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-![](_page_26_Figure_0.jpeg)
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-Figure 4. Car drivers killed and seriously injured in the UK, 1969-1984. From: Harvey and Durbin (1986)
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-Car passengers were further split into front seat passengers who were directly affected by the law and rear seat passengers. Applying statistical structural time-series models the authors were able to model the series for the before period and estimate the effect of the law by comparing the actual fatality and serious injury figures with their estimate of what those figures would have been without the introduction of the law. Their estimate of the effect was a significant reduction of 23 per cent in the numbers killed and seriously injuries for car drivers and 30 per cent for front seat passengers. For rear seat passengers, however, a small increase of 3 per cent was found. For the number of persons killed similar large reductions in the number of fatalities were found for car drivers and front seat passengers, but the number of rear seat passengers killed increased by 27 per cent. The authors could not find a satisfactory explanation for this increase. One explanation put forward was that there was the possibility that after the introduction of the law, which did not affect rear seat passengers, a certain number of passengers transferred from the front seat to the rear seat. It is exactly this type of question and outcome that could have been answered in detail, if at the time a series of exposure measurements had been in place counting, over time, the number of passengers in front seats and in the rear of cars.
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-Risk can serve as the basis for the selection of research and action themes which should then be justified on the basis of cost-benefit considerations. Decision-making can be carried out on the basis of comparative risk levels, on the basis of accident and injury data (without risk) and according to costbenefit considerations. Each method has advantages and disadvantages and should be selected according to the circumstances. Decision-making on the basis of the numbers of accidents, injuries or fatalies focuses on the size of the problem and prevents us from focusing our efforts on a segment of road safety that might look bad from the point-of-view of risk but which
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-might not be very significant in terms of the quantities involved. Decisionmaking on the basis of risk tends to lead to actions that are considered fair in terms of the road users involved. If a segment of road, or a certain type of road, or a group of road users has a risk that is higher than the risk for another group or higher than average, this is considered unfair and decision-makers are urged to adopt policies that would lead to an absolute and relative decrease in risk for that group. In practise, such decisions are not reasonable, not possible and almost certainly not economical. Motorcycle riders have higher risks than car occupants, pedestrians have higher risks of dying from an injury than car occupants, regional and local roads have higher levels of risk than motor-ways and so on. The final decision should generally be based on cost-effectiveness considerations because, in the field of road safety, like in any other field, the amount of expenditure is limited and investments should be allocated to those fields that produce the highest accident, casualty and fatality reduction that can be achieved per unit of money invested.
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-#### 4.4. **National safety levels and targets**
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-National road safety targets are generally set in terms of numbers of accidents, casualties and fatalities, or percentage reductions to be achieved. This has been the case in The United Kingdom, The Netherlands, Sweden, Australia and many other countries. There are several reasons for this approach. First, in terms of the impact on the population, it is the absolute numbers that count. Focusing on risk might show a reduction in risk which might be the result of an increase in exposure being greater than the increase in accidents or fatalities. This would thus not lead to an absolute reduction in the number of casualties or fatalities. Targets based on risk levels are also undesirable because of the fact that risk levels in general tend to decrease over time because of the whole range of road safety activities and because of inherent changes in the traffic volumes and composition. Once having set targets in terms of numbers, it is however possible to use risk levels to check for changes in certain areas, regions or municipalities. If the number of casualties or fatalities in a region is increasing faster than average, it is worth looking at the exposure and risk to study the differences before reaching conclusions.
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-When dealing with trend analyses of road safety phenomena, it might be possible to work with the rough accident and injury data for short-term prognosis. On the basis of past trends, when dealing with before-after evaluations, it is generally possible to apply time series models and intervention-type models to predict the short-term effect of a countermeasure using accident and casualty data. In the case of long-term projections, 5-10 years onwards, it is generally necessary to first estimate the underlying trend in exposure data before making a prognosis.
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-#### 4.5. **What is an acceptable level of risk?**
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-In a general way, the question of what is an acceptable level of risk in road traffic is virtually impossible to answer. Any limit other than zero risk is arbitrary and disputable. Zero risk, however, may be a valid ambition in theory, but hardly possible, if at all, to realise in practice. Furthermore, when talking about the acceptable level of risk a distinction must be made between various types of risk, for example individual risk versus societal
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-risk and voluntary risk versus compulsory risk. What risk level is acceptable for an individual road user when he or she makes a particular trip with a particular transport mode? What is the acceptable risk level for society as a whole, in other words how many accidents or casualties per year per unit of exposure are we as a society willing to accept at public roads? And is it reasonable to accept higher risks when these are taken on a voluntary basis (e.g. a recreational bicycle tour) than when taken on a compulsory basis (e.g. for professional drivers or home-work travel). Some of these questions are answered by society, through actions that government and local agencies take on the programs and budgets devoted to road safety. There are no absolute and correct levels, as becomes obvious when looking at the actions taken in different countries, which result in them having different levels of risk and safety. At the individual level there are also no correct and absolute answers. Individual risk varies from person to person, from task to task and can even vary for the same person depending on mood, timepressure and whether the risk is voluntary or compulsory.
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-An interesting issue related to risk and acceptability considerations is raised by Erskine (1996). Erskine makes the distinction between risks to those who have access to cars and those who do not have access. He points out that, although risks associated with the use of cars, in terms of both the number of casualties and fatalities per vehicle- km travelled, have been decreasing considerably over the years, it is the parts of the population that do not have access to cars, i.e. the poor, the young, the old and women (i.e the nondriving population) that are adversely affected by the increased exposure to motorised travel. In order to come to more socially responsible decisions, the needs of these separate populations should be taken into account.
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-Along a slightly different line of thought, one could also make the distinction between risks involved as a passenger or driver of a certain type of vehicle, and the risks associated with the use of such a vehicle to other road-users. In this way, lorries are a very safe means of transport to their occupants, because very few fatalities occur to drivers and passengers of lorries, both in absolute numbers and expressed as fatalities per vehicle-km travelled, but they are involved in a proportion of fatal accidents which is considerably larger than their share in vehicle kilometres driven. The same is the case for buses. Cars are also becoming safer and safer for their occupants, because most technological innovation and development has been applied to improve the safety of the occupant: US and European safety regulations, seat belts, air bags, etc. Thus the risk of being injured or killed per vehicle-km travelled continually decreases. However, the issue of injuries and fatalities caused by these vehicle to the vulnerable road-user are only now being addressed and still there are few technological developments that are effective.
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-Similarly, relevant for the transport of dangerous goods in particular, one could make a distinction between the accident or injury risk for road users and the risk for third parties, for example for those living in the area and being exposed to toxic chemicals following a road accident with a dangerous goods truck. In the Netherlands this distinction is made in the external safety report which is required for the dangerous goods transport industry. As an indicative value, the third party risk in this type of industry should not exceed 10-4 for events resulting in 10 victims and 10-6 for events resulting in 100 victims per year per kilometer route (RIVM, 1998).
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-### 4.6. **Risk versus effectiveness**
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-So far the issue of the economic costs associated with countermeasures that aim to change levels of risk and thereby the number of casualties or fatalities in the transport system has not been dealt with in this report. Riskbenefit assessment has been applied in such diverse fields as medicine, public health and environmental protection. The methods used in these fields might also be applied to the field of motor-vehicle safety (Thompson, Graham & Zellner, 2001). Risk-benefit analyses can be categorised according to the situation and may include cases where:
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-- neither risks nor benefits can be readily quantified;
-- risks can be quantified, but benefits cannot readily be quantified (or vice versa);
-- risks and benefits are quantified in very different terms or units;
-- risks and benefits are quantified in similar terms.
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-It is this last category which has been focal in medical and pharmaceutical research and which is of great interest to the transport safety field, especially when considering the introduction of road safety devices or other countermeasures. Thompson, Graham & Zellner (2001) discuss a number of quantitative approaches to deal with this issue.
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-One approach (QALY) calculates the quality-adjusted life years associated with a certain safety measure. QALYs combine information on duration of life and health-related quality of life into a numerical index. This technique is widely used in the medical field to assess new drugs or medical interventions and could be applied in the road safety field as well.
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-A slightly different technique, called DALY is promoted by the World Bank and the World Health Association (WHO). DALY also combines information on duration of life and quality of life into a numerical index that can be aggregated across people. The DALY approach evaluates health states in terms of a single dimension, degree of disability, whereas QALY addresses a wide range of dimensions of quality of life.
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-A third approach which has been suggested calculates the Normalised Injury-Fatality Costs (NIC). It is a research-based tool which assumes that all health-state values and preferences are reflected in the actual lifetime costs of medical treatment, ancillary costs (which include costs of permanent partial incapacity) and fatality costs to society. NIC intentionally neglects so-called 'pain and suffering' costs, as these were believed to be subject to enormous variations.
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-Despontin, Verbeke & Brucker (1997) suggest that decision-makers should approach the issue of road safety countermeasure evaluation in a twodimensional way as illustrated in Table 6.
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-| Factor contributing to<br>accidents / safety<br>characteristics | Road user                                                                                             | Vehicle                                                                                                                                         | Road                                                                                                                    |
-|-----------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|
-| Exposure                                                        | - Separation of different road<br>user categories<br>- Changes in number of<br>trips-                 | -<br>Regulation of vehicle<br>traffic                                                                                                           | -<br>Regional planning of<br>traffic control<br>-<br>Road improvements<br>reducing travel distances                     |
-| Risk                                                            | - Improvement of education,<br>information and road user<br>behaviour in relation to<br>traffic rules | -<br>Crash avoidance<br>measures: vehicle speed<br>regulations<br>-<br>Vehicle equipment<br>(studded tires, running<br>lights in daytime, etc.) | -<br>Improvements on roads,<br>streets or traffic network<br>-<br>Speed reduction signs<br>-<br>Road/street maintenance |
-| Accident consequence                                            | - Individual protection<br>equipment (seat belts,<br>helmets)<br>- First aid education                | -<br>Improved crash tolerance                                                                                                                   | -<br>'Soft' road side design<br>-<br>Alarm telephones                                                                   |
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-Table 6. A classification of road safety measures. In: Despontin, Verbeke & Brucker (1997) from OECD (1981).
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-Safety measures are classified on the basis of two criteria. The first criterion is related to 'factors contributing to accidents'- the road user, the vehicle and the road. The other criterion relates to the safety characteristic changed, namely exposure, risk or accident consequence. The final decision on which safety countermeasure should be preferred should in each case be taken on a cost-benefit basis.
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-Elvik (1997) also thinks that cost-benefit analysis will become increasingly important in the field of road safety decision-making. One reason is the limited financial resources that will be available for road safety. Funds should therefore be applied as efficiently as possible. Another reason is that more and more often a choice will have to be made between competing objectives, which could be environmental or based on mobility, energy or safety. Converting conflicting objectives to an economic yardstick is one way of treating problematic decisions.
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-One underlying dilemma has not been adequately resolved. On the one hand, in terms of accident or fatality risk, the transport system becomes safer as the years go by. In almost all countries for which reliable statistics are available, risks have been decreasing over time. At the same time, exposure, measured in vehicle kilometres travelled has continued to increase in most countries. The result is that in many cases the absolute number of accidents, and in some cases the number of fatalities have continued to increase. As to effectiveness it could be said that the transport system has become more efficient in terms of the number of accidents or fatalities it produces for each unit of exposure, however, in overall terms of safety, the system has become less safe by producing more casualties, or fatalities in some countries, as a result of the increased exposure. To produce further safety improvements it should therefore be attempted to reduce levels of exposure by modes of transport that have high risks associated with them and convert some of that exposure to modes that
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-have lower risks. Even that approach should be regarded with caution as the following two examples will demonstrate.
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-The first example is taken from Evans, Frick & Schwing (1989). It is often stated that the most dangerous part of a trip by plane is the trip to the airport. Evans, Frick & Schwing show this not to be the case. When comparing air travel with road travel, the risks look very much worse for road travel. Death rates per billion miles travelled are 0.6 by air, compared to 24 by road. But, there are three reasons why such a comparison is inappropriate. First, the airline rate is 'passenger fatalities per passenger mile', whereas the road rate is 'all fatalities (including occupants, pedestrians, motorcyclists, etc.) per vehicle mile'. Second, road travel that competes with air travel is mostly done on rural freeways, which are among the safest part of the road network. Third, driver and vehicle characteristics vary over a wide range of risks. In their study, Evans, Frick & Schwing make a number of assumptions and show that a 40-year-old, belted, alcohol-free driver of a car which is 700 pounds heavier than average is slightly less likely to be killed in a 600-mile-trip on the rural interstate freeway system than in a regularly scheduled airline trip of the same length. For 300 mile trips, the driving risk is about half that of flying.
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-In discussing costs and benefits associated with road safety measures two approaches are possible and are in widespread use. The first approach evaluates all the costs associated with the introduction of a certain safety measure and compares these with the benefits achieved from the reduced number of accidents, casualties or fatalities expressed in monetary values. The second approach does not convert the reductions in accidents and casualties into monetary values, but compares the numbers of accidents saved with the costs involved. This is the cost-effectiveness approach. In cost-benefit calculations different values are adopted for accidents, casualties of varying degrees of severity and for fatalities in different countries. Even the theoretical methods adopted in the different countries are not the same. Some are adopting the gross loss of output approach, some the net loss of output approach and some adopt the approach considered more advanced and correct, the willingness-to-pay approach. This approach is considered by some to be un-ethical and therefore to be avoided. It is however wrong to assume that by taking the cost-effectiveness approach such issues can be avoided. If one safety scheme prevents a certain number of casualties of varying degrees of severity and another scheme prevents a different mix of severities at a different cost, the decision still has to be made how to weigh these different mixes. The monetary conversion adopts one particular mix.
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-# 5. **Some examples of the use of risk calculations in a number of road safety applications**
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-Having established that there is no universal treatment of exposure and risk in safety studies and evaluations, it is illuminating to present some examples of the way in which exposure and risk should be used in a number of applications. The first examples are taken from the safety literature. These are followed by a number of examples taken from a number of studies conducted by researchers at SWOV.
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-#### 5.1. **The involvement of lorries in fatal accidents**
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-It is well known and documented that lorries are involved in a considerable percentage of fatal accidents in most countries. In Sweden, in 1992, out of the total number of fatalities- 759 in the year 1992, 132 fatalities (of which seven lorry occupants) occurred in collisions with lorries and another seven fatalities occurred in single vehicle lorry accidents (Table 7).
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-| Killed as      | Killed in          |              | In collision with |     |    |                |         |        |       |      |       |     |
-|----------------|--------------------|--------------|-------------------|-----|----|----------------|---------|--------|-------|------|-------|-----|
-|                | single<br>accident | Pass.<br>car | Lorry             | Bus | MC | Mop./<br>cycle | Tractor | Animal | Train | Tram | Other | Sum |
-| Car occupant   | 195                | 133          | 80                | 16  | 1  |                | 3       | 14     | 14    | 10   |       | 466 |
-| Lorry occupant | 7                  | 2            | 7                 |     |    |                | 1       |        |       |      |       | 17  |
-| Bus occupant   | 2                  | 0            | 0                 |     |    |                |         |        |       |      |       | 2   |
-| Motor cyclist  | 19                 | 10           | 1                 |     |    |                |         | 3      |       |      |       | 33  |
-| Mopedist       | 2                  | 7            | 7                 |     | 1  |                |         |        |       |      |       | 17  |
-| Bicyclist      | 9                  | 41           | 20                | 3   | 1  | 2              |         |        |       |      |       | 76  |
-| Pedestrian     | 0                  | 92           | 17                | 7   | 2  | 3              | 5       |        |       | 4    | 8     | 138 |
-| Tractor        | 1                  |              |                   |     |    |                |         |        | 3     |      |       | 4   |
-| Others         | 5                  | 1            |                   |     |    |                |         |        |       |      |       | 6   |
-| All killed     | 240                | 286          | 132               | 26  | 5  | 5              | 9       | 17     | 17    | 14   | 8     | 759 |
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-Table 7. Fatalities related to vehicles or road users and accident types in Sweden, 1992. From Nilsson, 1997).
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-From the absolute numbers it would seem that, on the one hand, lorries are a very safe mode to their occupants, with only 14 fatalities per year, compared with 466 killed car occupants, 138 killed pedestrians or 76 killed cyclists. On the other hand 132 fatalities in collisions with lorries is 17% of the total number of fatalities and is a much higher proportion than the number of lorries in the total vehicle population. This seems to lead to a number of possible conclusions.
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-- It is likely that lorries drive a much greater distance per year compared with other types of vehicles.
-- It is likely that because of their greater mass, once an accident occurs, the outcome will be more severe and the injured person is most likely to be in the other vehicle or to be a vulnerable road user.
-- It is possible that lorry drivers are bad drivers and are therefore involved in a higher proportion of collisions than their share in the vehicle park.
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-Without additional information on the exposure background of lorries and other types of vehicles it is not possible to come to the correct conclusions. Even with the correct exposure information for the various types of vehicles it is very difficult to obtain reliable exposure information for the vulnerable road users, the pedestrians and the cyclists, and even when that is available, from travel surveys or other means, it generally applies only to the adult population and not to children.
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-One additional complication manifests itself in the calculation of involvement rates (risks) of lorries. This issue was recently treated by Hauer (2001). Hauer's conclusion is that, for the rates to be calculated correctly, if vehicle exposure is used in the denominator, then the count of vehicles involved in accidents must be in the numerator (and not the number of accidents in which such vehicles are involved). Similarly, if driver exposure is in the denominator then the count of drivers of a certain category in accidents should be in the numerator and not the number of accidents involving that category of driver.
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-A second issue discussed by Hauer (2001) is the question of over-representation. His question is- if some type of entity is found over-represented in reported accidents of specific severity, may one conclude that entities of this type are in fact more likely to be involved in accidents? His answer is 'no'. If one is dealing with reported accidents of a specific severity, as one almost invariably is, then the over-representation may be caused by a mix of three factors: the probability to be in an accident per unit exposure, the probability of the accidents to be reported and the probability of the accident to be of the specified severity. Without knowing these three probabilities it is not possible to come to the right conclusions. This issue applies to both lorries and vulnerable road users in particular. It applies to lorries, because on account of their greater mass they are more involved in accidents with higher severity and are therefore likely to be over-reported in registered accidents. To calculate the accident risk of lorries, looking at the registered accidents will result in over-representation, which might not be the case if 'accidents with material damage only' had also been taken into account. The issue also applies to the vulnerable road user, especially the bicycle, for which it is known that accidents are greatly under-reported and are therefore not found in the general accident statistics in their representative share.
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-A recent report by Lyman and Braver (2001) also addresses the issue of large truck safety by analysing various crash involvement rates. The study examined different measures of truck crash fatality risk, to better understand how two main indicators – the public health aspect of large truck crashes and the risk per unit of travel - have changed over a period of 25 years. The study looked at large truck occupant fatalities (trucks over 10,000 pounds), and passenger vehicle occupant fatalities and calculated their rates per 100,000 members of the population, per 10,000 licensed drivers, per 10,000 registered trucks and per 100 million vehicle miles of travel (VMT) for each year over the period 1975-1999. Results show that in 1999, large truck crashes resulted in 3,916 occupant deaths in passenger vehicles and 747 in large trucks. Passenger vehicle occupant deaths in large truck crashes per 100,000 members of the population have increased somewhat since 1975 (1.28 in 1975, 1.44 in 1999). There has been a considerable decrease in occupant deaths per truck VMT since 1975, but the percentage reduction has been greater for occupants of large trucks (67%)
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-than for occupants of passenger vehicles (43%). However, truck drivers are at an elevated risk of dying relative to their numbers in the workforce. Large truck involvements in fatal crashes per truck VMT decreased by 68% for single-vehicle crashes and by 43% for multiple-vehicle crashes. In contrast, passenger vehicle involvements in fatal crashes (including those that did not involve a large truck) per passenger VMT have decreased less (33% for single-vehicle crashes, 23% for multiple-vehicle crashes).
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-Both arguments brought forward by safety groups and by the trucking industry are correct in their assertions. Large truck involvement in fatal crashes has dropped substantially when measured per unit of travel, but the public health burden of large truck crashes, as measured by death per 100,000 members of the population, has not improved over time because of the large increase in truck mileage. Countermeasures are needed to better protect both passenger vehicle occupants in collisions with large trucks and the occupants of large trucks.
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-#### 5.2. **Drivers involved in accidents while under the influence of alcohol**
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-One of the important topics in road safety and road safety countermeasures is the subject of driving under the influence of alcohol (DUI). Work on this subject began many years ago and has always been high on the agenda of decision-makers and the police. A major mile-stone in this field was the study of Borkenstein et al (1964). The study became known in the technical literature on the subject as the Grand Rapids study. Borkenstein collected information on alcohol levels of drivers involved in accidents and similar information on drivers in the population at large (not involved in accidents). Borkenstein's study involved about 5000 accident-involved drivers and a similar number of drivers as control group. From this information Borkenstein developed a risk function which describes the over-involvement of drinking drivers in accidents as a function of their blood-alcohol level (Figure 5). Without collecting the information on the general driving population, which serves as the exposure measure, it would be impossible to determine the over-involvement.
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-From the curve it can be seen that at alcohol levels of between 0.5 till 0.79 promille the chance of an accident increases significantly by 35% compared with the zero alcohol level. At 0.8 to 0.99 promille the chances have increased by a factor two, and above that the risk of involvement continues to increase exponentially. A subsequent study, conducted along similar lines in Germany (Kruger, Kazenwadel & Vollrath, 1995) produced similar results. At all levels of alcohol consumption accident-involved drivers were overrepresented. The degree of over-involvement was even somewhat higher than in the original Borkenstein study.
-
-Having collected accident data and exposure data on such a large scale the Borkenstein study was regarded as authoritative and provided the foundation on which many countries established their blood alcohol limits. A few countries, including the Netherlands, adopted a 50 g/100 l limit, more countries adopted an 80 g/100 l limit and the US initially adopted a limit of 100 g/100 l (Mathijssen and Twisk, 2001). Countries in the East-European block still maintain a zero-tolerance limit, as does Iceland. The Scandinavian countries maintain a 20 g/100 l limit.
-
-![](_page_35_Figure_0.jpeg)
-
-Figure 5. The relationship between Blood Alcohol Concentration (BAC) and accident risk. From: Mathijssen & Twisk (2001) after Borkenstein et al., 1964.
-
-One initial by-product of the original Borkenstein study was that an underinvolvement in accidents seemed to occur at low levels of alcohol consumption, between 10 g/100 l and 40 g/100 l. This phenomenon became known as the Grand Rapids Dip (Hurst, Harte & Frith, 1994). Subsequent reanalysis of the original data by Hurst and others has shown that this dip was a result of the statistical composition of the samples and when this is taken into account correctly, the dip does not exist.
-
-The use of exposure data in this example was crucial in determining the effect of alcohol in accident-involved accidents. Without the exposure data it would not have been possible to measure the extent of over-involvement and decision-makers would not have had the scientific basis on which to adopt countermeasures, which in this case were the alcohol limits. Through the adoption of this course of action the number of drivers who continue to drive while under the influence has decreased considerably in most countries.
-
-A similar approach is now essential in determining the correct course of action regarding regulations on the use of drugs and medication. Without extensive surveys on the use of these substances by the driving population at large, in comparison to the driving population involved in accidents, it will not be possible to come to scientifically based regulations. Without the scientific basis, it will be difficult to persuade the public and the decisionmakers to follow the right course of action.
-
-#### 5.3. **The effect of speed on the risk of accident involvement**
-
-Another issue which is of major importance in road safety research is the effect of speed on the risk of being involved in an accident. Because of the extensiveness of this topic it will not be treated in detail, but some points which bear upon the issue of risk will be discussed. Speed is not generally mentioned as a major contributor to accidents, when looking at the possible causes of accidents detailed in accident reports filled out by the police who arrive at the scene of an accident. This absence is sometimes taken to mean that speed is not a major contributing factor to accidents. The reason that speed is not mentioned in such reports is that it is almost impossible to determine the speed of a vehicle involved in a crash without an in-depth investigation, which is not normally conducted by the police. As a result police officers tend to stick to the manoeuvres and actions, prior to the accident, that they can prove and report; such actions as not giving way, changing lanes, not stopping for a stop sign etc.
-
-Research has shown, however, that speed is indeed a major contributory factor in accidents and should be dealt with as a topic of great importance. But speed in itself is not all that needs to be known in order to investigate the subject. Of importance are also the speed in relation to the speed of other vehicles using the road,this is generally called the speed differential and was described in detail by Solomon (1964). Another factor of major importance is the variation in speed (speed variance), which was described in detail by Finch et al. (1994). Both these variables indicate that, in order to study the importance of speed on accident involvement, one has to have further details on the speed of the traffic at large and not just of speeds of vehicles involved in crashes. The information on traffic speed should be regarded as a measure of exposure.
-
-Finch et al. (1994) reviewed data from a variety of sources that seems to indicate that the relationship between accident involvement rate and deviation from the mean traffic speed is U-shaped. Taylor, Lynam & Baruya (2000) referring to studies of Maycock et al. (1998) and Quimby et al. (1999) report different results. The increase in risk at higher speeds than average was established but the increase at lower speeds than average could not be determined. In this case the relevant exposure measure is the speed of vehicles travelling along a stretch of road. Not only the mean speed of such vehicles is of importance but also the speed variance. Once such information is known it becomes easier to determine the accident potential of such a road or to consider the possible effects of countermeasures that are contemplated.
-
-It seems that speed and speed distribution are significant factors in establishing the risk of an accident and in determining the severity of such an accident. The implication of this for accident and risk studies is, that speed information should be taken into account. As in many cases speed information is not available, great care should be taken when calculating and comparing risks on various parts of the road and transport system. It seems wise to avoid comparing risks between parts of the transport system that have very different speed distributions or between modes with widely different speeds, as much as possible.
-
-#### 5.4. **A preliminary study to explore directions for elaborating on risks**
-
-In 1998 the Dutch Ministry of Transport decided to reinforce its road safety policy by promoting the use of risk indicators. Roszbach (1998) prepared a report which was intended as a preliminary study on the subject exploring possible directions for the quantification of risks. Roszbach dealt with the definitions of exposure and risk, compared various types of risk and discussed problems associated with the quantification of risk in various applications. Roszbach points out that, in terms of policy setting, working with risk indices on a provincial and municipal level can be counterproductive. It might be better to work with absolute numbers of accidents, casualties and fatalities. Increased mobility leads to more vehicle kilometres (increased exposure). Accident rates (risks) decrease with increased exposure and as a result those provinces and municipalities with larger increases in mobility might be rewarded for improved performance. Another point made by Roszbach, as indeed also made in the present report, is that exposure data should reflect the problem being studied. The report also points out that for the assessment of infrastrucure safety it is better to work with accident rates as a measure of traffic volumes. In this report this has been termed Safety Performance Functions (SPF). This approach is to be preferred over working with average accident rates per type of road section and intersection (index numbers). Roszbach suggests a step-by-step approach to safety decision-making:
-
-- Determining that something has to be done. On this level it is often possible to work with the raw accident and casualty data;
-- Determining where something should be done. In some cases exposure data will already be required at this level;
-- Determining what should be done. At this level we generally need comparative and normalised data.
-
-#### 5.5. **Predictions of traffic safety in 2010 in the Netherlands**
-
-A report by Commandeur & Koornstra (2001) analyses road safety developments in the Netherlands during the period 1948-1998 at a nationally aggregated level. The purpose was to find models that could describe developments of mobility (exposure) and fatality and casualty risk as accurately and as simply as possible. Having developed fairly accurate models, prognoses were made of future developments in mobility, casualty risk, number of road fatalities and hospitalisations up to the year 2010. The models developed presented a fairly accurate presentation of the trends in past years. Yearly deviations from observed mobility and risk were studied. It was found that there is a relation between the deviations for mobility and fatal risk figures. It appears that mobility deviations show a strong, negative trend relation with the risk deviations ten to eleven years later. The models developed seemed to provide a strong basis for the prognoses of mobilty, casualties and fatalities produced in the report. One of the drawbacks of these models is related to the relationship between mobility and risk deviations. It seems to be that they are very country-specific and have to be carefully calibrated on local data. Negative trend correlations in different countries varied between four and twenty years and in some countries the correlations were positive instead of negative (Commandeur, 2002).
-
-#### 5.6. **The involvement of elderly drivers in accidents**
-
-One of the issues which has received attention in recent years is the involvement of elderly drivers in crashes. This interest is partly the result of various misconceptions about elderly drivers, and partly of the growing number of the elderly in the driving population in motorised societies. It seems therefore relevant to have a clear picture on this issue, based on data and research findings. A recent SWOV report by Davidse (2000) dealt with this issue. The report consists of three main parts. First, an analysis is conducted of the present situation in the Netherlands, based on crash data, population data and travel distance data. Secondly, a prognosis is made of future trends and implications and finally an inventory and evaluation of measures to possibly lower the risks for elderly drivers are presented. The present report will deal only with the first part of the study. Davidse calculated the risks, the number of casualties and the accident involvement of elderly car drivers and compared them with various other age groups. The elderly were defined as being 65 years old and older,and were sub-divided into two groups: aged 65-74 and aged 75 and older. Other age groups were 18-24, 25-29, 30-39, 40-49, 50-59 and 60-64 years old. Rates were defined as car driver casualties, or fatalities or accidents per thousand million (billion) vehicle kilometres travelled. In most rates, a U-shaped curve was established of risk against age groups, with increased rates for the young and old drivers. The main conclusions drawn by Davidse were that:
-
-- The 75-year-olds and older had the highest chance of being killed in a road accident (death risk). The 18-24 year olds came second. The 75-year-olds and older came second in victim risk (the chance of being killed or injured, regardless of the severity). The 18-24 years old had the highest victim risk.
-- The death risk of those 75-year-olds and older is high because they are (physically) more vulnerable than other age groups. On the other hand, however, the death risk of the (male) 18-24 years old is high because they are more often involved in accidents than other age groups. The elderly car driver will, therefore, profit from an improvement of secondary safety more than other car drivers.
-- A reduction of the number of accidents involving elderly car drivers (primary safety) can be achieved by designing measures that are specifically aimed at the types of accidents in which the elderly more often appear to be the 'guilty party'. These are especially accidents resulting from not giving the right of way and accidents involving leftturns at intersections.
-
-A recent study by Li, Braver & Chen (2001) came to very similar conclusions. Using national US data systems, the role of fragility (susceptibility to injury) versus excessive crash involvement in the increased fatality risk of older drivers per vehicle mile of travel (VMT) was estimated. For each age and gender group, deaths per driver involved in a crash (a marker of fragility) and drivers involved in crashes per VMT (a marker of excessive crash involvement) were computed. Compared with drivers aged 30-59, those younger than 20 and those aged 75 or older both had much higher driver death rates per VMT. The highest death rates per mile driven, 13-fold increases, were observed among drivers aged 80 or older, who also had the highest death rates per crash. Among older drivers, marked excesses in crash involvement did not begin until age 75, but explained no more than
-
-about 30-45% of the elevated risk in this group of drivers; excessive crashes explained the risk among drivers aged 60-74 to a lesser extent. In contrast, crash overinvolvement was a major factor contributing to the high risk of death among drivers younger than 20, accounting for more than 95% of their elevated death rates per VMT. These findings suggest that measures to improve the protection of older vehicle occupants in crashes will be more effective than measures directed towards reducing the involvement of older drivers in crashes.
-
-The above two studies demonstrate the usefulness of rates in the analysis of a problem. By focussing separately on the crash involvement rates and the fatality rates per vehicle kilometre for drivers of different age groups, the correct countermeasure approach could be identified. Some of these results could have been identified by just working with the fatality and casualty numbers, by calculating the number of fatalities per hundred crashes for each age group, but this would not have taken into account the relative amounts of travel for each age group.
-
-#### 5.7. **The involvement of young drivers in accidents**
-
-One example of the necessity to work with both the accident numbers and risk rates is demonstrated in Twisk (2000). In the Netherlands, in the period between 1985 and 1997 the number of young drivers involved in serious accidents dropped considerably. The number of drivers in the age group 20-24 involved in serious accidents decreased from 2829 in 1985 to 1502 in 1997, a decrease of 47%. The number of drivers in the age group 18-19 decreased from 682 in 1985 to 343 in 1997, a decrease of 50%. The number of accident-involved drivers in other age groups decreased by a much smaller percentage: from 9082 in 1985 to 8050 in 1997, a decrease of less than 12%. Figure 6 shows the absolute number of young drivers involved in serious accidents for 18-19 and 20-24 year old male and female drivers. Figure 7 shows the number of vehicle kilometres driven by these groups and Figure 8 shows the accident risk developments.
-
-It immediately becomes clear that the large reduction in the number of young drivers involved in serious crashes is the result of reduced exposure. In the report by Twisk the reduction in mileage is further traced to demographic and other changes that occurred over the period concerned, which had a major effect on the age group studied. An economic recession affected the number of licensed young drivers, and free public transport travel passes were issued to young students.
-
-The study demonstrates the need to work in an ad-hoc fashion, on both the raw casualty data and on exposure and risk rates to come to a comprehensive investigation of a certain phenomenon.
-
-![](_page_40_Figure_0.jpeg)
-
-Figure 6: Absolute number of young drivers involved in serious accidents by age group, sex and year in the Netherlands. From: Twisk, 2000.
-
-![](_page_40_Figure_2.jpeg)
-
-Figure 7: Mileage (kms) by age group, sex and year in the Netherlands. From: Twisk, 2000.
-
-![](_page_41_Figure_0.jpeg)
-
-Figure 8. Accident risk of young drivers versus total driver group by sex and year in the Netherlands. From: Twisk, 2000.
-
-#### 5.8. **Road safety in the Netherlands up to 1999**
-
-Van Schagen (2000) published a report which describes the extent and nature of road safety in the Netherlands over a period of 15 years and which served as the basis for prognoses on the developments in road safety to be expected in the future (the year 2010). Trends, backgrounds and explanations of the data were presented. A number of developments which are known to have a strong relation with safety, were studied in detail. These included certain driver behaviours-speeding, drinking-and-driving and the use of seat belts. Developments in certain driver groups were also analysed separately: moped riders, lorries, older car drivers and cyclists. It was possible to reach conclusions about the prognoses after making separate prognoses about the trends in vehicle kilometres and the casualty and fatality rates. Having established the development in exposure and in casualty rates, it became possible to predict the future number of casualties and fatalities in the year 2010. On the basis of these predictions conclusions were reached on the possibility of reaching the strategic targets the government had set for that year.
-
-# 6. **A more detailed discussion on risk and exposure in the context of infrastructure**
-
-#### 6.1. **The relationship between accidents and traffic volumes**
-
-One of the main reasons for defining and using exposure in studies related to infrastructure is to equalise for differences in intensity of use, so as to make comparisons meaningful. The unit of study might be a number of road sections or a number of intersections. Accident rate (Ar) is thus defined as:
-
-#### Ar = Expected number of accidents over a certain period of time Amount of exposure over a period of time
-
-The number of accidents is related to the exposure and generally increases with an increase in exposure. The shape of the curve that relates the two entities is nowadays termed a 'safety performance function' (Hauer, 1995a; Ekman, 1996; Persaud et al, 1999). It has by now been well- established that for road sections the shape of the safety performance function (SPF) is not a linear one. Figure 9 gives an example of a hypothetical SPF for a road. Accidents vary according to the amount of exposure in a non-linear way. In this context the exposure is generally expressed as traffic volume (AADT) or as vehicle kilometres travelled. When discussing intersection safety the relevant measure of exposure would be the number of vehicles entering the intersection, or some measure thereof. In that case too, the relationship is normally not linear.
-
-![](_page_42_Figure_5.jpeg)
-
-Figure 9. Safety performance function and accident rates. From: Hauer (1995a).
-
-It can thus be stated that division by exposure does the job of equalising only if the SPF is a straight line( through the origin). If the SPF is non-linear, the accident rate is of limited use, or incorrect. For the passing motorist a lower rate would still mean that per travel unit the chance of an accident is lower, hence we could talk of a lower risk. From the point of view of those who wish to consider the effect of a road safety intervention (a change to a road section or intersection) it would be impossible to relate the change in the accident rate to the intervention. One would first have to know the shape of the SPF before one could make an intelligent statement. A drop in accident rate does not necessarily mean that the intervention improved safety. The rate of accidents or fatalities per million vehicle kilometres travelled (million VKT), which is a widely used index of comparison, generally decreases over time for a variety of reasons. Vehicle kilometres travelled are generally defined as the distance travelled per vehicle multiplied by the number of vehicles. Each additional vehicle-km causes a smaller increase in the number of accidents. The decreasing slopes of curves OA and OB (see Figure 10) represent this fact. The ratio of accidents per vehicle-km is given by the angle a. The meaning of an improvement in safety is that, for each amount of travel, the resulting number of accidents is reduced. Therefore, curve OB describes an improvement over curve OA. For each amount of travel, the resulting number of accidents is smaller. It can be seen from Figure 10 that the change in accident rate by itself does not indicate a change in safety. Point C, having a smaller slope than D (a<b) is worse than D from the overall safety point of view, but the fact that the accident rate is lower means that for a passing motorist the risk is lower.
-
-![](_page_43_Figure_1.jpeg)
-
-Figure 10. The relationship between number of accidents and cumulative vehicle kilometres travelled. From: Hakkert, Livneh & Mahalel, 1976.
-
-Similarly, when taking this line of argument to other fields of safety intervention it can also be concluded that there is little ground for assuming that the number of aviation fatalities is linearly related to the number of flighthours, or that the number of truck accidents is proportional to truck-miles travelled or to ton-miles carried.
-
-The next part of this section will present findings from some studies that indicate the non-linearity of the relationship between accidents and traffic flow.
-
-#### 6.1.1. Safety Performance Functions (SPF)
-
-The detailed relationships between accidents and traffic volumes have been discussed extensively in the literature and during the past 5-10 years have become known as Safety performance Functions (SPF) in the North-American literature (Hauer, 1995a; Persaud et al.1999). A safety performance function (SPF) is the relationship between the traffic volume (AADT) on a highway and the safety of that highway, defined as the number of accidents (defined per unit of time and highway length). SPFs have been calibrated for a number of locations, mainly on rural highways, both twolane rural roads and freeways.
-
-Persaud et al. (1999) present a number of SPFs developed as part of their work on rural roads in Ontario, Canada. For a set of 197 rural four-legged signalised intersections in Ontario, during the years 1988-1993, the SPF was estimated as:
-
-```
-No. of accidents/year = 0.0005334 x (total entering AADT) exp 0.8776
-```
-
-For two-lane paved rural highways in Ontario, during the years 1988-1993, a set of 28,000 0.5 km long sections was selected. Only accidents with injury were included in the study. The SPF developed was:
-
-```
-No. of injury accidents/year = 0.000637 x (section length) x [exp(-0.08215)
-X (total road width)] x (AADT) exp 0.8993
-```
-
-It can be seen that for both the intersection and road section models the exponents for traffic volume were 0.8776 and 0.8993, indicating a nonlinear relationship between accidents and traffic volume.
-
-For 4-lane freeways in Canada, Persaud et al. (1999) calibrated the following SPF for injury accidents:
-
-```
-Injury accidents = (Section length) (0.0000537) (AADT) exp (1.01786)
-```
-
-In this case it is shown that the relationship is almost linear.
-
-#### 6.1.2. The relation between aggregate number of collisions and distance travelled
-
-Smeed (1974) studied the relationship between the aggregate number of collisions between certain types of vehicles and the distance travelled by these vehicles. He calculated and plotted the product of the distances travelled by vehicles of certain categories (cars, lorries, motor-cycles, pedalcycles) against the number of two-vehicle accidents of those categories. He concludes that "it seems that we must, to a large extent, give up the hypothesis that the number of two-vehicle accidents is generally proportional to the product of the distances travelled by the types of vehicles involved."
-
-As, however, traffic volumes vary greatly during different parts of the day, it might be possible to look for a relationship by clustering accidents and traffic volumes into groups with similar traffic volumes. The number of nonpedestrian single vehicle accidents was plotted against the estimated distances travelled by the vehicles in these hours. The numbers of two-vehicle accidents of various categories in the various hours was plotted against the functions of the distances travelled in these hours. It was found that, to a considerable extent, the number of single vehicle accidents involving any type of vehicle during any daylight hour was found to be proportional to the distance travelled by vehicles of that type during those hours. The number of collisions between any two categories of vehicles during any daylight hour was found to be proportional to the product of the distances travelled by each type of vehicle during that hour. Significant discrepancies were found, however, between the expected number of accidents and the actual numbers, indicating that other factors, not taken into consideration, might also have an influence. It could also be the case that significant errors exist in the estimation of distances travelled for each type of vehicle.
-
-In terms of safety studies related to infrastructure it seems advisable to develop and take account of Safety Performance Functions before making comparisons. The development and use of these SPFs is becoming widespread in many countries and the errors introduced by not accounting for differences in traffic volumes seem no longer warranted.
-
-#### 6.2. **Hazardous locations**
-
-The subject of hazardous locations plays a central role in safety management of road infrastructure. At each level of administration responsible for the construction and maintenance of roads - municipalities, provincial and central government - a certain number of locations (road sections and intersections) can be identified that have a 'higher than expected' number of accidents. Once identified, countermeasures can be proposed for such locations and the locations can be improved. Many models exist in the literature that deal with the identification of such locations, generally termed black-spots or hazardous locations. Intrinsic to all the more advanced methods is that they incorporate a procedure for the estimation of the number of accidents that should be expected at a location of the type under consideration. Hauer (1995b) provides a review of many of the models that have been proposed in the past (see also Mahalel, Hakkert & Prashker, 1982). The first task at hand is always to devise a method that will identify a limited number of locations (sections and/or intersections) for further consideration, to be chosen from the many thousands of such locations a road authority has under its jurisdiction.
-
-The final aim of this procedure should be the engineering treatment of a number of locations that will bring about a reduction in the number of accidents at the lowest costs possible. The locations do not therefore have to be hazardous or have a considerable concentration of accidents in order to qualify. In that sense the terms 'hazardous location' or 'black-spot' are not neutral words and Hauer suggested the cumbersome term "sites with promise" (Hauer, 1995b). The more recently proposed procedures for identifying sites for safety investigation are based on the Experimental Bayes (EB) technique. This essentially aims to smooth out the random fluctuation in accident data by specifying the safety of a site as an estimate of its longterm mean (m) instead of its short-term accident count (Persaud, B., Lyon, C. & Nguyen, T. (1999). The latter propose an improvement to the established methods by basing the selection of sites not only on the knowledge of the long-term mean but also on the calculated number of 'treatable' accidents.
-
-There are a number of selection principles to choose a limited number of locations for further consideration out of a long list of locations. Basically the selection principle can be the number of accidents or the accident rate, being the number of accidents divided by the exposure. Variations on these two principles are also possible. Therefore, the methods proposed in the literature can be divided into selection according to the highest number of accidents or selection according to the highest rate. Again, some methods suggest a combination of the two. In deciding which is the better method, it is important to ask what the purpose of the process is. Hauer(1995b) cites three possible motives:
-
-#### - Motive 1.
-
-Economic efficiency: identify sites at which remedial action would prove cost-effective.
-
-#### - Motive 2.
-
-Professional and institutional responsibility: recognise and rectify sites that are deficient either because of how they were built or because they have deteriorated while in use.
-
-#### - Motive 3.
-
-Fairness: identify sites that pose an unacceptably high hazard to the user.
-
-For motives 1 and 2 the selection should be based on the number of accidents or the changes in the number of accidents. For motive 3 the selection should be based on the accident rate. The list resulting from a selection on the basis of motive 3, i.e. risk to the road user, will generally result in a list which includes locations with a small number of accidents and low volumes of traffic, because these are the sites that have high accident rates. Such sites will generally have low rates of return and are not to be recommended on the basis of cost-benefit considerations.
-
-Table 8, taken from Hauer (1995b), describes five different selection criteria of which the first three are based on efficiency and rely on the number of accidents, and the last two assign priority on the basis of fairness and selection according to accident rate.
-
-To conclude this discussion of methods for the identification of hazardous locations it has been shown that methods based on the number of accidents would generally provide a more cost-effective approach, resulting in the higher number of accidents being saved per invested unit of money. Methods based on selection according to accident rate, seem to introduce a measure of fairness into the system, but on the whole will not result in a cost-effective allocation of budgets and will not result in achieving a significant reduction in accidents. If the methods are to be made more reactive to a deterioration in the safety situation, methods have been suggested which are a variation on the accident number method.
-
-| Selection<br>criteria |                                      | Consideration                                                                                                                | Rank by                                 | Comment                                                                                                                                                                                                                                                                                                                                   |                                 |
-|-----------------------|--------------------------------------|------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------|
-| A                     |                                      | Measures that reduce the probability<br>and/or severity of accidents (eg.<br>illumination, slope reduction)                  | F<br>(best done<br>by target<br>accid.) | These are usually high-exposure sites and tend to be near<br>the top of the list year after year. Once they are 'identified'<br>and examined, one can decide whether a safety<br>improvement is appropriate. Sites of this kind may not<br>need to be monitored and repeatedly identified.                                                |                                 |
-| B                     | E<br>F<br>F<br>I<br>C<br>I           | Deficiency since site opened to traffic<br>(or since reconstruction) that can<br>perhaps be cheaply rectified                | ûF/1F<br>(Total<br>accid.)              | Such sites should be identified as soon as possible. The<br>entire accident history (since opening or reconstruction)<br>seems relevant to identification. However, once a site is<br>shown to be normal, continued monitoring seems<br>pointless.                                                                                        | R<br>E<br>S<br>P<br>O<br>N<br>S |
-| C                     | E<br>N<br>C<br>Y                     | Deterioration of safety while in use that<br>perhaps can easily be rectified                                                 | Size of<br>jump in F                    | In this case periodic monitoring of all sites is required. It<br>may turn out that jumps in F are statistically difficult to<br>spot. It seems sensible to monitor for deterioration by<br>means other than accident occurrence.                                                                                                          | I<br>B<br>I<br>L<br>I<br>T<br>Y |
-| D                     | F<br>A<br>I<br>R<br>N<br>E<br>S<br>S | It is not acceptable to have sites where<br>the risk to road users is too large                                              | R                                       | These are often low-exposure sites. At such sites, a high<br>R may be a natural consequence of lower design<br>standards. It will take a long time to have enough accidents<br>to estimate R reliably. Use of accumulated accidents may<br>be necessary. Once such sites are labelled 'normal' further<br>monitoring may not be required. |                                 |
-| E                     | T<br>O<br>U<br>S<br>E<br>R           | It is not acceptable to have sites where<br>the risk to a road user is considerably<br>larger than at sites of similar class | ûR/1R                                   | For the reason listed above ûR is also difficult to estimate<br>reliably and use of the accumulated accident history and<br>clues other than accidents are important. Once such sites<br>are labelled 'normal' further monitoring may not be<br>required.                                                                                 |                                 |
-
-Table 8. Motives, considerations and ranking criteria. From: Hauer, 1995b.
-
-# 7. **Summary and conclusions**
-
-The report sets out with a definition of the three central terms used: accidents, exposure and risk. Each of the three terms is defined and problems of definition, accuracy of measurement and reporting are described.
-
-The conclusion reached is that there are general definitions of exposure and risk as used in the health prevention and risk analysis fields, but that in road safety practice these terms should be defined within the context of the issue studied. In the context of this report, exposure is meant as exposure to risk. To what extent are certain segments of the population, or certain types of vehicles likely to be involved in an accident? The measure of exposure is generally defined as some form of the amount of travel, either by vehicle or on foot. In the report, risk is used to mean the probability of an accident occurring, weighted in some way by the severity of the accident outcome. In many cases it would be better, and more neutral, to refer to rates and not to risks.
-
-As was discussed in the report, for each application, the correct exposure measure should be used. This is sometimes impossible because the required information is not available, or has to be collected at great cost. Generally, the more aggregate the exposure measure, the more indirect variables are introduced, which casts shadows over the resulting risk calculations.
-
-In the case of transport, the most widely used measure of exposure is the amount of travel for each travel mode. In some cases, useful additional insight is provided by taking into account the speed of travel, in which case exposure is expressed as the amount of time spent in the traffic system. Due to the very different conditions and circumstances, it is considered to be of little use to extend this comparison of risk per unit of time to other activities beyond the field of transport. Within the field of transport, transport modes can usefully be compared, up to a certain point, by risk per unit of travel or per unit of time. Taking bus and coach travel as a normative unity, it was shown that risk expressed in terms of person kilometres travelled is 10 times greater for cars and 100-200 times greater for the vulnerable modes (foot, bicycle and motor-cycle), with motor-cycles clearly being the most hazardous. Train and plane travel are also relatively safe. Looking at the same data, with risk expressed in terms of person hours of travel, does not change the picture greatly. Travel by foot and travel by bicycle become slightly less hazardous, because of the low speeds of these modes, whereas air travel becomes significantly more hazardous because of the high speeds (hence the small number of hours per kilometre).
-
-The lack of detailed and high quality exposure data is one of the reasons that in many cases international comparisons are conducted on a per capita or per vehicle basis, and deal mostly with fatalities because of the inaccuracy of injury reporting. One of the developments in recent years is the installation of electronics and telecommunications inside vehicles and along roads. The increasingly widespread use of mobile phones also presents many new possibilities. As a result it is becoming easier to collect up-to-date and reliable information on a variety of parameters that could be of importance in the calculation of vehicle exposure and risk. Additional information on the distribution of speeds, types of vehicles and following distances also seems to be a future possibility.
-
-On road sections, the number of vehicle kilometres is generally accepted as the relevant measure of exposure. At intersections, the correct measure of exposure is not that obvious, and has been the subject of many discussions. The product of the daily crossing flows and the sum of the products of the hourly crossing flows have been used as exposure measures in many accident studies. The definition of risk to pedestrians can be discussed in a way which is fairly similar to the discussion of risk and exposure at intersections. In this manner one obtains a measure of exposure which is the product of the traffic flow and the crossing number of pedestrians. The risk is then defined by dividing the annual number of pedestrian accidents by the exposure.
-
-One of the contexts in which risk is used, is in comparing risks between different parts of the transport system, different transport modes or even different activities outside the field of transport. The desire is to make various activities exposed to equal risks, so as to make risk distribution 'fair'. After discussing the various problems and pitfalls with this approach, it is concluded that the desire for equal risks in various segments of the transportation system is not practical. It is more useful to search for ways to make each segment of the transport system as safe as possible, keeping cost-effectiveness considerations into account.
-
-The report also describes studies in which useful safety evaluations were conducted without having to resort to risk and exposure measures. On a more general level, risk can serve as the basis for the selection of research and action themes, which should then be justified on the basis of costbenefit considerations. Decision-making can be carried out on the basis of comparative risk levels, on the basis of accident and injury data (without risk) and according to cost-benefit considerations. Each method has advantages and disadvantages and should be selected according to the circumstances. Decision-making on the basis of the number of accidents, injuries or fatalities focuses on the size of the problem, and prevents us from focussing our efforts on a segment of road safety that might look bad from the point of view of risk, but which might not be not very significant in terms of the quantities involved. Decision-making on the basis of risk tends to lead to actions that are considered fair in terms of the road users involved. It has been demonstrated in the report that this is actually not practical or reasonable. The final decision should generally be based on cost-effectiveness considerations because, in the field of road safety, like in any other field, the amount of expenditure is limited and investments should be allocated to those fields that produce the highest accident, casualty and fatality reductions that can be achieved per unit of money invested.
-
-In the context of national levels of safety and the setting of road safety targets, it was shown that targets are generally set in numbers of casualties and fatalities. This seems preferable over setting targets regarding risks, which can become intertwined with changes in exposure (mobility), which will affect the risk. When comparing risks and safety between different transport modes, different regions of a country or different countries,
-
-however, it seems that comparisons will have to be made on the basis of accidents and fatalities per vehicle kilometre travelled and in some cases, per passenger hour. In other cases a risk per passenger trip may be more meaningful.
-
-In chapter five, the report describes a number of studies from the major fields of road safety research and activity which deal with the issue of risk. The fields covered include the subject of the involvement of lorries in fatal accidents; driving under the influence of alcohol; the influence of speed in crashes and crash severity; risk used in assessing national safety levels; accident, injury and fatality trends; target setting; and elderly and young drivers.
-
-Chapter six discusses the relationship between accidents and traffic volumes in more detail. This is an issue of central importance when dealing with safety of the road infrastructure and has to be understood before making risk calculations. It was demonstrated that this relationship has to be determined first, in the form of a 'safety performance function (SPF)', before risks on various network sections can be compared. Many studies have demonstrated that the relationship between accidents and traffic volumes is not a linear one, which complicates matters greatly. When considering safety in the context of road infrastructure, there is a need to take information on traffic volumes into account. The report has documented that gross comparisons per vehicle kilometre travelled will lead to errors. Nowadays, enough information is available to develop safety performance functions (SPFs) for various segments of the road system to make meaningful comparisons. SPFs for intersections and for pedestrian and bicycle travel should also be developed.
-
-The report concludes with the issue of hazardous locations and establishes that locations had better be identified on the basis of the expected number of accidents, based on models developed for similar locations, rather than on the basis of accident rates (risks).
-
-## **References**
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Zotero/001_artiklid/Road Safety Indicators/hareruepidemiologyroadtrafficaccidents2022.md

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-category: literaturenote
-citekey: hareruepidemiologyroadtrafficaccidents2022
-title: "The epidemiology of road traffic accidents and associated factors among drivers in Dilla Town, Southern Ethiopia"
-authors: "Hareru, Habtamu Endashaw; Negassa, Belay; Kassa Abebe, Reta; Ashenafi, Eden; Zenebe, Getachew Assefa; Debela, Berhanu Gidisa; Ashuro, Zemachu; Eshete Soboksa, Negasa"
-year: 2022
-date: 2022-00-00 2022
-doi: 10.3389/fpubh.2022.1007308
-publication: Frontiers in Public Health
-url: "https://doi.org/10.3389/fpubh.2022.1007308"
-zotero_key: S6PNG8QA
-zotero_storage: ZPGBBZPU
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Hareru, Habtamu Endashaw"
-status: converted
----
-TYPE [Original Research](https://www.frontiersin.org/journals/public-health#editorial-board) PUBLISHED [10 November 2022](https://www.frontiersin.org/journals/public-health#editorial-board) DOI [10.3389/fpubh.2022.1007308](https://doi.org/10.3389/fpubh.2022.1007308)
-
-![](_page_0_Picture_2.jpeg)
-
-#### OPEN ACCESS
-
-Grace Sembajwe, Northwell Health, United States
-
-REVIEWED BY Chalachew Kassaw, Dilla University, Ethiopia Khosro Sadeghniiat, Tehran University of Medical Sciences, Iran Artur Petrov, Tyumen Industrial University, Russia
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-Habtamu Endashaw Hareru [habteenda@gmail.com](mailto:habteenda@gmail.com)
-
-This article was submitted to Occupational Health and Safety, a section of the journal Frontiers in Public Health
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-RECEIVED 30 July 2022 ACCEPTED 26 September 2022 PUBLISHED 10 November 2022
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-Hareru HE, Negassa B, Kassa Abebe R, Ashenafi E, Zenebe GA, Debela BG, Ashuro Z and Eshete Soboksa N (2022) The epidemiology of road trac accidents and associated factors among drivers in Dilla Town, Southern Ethiopia.
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-*Front. Public Health* 10:1007308. doi: [10.3389/fpubh.2022.1007308](https://doi.org/10.3389/fpubh.2022.1007308)
-
-© 2022 Hareru, Negassa, Kassa Abebe, Ashenafi, Zenebe, Debela, Ashuro and Eshete Soboksa. This is an open-access article distributed under the terms of the [Creative Commons](http://creativecommons.org/licenses/by/4.0/) [Attribution License \(CC BY\).](http://creativecommons.org/licenses/by/4.0/) The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
-
-# The epidemiology of road trac [accidents and associated factors](https://www.frontiersin.org/articles/10.3389/fpubh.2022.1007308/full) among drivers in Dilla Town, Southern Ethiopia
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-Habtamu Endashaw Hareru<sup>1</sup>\*, Belay Negassa<sup>2</sup> , Reta Kassa Abebe<sup>1</sup> , Eden Ashenafi<sup>3</sup> , Getachew Assefa Zenebe<sup>1</sup> , Berhanu Gidisa Debela<sup>1</sup> , Zemachu Ashuro<sup>2</sup> and Negasa Eshete Soboksa<sup>2</sup>
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-<sup>1</sup>School of Public Health, College of Medicine and Health Science, Dilla University, Dilla, Ethiopia, <sup>2</sup>Department of Environmental Health, College of Medicine and Health Science, Dilla University, Dilla, Ethiopia, <sup>3</sup>Department of Reproductive Health, College of Medicine and Health Science, Dilla University, Dilla, Ethiopia
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-Background: Data on the magnitude of road trac accidents (RTAs) were mostly obtained through police records and hospital registration data. However, insucient data reporting masked the gravity of the problem, and little attention was paid to the magnitude and correlation of road trac accidents from the driver's perspective. Therefore, this study aimed to assess the prevalence of RTA and related factors among drivers.
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-Methods: A community-based cross-sectional study involving 316 drivers was conducted in Southern Ethiopia. The participants were chosen using a systematic random sample technique, and the data were obtained using an interview-administered structured questionnaire. To analyze the data, SPSS software (version 20) was employed. In addition to descriptive statistics, binary logistic regression analysis was also employed to find factors connected to trac accidents. RTA factors were considered statistically significant if they had a *P*-value of 0.05 or below in the multivariate analysis.
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-Result: The RTA among drivers was 126 (39.9%) (95% confidence interval (CI): 34.2–45.6%) in the previous year. The following factors were associated with RTA: vehicle maintenance (AOR = 0.11, 95% CI: 0.09, 0.96), media utilization (AOR = 0.38, 95% CI: 0.18, 0.65), participation in driving-related training (AOR = 0.73, 95% CI: 0.28, 0.91), punishment for prior trac violations (AOR = 0.56, 95% CI: 0.47, 0.83), and risky driving behavior (AOR = 7.89, 95% CI: 3.22, 12.38).
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-Conclusion: Two-fifths of the drivers were involved in a trac accident. Risky driving behaviors, vehicle maintenance, media usage, attending driving-related training in the previous 2 years, and prior experience with trac police punishment or warning were all strongly linked to road trac accidents. In light of these statistics, the Federal Ministry of Transport of Ethiopia and
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-other stakeholders should support making it mandatory for drivers to check their vehicles' safety, provide them with safety training, raise awareness about vehicle maintenance and risky driving behaviors, and enforce strict penalties for trac violations.
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-associated factors, drivers, epidemiology, road trac accidents, vehicles, risky driving behavior
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-## Introduction
-
-A road traffic accident (RTA) refers to an incident on a public road or street that injures or kills one or more people and involves at least one vehicle in motion [\(1\)](#page-10-0). As a major threat to public health and economic growth, RTA is a worldwide issue of grave importance. RTA is the eighth largest cause of mortality worldwide. The World Health Organization (WHO) expects RTAs to rise to become the fifth-greatest cause of mortality by 2030. These accidents are one of the top three causes of death for people aged 5 to 44 years and the leading cause of death for young people aged 5 to 29 years [\(2\)](#page-10-1).
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-RTAs claim the lives of about 3,700 every day and roughly 1.35 million people each year. These RTA fatalities involved cars, buses, motorbikes, bicycles, trucks, and pedestrians. The high proportional burden is borne by pedestrians, cyclists, and motorcyclists, particularly those living in developing countries [\(3](#page-10-2)[–5\)](#page-10-3). Despite a minor decrease in road traffic deaths over the last decade, the current global fatality rate of 16.4 per 100,000 is staggeringly high [\(6\)](#page-10-4). Around 50 million people are injured every year due to non-fatal injuries sustained in RTAs, posing a severe threat to global public health [\(2\)](#page-10-1).
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-According to the WHO, despite having <60% of the world's registered vehicles, low and middle-income countries account for approximately 93% of deaths and 90% of disabilityadjusted life years (DALYs) lost annually in road traffic crashes. Projections show that these figures will continue to rise over the next 20 years, with Africa having the highest fatality rate [\(5,](#page-10-3) [7\)](#page-10-5).
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-An RTA is not only a tragedy that kills people but also a disaster that harms people, property, and the environment. The financial burden of these injuries falls heaviest on families, who must deal with both immediate and long-term costs, such as medical bills and lost savings. Road traffic injuries have a high financial impact nationally, especially in developing nations [\(7\)](#page-10-5). Therefore, more effective control measures for the causes of road
-
-Abbreviations: AOR, Adjusted Odd Ratio; CI, confidence interval; DALY, disability-adjusted life years; GDP, gross domestic product; IRB, institutional review board; LMIC, lower- and middle-income countries; RTA, road trac accidents; SNNPR, South Nation Nationalities of People Republic; WHO, the World Health Organization.
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-accidents are required if we are to achieve a reduction in road deaths and injuries by the year 2030 [\(8\)](#page-10-6).
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-Economic losses due to road traffic fatalities and injuries are estimated to be 3% of global GDP and 5% of GDP in low- and middle-income nations (LMICs) [\(4\)](#page-10-7). Africa has the highest rate of RTA fatalities per capita, with walkers and other vulnerable road users bearing the brunt of the burden. RTAs are now a major issue in Sub-Saharan Africa (SSA), accounting for roughly one-third of all regional deaths [\(9\)](#page-10-8).
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-Despite the rising toll of RTAs, road safety is still a neglected concern in many developing countries. The health sector has been hesitant to acknowledge it as a priority public health concern [\(10,](#page-10-9) [11\)](#page-10-10). A substantial amount of evidence implies that RTAs are easily avoided, and many high-income countries have successfully reduced the occurrence of these accidents through proven and cost-effective approaches [\(12\)](#page-10-11). While there has been progress toward worldwide road safety metrics, it is sluggish, and RTA rates remain disproportionately high in LMICs [\(6\)](#page-10-4). The WHO's global status report on road safety in 2018 predicts that SDG target 3.6 will not be met until 2020 [\(5\)](#page-10-3). According to the Third Global Ministerial Conference on Road Safety in 2020, approximately 1.35 million people are killed or injured on the world's roads each year [\(13\)](#page-10-12).
-
-Moreover, in September 2020, the United Nations General Assembly declared the "Decade of Action for Road Safety 2021– 2030," with a new goal of a 50% reduction in RTA-related deaths and injuries by 2030, emphasizing the importance of a holistic approach to road safety and calling for continued improvements in road and vehicle design, as well as enhanced laws and law enforcement, and timely, life-saving emergency care for the injured [\(14\)](#page-10-13). These alarming statistics highlight the importance of updating and enhancing road traffic accident data, which will aid policymakers in adopting evidence-based policies and programs for road safety.
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-In previous studies, the frequency of RTAs was reported to be 8% in Brazil [\(15\)](#page-10-14), 30 % in Vietnam [\(16\)](#page-10-15), 63.6% in another Brazilian study [\(17\)](#page-10-16), 41.4% in an Indian study [\(18\)](#page-10-17), and 51.50% in an Iranian study [\(19\)](#page-10-0). RTA prevalence varies between sub-Saharan African studies, ranging from 74.0% in a study from Ghana to 87.5% in one from Nigeria [\(20,](#page-10-18) [21\)](#page-10-19). In Ethiopian studies, the RTA varies from 23.5 to 62.5% [\(22](#page-10-20)[–28\)](#page-10-21).
-
-According to a global status assessment on road safety, Ethiopia has at least 114 deaths per 10,000 vehicles per year,
-
-compared to only 10 in the United Kingdom and Ireland and 60 in 39 Sub-Saharan African nations [\(23\)](#page-10-22). A number of studies have also indicated that Ethiopia has one of the world's highest fatality rates per vehicle [\(23,](#page-10-22) [24,](#page-10-23) [29](#page-10-24)[–32\)](#page-10-25). According to the World Health Organization (WHO) estimate, in 2013, the prevalence of road traffic fatalities in Ethiopia was 25.3 per 100,000 population, and the rate is among the highest in the world [\(25\)](#page-10-26).
-
-Although Ethiopia has a higher rate of road traffic morbidity and mortality, data on the scale of road traffic accidents were largely acquired through police records and hospital registration data. However, inadequate data reporting obscured the scale of the problem, and little concern was expressed [\(23,](#page-10-22) [30\)](#page-10-27). Furthermore, even though road crashes are increasing, the government and other stakeholders have given little attention to the issue due to a lack of data on its magnitude and contributing factors from the driver's perspective. Therefore, the objective of this study was to determine the epidemiology of RTA and its associated factors among drivers in Dilla, Southern Ethiopia. Moreover, understanding drivers' perspectives on road traffic accidents can help in the development of policies and strategies for community-based intervention to reduce road traffic accident fatalities. In addition, it can also be used as a sole input to the literature.
-
-## Materials and methods
-
-## Study area and period
-
-The study was conducted in Dilla Town, Gedeo Zone, Southern Ethiopia. Dilla town is one of the two city administrations established in 1904 in the Gedeo Zone. It is 377 km from the capital city, Addis Ababa, and has one main road passing through the town from Addis Ababa to Moyale. The total population in Dilla is 94,189; of which 46,058 (49.9%) are males, and 48,131 (50.1%) are females. During the data collection period, the estimated number of vehicles serving public transportation in Dilla town was 1,266. Bajaj 502, taxis 15, and 159 are private cars (automobiles), 85 are government vehicles, and 505 are motorcycles. There were 304 registered and 673 unregistered vehicles (Source: Dilla Town Administrative Road Transport Office). The study was conducted from 18 October 2021, to 20 November 2021.
-
-## Study design
-
-A community-based cross-sectional study design was conducted to assess the epidemiology of road traffic accidents and associated factors among drivers in Dilla Town, Southern Ethiopia.
-
-## Population
-
-The survey included everyone in Dilla town who drove a privately or government-owned vehicle (automobile, taxi, or Bajaj). All drivers of the selected vehicles outside Dilla, those with <12 months of driving experience before the data collection period, and those who were either unwell or unavailable throughout the study period were excluded.
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-## Sample size determination
-
-The sample size was calculated using the single-population proportion formula, with prevalence estimates from previous research conducted in Adama city of 56.4 % [\(27\)](#page-10-28), a margin of error of 5%, and a two-sided 95% confidence interval (Zα/2 = 1.96). After attaining the correction formula because the total number of vehicles was <10,000 and by including a 10% non-response rate, the appropriate sample size for this study was 319.
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-## Sampling techniques
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-During the study period, there were 1,266 vehicles in the town, of which 15,502, 159, 85, and 505 were taxis, Bajaj, private cars, automobiles, governmental cars, and motorcycles, respectively. A sampling frame of all cars was received from the Dilla Town Road and Transport Bureau, and the computed sample size was proportionally allocated. Individual drivers were also selected using systematic random sampling using their board numbers.
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-## Study variables
-
-The dependent variable in the study was a road traffic accident. The independent variables included sociodemographic variables (sex, age, marital status, educational status, family size, monthly income, and additional job), personnel (drivers) behavior (chewing khat, habits of alcohol drinking, talking on mobile phones while driving, and driving at speed), vehicle-related factors (service year of the vehicle (perceived vehicle age, vehicle maintenance, and frequency of vehicle health check), and failed brake or mechanical road-related conditions (congested, high traffic roads, damaged roads, and others), driving-related training in the past 2 years, years of holding a driving license, any prior violation of traffic rules, mass media utilization, perceived availability of sufficient traffic signs, and driving experience.
-
-## Operational definition
-
-#### Road trac accident
-
-In this study, a road traffic accident is defined as a collision between cars, between vehicles and pedestrians, between vehicles and animals, or between vehicles and a fixed object that occurred within the previous 12 months. The questions were close-ended. If the driver self-reported RTA experience at least once in the last 12 months, the answer was a "yes." Otherwise, the answer was a "no."
-
-#### Risky driving behavior
-
-If a respondent had engaged in any of the four risky driving behaviors in the last 12 months (i.e., driving after drinking alcohol, chewing khat, driving at high speeds, mobile phoning, or receiving while driving), the answer was a "yes." Otherwise, the answer was a "no."
-
-## Data collection instrument and procedure
-
-An interview-administered structured questionnaire was used to collect data related to the study objectives. Questions about road traffic accidents and related factors were adapted from previous research [\(27,](#page-10-28) [32–](#page-10-25)[37\)](#page-11-0). The data collection tools contained four sections: the first section included the sociodemographic characteristics of the participants; the second section identified the magnitude of road traffic accidents and risky driving behavior of the participants; the third section assessed vehicle-related factors; and the fourth section assessed road-related conditions. Fourth-year public health officers collected the data. After explaining the goal of the study, the interviewers approached the selected study participants at their station for Bajaj and taxi drivers, at their residence or fuel station for automobile drivers, and at their institution for government office cars using information obtained from the city's road and transport office.
-
-## Data quality control
-
-To assure the quality of the data, the questionnaire that was primarily written in English was translated into the local language (Gede'ogna) and then translated back to ensure that it was translated accurately. 1 week before the actual data collection, the instrument was pretested on 5% of drivers from areas other than the research area (Wonago town), and the questionnaire was modified accordingly. 2 days of training were given to the data collectors and supervisors about the research question, sampling technique, data handling, ethical conduct, and quality of data collection. The questionnaires were checked for accuracy and completeness every day by the primary investigator, and modifications were made if errors were discovered in the collected data.
-
-## Data processing and analysis
-
-The data were entered into EPI Info version 7 and analyzed using SPSS version 20 statistical software. Categorical variables were described using frequency or percentage, whereas continuous variables were described using mean and standard deviation. Binary logistic regression analyses were used to determine the influence of each independent variable on road traffic accidents. Those variables with a p-value of < 0.25 on bivariate analysis were chosen for multivariate analysis. Variables with a p-value of < 0.05 in the multivariate analysis were considered significant factors linked with road traffic accidents. Finally, the adjusted odds ratio (AOR) and 95% confidence interval (CI) for statistically significant variables were computed and interpreted.
-
-## Ethical consideration
-
-Ethical approval was secured from the Institutional Review Board of Dilla University (IRB-DU), College of Medicine and Health Science. Permission letters were also secured from Dilla town road and transport offices. Before data collection, information about the purpose of the study and the right not to participate was given to the participants. Oral consent was obtained from all the study participants, and the information collected from the participants was kept confidential.
-
-## Results
-
-## Socio-demographic characteristics
-
-In the current study, out of 319 participants, 316 were included, with a 99% response rate. The majority of the participants (94.6%) were males, with a mean standard deviation (SD) age of 31 (12.6) years and 174 (55%) in the age group of greater than 31 years. More than half (52.2%) of the participants were not married, 142 (45%) had secondary educational status, and 243 (76.9%) had no additional work [\(Table 1\)](#page-4-0).
-
-## Vehicle and driver related variables
-
-Of 316 respondents, ∼124 (39.2%) were driving their own car. Nearly 80% of the vehicles were Bajaj and motor bicycles. The majority of the respondents (64.6%) had between 1 and 4 years of driving experience. Of these, 201 (63.6%) had had the
-
-license for 1 to 4 years. Over half of the participants received drivers related training in the last 2 years.
-
-Moreover, 182 (57.6%) of the participants reported that their vehicles were served for about 1–4 years, and 216 (68.4%) of the participants informed us that their vehicles were checked every 6 months. About three-fourths (75.9%) of the participants also reported that their vehicles had some kind of service in the last year [\(Table 2\)](#page-5-0).
-
-## Risky driving behaviors of the participants
-
-A respondent's risky driving behavior was evaluated when they engaged in one of four behaviors: driving after consuming alcohol, chatting on a cell phone while driving, chewing khat while driving, and/or driving at high speeds. A total of 98 (31.0 %) of the 316 drivers in this study engaged in risky driving behaviors [\(Figure 1\)](#page-5-1). Driving at high speeds, chewing khat while driving, driving after drinking, and the habit of talking on the phone while driving accounted for 24.5, 35.7, 13.3, and 26.5% of the 98 risky driving behaviors, respectively.
-
-## The magnitude of road trac accidents
-
-The magnitude of RTA among drivers in the past year in Dilla Town was 126 (39.9%) [95% confidence interval (CI): 34.2–45.6%] [\(Figure 2\)](#page-6-0).
-
-A total of 42 (33.3%) of 126 participants who had an RTA cited pedestrian concerns as the leading cause of incidents that occurred during the previous 12 months; 39 (30.9%) cited excessive speed; 19 (15%) cited a lack of road signs/zebras or tags; and 20 (15%) cited a breach of traffic rules [\(Figure 3\)](#page-6-1).
-
-## Factors associated with road trac accidents
-
-Variables that have a p-value of < 0.25 in the bivariate logistic regression analysis and are eligible for multivariate logistic regression analysis include gender, educational status, additional job, average monthly income, driving experience, listening to mass media, attending recent driving-related training, receiving any kind of service to a vehicle in the past year, prior punishment by traffic police, availability of sufficient traffic signs or symbols, and risky driving behavior.
-
-Finally, independent variables significantly associated with road traffic accidents at p-value < 0.05 in multivariate logistic regression were those who received any kind of service to a vehicle in the past year, listened to mass media, received drivingrelated training in the last 2 years, prior punishment by traffic police; and risky driving behavior.
-
-<span id="page-4-0"></span>TABLE 1 Socio-demographic characteristics of the participants in Dilla Town, Gedeo Zone, Southern Ethiopia, 2021 (n = 316).
-
-| Variables            |                     | Frequency | Percent<br>(%) |
-|----------------------|---------------------|-----------|----------------|
-| Age (in years)       | <31                 | 142       | 45%            |
-|                      | ≥31                 | 174       | 55%            |
-| Sex                  | Male                | 299       | 94.6           |
-|                      | Female              | 17        | 5.4            |
-| Educational status   | No formal education | 19        | 6              |
-|                      | Primary             | 77        | 24.4           |
-|                      | Secondary           | 142       | 45             |
-|                      | College and above   | 78        | 24.6           |
-| Marital status       | Not married         | 165       | 52.2           |
-|                      | Married             | 151       | 47.2           |
-| Family size (number) | ≤5                  | 101       | 32.0           |
-|                      | >5                  | 215       | 68.0           |
-| Monthly income (ETB) | <1,000              | 26        | 8.2            |
-|                      | 1,000–2,000         | 107       | 33.9           |
-|                      | 2,000–3,000         | 100       | 31.6           |
-|                      | >3,000              | 83        | 26.3           |
-| Any additional work  | Yes                 | 73        | 23.1           |
-|                      | No                  | 243       | 76.9           |
-|                      |                     |           |                |
-
-ETB, Ethiopian Birr.
-
-RTA risk was 89 % lower in vehicles that had maintenance in the previous year than in vehicles that did not [AOR = 0.11, 95% CI: 0.09, 0.96, P-value = 0.001]. Drivers who had received driving-related training in the previous 2 years were 23% less likely to be involved in an RTA than those who did not [AOR= 0.73, 95%CI: 0.28, 0.91, P-value = 0.033]. Drivers who had been charged or warned by traffic cops in the previous year were 44% less likely to have RTA than those who had not [AOR = 0.56, 95% CI: 0.47, 0.83, P-value = 0.041]. RTA was 62 % less likely among drivers who could follow mass media than among those who could not [AOR = 0.38, 95% CI: 0.18, 0.65, P-value = 0.003].
-
-Moreover, drivers who engaged in risky driving behaviors were nearly eight times more likely to be involved in an RTA than those who did not [AOR = 7.89, 95%CI: 3.22, 12.38, P-value = 0.029] [\(Table 3\)](#page-8-0).
-
-## Discussion
-
-In this study, the magnitude of road traffic accidents (RTA) among drivers in the past year in Dilla Town was 126 (39.9%) (95% CI: 34.2–45.6%). This finding is consistent with previous studies conducted in India, in which 41.4% of road traffic accidents occurred among drivers [\(18\)](#page-10-17). Another Ethiopian study conducted in Amhara National Regional State reported 34.5% of RTA among passenger vehicles [\(23\)](#page-10-22). However, this study's
-
-<span id="page-5-0"></span>TABLE 2 Drivers and Vehicle-related characteristics of the participants in Dilla Town, Gedeo Zone, Southern Ethiopia, 2021 (n = 316).
-
-**Variables Frequency Percentage (%)** Vehicle type Bajaj 125 39.6 Motor bicycle 125 39.6 Taxi 4 1.3 Minibus 32 10.1 Mid bus 8 2.5 Public service 22 7 Total 316 100 Driver relation with the car Owner 124 39.2 Recruited 128 40.5 Relative to owner 41 13 Governmental 20 6.3 Other 3 0.9 Total 316 100 The driving experience of the drivers <1 year 64 20.3 1–4 years 204 64.6 5–10 years 48 15.1 Total 316 100 Years of holding a driving license <1year 39 12.3 1–4 years 201 63.6 4–10 years 70 22.2 Above 10 years 6 1.9 Total 316 100 Presence of sufficient zebra line Yes 54 20.7 No 262 82.3 Total 316 100 Have you attained any drivers related training in the last 2 years Yes 165 52.2 No 151 47.8 Total 316 100 For how long has this vehicle served? (in years) <1 67 21.2 1–4 182 57.6 5–10 63 19.9 >10 4 1.3 Total 316 100 Any kind of service in the last year for your vehicles Yes 272 86.1 No 44 13.9 Total 316 100 How frequently do your vehicle healthy checked? Every 6 month 216 68.4 Every year 73 23.1 Every 2 years 19 6.0 ≥3 years 8 2.5 Total 316 100 Any kind of service in the last year for your vehicles Yes 240 75.9 No 76 24.1
-
-(Continued)
-
-TABLE 2 (Continued)
-
-| Variables                            |               | Frequency Percentage |       |  |
-|--------------------------------------|---------------|----------------------|-------|--|
-|                                      |               |                      | (%)   |  |
-|                                      | Total         | 316                  | 100   |  |
-| Working hours                        | 8 h and less  | 107                  | 33.9  |  |
-|                                      | More than 8 h | 209                  | 66.1  |  |
-|                                      | Total         | 316                  | 100   |  |
-| Did you have a habit of following    | Yes           | 140                  | 44.3% |  |
-| mass media                           | No            | 176                  | 55.7% |  |
-|                                      | Total         | 316                  | 100   |  |
-| Perceived availability of sufficient | Yes           | 81                   | 25.6% |  |
-| traffic signs or symbol              | No            | 235                  | 74.4% |  |
-|                                      | Total         | 316                  | 100   |  |
-| Perceived road condition             | Good          | 43                   | 13.6% |  |
-|                                      | Poor          | 273                  | 86.4% |  |
-|                                      | Total         | 316                  | 100   |  |
-|                                      |               |                      |       |  |
-
-![](_page_5_Figure_6.jpeg)
-
-<span id="page-5-1"></span>finding is not consistent with the study conducted among motorcycle taxi drivers in 32 regions of Caicó; the Rio Grande do Norte, Brazil, which found that 63.6% of them were involved in at least one motorcycle accident [\(17\)](#page-10-16). In Southern Nigeria, (87.5%) of commercial motorcyclists were involved in road traffic accidents the year before the study [\(21\)](#page-10-19). In Ghana, 74.0% were reported to have been involved in crashes in the past year before the study [\(20\)](#page-10-18). A systematic review, GIS, and metaanalysis study in Iran showed that the prevalence of road traffic accidents was 51.50% [\(19\)](#page-10-0), and a study was conducted in Adama (54.7%) [\(29\)](#page-10-24). Moreover, it is not consistent with a hospital-based study conducted in Wolaita Zone, SNNPR, Ethiopia, in which 62.5% of trauma victims were due to road traffic accidents [\(30\)](#page-10-27). This might be due to the underreporting of accidents in the study settings.
-
-![](_page_6_Figure_1.jpeg)
-
-<span id="page-6-0"></span>![](_page_6_Figure_2.jpeg)
-
-<span id="page-6-1"></span>The prevalence rate found by this study was also higher than that in a study conducted in Brazil, where the prevalence of road traffic accidents among motorcyclists in the previous 12 months was 8% [\(15\)](#page-10-14). Studies conducted in Vietnam showed that 22.7% and 30% of RTA occurred among taxi drivers and motorcyclists over 1 year, respectively [\(16,](#page-10-15) [31,](#page-10-29) [38\)](#page-11-1); in Mekelle, Ethiopia, 26.4%
-
-of taxi drivers reported involvement in a road traffic crash within the past 3 years [\(32\)](#page-10-25). A study conducted in Chuko town, Sidama, Ethiopia, reported a lifetime prevalence of RTA of 23.55% [\(26\)](#page-10-30). In another study conducted in Mekelle, Ethiopia, where 26.4 % of taxi drivers reported involvement in a road traffic crash over the preceding 3 years [\(39\)](#page-11-2), a study conducted in East Wollega, Western Ethiopia, reported a lifetime prevalence of RTA of 33% [31], and a similar study conducted in Jigjiga, Ethiopia discovered the prevalence of RTA in the previous three years was 32.8% [\(27\)](#page-10-28). The main differences between current research and previous studies in road traffic accidents could be attributed to a lack of safety knowledge; risky driving practices by drivers, such as drinking alcohol and chewing khat while driving [\(33\)](#page-10-31); differences in the study period; the recent increase in the number of vehicles; inadequately experienced drivers; and the approval from drivers who were not adequately trained. Additional factors that may contribute to the high rate of RTA in the research area include a lack of formal training, mobile phone use while driving, dense traffic, failure to prioritize pedestrians, and a lack of awareness campaigns on road safety regulations. Sixtysix percent of drivers in the research setting had more than 8 h of work each day, suggesting that fatigue (which impairs decisionmaking) from working long hours may be linked to the high rate of road traffic accidents observed.
-
-According to this study, drivers who follow social media are less likely to be involved in a car accident than those who do not. This study was consistent with a previous study conducted in Mekelle town, northern Ethiopia [\(32\)](#page-10-25). Therefore, research done in India and Kenya found that media coverage of road traffic accidents can be used as a preventive measure by documenting the experiences of those hurt in RTAs and informing evidence-based solutions; emphasizing the systems approach, which encourages government action [\(34\)](#page-10-32) and encourages people to share information and opinions about road safety through social media [\(35\)](#page-11-3), which can change individual behavior by raising awareness and increasing understanding of the issue.
-
-This study found that prior traffic police punishment was less likely to be associated with road traffic accidents among taxi drivers, which is consistent with previous research conducted in Mekelle, northern Ethiopia [\(32\)](#page-10-25), Hanoi, Vietnam [\(36\)](#page-11-4), and India [\(37\)](#page-11-0), which found that improper enforcement of rules and regulations pertaining to road traffic causes accidents. Penalties for traffic violations may thereby reduce the risk of traffic violations and accidents by motivating drivers to observe traffic laws and improve their understanding of road safety. As a result, both the recent and earlier studies may have implications for altering the kind, degree, and scope of enforcement of traffic violation penalties used generally in Ethiopia, as well as the study's specific context. This may not always be the case. According to a study from China, driving experience significantly impacts how frequently violations occur and accidents happen [\(40\)](#page-11-5).
-
-The prevalence of RTA in this study was lower in vehicles that had been maintained in the preceding year than in vehicles that had not, which is similar to a study conducted in Ghana [\(41\)](#page-11-6) and Nigeria [\(42\)](#page-11-7). Because of this, well-maintained cars not only have a longer lifespan but also improve road safety for all parties involved, including drivers, passengers, and other road users. Accidents and injuries can result from failing to keep a vehicle in excellent operating condition, and a well-designed and maintained vehicle is less likely to be involved in accidents.
-
-According to the current study, drivers who received driving-related training in the previous 2 years were 23 % less likely to be involved in an RTA than those who did not. A study on the efficacy of pre-and post-license driver training found that post-license training programs help improve skills considered relevant to crash prevention, such as hazard perception and advanced vehicle control skills [\(43\)](#page-11-8). Therefore, it is recommended that drivers receive the necessary drivingrelated training to improve their skills and learn about safety measures to prevent traffic accidents and behavioral change in drivers toward risky driving behaviors. Training also enables drivers who had insufficient training during their license examinations to obtain the necessary knowledge.
-
-Moreover, drivers who engaged in risky driving behaviors were more likely to be involved in an RTA than those who did not. This finding is supported by research conducted in Mekelle City, where drivers who consumed alcohol were more likely to be involved in an RTA than those who did not [\(39\)](#page-11-2), as well as a study conducted in Pakistan, Korea [\(38\)](#page-11-1), and Tehran, Iran [\(44\)](#page-11-9). In addition, a study conducted in Hanoi, Vietnam, found that using a cell phone while riding in a taxi was linked with road traffic accidents [\(45\)](#page-11-10). As a result, to successfully minimize the magnitude of road traffic incidents, a proper countermeasure against drivers who engage in risky driving behaviors should be devised. Furthermore, to reduce road traffic accidents, alcohol intake tests, substance use, driver belt use, driver health status, driving speed, and vehicle maintenance status should be checked regularly.
-
-## Limitations of the study
-
-The study's limitations were that taxi drivers are a highly mobile community, which can contribute to discrepancies in the selection of study participants. The study only included living drivers, which can lead to an underestimation of the significance of the conclusion. Underestimation of the prevalence could also be due to social desirability bias. The current study also failed to evaluate drivers' perceptions of risky behavior and other experiences with it. Additionally, some demographic data were not collected, including height, weight, and BMI, which could be used to assess the risk of obstructive sleep apnea and determine whether a participant was at fault or injured in an accident.
-
-<span id="page-8-0"></span>TABLE 3 Factors associated with road trac accidents among vehicle drivers in Dilla Town, Gedeo Zone, Southern Ethiopia, 2021 (n = 316).
-
-| Variables                                              |     | Prevalence of RTA |                    | AOR [95%CI]       | P-value |
-|--------------------------------------------------------|-----|-------------------|--------------------|-------------------|---------|
-|                                                        | Yes | No                |                    |                   |         |
-| Age (years)                                            |     |                   |                    |                   |         |
-| <31                                                    | 62  | 80                | 1.33 [0.85, 2.09]  |                   |         |
-| ≥31                                                    | 64  | 110               | 1                  |                   |         |
-| Sex                                                    |     |                   |                    |                   |         |
-| Male                                                   | 119 | 180               | 0.94 [0.35, 2.55]  | 0.63 [0.69, 4.55] | 0.89    |
-| Female                                                 | 7   | 10                | 1                  | 1                 |         |
-| Educational status                                     |     |                   |                    |                   |         |
-| No formal education                                    | 8   | 11                | 1.54 [0.55, 4.31]  | 1.14 [0.23, 2.33] | 0.07    |
-| Primary school                                         | 37  | 40                | 1.96 [1.02, 3.77]  | 2.34 [0.79, 4.67] | 0.09    |
-| Secondary school                                       | 56  | 86                | 1.38 [0.77, 2.47]  | 1.61 [0.55, 3.42] | 0.17    |
-| College and above                                      | 25  | 53                | 1                  | 1                 |         |
-| Marital status                                         |     |                   |                    |                   |         |
-| Not married                                            | 69  | 96                | 1.96 [0.75, 1.86]  |                   |         |
-| Married                                                | 57  | 94                | 1                  |                   |         |
-| Family size                                            |     |                   |                    |                   |         |
-| Less than or equal to 5                                | 45  | 56                | 0.75 [0.46,1.21]   |                   |         |
-| Greater than 5                                         | 81  | 134               | 1                  |                   |         |
-| Additional job(s)                                      |     |                   |                    |                   |         |
-| Yes                                                    | 26  | 47                | 0.79 [0.46, 1.36]  | 2.45 [0.49, 2.38] | 0.628   |
-| No                                                     | 100 | 143               | 1                  | 1                 |         |
-| Average monthly income                                 |     |                   |                    |                   |         |
-| Less than 1,000                                        | 7   | 19                | 2.22 [0.86,5.75]   | 1.11 [0.92,6.65]  | 0.667   |
-| 1,000–2,000                                            | 32  | 62                | 1.58 [0.88,2.83]   | 4.56 [0.13,8.34]  | 0.083   |
-| 2,000–3,000                                            | 42  | 54                | 1.0 5 [0.59,1.85]  | 2.09 [0.93,7.34]  | 0.467   |
-| Greater than 3,000                                     | 45  | 55                | 1                  | 1                 |         |
-| Did you listen to mass media                           |     |                   |                    |                   |         |
-| Yes                                                    | 31  | 109               | [0.15, 0.41]       | 0.38 [0.18,0.65]  | 0.003** |
-| No                                                     | 95  | 81                | 1                  | 1                 |         |
-| Vehicle maintenance                                    |     |                   |                    |                   |         |
-| Yes                                                    | 21  | 55                | 0.49 [0.28,0.86]*  | 0.11 [0.09,0.96]* | 0.001** |
-| No                                                     | 105 | 135               | 1                  | 1                 |         |
-| Driving experience                                     |     |                   |                    |                   |         |
-| Less than one year                                     | 45  | 19                | 3.05 [1.39,6.66]   | 4.81 [0.89,7.86]  | 0.178   |
-| 1–4 years                                              | 60  | 144               | 0.54 [0.28,1.02]   | 0.78 [0.14, 2.61] | 0.073   |
-| 5–10 years                                             | 21  | 27                | 1                  | 1                 |         |
-| Working hours                                          |     |                   |                    |                   |         |
-| 8 h and less                                           | 42  | 65                | 0.96 [0.60, 1.55]  |                   |         |
-| Greater than 8 h                                       | 84  | 125               | 1                  |                   |         |
-| Attending driving-related training in the last 2 years |     |                   |                    |                   |         |
-| Yes                                                    | 38  | 127               | 0.21 [0.13, 0.35]* | 0.73 [0.28, 0.91] | 0.033** |
-| No                                                     | 88  | 63                | 1                  | 1                 |         |
-| Presence of sufficient zebra line                      |     |                   |                    |                   |         |
-| Yes                                                    | 21  | 33                | 0.95 [0.52, 1.73]  | 0.58 [0.22, 1.50] | 0.289   |
-| No                                                     | 105 | 157               | 1                  | 1                 |         |
-
-(Continued)
-
-TABLE 3 (Continued)
-
-| Variables                                          | Prevalence of RTA |     | COR [95% CI]        | AOR [95%CI]        | P-value |
-|----------------------------------------------------|-------------------|-----|---------------------|--------------------|---------|
-|                                                    | Yes               | No  |                     |                    |         |
-| Prior punishment by traffic polices                |                   |     |                     |                    |         |
-| Yes                                                | 30                | 91  | 0.34 [0.21, 0.56]*  | 0.56 [0.47, 0.83]  | 0.041** |
-| No                                                 | 96                | 99  | 1                   | 1                  |         |
-| Perceived road condition                           |                   |     |                     |                    |         |
-| Good                                               | 16                | 27  | 0.88 [0.45, 1.71]   | 0.53 [0.19, 1.47]  | 0.09    |
-| Poor                                               | 110               | 163 | 1                   | 1                  |         |
-| Availability of sufficient traffic signs or symbol |                   |     |                     |                    |         |
-| Yes                                                | 27                | 54  | 0.69 [0.40, 1.17]*  | 0.45 [0.98,3.07]   | 0.065   |
-| No                                                 | 99                | 136 | 1                   | 1                  |         |
-| Risky driving behaviors                            |                   |     |                     |                    |         |
-| Yes                                                | 75                | 23  | 10.68 [6.08, 18.74] | 7.89 [3.22, 12.38] | 0.029*  |
-| No                                                 | 51                | 167 | 1                   | 1                  |         |
-
-AOR, Adjusted Odds Ratio; COR, Crude Odds Ratio; \*p-value < 0.25 in the bivariate analysis; \*\*p-value ≤ 0.05 in the multivariate analysis; Not married- Single, widowed, and divorced.
-
-## Conclusion
-
-In this study, two-fifths of the drivers in Dilla town had a traffic accident in the previous year (2020) preceding the data collection period. Factors such as the experience of punishment or warning by traffic police in the last year, vehicle servicing, listening to mass media, attending drivingrelated training in the previous 2 years, and risky driving behaviors were all significantly associated with road traffic accidents. As a result, the Federal Ministry of Transport of Ethiopia and other stakeholders should be encouraged to advise drivers on vehicle safety and driving-related safety training, enhance their capacity to listen to the media, and raise awareness about vehicle maintenance and unsafe driving behaviors. Traffic officials should inspect the safety of the vehicles and advise all car owners to verify their vehicles' service. Additionally, it is recommended that drivers who disobey traffic laws and exhibit unsafe driving behaviors should be made aware of their actions and punished. Additional prospective and qualitative research is required to provide detailed accident characteristics and taxi driver behavior and perception.
-
-## Data availability statement
-
-The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.
-
-## Ethics statement
-
-The studies involving human participants were reviewed and approved by the Institutional Review Board of Dilla University (IRB-DU), College of Medicine and Health Science. The patients/participants provided their written informed consent to participate in this study.
-
-## Author contributions
-
-HH and BN were involved in the conceptualization, design, analysis, interpretation, and drafting of the manuscript and the critical revision of intellectual content. RK, EA, GZ, ZA, and NE were involved in the study's design, data analysis, data interpretation, and manuscript drafting and revision. All authors agree to be accountable for all aspects of the work and have approved the final version of the manuscript to be published.
-
-## Acknowledgments
-
-The authors would like to thank the study participants, Dilla University, Dilla Town Transport Office, and Dilla University staff, for their encouragement and constructive comments that contributed to the success of this manuscript. We would also like to thank the 4th-year public health students for participating in data collection and as supervisors.
-
-## Conflict of interest
-
-The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
-
-The reviewer CK declared a shared affiliation with the author(s) to the handling editor at the time of review.
-
-## Publisher's note
-
-All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
-
-## References
-
-- <span id="page-10-0"></span>1. Peden M, Scurfield R, Sleet D, Mohan D, Hyder AA, Jarawan E, et al. (editors). World Report on Road Traffic Injury Prevention. Geneva: World Health Organization (2004).
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Zotero/001_artiklid/Road Safety Indicators/harrisroadsafetyriskprediction2020.md

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-category: literaturenote
-citekey: harrisroadsafetyriskprediction2020
-title: A road safety risk prediction methodology for low volume rural roads
-authors: "Harris, Dale; Durdin, Paul; Brodie, Colin; Tate, Fergus; Gardener, Robyn"
-year: 2020
-date: 2020-11-08 2020-11-08
-doi: 10.3316/informit.842810685900972
-publication: Journal of the Australasian College of Road Safety
-url: "https://search.informit.org/doi/abs/10.3316/INFORMIT.842810685900972"
-zotero_key: 4DZ6336J
-zotero_storage: 27ZX3ZG5
-collections: imporditud
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Harris, Dale"
-status: converted
----
-Available under a Creative Commons Attribution Licence.
-
-- McCorry, B., & Murray, W. (1993). Reducing commercial vehicle road accident costs. *International Journal of Physical Distribution & Logistics Management, 23*(4), 35-35. doi: 10.1108/09600039310041491
-- Nævestad, T. (2009). Mapping research on high-risk organisations: Arguments for a sociotechnical understanding of safety culture. *Journal of Contingencies and Crisis Management, 7*, 126-136. doi: 10.1111/j.1468-5973.2009.00573.x
-- Short, J., Boyle, L., Shackelford, S., Inderbitzen, B., & Bergoffen, G. (2007). *Commercial truck and bus safety synthesis program: Synthesis of safety practice - Synthesis 14: The role of safety culture in preventing commercial motor vehicle crashes*. Washington: Transportation Research Board.
-- Snyder, B. H. (2012). Dignity and the Professionalized Body: Truck Driving in the Age of Instant Gratifi cation. *Hedgehog Review, 14*(3), 8-20.
-- Stake, R. E. (2005). Qualitative Case Studies. In N. K. Denzin & Y. S. Lincoln (Eds.), *The Sage Handbook of Qualitative Research* (3rd ed.). Thousand Oaks: Sage Publications, Inc.
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-- Sully, M. (2001). *When rules are not enough: Safety regulation and safety culture in the workplace*. Paper presented at the Road Safety Conference.
-- Transport Workers' Union. (2012). *Before the road safety remuneration tribunal: Application by the Transport Workers' Union of Australia for road safety remuneration orders*. Retrieved 2nd July, 2015, from http://www.twu.com. au/Home/Campaigns/Safe-Rates/TWU-Application-for-Road-Safety-Remuneration-Order/
-- Vaughan, D. (1996). *The Challenger launch decision: Risky technology, culture, and deviance at NASA*. Chicago: University of Chicago Press.
-- Williamson, A. (2007). Predictors of Psychostimulant Use by Long-Distance Truck Drivers. *American journal of epidemiology, 166(11),* 1320-1326. doi: 10.1093/aje/ kwm205
-- Williamson, A., & Friswell, R. (2013). The effect of external non-driving factors, payment type and waiting and queuing on fatigue in long distance trucking. *Accident Analysis & Prevention*, 58, 26-34. doi: 10.1016/j.aap.2013.04.017
-
-# A road safety risk prediction methodology for low volume rural roads
-
-*Dale Harris1 , Paul Durdin1 , Colin Brodie2 , Fergus Tate2 and Robyn Gardener3*
-
- *Abley Transportation Consultants, Christchurch New Zealand, 2 New Zealand Transport Agency, New Zealand, 3 Accident Compensation Corporation, New Zealand*
-
-*Winner of the John Kirby Award for best paper by a new researcher at the Australasian Road Safety Conference ARSC2015*
-
-# Abstract
-
-The roads of New Zealand's Eastern Bay of Plenty region have relatively low vehicle volumes and experience a number of rural road safety issues, including inappropriate speed, the use of drugs and alcohol, low levels of restraint use and young/inexperienced drivers. Over half of all rural crashes are loss-of-control crashes on curves. Due to low network traffi c volumes, crashes tend to be sporadic and diffi cult to predict using risk assessment techniques that rely on crash histories.
-
-This paper introduces a new risk prediction methodology that identifi es high-risk curves independent of crash history. Using geospatial data and innovative analysis techniques, existing methodologies for identifying curves and calculating vehicle operating speeds were modelled and automated to undertake a network-wide assessment of high risk curves.
-
-The new methodology extracted and classifi ed almost 7000 curves across 1500km of road network. When compared to the location of loss-of-control crashes, it was found that 66.6% of crashes occurred on 20.3% of curves classifi ed as 'high risk' in at least one direction. These results have been shared with road controlling authorities and will support prioritised road safety improvements targeting high risk curves.
-
-This methodology is the fi rst network screening tool that has been specifi cally developed to address road safety risk in low volume rural areas in New Zealand or Australia. The methodology demonstrates how existing research into vehicle operating speed behaviour can be applied to identify high risk road elements and support targeted improvements that have the potential to signifi cantly reduce road safety risk.
-
-# Introduction
-
-Safer Journeys, New Zealand's Road Safety Strategy 2010- 20, has a vision to provide a safe road system increasingly free of death and serious injury (Ministry of Transport, 2010). This Strategy adopts a safe system approach to road safety focused on creating safe roads, safe speeds, safe vehicles and safe road use. These four safe system pillars need to come together if the New Zealand Government's vision for road safety is to be achieved.
-
-![](_page_1_Picture_3.jpeg)
-
-**Figure 1. Eastern Bay of Plenty locality map** 
-
-Safe system signature projects are identifi ed in the Safer Journeys Action Plan 2013-2015 (New Zealand Transport Agency [NZTA], 2013) as exemplar projects that adopt a complete safe system approach to road safety. Safe systems signature projects provide a platform for trialling innovative approaches and treatments across the four safe system pillars.
-
-The Eastern Bay of Plenty region (Figure 1) was identifi ed as a candidate for a safe systems signature project as it is a region with signifi cant rural road safety issues; particularly inappropriate speed, use of alcohol/drugs, poor restraint use and inexperienced drivers. The scope of the project includes rural State highways and local roads. Most Eastern Bay of Plenty roads are low volume remote roads, where crashes tend to be sporadic and diffi cult to predict using reactive crash prediction models. Therefore a new approach to assessing road risk, independent of crash history, was required.
-
-Abley Transportation Consultants was commissioned by the New Zealand Transport Agency (the Transport Agency) to develop a risk prediction model and mapping interface "SignatureNET" to support the delivery of this signature project. This included building a vehicle speed model to identify high risk curves, assessing road risk using the urban KiwiRAP risk mapping methodology (Brodie et al., 2013), and applying rural road risk prediction models.
-
-# Methodology
-
-Many rural road crashes in Eastern Bay of Plenty occur on curves (57.9% of all fatal and serious rural road crashes 2004-2013) (NZTA, 2014). Due to the remote nature of the region's roads, fatal and serious crashes tend to occur on parts of the network where high-severity crashes have not occurred in the recent past. In these areas, relying on crash
-
-history alone to predict where future crashes will occur is unreliable. A new methodology that could identify and assess the risk of all the curves on the network that was independent of crash history was developed.
-
-The Austroads (2009) operating speed model for rural roads provides a procedure for calculating speeds along road sections based on the geometric features of the road. Using calculated speeds and horizontal curve radii, the model allows users to assess the design limit of curves.
-
-With 1500 km of rural road, manually assessing the risk of each curve in the Eastern Bay of Plenty region using the Austroads model would be time-consuming and cost-prohibitive. As the inputs to the Austroads operating speed model are available in a spatial format, the model was therefore automated using a new Geographic Information Systems (GIS) methodology. This included the development of GIS models that identify curves, predict vehicle operating speeds along road corridors, and assess curve risk using approach speeds and radius (Harris & Durdin, 2015). This methodology is discussed, in brief, below.
-
-### Curve identifi cation
-
-The fi rst step in speed modelling methodology is to identify curves using a high quality road centreline and a methodology adapted from Cenek et al. (2011). The spatial road dataset used in this methodology closely matched the actual centreline of the road. Using GIS linear referencing tools, the road centreline was divided into 10m sections and the rolling 30m average radius calculated for each arc section.
-
-Discrete curve sections were extracted by combining road segments where:
-
-- a. the radius was less than 800m;
-- b. at least one 10m section had a radius of 500m or less; and
-- c. the apex (direction) of the curve did not change.
-
-Contiguous 10m sections of road that met these criteria were dissolved into a single curved segment, with the radius (m) of the curve defi ned as the minimum radius across all the sections that make up the curve. The calculated curve radii represents a mid-road curvature value (rather than separate curve radii values for each lane or direction), noting that there are no divided carriageway roads in the Eastern Bay of Plenty region.
-
-#### Speed modelling
-
-The Austroads (2009) operating speed model predicts the operating (85th percentile) speed of cars travelling in each direction along a section of rural road. The model mimics the real-world behaviour of drivers based on a large number of car vehicle observations. As such, the model only applies to cars and cannot be used to predict the operating speeds of other types of vehicle.
-
-Available under a Creative Commons Attribution Licence.
-
-Once curves had been identifi ed, each road corridor was divided sequentially into a series of curves with known radii, and straights with known lengths. Speeds were then modelled along the road centreline in both directions. Sections of road with curves of a similar radius separated by short straights (less than or equal to 200m) were identifi ed as discrete sections with an operating speed identifi ed within a narrow range of values (minimum and maximum operating speeds). When drivers travel through a series of curves with a similar radii, their speeds stabilise to a level they feel comfortable with (Austroads, 2009). Section operating speeds for single, isolated curves were also calculated.
-
-Working along the road corridor, speed behaviour was modelled as either:
-
-- *a. Acceleration* on straights longer than 200m, or on curves where the approach speed is less than the operating speed of the curve.
-- *b. Speed maintenance* on straights less than 200m, or where the speed falls within the section operating speed range.
-- *c. Deceleration* on curves where the approach speed is higher than the operating speed for the curve (or series of curves).
-
-Rates of acceleration and deceleration were modelled using the methodology in Austroads (2009) (Figures 2 and 3). Extrapolation of values was required to estimate some acceleration and deceleration outputs, including acceleration for straights longer than 1000m (Figure 2) and deceleration where curve approach speeds are less than 60 km/h (Figure 3).
-
-The exit speed at the end of each curve or straight is applied as the approach speed for the following section of road. For each curve where deceleration is modelled, the design limit is identifi ed as either out-of-context (unacceptable or undesirable) or within context (desirable) (Figure 3). Curves where no deceleration is modelled are also considered to be 'within limit'.
-
-# Results
-
-The curve identifi cation methodology recognised 6,985 curves across the Eastern Bay of Plenty region. Each curve was classifi ed by design limit (in both directions) according to the Austroads speed model (Figure 3). The number of curves identifi ed by category are displayed in Table 1. Where curves were classifi ed differently in opposing directions, the worst (most out-of-context) classifi cation has been applied. For example, a curve that is 'undesirable' in one direction but 'within limit' in the reverse direction would be categorised as 'undesirable'.
-
-Because the curve identifi cation methodology developed for this project was new and untested, the results were compared against an existing Transport Agency curve dataset for the state highway network in the Eastern Bay of Plenty. The state highway curve dataset is based on horizontal curvature data collected in the fi eld as part of the Transport Agency's annual high speed surveys (Cenek et al., 2011).
-
-![](_page_2_Figure_14.jpeg)
-
-**Figure 2. Acceleration on straights (source: Austroads, 2009)** 
-
-![](_page_3_Figure_3.jpeg)
-
-**Figure 3. Deceleration on curves and design limits (source: Austroads, 2009)** 
-
-**Table 1. Eastern Bay of Plenty curve categorisation**
-
-| Curve Category | Total Curves | % of all Curves |
-|----------------|--------------|-----------------|
-| Unacceptable   | 600          | 8.6%            |
-| Undesirable    | 815          | 11.7%           |
-| Desirable      | 941          | 13.5%           |
-| Within Limit   | 4629         | 66.3%           |
-
-The new methodology identifi ed the location of 96.8% of curves in the state highway dataset, with a visible correlation between curve radii values (Figure 4). The two datasets met the assumptions required for linear regression, with 82% of calculated curve radii values within 20% of the Transport Agency's curve radii values. The degree of scatter between the two datasets, particularly for large-radius curves, is primarily attributed to the different collection methods. The state highway dataset is collected in-fi eld using lane-based radii, whereas the operating speed model methodology relies on radii values calculated from the centreline. Extreme outlier values are more likely to refl ect errors in the geometry and topology of the road centreline dataset.
-
-The results of the curve analysis were delivered through a mapping website ("SignatureNET"), which displayed the risk metrics alongside contextual road safety data including administrative boundaries and crash locations (Figure 5).
-
-### Correlation between curve category and loss-of-control crashes
-
-Further analysis was undertaken to identify the number and percentage of loss-of-control crashes by curve category. For this comparison, 10-years of crash data (2004-2013) was
-
-selected from New Zealand's Crash Analysis System (CAS) (NZTA, 2014a). For the purposes of this analysis, loss-ofcontrol crashes were defi ned as those with movement code 'BF' (head on - lost control on curve), 'DA' (lost control turning right) or 'DB' (lost control turning left) in CAS (NZTA, 2014b).
-
-In the 10-year period selected, there were 589 loss-ofcontrol crashes on the curves categorised using the curve risk assessment methodology. The number and percentage of loss-of-control crashes by curve category are presented in Table 2.
-
-**Table 2. Eastern Bay of Plenty loss-of-control crashes by curve category**
-
-| Curve Category | Total LOC<br>Crashes | % of all Curves |
-|----------------|----------------------|-----------------|
-| Unacceptable   | 226                  | 38.4%           |
-| Undesirable    | 166                  | 28.2%           |
-| Desirable      | 64                   | 10.9%           |
-| Within Limit   | 133                  | 22.6%           |
-
-The results show that two thirds (66.6%) of all loss-ofcontrol crashes occur on out-of-context curves i.e. those identifi ed as 'unacceptable or 'undesirable'. This is a particularly important fi nding as it means road controlling authorities in the Eastern Bay of Plenty can target efforts on 20.3% of all curves where 66.6% of all loss-of-control crashes occur.
-
-![](_page_4_Figure_3.jpeg)
-
-**Figure 4. Comparison of calculated curve radii against NZTA out-of-context dataset**
-
-#### Further analysis and model validation
-
-Since the fi rst development of the operating speed model for the Eastern Bay of Plenty, the operating speed model has been applied to the Top of the South region of New Zealand - the area encompassed by the South Island local authority districts of Marlborough, Tasman and Nelson City. It includes some 3382 km of rural roads, which is much larger than the Eastern Bay of Plenty region (approximately 1500 km).
-
-The operating speed model identifi ed a total of 21,158 curves in the Top of the South region. The results of the curve classifi cation are displayed in Table 5, showing that the proportion of curves in each classifi cation in the Top of the South region mirrored the Eastern Bay of Plenty region (Table 3) very closely.
-
-**Table 3. Top of the South curve categorisation**
-
-| Curve Category | Total Curves | % of all Curves |
-|----------------|--------------|-----------------|
-| Unacceptable   | 1772         | 8.4%            |
-| Undesirable    | 2215         | 10.5%           |
-| Desirable      | 2345         | 11.8%           |
-| Within Limit   | 14826        | 70.1%           |
-
-Analysis of loss-of-control crashes against curve category was also undertaken for the Top of the South region, identifying that 55.8% of all loss-of-control crashes occurred at 18.9% of curves identifi ed as out-of-context ('unacceptable' or 'undesirable') (Table 4). This result is similar to the analysis of the Eastern Bay of Plenty region, although the correlation is not as pronounced.
-
-**Table 4. Top of the South loss-of-control crashes by curve category**
-
-| Curve Category | Total LOC<br>Crashes | % of all LOC<br>Crashes |
-|----------------|----------------------|-------------------------|
-| Unacceptable   | 360                  | 32.3%                   |
-| Undesirable    | 262                  | 23.5%                   |
-| Desirable      | 135                  | 12.1%                   |
-| Within Limit   | 359                  | 32.2%                   |
-
-The Eastern Bay of Plenty and Top of the South data were combined so the relationship between curve categorisation and loss-of-control crashes could be better understood (Figure 6). This fi gure shows there is a strong relationship between curve category and loss-of-control crashes. The relationship indicates that focusing road safety improvement efforts on out-of-context curves is targeting to risk.
-
-![](_page_5_Picture_3.jpeg)
-
-**Figure 5. SignatureNET mapping website screenshot**
-
-# Discussion
-
-The methodology presented in this paper has been wellreceived by all the agencies involved in the delivery of the Eastern Bay of Plenty safe systems signature project. The curve risk data and SignatureNET web viewer is now available to all the local road controlling authorities to assist them in identifying and prioritising road safety interventions targeting loss-of-control crashes on curves.
-
-#### Current applications
-
-The curve assessment and automated operating speed model is currently being rolled-out across other locations in New Zealand.
-
-#### Informing speed management decisions
-
-The Transport Agency has developed a process for identifying parts of the state highway road network for speed management interventions based on road safety risk. Potential interventions include targeted enforcement, reducing speed limits, or upgrading parts of the road network to reduce the frequency and severity of crashes at current operating speeds. To determine and prioritise appropriate intervention strategies, the operating speed model was applied across the high speed state highway network to determine: (a) operating speeds under current speed limits; and (b) operating speeds under the identifi ed safe and appropriate speed limits, noting that speed limit is a factor in determining the maximum (desired) speed of a road (Austroads, 2009; Harris & Durdin, 2015).
-
-The different operating speeds are then being used to estimate the potential for death and serious injury (DSi) savings by using Nilsson's Power Model which connects changes in traffi c speeds with changes in road crashes at various levels of injury severity using a power relationship (Nilsson, as cited in Cameron & Elvik, 2008). Sections of state highway that exhibit little or no speed reduction are considered to be 'self-explaining' as the geometry and terrain of the road naturally prevents drivers from achieving higher speeds. Conversely sections with extreme differences when comparing the current operating speeds and the safe and appropriate operating speed are generally straight roads where enforcement or engineering improvements may be more appropriate, depending on potential DSi savings and the functional classifi cation of the corridor.
-
-#### Training safety practitioners
-
-As noted earlier, the methodology has been extended to the Top of the South region in New Zealand for exercises in the annual Safe Systems Engineering Workshop. The outputs were presented in a web viewer and combined with Urban KiwiRAP risk profi les for the region delivered as part of an Accident Compensation Corporation (ACC) funded road safety initiative. The web viewer was used as learning media to introduce Safe Systems Engineering Workshop attendees to the use of network screening tools to assist with the identifi cation of road safety issues across the region. By considering different risk metrics alongside one another and in combination with crash data, practitioners were able to identify potential factors contributing to highrisk locations and formulate responses at a desktop level prior to physically investigating sites.
-
-![](_page_6_Figure_3.jpeg)
-
-**Figure 6. Graph demonstrating the relationship between curve category and the location of loss-of-control crashes**
-
-#### Limitations and future enhancements
-
-The speed modelling and curve risk assessment methodology is of greatest value to road safety practitioners where it is used as a network screening tool. The methodology should be applied with care when considering individual curves. Site-specifi c factors such as roadside hazards should be taken into account when identifying or prioritising curve treatments. For this reason, the SignatureNET web viewer included aerial imagery basemaps, Google Street View and other contextual data to support users in undertaking desktop reviews.
-
-During the development and roll-out of the methodology, a number of further enhancements were suggested and are being explored. These include:
-
-- a. Enhancing the speed model by exploring the relationship between curve risk category, road surface type, carriageway width and actual road safety performance.
-- b. Exploring the relationship between curve risk category, KiwiRAP star rating and the road safety performance of state highways.
-- c. Enhancing the speed model by comparing calculated operating speeds against known operating speeds, for example data collected using GPS devices.
-- d. Exploring the feasibility of using high-resolution elevation data collected using LiDAR, (if available) to calculate super elevation and vertical curvature and incorporate these factors into the curve risk assessment models.
-
-# Conclusion
-
-The operating speed model and high risk curve assessment methodology demonstrate that innovative assessment methods and tools can be developed within a safe system signature project environment. Current applications of this methodology in New Zealand demonstrate the potential of this methodology in supporting the safe system philosophy, including the identifi cation of high risk curves for targeted safety investigation and treatment (Eastern Bay of Plenty), supporting the development of the New Zealand Transport Agency's Speed Management Guide, and being used as training media for Safe Systems Engineering Workshops. The curve identifi cation and analysis techniques presented in this paper will be of particular interest to any road controlling authorities wanting to reduce loss-of-control crashes on rural roads.
-
-# References
-
-Austroads. (2009). *Guide to road design part 3: Geometric design*. Sydney, NSW, Australia: Austroads Incorporated.
-
-Brodie, C., Durdin, P., Fleet, J., Minnema, R. & Tate, F. (2013, November). *Urban KiwiRAP road safety assessment programme*. Paper presented at the Australasian College of Road Safety Conference, Adelaide, Australia. Retrieved from http://acrs.org.au/fi les/papers/57%20Brodie\_NPR.pdf
-
-Cameron, M. H., & Elvik, R. (2008). *Nilsson's Power Model connecting speed and road trauma: Does it apply to urban roads?* Paper presented at the Australasian Road Safety Research, Policing and Education Conference, Adelaide, South Australia. Retrieved from http://acrs.org.au/fi les/ arsrpe/RS080079.pdf
-
-Available under a Creative Commons Attribution Licence.
-
-- Cenek, P. D., Brodie, C. A., Davies, R. B. & Tate, F. (2011, November). *A prioritisation scheme for the safety management of curves*. Paper presented at the 3rd International Road Surface Friction Conference, Gold Coast, Queensland, Australia. Retrieved from http://www. nzta.govt.nz/resources/safety-management-of-curves/docs/ safety-management-of-curves.pdf
-- Harris, D., & Durdin, P. (2015). *Developing a risk prediction model for a safe system signature project.* Paper presented at the IPENZ Transportation Group Conference, Christchurch, New Zealand. Retrieved from http://conf. hardingconsultants.co.nz/workspace/uploads/paper-harrisdale-developi-54f3936b2a318.pdf
-- Ministry of Transport. (2010). *Safer journeys New Zealand's road safety strategy 2010-2020*. New Zealand: Author.
-- New Zealand Transport Agency. (2013). *Safer journeys action plan 2013-2015*. New Zealand: Author.
-- New Zealand Transport Agency. (2014a). *Guide for the interpretation of coded crash reports from the crash analysis system (CAS).* New Zealand: Author. Retrieved from http://www.nzta.govt.nz/assets/resources/guide-tocoded-crash-reports/docs/guide-to-coded-crash-reports.pdf
-- New Zealand Transport Agency. (2014b). *Crash analysis system* [dataset]. Retrieved from http://www.nzta.govt.nz/resources/ crash-analysis-system-data/index.html
-
-# Laser ablated removable car seat covers for reliable deployment of side airbags
-
-*Arun Vijayan1 , Amit Jadhav1 , and Rajiv Padhye1*
-
-*School of Fashion and Textiles, RMIT University, Melbourne, arun.vijayan@rmit.edu.au* 
-
-*This paper is an extended version of an earlier conference paper that was presented at the ARSC 2015. It has undergone further extensive peer review; revised with additional material added.*
-
-# Abstract
-
-In the event of a collision, the safe and reliable deployment of side airbags which include side-torso and combination airbags through removable car seat covers that use tearseam technology has been recently questioned. It is well known that a side-torso airbag system in automotive seats can reduce injuries and prevent fatalities in the event of a collision. Removable car seat covers suitable for side airbag systems are popular accessories in the automotive industry from the perspective of upholstery protection and aesthetics. This study demonstrated a better-suited technology in which laser ablation down the side panel of removable car seat covers, produced a pre-determined strength within a weakened zone that allowed a reliable airbag deployment in the event of a collision. This paper investigates the effect of laser power as a key factor in the laser ablation process and its infl uence on the bursting strength of the car seat cover materials. The laser-ablation of the removable car seat covers can ensure the consistent airbag deployment under environmental conditions and temperatures ranging between ambient (22 ± 2°C), cold (-35 ± 2°C) and hot (85 ± 2°C). The durability and deployment performance of these car seat covers after UV exposure was also investigated.
-
-# Keywords
-
-Laser ablation, Textiles, Car seat covers, Side-torso and combination airbags, Bursting strength
-
-# Introduction
-
-Airbags in passenger vehicles are a supplementary restraint system to seat belts that further mitigates the injuries suffered in serious crashes. The use of airbags can help to minimise the chances of fatalities from an external collision, and limit passenger impact with the inside of the car. Airbags were fi rst introduced in passenger cars by Ford in 1971 to protect the driver and front passenger in frontal collisions. Since then, the number of airbags in modern cars has increased to fi ve and in some cases to nine; covering a wide range of accident scenarios. Side airbags were fi rst introduced into vehicles around 1995 to help protect passenger car occupants from serious injury in struck side crashes. International studies have shown that side airbags that include side-torso as well as head-and-torso (combination) airbags are effective in reducing the risk of death in near side impact situations, and mitigate injuries in vehicle rollovers (Braver, 2004; D'Elia, 2012). A driver involved in a side impact has twice as high a fatality risk as a driver involved in frontal impacts (Farmer 1997). In 2014 in Australia, 1299 passenger vehicles were involved in police-reported fatal crashes. These crashes resulted in 763 deaths (BITRE, 2014).
-
-Airbags in front occupant car seats are generally concealed in a moulded plastic enclosure contained within the side upholstery of the seat as either a combination or torsoonly system. The seat upholstery through which the side airbag would deploy involves a tear-seam technology that facilitates the infl ation of the airbag during a collision.

Zotero/001_artiklid/Road Safety Indicators/haueroverdispersionmodellingaccidentsroad2001.md

@@ -1,233 +0,0 @@
----
-category: literaturenote
-citekey: haueroverdispersionmodellingaccidentsroad2001
-title: Overdispersion in modelling accidents on road sections and in Empirical Bayes estimation
-authors: "Hauer, Ezra"
-year: 2001
-date: 2001-00-00 2001
-doi: 10.1016/s0001-4575(00)00094-4
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/s0001-4575(00)00094-4"
-zotero_key: 3JZ98TUI
-zotero_storage: 2ATV9SP8
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Hauer, Ezra"
-status: converted
----
-Accident Analysis and Prevention 33 (2001) 799–808
-
-![](_page_0_Picture_2.jpeg)
-
-www.elsevier.com/locate/aap
-
-# Overdispersion in modelling accidents on road sections and in Empirical Bayes estimation
-
-# Ezra Hauer \*
-
-35 *Merton Street*, *Apartment* 1706, *Toronto*, *Ont*., *Canada M*5*S* 3*G*<sup>4</sup>
-
-Received 28 September 2000; received in revised form 08 October 2000; accepted 30 October 2000
-
-#### **Abstract**
-
-In multivariate statistical models of road safety one usually finds that the accident counts are 'overdispersed'. The extent of the overdispersion is itself subject to estimation. It is shown that the assumption one makes about the nature of overdispersion will affect the maximum likelihood estimates of model parameters. If one assumes that the same overdispersion parameter applies to all road sections in the data base, then, the maximum likelihood estimate of parameters will be unduly influenced by very short road sections and insufficiently influenced by long road sections. The same assumption about the overdispersion parameter also leads to an inconsistency when one estimates the safety of a road section by the Empirical Bayes method. A way to avoid both problems is to estimate an overdispersion parameter () that applies to a unit length of road, and to set the overdispersion parameter for a road section of length *L* to *L*. How this would change the estimates of regression parameters for road section models now in use requires examination. Safety estimation by the Empirical Bayes method is altered substantially. © 2001 Elsevier Science Ltd. All rights reserved.
-
-*Keywords*: Empirical Bayes; Overdispersion; Negative binomial; Multivariate model
-
-# **1. Introduction**
-
-Entities have accident counts and traits. An entity can be a road section with traits such as length, traffic flow, number of lanes; entities can be drivers, with gender, age, annual distance travelled as traits; they can be grade crossings, with train traffic, vehicle traffic, sight distance, protective device used as traits; entities can be cities, with road density, number of inhabitants as traits, and so on. Often statistical regression models are formulated to express the expected accident count of an entity as a function of its traits. After the unknown model parameters are estimated, one usually finds that the accident counts are 'overdispersed'. That is, that the differences between the accident counts and model predictions, are larger than what would be consistent with the assumption that accident counts are Poisson distributed. This led researchers to use the Negative Binomial (NB) distribution to represent the distribution of accident counts.
-
-The NB distribution arises when accident counts for entity *i* are Poisson distributed with a mean *i* while is random variable drawn from a gamma distribution with mean=1 and variance=1/ (see, e.g. Guo 1996). We will call the 'overdispersion parameter'. Let *Yi* be the count of accidents on entity *i*. By the NB distribution,
-
-$$P(Y_i = y_i) = \frac{\Gamma(y_i + \phi)}{\Gamma(\phi)y_i!} \left(\frac{\phi}{\eta_i + \phi}\right)^{\phi} \left(\frac{\eta_i}{\eta_i + \phi}\right)^{y_i}.$$
- (1)
-
-The value of the overdispersion parameter is estimated along with all other unknown model parameters.
-
-The overdispersion parameter features in two uses to which the results of statistical regression modelling are put. First, when the results of modelling are used to estimate what is the mean accident frequency of entities with given traits, the overdispersion parameter serves to indicate how widely the accidents counts are distributed around the estimated mean. Second, when the results of modelling are used to estimate the safety of a specific entity for which the traits and the accident record are known by the Empirical Bayes (EB) method (see, e.g.
-
-<sup>\*</sup> Tel.: +1-416-483-4452; fax: +1-416-483-4415. *E*-*mail address*: ezra.hauer@utoronto.ca (E. Hauer).
-
-Hauer, 1997), the overdispersion parameter determines the relative weights given to the model prediction and the accident record.
-
-When fitting models to accident counts it is often assumed that one overdispersion parameter is common to all entities. Thus, e.g. a single common overdispersion parameter is used by Maycock and Hall (1984)to model accidents at roundabouts, by Hausman et al. (1984) to model patent counts; Hauer et al. (1989) to model accidents at urban signalized intersections; by Bonneson and McCoy (1993) to model accidents at stop-controlled rural intersections; by Guo (1996) to model quarterly counts of surgical procedures; by Miaou (1996) to model truck accidents; by Vogt and Bared (1998) to model accidents on rural road segments and intersections, and by many others.
-
-To use an overdispersion parameter that is common to all entities may be found limiting. There is no reason to expect that the gamma distribution from which is sampled is the same for all the entities that make up the data for the regression, or to all the entities to which the predictions may be thought to apply. One way to remove this limitation is to assume that *i*=*i* (see, e.g. Cameron and Trivedi, 1998 and Maher and Summersgill, 1996). When the parameter is set to 0, the standard NB model is obtained; when 0 is chosen then the variance of the gamma distribution from which is sampled decreases as *<sup>i</sup>* increases. Miaou and Lum (1993) modelled crashes on rural interstate highways using =1. Using the data from Maycock and Hall (1984) for four-arm roundabouts, Maher and Summersgill (1996) report that =0.25 maximized the likelihood of the data. Yet another possibility is to assume that itself is a function of the various covariates and unknown parameters. Heydecker and Wu (1999) used this approach when modelling accidents at three-arm junctions. The attraction of such generalizations is that they can better represent the reality of specific data sets.
-
-My experience with accident prediction models for road sections which differ in length leads me to believe that the choice of the expression for overdispersion is not only a matter of getting a better fit at the price of additional parameters in the model. The issue is not only empirical but also conceptual. In Section 2I show why problems of parameter estimation may arise when the same value of is assumed to apply to road sections that differ in length. In Section 3I discuss the general mechanism by which overdispersion arises in accident modelling. This understanding leads to a suggestion of a remedy to the aforementioned problem. The remedy is discussed in Section 4. In Section 5 I argue that the use of a single and common dispersion parameter also leads to a logical blemish in the Empirical Bayes Estimation of expected accident frequency and that the remedy in Section 4 removes the blemish.
-
-Table 1 Data for two road sections
-
-| Section | Traits         |         | Accident counts        |
-|---------|----------------|---------|------------------------|
-|         | L, length (km) | X, AADT | Y, accidents in a year |
-| 1       | 0.5            | 500     | 1                      |
-| 2       | 10.0           | 500     | 40                     |
-
-#### **2. A problem of influence**
-
-In this section a numerical example is used to illustrate how two alternative assumptions about the distribution of accident counts influence parameter estimates. In this example a parameter is estimated once assuming that accident counts are Poisson distributed and next using the NB distribution assumption. The data in this example are for two road sections with the traits and accident counts given in Table 1. Take, e.g. the model equation *<sup>i</sup>*=*Li Xi* 0.5. In this *<sup>i</sup>* is the expected number of accidents per year for road section *i*, *Li* is the length of road section *i*, and *Xi* is its Annual Average Daily Traffic (AADT). The parameter is to be estimated by the method of maximum likelihood.
-
-#### <sup>2</sup>.1. *Poisson regression*
-
-Assume first that the accident counts (*Y*) comes from a Poisson distribution. The Poisson log-likelihood functions with respect to for section 1, section 2, both sections on the same graph, and the sum for both sections together are shown in Figs. 1 and 2 Fig. 3 Fig. 4.
-
-The maximum in Fig. 1 is at 1=1/(0.5×5000.5)= 0.089, in Fig. 2 at 2=40/(10×5000.5)=0.179. Fig. 3 serves to show that the variation in log-likelihood for section 1 is very small compared to the variation in the log-likelihood for section 2. This is why the much longer road section 2 with its many accidents dominates the joint log-likelihood function in Fig. 4. As a result the maximum for the sum of two log-likelihoods is at <sup>1</sup> and 2=0.175, very close to the maximum for road section 2.
-
-That a data point with many accidents should have more influence on the estimate of than a data point with a few accidents is in accord with intuition and can be explained from basic principles. For road section *i*, is its mean number of accidents divided by *Li Xi* 0.5. When is estimated, the accident count replaces the mean number of accidents. If the accident count is Poisson distributed, the variance of this estimate of is the mean*i*/(*Li Xi* 0.5) <sup>2</sup>=/(*Li Xi* 0.5). Thus, whatever the value of , since *L*1/*L*2=1/20, the variance of <sup>1</sup> is 20 times the variance of 2. In combining two estimates of
-
-![](_page_2_Figure_2.jpeg)
-
-different precision, the combined estimate is most precise if we use a linear combination of <sup>1</sup> and <sup>2</sup> with the inverse of their variances as weights. That is, 1&2= (0.089/20+0.179/1)/(1/20+1/1)=0.175. In this, the weight of 1=0.089 is 1/21 and the weight of 2= 0.179 is 1/1.05.
-
-### <sup>2</sup>.2. *Negatie binomial regression*
-
-Assume now that accident counts (*Y*) come from the negative binomial distribution in Eq. (1) and that it is known that =1. The negative binomial log-likelihood functions with respect to for section 1, section 2, both sections on the same graph, and the sum for both sections together are shown in Fig. 5 Fig. 6 Fig. 7 Fig. 8. The maxima in Fig. 5 and Fig. 6 are, as before, at 1=0.089 and 2=0.179. However, as is evident from the comparison of Fig. 3 and Fig. 7, the log-likelihood function for road section 2 is now much flatter. Therefore, when the log-likelihoods for sections 1 and 2 (Fig. 8) are added, the 1 and 2 shifts a considerable way from <sup>2</sup> in the direction of 1. It is now 0.144. The mutual position of the estimates is depicted in Fig. 9.
-
-![](_page_2_Figure_6.jpeg)
-
-![](_page_2_Figure_7.jpeg)
-
-It is commonly believed that the estimates of model parameters do not change much when accident counts are assumed to be NB instead of Poisson distributed; the belief is that alternative assumptions about the distribution of accident counts affect mainly the precision with which the model parameters are estimated. In this numerical example, in which there are large differences in road section length, the maximum likelihood parameter estimate does depend on what is assumed to be the distribution of accident counts.
-
-The issue is now clear. We expect that road section 2, which is 20 times longer than road section 1 and has 40 times as many accidents, will always exert a strong pull on the maximum likelihood estimate 1&2. It is disturbing to find that assuming accident counts to be NB distributed with a common , considerably weakens the influence of what should be the dominant data point. Why is this so?
-
-The transparent explanation is again provided by examining the relative precision of the estimates <sup>1</sup> and 2. Under the Poisson assumption the variance of an estimate of was mean/(*LX*0.5) 2 . Under the NB assumption the variance of an estimate of is [mean× (1+mean/)]/(*LX*0.5) . If we take mean1=0.144×
-
-![](_page_2_Figure_11.jpeg)
-
-![](_page_3_Figure_2.jpeg)
-
-0.5×5000.5=1.6 accidents per year and mean2=20× 1.6=32 accidents per year, then the ratio of their variances is [1.6(1+1.6)/*L*<sup>1</sup> 2 ]/[(32(1+32)/(20*L*)1 ]= 4.16/2.64-1.5. Combining the two *i*'s we now obtain 1 and 2=(0.089/4.16+0.179/2.64)/(1/4.16+1/2.64)= 0.144. Whereas under the Poisson assumption the variance of <sup>1</sup> was 20 times that of 2, under the NB assumption it is only 1.5 times.
-
-The root of the problem in the numerical example is the innocuous but commonly made assumption, that the overdispersion parameter is the same for road sections 1 and 2. Recall that the NB distribution arises when *<sup>i</sup>* is multiplied by a random variable drawn from a gamma distribution with mean=1 and variance=1/*<sup>i</sup>* . This amounts to assuming that the variance of accident counts is *i*(1+*<sup>i</sup>*/*i*). For road section 2, under the Poisson assumption, the variance of accident counts was the same as the mean (32); using =1 in the NB assumption, the variance of accident counts is 33 times the mean [32(1+32/1)=1056]. The reason for the dramatic increase in the variance of the accident counts of road section 2 is that its mean (32) is
-
-![](_page_3_Figure_5.jpeg)
-
-large compared to the magnitude of the overdispersion parameter .
-
-The issue can be now stated generally:
-
-The lesser the variance of the (accident) counts for a data point (a road section), the larger is its influence on the estimates of model parameters. When counts are assumed to be NB distributed with a common overdispersion parameter , then, entities with a mean that is large in comparison to the magnitude of will suffer a dramatic increase in the variance of their (accident) counts. This brings about a correspondingly large decline in their influence over the most likely value of the model parameters, as compared to their influence when counts are assumed to be Poisson distributed. But entities with large means (e.g. long road sections and road sections with a lot of traffic) are precisely those entities that should influence parameter estimation most.
-
-Miaou and Lum (1993) provide essentially the same argument when they note that '… in estimating the
-
-![](_page_3_Figure_10.jpeg)
-
-![](_page_3_Figure_11.jpeg)
-
-![](_page_4_Figure_2.jpeg)
-
-Fig. 9. Comparison of estimates.
-
-(regression) coefficients for road sections with the same AADT the linear-regression models put considerably more weight on accidents observed on short road sections than those observed on long sections…' (p. 698). They argue in favour of using the Poisson models (where the variance of accident counts is the mean) as opposed to the Normal and Log-Normal models (which they, for some reason, call linear-regression models) in which the assumption is that the variance is proportional to the square of the road section length or the square of the mean.
-
-That under the NB error structure with common , road section 2 does not dominate the estimate of should raise eyebrows. To suggest a sensible remedy, it is necessary to be clear about why overdispersion arises in our circumstance.
-
-## **3. Why are accident counts overdispersed?**
-
-The usual circumstance of statistical road safety modelling is that we have data for many entities (say, road sections) and we wish to fit a model to this data set. For each entity we know the count of accidents that have been reported to the police. In addition, for each entity we know some traits that have been measured or estimated with varying degrees of accuracy. The measured traits are often averages over the road section length (e.g. roadside hazard rating) or averages over time (e.g. Annual Average Daily Traffic — AADT). We then choose to include some such measured traits in the model equation as covariates. These will be called the '*represented traits*'. In addition, there are the measured traits that are not represented in the model, some measurable but unmeasured traits, and perhaps some altogether unmeasurable traits. These are the '*unrepresented traits*'. Both represented and unrepresented traits are linked to accident occurrence.
-
-If for a specific entity, say entity '*a*' it was possible to keep constant all its traits — both represented and unrepresented — and then to repeat the same time period over and over, each time recording the count of accidents, then, the average of the counts would converge on the period mean for this specific entity — *a*. Consider now a different entity, say entity '*b*', with the same represented traits as entity '*a*'. The mean for entity '*b*' (*b*) will likely differ from the mean for entity '*a*' in spite of the fact that their represented traits are identical. This difference has several reasons. First, the measured traits are subject to estimation error. Thus, e.g. AADT is estimated on the basis of few days of traffic counting that may have been carried out in some other year at a nearby location. Therefore identical estimates of AADTs do not mean that, in fact, road sections '*a*' and '*b*' served the same traffic. Second, the same average may represent very different distributions. If road section '*a*' carries 100 vehicles at night and 900 during daytime while road section '*b*' serves 500 vehicles at night and in daytime, '*a*' and '*b*' will have the same AADT but different accident means. Third, entities '*a*' and '*b*' are sure to differ in unrepresented traits; one road may lead to a pub, the other may be popular with deer. For all these reasons, if we imagine a large group of entities (*a*, *b*, *c*, …) with the same represented traits, they must be thought to have different means (*a*, *b*, *c*, …). These means form a distribution with as a mean (when *E*[]=1) and with -2 / as variance (when VAR[]=1/). The roˆle of statistical modelling is to provide an estimate as a function of represented traits.
-
-We are interested in how can be expressed as a function of the represented traits for two common purposes. First, when we wish to ascertain how a certain trait (say, lane width or grade) is associated with (perhaps affects) the expected accident frequency. The second purpose is when we wish to estimate the expected accident frequency of a specific site by combining the prediction for sites with identical represented traits with the accident history of the site. In both instances (using the common statistical parlance) this means that we wish to estimate a random effects model and this is the framework of discussion in this paper. (Were a time series of accident counts and traits available for each entity in a data set, it would also be possible to estimate a parameter *<sup>i</sup>* , *i*= *a*, *b*, *c*, … doing so might be of interest if, e.g. one tried to examine why some entities in the data set seem to have fewer or more accidents than is predicted by their represented traits. In this case we might be interested in a fixed effects model.)
-
-Unless VAR[]=0, - (being the mean of the *i* , in the imagined group of entities *i*=*a*, *b*, *c*, … that have identical represented traits) will not be the same as the of the entity in the data. On this basis it is possible to further clarify the nature of overdispersion. Recall that the definition of 'variance' is the expected value of the squared difference between a random variable and its mean. The expected value of the squared differences between a random variable and any fixed value other than the mean, is larger than the variance. Overdispersion is judged by the magnitude of the squared difference between accident counts and model predictions —
-
-the squared residuals. But model predictions are estimates of the for an imagined population of entities with identical represented traits; model predictions are not estimates of the *<sup>i</sup>* of the entities for which we happen to have the accident counts. Since the squared residuals are computed with respect to a value other than the mean of the entities for which we have data, we must expect the residuals to be larger than the variance of the counts for the entities in the data set. Thus, overdispersion is the necessary consequence of the fact that the means of the entities that serve as data are not what the model aims to predict. The root cause of overdispersion is that entities with the same represented traits have different means.
-
-In the numerical example we noted that the influence of a data point diminishes as the variance of its accident counts increases and that the NB distribution implies that the variance of the accident counts for entity *i* is *i*×(1−*i*/*i*). Therefore, the central question is: how does the VAR[]=1/*<sup>i</sup>* change as a function of the value of represented traits. This question is answered in a very specific way if it is assumed that is always drawn at random from a Gamma distribution with mean 1 and variance 1/. In this case, since *<sup>i</sup>* is assumed to be , the same for all entities, the variance of the means in an imagined population of entities with identical represented traits is proportional to -. Thus, e.g. the use of =1 for both road sections in the numerical example implies that roads with the same represented traits as section 2 (10 mile long road sections with AADT=500) have a VAR[-] that is four hundred times that of roads with the same represented traits as road section 1 (0.5 mile long road sections with AADT=500). Should one expect such a large increase in the variance that is due to traits not represented in the model simply because one road section is much longer than the other road section?
-
-The condition under which this would be true will be examined next. However, before doing so, it is important to comment briefly on why in available data sets some road sections are long and others short. Miaou and Lum (1993)(pp. 697–699) provide a good discussion of this issue. Usually road sections in state or provincial inventories are delimited by intersections. In principle, the aim is to define the road sections so that the road in-between the end-points is of one class (e.g. two-lane, four-lane divided) and is fairly homogeneous in traffic. Additional considerations having to do with the history of major reconstructions also apply. When so defined, road sections are usually not homogeneous in features such as grade, curvature, roadside hazard and the like. In some data bases (e.g. the HSIS — the Highway Safety Information System) an attempt is made to define road sections that are homogeneous in all (or most) measured traits.
-
-#### **4. A remedy**
-
-Road section 2 of the numerical example can be thought to consist of 20 subsections like road section 1. Therefore -2=20-1. With this, under the usual NB assumption, the variance of accidents counts on road section 2 would be 20-1(1+20-<sup>1</sup>/). (The usual NB assumption is that the value of , drawn from the Gamma distribution with mean=1 and variance=1/, applies to the entire road section 2.)
-
-An alternative expression for the variance of accident counts on road section 2 is obtained when one assumes that for each of the 20 constituent subsections a separate is selected from a Gamma distribution with mean 1 and variance 1/. Under this assumption the variance of accident counts on each such sub-section is -1(1+ -1/). If the 20 subsection accident counts are statistically independent, the variance of their sum will be 20-1(1+-<sup>1</sup>/). We now have two different expressions for the variance of accident counts on road section 2 and, obviously, 20-1(1+20-1/)20-1(1+-<sup>1</sup>/).
-
-The reason for the noted difference can be stated in tangible terms. The expression 20-1(1+20-<sup>1</sup>/) has been obtained by assuming that all 20 constituent subsections have the same . That is, all 20 subsections of which road section 2 is made up have the very same 'errors in variables', the same 'distribution that underlies averages', and the same 'unrepresented traits'. In contrast, the expression 20-1(1+20-<sup>1</sup>/) is obtained by assuming that for each of the 20 constituent subsections a new is selected at random from the Gamma distribution with mean=1 and variance=1/. This implies that the 20 constituent sub-sections differ amongst each other in their 'errors in variables', 'distributions underlying averages' and in 'unrepresented traits' in the same manner as any two road sections with the same represented traits would, even when they were not stringed out one behind the other on the same road. In short, under the first assumption the 20 values of are perfectly correlated while under the second assumption they are uncorrelated.
-
-The assumption about the 's being either perfectly correlated or entirely uncorrelated brackets reality. Subsections of the same road will still differ in many respects. Therefore, there is no reason to think that the 's of all the subsections are the same. However, subsections with the same represented traits when stringed out consecutively along the same road are likely to differ less than would subsections that are not spatially proximal. Therefore the 's of subsections along the same road are likely to be correlated, if only because they are used by the same drivers under the same climatic conditions.
-
-A general expression for the variance of accident counts when the 's of subsections are not correlated can be obtained as follows. A road section of length *L*
-
-![](_page_6_Figure_2.jpeg)
-
-can be thought to consists of *L*/*l* subsections, all subsections having the same covariate values, and each subsection being *l* units long. If the entire road section has a mean accident frequency -, each subsection has mean accident frequency =-/(*L*/*l*). As before a mixing variable is drawn randomly from a distribution with mean=1, but now we assume that its variance depends on the length of the subsection *l*. That is, VAR[]=1/(*l*). Using the NB distribution, for each subsection of length *l*, VAR[Accident count for a subsection]=(1+/(*l*)). Let *Y* be the sum of accident counts for the *L*/*l* subsections. Since the subsection counts are statistically independent, VAR[*Y*]= (*L*/*l*)[1+(*L*/*l*)/((*l*)*L*/*l*)]=-[1+-/((*l*)*L*/*l*)].
-
-The sum of independent NB random variables is not NB distributed. However, we can use the result about VAR[*Y*] as a motivation to postulate an alternative negative binomial model for the count of accidents *Y* for a road of length *L*. The alternative assumption is that the NB distribution arises when for the road section of length *L* is multiplied by a random variable drawn from a gamma distribution with mean=1 and variance=1/[(*l*)*L*/*l*].
-
-![](_page_6_Figure_5.jpeg)
-
-To examine the implication of this alternative assumption on parameter estimation, I now return to the numerical example. Assume that (*l*=1km)=1. Then, for road section 1, VAR[]=1/[(*l*=1 km)×0.5/1]= 1/0.5=2 and for section 2, VAR[]=1/[(*l*=1 km)×10/1]=1/10. The corresponding four log-likelihood functions are in Figs. 10–13.The maxima in Figs. 10 and 11 are, as before, at 1=0.089 and 2=0.179. As is evident from the comparison of Figs. 10 and 11, the much larger influence of the log-likelihood function for the accident-rich road section 2 has been restored. Therefore, when the two log-likelihoods are added, the 1 and 2 (=0.175) is again very close to 2.
-
-When it comes to parameter estimation by maximizing a likelihood function then, instead of using in Eq. (1) a that is common to all entities, for entity *i* we used *i*=(*l*)*Li*/*l*. The question is what subsection length *l* should be used? Suppose that using a subsection length *l*=1 km we obtain the maximum likelihood estimate ML(*l*=1) of (*l*=1). This determines the values of all the *i*'s. Naturally, the same values of *<sup>i</sup>* maximize the likelihood function if subsections were, say, 2 km long. This implies that ML(*l*=1)=
-
-![](_page_6_Figure_10.jpeg)
-
-ML(*l*=2)/2. That is, that the ratio ML(*l*)/*l* is constant. Therefore we can choose *l*=1 unit of length, say 1 mile or 1 km and the numerical values of *i*=(1 mile)×*Li*(measured in miles) or *i*=(1 km)× *Li*(measured in km) will be the same. Using the same argument, namely, that there is a unique set of values *<sup>i</sup>* that maximize the likelihood function, we can establish the relationship between ML(1 mile) and ML(1 km); ML(1 mile)×*L*(measured in miles)=ML(1 mile)×0.622×L(measured in km). Therefore ML(1 km)=0.622×ML(1 mile).
-
-The gist of the argument in this section was that reality is somewhere between assuming that all subsections of a road section have precisely the same represented and unrepresented traits, and, alternatively, assuming that these traits vary from sub-section to sub-section of a certain road just as they vary between any subsections of the road system that have the same represented traits. The mathematical equivalent of these two bracketing assumptions is to either use a constant that does not depend on road length or to use *i*=×*Li* . (Both options can be represented algebraically by *i*=×*Li* with =0 or =1. A device for establishing a compromise that reflects reality might be to use a value of from within the (0, 1) range).
-
-# **5. Implication for EB estimation**
-
-In EB estimation the model prediction *<sup>i</sup>* and the accident count *Yi* are combined as follows (see, e.g. Hauer, 1997):
-
-Estimate of 
-$$\eta_i \theta = \text{weight}_i \times (1 - \text{weight}_i) \times (Y_i)$$
-. (2)
-
-In this, *<sup>i</sup>* is the expected number of accidents for entities that have the same represented traits as entity *i* and
-
-weight<sub>i</sub> = 
-$$\frac{1}{1 + \text{VAR}[\eta_i \theta]/E[\eta_i \theta]} = \frac{1}{1 + (\eta_i^2/\phi_i)/\eta_i}$$
-$$= \frac{1}{1 + \eta_i/\phi_i}.$$
- (3)
-
-The assumption that *i*=*L*<sup>0</sup>= was seen earlier to lead to potential distortions in parameter estimation. The same assumption may lead to an inconsistency when the safety of a road section is to be estimated by the EB method.
-
-To show the potential for inconsistency, consider a road that has along its entire length the same represented traits. The model for this kind of road predicts accidents in a period of specified length. Assume that we have in that period accident counts *y*<sup>1</sup> and *y*<sup>2</sup> on the first and the second half of this road. Since for the whole road weightwhole=(1+-/)−<sup>1</sup> , the estimate of for the road is weightwhole-+(1−weightwhole) (*y*1+ *y*2).
-
-Consider now the first half of this road on which *y*<sup>1</sup> accidents were counted and ask what is to be the estimate of the expected accidents there. One extreme option is to estimate as if both halves of the road had the same . To estimate in this manner reflects the belief that not only do the two halves of the road have the same represented traits, as postulated, but they are also have the same 'errors in variables', the same 'distributions that underlie averages', and the same 'unrepresented traits'. With these assumptions the estimate for the first half of the road would be half the estimate for the whole road. This amounts to asserting that not only is the count of accidents on the second half of the road (*y*2) relevant to estimating what is true about the first half of the road, but that it is just as important as the accident count on the first half of the road (*y*1). One may ask how far can this belief be stretched? How can one judge whether physically separate road segments with the same (or even similar) represented traits have the same , in which case their accident counts should be used jointly to estimate the expected number of accidents on any one of these segments? In any case, if the belief that the same applies to both halves of the road is correct, then the sum of EB estimates for the two halves equals the EB estimate for the whole road. With the assumption of a common the EB estimates are consistent.
-
-The other extreme option is to estimate as if the two road halves differed in their unrepresented traits, in variable errors and in the nature of averages. That is, even though the two halves have the same represented traits, their 's could differ in the same manner as 's differ amongst roads that are not physically contiguous; as if two values of were sampled independently from the same distribution, one for each road half. With this, for the first half of the road the EB estimate is : weighthalf×-/2+(1−weighthalf)×(*y*1), and for the second half of the road the EB estimate is: weighthalf× -/2+(1−weighthalf)×(*y*2). The sum of the two estimates is : weighthalf×-+(1−weighthalf)×(*y*1+*y*2). As was shown earlier, the EB estimate for the road as a whole is weightwhole-+(1−weightwhole) (*y*1+*y*2). We would like the EB estimator be such that, the sum of the estimates for the two halves be the same as the estimate for the whole. The only general way to satisfy this equality is to make weighthalf=weightwhole.
-
-The assumption *i*=*L*<sup>0</sup>= implies that weighthalf=(1+(-/2)/)−<sup>1</sup> and weightwhole=(1+-)−<sup>1</sup> . Because weighthalf weightwhole, the sum of the estimates for the two halves of a road section is not the same as the estimate for the whole. Under the assumption that *i*= the EB estimates are inconsistent when the two halves have different 's. The alternative assumption that *i*=*L* does not have the same blemish. Now weighthalf=[1+(-/2)/(*L*/2)]−1=[1+-(*L*)]−1=weightwhole. Since the two weights are now the same, the sum of the estimates for the two halves is the same as the estimate for the whole.
-
-That in our thought we divided the road section into halves only made the algebra transparent. Division into unequal parts would lead to the same conclusion. It appears that if it is not true that all subsections of a road that is homogeneous in represented traits have the same expected number of accidents per unit length, then use of the assumption that *i*= would lead to inconsistent EB estimates.
-
-# **6. Summary and discussion**
-
-In NB regression it is common to assume that the overdispersion parameter is the same for all entities. Experience with statistical modelling of accidents on road sections indicates that this assumption may have undesirable consequences. When road sections in the data base differ in length, two problems arise. First, if model parameters are estimated by maximum likelihood, the relative influence of long road sections is much diminished whereas very short road sections exert an unduly large influence. Second, when the expected accident frequency of a road section is estimated by the EB method and the entire road section does not have the same , the sum of estimates for subsections does not add up to the estimate for the road section as a whole.
-
-An alternative is to consider overdispersion for road section *i* to be given by *Li* . This has been motivated by the argument that each road section can be viewed as consisting of many subsections that differ in traits that are not represented in the model. Therefore, that these subsections have different 's drawn independently from the same Gamma distribution. The use of this assumption has been shown to remedy both problems.
-
-It can be argued that reality is somewhere between the unsatisfactory assumption that *i*=*L*<sup>0</sup> and the logically-pleasing-but-not-exactly-true assumption that *i*=*Li* 1 . It is therefore tempting to consider *i*=*Li* with in the range (0, 1). Two problems arise. First, any 1 will give rise to the problem that when the of a road section is not constant, the sum of EB estimates for parts will not be the EB estimate for the whole. Second, examination of residuals may give an illusory confirmation of incorrect assumptions about . The problem is that a value of close to 0 will give little weight to data points with large -. This, will make for large squared residuals at these data points. Large squared residuals for these data points will seem to confirm the assumption that is close to 0. Had a close to 1 been assumed, the data points where is large would have been more influential. The resulting parameter estimates, would make these residuals smaller, and thereby seem to confirm the assumption of  being close to 1. Whether the usual maximum likelihood estimation can overcome this built-in tendency needs to be explored.
-
-The recommendation here is not to regard the overdispersion parameter as a constant common to all entities, at least not without justification that is based on the data. At this time, the relationship *i*= *Li* seems to be the more attractive alternative. In this relationship *Li* is a 'logical variable'. It represents the logical requirement that if a road section is made up of two non-overlapping sub-sections the sum of the model predictions for the two subsections should be the same as the model prediction for the road section as a whole. This can be true only if the model prediction is proportional to section length. At times, section length is used in models not only as a logical variable but also as a statistical covariate. Since the length of a road section is determined mainly by the distance between major intersections, it is entirely possible that section length is correlated with speed, driveway density etc, and therefore legitimately serves as a proxy for several causal variables. Logical variables and statistical covariates must be kept separate. Thus, e.g. should parameter estimation lead to the result that accident frequency is proportional to (section length)0.79, this is best written as proportionality to *L*×*L*<sup>−</sup>0.21 in which *L* is the logical variable and *L* the covariate which serves as proxy for causal variables associated with section length.
-
-Would the generalization suggested by Cameron and Trivedi (1998) to use *i*=*i* with set to either 0 or 1 be a remedy? If is set to be 0 then we have the standard NB model and both noted deficiencies apply. If is chosen to be larger than 0 but lesser than 1, then weightpartweightwhole and the potential for estimation inconsistency discussed in Section 5 remains. When is set to be 1, then, in Eq. (2), weight*i*=(1+1/)<sup>−</sup><sup>1</sup> . This would remove the estimation inconsistency. Unfortunately, use of a weight that does not diminish as *i* increase would deprive EB estimation of its essence. The attraction of EB estimation is that it correctly accounts for differences in the precision of two separate sources of information. When *<sup>i</sup>* is large, most of the information about the expected accident frequency for the specific site should come from the accident count for that site, not the model prediction. Therefore weight*<sup>i</sup>* should be small. Conversely, when *<sup>i</sup>* is small the accident count will be small and therefore much of the information should come from the model prediction. In this case weight*<sup>i</sup>* should be large. It follows that the suggested generalization does not solve the problem.
-
-Heydecker and Wu (1999) suggest that itself be modelled as a function of the various covariates and a new set of parameters. This avenue can be pursued provided that the structure *i*=*Li*×*f*(covariates*i*) is preserved. However, since we suspect that in reality the 's for subsections of the same road are neither uncorrelated nor perfectly correlated, the fundamental remedy is in research that might provide details about the variation of 's among spatially proximate and spatially distant road sections.
-
-To make our point clearly, the numerical example on which Section 2 was based made use of one very short and one very long road section. The question arises whether in real data sets which consist of a mixture of road section lengths, the assumption made about the overdispersion parameter would have a noticeable effect on the results modelling. A related practical question is whether models that have been estimated in the past on the assumption of a single common overdispersion parameter should revisited. These questions can be answered fully only by modelling several real data sets using both assumptions about the overdispersion parameter. It is a task for future research. However, one issues can be settled now. The value of the overdispersion parameter under the differing assumptions is sure to be different. Therefore, if any use is to be made of the overdispersion parameter, the models need to be re-estimated.
-
-Another question is whether the two alternative assumptions about the dispersion parameter will have a noticeable effect on the EB estimation of the expected accidents. This question can be answered affirmatively. The EB estimate depends on the model prediction (-) and the 'weight' (Eq. (2)). Even if the model prediction was affected only mildly by the alternative assumptions, the 'weight' under the two assumptions will, as a rule, be substantially different. This may open another line of empirical inquiry. After fitting a model to data using both assumption, one can then compare the performance of the two alternative EB estimators.
-
-### **References**
-
-- Bonneson, J.A., McCoy, P.T., 1993. Estimation of safety at two-way stop-controlled intersections on rural highways. Transportation Research Record 1401, 83–89.
-- Cameron, A.C., Trivedi, P.K., 1998. Regression Analysis of Count Data. Cambridge University Press, Cambridge.
-- Guo, G., 1996. Negative multinomial regression models for clustered event counts. Sociological Methodology 26, 113–132.
-- Hauer, E., 1997. Observational Before–After Studies in Road Safety. Pergamon, Oxford, UK.
-- Hauer, E., Ng, J.C.N., Lovell, J., 1989. Estimation of safety at signalized intersections. Transportation Research Record 1185, 48–61.
-- Hausman, J., Hall, B.H., Griliches, Z., 1984. Econometric models for count data with an application to the patents-R&D relationship. Econometrica 52 (4), 909–938.
-- Heydecker, B.G, Wu, J., January/1999. Identification of sites for road accident remedial work by Bayesian statistical methods: an example of uncertain inference. The Fifth International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, Oxford.
-- Maher, M., Summersgill, I., 1996. A comprehensive methodology for the fitting of predictive accident models. Accident Analysis and Prevention 28 (6), 281–296.
-- Maycock, G, Hall, R.D., 1984. Accidents at four-arm roundabouts. Laboratory Report LR 1120, Transport Research Laboratory, Crowthorne, Berkshire, UK.
-- Miaou, S-P., 1996. Measuring the Goodness of Fit of Accident Prediction Models. FHWA- RD-96-040. FHWA, McLean, VA.
-- Miaou, S.P., Lum, H., 1993. Modelling vehicle accidents and highway geometric design relationships. Accident Analysis and Prevention 25 (6), 689–709.
-- Vogt, A., Bared, J., 1998. Accident models for two-lane rural segments and intersections. Transportation Research Record 1635, 18–29.

Zotero/001_artiklid/Road Safety Indicators/hauerpredictionroadsafety2010.md

@@ -1,588 +0,0 @@
----
-category: literaturenote
-citekey: hauerpredictionroadsafety2010
-title: On prediction in road safety
-authors: "Hauer, Ezra"
-year: 2010
-date: 2010-00-00 2010
-doi: 10.1016/j.ssci.2010.03.003
-publication: Safety Science
-url: "https://doi.org/10.1016/j.ssci.2010.03.003"
-zotero_key: MA7QJD6E
-zotero_storage: AJN8JVZ4
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Hauer, Ezra"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-Contents lists available at [ScienceDirect](http://www.sciencedirect.com/science/journal/09257535)
-
-# Safety Science
-
-![](_page_0_Picture_4.jpeg)
-
-![](_page_0_Picture_5.jpeg)
-
-# On prediction in road safety
-
-# Ezra Hauer \*
-
-35 Merton Street, Toronto, Ontario, Canada M4S 3G4
-
-#### article info
-
-Article history: Received 12 September 2009 Received in revised form 2 March 2010 Accepted 3 March 2010
-
-Keywords: Prediction Road safety Safety targets Evaluation
-
-#### abstract
-
-Prediction is about potential outcomes: what will happen if and what would have happened if. The first question arises when safety targets are set, the second when the effect of an intervention on safety is to be evaluated. There are many ways to predict. For the same data different prediction methods produce different predictions. What targets are set and what estimates of intervention effect are produced will depend on what method of prediction is chosen. Therefore one has to determine what method tends to predict best. To do so empirically one asks what method would have predicted best had it been applied in the past and then one assumes, inductively, that the same would apply in the future. Quantitative measures of prediction quality are suggested and it is shown how these measures of prediction quality allow one to determine which of two prediction methods should be preferred.
-
-The suggested approach was applied to two data sets: The time series of motor vehicle accident fatalities in Province A and in Province B. On the basis of this analysis one may draw tentative conclusions for these jurisdictions and the methods tested; one can say what method seems preferable, what is the average size of bias than needs to be corrected and how accurate is the prediction likely to be. Broader conclusions will emerge once many additional methods of prediction are applied to data from many other jurisdictions and pertaining to a variety of circumstances.
-
--2010 Elsevier Ltd. All rights reserved.
-
-#### 1. Introduction
-
-Safety predictions are needed for two main purposes:
-
-- 1. To set policy or program targets. For this purpose one needs to predict what would be the future safety of the unit1 if the policy or programs will not implemented. Safety targets are then set on this basis.
-- 2. To estimate what was the effect a policy, program, or treatment on the safety of the unit. For this purpose too one needs to predict what would have been the safety of the unit had the policy, program or treatment not been implemented.
-
-The difference between the two circumstances is one of timing: for target setting the prediction is produced before the program or policy is implemented whereas for effect estimation the prediction is produced after the intervention was implemented.2
-
-There are many commonly used methods of prediction. Some methods rely on the extrapolation of past trends; some make use comparison groups, another family of methods attempts to model the causal factors that govern the evolution of the safety of a unit. In this paper the question is not how to best describe the structure underlying a time series of data. The question is which method predicts best. This is why the emphasis is not on theory-motivated time series analysis methods and the characterization of their statistical properties. The emphasis is inductive and empirical; a method is attractive for future use if, had it been used in the past it would have predicted better than other methods. From this perspective the theoretical underpinnings of a method and the statistical properties of its fit do not matter.
-
-The quality of a method for safety prediction depends on the nature of the unit for which the prediction is prepared. That is, that one method of prediction may be best when the unit is an intersection or road section while a different method may be best when the unit is a province, state, or country. It is also possible that one method is best for making predictions about the next few years while another when predictions into the more distant future is of interest. In this paper the focus is on predictions when the unit is a province, state or country. In this context predictions for both the near and the more distant future are of interest. Near future predictions are needed to evaluate the effect of interventions in the short term; longer term predictions are needed to tell what might be the safety of some jurisdiction if past practices
-
-<sup>\*</sup> Tel.: +1 416 483 4452.
-
-E-mail address: [Ezra.Hauer@Utoronto.ca](mailto:Ezra.Hauer@Utoronto.ca)
-
-<sup>1</sup> Policies, programs, and treatments apply to units. A unit can be a state, a region, a city, a road section, drivers of an age cohort, trucks of some type, etc.
-
-<sup>2</sup> This has implications about the kind of information available at the time of the prediction is made. For target setting predictions one can only forecast what are likely to be the traits of the unit during the period to which the prediction applies. In contrast, since for effect evaluation the period of prediction is in the past, some such traits can be known.
-
-<span id="page-1-0"></span>were to continue and no novel programmes or new policies implemented.
-
-In this paper the approach to answering the question of which method predicts best is empirical. Available historical data will be used to ask how a method of prediction would have performed had it been used in the past. For clarity it is best to define the differences between Prediction, Forecast, and Estimate. Predictions are about a state of the world that did not and will not exist; they pertain to 'potential outcomes'.3 Thus, prediction is always associated with a conditional clause such as 'what would be if' or 'what would have been if'. Should it happen that, in the event, the policy, program, or intervention will not be implemented then, what was a prediction, turns into a forecast. Unlike a prediction, a forecast is about a state of the world that will exist and therefore the quality of a forecast can be judged by juxtaposing it to what eventually materialized. In contrast to forecasts which pertain to events that will materialize in the future, estimates are about events in the past. Thus, e.g., we can use the history of accident occurrence to estimate the expected number of accidents.
-
-The quality of estimates is the subject of much statistical theory and may be regarded well known. Similarly, there exists a very large body of knowledge about forecasting and forecast quality. This lore is embodied in many books and learned journals.4 The quality of 'potential outcome predictions'5 is less well explored.
-
-## 2. Previous work
-
-The issue of prediction in road safety was examined explicitly in [Hauer et al. \(1991\)](#page-11-0). Using a time series of annual fatal and injury accidents in each of 10 Canadian provinces, predictions were formed by four methods: (a) using 1 year's count to predict for the next, (b) using the average for 2 years to predict the for the next, (c) using the least-squares fit to three counts to predict the next and, (d) using comparison group, i.e. multiplying the count in Province A in year y by the ratio of counts Province B for years y + 1 and y to predict the expected accident count in Province A in year y + 1. It turned out that, in spite of using more data than method 'a', methods 'b' and 'c' gave worse predictions. Method 'd' gave good predictions if the comparison group had many accidents. We found that ''one can predict better not using a comparison group than using one that is too small" (p. 599). In spite of common belief in the importance of similarity between the 'treatment' and the 'comparison group', it turned out that similarity played only a minor role. The issue of predicting by method 'd' was analysed in [Hauer \(1991\).](#page-11-0)
-
-A more thorough examination of prediction in road safety is in the doctoral thesis of [Quaye \(1992\).](#page-11-0) Much of what follows draws on his work. Quaye's object was to propose means to measure the performance of prediction methods and, on this basis, to ascertain whether one method is better than another. Quaye made a distinction between prediction methods that extrapolate a time series
-
-![](_page_1_Figure_11.jpeg)
-
-Fig. 1. Count of road accident fatalities in Province A.
-
-of accident counts and between econometric methods that make use of information about some causal variables. For extrapolation methods his data were the fatality counts of Canadian provinces; for the econometric models he used also data about population, vehicle registration, and vehicle-kilometres of travel (Vkmt). Some of his results were published in [Quaye and Hauer \(1993\)](#page-11-0) and presented in [Quaye and Hauer \(1994\)](#page-11-0). In these we found, e.g., that using twelve different methods to predict the number of fatal accidents in Province A it seemed that extrapolation of a quadratic ordinary least-squares fit produced better predictions that the averaging of past 'y' accident counts and was also better than various econometric models that used data about population, registered vehicles and vehicle-kilometres of travel. In 1994 the trail seems to have grown cold. No further research into the quality of road safety predictions as produced by various prediction methods was found.
-
-#### 3. Same data, different predictions
-
-The purpose of this section is to show that methods of prediction are many and that these produce a multitude of diverse predictions. In the face of this multitude and diversity one asks: ''Which method of prediction should one believe more?" To explain the issues arising from this question in a tangible setting data about the number of persons killed in crashes in Province A (Fig. 1) will be used.
-
-This mountain-like shape depicts the evolution of a process over time.6 What forces shaped this process can be said only in general terms: they are the changes in population, the processes of urbanization and motorization, advances in medicine, improved roads, safer vehicles, changes attitude to risk and in other societal norms, etc.
-
-Suppose that we are at the end of 1985 and wish to predict what would be the number of fatalities for the next 10 years if the same forces continued to exert their influence and no substantially new programs and policies were implemented.<sup>7</sup> The predictions for 1986–1995 are to be based on data for the years to the left of the solid line; what is to the right of the solid line is at this point in time unknown.
-
-Prediction methods are many. As the purpose here is illustration, only relatively simple methods of prediction will be used. Thus, e.g., one could extrapolate the two most recent observed counts, or fit an ordinary least squares regression (OLS) a line to the n (=3, 4, 5, ...) most recent observed counts, or fit a polynomial
-
-<sup>3</sup> The 'potential outcome' terminology favoured by Rubin (see, e.g., [Rubin, 2005\)](#page-11-0) is used instead of the equivalent expression: 'counterfactual'.
-
-<sup>4</sup> One list [\(http://www.forecastingprinciples.com/books1.html\)](http://www.forecastingprinciples.com/books1.html) contains entries for 75 books on forecasting, The University of Toronto libraries presently subscribe to eight journal that have 'forecasting' in their title. Most such books and journals are oriented towards business and economics.
-
-<sup>5</sup> In the context of safety evaluation the word 'prediction' is a poor choice. Evaluations are undertaken after an intervention was implemented, and therefore 'prediction', as used in this context, refers to a time and state of affairs which is already in the past at the time when the evaluation commences. This is awkward because in common usage prediction refers to events in the future. It might have been better to use 'postdiction' or 'retrodiction' instead. However, both terms are already in use and have acquired specialized meanings. Besides, the word prediction has been used in the sense defined here earlier ([Hauer, 1997](#page-11-0)) and coining a new term might lead to confusion. In the rest of this paper the word 'prediction' will be used instead of the longhand expression: 'potential outcome safety prediction'.
-
-<sup>6</sup> The process evolved in a similar way in nearly all developed countries.
-
-<sup>7</sup> This is similar to what Broughton et al. (2000) aimed for when preparing the framework for setting the 2010 Target in the UK. Their goal was to predict ''what would be expected if there were no further DESS measures and only the core road safety activities were undertaken (at the 1998 level of effect) during the period to 2010; this was done by extrapolating trends from the 1983–1998 period." ([Brough](#page-11-0)[ton, 2006](#page-11-0)). The acronym DESS stands for Drink/driving, Engineering, Secondary Safety improvements to vehicles.
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-<span id="page-2-0"></span>Table 1 A variety of predictions.
-
-| Method and variant     | 1986  | 1987  | 1988  | 1989  | 1990  | 1991  | 1992  | 1993  | 1994   | 1995   |
-|------------------------|-------|-------|-------|-------|-------|-------|-------|-------|--------|--------|
-| OLS linear, n = 2      | 207.0 | 200.0 | 193.0 | 186.0 | 179.0 | 172.0 | 165.0 | 158.0 | 151.0  | 144.0  |
-| OLS linear, n = 3      | 202.3 | 191.8 | 181.3 | 170.8 | 160.3 | 149.8 | 139.3 | 128.8 | 118.3  | 107.8  |
-| OLS linear, n = 5      | 199.9 | 188.4 | 176.9 | 165.4 | 153.9 | 142.4 | 130.9 | 119.4 | 107.9  | 96.4   |
-| OLS linear, n = 10     | 215.0 | 207.7 | 200.5 | 193.2 | 185.9 | 178.6 | 171.4 | 164.1 | 156.8  | 149.5  |
-| OLS Quadratic, n = 3   | 214.0 | 221.0 | 235.0 | 256.0 | 284.0 | 319.0 | 361.0 | 410.0 | 466.0  | 529.0  |
-| OLS Quadratic, n = 5   | 210.4 | 209.4 | 211.4 | 216.4 | 224.4 | 235.4 | 249.4 | 266.4 | 286.4  | 309.4  |
-| OLS Quadratic, n = 10  | 181.9 | 156.6 | 128.3 | 96.9  | 62.6  | 25.2  | -15.1 | -58.5 | -104.8 | -154.2 |
-| Holt smoothing,a Ab    | 207.0 | 199.6 | 192.2 | 184.8 | 177.5 | 170.1 | 162.7 | 155.4 | 148.0  | 140.6  |
-| Holt smoothing, Bc     | 204.4 | 200.7 | 190.5 | 203.7 | 194.8 | 188.5 | 175.2 | 163.3 | 151.2  | 131.3  |
-| Hoerl function, n = 5  | 201.2 | 188.4 | 175.3 | 162.2 | 149.3 | 136.8 | 124.8 | 113.4 | 102.6  | 92.6   |
-| Hoerl function, n = 10 | 210.9 | 202.3 | 193.4 | 184.5 | 175.6 | 166.7 | 158.0 | 149.4 | 141.0  | 132.9  |
-| Hoerl function, n = 20 | 218.4 | 212.5 | 206.6 | 200.7 | 194.7 | 188.7 | 182.7 | 176.7 | 170.8  | 164.9  |
-| Oppe–Koornstra         | 256.7 | 254.7 | 252.5 | 250.1 | 247.4 | 244.6 | 241.6 | 238.4 | 235.2  | 231.8  |
-| What materialized      | 245   | 236   | 200   | 192   | 154   | 170   | 143   | 153   | 151    | 157    |
-
-<sup>a</sup> Let yi be the accident count at time i (i = 1, 2, ... , n) and si the 'smoothed' value. Holt smoothing consists of the recursive relationships si = ayi + (1 a)(si-<sup>1</sup> + ui-1) and ui = c(si si-1) + (1 c)ui-
-
-to these points, or use double exponential smoothing, or fit a Hoerl function or, following [Oppe and Koornstra \(1990\) and Oppe \(1991\),](#page-11-0) extrapolate risk and exposure and predict using their product.8 , 9 The resulting predictions are in Table 1.
-
-The juxtaposition of these predictions and the accident counts that materialized during 1986–1995 (the black squares) is in Figs. 2–5.
-
-One should resist the temptation to form conclusions on the basis of an illustration based on few simple methods of prediction that rely on data from only one jurisdiction (Province A), and use predictions for only one point in time (1985). The few rows of Table 1 are just a smattering of what could be listed. Instead of OLS one could use maximum likelihood, instead of linear or quadratic polynomial regressions one could choose from many function families, in addition to the few n's listed many others could be used, instead of Holt smoothing one could use one of the many autoregressive or state space models for time series data etc. It is obvious that as prediction methods are many and each method has many variants, predictions can be indeed very many. Furthermore, as in Table 1, the many predictions usually vary widely. Naturally one asks which method and variant are best in what circumstance. 'Circumstance' may refer to type of jurisdiction, the shape of the time series profile, whether the prediction is for but few years or for further into the future and so on. Determining which approach to prediction is best requires substantive empirical inquiry involving a juxtaposition of what would have been predicted and what has materialized for several jurisdictions and points in time. Before embarking on this task for two jurisdictions, a brief a discussion of two topics is in order: the issue of extrapolation versus causal modeling in prediction, and the question of how the quality of predictions can to be measured.
-
-![](_page_2_Figure_12.jpeg)
-
-Fig. 2. Prediction by straight lines fitted to the last n data points.
-
-![](_page_2_Figure_14.jpeg)
-
-Fig. 3. Predictions by extrapolating a quadratic polynomial fitted to the last n data points.
-
-#### 4. Approaches to prediction: extrapolation versus causation
-
-All the predictions in Table 1 rely on extrapolation. Even the Oppe–Koornstra method relies on the extrapolation of a time series of risks and another extrapolation of a time series of exposures. Extrapolation rests on the assumption that time-trends of the past will continue into the future. It is commonly believed that one would predict better if the causes of the evolution of a process
-
-<sup>1</sup> in which there are two smoothing constants (<sup>a</sup> and <sup>c</sup>) and starting values (s<sup>0</sup> and <sup>u</sup>0). <sup>b</sup> In variant <sup>A</sup> the smoothing and starting values were selected so as to minimize the sum of one-step-ahead absolute forecast errors. The prediction in Table 1 for years Y > 1985 is s<sup>1985</sup> + (Y - 1985) u1985. For the Province A data, 214.3 + (Year - 1985) (-7.37).
-
-<sup>c</sup> In variant B, the a, c, s<sup>0</sup> and u<sup>0</sup> for n-steps-ahead predictions are estimated so as to minimize the sum of n-step-ahead absolute prediction errors.
-
-<sup>8</sup> The Oppe–Koornstra approach entails two equations. The first traces the evolution of risk over time (Rt = exp(at + b) in which Rt is the risk at time t, with a and b as parameters to be estimated from data). The second traces the evolution of exposure over time (Vt = Vm/{1 + exp[-(at + b)]} in which Vt represents the vehiclekilometres of travel at time t, Vm is the maximum (saturation) Vkmt with a and b parameters estimate from data). The number of accidents is the product Rt Vt.
-
-<sup>9</sup> One could also use econometric models making use of variables such a population, income, alcohol, and consumption. Ranging from simple multivariable regressions to complex structural models. However, models of this kind will have difficulty replicating the fatality peak evident in [Fig. 1](#page-1-0) and will require the making predictions for all variables in the model.
-
-<span id="page-3-0"></span>![](_page_3_Figure_2.jpeg)
-
-Fig. 4. Predictions by extrapolating a Hoerl function fitted to the last n data points.
-
-![](_page_3_Figure_4.jpeg)
-
-Fig. 5. Two variants of Holt smoothing and the Oppe–Koornstra prediction.
-
-were understood and the magnitudes of these causes were known or could be predicted. Fig. 6 is an attempt to clarify.
-
-The cause of the change in the sequence of accident counts is not the passage of time; accidents depend causally on various factors. Thus, e.g., the number of traffic fatalities in a province depends on the number of drivers, amount of pedestrian traffic, quality of health care, norms of behaviour, on what roads and in what environment travel takes place, etc. These factors themselves change with time as indicated by the top grey arrow.
-
-Prediction by extrapolation assumes that causal factors will change with time is such a way that the future is a smooth continuation of past trends. This is represented by the curved dashed arrow on the right which bypasses the box containing causal factors. However, if one could know how expected accidents change under the influence of change in causal factors and if the value of these causal factors in the period for which the prediction is needed is could be known, estimated or predicted, then one could do away with the assumption that the future is an unspecified continuation of the past. The hope is that replacing the belief in an unspecified 'continuity of trends' by information about causal factors and knowledge about cause–effect relationship might improve prediction. This is represented by the looping solid arrow on the left.
-
-![](_page_3_Figure_9.jpeg)
-
-Fig. 6. Causal versus extrapolation of time-trend prediction.
-
-If data about the past values of all important causal factors were available, if the functional relationship between expected accident and causal factors was sufficiently understood, and if regression modeling was up to the task of distinguishing between association and causation, then it might be possible to describe how the expected number of accidents would change if the values of some causal factors changed. At present, none of these conditions fully holds. Furthermore, to predict what would be the expected number of accidents at some future time one would also have to be able to predict the magnitudes of all the causal factors for that future time, further complicating the task of prediction by causal models. For these reasons it is not clear whether predictions based on the use of data in statistical regression (structural) models will usually be better than those based on extrapolating time-trends.10
-
-In the present context of prediction for large jurisdictions there is an additional reason for shunning causal models. As in [Fig. 1,](#page-1-0) in most large jurisdictions there is a period during which fatalities increased, reached a peak and followed by a period of decline and stabilization. All the while causal variables such a population, exposure etc. increased. For a satisfactory explanation of the peak in the time series of fatalities one has to include in the model a cause– effect representation of how risk (accidents per unit of exposure) changes as a function of causal variables (such as changes in public mores, investment in infrastructure, medicine, and urbanization) At present, knowledge of this kind is insufficient. Even if it was known how risk depends on, say, public mores or the extent of urbanization, it still would be necessary to predict what these will be in the future. Unless the process of the evolution of public mores or of urbanization could be described as a function of some variables other than time, extrapolation would still be needed. The hope that the evolution of fatalities can be explained by cause and effect, without resorting to extrapolation of time-trends leads to infinite regress. For these reasons, in the present inquiry, only time-based extrapolation approaches will be examined.
-
-#### 5. Prediction quality
-
-In Section [3](#page-1-0) the story was that we are at the end of 1985 and need to predict what would be the number of fatalities in Province A for 1986–1995 if no substantially new policy or program were to be implemented in those 10 years. If we predict by, say, linear extrapolation of an OLS fit with n = 5, then the predictions are those in row 4 of [Table 1](#page-2-0) and repeated below in row P of [Table](#page-5-0) [2](#page-5-0). The count of fatalities that materialized in that period is in row X. The differences X - P between the two are in row D. In row d is the relative difference, the difference as proportion of the count that materialized. Thus e.g., if d (X - P)/X = 0.18, then the predicted value is 18% below the accident count.
-
-Were one to predict similarly at the end of 1984, one would obtain [Table 3.](#page-5-0)
-
-As the first year of data is for 1952 and when n = 5, the first such table could be produced at the end of 1956. Since the last year for which we have fatality counts is 2007, and each table is for 10 'steps ahead' years, the last table that can be produced is for 1997. Thus we can have 42 such tables and 42 rows of relative differences for 1, 2, ... , 10 steps-ahead predictions. Selected rows from this composite table are in [Table 4.](#page-5-0)
-
-<sup>10</sup> In the community of forecasters there is vigorous debate about the quality of forecasts obtainable by the relatively simple methods of time-series extrapolation and forecasts produced by more complex regression models that make use of important causal variables and often are often based on some theory which takes the form of simultaneous equations. The weight of the evidence at this time suggests that complex regression models do not necessarily produce better forecasts than simpler univariate time-trend extrapolations. As [Makridakis and Hibon \(2000\)](#page-11-0) say: ''Statistically sophisticated or complex methods do not necessarily provide more accurate forecasts than simpler ones." p. 452.
-
-<span id="page-4-0"></span>The interpretation of this table is as follows. Suppose that using the count of fatalities in [Fig. 1](#page-1-0) one was to predict what will be the annual number of fatalities in the next 10 years by extrapolating the ordinary-least-squares fit to the last 5 years of fatality counts. A one-step-ahead prediction (for 1957) when the last year of data is 1956 would prove to have d = -0.11 while a seven step ahead prediction (for 1991) when the last year of data is 1984 would prove to have d = 0.17. It should be clear that the entries in the table are not statistically independent. Thus, e.g., for s = 1 the d in 1956 0.11) and the d in 1957 (0.03) share the same four of five data points to estimate their prediction equations. The last three rows of the table contain summary statistics. Judging by d, the predictions (the P's) for the years 1956–1997 will be on the average about 1% higher11 than the count of fatalities (the X's)when s = 1, 2 and 3 while for s = 9 or 10 the P's would be, on the average, 6% higher than X's. Here the bias (d) increases with the number of steps ahead. The standard error of the d's around d is in the before last row of [Table 4.](#page-5-0) The further into the future one predicts the larger is the standard error of predictions. Thus, e.g., when predicting (by OLS with n = 5) 6 years into the future one should expect the average prediction to be 2% too high and its standard error to be ±39%. As is evident, the standard error of prediction increases with s.
-
-In sum, the quality of a method of predictions can be judged by two attributes. First, one would like predictions to be unbiased in the sense that when the prediction is P then the expected value of the corresponding X's is also P. Second, one would like predictions to be 'close' to the expected value of X. The notion of being 'close' can be variously defined. One common measure of closeness is the standard error (SE); another is the mean absolute error (MAE). While many measures of prediction error are in use12, in what follows the measures of prediction quality will be the bias, as measured by the average of d's (the d or d-bar), and the standard error of the d's denoted by SEdbar. The attributes of being unbiased and that of closeness are not independent. Methods that produce predictions with significant bias will tend to also have large SE and MAE. Therefore in many cases one can combine them into one measure of performance, the standard error around 0. This measure, to be denoted by SE0 is defined by SE0 ¼ ðSE2 dbar <sup>þ</sup> bias2 . Having created the yardstick for measuring prediction quality we can now turn to the task of comparing methods and their variants in terms of the quality of predictions that they produce.
-
-#### 6. Comparing variants and methods
-
-The central question is which of two prediction methods and variants would have predicted better. Thus, e.g., had we predicted by the linear extrapolation of an OLS fit, would we have predicted better with n = 5 or with n = 10? Having specified what will measure the quality of predictions an answer can now be attempted. The raw material is in [Table 5](#page-5-0) the top half of which was taken from the summary rows of [Table 4](#page-5-0) and the bottom half of which was similarly generated except that the extrapolation was of an OLS fit to the last ten fatality counts (n = 10).
-
-The three comparisons are in the Figs. 7–9.
-
-The negative bias is larger with n = 10 than with n = 5 for all s. On the other hand, with the exception of s = 1, when n = 10 the pre-
-
-![](_page_4_Figure_10.jpeg)
-
-Fig. 7. Bias as a function of s for n = 5 and n = 10.
-
-![](_page_4_Figure_12.jpeg)
-
-Fig. 8. Standard error around d as a function of s for n = 5 and n = 10.
-
-![](_page_4_Figure_14.jpeg)
-
-Fig. 9. Standard error around 0 as a function of s for n = 5 and n = 10.
-
-dictions have a smaller standard error (around d and around 0) than when n = 5. One may conclude that when s = 1 it would have been better to use n = 5 while n = 10 would have been preferred for s > 1. The predictions would have been better, on the average,13 had they been reduced by d. Thus, e.g., if s = 5 then the predictions obtained by linear extrapolation should have been reduced by 5% (as per [Table 5.](#page-5-0))
-
-The method that allowed the comparison of n = 5 and n = 10 can be used to compare any two n's. How the three measures of prediction quality vary with s and n is shown in the three figures below.
-
-The bias [\(Fig. 10\)](#page-6-0) increases with s and n. The standard error around d is smallest between n = 9 and 10. The standard error around 0 is also smallest between n = 9 and 10. It follows that if
-
-<sup>11</sup> When P > X then D (and also d) are negative. A negative d (or d) indicates the predictions (P) are to high. When a linear prediction equation is used P will tend to be too high when the underlying process is a downward bending curve (the second derivative is negative).
-
-<sup>12</sup> Common measures of forecast accuracy are also the MAPE (Mean Absolute Percentage Error), MdAPE (Median Absolute Percentage Error), sMAPE (Symmetric Mean Absolute Percentage Error), sMdAPE (Symmetric Median Absolute Percentage Error), MdRAE (Median Relative Absolute Error), GMRAE (Geometric Mean Relative Absolute Error), and the MASE (Mean Absolute Scaled Error). From [Hyndman and](#page-11-0) [Koehler \(2006\).](#page-11-0)
-
-<sup>13</sup> d is the right correction if it is not clear whether, at the time when the prediction is made, that the underlying process is non-linear. If at the time the prediction is made it be clear that the underlying process is upward or downward bending then the use of a linear predictor does not make much sense.
-
-<span id="page-5-0"></span>Table 2 Predictions for the 10 years following 1985.
-
-|   | Years               | 1986  | 1987  | 1988  | 1989  | 1990  | 1991  | 1992  | 1993  | 1994  | 1995 |
-|---|---------------------|-------|-------|-------|-------|-------|-------|-------|-------|-------|------|
-| s | Steps ahead         | 1     | 2     | 3     | 4     | 5     | 6     | 7     | 8     | 9     | 10   |
-| P | OLS linear, n = 5   | 199.9 | 188.4 | 176.9 | 165.4 | 153.9 | 142.4 | 130.9 | 119.4 | 107.9 | 96.4 |
-| X | Count of fatalities | 245   | 236   | 200   | 192   | 154   | 170   | 143   | 153   | 151   | 157  |
-| D | Differences         | 45.1  | 47.6  | 23.1  | 26.6  | 0.1   | 27.6  | 12.1  | 33.6  | 43.1  | 60.6 |
-| d | Rel. diff. D/X      | 0.18  | 0.20  | 0.12  | 0.14  | 0.00  | 0.16  | 0.08  | 0.22  | 0.29  | 0.39 |
-
-Table 3 Predictions for the 10 years following 1984.
-
-|   | Years               | 1985  | 1986  | 1987  | 1988  | 1989  | 1990  | 1991  | 1992  | 1993  | 1994  |
-|---|---------------------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|
-| s | Steps ahead         | 1     | 2     | 3     | 4     | 5     | 6     | 7     | 8     | 9     | 10    |
-| P | OLS linear, n = 5   | 210.1 | 198.6 | 187.1 | 175.6 | 164.1 | 152.6 | 141.1 | 129.6 | 118.1 | 106.6 |
-| X | Count of fatalities | 214   | 245   | 236   | 200   | 192   | 154   | 170   | 143   | 153   | 151   |
-| D | Differences         | 3.9   | 46.4  | 48.9  | 24.4  | 27.9  | 1.4   | 28.9  | 13.4  | 34.9  | 44.4  |
-| 2 | Rel. diff. D/X      | 0.02  | 0.19  | 0.21  | 0.12  | 0.15  | 0.01  | 0.17  | 0.09  | 0.23  | 0.29  |
-
-Table 4 Relative differences [d (X -P)/X] by 'year' and 'steps ahead' with OLS, linear, n = 5.
-
-| Last year of data          | Steps ahead |          |          |          |          |          |          |          |          |          |
-|----------------------------|-------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
-|                            | 1           | 2        | 3        | 4        | 5        | 6        | 7        | 8        | 9        | 10       |
-| 1956                       | 0.11<br>    | 0.05     | 0.03<br> | 0.05<br> | 0.01     | 0.01     | 0.09     | 0.02     | 0.18     | 0.17     |
-| 1957                       | 0.03        | <br>0.08 | <br>0.12 | <br>0.07 | <br>0.09 | <br>0.01 | <br>0.10 | 0.07     | 0.05     | <br>0.09 |
-| 1958                       | <br>0.02    | <br>0.02 | 0.04     | 0.04     | 0.12     | 0.06     | 0.22     | 0.21     | 0.11     | <br>0.09 |
-|                            |             |          |          |          |          |          |          |          |          |          |
-| 1980                       | <br>0.09    | <br>0.20 | <br>0.24 | <br>0.33 | <br>0.39 | <br>0.22 | <br>0.28 | <br>0.53 | <br>0.61 | <br>1.02 |
-| 1981                       | 0.10<br>    | 0.11<br> | 0.17<br> | 0.19<br> | 0.02<br> | 0.05<br> | 0.22<br> | 0.25<br> | 0.54<br> | 0.37<br> |
-| 1982                       | 0.03        | 0.03     | 0.06     | 0.24     | 0.27     | 0.21     | 0.25     | 0.15     | 0.31     | 0.28     |
-| 1983                       | 0.01        | 0.04     | 0.22     | 0.25     | 0.18     | 0.22     | 0.11     | 0.27     | 0.23     | 0.37     |
-| 1984                       | 0.02        | 0.19     | 0.21     | 0.12     | 0.15     | 0.01     | 0.17     | 0.09     | 0.23     | 0.29     |
-| 1985                       | 0.18        | 0.20     | 0.12     | 0.14     | 0.00     | 0.16     | 0.08     | 0.22     | 0.29     | 0.39     |
-|                            |             |          |          |          |          |          |          |          |          |          |
-| 1995                       | 0.12<br>    | 0.11     | 0.01     | 0.23     | 0.06     | 0.15     | 0.00     | 0.08     | 0.07<br> | 0.09     |
-| 1996                       | 0.14        | 0.05     | 0.25     | 0.09     | 0.18     | 0.02     | 0.10     | 0.04<br> | 0.12     | 0.06     |
-| 1997                       | <br>0.04    | 0.17     | <br>0.03 | 0.06     | <br>0.13 | <br>0.06 | <br>0.25 | <br>0.07 | <br>0.16 | <br>0.11 |
-| <br>d = average d (bias)   | <br>0.01    | <br>0.01 | <br>0.01 | <br>0.02 | <br>0.02 | <br>0.02 | <br>0.03 | <br>0.04 | <br>0.06 | <br>0.06 |
-| Standard error around<br>d | 0.13        | 0.20     | 0.27     | 0.31     | 0.35     | 0.39     | 0.42     | 0.48     | 0.55     | 0.61     |
-| Standard error around 0    | 0.13        | 0.20     | 0.27     | 0.31     | 0.35     | 0.39     | 0.42     | 0.48     | 0.55     | 0.62     |
-
-Table 5 Statistics for n = 5 and n = 10.
-
-| n  |                                                                                   | Steps ahead              |                          |                          |                          |                          |                          |                          |                          |                          |                          |
-|----|-----------------------------------------------------------------------------------|--------------------------|--------------------------|--------------------------|--------------------------|--------------------------|--------------------------|--------------------------|--------------------------|--------------------------|--------------------------|
-|    |                                                                                   | 1                        | 2                        | 3                        | 4                        | 5                        | 6                        | 7                        | 8                        | 9                        | 10                       |
-| 5  | <br>d = average d (bias)<br>Standard error around<br>d<br>Standard error around 0 | 0.01<br><br>0.13<br>0.13 | 0.01<br><br>0.20<br>0.20 | 0.01<br><br>0.27<br>0.27 | 0.02<br><br>0.31<br>0.31 | 0.02<br><br>0.35<br>0.35 | 0.02<br><br>0.39<br>0.39 | 0.03<br><br>0.42<br>0.42 | 0.04<br><br>0.48<br>0.48 | 0.06<br><br>0.55<br>0.55 | 0.06<br><br>0.61<br>0.62 |
-| 10 | <br>d = average d (bias)<br>Standard error around<br>d<br>Standard error around 0 | <br>0.02<br>0.15<br>0.15 | <br>0.02<br>0.19<br>0.19 | <br>0.03<br>0.23<br>0.23 | <br>0.03<br>0.26<br>0.26 | <br>0.05<br>0.28<br>0.28 | <br>0.06<br>0.30<br>0.31 | <br>0.07<br>0.33<br>0.34 | <br>0.08<br>0.37<br>0.38 | <br>0.09<br>0.42<br>0.43 | <br>0.10<br>0.49<br>0.50 |
-
-for Province A one was to predict using the extrapolation of a linear OLS fit, it would be best to do so using an n of about10. (In general there is no reason to expect that the same n is best for all s. In the present case, however, the trough14 of the function is not a function of s.) The performance of such predictions for n = 10 and s = 1–10 was shown in the lower part of Table 5.
-
-The merit of the suggested approach is now evident. First, it can answer questions of practical interest.<sup>15</sup> Second, it provides an estimate of the average bias inherent in the prediction method and thereby a way for removing it. Third it produces straightforward
-
-<sup>14</sup> While the process underlying [Fig. 1](#page-1-0) is not a straight line it can be locally approximated by one. For the approximation to be 'local', n has to be small. However, for the slope of the regression line to be reliably estimated n must not be too small. The trough in [Figs. 11 and 12](#page-6-0) is the result of these two opposite considerations.
-
-<sup>15</sup> Here the question was how many data points should be used in fitting the straight line used for producing predictions.
-
-<span id="page-6-0"></span>![](_page_6_Figure_2.jpeg)
-
-Fig. 10. Linear prediction: bias as a function of s and n.
-
-and realistic estimates of the standard error of the predictions for every 's' (such as those in the two last rows of [Table 5\)](#page-5-0).
-
-Having established what quality of prediction is attainable in Province A by the extrapolation of a linear OLS fit, the next question is whether a different method of prediction would do better. As noted in Section [2,](#page-1-0) Quaye found that the extrapolation of a quadratic OLS fit produced good predictions. Therefore we will attempt to determine whether in Province A the quadratic OLS fit usually out-performs the linear OLS fit (in spite of the impression from [Fig. 3\)](#page-2-0).
-
-Fig. 13 for the quadratic prediction corresponds to Fig. 10 for the linear one. The main difference is that here the bias is mostly positive (P is too low) whereas with the linear prediction the bias is mostly negative (P was too high). This difference is due to the shape of the 'mountain' in [Fig. 1](#page-1-0). With both methods the bias tends to increase with s and n.
-
-Fig. 14 for the quadratic prediction corresponds to Fig. 11 for the linear one. For ease of comparison the vertical scale has been truncated so as to show the same portion of the function in both figures. It is now clear that for Province A the standard error of the quadratic predictions is always larger than that for the linear one.
-
-Fig. 15 corresponds to Fig. 12 and their comparison leads to the same conclusion: for fatalities in Province A predicting by the extrapolation of a straight line OLS fit would have predicted better than using a quadratic polynomial. Once again the suggested approach for measuring the quality of predictions helps to answer the question of interest straightforwardly.
-
-In this section we established the procedure for determining which variant of a method to use under what circumstances and which of two methods would have usually given better predictions. It should be noted that what has been concluded so far pertains to a specific data set, the time series of fatalities in Province A.
-
-![](_page_6_Figure_10.jpeg)
-
-Fig. 11. Linear prediction: standard error around d as a function of s and n.
-
-![](_page_6_Figure_12.jpeg)
-
-Fig. 12. Linear prediction: standard error around 0 as a function of s and n.
-
-![](_page_6_Figure_14.jpeg)
-
-Fig. 13. Quadratic prediction: bias as a function of s and n.
-
-![](_page_6_Figure_16.jpeg)
-
-Fig. 14. Quadratic prediction: standard error around d as a function of s and n.
-
-![](_page_6_Figure_18.jpeg)
-
-Fig. 15. Quadratic prediction: standard error around 0 as a function of s and n.
-
-Table 6 Measures of performance for Holt smoothing A.
-
-|                            | Steps ahead |          |          |          |          |          |          |          |          |          |
-|----------------------------|-------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
-|                            | 1           | 2        | 3        | 4        | 5        | 6        | 7        | 8        | 9        | 10       |
-| <br>d = average d (bias)   | <br>0.01    | <br>0.02 | <br>0.02 | <br>0.03 | <br>0.04 | <br>0.05 | <br>0.06 | <br>0.07 | <br>0.09 | <br>0.10 |
-| Standard error around<br>d | 0.12        | 0.17     | 0.23     | 0.26     | 0.28     | 0.30     | 0.33     | 0.36     | 0.42     | 0.46     |
-| Standard error around 0    | 0.13        | 0.17     | 0.23     | 0.26     | 0.29     | 0.31     | 0.33     | 0.36     | 0.43     | 0.47     |
-
-Table 7 Measures of performance for Holt smoothing B.
-
-|                            | Steps ahead |          |          |          |          |          |          |          |          |          |
-|----------------------------|-------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
-|                            | 1           | 2        | 3        | 4        | 5        | 6        | 7        | 8        | 9        | 10       |
-| <br>d = average d (bias)   | 0.01<br>    | 0.04<br> | 0.02<br> | 0.05<br> | 0.03<br> | 0.05<br> | 0.05<br> | 0.10<br> | 0.07<br> | 0.11<br> |
-| Standard error around<br>d | 0.12        | 0.17     | 0.21     | 0.28     | 0.29     | 0.34     | 0.38     | 0.47     | 0.52     | 0.66     |
-| Standard error around 0    | 0.13        | 0.17     | 0.21     | 0.28     | 0.30     | 0.34     | 0.38     | 0.48     | 0.53     | 0.67     |
-
-Whether similar conclusions hold for other jurisdictions can be established only by performing similar analyses for data from many other jurisdictions.
-
-The following two sections are best regarded as more complete illustration of the approach to determining which method tends to predict best. The illustration is still limited to the simple methods first introduced in [Table 1](#page-2-0) and there is no intent to claim that other prediction methods would not perform better. In the next section we return to the Province A data to determine whether the other prediction methods introduced earlier in Section [3](#page-1-0) can do better than the linear prediction which reigns at this point.
-
-## 7. Province A: examining other methods
-
-Section [6](#page-4-0) served to show how the yardstick for measuring prediction quality introduced in Section [5](#page-3-0) can be used to determine which of two variants or prediction methods tends to predict better for the data of Province A. Several other simple prediction methods and their variants were introduced for illustration in [Table 1.](#page-2-0) The present section will examine how these would have performed had they been used.
-
-# 7.1. Holt smoothing A<sup>16</sup>
-
-For each of the 43 years 1955–1997 the predictions (P(s), s = 1, 2, ... , 10) were computed. From these the relative differences d(s) were obtained. The summary statistics d, SE around d and SE around 0 are in Table 6.
-
-These measures of performance are very similar to those obtained by linear regression with n = 10 as shown in [Table 5.](#page-5-0) It follows that, in Province A, the Holt smoothing (A) is as good or slightly better as the extrapolation of a linear OLS fit with 10 data points. Both outperform the extrapolation of a quadratic fit.
-
-# 7.2. Holt smoothing B<sup>17</sup>
-
-The results for s = 1–10 for Holt smoothing B are in Table 7. Except at s = 3 they are not as good as those for Holt smoothing A.
-
-## 7.3. Fitting a Hoerl function
-
-The function aeb(t-(t d) <sup>c</sup> was fitted to n = 5, 6, ... , 20 data points by minimizing the sum of absolute deviations. How the d depends on n and s is shown in Fig. 16.
-
-![](_page_7_Figure_19.jpeg)
-
-Fig. 16. Hoerl prediction: bias as a function of s and n.
-
-The shape of the bias function lacks regularity. It depends heavily on how well the chosen function fits the data and may be influenced by how well the Excel 'SOLVER' determines the minimum. Bias is minimal when n = 7, 8 or 9 and when n = 18, 19. How the two standard errors depend on s and n is shown in Figs. 17 and 18.
-
-Considering the size of bias and of standard error, the best predictions for all s are when n = 19. The measures of performance at n = 19 are in [Table 8](#page-8-0). They are significantly better than those of the linear prediction and the Holt smoothing A.
-
-The measures of performance of the Hoerl function when n = 10 is in [Table 9.](#page-8-0) These too are better than those of Holt smoothing A. Thus, for Province A, even when only 10 points were available for fitting the Hoerl function, the predictions were, on the average, better than by the other methods examined so far.
-
-![](_page_7_Figure_24.jpeg)
-
-Fig. 17. Hoerl prediction: standard error around d as a function of s and n.
-
-<sup>16</sup> For description see Footnotes 9 and 10.
-
-<sup>17</sup> See Footnote 12.
-
-<span id="page-8-0"></span>![](_page_8_Figure_2.jpeg)
-
-Fig. 18. Hoerl prediction: standard error around 0 as a function of s and n.
-
-## 7.4. The Oppe–Koornstra model
-
-The Oppe–Koornstra model consists of two equations. The first equation traces the evolution of risk over time [Rt = exp(at + b) in which Rt is the risk at time t, with a and b as parameters to be estimated from data]. The second equations traces the evolution of exposure over time [Vt = Vm/{1 + exp - [(a(t c) + b]} in which Vt represents the vehicle-kilometres of travel (Vkmt) at time t, Vm is the maximum (saturation) Vkmt and a, b and c are parameters estimated from data]. The number of accidents is the product Rt Vt.
-
-The Vkmt information for Province A is available from the period 1960–2007. For each year between 1966 and 1998 the parameters a, b, a, b, c and Vm were estimated using all the data from 1960 till that year. Using these parameters Rt and Vt were predicted for s = 1, 2, ... , 10 steps ahead and the relative differences [d(s)] calculated. The usual measures of performance are in Table 10. As is obvious the biases (the d and all the d's that make it up) are negative and large. Had the Oppe–Koornstra model been applied to Province A it would have predicted more fatalities than what actually occurred. However, even if the bias is disregarded, the standard error around d is larger than when prediction is based on fitting a Hoerl function.
-
-#### 7.5. Summary for Province A
-
-The performance of several methods and their variants for predicting the number of fatalities in Province A up to ten steps ahead was examined. It seems that of the many options compared, fitting a Hoerl function with n = 10 and n = 19 would have performed best. This conclusion has to be qualified in several ways. First, there are many other prediction methods and variants that could be tried. Thus, e.g., with Holt smoothing one could take the last n u's (slopes), extrapolate them in some suitable way, and use the sum of extrapolated u's in the prediction. Or, e.g., one could use the Oppe–Koornstra model so that the fitted Risk and Exposure functions are based on the most recent n data points, rather than all data points. Second, the method that, had it been used, would have performed best in Province A, may not be best in another jurisdiction. To draw more general conclusions, similar analyses need to be performed on data from many other jurisdictions.
-
-Table 8 Measures of performance for Hoerl function {a exp[b(t d)](t d) } when n = 19.
-
-|                            | Steps ahead |          |          |          |          |          |          |          |          |          |
-|----------------------------|-------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
-|                            | 1           | 2        | 3        | 4        | 5        | 6        | 7        | 8        | 9        | 10       |
-| <br>d = average d (bias)   | 0.03<br>    | 0.02<br> | 0.02<br> | 0.02<br> | 0.02<br> | 0.02<br> | 0.03<br> | 0.03<br> | 0.03<br> | 0.03<br> |
-| Standard error around<br>d | 0.13        | 0.14     | 0.16     | 0.18     | 0.19     | 0.22     | 0.24     | 0.27     | 0.29     | 0.32     |
-| Standard error around 0    | 0.13        | 0.15     | 0.16     | 0.18     | 0.19     | 0.22     | 0.24     | 0.27     | 0.30     | 0.33     |
-
-Table 9 Measures of performance for Hoerl function {a exp[b(t d)](t d) } when n = 10.
-
-|                                                       | Steps ahead  |              |              |              |              |              |              |              |              |              |
-|-------------------------------------------------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|
-|                                                       | 1            | 2            | 3            | 4            | 5            | 6            | 7            | 8            | 9            | 10           |
-| <br>d = average d (bias)                              | 0.00         | 0.00         | 0.00         | 0.00         | 0.01<br>     | 0.02<br>     | 0.04<br>     | 0.05<br>     | 0.07<br>     | 0.08<br>     |
-| Standard error around<br>d<br>Standard error around 0 | 0.15<br>0.15 | 0.19<br>0.19 | 0.19<br>0.19 | 0.22<br>0.22 | 0.24<br>0.25 | 0.27<br>0.27 | 0.28<br>0.29 | 0.31<br>0.31 | 0.35<br>0.35 | 0.40<br>0.41 |
-
-Table 10 Measures of performance for the Oppe–Koonstra model.
-
-|                            |          | Steps ahead |          |          |          |          |          |          |          |          |  |  |  |
-|----------------------------|----------|-------------|----------|----------|----------|----------|----------|----------|----------|----------|--|--|--|
-|                            | 1        | 2           | 3        | 4        | 5        | 6        | 7        | 8        | 9        | 10       |  |  |  |
-| <br>d = average d (bias)   | <br>0.30 | <br>0.34    | <br>0.39 | <br>0.43 | <br>0.47 | <br>0.51 | <br>0.56 | <br>0.59 | <br>0.64 | <br>0.69 |  |  |  |
-| Standard error around<br>d | 0.28     | 0.30        | 0.31     | 0.31     | 0.30     | 0.31     | 0.33     | 0.35     | 0.36     | 0.37     |  |  |  |
-| Standard error around 0    | 0.42     | 0.46        | 0.50     | 0.53     | 0.56     | 0.59     | 0.65     | 0.69     | 0.74     | 0.78     |  |  |  |
-
-Table 11 Predictions for 1986–1995.
-
-| Year       | 1986  | 1987  | 1988  | 1989  | 1990  | 1991  | 1992  | 1993  | 1994  | 1995  |
-|------------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|
-| Prediction | 216.6 | 210.0 | 203.4 | 196.6 | 189.8 | 183.0 | 176.2 | 169.4 | 162.7 | 156.1 |
-
-<span id="page-9-0"></span>![](_page_9_Figure_2.jpeg)
-
-Fig. 19. Time series of fatalities, population, etc. in Province B.
-
-![](_page_9_Figure_4.jpeg)
-
-Fig. 20. Amount of travel in Province B.
-
-To round out this section recall the story line around which the narration revolved. We imagined that we are at the end of 1985 and wish to predict for the years 1986–1995. It now seems that the Hoerl function with n = 19 would be sensible choice of method and variant. The parameter estimates based on the 1967–1985 data are a = 0.3438, b = -0.0982, c = 2.822 and d = 1944.7. Using these parameters the predictions are in [Table 11.](#page-8-0)
-
-Based on [Table 8](#page-8-0) the average bias is -0.02 to -0.03. It would therefore seem appropriate to reduce the prediction by 2–3%. With this the unbiased prediction, e.g., for 1990 (s = 5) is 189.8 0.98 = 186.0. Thus, at the end of 1985 one should predict that if the prevailing trends will continue and no substantially new policies and measures are implemented, in 1990 there will be in Province A about 186 fatalities. Also based on [Table 8](#page-8-0) the standard error is ±0.19 186 = ±35.3 fatalities. Using the usual rule of thumb of ±2 standard deviations, the number of fatalities is almost certain to be in the 116–256 range.
-
-# 8. Province B
-
-The main time series information for Province B is in Figs. 19 and 20. While fatalities reached a peak and later declined, population, licensed drivers, and registered vehicles continued to increase over time. Till 1999 the Vkmt information (in Fig. 20) was based on fuel sales data and after 2000 it is based on the Annual Canadian Vehicle Survey by Statistics Canada. Because of the uncertainties surrounding this data the number of Licensed Drivers is used as a proxy for Exposure.
-
-#### 8.1. Linear prediction
-
-The performance of the linear OLS predictor on the Province B data is best for n between 8 and 13 as shown in Table 12.
-
-#### 8.2. Quadratic prediction
-
-For this method the quality of predictions improves with n. The measures of performance for n from 15 to 19 are in [Table 13.](#page-10-0) For s = 1 and 2 the quadratic predictor has a slight edge over the linear one but is significantly worse for larger s.
-
-#### 8.3. Holt smoothing A
-
-It appears that the extrapolation of a linear fit to n between 8 and 13 is about as good as the Holt Smoothing 'A'.
-
-Table 12 Measures of performance for the linear OLS predictor.
-
-| n           | Steps ahead |          |          |          |          |          |          |          |          |          |
-|-------------|-------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
-|             | 1           | 2        | 3        | 4        | 5        | 6        | 7        | 8        | 9        | 10       |
-| <br>d, bias |             |          |          |          |          |          |          |          |          |          |
-| 8           | <br>0.01    | <br>0.02 | <br>0.03 | <br>0.03 | <br>0.03 | <br>0.03 | <br>0.03 | <br>0.03 | <br>0.04 | <br>0.04 |
-| 9           | 0.01<br>    | 0.02<br> | 0.03<br> | 0.03<br> | 0.03<br> | 0.02<br> | 0.03<br> | 0.03<br> | 0.04<br> | 0.04<br> |
-| 10          | <br>0.02    | <br>0.02 | <br>0.03 | <br>0.02 | <br>0.02 | <br>0.02 | <br>0.03 | <br>0.03 | <br>0.04 | <br>0.04 |
-| 11          | <br>0.02    | <br>0.02 | <br>0.02 | <br>0.02 | <br>0.02 | <br>0.02 | <br>0.03 | <br>0.03 | <br>0.04 | <br>0.05 |
-| 12          | <br>0.02    | <br>0.02 | <br>0.01 | <br>0.01 | <br>0.02 | <br>0.02 | <br>0.03 | <br>0.04 | <br>0.04 | <br>0.05 |
-| 13          | 0.01<br>    | 0.01<br> | 0.01<br> | 0.01<br> | 0.02<br> | 0.02<br> | 0.03<br> | 0.04<br> | 0.05<br> | 0.06<br> |
-| SE around   | d           |          |          |          |          |          |          |          |          |          |
-| 8           | 0.13        | 0.17     | 0.22     | 0.24     | 0.26     | 0.28     | 0.31     | 0.35     | 0.39     | 0.42     |
-| 9           | 0.13        | 0.18     | 0.21     | 0.23     | 0.25     | 0.26     | 0.30     | 0.33     | 0.37     | 0.40     |
-| 10          | 0.14        | 0.17     | 0.20     | 0.22     | 0.23     | 0.26     | 0.29     | 0.32     | 0.36     | 0.39     |
-| 11          | 0.14        | 0.17     | 0.19     | 0.20     | 0.23     | 0.26     | 0.29     | 0.32     | 0.35     | 0.38     |
-| 12          | 0.13        | 0.16     | 0.18     | 0.20     | 0.23     | 0.25     | 0.29     | 0.32     | 0.35     | 0.38     |
-| 13          | 0.13        | 0.15     | 0.17     | 0.20     | 0.23     | 0.26     | 0.29     | 0.32     | 0.35     | 0.39     |
-| SE around 0 |             |          |          |          |          |          |          |          |          |          |
-| 8           | 0.13        | 0.18     | 0.22     | 0.24     | 0.26     | 0.28     | 0.31     | 0.35     | 0.39     | 0.42     |
-| 9           | 0.14        | 0.18     | 0.21     | 0.23     | 0.25     | 0.27     | 0.30     | 0.34     | 0.37     | 0.40     |
-| 10          | 0.14        | 0.18     | 0.21     | 0.22     | 0.23     | 0.26     | 0.30     | 0.33     | 0.36     | 0.39     |
-| 11          | 0.14        | 0.17     | 0.19     | 0.20     | 0.23     | 0.26     | 0.29     | 0.32     | 0.36     | 0.39     |
-| 12          | 0.14        | 0.16     | 0.18     | 0.20     | 0.23     | 0.25     | 0.29     | 0.32     | 0.36     | 0.39     |
-| 13          | 0.13        | 0.15     | 0.18     | 0.20     | 0.23     | 0.26     | 0.29     | 0.32     | 0.36     | 0.39     |
-
-<span id="page-10-0"></span>Table 13 Measures of performance for the quadratic OLS predictor.
-
-| n           | Steps ahead |          |          |          |          |          |          |          |          |          |
-|-------------|-------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
-|             | 1           | 2        | 3        | 4        | 5        | 6        | 7        | 8        | 9        | 10       |
-| <br>d, bias |             |          |          |          |          |          |          |          |          |          |
-| 15          | 0.01        | 0.01     | 0.01     | 0.01     | 0.01     | 0.01     | 0.00     | 0.00     | 0.01<br> | 0.01<br> |
-| 16          | 0.00        | 0.00     | 0.00     | 0.00     | 0.01<br> | 0.01<br> | 0.01<br> | 0.02<br> | 0.03<br> | 0.03<br> |
-| 17          | 0.00        | <br>0.01 | <br>0.01 | <br>0.01 | <br>0.02 | <br>0.02 | <br>0.03 | <br>0.04 | <br>0.05 | <br>0.05 |
-| 18          | <br>0.01    | <br>0.01 | <br>0.02 | <br>0.02 | <br>0.03 | <br>0.03 | <br>0.04 | <br>0.05 | <br>0.05 | <br>0.06 |
-| 19          | 0.01<br>    | 0.01<br> | 0.02<br> | 0.02<br> | 0.03<br> | 0.03<br> | 0.04<br> | 0.05<br> | 0.05<br> | 0.06<br> |
-| 20          | <br>0.01    | <br>0.01 | <br>0.02 | <br>0.03 | <br>0.03 | <br>0.03 | <br>0.04 | <br>0.04 | <br>0.05 | <br>0.05 |
-| SE around   | d           |          |          |          |          |          |          |          |          |          |
-| 15          | 0.12        | 0.17     | 0.22     | 0.28     | 0.35     | 0.41     | 0.50     | 0.59     | 0.68     | 0.78     |
-| 16          | 0.12        | 0.16     | 0.22     | 0.27     | 0.33     | 0.40     | 0.48     | 0.56     | 0.65     | 0.75     |
-| 17          | 0.12        | 0.17     | 0.22     | 0.27     | 0.33     | 0.39     | 0.47     | 0.55     | 0.64     | 0.73     |
-| 18          | 0.12        | 0.17     | 0.22     | 0.27     | 0.32     | 0.38     | 0.46     | 0.54     | 0.63     | 0.72     |
-| 19          | 0.13        | 0.17     | 0.22     | 0.26     | 0.32     | 0.38     | 0.45     | 0.53     | 0.62     | 0.71     |
-| 20          | 0.13        | 0.17     | 0.21     | 0.26     | 0.32     | 0.38     | 0.45     | 0.52     | 0.61     | 0.70     |
-| SE around 0 |             |          |          |          |          |          |          |          |          |          |
-| 15          | 0.12        | 0.17     | 0.22     | 0.28     | 0.35     | 0.41     | 0.50     | 0.59     | 0.68     | 0.78     |
-| 16          | 0.12        | 0.16     | 0.22     | 0.27     | 0.33     | 0.40     | 0.48     | 0.56     | 0.66     | 0.75     |
-| 17          | 0.12        | 0.17     | 0.22     | 0.27     | 0.33     | 0.39     | 0.47     | 0.55     | 0.64     | 0.74     |
-| 18          | 0.12        | 0.17     | 0.22     | 0.27     | 0.32     | 0.38     | 0.46     | 0.54     | 0.63     | 0.72     |
-| 19          | 0.13        | 0.17     | 0.22     | 0.26     | 0.32     | 0.38     | 0.46     | 0.53     | 0.62     | 0.71     |
-| 20          | 0.13        | 0.17     | 0.22     | 0.26     | 0.32     | 0.38     | 0.45     | 0.52     | 0.61     | 0.70     |
-
-Table 14 Measures of performance for Holt smoothing A.
-
-|                            | Steps ahead |          |      |      |      |      |      |          |          |          |
-|----------------------------|-------------|----------|------|------|------|------|------|----------|----------|----------|
-|                            | 1           | 2        | 3    | 4    | 5    | 6    | 7    | 8        | 9        | 10       |
-| <br>d = average d (bias)   | 0.00        | 0.01<br> | 0.00 | 0.01 | 0.01 | 0.01 | 0.00 | 0.01<br> | 0.03<br> | 0.04<br> |
-| Standard error around<br>d | 0.11        | 0.15     | 0.20 | 0.21 | 0.21 | 0.23 | 0.28 | 0.31     | 0.36     | 0.40     |
-| Standard error around 0    | 0.11        | 0.15     | 0.20 | 0.21 | 0.22 | 0.23 | 0.28 | 0.31     | 0.36     | 0.40     |
-
-Table 15 Measures of performance for the Hoerl function.
-
-|                            | Steps ahead |      |      |      |      |      |      |      |      |      |
-|----------------------------|-------------|------|------|------|------|------|------|------|------|------|
-|                            | 1           | 2    | 3    | 4    | 5    | 6    | 7    | 8    | 9    | 10   |
-| <br>d = average d (bias)   | 0.01        | 0.02 | 0.03 | 0.04 | 0.05 | 0.06 | 0.07 | 0.07 | 0.08 | 0.09 |
-| Standard error around<br>d | 0.16        | 0.18 | 0.19 | 0.21 | 0.24 | 0.26 | 0.29 | 0.31 | 0.34 | 0.36 |
-| Standard error around 0    | 0.16        | 0.18 | 0.19 | 0.22 | 0.24 | 0.27 | 0.29 | 0.32 | 0.35 | 0.37 |
-
-# 8.4. The Hoerl function
-
-For the Province B time series of fatalities the Hoerl Function performs best when n is about 12. The measures of performance at this point are in Table 15.
-
-### 8.5. The Oppe–Koornstra method
-
-To apply this method one has to compute risk which, in the Oppe–Koornstra formulation is the ratio of fatalities per vehiclekilometres of travel. Inasmuch as for Province B the information about Vkmt is sketchy as shown in [Fig. 20](#page-9-0), the number of licensed drivers will be the proxy for exposure.18 Even though the predictions of risk and exposure are quite good, their product does not yield satisfactory predictions as is obvious from comparing the measures of performance in [Table 16](#page-11-0) to these in [Table 12](#page-9-0) or Table 14.
-
-For Province B the count of fatalities begins in 1931. To produce [Table 16](#page-11-0) the parameters of the risk and exposure function were estimated using data from 1931 till the year before the prediction for s = 1. This is in line with the spirit of the Oppe–Koornstra thinking which regards the regularities in the evolution of risk and exposure as representing some deeper theoretical principles. It is possible the predictions would be better if the parameter estimates were based on the n most recent data points rather than on all available data.
-
-#### 8.6. Summary for Province B
-
-The performance of several methods and their variants for predicting the number of fatalities in Province B for up to ten steps (years) ahead was examined. It seems that the linear predictor with n around 10, Holt smoothing A, and the Hoerl function predictor perform similarly. The quadratic predictor was better than the
-
-<sup>18</sup> Vkmt and the number of licensed drivers are usually closely correlated. It is possible that the number of kilometres of travel per licensed driver changes gradually over time. However, in the period 2000–2007 for which reliable Vkmt data are available there is no evidence of any such trend.
-
-<span id="page-11-0"></span>Table 16 Measures of performance for the Oppe–Koornstra method.
-
-|                            | Steps ahead |          |          |          |          |          |          |          |          |          |
-|----------------------------|-------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
-|                            | 1           | 2        | 3        | 4        | 5        | 6        | 7        | 8        | 9        | 10       |
-| <br>d = average d (bias)   | <br>0.09    | <br>0.10 | <br>0.12 | <br>0.13 | <br>0.15 | <br>0.17 | <br>0.19 | <br>0.20 | <br>0.21 | <br>0.22 |
-| Standard error around<br>d | 0.31        | 0.33     | 0.34     | 0.36     | 0.37     | 0.38     | 0.39     | 0.41     | 0.42     | 0.44     |
-| Standard error around 0    | 0.32        | 0.34     | 0.36     | 0.38     | 0.40     | 0.42     | 0.44     | 0.46     | 0.47     | 0.49     |
-
-linear one for one or two steps ahead and significantly worse for predicting further into the future. The Oppe–Koornstra method has a relatively large average bias and standard error. It is possible that its performance would improve if the number of data points used in the curve fitting was limited.
-
-## 9. Summary and discussion
-
-Prediction is about potential outcomes: what will happen if and about what would have happened if. As there are many ways to predict one has to determine what method tends to predict best. To do so empirically one asks what method would have predicted best had it been applied in the past and assumes, inductively, that the same will be true in the future.
-
-To say which of two methods is better, one has to have a yardstick for prediction quality. It is suggested that d (the average bias) and the standard error of d be used for this purpose. The d tells by what proportion, on the average, the count of accidents would exceed the prediction if the method was applied year after year. The standard error (around d or around 0) measures the variability of the d's in the usual manner. It is shown how these measures of prediction quality allow one to determine which of two methods should be preferred in what circumstance.
-
-The approach was applied to two data sets: The time series of motor vehicle accident fatalities in Province A and in Province B. On the basis of this analysis one may draw tentative conclusions for these jurisdictions and the methods tested; one can say what method seems preferable, what is the average size of bias that needs to be corrected, and how accurate is the prediction likely to be.
-
-Broader conclusions will emerge once many additional methods of prediction are applied to data from many other jurisdictions and pertaining to other circumstances. As there are many jurisdictions that have long time series of road safety related data, research along these lines is both feasible and attractive. The hope is that the approach outlined here will prove attractive to others and that the cumulative results of research on prediction will make for better safety evaluation and improved setting of safety targets.
-
-#### Acknowledgements
-
-The help of Transport Canada, Road Safety is gratefully acknowledged. Kwei Quaye of the Saskatchewan Government Insurance, Leo Tasca and Chris Janusz of the Ontario Ministry of Transport provided important data.
-
-#### References
-
-Broughton, J., Allsop, R.E., Lynam, D.A., McMahon, C.M., 2000. The numerical context for setting national casualty reduction targets. TRL Report 382, TRL Limited, Wokingham.
-
-Broughton, J., 2006. Monitoring progress towards the GB casualty reduction target. In: Proceedings, European Transport Conference, Strasbourg, France.
-
-Hauer, E., Ng, J.N.C., Papaioannou, P., 1991. Prediction in road safety studies: an empirical inquiry. Accident Analysis and Prevention 23 (6), 595–607.
-
-Hauer, E., 1991. Comparison groups in road safety studies: an analysis. Accident Analysis and Prevention 23 (6), 609–622.
-
-Hauer, E., 1997. Observational Before–After Studies in Road Safety. Pergamon.
-
-Hyndman, R.J., Koehler, A.B., 2006. Another look at measures of forecast accuracy. International Journal of Forecasting 22 (4), 679–688.
-
-Makridakis, S., Hibon, M., 2000. The M3-competition: results, conclusions, and implications. International Journal of Forecasting 16, 451–476.
-
-Oppe, S., Koornstra, M.J., 1990. A mathematical theory for related long term developments of road traffic and safety. In: Koshi, M. (Ed.), Transportation and Traffic Theory. Elsevier, New York, pp. 113–132.
-
-Oppe, S., 1991. Development of traffic and traffic safety: global trends and incidental fluctuations. Accident Analysis and Prevention 23 (5), 413–422.
-
-Quaye, K.E., 1992. Forecasting models in road safety studies. PhD Dissertation, Department of Civil Engineering, University of Toronto.
-
-Quaye, K.E., Hauer, E., 1993. The use of forecasting models in the evaluation of safety interventions: a theoretical inquiry. In: Daganzo, C.F. (Ed.), Transportation and Traffic Theory. Elsevier Science Publishers, pp. 313–332.
-
-Quaye, K.E., Hauer, E., 1994. Assessing forecasting methods used in before after studies. Paper presented at the 72nd Annual Meeting of the Transportation Research Board, Washington.
-
-Rubin, D.B., 2005. Causal inference using potential outcomes: design, modeling, decisions. Journal of the American Statistical Association 100 (469), 322–331.

Zotero/001_artiklid/Road Safety Indicators/helseffectroundaboutdesignfeatures2007.md

@@ -1,309 +0,0 @@
----
-category: literaturenote
-citekey: helseffectroundaboutdesignfeatures2007
-title: The effect of roundabout design features on cyclist accident rate
-authors: "Hels, Tove; Orozova-Bekkevold, Ivanka"
-year: 2007
-date: 2007-00-00 2007
-doi: 10.1016/j.aap.2006.07.008
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2006.07.008"
-zotero_key: 2RFJFRDB
-zotero_storage: N4984AUP
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Hels, Tove"
-status: converted
----
-![](_page_0_Picture_0.jpeg)
-
-Accident Analysis and Prevention 39 (2007) 300–307
-
-![](_page_0_Picture_2.jpeg)
-
-# The effect of roundabout design features on cyclist accident rate
-
-Tove Hels ∗, Ivanka Orozova-Bekkevold
-
-*Danish Transport Research Institute, Knuth-Wintherfeldts All´e, Building 116 West, DK-2800 Lyngby, Denmark* Received 27 January 2006; received in revised form 27 July 2006; accepted 30 July 2006
-
-### **Abstract**
-
-Roundabouts are known to result in fewer traffic accidents than traditional intersections. However, this is to a lesser degree true for bicycles than for vehicles. In this paper, we aimed at establishing statistical relationships through Poisson regression and logistic regression analyses between yearly rate of cyclist accidents on one hand and roundabout geometry, age and traffic volume (vehicles and cyclists) on the other. We related all roundabout cyclist accidents recorded by the hospital emergency department of the town of Odense, Denmark, through the years 1999–2003 (*N*= 171) to various geometric features, age and traffic volume of all roundabouts on the Danish island of Funen (*N*= 88). Cyclist and vehicle volumes turned out to be significant predictors in most of our models—the higher the volumes, the more accidents. Moreover, potential vehicle speed was a significant predictor, and so was age of the roundabout—older roundabouts related to more accidents and higher accident probability. Excluding 48 single cyclist accidents strengthened the relationship between accidents on one hand and vehicle and cyclist volume and potential vehicle speed on the other. This stresses the significance of speed and traffic volume for traffic accidents with more than one partner involved. The 48 single cyclist accidents were significantly related to the traffic volume of cyclists only. Due to our limited number of observations, the models should be regarded as indicative.
-
-© 2006 Elsevier Ltd. All rights reserved.
-
-*Keywords:* Roundabout; Cyclist accident; Geometry; Speed
-
-#### **1. Introduction**
-
-Roundabouts are popular. During the latest 20 years, more and more intersections have been converted to roundabouts, both in Europe (e.g. Britain: [Brown, 1995, D](#page-6-0)enmark: [Jørgensen and](#page-7-0) [Jørgensen, 1994,](#page-7-0) Finland: Ras¨ [anen and Summala, 2000](#page-7-0) ¨ , Sweden: [Brude and Larsson, 2000](#page-6-0) ¨ , Switzerland: [Huber, 1995,](#page-7-0) The Netherlands: [Schoon and van Minnen, 1994,](#page-7-0) France: Guichet pers. com.) and in the US, although the development in the US has come later than in Europe [\(Perseaud et al., 2001; Flannery,](#page-7-0) [2001\).](#page-7-0) The advantages of roundabouts as compared to intersections concern two aspects: safety and mobility. Safety: roundabouts produce slowed and smooth traffic flows with few stops and no left turns in front of left coming traffic in countries where traffic drives on the right. Mobility: roundabouts have a larger capacity than give way intersections and signalised junctions due to the fact that left turns are omitted ([Elvik and Vaa, 2004\).](#page-7-0) [Elvik and Vaa \(2004\)](#page-7-0) have reviewed 34 studies from Northern Europe, Australia and the US evaluating the effect on accidents
-
-The aim of this study was to identify reasons for the relatively higher prevalence of cyclist accidents in Danish roundabouts compared to intersections [\(Herslund and Jørgensen, 2003\).](#page-7-0) Our primary aim was to find relations between the number of cyclist accidents in roundabouts and the geometric features of a roundabout, its age and traffic volume (vehicles as well as cyclists).
-
-of converting an intersection into a roundabout. They find a mean reduction in injury accidents of 10 to 40%. Generally, the reduction in serious injury accidents is larger than that in less serious injury accidents, while property damage only accidents typically increase in number, although this effect is highly uncertain. Roundabouts, however, do not have the same positive effect on the number of cyclist accidents ([Schoon and van](#page-7-0) [Minnen, 1994; Jørgensen and Jørgensen, 1994\).](#page-7-0) There may be more than one explanation to this. Firstly, a roundabout represents a more challenging orientation task for all road users than an ordinary junction because of the circular design. This may increase the probability of orientation failure and the risk of accidents. Secondly, passing through a roundabout is physically more demanding due to the circular deflection of the road. This may increase the number and proportion of one-cyclist accidents.
-
-<sup>∗</sup> Corresponding author. Tel.: +45 45 25 65 00; fax: +45 45 93 65 33. *E-mail addresses:* [ths@dtf.dk](mailto:ths@dtf.dk) (T. Hels), [iob@dtf.dk](mailto:iob@dtf.dk) (I. Orozova-Bekkevold).
-
-| Variables recorded for the 88 roundabouts the number of bicycle accidents is the dependent variable, the rest are the independent variables |                     |                              |              |    |
-|---------------------------------------------------------------------------------------------------------------------------------------------|---------------------|------------------------------|--------------|----|
-| Variable (NAME)                                                                                                                             | Type of variable    | Range                        | Mean/median  | N  |
-| Number of legs (LEGS)                                                                                                                       | Numeric, discrete   | 2–6                          | Median: 4    |    |
-| Diameter of central island (ISLAND)                                                                                                         | Numeric, continuous | 5–53 m                       | Mean: 19.0 m | 85 |
-| Circulatory roadway width (CIRC)                                                                                                            | Numeric, continuous | 4–15 m                       | Mean: 6.4 m  | 86 |
-| Apron width (APRON)                                                                                                                         | Numeric, continuous | 0–6 m                        | Mean: 1.6 m  | 84 |
-| Drive curve (DRIVCURV)                                                                                                                      | Numeric, continuous | 9–40 m                       | Mean: 21 m   | 62 |
-| Cyclist facility (CYCFAC)                                                                                                                   | Binary              | 0 (no), 1 (yes)              | –            | 81 |
-| Traffic volume of cyclists (CYCLISTS)                                                                                                       | Semi-quantitative   | 1 (≤1000/day), 2 (>1000/day) | –            | 60 |
-| Traffic volume of vehicles (VEHIC)                                                                                                          | Numeric, continuous | 1100–24,500/day              | Mean: 7200   | 69 |
-| Year of construction (CONSTRYEAR)                                                                                                           | Numeric, discrete   | 1981–2004                    | Median: 1996 | 80 |
-
-Location (urban or rural zone) (ZONE) Qualitative Urban, rural, urban/rural – 88 Number of bicycle accidents in each roundabout Numeric, discrete 0–29 Median: 1 152
-
-<span id="page-1-0"></span>Table 1 Variables recorded for the 88 roundabouts the number of bicycle accidents is the dependent variable, the rest are the independent variables
-
-Secondly, we wanted to clarify the types and prevalence of various cyclist accidents. Thirdly, our aim was to quantify the degree of underreporting of cyclist accidents (cyclist accidents recorded by the police compared to the cyclist accidents recorded by the hospital emergency department).
-
-#### *1.1. Hypotheses*
-
-The main hypotheses of this study were:
-
-- The number of accidents with cyclists in a roundabout increases with increasing of the traffic volume of cyclists and vehicles.
-- Roundabout geometry, which allows higher speed of the vehicles leads to higher number of cyclist accidents.
-- Existence of a cyclist facility makes the roundabout safer for cyclists if the traffic volume of vehicles is high.
-
-## **2. Data**
-
-The data set consisted of data on cyclist accidents and data on geometric features, age and position of the roundabouts, besides the daily number of vehicles and cyclists entering the roundabouts (measured per 24 h).
-
-The cyclist roundabout accidents were recorded in two different ways: by the police and by the emergency department of the hospital in Odense, Funen, in Denmark. Accidents involving cyclists between the years 1999 and 2003 on the Danish island of Funen were used.
-
-For all roundabouts at the island of Funen (*N*= 88) the following features were recorded (abbreviated variable name in parentheses, cf. Table 1):
-
-The number of legs was recorded from digital maps and from construction maps of the roundabouts. The diameter of the central island, the circulatory roadway width and the apron width were all measured from construction maps of the roundabouts. If there was a cyclist facility, this was not considered part of the circulatory roadway. The drive curve was measured from construction maps according to [CROW, 1998](#page-6-0) (cf. Fig. 1):
-
-$$D = \frac{(0.25 \times L)^2 + (0.50 \times (U+2))^2}{U+2},$$
- (1)
-
-where *D* (in m) is the drive curve (R on Fig. 1), and *L* and *U* can be seen from Fig. 1. Drive curve was measured for symmetrical roundabouts only (*N*= 62). In these roundabouts, drive curves were the same for all directions. The drive curve variable is a proxy for potential vehicle speed through roundabout in that it is a measure of how much deflection is needed over a given distance to pass through the roundabout. The drive curve was considered a continuous variable.
-
-Information on the existence of a cyclist facility was provided by the road authorities, i.e. the municipalities (*N*= 46), the county of Funen (*N*= 38) or in the four cases of large roundabouts, the Danish Road Directorate. Any kind of cyclist facility was considered to have equal potential impact on cyclist accidents. Traffic volume of cyclists was assessed by the road authorities as the total number of cyclists riding into the roundabout per 24 h. The number was placed into an ordinal scale: 1: 0–250, 2: 250–500, 3: 500–1000, 4: 1000–2000 and 5: above 2000. The traffic volume of vehicles was counted by the road authorities as the total number of ingoing vehicles per 24 h in the roundabout. The traffic volume was counted in different years; in order to compare the numbers directly, they were all standardised to year 2001-numbers. This standardisation was done by calculating the development in the total number of kilometres driven by motor vehicles in Denmark. In 2002 the
-
-![](_page_1_Picture_18.jpeg)
-
-Fig. 1. A sketch of a roundabout showing the measures necessary for the calculation of the drive curve. After [CROW \(1998\).](#page-6-0)
-
-<span id="page-2-0"></span>number of kilometres driven equalled 1.22 times the number of kilometres driven in 1992 (source: Danish Road Directorate: [http://www.vd.dk](http://www.vd.dk/)), corresponding to a yearly increase by 1.02. This factor was then used—either to multiply or to divide traffic volume numbers from years other than 2001 to standardise them to 2001-numbers. The construction years of the roundabouts were provided by the road authorities and was considered a discrete variable with values ranging from 1981 to 2004 (cf. [Table 1\).](#page-1-0) The location of the roundabouts (urban surroundings, country surroundings or a combination) was found on digital GIS-maps and was considered a class variable.
-
-#### **3. Data analysis**
-
-The goal of the analysis was to explain as much variation as possible in the number of cyclist accidents in the roundabouts by the independent variables mentioned above. We have used a
-
-Table 2 Number of roundabouts and cyclist accidents in the various categories
-
-| Variable               | Category               | Number of<br>roundabouts | Number of emergency<br>department recorded accidents |  |
-|------------------------|------------------------|--------------------------|------------------------------------------------------|--|
-| LEGS (no.)             | 2–3                    | 24                       | 18                                                   |  |
-|                        | 4                      | 59                       | 108                                                  |  |
-|                        | 5–6                    | 5                        | 26                                                   |  |
-| Total                  |                        | 88                       | 152                                                  |  |
-|                        |                        |                          |                                                      |  |
-| ISLAND (m)             | 0–10                   | 10                       | 5                                                    |  |
-|                        | 10–20                  | 34                       | 96                                                   |  |
-|                        | 20–30                  | 26                       | 21                                                   |  |
-|                        | 30–40                  | 12                       | 21                                                   |  |
-|                        | 40–50                  | 2                        | 9                                                    |  |
-|                        | 50–60                  | 1                        | 0                                                    |  |
-| Total                  |                        | 85                       | 152                                                  |  |
-| CIRC (m)               | 4–8                    | 76                       | 116                                                  |  |
-|                        | 8–12                   | 7                        | 35                                                   |  |
-|                        | 12–16                  | 3                        | 0                                                    |  |
-| Total                  |                        | 86                       | 151a                                                 |  |
-| APRON (m)              | 0–2                    | 46                       | 111                                                  |  |
-|                        | 2–4                    | 36                       | 40                                                   |  |
-|                        | 4–6                    | 2                        | 0                                                    |  |
-| Total                  |                        | 84                       | 151a                                                 |  |
-|                        |                        |                          |                                                      |  |
-| DRIVCURV (m)           | 9–20                   | 28                       | 30                                                   |  |
-|                        | 20–30                  | 28                       | 53                                                   |  |
-| Total                  | 30–40                  | 6<br>62                  | 49<br>132a                                           |  |
-|                        |                        |                          |                                                      |  |
-| CYCFAC                 | 0 (no)                 | 19                       | 18                                                   |  |
-|                        | 1 (yes)                | 62                       | 129                                                  |  |
-| Total                  |                        | 81                       | 147a                                                 |  |
-| CYCLISTS (no. per day) | <1000                  | 46                       | 28                                                   |  |
-|                        | ≥1000                  | 14                       | 87                                                   |  |
-| Total                  |                        | 60                       | 115a                                                 |  |
-| VEHIC (no. per day)    | 0–5000                 | 23                       | 19                                                   |  |
-|                        | 5000–10,000            | 34                       | 45                                                   |  |
-|                        | 10,000–15,000          | 7                        | 19                                                   |  |
-|                        | 15,000–20,000          | 4                        | 50                                                   |  |
-|                        | 20,000–25,000          | 1                        | 0                                                    |  |
-| Total                  |                        | 69                       | 133a                                                 |  |
-|                        |                        |                          |                                                      |  |
-| CONSTRYEAR             | 1981–1995<br>1996–2004 | 38<br>42                 | 108<br>37                                            |  |
-| Total                  |                        | 80                       | 145a                                                 |  |
-|                        |                        |                          |                                                      |  |
-| ZONE                   | Urban                  | 60                       | 132                                                  |  |
-|                        | Rural                  | 10                       | 2                                                    |  |
-|                        | Urban/rural            | 18                       | 18                                                   |  |
-| Total                  |                        | 88                       | 152                                                  |  |
-
-Categorisation of the continuous variables is arbitrary and only serves the purpose of giving an overview of data.
-
-<sup>a</sup> There are 152 cyclist accidents registered by the emergency department for which the location is known. Some of the totals are less than 152; this is because the rest of the accidents took place in roundabouts where the variable in question was not recorded.
-
-<span id="page-3-0"></span>Poisson model with the formal expression:
-
-$$\log(y) = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \ldots + \beta_n x_n + \varepsilon, \tag{2}$$
-
-where *y* is the dependent variable (number of accidents per year after the construction of the roundabout), *x*1, ..., *xn* denote the independent variables mentioned above, and β0, ..., β*<sup>n</sup>* are coefficients scaling the variables. Binary variables (e.g. existence of a cyclist facility) are given the value of one when a cyclist facility is present and 0 otherwise. Ordinally scaled variables are given a separate coefficient for each level of the variable. One level is set to be the reference level with a coefficient equal to 0. ε is the variation in the dependent variable not accounted for by the model.
-
-However, because of the limited number of roundabouts, building a complete model and subsequently eliminate insignificant variables was not possible. Therefore, we adopted a different strategy: We checked each independent variable separately for significance and eventually put the significant variables together into a model. Proc GENMOD (SAS 9.1) was used to quantify the coefficients and the significance of the independent variables (*F*-tests). In order to save degrees of freedom, the categorization of the variables LEGS, CYCLISTS and CONSTRYEAR was slightly changed (cf. [Table 2\):](#page-2-0)
-
-*LEGS.* Three categories were used: category 1: two to three legs, category 2: four legs and category 3: five to six legs.
-
-*CYCLISTS.* Instead of five categories, two were used: above and below 1000 cyclists per 24 h.
-
-*CONSTRYEAR.* Two categories were established: 1: 1981– 1995 and 2: 1996–2004.
-
-Secondly, we built a logistic regression model where the dependent variable instead of being the number of cyclist accidents as above, was the probability of a cyclist accident taking place given certain values of independent variables. The same categories for variables LEGS, CYCLISTS and CONSTRYEAR as mentioned above, were used in the logistic regression analyses.
-
-A logistic relation is described by the general formula:
-
-$$P(y) = \frac{\exp(y)}{1 + \exp(y)},\tag{3}$$
-
-where *P*(*y*) denotes the probability of an event, and log(*y*) is a linear expression of *x*, given in Eq. (2) [\(Hosmer and Lemeshow,](#page-7-0) [1989\).](#page-7-0) The model building strategy was the same as for the Poisson model given above. Proc LOGISTIC (SAS 9.1) was used to quantify the coefficients and the significance of the independent variables (goodness of fit-tests).
-
-Table 3 Details of Poisson models including one variable at a time
-
-| Variable    | Poisson regression univariate analyses |      |       |        |                                                               | Logistic regression univariate analyses |                              |                                                               |                                                       |
-|-------------|----------------------------------------|------|-------|--------|---------------------------------------------------------------|-----------------------------------------|------------------------------|---------------------------------------------------------------|-------------------------------------------------------|
-|             | N                                      | d.f. | F     | P      | Parameter estimate                                            | χ2                                      | P                            | Parameter estimate                                            | Odds ratio estimate                                   |
-| LEGS        | 75                                     | 2    | 2.89  | 0.06   | Int.: 0.26<br>Cat. 1: −2.01<br>Cat. 2: −1.07<br>Cat. 3: ref.  | 0.36                                    | 0.83                         | Int: 4.5 × 10−16<br>3: −0.20<br>4: 0.12<br>5: ref.            | Legs 3 vs. 5: 0.82<br>Legs 4 vs. 5: 1.13              |
-| DIAMETER    | 74                                     | 1    | 0.21  | 0.65   | Int.: −1.08<br>0.71<br>0.40<br>Diameter: 0.01                 |                                         | Int: 0.46<br>Diameter: −0.02 | Island: 0.98                                                  |                                                       |
-| CIRC        | 75                                     | 1    | 2.41  | 0.13   | Int.: −2.04<br>0.28<br>0.60<br>Circ: 0.18                     |                                         | Int: 0.49<br>Circ: −0.07     | Circ: 0.93                                                    |                                                       |
-| APRON       | 73                                     | 1    | 6.52  | 0.01   | Int: −0.28<br>2.75<br>Apron: −0.42                            |                                         | 0.10                         | Int: 0.59<br>Apron: −0.30                                     | Apron: 0.74                                           |
-| DRIVCURV    | 55                                     | 1    | 32.95 | 0.0001 | Int: −3.78<br>Drivcurv: 0.13                                  | 3.97                                    | 0.05                         | Int: −1.95<br>Drivcurv: 0.10                                  | Drivcurv: 1.11                                        |
-| CYCFAC      | 70                                     | 2    | 1.81  | 0.18   | Int: −0.69<br>0: −0.96<br>1: ref.                             | 2.00                                    | 0.16                         | Int: 0.29<br>0: −0.88<br>1: ref.                              | Cycfac 0 vs. 1: 0.42                                  |
-| CYCLISTS    | 53                                     | 2    | 33.89 | 0.0001 | Int: 1.81<br>≤1000: ref.<br>>1000: 2.17                       | 6.02                                    | 0.01                         | Int: −0.45<br><1000: ref.<br>>1000: 2.06                      | Cyclists > 1000 vs. ≤ 1000: 7.81                      |
-| VEHIC       | 63                                     | 1    | 19.71 | 0.0001 | Int: −2.04<br>Vehic: 0.0001                                   | 2.21                                    | 0.14                         | Int: −0.60<br>Vehic: 9.4 × 10−5                               | Vehic: 1.00                                           |
-| CON-STRYEAR | 75                                     | 1    | 2.70  | 0.10   | Int: −1.33<br>1: 0.76<br>2: ref.                              | 4.70                                    | 0.03                         | Int: −0.50<br>1: 1.04<br>2: Ref.                              | Constryear 1 vs. 2: 2.82                              |
-| ZONE        | 75                                     | 2    | 2.67  | 0.08   | Int: −3.81<br>Urban: 3.17<br>Urban/rural: 2.63<br>Rural: ref. | 4.64                                    | 0.10                         | Int: −2.08<br>Urban: 2.37<br>Urban/rural: 2.20<br>Rural: ref. | Urban vs. rural: 10.67<br>Urban/rural vs. rural: 9.00 |
-
-Table 4 Details of the multiple Poisson model including all significant variables from the single-variable models
-
-| Variable  | d.f. | F    | P     | Coefficient estimate | P      |
-|-----------|------|------|-------|----------------------|--------|
-| INTERCEPT | 1    | –    | –     | −3.23                | 0.0001 |
-| CYCLISTS  | 2    | 3.86 | 0.028 | ≤1000: ref.          | ref.   |
-|           |      |      |       | >1000: 1.28          | 0.0045 |
-| VEHIC     | 1    | 3.68 | 0.062 | 0.00006              | 0.057  |
-| DRIVCURV  | 1    | 6.50 | 0.014 | 0.06                 | 0.010  |
-
-*N*= 50. Poisson regression analyses at left, logistic regressions at right.
-
-Since we have data only on accidents in the period 1999–2003, roundabouts constructed in 2003 and 2004 (*N*= 5) were excluded from the regression analyses.
-
-Both Poisson and logistic regression models were built with and without single cyclist accidents. Finally, logistic regression models were built with single cyclist accidents alone.
-
-## **4. Results**
-
-All roundabouts at the island of Funen were included in the analysis. Details on the variables describing them are summarised in [Tables 1 and 2.](#page-1-0)
-
-A total of 171 accidents involving cyclists were recorded by the emergency department of the hospital of Odense through the period 1999–2003, 152 of which were unambiguously assigned to a given roundabout. Only 42 of these 171 cyclist accidents were recorded by the police. Of the 171 accidents recorded by the emergency department, 69 (40%) were one-cyclist accidents; of these, 48 were unambiguously assigned to a given roundabout. Almost half of the accidents (i.e. 81, equal to 47%) were vehiclefailed-to-give-way-accidents; in 57 accidents the vehicle hit a circulating cyclist on its way into the roundabout; in 24 accidents the vehicle was about to leave the roundabout when it hit the cyclist. The rest of the emergency department cyclist accidents (21 equal to 13%) were of various kinds – e.g. cyclists hitting each other – or insufficiently described.
-
-The pattern of the 42 accidents recorded by the police in the same roundabouts in the years 1999–2003 was similar to the one above except that the one-cyclist accidents were fewer, namely no more than two accidents (equal to 5% of the recorded). Vehicle-failed-to-give-way-accidents had a higher proportion: 34 were recorded, equal to 81%. Of these, in 22 accidents the vehicle hit a circulating cyclist on its way into the roundabout; in 12 accidents the vehicle hit the cyclist on its way out of the roundabout. Six of the accidents (14%) contained too little information to be categorized.
-
-In the univariate Poisson model drive curve, apron width and traffic volume of cyclists and of vehicles significantly explained the variation in the number of cyclist accidents per year in the roundabouts ([Table 3\).](#page-3-0) More cyclist accidents per year were connected to roundabouts with a narrow apron, a large drive curve and with high volumes of cyclists and vehicles. The existence of a cyclist facility did not significantly explain the variation in the number of cyclist accidents, and neither did the number of legs. There was a trend for roundabouts with more legs to have more cyclist accidents. Just about the same variables were significant in the univariate logistic regression analysis. The probability of an accident was significantly positively related to the number of cyclists and to the age of the roundabout: the older the roundabout, the higher the probability of a cyclist accident. Like in the Poisson models, there was a higher accident probability connected to roundabouts with a small apron (non-significant) and a large drive curve (significant).
-
-In the multiple models the explanatory variables from the univariate analyses were put together into the same model to correct for each other. In Tables 4 and 5 are reported the coefficients and odds ratios only for the significant variables. In doing this, it turned out that apron width was not a significant predictor of the number of accidents in the linear model, whereas cyclist and vehicle volume and drive curve still were. In the multiple logistic model, the drive curve was significant, the number of cyclists was only an insignificant trend whereas construction year was close to being significant, the older roundabouts related to the higher accident probability.
-
-Excluding 48 single cyclist accidents from the analyses did not qualitatively change the outcome of the univariate or the multiple Poisson models. However, it did somewhat change the results of the logistic regression analysis. The significant variables (i.e. the number of cyclists and the age of roundabout) became more significant (*P* = 0.001 and *P* = 0.004, respectively). Moreover, apron, drive curve and traffic volume of vehicles changed from insignificant to significant (*P*-values 0.03, 0.03 and 0.03, respectively). In the multiple logistic model, traffic
-
-Table 5 Details of the multiple logistic model including all variables that were significant in the single-variable models
-
-| Variable   | d.f. | χ2   | P    | Coefficient estimate | Odds ratio estimate | 95% confidence limits |
-|------------|------|------|------|----------------------|---------------------|-----------------------|
-| INTERCEPT  | 1    | 5.91 | 0.02 | −4.80                | –                   | –                     |
-| CONSTRYEAR | 1    | 3.94 | 0.05 | 1.60                 | 1 vs. 2: 4.95       | 1.02–23.99            |
-| DRIVCURV   | 1    | 5.24 | 0.02 | 0.22                 | 1.24                | 1.03–1.50             |
-
-Table 6 Details of the multiple logistic model excluding single cyclist accidents
-
-| Variable   | d.f. | χ2    | P     | Coefficient estimate | Odds ratio estimate | 95% confidence limits |
-|------------|------|-------|-------|----------------------|---------------------|-----------------------|
-| INTERCEPT  | 1    | 10.23 | 0.001 | −2.25                | –                   | –                     |
-| CONSTRYEAR | 1    | 8.36  | 0.004 | 1.68                 | 1 vs. 2: 5.38       | 1.72–16.84            |
-| VEHIC      | 1    | 4.33  | 0.04  | 0.00015              | 1.00                | 1.00–1.00             |
-
-*N*= 63. For simplicity, variables which showed insignificant effect were omitted from the table.
-
-volume of vehicles stayed in the model instead of drive curve (Table 6).
-
-Analysing the 48 single cyclist accidents in the 88 roundabouts separately revealed very few significant relations to the recorded independent variables. Thus, only drive curve and traffic volume of cyclists turned out to be significantly related to the probability of single cyclist accidents in a roundabout, for both variables in a positive way (*N*= 55, *P* = 0.03 and *N*= 53, *P* = 0.004, respectively). The significance of the drive curve disappeared in a multiple logistic model containing these two variables.
-
-## **5. Discussion**
-
-The 88 roundabouts included in this analysis differ considerably, both in terms of geometry and traffic volumes. All of them are built in the speed-reducing way with legs more or less perpendicularly radiating from the circulatory roadway as opposed to the dynamic design that gives the roundabout a higher capacity but is not recommended where there are many cyclists ([Jørgensen and Jørgensen, 2002\).](#page-7-0)
-
-Single-cyclist accidents were the type of accident most seriously underreported by the police, i.e. only 3% of this type of accident gets reported by the police. This was expected due to the nature of these accidents, since in single-cyclist accidents there are no conflicting interests for the involved partner regarding insurance matters, and the cyclist – if seriously injured – probably goes straight to the emergency department without contacting the police. It is highly likely that the number of single-cyclist accidents is vastly underreported by the emergency department as well, since most cyclists – if not seriously injured – probably go home after such an accident and then, if needed, later consults his or her general practitioner.
-
-About 75% of the cyclist accidents recorted by the hospital were not found in the police reports. In a comparable Danish study ([Bach, 2004\)](#page-6-0) the author found that cyclist accidents were by far the most underreported group of accidents since the percentage of reported cyclist accidents is only 5% of the actual number.
-
-A large part of the accidents were vehicle-failed-to-give-wayaccidents. This is in accordance with other findings [\(Alphand](#page-6-0) [et al., 1991; Jørgensen and Jørgensen, 2002\).](#page-6-0) Some or all of these may be what is also described as looked-but-failed-to-seeaccidents ([Hills, 1980\).](#page-7-0) In this type of accident the cyclist usually reports the driver to have looked at him but then moves on as if the cyclist was not there. [Herslund and Jørgensen \(2003\)](#page-7-0) have examined this type of accident closely and concluded tentatively that the drivers' visual search strategy and/or mental processing somehow fails, so the driver sees but does not perceive the cyclist.
-
-As a rule of thumb when building causal regression models, the number of observations must be about ten times the number of independent variables, including the number of levels of the semi quantitative variables. This is to ensure a fair number of observations in all categories. With a limited amount of data (i.e. 171 accidents distributed over 88 roundabouts) building models with a method like backwards elimination would clearly violate this rule. Therefore, we started out constructing univariate models to get an indication of the significance of the various variables. Later the explanatory variables were put together into multiple models to quantify the effects of the variables when correcting for other significant variables. The models developed here must be seen only as indicative due to the limited number of observations.
-
-When working with discrete data such as counts, the most used distributions are the Poisson and the binomial distributions. The negative binomial distribution also provides a good model for discrete data, and it can be derived from the Poisson distribution by providing a prior distribution to the mean. We decided to use a Poisson instead of a negative binomial distribution since Poisson provides a good model for our data and does not require a prior distribution (which means additional assumptions) for the mean.
-
-Many of the same explanatory variables turned out to be significantly related to the number of accidents and to the predicted accident probability (Poisson and logistic regression, respectively). It seems intuitively obvious that traffic volume is an important predictor of the number of accidents. We found significantly positive relationships between the number of cyclists and the number of accidents per year after construction of the roundabout as well as accident probability—a relation that was strengthened by excluding single cyclist accidents. By relating the original five class ordinal scale for cyclist traffic volume to the number of cyclist accidents and cyclist accident probability, it became evident that there was a significant increase in both the number of accidents and the accident probability at 1000 cyclists/day. Other authors have found positive relationships between the number of cyclists and the number of accidents as well ([Schoon and van Minnen, 1994;](#page-7-0) [Brude and Larsson, 1997, 2000](#page-7-0) ¨ ). In most other investigations of cyclist safety in roundabouts, traffic volume of cyclists is not included. In cases where it is included, the relation turns out to be like the one we found: the more cyclists, the more cyclist accidents. [Ekman \(1996\)](#page-6-0) suggested that even if the number of accidents increases with increasing number of cyclists, accident risk per cyclist is higher for low cyclist volumes. Unfortunately, <span id="page-6-0"></span>our data does not allow calculating accident risk per cyclist because the number of cyclists is recorded on a semi quantitative scale.
-
-According to the literature the relation between vehicle volume and number of cyclist accidents is clearly positive ([Layfield](#page-7-0) [and Maycock, 1986; Schoon and van Minnen, 1994; Brude and](#page-7-0) ¨ [Larsson, 2000; Perseaud et al., 2001; Elvik, 2003; Alphand et](#page-7-0) [al., 1991\).](#page-7-0) This was confirmed by our models. In the logistic regression models the relation between accident probability and vehicle volume was strengthened considerably, not surprisingly, by excluding single cyclist accidents. In doing so, focus was put on accidents involving cyclists and vehicles, which is probably why vehicle volume turned out to be more significant. Refined models usually fit a levelling off of number of accidents per circulating vehicle for large vehicle volumes. We, however, regard our models indicative due to the limited amount of data.
-
-Speed of the circulating vehicles has an intuitively obvious impact on accident risk; the speed also has a very high impact on the severity of the potential injury. We used a measure of deflection related to the length of the road deflected as a proxy for potential vehicle speed through the roundabout (i.e. drive curve). This measure turned out to be positively related to the number of cyclist accidents. A positive relation between vehicle speed and the number of cyclist accidents is also found by Brude ¨ and Larsson (2000) who measured speed of entering, circulating and exiting vehicles directly.
-
-Excluding single cyclist accidents also strengthened the positive relation between the traffic volume of cyclists and the probability of cyclist accidents. Overall, the probability of cyclist accidents turned out to be high in older roundabouts with high vehicle volumes. These are roundabouts often situated in the town centres where the cyclist volume is high, too. The location of the roundabouts is probably more important than its age, but since the location was not recorded accurately enough, age became a significant predictor of accident probability.
-
-Of the geometric features recorded, surprisingly few had any relation whatsoever to the number of cyclist accidents or cyclist accident probability. The effect of a cyclist facility on cyclist accidents was investigated by [Schoon and van Minnen \(1994\).](#page-7-0) In their much cited paper they found no effect on the number of accidents of a cyclist facility in roundabouts with vehicle volumes below 8000 vehicles/day. Above 8000 vehicles/day, however, they recommended construction of cycle paths completely separated from the circulatory roadway. [Jørgensen and](#page-7-0) [Jørgensen \(2002\)](#page-7-0) did not find a difference in number of cyclist accidents with different types of cyclist facilities (no facility, cycle lane, white markings on the asphalt) or cycle path (not separated from the circulatory roadway). We did not find a relation between the existence of a cyclist facility (0/1-variable) and the number of cyclist accidents or cyclist accident probability, but it might be due to the limited amount of observations, and it does not necessarily mean that there is no relation. In Denmark roundabouts without cyclist facilities tend to be rare, and in our data set we have only 19 of them. Moreover, in our data set we have different kinds of cyclist accidents lumped together due to the relatively small number of observations. It may be that the number of, e.g. vehicle-failed-to-give-way-accidents is related to the existence of a cyclist facility whereas other types of accidents are not.
-
-Excluding the single cyclist accidents strengthened the significance of the relation between drive curve, apron width and cyclist accident probability considerably. This stresses the connection between potential speed of vehicles and accident probability in accidents with more than one partner involved.
-
-According to our results, the probability of single cyclist accidents is apparently related to the number of cyclists entering the roundabout, only. As it turns out, the geometric features recorded in these particular roundabouts do not pose a challenge to bicycle riders. A look at the verbal descriptions of the single cyclist accidents reveals that single cyclist accidents often happen in situations where cyclists collide with the kerb or permanent installations like lamp posts, where cyclists are driving under influence and fall over, where the road is slippery due to winter weather conditions and/or where the cyclist fails at manoeuvring the bicycle properly, e.g. by braking too hard.
-
-One of the limitations of our study is clearly the relatively small number of observations, all of which were collected on the island of Funen. Another limitation, which is a general problem all over, is the high percentage of underreporting of cyclist accidents. Clearly, a larger database is needed, both concerning roundabouts and number of accidents, in order to construct a reliable statistical model. Until then, our results may serve as preliminary ones.
-
-## **Acknowledgements**
-
-We thank Lars Binderup-Larsen, consultant orthopaedic surgeon, for a great help in extracting relevant data from the databases of the Accident Analysis Group of the Odense University Hospital. Moreover, we thank all employees of the road authorities who provided us with invaluable information: The Danish Road Directorate, The County and the Communities of Funen. This work was supported by a grant from the Danish Ministry of Transport and Energy.
-
-# **References**
-
-Alphand, F., Noelle, U., Guichet, B., 1991. Roundabouts and road safety. State of the art in France. In: Brilon, W. (Ed.), Intersections Without Traffic Signals II. Springer-Verlag, pp. 126–140.
-
-Bach, U., 2004. Færdselsuheld registreret pa Sydvestjysk Sygehus [Traffic acci- ˚ dents recorded at the hospital in South West Jutland]. Carl Bro., 30 (in Danish).
-
-Brown, M., 1995. The design of roundabouts. HMSO, 270.
-
-Brude, U., Larsson, J., 1997. The safety of cyclists at roundabouts—a compari- ¨ son between Swedish, Danish and Dutch results. Nordic Road Transp. Res. 1, 23–25.
-
-Brude, U., Larsson, J., 2000. What roundabout design provides the highest pos- ¨ sible safety? Nordic Road Transp. Res. 2, 17–21.
-
-CROW, 1998. Eenheid in rotondes [in Dutch]. Publicatie 126, 112.
-
-Ekman, L., 1996. On the treatment of flow in traffic safety analysis. A nonparametric approach applied on vulnerable road users. Lunds Tekniska Hogskola, Bull. 136, 99. ¨
-
-Elvik, R., 1847. Effects on road safety of converting intersections to roundabouts. Transport. Res. Rec., 1–10.
-
-- <span id="page-7-0"></span>Elvik, R., Vaa, T., 2004. The Handbook of Road Safety Measures. Elsevier, 1078 pp.
-- Flannery, A., 1751. Geometric design and safety aspects of roundabouts. Transport. Res. Rec., 76–82.
-- Herslund, M.-B., Jørgensen, N.O., 2003. Looked-but-failed-to-see-errors in traffic. Accid. Anal. Prevent. 35, 885–891.
-- Hills, B.L., 1980. Vision, visibility and perception in driving. Perception 9, 183–216.
-- Hosmer, D.W., Lemeshow, S., 1989. Applied Logistic Regression. Wiley, 307 pp.
-- Huber, C.A., 1995. Sicherheit von Kreiselanlagen [in German]. Zeitschrift fur¨ Verkehrssicherheit 41 (2), 83–85.
-- Jørgensen, E., Jørgensen, N.O., 1994. Trafiksikkerhed i 82 danske rundkørsler—anlagt efter 1985 [Traffic safety in 82 Danish roundabouts], Institut for Veje, Trafik og Byplan, DTU, 49 pp. (in Danish).
-- Jørgensen, E., Jørgensen, N.O., 2002. Trafiksikkerhed i rundkørsler i Danmark. En analyse af uheld i danske rundkørsler i arene 1991–1996 [Traffic safety in ˚ roundabouts in Denmark. An analysis of accidents in Danish roundabouts in 1991–1996], Vejdirektoratet, rapport 235, 50 pp. + 16 siders notat (in Danish).
-- Layfield, R.E., Maycock, G., 1986. Pedal-cyclists at roundabouts. Traff. Eng. Contr. Juni, 343–349.
-- Perseaud, B.N., Retting, R.A., Garder, P.E., Lord, D., 1751. Safety effect of roundabout conversions in the United States: empirical bayes observational before-after study. Transport. Res. Rec., 1–9.
-- Ras¨ anen, M., og Summala, H., 2000. Car drivers' adjustments to cyclists at ¨ roundabouts. Transport. Hum. Factors 2 (1), 1–17.
-- Schoon, C., van Minnen, J., 1994. The safety of roundabouts in The Netherlands. Traffic Eng. Contr. 35 (3), 142–148.

Zotero/001_artiklid/Road Safety Indicators/heroadtrafficinjurymortality2021.md

@@ -1,241 +0,0 @@
----
-category: literaturenote
-citekey: heroadtrafficinjurymortality2021
-title: "Road traffic injury mortality and morbidity by country development status, 2011-2017"
-authors: "He, Jie-Yi; Xiao, Wang-Xin; Schwebel, David C.; Zhu, Mo-Tao; Ning, Pei-Shan; Li, Li; Cheng, Xun-Jie; Hua, Jun-Jie; Hu, Guo-Qing"
-year: 2021
-date: 2021-03-00 2021-3
-doi: 10.1016/j.cjtee.2021.01.007
-publication: Chinese Journal of Traumatology
-url: "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8071733/"
-zotero_key: T7YIZNII
-zotero_storage: PCIGGLWT
-collections: doktoritöö / HLO
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "He, Jie-Yi"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-## Chinese Journal of Traumatology
-
-journal homepage: <http://www.elsevier.com/locate/CJTEE>
-
-![](_page_0_Picture_5.jpeg)
-
-## Original Article
-
-# Road traffic injury mortality and morbidity by country development status, 2011- 2017
-
-Jie-Yi He [a](#page-0-0) , Wang-Xin Xiao [a](#page-0-0) , David C. Schwebel [b](#page-0-1) , Mo-Tao Zhu [c](#page-0-2) , Pei-Shan Ning [a](#page-0-0) , Li Li [d](#page-0-3) , Xun-Jie Cheng [a](#page-0-0) , Jun-Jie Hua [a](#page-0-0) , Guo-Qing Hu [a,](#page-0-0) [\\*](#page-0-4)
-
-- <span id="page-0-0"></span><sup>a</sup> Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, 410078, China
-- <span id="page-0-1"></span><sup>b</sup> Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
-- <span id="page-0-2"></span><sup>c</sup> Center for Injury Research and Policy, Abigail Wexner Research Institute at Nationwide Children's Hospital, Department of Pediatrics, The Ohio State University, Columbus, OH, 43205, USA
-- <span id="page-0-3"></span><sup>d</sup> Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, 43210, USA
-
-#### article info
-
-#### Article history: Received 31 May 2020 Received in revised form 1 January 2021 Accepted 4 January 2021 Available online 19 January 2021
-
-Keywords: Road traffic injury Mortality Morbidity Socioeconomic disparity Sustainable development goals
-
-#### abstract
-
-Purpose: This research examined road traffic injury mortality and morbidity disparities across of country development status, and discussed the possibility of reducing country disparities by various actions to accelerate the pace of achieving Sustainable Development Goals target 3.6 e to halve the number of global deaths and injuries from road traffic accidents by 2020.
-
-Methods: Data for road traffic mortality, morbidity, and socio-demographic index (SDI) were extracted by country from the estimates of the Global Burden of Disease study, and the implementation of the three types of national actions (legislation, prioritized vehicle safety standards, and trauma-related post-crash care service) were extracted from the Global Status Report on Road Safety byWorld Health Organization.We fitted joinpoint regression analysis to identify and quantify the significant rate changes from 2011 to 2017.
-
-Results: Age-adjusted road traffic mortality decreased substantially for all the five SDI categories from 2011 to 2017 (by 7.52%e16.08%). Age-adjusted road traffic mortality decreased significantly as SDI increased in the study time period, while age-adjusted morbidity generally increased as SDI increased. Subgroup analysis by road user yielded similar results, but with two major differences during the study period of 2011 to 2017: (1) pedestrians in the high SDI countries experienced the lowest mortality (1.68e1.90 per 100,000 population) and morbidity (110.45e112.72 per 100,000 population for incidence and 487.48e491.24 per 100,000 population for prevalence), and (2) motor vehicle occupants in the high SDI countries had the lowest mortality (4.07e4.50 per 100,000 population) but the highest morbidity (428.74e467.78 per 100,000 population for incidence and 1025.70e1116.60 per 100,000 population for prevalence). Implementation of the three types of national actions remained nearly unchanged in all five SDI categories from 2011 to 2017 and was consistently stronger in the higher SDI countries than in the lower SDI countries. Lower income nations comprise the heaviest burden of global road traffic injuries and deaths.
-
-Conclusion: Global road traffic deaths would decrease substantially if the large mortality disparities across country development status were reduced through full implementation of proven national actions including legislation and law enforcement, prioritized vehicle safety standards and trauma-related postcrash care services.
-
-© 2021 Production and hosting by Elsevier B.V. on behalf of Chinese Medical Association. This is an open access article under the CC BY-NC-ND license [\(http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)).
-
-#### Introduction
-
-"To halve the number of global deaths and injuries from road traffic accidents by 2020" was proposed by the United Nations as Sustainable Development Goals (SDGs) target 3.6[.1](#page-5-0) However, according to the Global Status Report on Road Safety 2018,[2](#page-5-1) the ambitious target is unlikely to be reached despite global improvement in key domains of legislation, vehicle standards and access to
-
-<span id="page-0-4"></span><sup>\*</sup> Corresponding author. E-mail address: [huguoqing009@gmail.com](mailto:huguoqing009@gmail.com) (G.-Q. Hu). Peer review under responsibility of Chinese Medical Association.
-
-post-crash care services. This situation raises a challenge for global health decision-makers and researchers as we move into and beyond the year 2020, how can we accelerate global progress to reduce the burden of road traffic injuries (RTIs) and deaths worldwide and achieve the SDG target belatedly?
-
-Substantial research evidence suggests that there is much heavier road traffic injury burden in underdeveloped countries compared to developed ones.[2](#page-5-1)e[6](#page-5-1) The Global Status Report on Road Safety 2018 suggested that 93% of the 1.35 million global road traffic deaths occurred in low- and middle-income countries in 2016, and age-adjusted road traffic mortality was 3 times higher in lowincome countries versus high-income countries (27.5 vs. 8.3 deaths per 100,000 population)[.2](#page-5-1) Remarkably, the report also indicated the number of road traffic deaths did not decrease in any low-income country between 2013 and 2016, but reductions were observed over that time span in 48 middle- and high-income countries[.2](#page-5-1)
-
-In light of the disproportional road traffic injury burden and slow or no progress in reducing RTIs in underdeveloped countries, a feasible and reasonable approach to address the global challenge to halve RTIs is to reduce heavier burden in countries with lower development statuses. This goal also corresponds to the global goal to offer all humans an equal opportunity to enjoy a better quality of life.[4](#page-5-2)[,6](#page-5-3)[,7](#page-5-4)
-
-The present study aims to examine the recent progress in reducing road traffic injury both mortality and morbidity disparities across country development statuses, as well as to assess how that progress corresponds to the three types of recommended national actions from the World Health Organization (WHO): legislation and enforcement, adoption of motor vehicle safety standards, and implementation of trauma-related post-crash care services. These three actions have been proven to be effective in reducing road traffic injury burden.[2](#page-5-1) In order to explore the global performance, to indicate where the weaknesses were and to offer the reference for adopting relevant actions, our analysis purposely linked and examined the implementation of WHO recommended actions with country development statuses.
-
-Morbidity rates do not always correspond to mortality rates, as an example, improved post-crash care services may lead to a crucial reduction in mortality but an increase in prevalence of hospitalization. Other prevention strategies, such as road traffic legislation and prioritized vehicle safety standards, may reduce both morbidity and mortality.
-
-We used two data sources for our analysis, the latest mortality and morbidity estimates by the Global Burden of Disease (GBD) study 201[78](#page-5-5) and implementation data concerning the three national actions from the Global Status Report on Road Safety.[2,](#page-5-1)[9,](#page-5-6)[10](#page-5-7) We examined the progress to reduce disparities in road traffic mortality and morbidity across country development status from 2011 to 2017 as well as progress in implementing the recommended three types of national actions during the study time period.
-
-## Methods
-
-### Data sources
-
-The GBD study is the only data source providing annual and comparable estimates of both fatal and non-fatal road traffic injury indicators by age group, sex, sub-cause, year, and geography at global, regional, national, and subnational levels from 1990 to 2017.[11,](#page-5-8)[12](#page-5-9) The GBD 2017 updated estimates can be freely accessed through the online data visualization tool "GBD Compare | Viz Hub" which was established and maintained by the Institute for Health Metrics and Evaluation at the University of Washington.[8](#page-5-5) Mortality and morbidity (incidence and prevalence) were both considered in our analyses.
-
-The Global Status Report on Road Safety is regularly released by the WHO to offer information concerning implementation of national actions recommended for road traffic injury prevention.[2](#page-5-1)[,9](#page-5-6)[,10](#page-5-7) We extracted data concerning three key national actions: legislation and law enforcement from 180 countries, vehicle safety standards from 183 countries, and trauma-related post-crash care services from 185 countries, and matched them with GBD estimates one country by one country for subsequent analyses. It was noted that data concerning the three types of national actions were missing for a few countries in the WHO reports.
-
-We used the socio-demographic index (SDI) to measure country development status. The SDI is a composite indicator defined by the GBD study group based on per capital income, educational attainment, and total fertility rate in each country. SDI ranges continuously from zero (the lowest developmental status) to one (the highest developmental status) and reflects the development status of a country or a region. The SDI is strongly correlated with health outcomes[.13](#page-5-10) Using the SDI values of 2017, the GBD study group divided 195 countries and territories into five development levels: high, high-middle, middle, low-middle, and low.
-
-#### Data analysis
-
-Corresponding with the time period for the Global Plan for the Decade of Action for Road Safety 2011e2020[14](#page-5-11) and the SDGs[,1](#page-5-0) we selected data from 2011 to 2017 for analyses. Line graphs were plotted to demonstrate trends in age-standardized road traffic injury mortality, incidence, and prevalence from 2011 to 2017 by country development level (SDI category). Mortality, incidence and prevalence were calculated based on the number of deaths, new cases, prevalent cases and population estimated by the GBD 2017. Joinpoint regression analysis was fitted to examine the trends in mortality and morbidity from 2011 to 2017 to describe and distinguish significant changes over time[.15](#page-5-12) The average annual percent change (AAPC) and its 95% confidence interval (CI) both from joinpoint regression analysis were used to quantify the average speed of rate change from 2011 to 2017. Subgroup analyses were conducted to explore disparities in mortality and morbidity across SDI category by road users (pedestrian, pedal cyclist, motorcyclist and motor vehicle occupant).
-
-By matching SDI data from the GBD updates and the WHO reports one country by one country, we also examined progress in the implementation of the three national actions (legislation, prioritized vehicle safety standards, and trauma-related post-crash care services)[2](#page-5-1) from 2011 to 2017 by country SDI category. Legislation involves the enactment and enforcement of five national road traffic laws (speed limit law, drink-driving law, motorcycle helmet law, seat-belt law, and child restraint law). A score from zero to ten was used to quantify the enactment and enforcement of the road traffic laws; a score of zero reflects the lack of relevant laws or the weakest enforcement and a score of ten reflects the law's presence plus the strongest enforcement.[2](#page-5-1) The implementation of the four prioritized United Nations vehicle safety standards (frontal impact protection, electronic stability control, pedestrian protection, and motorcycle anti-lock braking system)[2](#page-5-1) and three trauma-related post-crash care services (national single emergency care access phone number, formal training and certification for prehospital providers, and presence of a national trauma registry)[2](#page-5-1) were also assessed. Please note that data concerning implementation of vehicle safety standards were available for 2014 and 2018 only.
-
-Joinpoint regression analysis was conducted through Joinpoint Regression Program Desktop version 4.7.0.0. Mortality and morbidity changes with p values less than 0.05 were considered statistically significant.
-
-### Results
-
-#### Primary analysis
-
-Generally, age-adjusted road traffic mortality decreased significantly as the SDI increased, which suggested that countries with higher SDI tended to have a lower mortality rate. Mortality disparities consistently existed across the five SDI categories from 2011 to 2017 ([Fig. 1A](#page-2-0)). Age-adjusted mortality significantly decreased in all five SDI categories, with the greatest decrease in the high-middle SDI countries (AAPC: -2.7%, 95% CI: -2.9% e -2.5%, p < 0.05) and the smallest decrease in the high SDI countries (AAPC: -1.2%, 95% CI: -1.4% e -0.9%, p < 0.05).
-
-In contrast, age-adjusted morbidity showed a very different spectrum across the five SDI categories. Countries with a higher SDI tended to have a higher morbidity rate [\(Fig. 1](#page-2-0)B for incidence and [Fig. 1C](#page-2-0) for prevalence). Notably, both the incidence and prevalence gradually increased in the middle SDI countries from 2011 to 2017 (AAPC: 1.3%, p < 0.05 for incidence and 1.2%, p < 0.05 for prevalence) but decreased slightly in the other four SDI categories during the study time period (AAPC: -1.0% -0.2%, all p < 0.05 for incidence and -0.9% -0.4%, all p < 0.05 for prevalence). Although morbidity in the higher SDI countries was initially higher, high (AAPC: -1.0%, p < 0.05 for incidence and -0.9%, p < 0.05 for prevalence) and high-middle (AAPC: -0.8%, p < 0.05 for incidence and -0.9%, p < 0.05 for prevalence) SDI countries showed the decreases which were more dramatic than the decreases in low-middle (AAPC: -0.2%, p < 0.05 for incidence and -0.4%, p < 0.05 for prevalence) and low (AAPC: -0.5%, p < 0.05 for incidence and -0.8%, p < 0.05 for prevalence) SDI countries.
-
-#### Subgroup analysis by road user
-
-Subgroup analysis demonstrated generally similar results to those for overall road traffic mortality, incidence and prevalence. The two most notable differences were during the study period of 2011 to 2017: (1) pedestrians in the high SDI category generally had the lowest mortality and morbidity (incidence and prevalence) [\(Fig.1](#page-2-0)-A1, B1 and C1); and (2) the mortality and morbidity of motor vehicle occupants demonstrated quite distinct gaps across the five SDI categories e the high SDI category had the lowest mortality but the highest morbidity (incidence and prevalence) ([Fig. 1](#page-2-0)-A4, B4 and C4).
-
-<span id="page-2-0"></span>![](_page_2_Figure_9.jpeg)
-
-Fig. 1. Age-standardized road traffic injury mortality, incidence and prevalence by socio-demographic index (SDI), 2011-2017.
-
-Implementation of the three types of recommended national actions
-
-The median score of legislation and enforcement of the five types of national road traffic laws (speed limit law, drink-driving law, motorcycle helmet law, seat-belt law, and child restraint law) gradually increased as the SDI level increased ([Table 1](#page-3-0)). The highest SDI countries had median scores between seven and nine, while the lowest SDI countries had median scores between zero and four. The median scores generally remained stable for all the five SDI countries between 2011 and 2017, except for a sharp increase in child restraint laws for the high-middle SDI category. Notably, the median scores remained at zero for child restraint laws in the three lower SDI categories in both 2011 and 2017, suggesting no progress in those countries in using child restraint laws to protect child motor vehicle occupants.
-
-Implementation of the four prioritized motor vehicle safety standards increased as country SDI increased [\(Table 2](#page-3-1)). Four prioritized United Nations vehicle safety standards[2](#page-5-1) : (1) frontal impact protection ensures that cars withstand the impact of frontal impact crashes at certain speeds to protect occupants, (2) electronic stability control prevents skidding and loss of control in cases of oversteering or understeering to reduce both fatal crash deaths and non-fatal crash injuries, (3) pedestrian protection provides softer bumpers and modifies the front ends of vehicles to reduce the severity of a pedestrian impact with a car, (4) motorcycle anti-lock braking system helps motorcycle operators maintain control during an emergency braking situation and reduces the likelihood of both fatal crash deaths and non-fatal crash injuries. Almost all high SDI countries adopted these standards, while countries with highmiddle or lower SDI only partially implemented the four standards. Remarkably, no low SDI countries and very few low-middle SDI countries implemented any prioritized vehicle safety standards designed to guarantee the safety of pedestrians and occupants.
-
-As for the performance of three trauma-related post-crash care services, the higher SDI countries had comparatively higher proportions of having national and single emergency care access numbers, of providing formal training and certification for all prehospital providers, and of having a national trauma registry compared to the lower SDI countries ([Table 3\)](#page-4-0).
-
-## Discussion
-
-Key findings
-
-This study identified five key findings: (1) age-adjusted road traffic mortality significantly decreased in all the five SDI categories, but large disparities persisted from 2011 to 2017, with the higher SDI countries having lower mortality rates than the lower SDI countries, (2) age-adjusted road traffic morbidity (incidence and prevalence) presented contrary disparities across the study time period, with the higher SDI countries having higher morbidity rates than the lower SDI countries, (3) age-adjusted morbidity decreased slightly in all the SDI categories from 2011 to 2017, except for a significant increase in the middle SDI category, (4) both ageadjusted mortality and morbidity for pedestrians were the lowest in the high SDI category during 2011-2017, a contrast to the broad road traffic injury mortality and morbidity spectrum across the five SDI categories as well as the spectrum for other road users, and (5) the implementation of five national road traffic laws, four prioritized motor vehicle safety standards, and three trauma-related post-crash care services remained nearly unchanged in all five SDI categories from 2011 to 2017, but continued to be stronger in the higher SDI countries than in the lower SDI countries.
-
-#### Interpretation of findings
-
-Mortality disparities across the country SDI concord with differences reported in previous publications: mortality rates are lower in the developed countries compared to the developing countries.[2](#page-5-1),[3](#page-5-13),[16](#page-5-14) These disparities are often attributed to a range of factors, including discrepancies in road infrastructure, unsafe behaviors of road users, legislation and enforcement of road traffic laws, implementation of vehicle safety standards, and pre-hospital emergency medical services and hospital treatments.[2,](#page-5-1)[9,](#page-5-6)[10](#page-5-7),[17](#page-5-15) The
-
-<span id="page-3-0"></span>Table 1 Median and quartile range enforcement score of five types of national road traffic laws by country SDI (2011 vs. 2017).
-
-| SDI group            | Speed limit law |      |      | Drink-driving law |      |      | Motorcycle helmet law |      |      | Seat-belt law |      |      | Child restraint law |      |      |      |      |      |      |      |
-|----------------------|-----------------|------|------|-------------------|------|------|-----------------------|------|------|---------------|------|------|---------------------|------|------|------|------|------|------|------|
-|                      | 2011            |      | 2017 |                   | 2011 |      | 2017                  |      | 2011 |               | 2017 |      | 2011                |      | 2017 |      | 2011 |      | 2017 |      |
-|                      | M               | QR   | M    | QR                | M    | QR   | M                     | QR   | M    | QR            | M    | QR   | M                   | QR   | M    | QR   | M    | QR   | M    | QR   |
-| High (n ¼ 35)        | 7.00            | 1.00 | 7.00 | 2.00              | 7.00 | 3.00 | 7.00                  | 2.00 | 8.00 | 2.00          | 9.00 | 2.00 | 7.00                | 2.00 | 7.00 | 2.00 | 7.00 | 3.00 | 7.00 | 2.00 |
-| High-middle (n ¼ 35) | 6.00            | 2.00 | 6.00 | 2.00              | 7.00 | 3.00 | 7.00                  | 3.00 | 6.00 | 3.00          | 7.00 | 4.00 | 6.00                | 4.00 | 7.00 | 2.00 | 1.00 | 5.00 | 4.00 | 7.00 |
-| Middle (n ¼ 39)      | 4.00            | 4.00 | 5.00 | 3.00              | 5.00 | 3.00 | 5.00                  | 4.00 | 6.00 | 3.00          | 7.00 | 4.00 | 6.00                | 3.00 | 6.00 | 2.00 | 0    | 2.00 | 0    | 4.00 |
-| Low-middle (n ¼ 37)  | 4.00            | 2.50 | 5.00 | 2.00              | 4.00 | 3.50 | 5.00                  | 4.00 | 6.00 | 4.00          | 6.00 | 4.50 | 5.00                | 4.00 | 6.00 | 4.00 | 0    | 0    | 0    | 0    |
-| Low (n ¼ 34)         | 3.00            | 2.25 | 3.00 | 4.25              | 3.00 | 2.00 | 2.00                  | 5.00 | 2.00 | 4.25          | 4.00 | 3.50 | 3.00                | 5.00 | 4.00 | 6.00 | 0    | 0    | 0    | 0    |
-
-SDI: socio-demographic index, M: median, QR: quartile range, QR is the 3rd quartile minus the 1st quartile. Data source: global status report on road safety 2013[9](#page-5-6) and global status report on road safety 2018.[2](#page-5-1)
-
-<span id="page-3-1"></span>Table 2 Number of countries adopting four prioritized United Nations vehicle safety standards by SDI (2014 vs. 2018), n (%).
-
-| SDI group            | Frontal impact protection |            | Electronic stability control |            | Pedestrian protection |            | Motorcycle anti-lock<br>braking system |            |  |
-|----------------------|---------------------------|------------|------------------------------|------------|-----------------------|------------|----------------------------------------|------------|--|
-|                      | 2014                      | 2018       | 2014                         | 2018       | 2014                  | 2018       | 2014                                   | 2018       |  |
-| High (n ¼ 35)        | 34 (97.14)                | 34 (97.14) | 34 (97.14)                   | 34 (97.14) | 32 (91.43)            | 32 (91.43) | /                                      | 29 (82.86) |  |
-| High-middle (n ¼ 35) | 10 (28.57)                | 10 (28.57) | 7 (20.00)                    | 8 (22.86)  | 7 (20.00)             | 7 (20.00)  | /                                      | 4 (11.43)  |  |
-| Middle (n ¼ 40)      | 2 (5.00)                  | 2 (5.00)   | 2 (5.00)                     | 2 (5.00)   | 2 (5.00)              | 2 (5.00)   | /                                      | 1 (2.50)   |  |
-| Low-middle (n ¼ 39)  | 1 (2.56)                  | 2 (5.13)   | 1 (2.56)                     | 1 (2.56)   | 1 (2.56)              | 2 (5.13)   | /                                      | 1 (2.56)   |  |
-| Low (n ¼ 34)         | 0 (0)                     | 0 (0)      | 0 (0)                        | 0 (0)      | 0 (0)                 | 0 (0)      | /                                      | 0 (0)      |  |
-
-<sup>&</sup>quot;/": Data were not included in the global status report on road safety 2015.
-
-SDI:socio-demographic index.
-
-Data sources: global status report on road safety 2015[10](#page-5-7) and global status report on road safety 2018.[2](#page-5-1)
-
-<span id="page-4-0"></span>Table 3 Number of countries with trauma-related post-crash care services by SDI (2011 vs. 2017), n (%).
-
-| SDI group            | National single emergency care<br>access phone number |            | prehospital providers | Formal training and certification or | National trauma registry |            |  |  |
-|----------------------|-------------------------------------------------------|------------|-----------------------|--------------------------------------|--------------------------|------------|--|--|
-|                      | 2011                                                  | 2017       | 2011                  | 2017                                 | 2011                     | 2017       |  |  |
-| High(n ¼ 36)         | 31 (86.11)                                            | 33 (91.67) | 24 (66.67)            | 25 (69.44)                           | /                        | 20 (55.56) |  |  |
-| High-middle (n ¼ 35) | 27 (77.14)                                            | 31 (88.57) | 19 (54.29)            | 26 (74.29)                           | /                        | 13 (37.14) |  |  |
-| Middle (n ¼ 40)      | 19 (47.50)                                            | 21 (52.50) | 22 (55.00)            | 20 (50.00)                           | /                        | 13 (32.50) |  |  |
-| Low-middle (n ¼ 40)  | 16 (40.00)                                            | 16 (40.00) | 13 (32.50)            | 17 (42.50)                           | /                        | 10 (25.00) |  |  |
-| Low (n ¼ 34)         | 12 (35.29)                                            | 13 (38.24) | 3 (8.82)              | 8 (23.53)                            | /                        | 3 (8.82)   |  |  |
-
-<sup>&</sup>quot;/": Data were not included in the global status report on road safety 2013.
-
-Data sources: Global status report on road safety 2013[9](#page-5-6) and Global status report on road safety 2018.[2](#page-5-1)
-
-research also suggests that mortality risks decrease with the implementation of interventions addressing these problems, including road infrastructure improvement,[18](#page-5-16) reduction of unsafe behaviors[,2](#page-5-1) stronger legislation and enforcement of road traffic laws,[19](#page-5-17) stricter implementation of vehicle safety standards,[20](#page-5-18) and higher quality pre-hospital emergency rescue and hospital treatments.[21](#page-5-19) One challenge for safety intervention is that road users in the developing countries display higher rates of unsafe behavior than those in the developed countries, such as failing to abide by road signs and signals, not using seatbelts and helmets, reckless and speedy driving, drinking and distracted driving and walking, and failing to respect a pedestrian's right-of-way.[2](#page-5-1)[,22](#page-5-20) Continued effort to address these behaviors through cultural diversity, normative behavior, and legislation is needed worldwide, and particularly in the lower SDI countries.
-
-Our findings offer novel results to the field. First, we found inconsistent results for mortality and morbidity data across the five SDI categories. Over the course of our study, the mortality from road traffic crashes decreased but the morbidity rose. This may be partly result from higher quality emergency response and postinjury care, and motorization. Crashes may occur, but survival rates become higher. Whatever the cause, the burden of RTIs appears to be slowly transformed from deaths to non-fatal injuries, some of which lead to lifelong disability. Globally, humans may survive road traffic crashes and experience extended life, but they may also suffer disability and lost quality of life[.23](#page-5-21),[24](#page-5-22) Researchers and policy-makers should consider the consequences of these data for efforts to improve injury rehabilitation, work placements for disabled individuals, and improved quality of life for victims of serious RTIs.
-
-We also found disparities across road users among the five SDI categories. In particular, pedestrians in the high SDI category had the lowest mortality and morbidity rates while motor vehicle occupants in the high SDI countries had the lowest mortality but the highest morbidity rates. These data patterns likely reflect the effect of exposure: people may drive and ride in cars more often in the wealthy nations than in poor nations, but walk less often.
-
-The pattern of rapid global motorization is also likely contributing to our results, especially concerning increased road traffic morbidity.[2](#page-5-1),[14](#page-5-11) According to WHO reports,[2](#page-5-1) the cumulative number of total registered vehicles in circulation in 2016 was 766,060,506 (37.5%) in the high SDI countries, 532,765,962 (26.0%) in the highmiddle SDI countries, 430,922,455 (21.1%) in the middle SDI countries, 294,186,940 (14.4%) in the low-middle SDI countries, and 21,460,672 (1.0%) in the low SDI countries; the proportions of registered motor vehicles clearly differ from the proportions of populations for the five SDI categories in 2016 (15.1%, 18.3%, 27.5%, 22.3%, and 16.8%), causing large discrepancies between exposurebased metrics and population-based metrics. Equally important, however, the growth of the number of registered motor vehicles from 2010 to 2016 varied across the SDI categories (8.1%, 35.7%, 52.6%, 76.8%, and 87.3% for high, high-middle, middle, low-middle, and low SDI countries, respectively). Thus, the rapid rise of registered motor vehicles in the lower SDI countries partially influences the pattern of RTIs globally. A recent study by Cheng et al.,[25](#page-5-23) for example, reported large discrepancies in using exposure-based and population-based road traffic mortality to compare rates across countries and over time.
-
-## Implications
-
-Our findings have at least two major implications. First, we illustrated the presence of significant road traffic mortality differences across the five SDI categories between 2011 and 2017. The high SDI countries had the lowest mortality rates and the strongest prevention efforts. The global road injury deaths in 2017 would have decreased by 58% (from 1.24 million to 0.52 million, saving over 700,000 lives)[8](#page-5-5) if the four lower SDI categories had the same mortality rate as the high SDI category (9.1 per 100,000 population).[8](#page-5-5) The SDGs target to halve road traffic deaths and injuries by 2020 might be met in the upcoming years if our society implement technically feasible prevention not only to strengthen legislation and law enforcement, apply the recommended vehicle safety standards, but also to improve the pre-hospital rescue services and hospital treatments effectively. Of course, implementation of these initiatives worldwide would require substantial resources. Continued work, likely under the leadership of the United Nations and the WHO, is needed to mobilize and coordinate each country of the world to implement prevention strategies. International aid may be required to support many the low- and middle-income countries.
-
-Second, the inconsistent morbidity disparities across the five SDI categories reveal the challenge of using population-based metrics instead of exposure-based metrics to study road traffic injury rates. When exposure data are preferential and available, data patterns are easier to interpret.[25](#page-5-23) To facilitate monitoring and evaluating the SDGs progress for road traffic injury precisely, we encourage key stakeholders in the field, including the GBD study group and the WHO, to include valid exposure-based metrics in their annualized updates[8](#page-5-5) and regular reports.[2](#page-5-1) Besides this, improving the reliability and quality of existing data and integration of distinct data sources to diminish the inconsistency of various data and misreporting (including under-reporting and over-reporting) are urgently needed[.26](#page-5-24)<sup>e</sup>[28](#page-5-24)
-
-#### Limitations
-
-This study was primarily limited by the GBD estimates, which are restricted by the availability of high-quality mortality, morbidity and relevant covariates at the country level. In fact, very few countries in the world have regular and high-quality data sources for the estimation of burden of diseases, particularly for
-
-SDI:socio-demographic index.
-
-morbidity and covariate data[.29](#page-5-25)<sup>e</sup>[31](#page-5-25) Therefore, the GBD study group uses a variety of models and methods to address data challenges and estimate road traffic injury mortality and morbidity[.12](#page-5-9) Assumptions used in the GBD estimation models are optimized to the extent possible but remain sub-optimal in some cases and may yield unexpected biases to the final estimates.[32](#page-5-26) This limitation could be solved to some extent by integrating new high-quality data sources and improving the GBD models, stepping the GBD study group routinely engages in[.11](#page-5-8)[,12](#page-5-9)
-
-In conclusion, the lower SDI countries continued to have a higher mortality from road traffic injury, and weaker prevention efforts, from 2011 to 2017. Prevention efforts recommended by the United Nations and the WHO should be fully implemented in all countries globally, including but not limited to legislation and law enforcement, mandated vehicle safety standards, and improved pre-hospital rescue services & hospital treatments. International aid and cooperation are needed, as implementation of evidence-based prevention strategies could have a substantial impact on global public health. In addition, efforts to improve the reliability and validity of road traffic mortality and morbidity estimates should be expedited. Such efforts include use of exposure-based metrics, regular collection of highquality data, integration of diverse data sources, and improvment of the accuracy of models for estimating RTIs.
-
-#### Funding
-
-This work was supported by the National Natural Science Foundation of China (No. 82073672).
-
-#### Ethical statement
-
-This research used anonymous open-access data and did not involve personal information from individuals. This analysis was approved by ethics committee of Xiangya School of Public Health, Central South University, Changsha, China (NO.XYGW-2019-033).
-
-#### Declaration of competing interest
-
-The authors declare that they have no conflicts of interest.
-
-## References
-
-- <span id="page-5-0"></span>1. United Nations. Transforming Our World, the 2030 Agenda for Sustainable Development; 2015. [https://sustainabledevelopment.un.org/content/](https://sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf) [documents/21252030%20Agenda%20for%20Sustainable%20Development%](https://sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf) [20web.pdf](https://sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf).
-- <span id="page-5-1"></span>2. World Health Organization. Global Status Report on Road Safety 2018; 2018. [https://www.who.int/violence\\_injury\\_prevention/road\\_safety\\_status/2018/en/](https://www.who.int/violence_injury_prevention/road_safety_status/2018/en/)
-- <span id="page-5-13"></span>3. GBD 2017 SDG Collaborators. Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:2091e2138. [https://doi.org/](https://doi.org/10.1016/S0140-6736(18)32281-5) [10.1016/S0140-6736\(18\)32281-5](https://doi.org/10.1016/S0140-6736(18)32281-5).
-- <span id="page-5-2"></span>4. Department of Economic and Social Affairs. The Sustainable Development Goals Report 2017; 2017. [https://unstats.un.org/sdgs/](https://unstats.un.org/sdgs/files/report/2017/TheSustainableDevelopmentGoalsReport2017.pdf)files/report/2017/
-- 5. World Health Organization. World Health Statistics 2018: Monitoring Health for the SDGs; 2018. [https://apps.who.int/iris/bitstream/handle/10665/272596/](https://apps.who.int/iris/bitstream/handle/10665/272596/9789241565585-eng.pdf) [9789241565585-eng.pdf.](https://apps.who.int/iris/bitstream/handle/10665/272596/9789241565585-eng.pdf)
-- <span id="page-5-3"></span>6. World Bank. Atlas of Sustainable Development Goals 2018: From World Development Indicators; 2018. [https://openknowledge.worldbank.org/handle/10986/](https://openknowledge.worldbank.org/handle/10986/29788) [29788](https://openknowledge.worldbank.org/handle/10986/29788).
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-- <span id="page-5-5"></span>8. Institute for Health Metric and Evaluation, University of Washington. GBD compare Viz Hub. <https://vizhub.healthdata.org/gbd-compare/>.
-- <span id="page-5-6"></span>9. World Health Organization. Global Status Report on Road Safety 2013: Supporting a Decade of Action; 2013. [https://www.who.int/violence\\_injury\\_](https://www.who.int/violence_injury_prevention/road_safety_status/2013/en/) [prevention/road\\_safety\\_status/2013/en/](https://www.who.int/violence_injury_prevention/road_safety_status/2013/en/).
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-- <span id="page-5-7"></span>10. World Health Organization. Global Status Report on Road Safety 2015; 2015. [https://www.who.int/violence\\_injury\\_prevention/road\\_safety\\_status/2015/en/](https://www.who.int/violence_injury_prevention/road_safety_status/2015/en/)
-- <span id="page-5-8"></span>11. GBD 2017 Mortality Collaborators. Global, regional, and national age-sexspecific mortality and life expectancy, 1950-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1684e1735. [https://doi.org/10.1016/S0140-6736\(18\)31891-9.](https://doi.org/10.1016/S0140-6736(18)31891-9)
-- <span id="page-5-9"></span>12. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392: 1789e1858. [https://doi.org/10.1016/S0140-6736\(18\)32279-7.](https://doi.org/10.1016/S0140-6736(18)32279-7)
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-- <span id="page-5-16"></span>18. World Health Organization. Save LIVES-A Road Safety Technical Package; 2017. [https://apps.who.int/iris/bitstream/handle/10665/255199/9789241511704](https://apps.who.int/iris/bitstream/handle/10665/255199/9789241511704-eng.pdf?sequence=1) [eng.pdf?sequence](https://apps.who.int/iris/bitstream/handle/10665/255199/9789241511704-eng.pdf?sequence=1)¼[1.](https://apps.who.int/iris/bitstream/handle/10665/255199/9789241511704-eng.pdf?sequence=1)
-- <span id="page-5-17"></span>19. Zhao A, Chen R, Qi Y, et al. Evaluating the impact of criminalizing drunk driving on road-traffic injuries in guangzhou, China: a time-series study. J Epidemiol. 2016;26:433e439. <https://doi.org/10.2188/jea.JE20140103>.
-- <span id="page-5-18"></span>20. Walker C, Thompson J, Stevenson M. Road trauma among young Australians: implementing policy to reduce road deaths and serious injury. Traffic Inj Prev. 2017;18:363e368. <https://doi.org/10.1080/15389588.2016.1212189>.
-- <span id="page-5-19"></span>21. World Health Organization. Post-crash Response: Supporting Those Affected by Road Traffic Crashed; 2016. [https://apps.who.int/iris/bitstream/handle/10665/](https://apps.who.int/iris/bitstream/handle/10665/251720/WHO-NMH-NVI-16.9-eng.pdf?sequence=1) [251720/WHO-NMH-NVI-16.9-eng.pdf?sequence](https://apps.who.int/iris/bitstream/handle/10665/251720/WHO-NMH-NVI-16.9-eng.pdf?sequence=1)¼[1](https://apps.who.int/iris/bitstream/handle/10665/251720/WHO-NMH-NVI-16.9-eng.pdf?sequence=1).
-- <span id="page-5-20"></span>22. World Health Organization. Make Walking Safe: A Brief Overview of Pedestrian Safety Around the World; 2013. [https://www.who.int/violence\\_injury\\_](https://www.who.int/violence_injury_prevention/publications/road_traffic/make_walking_safe.pdf?ua=1&ua=1) [prevention/publications/road\\_traf](https://www.who.int/violence_injury_prevention/publications/road_traffic/make_walking_safe.pdf?ua=1&ua=1)fic/make\_walking\_safe.pdf?ua¼[1](https://www.who.int/violence_injury_prevention/publications/road_traffic/make_walking_safe.pdf?ua=1&ua=1)&[amp;](https://www.who.int/violence_injury_prevention/publications/road_traffic/make_walking_safe.pdf?ua=1&ua=1)
-- <span id="page-5-21"></span>[ua](https://www.who.int/violence_injury_prevention/publications/road_traffic/make_walking_safe.pdf?ua=1&ua=1)¼[1.](https://www.who.int/violence_injury_prevention/publications/road_traffic/make_walking_safe.pdf?ua=1&ua=1) 23. GBD 2015 Mortality and Causes of Death Collaborators. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1459e1544. [https://doi.org/10.1016/](https://doi.org/10.1016/S0140-6736(16)31012-1) [S0140-6736\(16\)31012-1](https://doi.org/10.1016/S0140-6736(16)31012-1).
-- <span id="page-5-22"></span>24. GBD 2015 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1545e1602. [https://doi.org/](https://doi.org/10.1016/S0140-6736(16)31678-6) [10.1016/S0140-6736\(16\)31678-6.](https://doi.org/10.1016/S0140-6736(16)31678-6)
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Zotero/001_artiklid/Road Safety Indicators/hiseliusestimatingrelationshipbetweenaccident2004.md

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-category: literaturenote
-citekey: hiseliusestimatingrelationshipbetweenaccident2004
-title: Estimating the relationship between accident frequency and homogeneous and inhomogeneous traffic flows
-authors: "Hiselius, Lena Winslott"
-year: 2004
-date: 2004-00-00 2004
-doi: 10.1016/j.aap.2003.11.002
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2003.11.002"
-zotero_key: WHT97JG4
-zotero_storage: VA46EBG7
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Hiselius, Lena Winslott"
-status: converted
----
-![](_page_0_Picture_0.jpeg)
-
-Accident Analysis and Prevention 36 (2004) 985–992
-
-![](_page_0_Picture_2.jpeg)
-
-# Estimating the relationship between accident frequency and homogeneous and inhomogeneous traffic flows
-
-# Lena Winslott Hiselius∗
-
-*Department of Economics or Department of Technology and Society, Lund University, P.O. Box 118, Lund 22100, Sweden* Received 5 January 2002; received in revised form 19 November 2003; accepted 24 November 2003
-
-#### **Abstract**
-
-This paper estimates the relationship between accident frequency and the traffic flow empirically treating the hourly traffic flow in two different ways, as consisting of homogenous vehicles and as consisting of cars and lorries. Rural roads in Sweden are studied using Poisson and Negative Binominal regression models. It is found that important information is lost if no consideration is taken to differences between vehicle types when estimating the marginal effect of the traffic flow. The accident rate decreases when the traffic flow is treated as if homogeneous. However, when cars are studied separately the result suggests that the accident rate is constant or increases. The result with respect to lorries is reversed, indicating a decreasing number of accidents as the number of lorries increases. © 2004 Elsevier Ltd. All rights reserved.
-
-*Keywords:* Accident prediction models; Road accidents; Hourly traffic flow; Poisson; Negative Binomial
-
-# **1. Introduction**
-
-Studies that analyse the relationship between accident frequency and traffic flows show a great variation in their results. An early report from [Vickery \(1968\)](#page-7-0) suggests that the marginal accident rate is 1.5 times the average accident rate. On the other hand, [Vitaliano and Held \(1991\)](#page-7-0) cannot detect any significant increase in the accident rate as the traffic flow increases and, according to [Hauer and Bamfo \(1997\), a](#page-7-0)nd a majority of the results reviewed in [Ardekani et al. \(1997\),](#page-7-0) the accident rate even decreases with an increasing number of vehicles. Furthermore, only a few empirical studies analyse the effect of the traffic flow separated for different road user groups. One example is the study by [Jovanis and](#page-7-0) [Chang \(1986\), w](#page-7-0)here they found that the accident frequency increased with the number of car and lorry miles travelled. However, it is difficult to distinguish the marginal effect of another car or lorry as the number of miles travelled varies in their study.
-
-There is, thus, an interest in studying the effect of the traffic flow more thoroughly, and the aim of this article is to estimate the relationship between the accident frequency and the traffic flow treated as both homogeneous, i.e. consisting of homogeneous vehicles, and inhomogeneous, i.e.
-
-*E-mail address:* lena.hiselius@tft.lth.se (L.W. Hiselius).
-
-consisting of cars and lorries. The data is restricted to accidents on sections in rural areas of Sweden.
-
-### **2. Model**
-
-The model seeks to describe the relationship between the number of vehicles per hour and the accident frequency. It is developed under the assumption that a limited and homogeneous road system is studied and only the traffic flow affects the number of accidents. In the first case the flow of vehicles per hour, *q*, is assumed to be homogeneous, and with all vehicles influencing the occurrence of an accident the same way. In the second case the traffic flow is defined as the number of cars and lorries per hour, *q*<sup>c</sup> and *q*l. The accident frequency, *Z*, is calculated as the expected number of accidents per unit of time and kilometer in order to apply the model to time periods and road systems of different length.
-
-### **3. Data**
-
-Data is collected from 83 road sections in rural areas of Sweden, where the number of passing vehicles is continuously counted by the Swedish National Road Administration. The assumption is made that the counted traffic flow at a stationary place is valid along the section. Information is collected on police reported accidents with personal
-
-<sup>∗</sup> Fax: +46-46-12-32-72.
-
-Table 1 Number of accidents in the data set
-
-| Road<br>type |       | Number of accidents | Total number of |                   |  |
-|--------------|-------|---------------------|-----------------|-------------------|--|
-|              | Total | Single-vehicle      | Multi-vehicle   | daytime accidents |  |
-| I            | 59    | 29                  | 30              | 49                |  |
-| II           | 83    | 52                  | 31              | 51                |  |
-| III          | 179   | 107                 | 72              | 111               |  |
-| IV           | 186   | 121                 | 65              | 129               |  |
-
-casualties that occurred on the studied road sections from 1989 to the middle of 1995. Given the time and date of the accidents, the hourly traffic flow that prevailed at the time of each accident is obtained. Furthermore, in order to calculate traffic flow frequencies, information on the number of hours that each traffic flow was observed during 1990 is also collected on the assumption that this is a representative year.
-
-Only accidents occurring on sections without intersections are included. Accidents involving animals are excluded together with accidents that may be considered specific for each road section. Since it is not possible to obtain information on driving speed, the analysis will be made bearing in mind that the estimated effect of the traffic flow may also be an effect of speed adjustment. Moreover, there are several other factors likely to influence the occurrence of accidents, e.g. weather, road conditions, type of vehicle and drivers' characteristics. In order to take some of these factors into account, daylight accidents are studied separately.
-
-Four road types are distinguished. Road type I with speed limit 70 km/h and road width 6–9.7 m, road type II with speed limit 90 km/h and road width 6–7.9 m, road type III with speed limit 90 or 110 km/h and road width 8–13 m, without separated road lanes, and road type IV, motorways, with speed limit 90 or 110 km/h. The numbers of accidents that occurred on the studied sections are presented in Table 1.
-
-For the road types I, II and III the traffic flow is counted as the number of vehicles per hour driving in both directions. Thus, there is no information on the traffic flow per road lane. This is a problem since depending on the distribution of the traffic flow, different types of accidents may occur. The same situation occurs when studying road type IV. Even though the traffic flow is counted for each direction for this road type, there is no information on the traffic in each lane.
-
-### **4. Accident frequency**
-
-In order to analyse the accident frequency, *Z*, the distribution model of the number of accidents, *Y*, is first discussed. Several authors use a Poisson distribution to approximate the number of accidents occurring during a certain time interval, e.g. [Hall \(1986\)](#page-7-0) and [Nicholson and Wong \(1993\).](#page-7-0) The assumption is based on the fact that although the total number of accidents is reasonably stable over a period, each accident is unpredictable. A reasonable assumption is furthermore that accidents are independent events. However, it may be argued that individual collisions in a multi-collision accident are not mutually independent events. This problem is avoided here, since a traffic accident in this study is defined without consideration being given to the number of vehicles involved. Let *Y*<sup>i</sup> denote the number of accidents occurring on a specific road site during a given time period. If we assume that the number of accidents follows a Poisson distribution with expected value, and thus variance, equal to λi, the probability that the number of accidents during this period will be equal to *y*i, may be written as
-
-$$P(Y_i = y_i) = \frac{\lambda_i^{y_i} e^{-\lambda_i}}{y_i!}$$
-  $y_i = 0, 1, 2, 3, ...$ 
-
-There is, however, no reason to believe that λi is same at all sites, and there will usually be a variation of the λi around a mean, λ. The variation of the *Y*<sup>i</sup> (the number of accidents occurring at different sites) will thus comprise both the variation of *y*<sup>i</sup> around λi and the variation of λi around λ. The variance will therefore tend to be larger than the mean, i.e. we can observe so called over dispersion with respect to the Poisson distribution. The variation in λi is often assumed to be Gamma distributed. Assuming λi to have a Gamma distributed parameter κ, it can be shown that the number of accidents, *Y*, follows a Negative Binomial distribution, e.g. in [Kulmala \(1995\).](#page-7-0) The probability density function for *Y* can now be written as
-
-$$P(Y = y) = \frac{\Gamma(\kappa + y)}{\Gamma(\kappa)y!} \left(\frac{\kappa}{\kappa + \lambda}\right)^{\kappa} \left(\frac{\lambda}{\kappa + \lambda}\right)^{y}$$
-$$y = 0, 1, 2, 3, \dots$$
-
-and variance
-
-$$\sigma^2 = \lambda + \frac{\lambda^2}{\kappa}$$
-
-The Negative Binomial distribution is thus an extension of the Poisson distribution, where the gamma parameter, κ, determines the shape of the distribution. As κ → ∞, the rate of over dispersion declines and the distribution approaches the Poisson.
-
-So far the distribution model of the number of accidents has been discussed. However, when analysing accident frequency, there is also need for a regression model that can describe this phenomenon. An exponential function is a common formulation, since this function ensures that the expected number of accidents is a positive number. Furthermore, measuring the explanatory variables on the logarithmic scale, Gj = ln(Xj), the accident frequency may be written as a multiplicative function for which the value of the exponents can be estimated directly
-
-$$E(Y) = \exp\left[\sum \beta_j \ln(X_j)\right] = \Pi X_j^{\beta_j}$$
-
-The result of a regression model is, however, often dependent on the choice of model function, [Hauer and Bamfo](#page-7-0) [\(1997\). U](#page-7-0)sing an exponential regression model, as described
-
-<span id="page-2-0"></span>Table 2 Statistics on traffic flow data
-
-| Road type | Number of traffic flow intervals |                               | Average traffic flow        |                               |         | Maximum traffic flow        |                               |         |  |
-|-----------|----------------------------------|-------------------------------|-----------------------------|-------------------------------|---------|-----------------------------|-------------------------------|---------|--|
-|           | Homogeneous<br>traffic flow      | Inhomogeneous<br>traffic flow | Homogeneous<br>traffic flow | Inhomogeneous<br>traffic flow |         | Homogeneous<br>traffic flow | Inhomogeneous<br>traffic flow |         |  |
-|           |                                  |                               |                             | Cars                          | Lorries |                             | Cars                          | Lorries |  |
-| I         | 98                               | 408                           | 153                         | 139                           | 12      | 2712                        | 2328                          | 312     |  |
-| II        | 38                               | 159                           | 94                          | 81                            | 8       | 987                         | 938                           | 132     |  |
-| III       | 131                              | 595                           | 250                         | 232                           | 24      | 1687                        | 1645                          | 267     |  |
-| IV        | 121                              | 458                           | 550                         | 498                           | 55      | 3088                        | 2937                          | 326     |  |
-
-Table 3 Statistics on total number of accidents (Tot), single-vehicle (Sing) and multi-vehicle accidents (Mult)
-
-| Road type |                             |      | Number of accidents per traffic flow interval, average | Number of accidents per traffic flow interval, variance |      |                             |     |      |                               |     |      |      |
-|-----------|-----------------------------|------|--------------------------------------------------------|---------------------------------------------------------|------|-----------------------------|-----|------|-------------------------------|-----|------|------|
-|           | Homogeneous traffic<br>flow |      |                                                        | Inhomogeneous<br>traffic flow                           |      | Homogeneous traffic<br>flow |     |      | Inhomogeneous traffic<br>flow |     |      |      |
-|           | Tot                         | Sing | Mult                                                   | Tot                                                     | Sing | Mult                        | Tot | Sing | Mult                          | Tot | Sing | Mult |
-| I         | 0.6                         | 0.3  | 0.3                                                    | 0.1                                                     | 0.1  | 0.1                         | 1.4 | 0.7  | 0.4                           | 0.4 | 0.2  | 0.1  |
-| II        | 2.2                         | 1.4  | 0.8                                                    | 0.5                                                     | 0.3  | 0.2                         | 4.4 | 3.8  | 0.7                           | 1.7 | 0.9  | 0.4  |
-| III       | 1.4                         | 0.8  | 0.5                                                    | 0.3                                                     | 0.2  | 0.1                         | 4.5 | 2.5  | 0.9                           | 0.6 | 0.3  | 0.2  |
-| IV        | 1.5                         | 1.0  | 0.5                                                    | 0.4                                                     | 0.3  | 0.1                         | 3.8 | 2.2  | 0.7                           | 1.0 | 0.5  | 0.2  |
-
-previously, restricts the model to the description of monotonic functions, i.e. the functions may be either increasing or decreasing, not both. The use of this model function is supported by studying the data visually and by analysing the residuals.
-
-### *4.1. Variables and regression models*
-
-In this study, the relationship between the accident frequency and the traffic flow is estimated empirically. In order to apply the result to time periods and road systems of different length, the accident frequency, *Z*, is defined as the expected number of accidents, *E*(*Y*), per hour and kilometre. A parameter, *H*, is also defined, see [Appendix A.](#page-7-0) This parameter describes the exposure, i.e. the number of hours and kilometres that each road type has been studied.
-
-The accident frequency cannot be estimated as a function of the traffic flow directly, since the variable describing the accident frequency is not Poisson or Negative Binomial distributed. The regression model is therefore rewritten as E(Yt) = Z<sup>t</sup> ×H<sup>t</sup> so that the accident frequency may be estimated separately as a function of the traffic flow. The complete regression model for the case of homogeneous traffic flow is estimated as
-
-$$E(Y_1) = \exp[\beta_1 + \beta_2 \ln(q_1) + \beta_3 \ln(H_1)] = [e^{\beta_1} \times (q_1)^{\beta_2}]H_1$$
- with the restriction,  $\beta_3 = 1$ . When treating the traffic flow as consisting of cars and lorries, the following regression model is estimated
-
-$$\begin{split} E(Y_t) &= \exp[\beta_1 + \beta_2 \ln(q_c)_t + \beta_3 \ln(q_l)_t + \beta_4 \ln(H)_t] \\ &= \left| e^{\beta_1} \times (q_c)_t^{\beta_2} \times (q_l)_t^{\beta_3} \right| H_t \end{split}$$
-
-with the restriction β<sup>4</sup> = 1.
-
-The regression models are estimated applying both the Poisson and the Negative Binomial distribution. The statistical program LIMDEP version 7 is used. In order to evaluate the goodness-of-fit, four indicators are studied; the scaled deviance statistic, the over dispersion parameter, κ, estimated in the Negative Binomial regression model and two test functions for over dispersion.
-
-# **5. Results**
-
-The regression analysis of this study is based on aggregates of hours with similar traffic flows, see [Appendix A.](#page-7-0) Traffic flow statistics per road type are presented in Table 2 together with the number of traffic flow intervals used. In Table 3, statistics on the aggregated number of accidents per traffic flow interval is presented for various accident types1. The average number of accidents per traffic flow interval is generally low when inhomogeneous traffic flows or different accidents types are studied.
-
-The results from the regression analysis generally suggest a good fit for both the Poisson and the Negative Binominal models. Distributional assumptions do not seem to affect the results since there are only small differences. The test statistics that are used for over dispersion occasionally contradict the size of the dispersion parameter. Contradictions tend to arise when the scaled deviance is almost the same for the Poisson and the Negative Binominal regression models, suggesting a good fit for both. Since the estimated coefficients are practically the same, only the results of the
-
-<sup>1</sup> Statistics for daytime traffic flows and accidents are left out since these statistics corresponds largely to the presented statistics.
-
-<span id="page-3-0"></span>
-
-<span id="page-4-0"></span>Table 6Expected number of accidents occurring all day and in daylight, inhomogeneous traffic flowa
-
-|                                                                                                                                  | Ex<br>ted<br>mb<br>of<br>pec<br>nu<br>er                                                                                  | ide<br>nts<br>acc                                                                                                         |                                                                                                                           |                                                                                                                                     | Ex<br>ted<br>mb<br>of<br>ide<br>in<br>day<br>lig<br>ht<br>nts<br>pec<br>nu<br>er<br>acc                                   |                                                                                                                           |                                                                                                                           |                                                                                                                            |  |
-|----------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------|--|
-|                                                                                                                                  | Ro<br>ad<br>e I<br>typ                                                                                                    | Ro<br>ad<br>e I<br>I<br>typ                                                                                               | Ro<br>ad<br>e I<br>II<br>typ                                                                                              | Ro<br>ad<br>e I<br>V<br>typ                                                                                                         | Ro<br>ad<br>e I<br>typ                                                                                                    | Ro<br>ad<br>e I<br>I<br>typ                                                                                               | Ro<br>ad<br>e I<br>II<br>typ                                                                                              | Ro<br>ad<br>e I<br>V<br>typ                                                                                                |  |
-| Co<br>nst<br>ant<br>ln(<br>)<br>qc<br>ln(<br>)<br>ql<br>ln(<br>H)                                                                | b<br>−3<br>1.5<br>4 (<br>−1<br>6.4<br>2)<br>b<br>3.6<br>2 (<br>7.1<br>0)<br>b<br>−2<br>.66<br>(−<br>5.4<br>0)<br>1.0<br>0 | b<br>−2<br>3.4<br>8 (<br>−3<br>3.0<br>3)<br>b<br>1.4<br>3 (<br>7.2<br>8)<br>b<br>−0<br>.94<br>(−<br>5.6<br>9)<br>1.0<br>0 | b<br>−2<br>1.1<br>6 (<br>−5<br>0.5<br>6)<br>b<br>1.0<br>4 (<br>9.9<br>6)<br>b<br>−0<br>.77<br>(−<br>9.0<br>7)<br>1.0<br>0 | b<br>−2<br>2.8<br>4 (<br>−3<br>5.3<br>8)<br>b<br>1.9<br>0 (<br>13.<br>04<br>)<br>b<br>−1<br>.84<br>(−<br>14.<br>44<br>)<br>1.0<br>0 | b<br>−3<br>1.1<br>4 (<br>−1<br>5.6<br>5)<br>b<br>3.6<br>0 (<br>6.9<br>5)<br>b<br>−2<br>.66<br>(−<br>5.4<br>1)<br>1.0<br>0 | b<br>−2<br>4.1<br>8 (<br>−2<br>3.8<br>4)<br>b<br>1.6<br>8 (<br>6.1<br>8)<br>b<br>−1<br>.26<br>(−<br>5.4<br>0)<br>1.0<br>0 | b<br>−2<br>1.4<br>2 (<br>−2<br>9.9<br>7)<br>b<br>1.1<br>6 (<br>7.3<br>6)<br>b<br>−0<br>.92<br>(−<br>7.8<br>5)<br>1.0<br>0 | b<br>−1<br>9.5<br>2 (<br>−2<br>1.6<br>0)<br>b<br>1.3<br>0 (<br>7.0<br>2)<br>b<br>−1<br>.61<br>(−<br>11.<br>39)<br>1.0<br>0 |  |
-| d.f<br>Sc<br>ale<br>d d<br>evi<br>atio<br>n<br>LM<br>-st<br>at<br>t-S<br>isti<br>1<br>tat<br>cs<br>t-S<br>isti<br>2<br>tat<br>cs | 40<br>4<br>56<br>.31<br>61<br>c<br>.97<br>0.6<br>0<br>3.6<br>9c                                                           | 15<br>5<br>35<br>.44<br>17.<br>52c<br>−1<br>.21<br>0.0<br>3                                                               | 59<br>1<br>16<br>0.2<br>3<br>12.<br>55c<br>1.4<br>6<br>0.9<br>4                                                           | 45<br>4<br>15<br>3.9<br>7<br>14.<br>40<br>c<br>−0<br>.39<br>0.6<br>3                                                                | 39<br>6<br>55<br>.49<br>54<br>c<br>.67<br>0.7<br>4<br>4.2<br>8c                                                           | 15<br>4<br>26<br>.54<br>7.4<br>6<br>−2<br>.11<br>c<br>−3<br>.26<br>c                                                      | 57<br>1<br>11<br>3.8<br>8<br>5.5<br>4<br>−2<br>.11<br>c<br>−2<br>.43<br>c                                                 | 45<br>4<br>17<br>8.1<br>9<br>19<br>.70<br>c<br>1.2<br>5<br>1.7<br>9                                                        |  |
-
-Values in parenthesis are *<sup>t</sup>*-statistics.
-
-Table 7Expected number of single and multi-vehicle accidents, inhomogeneous traffic flowa
-
-|                                      | Ex<br>ted<br>mb<br>of<br>pec<br>nu<br>er | sin<br>le-<br>veh<br>icl<br>cci<br>den<br>ts<br>g<br>e a |                                          |                                          | Ex<br>ted<br>mb<br>of<br>lti-<br>veh<br>icl<br>cci<br>den<br>ts<br>pec<br>nu<br>er<br>mu<br>e a |                                          |                                          |                                          |  |
-|--------------------------------------|------------------------------------------|----------------------------------------------------------|------------------------------------------|------------------------------------------|-------------------------------------------------------------------------------------------------|------------------------------------------|------------------------------------------|------------------------------------------|--|
-|                                      | Ro<br>ad<br>e I<br>typ                   | Ro<br>ad<br>e I<br>I<br>typ                              | Ro<br>ad<br>e I<br>II<br>typ             | Ro<br>ad<br>e I<br>V<br>typ              | Ro<br>ad<br>e I<br>typ                                                                          | Ro<br>ad<br>e I<br>I<br>typ              | Ro<br>ad<br>e I<br>II<br>typ             | Ro<br>ad<br>e I<br>V<br>typ              |  |
-| Co<br>nst<br>ant                     | b<br>−2<br>9.0<br>0 (<br>−1<br>2.5<br>7) | b<br>−2<br>2.8<br>5 (<br>−2<br>7.2<br>3)                 | b<br>−2<br>0.3<br>6 (<br>−4<br>2.9<br>9) | b<br>−2<br>2.0<br>8 (<br>−3<br>0.0<br>4) | b<br>−3<br>7.4<br>1 (<br>−1<br>0.8<br>0)                                                        | b<br>−2<br>6.5<br>8 (<br>−2<br>0.1<br>8) | b<br>−2<br>4.9<br>9 (<br>−2<br>9.0<br>2) | b<br>−2<br>6.8<br>9 (<br>−2<br>0.4<br>7) |  |
-| ln(<br>)<br>qc                       | b<br>2.9<br>0 (<br>4.6<br>5)             | b<br>1.1<br>4 (<br>4.8<br>2)                             | b<br>0.7<br>6 (<br>6.1<br>0)             | b<br>1.7<br>0 (<br>9.9<br>2)             | b<br>4.6<br>9 (<br>5.2<br>3)                                                                    | b<br>1.9<br>6 (<br>5.5<br>7)             | b<br>1.6<br>1 (<br>8.3<br>8)             | b<br>2.4<br>0 (<br>8.6<br>4)             |  |
-| ln(<br>)<br>ql                       | b<br>−2<br>.29<br>(−<br>3.8<br>3)        | b<br>−0<br>.85<br>(−<br>4.3<br>4)                        | b<br>−0<br>.73<br>(−<br>7.0<br>9)        | b<br>−1<br>.82<br>(−<br>11.<br>91<br>)   | b<br>−3<br>.15<br>(−<br>3.6<br>7)                                                               | b<br>−1<br>.11<br>(−<br>3.6<br>8)        | b<br>−0<br>.83<br>(−<br>5.6<br>9)        | b<br>−1<br>.90<br>(−<br>8.2<br>3)        |  |
-| ln(<br>H)                            | 1.0<br>0                                 | 1.0<br>0                                                 | 1.0<br>0                                 | 1.0<br>0                                 | 1.0<br>0                                                                                        | 1.0<br>0                                 | 1.0<br>0                                 | 1.0<br>0                                 |  |
-| d.f                                  | 40<br>4                                  | 15<br>5                                                  | 59<br>1                                  | 45<br>4                                  | 40<br>4                                                                                         | 15<br>5                                  | 59<br>1                                  | 45<br>4                                  |  |
-| Sc<br>ale<br>d d<br>evi<br>atio<br>n | 59<br>.34                                | 47<br>.30                                                | 22<br>8.5<br>5                           | 17<br>4.2<br>7                           | 78<br>.34                                                                                       | 49<br>.39                                | 19<br>2.3<br>0                           | 19<br>0.3<br>1                           |  |
-| LM<br>-st<br>at                      | c<br>13<br>.54                           | 95c<br>13.                                               | 8.9<br>3                                 | c<br>11<br>.15                           | 4c<br>9.9                                                                                       | 7.1<br>9                                 | c<br>13<br>.67                           | c<br>56<br>.60                           |  |
-| t-S<br>isti<br>1<br>tat<br>cs        | 0.0<br>2                                 | −0<br>.03                                                | 0.0<br>3                                 | −0<br>.04                                | −0<br>.01                                                                                       | −0<br>.05                                | 0.0<br>4                                 | 0.0<br>2                                 |  |
-| t-S<br>isti<br>2<br>tat<br>cs        | 0c<br>0.3                                | 0.0<br>2                                                 | 0c<br>0.2                                | −0<br>.02                                | 0.1<br>2                                                                                        | c<br>−0<br>.22                           | −0<br>.09                                | 0.0<br>8                                 |  |
-
-Values in parenthesis are *<sup>t</sup>*-statistics.
-
-Significantly different from zero at the 5% level.
-
-The null-hypothesis of over dispersion is significantly rejected at the 5% level.
-
-Significantly different from zero at the 5% level.
-
-The null-hypothesis of over dispersion is significantly rejected at the 5% level.
-
-<span id="page-5-0"></span>![](_page_5_Figure_2.jpeg)
-
-Fig. 1–4. Number of accidents per hour and kilometre (×1E7) at averge number of cars per hour +/−20%. *Remark*: The number of dots does not correspond to the number of accidents since more than one accident have occurred at some traffic flows. The total number of accidents in the figure of road type I–IV is 8, 18, 15 and 37.
-
-![](_page_5_Figure_4.jpeg)
-
-Fig. 5–8. Number of accidents per hour and kilometre (×1E7) at averge number of lorries per hour +/−20%. *Remark*: The total number of accidents in the figure of road type I–IV is 16, 10, 21 and 14.
-
-Poisson regression model will be presented. A complete report of the results is to be found in [Winslott \(1998\).](#page-7-0)
-
-### *5.1. Homogeneous traffic flow*
-
-The results of the regression analysis are presented in [Table 4](#page-3-0) for accidents occurring all day and in daylight only. All estimated parameter values are significantly different from zero. Furthermore, for all road types, except for road type I, the estimated exponent is significantly different from 1. Thus, we may reject the hypothesis that the expected accident frequency increases in proportion with the traffic flow for these road types, i.e. that the accident rate is constant. Instead, an additional vehicle lowers the accident rate and increases the traffic safety.
-
-For the road types I, II and III the estimated parameters are lower than when studying accidents that have occurred throughout the day. The difference is, however, not significant. The result for road type IV suggests that the model of the expected number of accidents occurring in daylight is not a correct choice. The estimated value of the exponent should be interpreted with caution.<sup>2</sup>
-
-The results for single and multi-vehicle accidents are presented in [Table 5.](#page-3-0) The results differ in some respects from the case when all accident types are studied together. The estimated values of the exponent of the traffic flow are significantly larger for multi-vehicle accidents than single-vehicle accidents. For multi-vehicle accidents, we cannot reject the hypothesis that the expected number of multi-vehicle accidents increases in proportion with the traffic flow. However,
-
-<sup>2</sup> The chosen regression model may only describe a monotonic function, and when the graph of the number of daytime accidents versus the number of vehicles per hour is studied for road type IV, there is no sign of a monotonic relationship, either increasing or decreasing.
-
-for single-vehicle accidents, the estimated exponents of the traffic flow are significantly less than one for all road types.
-
-### *5.2. Inhomogeneous traffic flow*
-
-[Table 6](#page-4-0) shows the results for all accidents and accidents occurring in daylight only when separating the traffic flow for cars and lorries. The estimated coefficients are significantly different from zero for all road types. For road type I and IV the exponents are significantly different from 1, indicating that the expected number of accidents increases more than proportionally with the number of cars per hour, i.e. that the accident rate increases. However, for the road types II and III the null-hypothesis cannot be rejected. The estimated exponent for the flow of lorries is generally negative and different from zero. This suggests that an increasing number of lorries per hour will lower the expected number of accidents independently of the flow of cars. When studying accidents in daylight, the results are not significantly different.
-
-The estimates when studying single and multi-vehicle accidents are presented in [Table 7.](#page-4-0) The value of the exponent for the number of cars per hour is generally different from 1 when both single and multi-vehicle accidents are studied. Accordingly, the expected number of accidents increases more than proportionally for both single and multi-vehicle accidents. However, the exponent for cars is significantly lower for single-vehicle accidents than for multi-vehicle accidents. The exponent for the number of lorries per hour is also negative here. The value of the exponent is generally, however not significantly, lower when multi-vehicle accidents are studied compared to when all accidents are studied together.
-
-The difference between the studied road types is also tested. The results for the road types I and IV are significantly higher and lower than the results for the road type II and III, respectively. This corresponds to the results when all accidents are studied together.
-
-The result using an inhomogeneous traffic flow differs greatly to that of homogeneous traffic flow. Since the estimates raise certain questions the data is also studied visually. [Figs. 1–4](#page-5-0) present the number of accidents per hour and kilometre in relation to the flow of lorries per hour. [Figs. 5–8](#page-5-0) present the number of accidents per hour and kilometre in relation to the flow of cars per hour. The figures are drawn for the average number of cars ([Figs. 1–4\) a](#page-5-0)nd lorries ([Figs. 5–8\)](#page-5-0) per hour for each road type±20%.<sup>3</sup> Generally, there are few accidents in the data set and when restricting the analysis to certain traffic flows, the number of accidents is reduced even further. In spite of a limited number of observations, the plots can be argued to indicate the type of relation between the accident frequency and the flow of cars and lorries.
-
-Data presented in [Figs. 1–4](#page-5-0) indicates that the number of accidents is constant or decreasing with an increasing number of lorries on the road, whereas in [Figs. 5–8](#page-5-0) the number of accidents is increasing with an increasing number of cars. Consequently, the visual analysis is not in conflict with the estimates received in the regression analysis.
-
-### **6. Discussion**
-
-The estimated relationship between the expected number of accidents per hour and kilometre, and the traffic flow differs considerably depending on whether different types of traffic modes are considered or not, i.e. if a homogeneous or a inhomogeneous traffic flow analysis is carried out. Differences appear both in relation to accidents occurring throughout the day, accidents occurring in daylight, singleand multi-vehicle accidents. In the homogeneous traffic flow analysis regarding accidents that have occurred throughout the day, we reject the null-hypothesis that the expected number of accidents increases proportionally with the traffic flow for the road types II, III and IV. The value of the exponent is less than 1. This result is in line with a number of studies reviewed in [Satterthwaite \(1981\),](#page-7-0) [Ardekani et al. \(1997\)](#page-7-0) and [Hauer and Bamfo \(1997\).](#page-7-0)
-
-As cars constitute the main part of the traffic flow, one may expect the outcome, with respect to the number of cars per hour, to be similar to that of the homogeneous traffic flow analysis. However, here the suggestion is that the expected number of accidents increases in proportion or more, to the number of cars per hour. Furthermore, studying the effect of an increasing number of lorries one might expect that the presence of more lorries on the road would increase the number of accidents since the incidence of possibly dangerous overtaking manoeuvres increases. The result of this study indicates, however, that at a given number of cars per hour, the expected number of accidents will decrease with increasing number of lorries per hour. This safety effect of lorries may be regarded as counter intuitive at first glance. It is, however, possible that the number of accidents decreases as an effect of a speed reduction as the speed limit for lorries is lower than for cars. An increasing number of lorries per hour will accordingly slow down the average speed. The result with respect to the flow of lorries may also be a result of people's unease when sharing the road-space with a lorry causing the attention to increase. There is, furthermore, a possibility that the number of lorries per hour is correlated with an unknown factor that lowers the number of accidents. Hours with many lorries may coincide with hours with good road conditions and hours when experienced drivers are driving.
-
-It is obvious that there are shortcomings in analysis indicating that some of the results in this paper can be questioned. For instance, the model for inhomogeneous traffic flow analysis may be considered as a better model in relation to differences between traffic modes. As this model can be assumed to have more explanatory power, no over dispersion is expected in the Poisson regression model. However, in the inhomogeneous traffic flow model, over dispersion becomes
-
-<sup>3</sup> The same pattern is given irrespective of the flow of cars and lorries used.
-
-<span id="page-7-0"></span>evident, while in the homogeneous traffic flow model, when the traffic flow is treated without consideration to different types of traffic modes, there is generally no over dispersion.
-
-In addition, the accident sample is rather small even though data on accidents for 6.5 years is used. According to [Table 3, t](#page-2-0)he average numbers of accidents per traffic flow interval is generally less than 1, indicating low statistical power in especially the analysis of inhomogeneous traffic flows. Random fluctuations may then be a source of error. The maximum hourly flow of lorries is furthermore a fraction of that of cars which may lead to unstable results.<sup>4</sup>
-
-Consequently, the marginal effect of the flow of different vehicle types may have been miscalculated and exaggerated in the regression analysis due to shortcomings in the data set and in the regression models. However, visual analysis of the inhomogeneous traffic flow data, presented in [Figs. 1–8,](#page-5-0) indicates that the flow of lorries affects the accident frequency differently than the flow of cars. The result implies that important information is lost if no consideration is taken to vehicle type. Literature of today shows limited interest in studying traffic safety models distinguishing between different vehicle types, though. This is a shortcoming since the characteristics of our vehicles and their driving pattern differ in a number of respects, undoubtedly affecting the accident frequency in various ways. Other model structures ought to be applied in future research including, for instance, speed as an explanatory variable.
-
-### **Acknowledgements**
-
-Finical support from the Swedish National Road Administration is gratefully acknowledged. I am grateful for insightful suggestions from Risto Kulmala, and Karin Brundell-Freij. Any errors are my sole responsibility.
-
-### **Appendix A**
-
-Traffic flow data from the stationary counters are applied to calculate traffic flow frequencies for each road section, i.e. set of hours of different traffic volumes. An interval level of 25 vehicles per hour is used when treating the traffic flow as homogeneous. When treating the traffic flow as inhomogeneous, an interval level of 25 cars per hour is used together with an interval level of five lorries per hour. The traffic flow frequency for interval *t* is denoted by τt. For each road type the number of kilometres, η, is calculated for each section, i.e. ηr kilometres for road type r = I, II, III and IV. The number of kilometres is then multiplied by the traffic flow frequencies for the studied road section. The analysis is based on four road types and each road type is represented on several road sections. If *s* is the number of sections of road type *r*, the exposure, *H*, is calculated as the total number of hours and kilometres studied.
-
-$$H_{r,t} = \sum_{s=1}^{n} \eta_{r,s} \times \tau_{t,s}$$
-  
-for  $t = 1, \dots, p$  and  $r = I, \dots, IV$ 
-
-The traffic flow variables are calculated as the average number of vehicles, *q*, and the average number of cars and lorries per hour, *q*<sup>c</sup> and *q*l, for each traffic flow interval.
-
-The number of accidents throughout the day and in daylight is calculated by aggregation using the same traffic flow interval as when calculating the traffic flow frequencies. These variables are used as dependent variables. When calculating the exposure for daytime traffic, frequencies of traffic flows observed between 6 a.m. and 6 p.m. are used.
-
-## **References**
-
-Ardekani, S., Hauer, E., Jamei, B., 1997. In: Gartner, N. et al. (Eds.), Traffic Impact Models in Traffic Flow Theory—A State-of-the-Art Report, Oak Ridge National Laboratory.
-
-Hall, R.D.,1986. Accidents at Four-arm Single Carriageway Urban Traffic Signals. Transport and Road Research Laboratory Contractor Report, 65.
-
-Hauer, E., Bamfo, J., 1997. In: Proceedings of the ICTCT conference on Two Tools for Finding what Function Links the Dependent Variable to the Explanatory Variables, Lund.
-
-Jovanis, P.P., Chang, H.L., 1986. Modelling the relationship of accidents to miles travelled. Transportation Res. Rec. 1068, 42–51.
-
-Kulmala, R., 1995. Safety at Rural Three- and Four-arm Junctions, VTT-Publications, 233.
-
-Nicholson, A., Wong, Y.D., 1993. Are accidents Poisson distributed? a statistical test. Accid. Anal. Prev. 1, 91–97.
-
-Satterthwaite, P.S., 1981. A Survey of Research into Relationship Between Traffic Accidents and Traffic Volumes, Transport and Road Research Laboratory Contractor Report, 692.
-
-Vickery, W.S., 1968. Automobile Accidents, Tort Law, Externalities and Insurance. Journal of Law and Contemporary problems 33, 464–484. Vitaliano, D.F., Held, J., 1991. Road Accident External Effects: an
-
-Empirical Assessment. Applied Economics 23, 373–378. Winslott, L., 1998. The external costs of traffic accidents: an empirical analysis of the traffic flow. Department of Economics Working Paper Series, 1.
-
-<sup>4</sup> In this type of data one could also expect a correlation between the flow of cars and lorries, leading to spurious effects in the regression analysis. No correlation can be detected, however. Even though high flows of lorries are more frequently found at high flows of cars there are also hours in the data set with high flows of lorries and low flows of cars.

Zotero/001_artiklid/Road Safety Indicators/huangeffectsdaylightsavingtime2010.md

@@ -1,353 +0,0 @@
----
-category: literaturenote
-citekey: huangeffectsdaylightsavingtime2010
-title: The effects of daylight saving time on vehicle crashes in Minnesota
-authors: "Huang, Arthur; Levinson, David"
-year: 2010
-date: 2010-00-00 2010
-doi: 10.1016/j.jsr.2010.10.006
-publication: Journal of Safety Research
-url: "https://doi.org/10.1016/j.jsr.2010.10.006"
-zotero_key: BJ9EXQDF
-zotero_storage: 8NGEZ4AN
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Huang, Arthur"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-# Journal of Safety Research
-
-j o u r n a l h om e p a g e : www. e l s ev i e r. c om / l o c a t e / j s r
-
-![](_page_0_Picture_5.jpeg)
-
-# The effects of daylight saving time on vehicle crashes in Minnesota
-
-Arthur Huang a, ⁎, David Levinson <sup>b</sup>
-
-- <sup>a</sup> University of Minnesota, Department of Civil Engineering, 500 Pillsbury Drive SE, Minneapolis, MN 55455 USA
-- <sup>b</sup> RP Braun-CTS Chair of Transportation Engineering, University of Minnesota, Department of Civil Engineering, USA
-
-## article info abstract
-
-Available online 25 November 2010
-
-Keywords: Daylight saving time Vehicle crashes Exposure Traffic volume
-
-Introduction: Daylight saving time (DST), implemented as an energy saving policy, impacts many other aspects of life; one is road safety. Based on vehicle crash data in Minnesota from 2001 to 2007, this paper evaluates long- and short-term effects of DST on daily vehicle crashes. Method: To provide evidence to explain the causes of more/fewer crashes in DST, we examine the impact of DST on crashes in four periods of a day: 3 a.m.-9 a.m., 9 a.m.-3 p.m., 3 p.m.-9 p.m., 9 p.m.-midnight. The effects of risk and exposure to traffic are also separated. Our statistical models not only include weather conditions and dummy variables for days in DST as independent variables, but also consider traffic volumes on major roads in different periods of a day. Our major finding is that the short-term effect of DST on crashes on the morning of the first DST is not statistically significant. Moreover, it is interesting to notice that while DST per se is associated with fewer crashes during dusk, this is in part offset because it is also associated with more traffic on roads (and hence more crashes). Our path analysis shows that overall DST reduces crashes. Impact on industry: Daylight saving time can lead to fewer crashes on roads by providing better visibility for drivers.
-
-© 2010 National Safety Council and Elsevier Ltd. All rights reserved.
-
-## 1. Introduction
-
-Daylight saving time (DST) in the United States dates to World War I as an energy saving measure, and in its current incarnation was officially enacted by Congress in 1974. DST aims to match activities with hours of daylight by adjusting the standard time (ST) forward or backward one hour at different times of year. This keeps sunset and sunrise one hour later in spring, summer, and early fall, so that people have one more evening hour of daylight (and therefore one less morning hour of daylight) in DST [\(Coate & Markowitz, 2003\)](#page-7-0). In addition to its claimed feature of energy conservation, DST may impact many other aspects of daily life, one of which is road safety. Therefore, quantitatively examining the effects of DST on road safety is of interest.
-
-The relationship between DST and road injuries or fatalities has been a topic of extensive research. [Sood and Ghosh \(2007\)](#page-7-0) indicated that there were generally two schools in studying the effects of DST on road safety, which respectively concentrated on long-term effects and shortterm effects. Here short-term effects indicate the impact of DST on vehicle crashes in a few days after time changes, while long-term effects refer to its impact of a longer time period, say, a few months or year round.
-
-URL: <http://nexus.umn.edu> (D. Levinson).
-
-To be specific, one of the schools claimed that DST in the long run decreases vehicle crashes due to better visibility in the evenings, reducing the likelihood of crashing in darkness. [Table 1](#page-1-0) summarizes previous research. These studies argued that if driving in darkness contributes to more traffic crashes, the delayed sunset time can lead to fewer crashes.
-
-The other school contended that time change to DST resulted in more vehicle crashes in the short run. Some representative studies are reviewed in [Table 2](#page-1-0), where many research reported an increase of general/fatal crashes after time change in spring. The main hypothesis was that time change in spring deprived people of one-hour sleep, which, in the short run, could induce drivers' sleepiness or fatigue while driving. Some clinical research performed controlled experiments to examine the relationship between hours of sleep and drivers' response time and vigilance; their main conclusion was that one-hour less sleep can boost the rise of traffic crashes. In addition, some other studies found that time change to DST was associated with an increase of fatal vehicle crashes, and attributed it to possible drivers' alcohol-drinking or late-night driving behavior out of one extra hour during dusk.
-
-In contrast, other research found that time change did not have a statistically significant impact on vehicle crashes. For example, [Lambe](#page-7-0) [and Cummings \(2000\)](#page-7-0), based on the Swedish vehicle crash data from 1984 to 1995, concluded that the shifts to and from DST had no effect on vehicle crashes. [Vincent \(1998\)](#page-7-0) also drew a similar conclusion using the crash data from Canada.
-
-While different data may produce different results, the effects of DST do not seem to have been sufficiently investigated. First of all,
-
-<sup>⁎</sup> Corresponding author.
-
-E-mail addresses: [huang284@umn.edu](mailto:huang284@umn.edu) (A. Huang), [dlevinson@umn.edu](mailto:dlevinson@umn.edu) (D. Levinson).
-
-<span id="page-1-0"></span>Table 1 Selected previous studies supporting positive effects of daylight saving time on road safety.
-
-| Study                                 | Data set                                                           | Method                             | Results                                                                                                                                                                                          |
-|---------------------------------------|--------------------------------------------------------------------|------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Meyerhoff (1978)                      | US fatal vehicle crash<br>data (1973-1974)                         | Fourier analysis                   | Several weeks after time change from ST to DST<br>witnessed a net reduction of about 0.7% in fatal<br>vehicle crashes; little effect of DST was found on<br>fatal crashes in the winter.         |
-| Ferguson (1995)                       | US fatal vehicle crash<br>data (1987-1991)                         | Generalized linear models<br>(GLM) | Year-round DST from 1987 to 1991 would have<br>helped reduce 901 fatal crashes (727 involving<br>pedestrians, 174 involving vehicle occupants).                                                  |
-| Broughton and Sedmand (1989)          | UK accident data (1968-1971)                                       | GLM                                | Year-round DST in 1987 would have helped reduce<br>159 fatal crashes, 654 seriously injured crashes<br>and 1247 overall injured crashes.                                                         |
-| Hillman (1989)                        | Survey data in Britain                                             | GLM                                | Transferring one hour of daylight from the early<br>morning to daytime may save more lives on road,<br>especially for rural areas.                                                               |
-| Ward et al. (1994)                    | Survey data in Northampton, England                                | GLM                                | 237 pedestrians were injured per 100 million road<br>in darkness, compared with 52 pedestrians in daylight.<br>This implied that better light condition at night could<br>lead to fewer crashes. |
-| Adams (2005)                          | UK crash data (1968–1971)                                          | GLM                                | Year-round DST was associated with a 6.9%<br>decrease in serious or fatal road injuries of children.                                                                                             |
-| Coate and Markowitz (2003)            | US fatal vehicle crash data (1998–1999)                            | GLM                                | Year-round DST would reduce pedestrian fatalities in<br>morning peak hours and afternoon peak hours by 13%;<br>motor vehicle occupant fatalities would downsize by 3%.                           |
-| Broughton and Stone (1998)            | UK vehicle crash data (1969 to 1994)                               | GLM                                | Year-round DST would reduce 104 to 138 fatal crashes<br>per year.                                                                                                                                |
-| Broughton, Hazelton, and Stone (1999) | UK vehicle crash data (1969–1973<br>and 1985–1994), USA (19911995) | Statistical models                 | Year-round DST from 1987 to 1991 would have helped<br>reduce 833 fatal crashes involving pedestrians, and 140<br>fatal crashes involving vehicle occupants .                                     |
-| Stevens and Lord (2006)               | Fatal and nonfatal jnjury<br>Crashes in Texas                      | GLM                                | Time change to DST can improve traffic safety due to<br>better light condition.                                                                                                                  |
-
-most studies only looked at crashes for the whole day, which, while useful in comparing crashes in DST and ST on a daily basis, cannot provide insight about the effects of DST on different periods of a day. Therefore, to test the hypotheses about the reasons for more crashes (such as sleepiness or late-night driving) calls for studying crashes in different time periods of a day where the causes may exist. Second, previous research has not addressed the issue of exposure. In addition to a direct influence on crashes, DST may change traffic flow patterns near dawn and dusk, which can further impact crashes.
-
-This paper, based on the crash data in Minnesota (MN) from 2001 to 2007, endeavors to fill this research niche by considering vehicles' exposure to traffic. The research question is whether the policy of DST can impact vehicle crashes in MN. Over the long-term, we hypothesize that the impact of DST on crashes includes two parts: exposure and risk of crashes. First, in terms of exposure, we hypothesize that DST may reduce traffic during dawn yet increase traffic during dusk. Second, DST may lower the risk of crashes during dusk due to one more hour of visibility for drivers. In the short term, DST, per se, may be associated with more crashes on morning of the first day of DST because of drivers' tiredness/sleepiness after losing one hour's sleep.
-
-The rest of the paper is organized as follows. Section 2 describes the background of daylight saving time and the dates of time change in the United States. [Section 3](#page-2-0) shows the results of exploratory analysis on crashes. [Section 4](#page-2-0) introduces the data sets used in this research. In [Section 5,](#page-4-0) statistical models are constructed to assess the effects of DST on crashes in MN; two-stage least squared models are built to look at the effects of DST on traffic as well as on crashes. The results and analysis are depicted in [Section 6](#page-5-0). Lastly, [Section 7](#page-7-0) concludes the paper.
-
-## 2. Background of daylight saving time
-
-The idea of daylight saving time is to transfer an hour of morning daylight to the evening. It is achieved by setting clocks ahead one hour in spring and turning the clocks back one hour in fall. To illustrate, [Fig. 1](#page-2-0) plots the times of sunrise and sunset (with DST adjustment as separate lines) in Minneapolis/St. Paul, MN, USA for 2007. Note that not all places in the United States observe daylight time; in particular, Hawaii and most of Arizona do not use it. In 2006 and earlier years, DST begins at 2 a.m. local time on the first Sunday in April, and ends at
-
-Table 2 Selected previous studies supporting negative effects of daylight saving time on road safety.
-
-| Study                               | Data set                                 | Method                 | Results                                                                                                                                               |
-|-------------------------------------|------------------------------------------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Ledger (1994)                       | US vehicle crashes 1998                  | Human capital approach | Sleepiness and sleep disorders are major<br>causes of traffic crashes.                                                                                |
-| Coren (1996a)                       | US fatal crash data (3 years)            | GLM                    | Sleep loss in spring can increase the likelihood<br>of fatal crashes on road.                                                                         |
-| Coren (1996b)                       | Canada traffic crash data (1991 to 1992) | GLM                    | Deprivation of one-hour sleep can boost the risk<br>of traffic crashes.                                                                               |
-| Hicks, Lindseth, and Hawkins (1983) | US fatal vehicle crashes                 | Qualitative analysis   | Crashes increased significantly in the first seven<br>days after the time change both in spring and in fall.                                          |
-| Hicks, Davis, and Hicks (1998)      | Fatal crashes in New Mexico (1989-1992)  | GLM                    | Percentage of alcohol-related fatal crashes increased<br>significantly in the first seven days after the time change.                                 |
-| Varughese and Allen (2001)          | US fatal vehicle crashes (21 years)      | GLM                    | Crashes on Sundays in fall rised significantly after shifting<br>from DST to ST, which may be highly associated with drivers'<br>alcohol consumption. |
-
-<span id="page-2-0"></span>![](_page_2_Figure_2.jpeg)
-
-Fig. 1. Times of sunrise and sunset (with DST adjustment as separate lines) in Minneapolis/St. Paul, MN, USA for 2007 (plotted by Daylight Chart 3.0 ([Fatehi, 2009\)](#page-7-0)).
-
-Table 3 Beginning and ending dates of daylight saving time in the US (Hawaii and Arizona excluded).
-
-| Year | DST Begins | DST ends |  |
-|------|------------|----------|--|
-| 2000 | 4/2        | 10/29    |  |
-| 2001 | 4/1        | 10/28    |  |
-| 2002 | 4/7        | 10/27    |  |
-| 2003 | 4/6        | 10/26    |  |
-| 2004 | 4/4        | 10/31    |  |
-| 2005 | 3/3        | 10/30    |  |
-| 2006 | 3/2        | 10/29    |  |
-| 2007 | 3/11       | 11/4     |  |
-| 2008 | 3/9        | 11/2     |  |
-| 2009 | 3/8        | 11/1     |  |
-
-2 a.m. local time on the last Sunday in October. Starting in 2007, DST begins on the second Sunday in March, and ends on the first Sunday in November. The dates of time change from 2000 to 2009 are shown in Table 3.
-
-Before we perform statistical analysis, it may be important to first obtain an intuitive sense of the trend of crashes as time changes (from ST to DST or from DST to ST).
-
-## 3. Exploratory analysis
-
-The exploratory analysis involves two parts. First, based on historical crash data in Minnesota (2001–2007), this study plots weekly crashes during the 16 weeks crossing the time change in spring (from ST to DST) and in fall from (DST to ST), categorized by four parts of day (hereafter dayparts) (3 a.m.-9 a.m., 9 a.m.-3 p.m., 3 p.m.-9 p.m., 9 p.m.-midnight).1 [Fig. 2](#page-3-0) shows the crashes during the transition period in spring. The first week in DST in all four sections has fewer crashes than its previous week, although afterwards crashes rise again. However, the average weekly crashes in the first eight weeks in DST are lower than the last eight weeks in ST. [Fig. 3](#page-3-0) sketches weekly crashes during the transition period in fall. During 3 p.m. to 9 p.m., the first week in ST suffers from more crashes than the last week in DST, while the opposite holds for 3 a.m. to 9 p.m. For other time periods, the difference is not easy to be discerned. It is interesting to notice that in the 5th week in ST (around the first week in November), there is a surge of crashes for most periods of a day. This is around the time when snow starts to fall in Minnesota, increasing the probability of crashes.
-
-Second, to inspect the short-term effect of DST, we single out the first day (Sunday) of time change and compare it with other Sundays. [Fig. 4](#page-4-0) displays the crashes on Sundays during the transition period in spring. It can be found that crashes decline sharply in the 3rd week before DST, and level off afterwards. For 9 a.m.-3 p.m. the first week in DST has slightly fewer crashes than the last week in ST; yet during other periods more crashes happen in the first week of DST. [Fig. 5](#page-4-0) further shows the Sunday crashes during the transition period in fall. The first Sunday in ST seems to be afflicted with more crashes than its previous week in all four periods; the difference of crashes, nevertheless, are generally less than 5%.
-
-Overall, based on the above analysis, it is hard to pinpoint the effect of time change. For example, as shown in [Figs. 2 and 4](#page-3-0), the first day and first week of DST have fewer crashes than their previous ones, but a longer effect of time change cannot be clearly eyeballed. In addition, we have not yet controlled for other crash-related factors such as weather and season. Further statistical analysis is thus needed.
-
-One frequently asked question is: how much more dangerous, if any, is travel in daylight saving time? This question includes two aspects. First, what natural conditions in DST, different from those in ST, lead to more crashes? Second, what changes in the amount of driving happen? The next two sections will introduce the variables and models used to answer this question.
-
-### 4. Data
-
-To answer the first aspect of the question, the data we use are the crash data of Minnesota, USA from year 2001 and 2007. The crash data set, complied by the Minnesota Office of Traffic Safety (OTS), documents all reported vehicle-related crash data for Minnesota. The complied crash data have one record for each crash and contain overall information about the crash, such as date and time, location, whether property besides vehicles was damaged, and type of roadway.
-
-<sup>1</sup> While our major interests are in the dawn and dusk periods, we also would like to explore if there is any ripple effect for other dayparts.
-
-<span id="page-3-0"></span>![](_page_3_Figure_2.jpeg)
-
-Fig. 2. Weekly vehicle crashes from eight weeks before DST through the first eight weeks in DST in MN (2001-2007), categorized by four periods of day (3 am-9 am, 9 am-3 pm, 3 pm-9 pm, 9 pm-midnight).
-
-![](_page_3_Figure_4.jpeg)
-
-Fig. 3. Weekly vehicle crashes from eight weeks before ST through the first eight weeks in ST in MN (2001-2007), categorized by four periods of day (3 am-9 am, 9 am-3 pm, 3 pm-9 pm, 9 pm-midnight).
-
-The second aspect of the question concerns the issue of exposure to traffic. It would be ideal to use vehicle travel by hour of day, but this information is not available statewide. But we can obtain hourly traffic volume data on some major roads, which are counted by Automatic Traffic Recorder Stations (ATR) located on the state's interstates, trunk highways, county state aid highways, and municipal state aid streets. The ATR data are maintained by the MN Traffic Forecast and Analysis Section of the Office of Transportation data and Analysis.2 In this paper total traffic volumes from all ATR stations in different dayparts are used to indicate changes in the amount of travel statewide.3 The basic statistics of crashes and ATR traffic volumes in different periods of a day from 2001 to 2007 are shown in [Table 4.](#page-4-0)
-
-As noted above, we categorize crashes by four dayparts. The dependent variables are statewide daily vehicle crashes given a period of day from 2001 to 2007. Regarding dependent variables, we are mainly interested in dummy variables for the first Sunday of DST, first Monday of DST, first Sunday of ST, first Monday of ST, and the rest of the days of DST (the base case being the rest of the days in ST). Other independent variables include: traffic volumes (from statewide ATR stations); weather indicators such as rain and snow; dummy variables for year, dummy variables for month; day length <sup>4</sup> ; monthly gas price statewide. It should be noted that dummy variables for month and day length (sunlight hours per day) are included to separate the seasonal effects from DST. To elaborate, monthly dummy variables indicate
-
-<sup>2</sup> The data can be downloaded from [http://www.dot.state.mn.us/traf](http://www.dot.state.mn.us/traffic/data/atr/atr.html)fic/data/atr/atr.
-
-<sup>3</sup> It is assumed that the fluctuation of traffic volumes in different dayparts from the ATR data set is representative of the general trend of traffic flows statewide.
-
-<sup>4</sup> Day length is calculated as the period between the rising and setting of the sun. The statewide sunrise/sunset time is averaged over sunrise/sunset times of all counties in Minnesota. The data set was downloaded from the website of Astronomical Applications Department of the U.S. Naval Observatory: [http://aa.usno.navy.mil/data/](http://aa.usno.navy.mil/data/docs/RS-OneYear.php) [docs/RS-OneYear.php.](http://aa.usno.navy.mil/data/docs/RS-OneYear.php)
-
-<span id="page-4-0"></span>![](_page_4_Figure_2.jpeg)
-
-Fig. 4. Total vehicle crashes on Monday from eight weeks before DST through the first eight weeks in DST in MN (2001-2007), categorized by four periods of day (3 am-9 am, 9 am-3 pm, 3 pm-9 pm, 9 pm-12 midnight).
-
-![](_page_4_Figure_4.jpeg)
-
-Fig. 5. Total vehicle crashes on Monday from eight weeks before ST through the first eight weeks in ST in MN (2001-2007), categorized by four periods of day (3 am-9 am, 9 am-3 pm, 3 pm-9 pm, 9 pm-12 midnight).
-
-seasonal effects on a monthly basis, while day length is used to represent the effect of hours of daylight.
-
-## 5. The models
-
-The dependent variables are crash counts, so the Poisson or negative binomial distribution is often used to describe them ([Long, 1997](#page-7-0)). Deviance and Pearson Chi-square divided by the degrees of freedom are used to detect over-dispersion and under-dispersion. If the value is larger than 1, then variance is larger than the mean, indicating overdispersion, and under-dispersion if the value is less than 1. For count data with over-dispersion, the negative binomial distribution is often used. Our initial analysis reveals that the Minnesota crash data show over-dispersion. We further test the null hypothesis equality of mean and variance imposed by the Poisson distribution against the alternative that the variance exceeds the mean. The method we use is based on [Cameron and Trivedi \(1998\)](#page-7-0): first we run the regression model using negative binomial distribution, and keep the value of LLnb (loglikelihood value); second, the log-likelihood value for fitting the Poisson distribution (LLp) is also documented; third, we calculate the log likelihood ratio statistic of −2(LLp−LLnb). The asymptotic distribution of the log likelihood ratio statistic follows the Chi-square distribution with one degree of freedom. For all our models, we reject the null hypothesis that the mean equals and the variance at the 5% level.
-
-Table 4 Descriptive statistics of crashes and traffic volumes.
-
-|                | Time period   | Mean   | St. dev. | Min    | Max     |
-|----------------|---------------|--------|----------|--------|---------|
-| Crashes        | 3 am-9 am     | 42.78  | 3.30     | 5      | 360     |
-|                | 9 am-3 pm     | 75.12  | 41.06    | 19     | 562     |
-|                | 3 pm-9 pm     | 84.74  | 33.01    | 23     | 483     |
-|                | 9 pm-midnight | 21.89  | 13.51    | 1      | 108     |
-| Traffic volume | 3 am-9 am     | 415596 | 160994   | 76469  | 574082  |
-|                | 9 am-3 pm     | 792097 | 77569    | 363397 | 1015998 |
-|                | 3 pm-9 pm     | 813235 | 97030    | 292490 | 1004692 |
-|                | 9 pm-midnight | 177408 | 32536    | 49633  | 255546  |
-
-<span id="page-5-0"></span>Table 5 Regressing log(traffic) on days in daylight saving time (2001-2007).
-
-| Dependent Var.    |                  | Log-traffic volumes in the following periods |                    |                  |                   |  |  |  |
-|-------------------|------------------|----------------------------------------------|--------------------|------------------|-------------------|--|--|--|
-|                   | 3 am-9 am        | 9 am-3 pm                                    | 3 pm-9 pm          | 9 pm-midnight    | all day           |  |  |  |
-| 1st Sunday(DST)   | 0.004(0.072)     | -0.008(0.023)                                | -0.005(0.030)      | -0.057(0.032)    | -0.009(0.027)     |  |  |  |
-| 1st Monday(DST)   | 0.090(0.072)     | -0.006(0.023)                                | 0.039(0.030)       | 0.066(0.032) *   | 0.033(0.027)      |  |  |  |
-| Rest of days(DST) | 0.017(0.027)     | 0.015(0.009)                                 | 0.045(0.011) **    | 0.049(0.012) *   | 0.03(0.010) **    |  |  |  |
-| 1st Sunday(ST)    | 0.028 (0.072)    | 0.0003(0.023)                                | -0.005(0.030)      | -0.059(0.032) *  | -0.006(0.027)     |  |  |  |
-| 1st Monday(ST)    | 0.114(0.067)     | 0.010(0.021)                                 | 0.026(0.027)       | 0.0324(0.029)    | 0.039(0.025)      |  |  |  |
-| rain              | -0.032(0.013) *  | -0.025(0.005) **                             | -0.029(0.006) **   | -0.049(0.006) ** | -0.021(0.005) **  |  |  |  |
-| snow              | -0.059(0.017) ** | -0.092(0.005) **                             | -0.115(0.007) **   | -0.091(0.007) ** | -0.079(0.006) **  |  |  |  |
-| cloudy            | 0.002(0.008)     | -0.002(0.002)                                | -0.008(0.003) *    | -0.011(0.003) ** | -0.008(0.003) **  |  |  |  |
-| windy             | -0.040 (0.025)   | 0.014 (0.015)                                | 0.0003(0.018)      | 0.0182(0.013)    | -0.001(0.016)     |  |  |  |
-| mon               | 1.246(0.014) **  | 0.068 (0.004) **                             | 0.124(0.006) **    | 0.056 (0.006) ** | 0.023(0.005) **   |  |  |  |
-| tue               | 1.329(0.013) **  | 0.078(0.004) **                              | 0.160(0.005) **    | 0.153(0.006) **  | 0.270(0.005) **   |  |  |  |
-| wed               | 1.333(0.013)**   | 0.096(0.004) **                              | 0.179 (0.005) **   | 0.192 (0.006) ** | 0.287 (0.005) **  |  |  |  |
-| thur              | 1.316 (0.013) ** | 0.115(0.004) **                              | 0.186 (0.005) **   | 0.248 (0.006) ** | 0.299 (0.005) **  |  |  |  |
-| fri               | 1.288 (0.013) ** | 0.213 (0.004) **                             | 0.253 (0.005) **   | 0.408 (0.006) ** | 0.364 (0.005) **  |  |  |  |
-| sat               | 0.498(0.013) **  | 0.131(0.004) **                              | -0.00007(0.006) ** | 0.331(0.006) **  | 0.128(0.005) **   |  |  |  |
-| y2002             | -0.003(0.014)    | 0.008(0.004)                                 | -0.005(0.006)      | 0.001(0.006)     | 0.0002(0.005)     |  |  |  |
-| y2003             | -0.016(0.013)    | -0.0005 (0.004)                              | -0.016(0.005) **   | -0.006 (0.006)   | -0.009(0.005)     |  |  |  |
-| y2004             | 0.036(0.016) **  | 0.035(0.005) **                              | 0.014(0.006) **    | 0.028(0.007) **  | 0.025(0.006) **   |  |  |  |
-| y2005             | 0.067(0.021) **  | 0.058(0.007) **                              | 0.035(0.008) **    | 0.041(0.009) **  | 0.047(0.008) **   |  |  |  |
-| y2006             | 0.067(0.026) **  | 0.059(0.008) **                              | 0.032 (0.011) **   | 0.031(0.0112) ** | 0.045(0.010) **   |  |  |  |
-| y2007             | 0.050(0.029)     | 0.045(0.009) **                              | 0.008(0.012) **    | -0.001(0.013)    | 0.024(0.011) *    |  |  |  |
-| Feb.              | 0.052(0.022) *   | 0.013(0.007)                                 | 0.023(0.009) **    | 0.016(0.009)     | 0.020(0.008) *    |  |  |  |
-| Mar.              | 0.085(0.032 ) ** | 0.023(0.010) **                              | 0.040(0.013) **    | 0.0004(0.014)    | 0.037(0.012) **   |  |  |  |
-| Apr.              | 0.110(0.049) *   | 0.043(0.016) **                              | 0.050(0.020) *     | 0.015(0.021)     | 0.053(0.018) *    |  |  |  |
-| May               | 0.102(0.061)     | 0.049(0.019) **                              | 0.049(0.025) *     | 0.0436(0.026)    | 0.057(0.023) *    |  |  |  |
-| June              | 0.157(0.067)     | 0.083(0.021) **                              | 0.085(0.0273) **   | 0.136(0.029) **  | 0.099(0.025) **   |  |  |  |
-| July              | 0.084(0.064)     | 0.075(0.020) **                              | 0.054(0.026) *     | 0.156(0.028) **  | 0.077(0.024) **   |  |  |  |
-| Aug.              | 0.138(0.054) **  | 0.109(0.017) **                              | 0.086(0.022) **    | 0.141(0.023) **  | 0.104(0.020) **   |  |  |  |
-| Sep               | 0.117(0.0424) ** | 0.078( 0.013) **                             | 0.063(0.0174) **   | 0.028(0.018)     | 0.071(0.016) **   |  |  |  |
-| Oct.              | 0.142(0.033) **  | 0.099(0.010) **                              | 0.077(0.0135) **   | 0.035(0.014) *   | 0.087(0.012) **   |  |  |  |
-| Nov.              | 0.070(0.018 ) ** | 0.093(0.006) **                              | 0.059(0.007) **    | 0.056(0.008) **  | 0.071(0.007) **   |  |  |  |
-| Dec.              | -0.025(0.018)    | 0.094(0.006) **                              | 0.035(0.007) **    | 0.119(0.008) **  | 0.052(0.007) **   |  |  |  |
-| daylight hours    | 0.003(0.010)     | 0.013(0.003) **                              | 0.003(0.004)       | 0.015(0.004) **  | 0.006(0.004)      |  |  |  |
-| gas price (log)   | -0.042(0.042)    | -0.056(0.013) **                             | -0.031(0.0170)     | -0.047(0.018) ** | -0.041(0.0155) ** |  |  |  |
-| R2                | 0.89             | 0.70                                         | 0.68               | 0.83             | 0.79              |  |  |  |
-
-<sup>\*\*</sup> statistically significant at the 1% level.
-
-Note 1: the OLS model is used to run the regressions.
-
-Note 2: number in (parentheses) indicates std. error.
-
-Therefore, the negative binomial distribution is used to characterize the crash counts in this paper.
-
-Among the independent variables, special attention is paid to the traffic volume variable in that we suspect that it may be an endogenous one. Our model fit tests show that the variable of traffic is endogenous in all our models. Based on [Wooldridge \(2003\),](#page-7-0) the 2SLS model can be considered if an independent variable is endogenous and the instrumental variables (IV) of this variable are uncorrelated with crashes. In our search for the IVs, we have found that the dummy variables for day of week are highly associated with traffic yet are not correlated with crashes at the 5% level; moreover, our tests further show that gas price (at the monthly level statewide) is also uncorrelated with crashes. Therefore dummy variables for day of week and the variable of gas price are used as instrumental variables (IV) for traffic.5 Therefore the 2SLS model is adopted in our analysis.
-
-We also look at fatal crashes statewide. Yet the fatal crash data, when grouped by different dayparts, display obvious under-dispersion, especially for 3 a.m.-9 a.m. (most of the days where fatal crashes happen only have one or two observations). Therefore we examine fatal crash data on a daily basis; the traffic variable is calculated as daily average traffic volume from all ATR stations. The model fit test also allows us to use the 2SLS model; the model structure is otherwise the same as other models mentioned above.
-
-## 6. Results and analysis
-
-A 2SLS model includes two functions: the reduced function to predict traffic and the structural function to predict crashes. These are discussed in turn.
-
-## 6.1. Traffic
-
-In the reduced function, traffic volumes are used as dependent variables, with day-of-week dummy variables and gas price being instrumental variables; dummy variables for year, month, and weather are also included. The hypothesis is that all else equal, weekdays are associated with more traffic than Sunday in daytime; higher gas price is associated with lower traffic volume. The results for the reduced functions are listed in Table 5; we differentiate the estimated coefficients at the 1% and 5% level. As can be seen, the coefficients of first Sunday and first Monday in DST are generally not statistically significant (except for the first Monday in DST during 9 a.m.-midnight), meaning that traffic in the first two days of DST, ceteris par ribus, is no different from an ordinary day in ST. The rest of the days in DST, while not statistically significant in the 3 a.m.-9 a.m. or 9 a.m.-3 p.m. dayparts, are positively associated with traffic during 3 p.m.-9 p.m. and 9 p.m.-midnight. These results accord with our hypotheses that one more hour daylight in the afternoon in DST encourages more out-of-home activities, and thus results in more vehicles on the road. The signs of the coefficients of other variables are consistent with our hypotheses too. For example, during the first two dayparts: 3 a.m.-9 a.m. and 9 a.m.-3 p.m., traffic in weekdays
-
-<sup>\*</sup> statistically significant at the 5% level.
-
-<sup>5</sup> In two of the four models, the correlation between gas price and traffic is not statistically significant; but we still include them in the model for the convenience of comparing the results with those of other models.
-
-are higher than weekends; yet the opposite holds for dusk and night (probably due to night-time activities on weekends). Moreover, many of the monthly dummy variables are statistically significant, displaying a seasonal trend; the coefficients of day length, representing the impact of daily sunlight differences on traffic, are positive; yet the variable is only statistically significant for the off-peak 9 a.m.-3 p.m. and 9 p.m.-midnight periods.
-
-## 6.2. Crashes
-
-After obtaining the OLS estimate of traffic volume, we employ it as an independent variable in our structural function with crashes as the dependent variable. Table 6 shows the results of the structural forms given different time periods. The coefficient of first Sunday of DST equals 0.026 (the increase may result from our previous hypothesis that losing one hour's sleep affects drivers' behavior); however, it is not statistically significant. During 3 p.m.-9 p.m., the coefficient of the first Sunday in DST equals -0.29, suggesting approximately 0.28% fewer crashes on the first day of DST than a day in ST. We also find that during mid-day (9 a.m.-3 p.m.) the first day of DST also has a negative and statistically significant coefficient, but the rest of the days in DST are not statistically significant; it is still unclear why the crashes on the mid-day of the first day in DST are affected, but it must be remembered this first day of DST only occurs on 7 days between 2001 and 2007, so may be anomalous. For the rest of the days in DST, they have negative and statistically significant coefficients for most periods of day except for 9 a.m.-3 p.m. In particular, for sunset and dusk period, the decrease of crashes may be attributed to longer visibility for drivers. Furthermore, none of the estimates for the first Sunday of ST and first Monday of ST are statistically significant, which probably illustrates that crashes on the first two days of ST are no different from other days in ST.
-
-Clearly, traffic has a salient impact on crashes. Particularly for 3 p.m.- 9 p.m., a 1% increase of traffic volume, all else equal, is associated with
-
-Table 7 The overall effect of DST on crashes(log form), MN crashes data, 2001-2007 (base case: days in ST).
-
-|                            | 3 am-9 am | 9 am-3 pm | 3 pm-9 pm | 9 pm-midnight | Fatal<br>(all day) |
-|----------------------------|-----------|-----------|-----------|---------------|--------------------|
-| first Sunday<br>in DST     | -         | -0.367    | -0286     | -             | -                  |
-| first Monday<br>in DST     | -         | -         | - 0.246   | 0.107         | -                  |
-| Rest of the<br>days in DST | -0.164    | -         | -0.090    | -0.075        | -0.010             |
-
-2.2% more crashes. Take note that the exposure issue is also influenced by DST. To assess the overall effect of DST on crashes, therefore, we need to consider both the impact of DST per se and exposure to traffic affected by DST (using path analysis). For example, for 3 p.m.-9 a.m., the overall effect of the rest of the days in DST on log-crashes is calculated as: 0.045×2.214−0.190=−0.090. Thus, a day in DST, all else equal, is associated with about 0.09% fewer crashes than a day in ST. This reminds us that the benefit of better visibility during dusk may be diminished (but not eliminated) by the corresponding uptick in traffic.
-
-The final results of the influence of DST on crashes are described in Table 7 (in calculation we only consider the statistically significant coefficients). They demonstrate that a day DST, largely speaking, is associated with fewer crashes than its counterpart in ST when all else is equal. In addition, we do not find a statistically significant rise of crashes on the first day of DST during 3 a.m.-9 a.m.
-
-## 6.3. Fatal crashes
-
-Our results from the fatal crash data show that none of the coefficients for the first Sunday day in DST, first Monday in DST, and the rest of days in DST are statistically significant (after controlling for
-
-Table 6 Regressing vehicle crashes on days in daylight saving time (MN crash data , 2001-2007).
-
-| Dependent Var.      | Log-crashes in the following periods |                   |                    |                  |                 |  |  |
-|---------------------|--------------------------------------|-------------------|--------------------|------------------|-----------------|--|--|
-|                     | 3 am-9 am                            | 9 am-3 pm         | 3 pm-9 pm          | 9 pm-midnight    | Fatal(all day)  |  |  |
-| 1st Sunday(DST)     | 0.026(0.171)                         | -0.367(0.134) **  | -0.286(0.111) **   | -0.032(0.179)    | -0.346(0.410)   |  |  |
-| 1st Monday(DST)     | -0.201 (0.160)                       | -0.115(0.128)     | -0.246(0.106) *    | -0.293(0.178)    | 0.168(0.234)    |  |  |
-| Rest of days(DST)   | -0.164 (0.062) **                    | -0.093 (0.049)    | -0.190(0.039) **   | -0.155(0.060) ** | -0.058(0.113)   |  |  |
-| 1st Sunday(ST)      | -0.168(0.170)                        | -0.142(0.129)     | -0.129(0.1066)     | 0.237(0.164)     | 0.168(0.234)    |  |  |
-| ^<br>1st Monday(ST) | -0.204 (0.147)                       | -0.080(0.116)     | 0.126 (0.092)      | -0.024(0.146)    | -0.167(0.256)   |  |  |
-| traff<br>(log)      | 0.634(0.017) **                      | 1.978(0.104) **   | 2.214 (0.059) **   | 1.634(0.058) **  | -0.323(0.104) * |  |  |
-| rain                | 0.296(0.029 ) **                     | 0.375(0.025) **   | 0.238(0.020) **    | 0.314(0.028) **  | 0.006(0.056)    |  |  |
-| snow                | 0.639(0.037) **                      | 0.926(0.033) **   | 0.752(0.025) **    | 0.814(0.033) **  | 0.082(0.065)    |  |  |
-| cloudy              | -0.033(0.018) **                     | -0.013(0.013)     | 0.0106(0.011)      | 0.040(0.018) *   | -0.002(0.031)   |  |  |
-| windy               | 0.100(0.057) **                      | -0.077(0.085)     | -0.010(0.063)      | 0.038(0.063)     | 0.052(0.089)    |  |  |
-| y2002               | -0.028(0.030)                        | -0.067(0.023) **  | -0.003(0.019)      | -0.069(0.030) *  | 0.088(0.051)    |  |  |
-| y2003               | -0.288(0.030) **                     | -0.313(0.023) **  | -0.056(0.019) **   | 0.291(0.029) **  | 0.056(0.051)    |  |  |
-| y2004               | -0.126(0.030) **                     | -0.248(0.023) **  | -0.033(0.019)      | 0.288(0.029) **  | -0.015(0.052)   |  |  |
-| y2005               | -0.191 (0.030) **                    | -0.310 (0.023) ** | -0.076 (0.0191) ** | 0.256(0.029) **  | -0.018(0.053)   |  |  |
-| y2006               | -0.223(0.030) **                     | -0.385(0.023) **  | -0.149(0.019) **   | 0.197(0.030) **  | -0.022(0.054)   |  |  |
-| y2007               | -0.231(0.030) **                     | -0.344(0.023) **  | -0.128(0.0194) **  | 0.154(0.030) **  | -0.067(0.055)   |  |  |
-| Feb.                | 0.177(0.047) **                      | -0.010(0.037)     | 0.004(0.031)       | -0.0120(0.046)   | -0.161(0.091)   |  |  |
-| Mar.                | -0.001(0.071)                        | -0.116(0.056) *   | -0.181(0.046) **   | -0.100(0.070)    | -0.300(0.133) * |  |  |
-| Apr.                | 0.012(0.109)                         | -0.091(0.085)     | 0.007(0.067)       | 0.057(0.105)     | -0.071(0.192)   |  |  |
-| May                 | 0.208(0.135)                         | 0.156(0.106)      | 0.208(0.087) *     | 0.240(0.131)     | -0.080(0.238)   |  |  |
-| June                | 0.301(0.149) *                       | 0.280(0.116) **   | 0.229(0.095) *     | 0.401(0.144) **  | -0.025(0.261)   |  |  |
-| July                | 0.178(0.142)                         | 0.228(0.111) *    | 0.214(0.091) *     | 0.261(0.138)     | -0.049(0.248)   |  |  |
-| Aug.                | 0.063(0.118)                         | 0.094(0.093)      | 0.081(0.076)       | 0.060(0.115)     | 0.090(0.209)    |  |  |
-| Sep                 | 0.201(0.093)                         | -0.027(0.073)     | 0.119(0.060) **    | 0.067(0.090)     | -0.007(0.166)   |  |  |
-| Oct.                | 0.055 (0.073)                        | -0.153 (0.059) ** | 0.031(0.047)       | 0.084(0.071)     | 0.121(0.130)    |  |  |
-| Nov.                | -0.091(0.039) *                      | -0.273(0.032) **  | 0.019(0.026)       | -0.018(0.039)    | 0.036(0.072)    |  |  |
-| Dec.                | -0.056(0.039)                        | -0.086(0.032) **  | 0.010(0.025)       | -0.124(0.039)    | 0.141(0.069) *  |  |  |
-| daylight hours      | -0.1010(0.023) **                    | -0.090(0.018) **  | -0.051(0.015) **   | -0.064(0.022) ** | 0.039(0.040)    |  |  |
-
-<sup>\*\*</sup> statistically significant at the 1% level.
-
-Note 2: number in (parentheses) indicates std. error.
-
-<sup>\*</sup> statistically significant at the 5% level.
-
-Note 1: the negative binomial (NB) model is used to run the regressions.
-
-<span id="page-7-0"></span>traffic and months). Yet it is interesting to notice that the coefficient of traffic in the structural model equals -0.323 and is statistically significant at the 5% level, meaning that the increase of traffic is associated with a decrease of fatal crashes. This may be because higher traffic volumes limit vehicles' speed, which makes drivers less likely to drive recklessly; a similar theory can be found in John Adams's thoughts in Smeed's Law (Adams, 1987). In the reduced function with traffic as the dependent variable, the coefficient of the rest of the days in DST equals 0.027. The overall effect of DST on vehicle crashes can be calculated as −0.323× 0031= 0.010, indicating that a day in DST, all else equal, is associated with about 0.008% fewer fatal crashes than a day in ST.
-
-## 7. Conclusions
-
-Considering the issue of exposure to traffic, this paper explores long- and short-term effects of DST on vehicle crashes. The contribution of this research includes two aspects. First, we separate the effects of DST on traffic volume and the effect of DST per se on crashes. The two stage least square models are further developed to examine crashes in four periods of day (including dawn and dusk periods). Second, we have looked at crashes at different periods of day, which is convenient for explaining the possible causes for the effects. Our results show that DST per se, by providing one more hour's visibility in the afternoon, can help reduce crashes during and after dusk; but it can also increase traffic. Overall, we find that DST, all else equal, is associated with fewer crashes for most dayparts; in addition, DST is also correlated with fewer fatal crashes than ST.
-
-#### Acknowledgements
-
-We thank Nathan Drews and Gene Hicks from the Minnesota Department of Transportation for providing the crash data set for this study.
-
-#### References
-
-- Adams, J. (1987). Smeed's Law: some further thoughts. Traffic Engineering and Control, 28, 70−73.
-- Adams, J. M. (2005). Year-round daylight saving and serious or fatal road traffic injuries in children in the Northeast of England. Journal of Public Health, 27(4), 316−317.
-- Broughton, J., Hazelton, M., & Stone, M. (1999). Influence of light level on the incidence of road casualties and the predicted effect of changing 'summertime'. Journal of the Royal Statistical Society: Series A, 162(2), 137−175.
-- Broughton, J., & Sedmand, R. J. (1989). The potential effects on road causalties of double British Summer Time, TRL report 228. Crowthorne: Transport Research Laboratory. Broughton, J., & Stone, M. (1998). A new assessment of the likely effects on road accidents of adopting SDST, TRL report 368. Crowthorne: Transport Research Laboratory.
-
-- Cameron, A., & Trivedi, P. (1998). Regression Analysis of Count Data. Cambridge: Cambridge University Press.
-- Coate, D., & Markowitz, S. (2003). The effects of daylight and daylight saving time on us pedestrian fatalities and motor vehicle occupant fatalities. Accident Analysis and Prevention, 36(3), 351−357.
-- Coren, S. (1996a). Acciental death and shift to daylight saving time. Perceptual and Motor Skills, 83, 921−922.
-- Coren, S. (1996b). Daylight savings time and traffic accidents. The New England Journal of Medicine, 334, 924.
-- Fatehi, S. (2009). Daylight Chart 3.0. <http://daylightchart.sourceforge.net/sunposition/>. Ferguson, S. A. (1995). Daylight savings time and motor vehicle crashes: the reduction
-- in pedestrian and vehicle occupant fatalities. American Journal of Public Health, 85 (1), 92−95. Hicks, G. J., Davis, J. W., & Hicks, R. A. (1998). Fatal alcohol-related traffic crashes
-- increase subsequent to changes to and from daylight savings time. Perceptual and Motor Skills, 86(3 Pt 1), 879−882.
-- Hicks, R. A., Lindseth, K., & Hawkins, J. (1983). Daylight savings time changes increase traffic accidents. Perceptual and Motor Skills, 56, 64−66.
-- Hillman, M. (1989). More daylight less accidents. Traffic Engineering and Control, 4(30), 191−193.
-- Lambe, M., & Cummings, P. (2000). The shift to and from daylight savings time and motor vehicle crashes. Accident Analysis and Prevention, 32, 609−611.
-- Ledger, D. (1994). The cost of sleep related accidents: a report for the national commission on sleep disorders research. Sleep, 17, 84−93.
-- Long, J. (1997). Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: Sage Publications Inc.
-- Meyerhoff, N. J. (1978). The influence of daylight saving time on motor vehicle fatal traffic accidents. Accident Analysis and Prevention, 10, 207−221.
-- Sood, N., & Ghosh, A. (2007). The short and long run effects of daylight saving time on fatal automobile crashes.B.E. Journal of Economic Analysis & Policy, 7(1) (Article 11).
-- Stevens, C. R., & Lord, D. (2006). Evaluating safety effects of daylight savings time on fatal and nonfatal injury crashes in texas. Transportation Research Record, 1953, 147−155.
-- Varughese, J., & Allen, R. P. (2001). Fatal accidents following changes in daylight savings time: the American experience. Sleep Medicine, 2(1), 31−36.
-- Vincent, A. (1998). Effects of daylight savings time on collision rates. The New England Journal of Medicine, 339, 1167−1168.
-- Ward, H., Cave, J., Morrison, A., Allsop, R., Evans, A., Kuiper, C., et al. (1994). Pedestrian activity and accident risk, Technical report. London: Automobile Association Foundation for Road Safety Research.
-- Wooldridge, J. M. (2003). Introductory Econometrics: A Modern Approach. OH: South-Western Mason.
-
-Arthur Huang is a Ph.D. student at the Department of Civil Engineering at the University of Minnesota, Twin Cities. He is also enrolled in the Master of Urban and Regional Planning program at the Humphrey Institute of Public Affairs, University of Minnesota. His major research interests are the evolution of transportation and land use, business cluster analysis, and traffic safety. He is a student member of American Planning Association, Institute of Transportation Engineers, and Air & Waste Management Association.
-
-Dr. David Levinson is an Associate Professor in the Department of Civil Engineering at the University of Minnesota and Director of the Networks, Economics, and Urban Systems (NEXUS) research group. He currently holds the Richard P. Braun/CTS Chair in Transportation. Levinson has authored or edited several books and numerous peer reviewed articles. He is the editor of the Journal of Transport and Land Use.

Zotero/001_artiklid/Road Safety Indicators/ivandifferencescausalityfactorssingle1999.md

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----
-category: literaturenote
-citekey: ivandifferencescausalityfactorssingle1999
-title: Differences in causality factors for single and multi-vehicle crashes on two-lane roads
-authors: "Ivan, John N.; Pasupathy, Raghubhushan K.; Ossenbruggen, Paul J."
-year: 1999
-date: 1999-00-00 1999
-doi: 10.1016/s0001-4575(99)00030-5
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/s0001-4575(99)00030-5"
-zotero_key: KEEZ8SAF
-zotero_storage: NG72WZ99
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Ivan, John N."
-status: converted
----
-on two-lane roads
-
-<sup>a</sup> *Department Ci*6*il and En*6*ironmental Engineering U*-37, *Uni*6*ersity of Connecticut*, *Connecticut Transportation Institute*, *Storrs*, *CT* 06269-2037, *USA* <sup>b</sup> *Edwards and Kelcey*, <sup>529</sup> *Main Street*, *Suite* <sup>203</sup>, *Charlestown*, *MA* <sup>02129</sup>, *USA* <sup>c</sup> *Department of Ci*6*il Engineering*, *Uni*6*ersity of New Hampshire*, *Durham*, *NH* <sup>03824</sup>, *USA* Received 1 October 1998; received in revised form 9 February 1999; accepted 26 March 1999
-
-Past research has found a non-linear relationship between traffic intensity or level of service (LOS) and highway crash rates. This paper investigates this relationship further by including the effects of site characteristics and estimating Poisson regression models for predicting single and multi-vehicle crashes separately. Analysis focuses on rural two-lane highways, with hourly LOS, traffic composition, and highway geometric characteristics as independent variables. The resulting models for single and multi-vehicle crashes have different explanatory variables. Single-vehicle crash rates decrease with increasing traffic intensity (lower LOS), shoulder width and sight distance. Multi-vehicle crash rates increase with the number of signals, the daily single-unit truck percentage, and the shoulder width, and decreased on principal arterials compared to other roadway classes. LOS does not significantly explain variation in the number of multi-vehicle crashes. Ongoing research by the authors is aimed at identifying other site factors, such as driveway density and intersection LOS, that can better explain the differing effects reported here and
-
-John N. Ivan a,\*, Raghubhushan K. Pasupathy <sup>b</sup>
-
-predict crash rates of both types better. © 1999 Elsevier Science Ltd. All rights reserved.
-
-*Keywords*: Highway crash rates; Level of service; Crash type; Two-lane roads; Daylight
-
-One of many important aspects of highway safety research is developing crash prediction models to quantify the relationship between site characteristics and the number of crashes observed. These prediction models can help identify the site characteristics tending to cause highway crashes. Crash prediction models, till now, have tended to be macroscopic in nature, meaning that they predict using summary statistics on traffic such as annual average daily traffic (AADT) rather than microscopic measures such as hourly volume counts. These macroscopic models also tended to consider all crashes together rather than separately by single and multi-vehicle crash types. Studies that have considered hourly volumes have generally not consid-
-
-\* Corresponding author. Tel.: +1-860-486-0352; fax: +1-860-486-
-
-0001-4575/99/\$ - see front matter © 1999 Elsevier Science Ltd. All rights reserved.
-
-*E*-*mail address*: johnivan@eng2.uconn.edu (J.N. Ivan)
-
-PII: S0001-4575(99)00030-5
-
-![](_page_0_Picture_1.jpeg)
-
-![](_page_0_Picture_2.jpeg)
-
-, Paul J. Ossenbruggen <sup>c</sup>
-
-ered geometric effects. In this paper, the significance of all of these factors—site location, highway geometry, daylight conditions, and traffic composition—are in-
-
-The focus of this research was to find differences in the effects of both traffic condition and site characteristics as independent variables for the expected single and multi-vehicle crash rates, measured in crashes per year. Eight different rural two-lane state highway locations across Connecticut with hourly volume counts between September 1990 and October 1996 were studied. Poisson regression was used to estimate and evaluate models for predicting the expected number of single and
-
-A number of researchers have investigated the effect of exposure on the number of crashes per year, or crash
-
-vestigated together.
-
-**2. Previous work**
-
-multi-vehicle crashes at these sites.
-
-Differences in causality factors for single and multi-vehicle crashes
-
-**Abstract**
-
-**1. Introduction**
-
-2298.
-
-![](_page_1_Picture_0.jpeg)
-
-![](_page_2_Picture_0.jpeg)
-
-![](_page_3_Picture_0.jpeg)
-
-![](_page_4_Picture_0.jpeg)
-
-![](_page_5_Picture_0.jpeg)
-
-Table 3 Preliminary models for multi-vehicle crashes<sup>a</sup>
-
-| Variable             | Model 1         | Model 2        | Model 3        | Model 4        |
-|----------------------|-----------------|----------------|----------------|----------------|
-| Intercept            | −10.08 (−11.53) | −9.88 (−12.04) | −9.98 (−10.72) | −11.31 (−7.33) |
-| Route 1              | Base            |                | Base           | Base           |
-| Route 5              | −2.03 (−3.72)   |                | −2.28 (−3.86)  | −2.30 (−3.78)  |
-| Route 7              | −1.86 (−4.19)   |                | −1.84 (−4.12)  | −1.81 (−3.99)  |
-| Route 8              | −1.17 (−2.10)   |                | −1.11 (−1.90)  | −0.94 (−1.50)  |
-| Route 12             | 0.88 (3.13)     |                | 0.86 (3.00)    | −1.01 (−3.18)  |
-| Route 66             | −1.95 (−5.70)   |                | −2.09 (−5.45)  | −2.21 (−5.35)  |
-| Route 81             | −2.59 (−3.15)   |                | −2.67 (−3.22)  | −2.63 (−3.05)  |
-| Route 124            | −0.99 (−4.00)   |                | −1.54 (−3.18)  | −1.66 (−3.13)  |
-| LOS A                | Base            | Base           | Base           | Base           |
-| LOS B                |                 | 0.64 (1.67)    | 0.15 (0.38)    | 0.14 (0.32)    |
-| LOS C                |                 | −0.14 (−0.32)  | 0.21 (0.49)    | 0.33 (0.67)    |
-| LOS D                |                 | −0.18 (−0.39)  | 0.63 (1.16)    | 0.76 (1.24)    |
-| LOS E                |                 | 0.46 (1.02)    | 0.83 (1.28)    | 0.86 (1.21)    |
-| Light                |                 |                |                | Base           |
-| Dark                 |                 |                |                | 0.26 (0.79)    |
-| Dusk                 |                 |                |                | 0.45 (1.27)    |
-| Exponent on exposure | 0.88 (12.20)    | 0.76 (10.37)   | 0.86 (10.73)   | 0.96 (7.96)    |
-| Dispersion           | 1.37            | 1.44           | 1.38           | 1.40           |
-| Null deviance        | 788.97          | 788.97         | 788.97         | 788.97         |
-| Residual deviance    | 373.28          | 504.44         | 370.26         | 367.97         |
-| AIC value            | 391.28          | 516.44         | 396.26         | 397.97         |
-
-<sup>a</sup> Values in parentheses are *t*-statistics after correction for dispersion.
-
-variables, is clearly the best. It has the smallest AIC value, and none of the LOS or light condition coefficients in the other models are significantly different from zero at 95% confidence. It seems that segment LOS and light conditions are not useful for predicting multi-vehicle crashes. This point will also be discussed later.
-
-### <sup>5</sup>.2. *Site characteristics*
-
-The preliminary analysis clearly showed that each site has a different average crash rate for both crash types. The next step is to replace the site variable with the actual site characteristics in the model estimations. These site characteristics are represented by a linear combination of specific continuous variables, as represented in the initial model form. The LOS and light condition variables were retained as categorical variables. The list of site variables and their abbreviations as used in the models are shown in Table 4. The base model (with respect to LOS and light conditions) was defined as before. All site characteristic, traffic, and light condition variables, and all two-way interactions between the categorical variable levels, were considered as predictors in estimating models for both types of crashes.
-
-Table 5 shows the results from estimating models for single-vehicle crashes. Model 5 includes the five LOS categories, along with three site-related variables: the percentage of the section with a sight distance of at least 1500 feet, the right shoulder width (feet), and the percentage of single-unit trucks in the peak traffic stream. All coefficients are significantly different from zero with at least 95% confidence; however, paired *t*-tests show that the coefficients on LOS D and LOS E are not significantly different from each other. Consequently, model 6 was run, which combines LOS D and E into a single category. The resulting increase in residual deviance is offset by the reduction in model complexity (one less parameter), so this model has a better (smaller) AIC value.
-
-Table 4 Site characteristic variables
-
-| Variable name                                       | Type of variable | Observed values                                                       |
-|-----------------------------------------------------|------------------|-----------------------------------------------------------------------|
-| Functional classifica-<br>tion                      | Categorical      | Principal arterial—<br>othera<br>; minor arterial;<br>major collector |
-| Right shoulder width<br>(feet)                      | Continuous       | 0–8                                                                   |
-| Sight distance over<br>1500 feet (% of sec<br>tion) |                  | 0, 10, 80                                                             |
-| Number of signals                                   | Continuous       | 0–1                                                                   |
-| % Single unit trucks<br>(daily)                     | Continuous       | 0–4                                                                   |
-| % Single unit trucks<br>(peak)                      | Continuous       | 0–2                                                                   |
-| Speed limit (mph)                                   | Continuous       | 35, 40, 45                                                            |
-
-<sup>a</sup> Non-freeway.
-
-Table 5 Final models for single vehicle crashes<sup>a</sup>
-
-| Variable                      | Model 5         | Model 6         | Model 7         | Model 8         | Model 9       |
-|-------------------------------|-----------------|-----------------|-----------------|-----------------|---------------|
-| Intercept                     | −14.78 (−11.74) | −14.88 (−12.22) | −13.06 (−10.48) | −13.06 (−11.84) | −8.91 (−4.13) |
-| LOS A                         | Base            | Base            | Base            | Base            | Base          |
-| LOS B                         | −1.65 (−5.22)   | −1.66 (−5.30)   | −1.66 (−4.89)   | −1.65 (−4.91)   | −1.59 (−4.90) |
-| LOS C                         | −2.81 (−6.27)   | −2.83 (−6.31)   | −2.53 (−5.36)   |                 |               |
-| LOS D                         | −3.90 (−5.72)   |                 |                 |                 |               |
-| LOS E                         | −3.70 (−6.31)   |                 |                 |                 |               |
-| LOS (D, E)                    |                 | −3.77 (−7.01)   | −2.59 (−5.90)   |                 |               |
-| LOS (C, D, E,)                |                 |                 |                 | −2.56 (−6.57)   | −2.76 (−6.86) |
-| Sight distance over 1500 feet | −0.02 (−2.30)   | −0.02 (−2.30)   | −0.02 (−2.31)   | −0.02 (−2.44)   | −0.02 (−2.49) |
-| Right shoulder width (feet)   | −0.27 (−4.23)   | −0.27 (−4.36)   | −0.12 (−2.32)   | −0.12 (−2.33)   | −0.15 (−2.84) |
-| % Single-unit trucks (peak)   | 1.47 (3.68)     | 1.49 (3.80)     |                 |                 |               |
-| % Single-unit trucks (daily)  |                 |                 | −0.01 (−0.06)   |                 |               |
-| Speed limit (mph)             |                 |                 |                 |                 | −0.09 (−2.10) |
-| Exponent on exposure          | 1.18 (11.71)    | 1.19 (12.13)    | 1.16 (11.26)    | 1.16 (11.50)    | 1.13 (11.53)  |
-| Dispersion                    | 0.64            | 0.64            | 0.75            | 0.75            | 0.71          |
-| Null deviance                 | 363.50          | 363.50          | 363.50          | 363.50          | 363.50        |
-| Residual deviance             | 185.95          | 186.01          | 194.94          | 194.96          | 191.44        |
-| AIC value                     | 203.95          | 202.01          | 210.94          | 206.96          | 205.44        |
-
-<sup>a</sup> Values in parentheses are *t*-statistics after correction for dispersion.
-
-The peak hour truck percentage variable seemed inappropriate, since each case potentially represents conditions aggregated over periods of time other than the peak. We therefore ran model 7, which replaces the peak truck variable with a corresponding daily variable; this model performs much worse; the coefficient on the daily truck percentage is not significantly different from zero at even 90% confidence. Furthermore, a paired *t*-test comparing the coefficients on LOS C and the combined LOS D/E category showed them to be statistically identical at 95% confidence (*t\**=1.96).
-
-In consideration of these findings, we then combined LOS C, D and E into one category and removed both truck variables: this was run as model 8. Because other research by the senior author (Ivan and O'Mara 1997) has found the posted speed limit to be a significant variable for predicting crash rates, model 9 was then run adding 'speed limit' to model 8. Model 9 performs better than model 8, having a smaller AIC, but not as well as model 6, which included the suspect peak singleunit truck percentage variable.
-
-Similar analysis was performed with the number of multi-vehicle crashes as the dependent variable; these results are shown on Table 6. In the first model (number 5), the best predictor variables were found to be the functional classification of the road, the number of traffic signals, the daily truck percentage, and the right shoulder width. Only the coefficient on the principal regional arterial roadway class was significantly different from the others (minor arterial or major collector) at 95% confidence; this class has a significantly lower average crash rate than the others. The positive coefficients on both the number of signals and the daily single unit truck percentage make intuitive sense; one would expect both of these variables to increase the intensity of conflicts between vehicles.
-
-However, the positive coefficient on right shoulder width is troubling; one normally expects a wider shoulder to be a safety feature. Consequently, we examined the data surrounding this variable more carefully. Figure 3 shows how the multi-vehicle crash rate varies with the right shoulder width: note how there is no clear linear relationship between the two quantities. We therefore examined several categorical representations of right shoulder width; those that performed best are shown as models 6, 7 and 8. These three models each divide the right shoulder width into two categories, with the lower category having shoulder widths up to 2, 3 and 4 feet, respectively. None of these models perform as well as model 5. The issue of right shoulder width will be discussed more in the next section.
-
-### **6. Discussion**
-
-Several general observations may be made about these results:
-
-- 1. The coefficient on shoulder width was *negati*6*e* for predicting single vehicle crashes, but *positi*6*e* for multi-vehicle crashes.
-- 2. For single-vehicle crashes, the most important predictor variables are the LOS and the percent sight distance over 1500 feet.
-- 3. For multi-vehicle crashes, the most important predictor variables are the class of roadway, number of signals and daily single unit truck percentage.
-
-Single vehicle crashes seem to occur when traffic volume is low and on roads with less forgiving geometry. This makes sense, since a single vehicle crash involves a driver losing control and colliding with a fixed object off the roadway (or an object on the roadway). Under low traffic volumes, there is less likely to be another vehicle to hit, and the less than ideal geometry (sharp horizontal curves and narrow shoulders) reduces the maneuvering room for the driver to recover without leaving the pavement, for example, if the driver had been speeding.
-
-Conversely, multi-vehicle crashes appear to occur in the presence of intense traffic conflicts such as would be observed at a traffic signal, or on roads serving local trips, or when there are many trucks in the traffic stream. Traffic signals tend to be placed in locations where intersecting streets have high traffic volumes. Minor arterials and major collector roads tend to serve local trips and thus have more driveways and local street intersections, increasing the potential for vehicle conflicts.
-
-Clearly, more analysis is required to properly interpret these findings. For one thing, the higher levels of LOS—which correspond to the higher numbers of single-vehicle crashes—are observed more typically at night rather than during the day. Consequently, it is not immediately clear from this analysis whether it is the low volumes, the darkness, or drowsiness of late night drivers that results in greater numbers of singlevehicle crashes. Each of these factors needs to be accounted for explicitly in order to fully understand this phenomenon.
-
-A second issue with these findings is that the LOS was computed for the analysis *segment*, not for the *intersections* on the segment. This might explain why LOS was not significant for multi-vehicle crashes, which occur where there is potential for vehicle conflicts, such as at intersections. If LOS were computed for the intersections on the segment, then it might become significant for predicting multi-vehicle crashes.
-
-Finally, it is difficult to explain the positive coefficient on right shoulder width in the multi-vehicle crash models. One interpretation is that with wider shoulders drivers are more likely to drive too fast and collide with other vehicles exiting or entering driveways. Another is that drivers might be tempted to use a wide shoulder to pass drivers waiting to make left turns, and thus not see a hazardous situation beyond the stopped vehicle. Both of these explanations are related to the presence of intersections on the segment; this issue is addressed in the next section.
-
-#### **7. Conclusions**
-
-In response to these findings, ongoing research by this research team is aimed at including new variables which attempt to clarify the effects of the predictive factors used in this study. Specifically, intersection volumes and the potential for conflicts leading to angle and turning collisions are incorporated into the analysis by considering the AADT on roads intersecting each analysis section. Second, the number of driveways of different types (e.g. residential, service station, retail, etc.) is included to account both for the land use intensity and for conflicts associated with driveway intersections. Third, the potential effect of time of day is being investigated by incorporating it as an additional variable along with daylight conditions.
-
-Table 6 Final models for multi-vehicle crashes<sup>a</sup>
-
-| Variable                           | Model 5         | Model 6         | Model 7         | Model 8         |
-|------------------------------------|-----------------|-----------------|-----------------|-----------------|
-| Intercept                          | −15.87 (−14.69) | −14.72 (−14.07) | −15.32 (−11.13) | −18.57 (−12.89) |
-| Minor arterial or major collector  | Base            | Base            | Base            | Base            |
-| Principal arterial                 | −5.79 (−9.83)   | −3.34 (−8.79)   | −4.05 (−7.55)   | −7.98 (−7.09)   |
-| Number of signals                  | 2.31 (7.78)     | 1.50 (4.40)     | 2.53 (6.39)     | 3.55 (8.53)     |
-| Daily single-unit truck percentage | 1.26 (6.84)     | 1.01 (5.39)     | 1.40 (4.92)     | 2.24 (8.17)     |
-| Right shoulder width (feet)        | 0.44 (5.15)     |                 |                 |                 |
-| Right shoulder width (52 feet)     |                 | Base            |                 |                 |
-| Right shoulder width (\2 feet)     |                 | 1.16 (5.07)     |                 |                 |
-| Right shoulder width (53 feet)     |                 |                 | Base            |                 |
-| Right shoulder width (\3 feet)     |                 |                 | 0.71 (1.84)     |                 |
-| Right shoulder width (54 feet)     |                 |                 |                 | Base            |
-| Right shoulder width (\4 feet)     |                 |                 |                 | 3.79 (4.48)     |
-| Exponent on exposure               | 0.88 (13.83)    | 0.90 (13.57)    | 0.87 (11.67)    | 0.91 (13.22)    |
-| Dispersion                         | 1.30            | 1.32            | 1.69            | 1.33            |
-| Null deviance                      | 788.97          | 788.97          | 788.97          | 788.97          |
-| Residual deviance                  | 370.88          | 374.07          | 400.26          | 384.67          |
-| AIC value                          | 382.88          | 386.07          | 412.26          | 396.67          |
-
-<sup>a</sup> Values in parentheses are *t*-statistics after correction for dispersion.
-
-![](_page_9_Figure_2.jpeg)
-
-Fig. 3. Single and multi-vehicle crashes per million vehicle miles by right shoulder width.
-
-Intersection traffic volumes and driveway counts are not as readily available as the other data used in this analysis. The permanent count stations only provide data for the main road; consequently, the precise exposure and volumes gathered for each analysis section are not available for the roads intersecting them. Instead, in this new research, these intersecting road volumes are estimated using an AADT observed for each road and the observed hourly volumes. Similarly, the HPMS and other roadway inventory datasets do not provide detailed information about the driveways along the section. This information is being collected using the ConnDOT photolog library, a unique data source comprised of driver's eye view images of the entire length of every state highway in Connecticut, updated once each year. Using the photolog library permits expanding our analysis beyond HPMS sites; consequently, we have identified ten new analysis sites in the vicinity of two-lane ATR stations, each 0.5 mile in length.
-
-This newly expanded dataset permits us to estimate additional models for predicting single and multi-vehicle crashes which consider these new variables. In addition, in this analysis we are including the time of day (divided into five categories), permitting us to isolate time of day, daylight and LOS effects and identify the value of each. We are also computing new variables for measuring exposure to multi-vehicle crashes which consider not only the traffic volume on the section being analyzed, but also the traffic volume on intersecting roads. We expect these new models to explain more variation than the models presented in this paper.
-
-# **Acknowledgements**
-
-The research described in this paper was funded by a grant from the United States Department of Transportation through the New England (Region 1) University Transportation Center and was conducted at the Connecticut Transportation Institute of the University of Connecticut.
-
-## **References**
-
-Agresti, A., 1990. Categorical Data Analysis. Wiley, New York.
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-Bozdogan, H., 1987. Model selection and Akaike's Information Criterion (AIC): the general theory and its analytical extensions. Psychometrika 52, 345–370.
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-Frantzeskakis, J., 1983. Accident analysis on two non-controlled access national highways in Greece. ITE Journal 65 (10), 32–34. Gwynn, D., 1967. Relationship of accident rates and accident involvements with hourly volumes. Traffic Quarterly 21, 407–418.
-
-TRB, 1994. Highway capacity manual. Special Report 209. National Research Council, Washington, DC.
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-Ivan, J., O'Mara, P., 1997. Prediction of traffic accident rates using Poisson regression. Presented at 76th Annual Meeting of the Transportation Research Board, Washington, DC, paper no. 970861.
-
-Persaud, B., Mucsi, K., 1995. Microscopic accident potential models for two-lane rural roads. Transportation Research Record 1485, TRB, National Research Council, Washington, DC, pp. 134–139. S-PLUS User's Manual, 1995. Version 3.3 for Windows. Statistical
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-Sciences Division, Mathsoft, Seattle. Valavanis, S., 1959. Econometrics: An Introduction to Maximum
-
-Likelihood Methods. McGraw-Hill, New York.
-
-Vogt. A., Bared, J., 1998. Accident Models for Two-Lane Rural Segments and Intersections. Transportation Research Record 1635, TRB, National Research Council, Washington, DC, 18–29.
-
-Zhou, M., Sisiopiku, V., 1997. Relationship Between Volume to Capacity Ratios and Accident Rates. Transportation Research Record 1581, TRB, National Research Council, Washington, DC, pp. 47–52.

Zotero/001_artiklid/Road Safety Indicators/jafariinvestigatinginfluencesocioeconomicfactors2025.md

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----
-category: literaturenote
-citekey: jafariinvestigatinginfluencesocioeconomicfactors2025
-title: Investigating the influence of socioeconomic factors on the relationships between road characteristics and traffic crash frequency and severity-- A hybrid structural equation modelling − artificial neural networks approach
-authors: "Jafari, Mahsa; Persaud, Bhagwant"
-year: 2025
-date: 2025-00-00 2025
-doi: 10.1016/j.aap.2025.108076
-publication: "Accident Analysis & Prevention"
-url: "https://doi.org/10.1016/j.aap.2025.108076"
-zotero_key: GR6VQ68S
-zotero_storage: ASEB5UDE
-collections: transpordi_planeerimine
-folder: 001_artiklid/Road Safety Indicators
-firstAuthor: "Jafari, Mahsa"
-status: converted
----
-![](_page_0_Picture_1.jpeg)
-
-Contents lists available at [ScienceDirect](www.sciencedirect.com/science/journal/00014575)
-
-# Accident Analysis and Prevention
-
-journal homepage: [www.elsevier.com/locate/aap](https://www.elsevier.com/locate/aap)
-
-![](_page_0_Picture_5.jpeg)
-
-![](_page_0_Picture_6.jpeg)
-
-Investigating the influence of socioeconomic factors on the relationships between road characteristics and traffic crash frequency and severity-- A hybrid structural equation modelling − artificial neural networks approach☆
-
-Mahsa Jafari \* , Bhagwant Persaud
-
-*Department of Civil Engineering, Toronto Metropolitan University, 350 Victoria St, Toronto, ON M5B 2K3, Canada*
-
-#### ARTICLE INFO
-
-*Keywords:* Crash severity Traffic safety Structural equation modeling Moderation Artificial neural networks
-
-## ABSTRACT
-
-Traffic crashes result from complex interactions between driver, roadway, and environmental factors, which traditional methods often fail to capture. This paper investigates the influence of road, weather, and socioeconomic factors on traffic crashes, using a two-stage hybrid Structural Equation Modelling (SEM)-Artificial Neural Networks (ANN) approach to capture the complex relationships between these factors and crash intensity, a variable that jointly captures the frequency and severity of crashes. A database from Ohio collector road segments served as the case study in this novel hybrid approach, which utilized SEM to analyze the complex and moderating relationships between different factors and crash intensity. SEM revealed significant relationships between crash intensity and factors such as "Horizontal Curve," "Road" (AADT and surface width index), "Segment Length," "Speed Limit," "Vertical Curve," and "Vehicle Possession." Based on the SEM results, "Vehicle Possession" significantly moderated the relationship between "Horizontal Curve" and crash intensity. In the next step, ANN further identified key predictors, including "Segment Length," "Road," the interactions of "Vehicle Possession-Speed Limit," "Vehicle Possession-Vertical Curve," and "Age-Road." The findings highlight the advantage of the complementary application of linear and nonlinear methods in providing invaluable theoretical and methodological insights for crash data analysis.
-
-## **1. Introduction**
-
-Globally, traffic fatalities are unacceptably high ([Sheykhfard et al.,](#page-6-0)  [2023\)](#page-6-0). According to statistics published by the World Health Organization (WHO), traffic crashes are projected to rank as the fifth leading cause of death in the world by 2030 ([World Health Organization, 2018](#page-6-0)). The WHO revealed that despite various efforts to reduce road crashes, the reported results were not even close to the 50 % reduction target for 2020 ([World Health Organization, 2018\)](#page-6-0). In the United States, for example, 40,990 people were killed in highway crashes in 2023, with a fatality rate of 1.26 per 100 million vehicle miles ([National Highway](#page-5-0)  [Traffic Safety Administration, 2024\)](#page-5-0). These numbers suggest that investigating traffic crashes and evaluating their influencing factors are essential elements of road safety research. Such investigations typically involve analyzing multiple interrelated factors while considering their inherent complexity [\(Haghshenas et al., 2023\)](#page-5-0). Key contributing factors to traffic crashes can be grouped into categories pertaining to driver traits, road conditions, vehicle characteristics, and crash attributes (Chang & [Wang, 2006\)](#page-5-0). Additionally, socioeconomic elements can also play a significant role in explaining traffic collision patterns and disparities across diverse populations ([Liu et al., 2024\)](#page-5-0). While previous literature has explored direct relationships between crashes and socioeconomic factors ([Gehrke et al., 2023; Mahmoudi et al., 2023; Osama](#page-5-0) & [Sayed, 2017; Saleem et al., 2020](#page-5-0)), limited attention has been given to potential mediator or moderator impacts that might be exerted on underlying relationships between crashes and contributing factors.
-
-Various statistical approaches have been employed to study the influence of different factors on crash frequency and severity (See ([Savolainen et al., 2011](#page-6-0)) and (Lord & [Mannering, 2010\)](#page-5-0) for detailed reviews.); these encompass regression models, Structural Equation
-
-*E-mail address:* [mahsa.jafari@torontomu.ca](mailto:mahsa.jafari@torontomu.ca) (M. Jafari).
-
-<sup>☆</sup> This article is part of a special issue entitled: 'RSS 2024' published in Accident Analysis and Prevention.
-
-<sup>\*</sup> Corresponding author.
-
-Models (SEM), and machine learning techniques. It is important to note that some of these methodologies (e.g., regressions models) have limitations, including issues related to non-normal distributions of data, over- or under-dispersion, and endogeneity (Lord & [Mannering, 2010;](#page-5-0)  [Savolainen et al., 2011](#page-5-0)), that may influence the outcome, potentially making them biased (Wang & [Wang, 2012](#page-6-0)). SEM offers distinct benefits compared to conventional methods through enhanced capacity to model complex interactions among variables, evaluate latent constructs, concurrently test multiple dependent and independent variables, and accommodate both normal and non-normal datasets [\(Hassan, 2016;](#page-5-0)  [Kashani et al., 2021](#page-5-0)).
-
-Generally, in some SEMs, relationships imply that independent constructs directly influence dependent constructs without any systematic influences from other variables. This assumption, however, is only sometimes valid. The understanding of the nature of direct relationships can change dramatically when a third variable is introduced into the analysis. What is referred to as "moderation" occurs when a third variable (i.e., the moderator) influences the strength or direction of a relationship between two constructs ([Hair et al., 2022\)](#page-5-0). The moderation analysis has been employed in a few studies on transportation [\(Guo](#page-5-0)  [et al., 2023; Haerani et al., 2019; Jing et al., 2023; Taiwo et al., 2024;](#page-5-0)  [Zhang et al., 2019](#page-5-0)). However, there has been little or no emphasis on exploring socioeconomic modifiers affecting both crash frequency and severity.
-
-Despite the strengths of SEM, critics point to its incapacity to account for nonlinear variable impacts ([Lee et al., 2020](#page-5-0)), thereby simplifying the complex decision-making process [\(Leong et al., 2013\)](#page-5-0). To address this issue, artificial neural networks (ANNs), a form of artificial intelligence (AI), can be applied to model complex problems with linear and nonlinear relationships ([Parhi et al., 2022\)](#page-5-0). Thus, it seems of interest to complement linear SEM methods with nonlinear ANN methods (Li´[ebana-Cabanillas et al., 2018](#page-5-0)) in that ANNs can be used to deal with these complex problems by modelling linear and nonlinear relationships. It is, however, inappropriate to use ANN methodology to test hypotheses since it is a black-box approach [\(Leong et al., 2013](#page-5-0)). Consequently, it is always recommended that a linear SEM model be complemented with a nonlinear ANN model (Li´[ebana-Cabanillas et al.,](#page-5-0)  [2018\)](#page-5-0).
-
-Among the notable applications of this hybrid method beyond the traffic safety area are those in the fields of business (Arpaci & [Bahari,](#page-5-0)  2023; Xia & [Zhang, 2022](#page-5-0)), health ([Sun et al., 2022](#page-6-0)), data analysis [\(Leong](#page-5-0)  et al., 2020; Li´[ebana-Cabanillas et al., 2018](#page-5-0)), hospitality and tourism (Xia & [Zhang, 2022\)](#page-6-0), structural engineering [\(Elseufy et al., 2022\)](#page-5-0), and urban planning [\(Zhou et al., 2021\)](#page-6-0). In one study in the latter category, the annoyance caused by traffic noise was assessed using the "two-stage structural equation modelling with artificial neural networks" approach. PLS-SEM was utilized to determine the weight and importance of each latent factor, test hypotheses and evaluate the reliability and validity of measurements. Then, ANN was used to uncover nonlinear relationships within the research model ([Das et al., 2022](#page-5-0)).
-
-To the best of the authors' knowledge, no study has thoroughly analyzed crashes in the traffic safety area using a hybrid method such as SEM-ANN to jointly consider both crash frequency and severity level while analyzing the moderation effect of socioeconomic variables. Typically, socioeconomic variables represent differences in travel behaviour, driving behaviour, and driving capabilities that are important for safety analyses but are not usually incorporated in traffic safety analysis. To help fill this void, the primary goal, and the central novelty, of this study is the investigation of crash frequency and severity using the hybrid SEM-ANN method to tease out influential road and environmental factors and the moderation effects of socioeconomic variables. The amalgamated measure of crash frequency and severity in the context of this investigation is referred to as crash intensity. Thus, the higher the crash frequency and the greater the severity of these crashes, the larger will be the crash intensity. A database of Ohio collector road segments was used as a case study for the investigation. In the process,
-
-the research sought to address the following questions: 1) Which factors significantly affect crash intensity? 2) Do socioeconomic factors significantly moderate the relationships between road characteristics and crash intensity? 3) What are the most important variables in predicting crash intensity, considering nonlinear and more complex relationships? To address these questions, PLS-SEM was first employed to analyze and capture the moderating effect of certain variables. Then, ANN was applied to evaluate the potentially nonlinear relationships between latent variables and crash intensity.
-
-#### **2. Methodology**
-
-As noted above, a two-stage hybrid SEM-ANN approach was adopted. The first stage tested the hypotheses and identified factor scores for the ANN input to the second stage. The Smart-PLS 4.1 software [\(Ringle](#page-5-0)  [et al., 2024\)](#page-5-0) was used to develop the PLS-SEM model. The PLS-SEM algorithm involves combining two models: the measurement model, which links latent variables to their observed variables, and the structural model, which maps how the latent variables relate to each other. The process begins by estimating initial scores for all available latent variables, usually with ordinary regression. It then refines these estimates by calculating how the observed data contribute to each latent variable and by analyzing the relationships between the latent variables. This iterative process ensures that the model maximizes the explained variance in the dependent constructs [\(Hair et al., 2022](#page-5-0)).
-
-The next step after estimating the model parameters was to evaluate the PLS-SEM results. The first stage of evaluating the PLS-SEM model was to assess the measurement models. For this purpose, indicator reliability, internal consistency, convergent validity, and discriminant validity were evaluated for each factor following specifications recommended by Hair et al. ([Hair et al., 2022](#page-5-0)). The indicator reliability criterion states that the factor loadings between the indicators and the construct should be at least 0.708. For internal consistency, Cronbach's alpha is a traditional measure, which should be larger than 0.7. However, Cronbach's alpha assumes that the reliability of all indicators is equal and is sensitive to the number of indicators within each construct. Therefore, the reliability coefficient (*ρA*) was used instead. For internal consistency, values below 0.6 indicate a lack of consistency. Convergent validity measures the share of the variance of the indicators explained by the construct, denoted by Average Variance Extracted (AVE). AVE should be 0.5 or higher, meaning that at least 50 % of the variance is explained by the latent variable. Discriminant validity measures how distinct the construct is from other constructs, which is assessed using the "Fornell-Larcker" criteria. According to the "Fornell-Larcker" criterion, the square root of the Average Variance Extracted (AVE) should be greater than the correlation coefficient with other constructs in the model.
-
-For the second stage, to measure relationships between the predictors and the output, SPSS 29.0 software (IBM [Corp, 2023\)](#page-5-0) was used to develop the ANN model, employing a multi-layer perceptron with a Feedforward-Back Propagation algorithm. To specify the model, all independent and moderator variable scores derived from the preceding SEM analysis formed inputs within the initial layer. At the same time, crash intensity served as the ultimate output target. A 10-fold crossvalidation with a 90:10 training–testing data partition was applied to avoid overfitting [\(Parhi et al., 2022; Sim et al., 2014\)](#page-5-0). The modelling process is shown in [Fig. 1](#page-2-0).
-
-# **3. Data**
-
-The data set, which was assembled for a study conducted by Saleem et al. ([Saleem et al., 2020](#page-5-0)), combines data from three sources: crash and roadway inventory data ([FHWA, 2018](#page-5-0)), climate data [\(National Oceanic](#page-5-0)  [and Atmospheric Administration, 2018\)](#page-5-0), and socioeconomic census data (U.S. [Census Bureau, 2018](#page-6-0)). The data pertain to 6,971 traffic crashes on Ohio collector road segments during a 6-year period. In creating that
-
-<span id="page-2-0"></span>![](_page_2_Figure_2.jpeg)
-
-**Fig. 1.** Conceptual Framework of the study.
-
-dataset, road segments were linked to climate and census data by representing each road segment as a single point at its midpoint; each midpoint was then assigned to the corresponding census-block group polygon that contained it, and a polygon file was used to map each census-block group as a unique geographic area. This process facilitated the spatial matching of road segments with relevant climate and census data ([Saleem et al., 2020\)](#page-5-0). Collector road segments were selected for use in this study because drivers from surrounding neighborhoods most likely use them; as a result, the socioeconomic characteristics of those neighborhoods logically become pertinent.
-
-Table 1 summarizes the descriptive statistics for each variable in the dataset. Descriptive labels for the indicator variables used in the analysis, which are found in the last column, were sourced from the original dataset [\(Saleem et al., 2020](#page-5-0)). According to this Table, over half of the curves and grades fall into the "tangent" and "level" categories. Similarly, more than half of the population hold a diploma or higher degree and earn over \$50,000 annually. Table 1 also summarizes data on the indicators of crash intensity, for which frequency was modelled as the count of fatal plus severe lane departure crashes, while severity was considered as an ordinal variable, with 1 indicating property damage only (PDO) crashes, 2 indicating minor injury crashes, and 3 indicating fatal and severe injury crashes combined.
-
-# **4. Analysis and results**
-
-As depicted in [Fig. 2,](#page-3-0) the SEM model incorporates independent latent variable indicators comprising horizontal curvature, Ln(AADT), surface width, Ln(segment length), speed limits categorized as 40–50 mph and 50–60 mph, vertical grade, rainfall, and temperatures. Furthermore, socioeconomic variables considered as independent and moderator variables consist of age, the percentage of the population holding a higher degree of education, the percentage of household earnings surpassing \$50,000, and the percentage of the population owning more than one vehicle. Ultimately, crash intensity, the integrated measure of crash frequency and severity level, as defined earlier, was considered as the dependent variable.
-
-In developing the SEM model, the construct reliability and validity were evaluated using criteria and threshold values presented earlier for indicator reliability, consistency reliability, convergent validity, and discriminant validity, which were gleaned from the book published by Hair et al. ([Hair et al., 2022](#page-5-0)). Indicator reliability was assessed using the loadings of indicators on the corresponding latent variables. According to [Table 2,](#page-3-0) construct validity is achieved in the model since the absolute loading values are higher than 0.7. In addition, consistency reliability and convergent validity criteria were also met, as the reliability coefficient (*ρA*) and Average Variance Extracted (AVE) exceeded 0.7 and 0.5, respectively [\(Hair et al., 2022](#page-5-0)).
-
-The discriminant validity of the identified constructs was evaluated using the "Fornell-Larcker" criterion. According to [Table 3](#page-3-0), discriminant validity of latent variables was also achieved as the square root of AVE values (the diagonal values) are greater than the inter-construct correlation coefficients values (the non-diagonal values).
-
-Lastly, the structural model was evaluated for its explanatory power
-
-**Table 1**  Descriptive Statistics and Variable Names.
-
-| Variable<br>description                                                             | mean           | std          | Min         | max         | Variable name/<br>category<br>used in the analysis                                    |
-|-------------------------------------------------------------------------------------|----------------|--------------|-------------|-------------|---------------------------------------------------------------------------------------|
-| Segment length<br>(mi)                                                              | 0.58           | 0.94         | 0.001       | 8.03        | Ln(Segment Length)                                                                    |
-| Curve radius (ft)                                                                   | 69.6           | 195.6        | 0           | 1311        | radiusft_moderate<br>(500–1000 ft)<br>radiusft_tangent<br>(>1000 ft and no<br>radius) |
-| Grade percentage                                                                    | 2.22           | 3.78         | 0           | 20          | grade steepness_level<br>(0–3)<br>grade<br>steepness_steep (≥ 6)                      |
-| Annual Average<br>Daily Traffic                                                     | 2132           | 1648         | 92          | 12,360      | Ln(AADT)                                                                              |
-| Surface width (ft)<br>Speed limit (mph)                                             | 20.88<br>52.78 | 2.58<br>5.33 | 16.00<br>25 | 46.00<br>60 | surf_wid<br>speedlimit 40–50<br>mph<br>speedlimit 50–60<br>mph                        |
-| Total average<br>rainfall and<br>snowfall (in)                                      | 69.7           | 21.73        | 48.2        | 158.64      | average<br>precipitation                                                              |
-| Average annual<br>maximum<br>temperature (F)                                        | 61.09          | 2.54         | 57.00       | 69.00       | Tempmaxavg                                                                            |
-| Percentage of<br>population ages<br>25 + with a<br>diploma and<br>university degree | 0.56           | 0.10         | 0.14        | 0.89        | diploma and<br>universityplus                                                         |
-| Percentage of<br>households with<br>income<br>>\$50,000                             | 0.51           | 0.14         | 0.03        | 0.96        | income more than<br>50 k                                                              |
-| Percentage of<br>households with<br>at least one<br>vehicle                         | 0.94           | 0.09         | 0.12        | 1.00        | vehicle 1 and more                                                                    |
-| Percentage of<br>population ages<br>45+                                             | 0.46           | 0.1          | 0.04        | 0.83        | Age45plus                                                                             |
-| Fatal plus severe<br>lane departure<br>crashes                                      | 0.59           | 0.88         | 0           | 10          | LNDP_KAB                                                                              |
-| Minor lane<br>departure<br>crashes                                                  | 0.16           | 0.42         | 0           | 5           | n/a                                                                                   |
-| PDO lane departure<br>crashes                                                       | 1.06           | 1.19         | 0           | 18          | n/a                                                                                   |
-
-using the coefficient of determination (R2 ), the traditional measure of model fit. According to the output of PLS-SEM, the value of R2 was 0.085, indicating a weak explanatory power, possibly due to complex relationships between variables. This outcome suggested the need for further exploration using the hybrid SEM-ANN approach, which is described later.
-
-It is noteworthy that SmartPLS, the software used for PLS-SEM,
-
-<span id="page-3-0"></span>![](_page_3_Figure_2.jpeg)
-
-**Fig. 2.** The Developed SEM Model.
-
-**Table 2**  Loadings, Reliability coefficients and Average Variance Extracted values of constructs.
-
-| Constructs          | Indicators                                           | Loading          | Composite<br>reliability<br>(ρA) | Average<br>variance<br>extracted<br>(AVE) |
-|---------------------|------------------------------------------------------|------------------|----------------------------------|-------------------------------------------|
-| Horizontal<br>curve | Radius ft_tangent<br>Radiusft_moderate               | 0.981<br>− 0.806 | 1.184                            | 0.805                                     |
-| Road                | AADT<br>Surface Width                                | 0.943<br>0.681   | 0.818                            | 0.677                                     |
-| Intensity           | LNDP_KAB<br>Severity                                 | 0.952<br>0.884   | 0.932                            | 0.844                                     |
-| Speedlimit          | speedlimit 40–50<br>speedlimit 50–60                 | − 0.938<br>0.977 | 1.003                            | 0.917                                     |
-| Vertical<br>curve   | Grade steepness<br>level<br>Grade steepness<br>steep | 0.977<br>− 0.957 | 0.971                            | 0.935                                     |
-| Weather             | average<br>precipitation<br>Tempmaxavg               | 0.935<br>− 0.836 | 0.838                            | 0.786                                     |
-
-provides various fit measures, including SRMR, Exact fit criteria d\_ULS and d\_G, NFI, and Chi<sup>2</sup> . Nevertheless, Hair et al. emphasize that PLS-SEM focuses on prediction rather than explanatory modeling. Consequently, fit measures have limited utility in PLS-SEM models and can even be detrimental, as researchers may compromise predictive power in pursuit of a better fit [\(Hair et al., 2022](#page-5-0)).
-
-After confirming the validity and reliability of the constructs, the path coefficients and p-values for all relationships were evaluated, as shown in [Table 4,](#page-4-0) which reveals that six key factors − horizontal curve, road, segment length, speed limits, vertical grade, and vehicle possession − significantly influence crash intensity.
-
-Considering road alignments, the SEM results revealed that decreasing radii are associated with more severe and frequent crashes, i. e., crash intensity (estimate = -0.233, p *<* 0.05); this is consistent with previous studies that indicated that sharper curves are associated with increased severity and frequency [\(Himes et al., 2019; Wu et al., 2017\)](#page-5-0). In terms of vertical grade, the results show that level vertical alignments are associated with increasing crash intensity and that steep grades are related to lower intensity (estimate = -0.091, p = 0.001), which is consistent with the results of Saleem et al. ([Saleem et al., 2020\)](#page-5-0). These results can potentially be attributed to higher operating speeds on straight and level segments. For the road characteristics, the results showed that increasing AADT and surface width (which comprise the "road" variable) are associated with increasing crash intensity (estimate = 0.064, p *<* 0.05). The results also show that higher speed limits are associated with higher intensity (estimate = 0.102, p = 0.002), as indicated by Yu et al. (Yu & [Xu, 2018](#page-6-0)).
-
-Among the socioeconomic factors, vehicle possession positively impacts crash intensity, indicating that an increasing percentage of the population with more than one vehicle per household results in higher crash intensity (estimate = 0.066, p = 0.027), which is consistent with the results of Forrest et al. [\(Forrest et al., 2023](#page-5-0)). Regarding the interactions of socioeconomic variables on road and weather characteristics, the results show that vehicle possession moderates the impact of the horizontal curve on the intensity with a coefficient of − 0.072 (p = 0.04). The results further show that as the percentage of the higherincome population rises, the impact of horizontal grade on intensity
-
-**Table 3**  Discriminant Validity using AVE Square Roots and Correlation Coefficients.
-
-|                    | Age    | Intensity | education | horizontal curve | income | road   | segment length | Spd<br>limt | vehicle possession | vertical curve | weather |
-|--------------------|--------|-----------|-----------|------------------|--------|--------|----------------|-------------|--------------------|----------------|---------|
-| Age                | 1.00   |           |           |                  |        |        |                |             |                    |                |         |
-| Intensity          | 0.00   | 0.92      |           |                  |        |        |                |             |                    |                |         |
-| education          | 0.30   | 0.00      | 1.00      |                  |        |        |                |             |                    |                |         |
-| horizontal curve   | 0.02   | 0.05      | 0.01      | 0.90             |        |        |                |             |                    |                |         |
-| income             | 0.06   | 0.05      | 0.29      | 0.09             | 1.00   |        |                |             |                    |                |         |
-| road               | − 0.01 | 0.09      | 0.02      | 0.19             | 0.22   | 0.82   |                |             |                    |                |         |
-| segment length     | 0.00   | 0.27      | 0.05      | 0.39             | 0.15   | 0.16   | 1.00           |             |                    |                |         |
-| spdlimt            | − 0.01 | 0.07      | 0.07      | 0.02             | 0.03   | − 0.05 | 0.13           | 0.96        |                    |                |         |
-| vehicle possession | 0.36   | 0.03      | 0.50      | 0.02             | 0.21   | − 0.02 | 0.06           | 0.03        | 1.00               |                |         |
-| vertical curve     | − 0.01 | 0.09      | 0.05      | 0.11             | 0.06   | 0.08   | 0.44           | 0.04        | 0.05               | 0.97           |         |
-| weather            | 0.03   | 0.06      | − 0.01    | 0.14             | 0.17   | 0.24   | 0.15           | 0.06        | − 0.10             | 0.04           | 0.89    |
-
-<span id="page-4-0"></span>**Table 4**  Path coefficients of SEM model and their significance.
-
-| Relationships                                       | Coefficient | P values |
-|-----------------------------------------------------|-------------|----------|
-| Age − > Intensity                                   | − 0.013     | 0.691    |
-| education − > Intensity                             | − 0.051     | 0.155    |
-| horizontal curve − > Intensity                      | − 0.233     | <0.001   |
-| income − > Intensity                                | − 0.016     | 0.597    |
-| road − > Intensity                                  | 0.064       | <0.001   |
-| segment length − > Intensity                        | 0.3         | <0.001   |
-| spdlimt − > Intensity                               | 0.102       | 0.002    |
-| vehicle possession − > Intensity                    | 0.066       | 0.027    |
-| vertical curve − > Intensity                        | − 0.091     | 0.001    |
-| weather − > Intensity                               | 0.016       | 0.274    |
-| Age − vertical curve − > Intensity                  | − 0.04      | 0.226    |
-| vehicle possession − vertical curve − > Intensity   | − 0.014     | 0.608    |
-| education − road − > Intensity                      | − 0.009     | 0.57     |
-| income − spdlimt − > Intensity                      | − 0.047     | 0.188    |
-| income − horizontal curve − > Intensity             | 0.022       | 0.532    |
-| education − horizontal curve − > Intensity          | 0.013       | 0.734    |
-| income − vertical curve − > Intensity               | 0.047       | 0.093    |
-| Age − spdlimt − > Intensity                         | − 0.022     | 0.577    |
-| education − spdlimt − > Intensity                   | − 0.001     | 0.99     |
-| vehicle possession − spdlimt − > Intensity          | 0.018       | 0.605    |
-| vehicle possession − horizontal curve − > Intensity | − 0.072     | 0.04     |
-| vehicle possession − road − > Intensity             | 0.013       | 0.354    |
-| income − road − > Intensity                         | 0.024       | 0.069    |
-| education − vertical curve − > Intensity            | 0.032       | 0.315    |
-| Age − horizontal curve − > Intensity                | 0.058       | 0.093    |
-| Age − road − > Intensity                            | − 0.002     | 0.916    |
-
-gets lower. Based on the SEM results, most moderation effects appear negligible and insignificant, likely due to unobserved nonlinearity among relationships. Applying Artificial Neural Networks (ANN) facilitated the exploration of such nonlinear relationships. The ANN investigation is described next.
-
-For the ANN investigation, a 10-fold cross-validation with a 90:10 training–testing data partition was used to avoid overfitting. The 10-fold cross-validation assesses the performance of the ANN model by dividing data into ten subsets. This process helps to reduce the variability in the model performance and provides a more robust evaluation. As shown in Table 5, nine folds train the model in each iteration or network (the training sample), while the remaining fold validates it (the test sample). This process is repeated ten times. The performance metrics of the model are then averaged, providing a robust estimate of its expected performance on unseen data. Root Mean Square Error (RMSE), a measure that reflects the difference between predicted and actual observed values, was then used to evaluate the ANN models as a proxy for prediction accuracy (Li´[ebana-Cabanillas et al., 2017\)](#page-5-0). Ideally, minimal values should be achieved in the RMSE scores attained from train or test samples. Table 5 shows the sample sizes of training and test samples in each iteration and their corresponding RMSE. It can be seen that the
-
-**Table 5**  ANN validation results (RMSE values).
-
-| Network                       | Sample<br>size<br>(Training) | Sample<br>size (Test) | RMSE<br>(Training) | RMSE<br>(Test) | RMSE<br>(Training<br>Test) |
-|-------------------------------|------------------------------|-----------------------|--------------------|----------------|----------------------------|
-| 1                             | 6278                         | 693                   | 0.077              | 0.078          | 0.000                      |
-| 2                             | 6225                         | 746                   | 0.081              | 0.075          | 0.005                      |
-| 3                             | 6215                         | 756                   | 0.078              | 0.079          | 0.001                      |
-| 4                             | 6291                         | 680                   | 0.077              | 0.077          | 0.000                      |
-| 5                             | 6242                         | 729                   | 0.084              | 0.094          | 0.010                      |
-| 6                             | 6274                         | 697                   | 0.077              | 0.077          | 0.000                      |
-| 7                             | 6221                         | 750                   | 0.078              | 0.079          | 0.000                      |
-| 8                             | 6253                         | 718                   | 0.079              | 0.085          | 0.006                      |
-| 9                             | 6272                         | 699                   | 0.079              | 0.075          | 0.005                      |
-| 10                            | 6249                         | 722                   | 0.079              | 0.077          | 0.002                      |
-| Mean                          |                              |                       | 0.079              | 0.080          | 0.003                      |
-| Standard<br>Deviation<br>(SD) |                              |                       | 0.002              | 0.006          | 0.003                      |
-
-RMSE values ranged from 0.07 to 0.09 for the outcome variable. In addition, there is a very slight average difference between training and testing RMSE values in the model (Mean = 0.003, SD = 0.003), further corroborating the substantial relative prediction accuracy and predictive power of the approach used in this study.
-
-Following the optimal ANN architecture configuration, a sensitivity analysis ensued to discern the comparative significance of each variable. Sensitivity analysis in the ANN model shows the normalized importance scores as percentages that represent the variation in the dependent variable with changes in the independent variables associated with it. The sensitivity analysis results are shown in [Table 6. Table 6](#page-5-0) shows the ten most important variables obtained through sensitivity analysis. The normalized ranks from the sensitivity analysis indicate that the segment length is the most important predictor of intensity, followed by "Road" factor (an index of AADT and surface width), "Education-Road", "Vehicle Possession-Vertical Curve", and "Income-Road". These results seem to be intuitively logical and reasonable.
-
-#### **5. Summary and Conclusions**
-
-The paper aimed to apply a novel hybrid technique to predict the intensity of traffic crashes, which is an amalgamated measure of the frequency of crashes and their severity level. To accomplish this objective, a dataset containing crash and roadway inventory data, climate data and socioeconomic census data for Ohio collector road segments was used. A hybrid PLS-SEM-ANN technique was employed to analyze the factors affecting the intensity of traffic crashes. Using SEM, the influence of socioeconomic variables as moderators on the impact of road and weather characteristics on crash intensity was assessed, and, using ANN, the nonlinear complex relationships between independent, moderating and dependent variables were captured. SEM results showed that "Horizontal Curve", "Road", "Speed Limit", "Segment Length", "Vertical Curve", and "Vehicle Possession" have significant linear impacts on crash intensity. Still, most of the moderating effects were insignificant. However, ANN results revealed that the interactions are among the most important predictors of crash predictors, suggesting that using ANN to enrich SEM analyses offers unique insights into model relationships that would otherwise remain undetected.
-
-Combining these complementary approaches can provide a more holistic understanding of contributing factors to traffic crashes, allowing for better managerial decisions. The in-depth analyses resulting from the hybrid approaches described can lead to designing appropriate countermeasures to mitigate crash intensity. Although SEM-ANN modelling may be considered complex, the approach can potentially help safety professionals identify key crash factors, prioritize them, and allocate resources effectively, thereby enabling a more strategic approach to improving traffic safety. In so doing, it offers several benefits, including enhanced accuracy, a deeper understanding of non-linear relationships, greater robustness to outliers, and improved model interpretability through prioritized factor analysis. Nevertheless, there are some limitations to this research. First, this research was only conducted for a database from Ohio; hence, the findings themselves cannot be generalized for application elsewhere. To further extend the scope of the current study, future studies may involve cross-national or cross-cultural perspectives. Second, although several variables are considered in the current study, more comprehensive information would be helpful, for example, more information about neighborhoods, such as religion and ethnicity, driver sociodemographic and behavioral characteristics, and characteristics of the vehicles involved in crashes. Third, a hybrid framework can be developed using more advanced machine learning algorithms to enhance the investigation of factors associated with crashes and their severity.
-
-#### **CRediT authorship contribution statement**
-
-**Mahsa Jafari:** Conceptualization, Data curation, Investigation,
-
-<span id="page-5-0"></span>**Table 6**  ANN Sensitivity Analysis Results.
-
-| Network<br>Variables                | Normalized Importance |      |      |      |      |      |      |      |      |      |                 |
-|-------------------------------------|-----------------------|------|------|------|------|------|------|------|------|------|-----------------|
-|                                     | 1                     | 2    | 3    | 4    | 5    | 6    | 7    | 8    | 9    | 10   | Normalized rank |
-| segment length                      | 100                   | 100  | 100  | 100  | 100  | 100  | 91.8 | 100  | 100  | 100  | 1               |
-| road                                | 46.5                  | 29.2 | 32.2 | 47.3 | 48.3 | 41.4 | 21.9 | 94.9 | 64.1 | 13.7 | 0.44            |
-| vehicle possession − spdlimt        | 18.9                  | 72.2 | 25.8 | 13.5 | 14.8 | 31.8 | 100  | 68.8 | 24.2 | 46.5 | 0.42            |
-| vehicle possession − vertical curve | 10.9                  | 21.5 | 53.1 | 17.2 | 16.6 | 14.2 | 47.7 | 49.2 | 48.1 | 70.2 | 0.35            |
-| Age − road                          | 13.5                  | 36.4 | 47.1 | 15.4 | 13.1 | 15.7 | 10.8 | 62.9 | 50.2 | 83.3 | 0.35            |
-| education − road                    | 21.4                  | 58.6 | 12.0 | 14.7 | 12.4 | 26.8 | 9.30 | 62.2 | 47.9 | 63.9 | 0.33            |
-| income − road                       | 11.9                  | 68.0 | 24.3 | 18.4 | 17.9 | 17.4 | 41.0 | 43.3 | 24.9 | 37.6 | 0.31            |
-| vehicle possession − road           | 24.5                  | 41.0 | 17.2 | 16.6 | 35.4 | 13.2 | 44.9 | 36.8 | 53.6 | 12.8 | 0.29            |
-| income                              | 13.2                  | 21.1 | 17.3 | 23.3 | 34.1 | 26.2 | 7.80 | 67.3 | 26.2 | 43.8 | 0.28            |
-| education- horizontal curve         | 16.9                  | 53.4 | 17.4 | 9.30 | 43.0 | 17.4 | 18.3 | 22.4 | 11.8 | 46.9 | 0.25            |
-
-Methodology, Software, Writing – original draft. **Bhagwant Persaud:**  Conceptualization, Funding acquisition, Supervision, Writing – review & editing.
-
-## **Declaration of Generative AI and AI-assisted technologies in the writing process**
-
-No generative AI or AI-assisted technologies are used in this manuscript.
-
-#### **Declaration of competing interest**
-
-The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
-
-#### **Acknowledgment**
-
-The financial support provided by a discovery grant (Appl. ID RGPIN-2023-03787) from the Natural Sciences and Engineering Research Council of Canada (NSERC) is gratefully acknowledged, as is the provision of the data set by the University of North Carolina Highway Safety Research Center.
-
-#### **Appendix A. Supplementary data**
-
-Supplementary data to this article can be found online at [https://doi.](https://doi.org/10.1016/j.aap.2025.108076)  [org/10.1016/j.aap.2025.108076.](https://doi.org/10.1016/j.aap.2025.108076)
-
-## **Data availability**
-
-Data will be made available on request.
-
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