category: literaturenote citekey: barrettanalysistrafficgrowthrates2001 title: Analysis of Traffic Growth Rates authors: "Barrett, Monica; Graves, R.; Allen, David; Pigman, Jerry; Abu-Lebdeh, Ghassan; Aultman-Hall, Lisa; Bowling, Sarah" year: 2001 date: 2001-08-01 2001-08-01 doi: "http://dx.doi.org/10.13023/KTC.RR.2001.15" publication: Kentucky Transportation Center Research Report url: "http://uknowledge.uky.edu/ktc_researchreports/284" zotero_key: CN3FPRE7 zotero_storage: UJC55PSD collections: magistritöö / kohalikud teed folder: 001_artiklid firstAuthor: "Barrett, Monica"

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K T C ENTUCKY RANSPORTATION ENTER

College of Engineering

ANALYSIS OF TRAFFIC GROWTH RATES

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Research Report KTC-01-15/SPR213-00-1F

ANALYSIS OF TRAFFIC GROWTH RATES

Monica L. Barrett R. Clark Graves David L. Allen Jerry G. Pigman

Kentucky Transportation Center

Ghassan Abu-Lebdeh Lisa Aultman-Hall Sarah T. Bowling

Department of Civil Engineering

College of Engineering University of Kentucky Lexington, Kentucky

in cooperation with

Kentucky Transportation Cabinet Commonwealth of Kentucky

and

Federal Highway Administration U.S. Department of Transportation

The contents of this report reflect the views of the authors, who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the University of Kentucky, the Kentucky Transportation Cabinet, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. The inclusion of manufacturer names and trade names is for identification purposes, and is not considered an endorsement.

| 1.
Report Number
KTC-01-15 / SPR213-00-1F | 2. Government Accession No. | 3. Recipient's Catalog No. | | |------------------------------------------------------------------------------------------------------------------------------------|-----------------------------|----------------------------------------------------------------------|--| | 4. Title and Subtitle
Analysis of Traffic Growth Rates | | 5.
Report Date
August 2001 | | | | | 6.
Performing Organization Code | | | 7.
Author(s)
D.L. Allen, M. L. Barrett, R. C. Graves, J. G. Pigman,
G. Abu-Lebdeh, L. Aultman-Hall, S. T. Bowling | | 8.
Performing Organization Report No.
KTC-01-15 / SPR213-00-1F | | | 9.
Performing Organization Name and Address | | 10. Work Unit No. | | | Kentucky Transportation Center | | | | | College of Engineering
University of Kentucky
Lexington, Kentucky 40506-0281 | | 11.
Contract or Grant No. | | | 12.
Sponsoring Agency Name and Address
Kentucky Transportation Cabinet
State Office Building
Frankfort, Kentucky 40602 | | 13.
Type of Report and Period Covered
Final | | | | | 14. Sponsoring Agency Code | |

15. Supplementary Notes

Prepared in cooperation with the Kentucky Transportation Cabinet and the Federal Highway Administration

16. Abstract

The primary objectives of this study were to determine patterns of traffic flow and develop traffic growth rates by traffic composition and highway type for Kentucky's system of highways. Additional subtasks included the following: 1) a literature search to determine if there were new procedures being used to more accurately represent traffic growth rates, 2) development of a random sampling procedure for collecting traffic count data on local roads and streets, 3) prediction of vehicle miles traveled based on socioeconomic data, 4) development of a procedure for explaining the relationship and magnitude of traffic volumes on routes functionally classified as collectors and locals, and 5) development of county-level growth rates based on procedures to estimate or model trends in vehicle miles traveled and average daily traffic.

Results produced a random sampling procedure for traffic counting on local roads which were used as part of the effort to model traffic growth at the county level in Kentucky. Promising results were produced to minimize the level of effort required to estimate traffic volumes on local roads by development of a relationship between functionally classified collector roads and local roads. Both regression and logarithmic equations were also developed to explain the relationship between local and collector roads. County-level growth rates in traffic volumes were analyzed and linear regression was used to represent changes in ADT to produce county-level growth rates by functional class. Linear regression and Neural Networks models were developed in an effort to estimate interstate and non-interstate vehicle miles traveled.

17. Key Words Volume Estimates Regression Analysis Local Roads Random Sampling Vehicle Miles Traveled (VMT) Estimates	18. Distribution Statement Neural Network Models Unlimited, with Transportation Cabinet	approval of	the Kentucky
19. Security Classification (report) Unclassified	20. Security Classification (this page) Unclassified	21. No. of Pages 158	22. Price

List of Tables Executive Summaryvii Acknowledgments ix 1.0 Background and Objectives1 2.0 Review of Literature and Survey of States2 2.1 Review of Literature 2 2.2 Survey of States5 3.0 Development of a Random Sampling Procedure for Local Road Traffic Count Locations 5 3.1 Introduction 5 3.2 Other Efforts to Estimate Local Road VMT 7 3.3 The GIS Grid-Based Sampling Methodology8 3.3.1 The Challenges of Finding a Methodology8 3.3.2 Creating the Point-like Sections for Three Study Areas 3.4 Consideration of Bias in the Point-like Sections 3.5 Conclusions 3.6 Local Road ADT Sample with Spatial Variables 3.7 Local Road Traffic Volume Summary 3.8 Regression Analysis to Predict Local Road ADT 4.0 Prediction of VMT Based on Socioeconomic Data 4.1 Introduction 4.2 Background 4.3 Objective 4.4 Data Input 4.4.1 Data Measures of Quality 4.5 Methodology 4.5.1 Regression Modeling 4.5.2 Neural Networks (NNets) Modeling 4.6 Results 4.6.1 Descriptive Summary of All Trials and Results 4.7 Final Models 4.7.1 Non-Interstate VMT 4.7.2 Interstate VMT 4.8 Discussion 4.9 Conclusion Recommendations		List of Figuresiii
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	4.10		51

5.0		Development of Ratios for Relationship Between Collectors and Local Roads	52
	5.1	Local to Collector Ratio Analysis	52
	5.2	Data Analysis	53
	5.3	2000 Local Sample	56
6.0		Development of County-Level Growth Rates	61
	6.1	Growth Rate Development	61
	6.2	Statewide Functional Class Averages	63
	6.3	Interstate Corridor Analysis	66
7.0		References	67
8.0		Appendices	69
	8.1	Appendix A – Kentucky Year 2020 VMT Forecast Procedures	71
	8.2	Appendix B – State Survey Results	87
	8.3	Appendix C – VMT From Historical KYTC Data Files (1993-1999)	105
	8.4	Appendix D – Socioeconomic Census-Based Data	109
	8.5	Appendix E – Traffic Volume System (TVS) Estimating Procedure	115
	8.6	Appendix F – Weighted County Level Functional Class Growth Rates	117
	8.7	Appendix G – Unweighted County Level Functional Class Growth Rates	129
	8.8	Appendix H - Corridor Interstate Growth Rates	141

LIST OF FIGURES

1.	A "Cookie Cutter" Grid on a Network of Roads	11
2.	Bias Analysis for Pike County (0.20 mile grid)	12
3.	Pike County Weights	16
4.	Counties Used in Local Road ADT Data Collection	18
5.	Major Highways Used in Distance Calculation	23
6.	Cities Used in Distance Calculation	24
7.	Prediction of State VMT (combined interstate and non-interstate VMT)	32
8.	Distribution of Observation Error Over The Error Range (interstate and non-interstate VMT combined)	33
9.	Distribution of Non-Interstate VMT Prediction Error Over Error Range	35
10.	Distribution of Interstate VMT Prediction Error Over Error Range	35
11.	Distribution of Prediction Error over the Error Range (non-interstate VMT)	39
12.	Projected Increase in Non-Interstate VMT between 1999 and 2000 as a Percentage of 1999 VMT	40
13.	Distribution of Prediction Error over the Error Range (interstate VMT)(1993-1999 data)	41
14.	Projected Increase in Interstate VMT between 1999 and 2000 as a Percentage of 1999 VMT	42
15.	A Sample of Corridor-Based VMT Models	43
16 16	a. Comparison of Actual and Predicted VMT – Training Data (Interstate 75 Corridor) b. Comparison of Actual and Predicted VMT – Testing Data (Interstate 75 Corridor)	44 45
17.	Relationship between Change in Interstate VMT and Retail Sales (Interstate 24 Corridor)	46
18.	Relationship between Change in Interstate VMT and Retail Sales (Interstate 64 Corridor)	47
19.	Variation of VMT over Time for Selected Counties (VMT figures are in thousands)	49
20.	Distribution of Yearly Interstate VMT	49

21. Distribution of Yearly Non-Interstate VMT	50
22. Comparison of Functional Class Ratios	54
23. Non-Urbanized Counties Functional Class Ratio FC 09/08	55
24. Urbanized Counties Functional Class Ratio FC 09/08	55
25. All Counties Functional Class Ratio FC 19/17	56
26. Comparison of Collector ADT and Local ADT	58
27. Comparison of Predictive Relationships	60
28. Allen County Functional Class 06 Traffic Monitoring Stations	62
29. Average Annual Functional Class 06 ADT for Allen County	62
30. Statewide Unweighted Average ADT for Functional Class 01 and 11	64
31. Statewide Unweighted Average ADT for Rural Functional Classes	65
32. Statewide Unweighted Average ADT for Urban Functional Classes	65

LIST OF TABLES

1.	Y-Intercept, R-Squared, and X2 Coefficient	13
2.	Slope Comparison	15
3.	Corrected and Uncorrected ADT and VMT Values (0.2 mile grid-based sample)	16
4.	Local Road ADT Sample Locations by County	19
5.	Local Rural Road ADT Summary	21
6.	Local Urban Road ADT Summary	21
7.	Regression Results for County-Level Variables	22
8.	Urban ADT R-Squared Regression Results for Spatial Variables	25
9.	Rural ADT R-Squared Regression Results for Spatial Variables	26
10.	Socioeconomic Variables Used	28
11.	Correlation Matrix (using data from all counties for 1993-1999)	32
12.	Percent Error at the County Level (based on state-wide models)	33
13.	Historical Functional Class Ratios	52
14.	Average FC 08 and FC 09 ADT for 2000 Local Sample Counties	57
15.	Average FC 17 and FC 19 ADT for 2000 Local Sample Counties	58
16.	Summary of Statewide County Level Functional Classification Growth Rates	63
17.	Summary of Statewide Functional Class Growth Rates	66
18.	Interstate Corridor Weighted ADT Growth	66

EXECUTIVE SUMMARY

The primary objectives of this study were to determine patterns of traffic flow and develop traffic growth rates by highway type for Kentucky's system of highways. Additional subtasks included the following: 1) a literature search to determine if there were new procedures being used to more accurately represent traffic growth rates, 2) development of a random sampling procedure for collecting traffic count data on local road and streets, 3) prediction of VMT based on socioeconomic data, 4) development of a procedure for explaining the relationship and magnitude of traffic volumes on routes functionally classified as collectors and locals, and 5) development of county-level growth rates based on procedures to estimate or model trends in vehicle miles traveled and average daily traffic.

The literature review produced reference documents that were related to the objectives of the research study; however, none offered any new approaches that could be adopted and directly applied to the prediction of growth rates in Kentucky. The survey of states produced responses from 29 of 45 agencies that received the questionnaire. In general, the survey indicated that states were using historical data and regression analysis to predict growth rates.

The development of a random sampling procedure for count locations on local roads produced a GIS grid-based process. Results from the bias analysis were presented along with a description of procedures used to correct for the sampling bias. It was concluded that large samples were need in order to obtain confidence in mean ADT values for local roads.

Efforts to estimate or model traffic growth at the county level in Kentucky produced several socioeconomic variables which offered promise as reliable independent variables. The analysis procedures included linear regression models and Neural Networks models, with separate analyses for interstate and non-interstate VMT. Results indicated that predictions of VMT based on socioeconomic data was not entirely successful, even though it was determined that available data has the potential to be used predicting non-interstate VMT for most counties. It was noted that national and regional data should be used for predicting interstate VMT. Neural Networks has shown significant potential for use as a modeling technique; however, the nature and structure of specific data should be used to determine which modeling approach is best.

The need to estimate traffic volumes on local roads, without excessive data collection efforts, resulted in development of a relationship between functionally classified collector roads and local roads. Ratios of local road ADT to collector road ADT were developed for both rural and urban classifications. Regression relationships were also developed to explain the relationship between local and collector roads. Models were evaluated for goodness of fit and ability to predict local ADT beyond the limits of the 2000 local sample data. Based on evaluation of regression and logarithmic equations, the power equation was found to provide the best fit.

County level growth rates in traffic volumes were analyzed and linear regression was used to represent changes in ADT. Historical data for the period 1991 through 2000 were used to produce county-level growth rates by functional class.

ACKNOWLEDGMENTS

An expression of appreciation is extended to the following Study Advisory Committee members and others that participated in the project.

Rob Bostrom	Kentucky Transportation Cabinet – Multimodal Programs
Annette Coffey	Kentucky Transportation Cabinet - Transportation Planning
Dan Inabnitt	Kentucky Transportation Cabinet – Transportation Planning
Glenn Jilek	Federal Highway Administration
Jesse Mayes	Kentucky Transportation Cabinet - Multimodal Programs
Peter Rogers	Kentucky Transportation Cabinet - Transportation Planning
Charles Schaub	Kentucky Transportation Cabinet - Multimodal Programs
James Simpson	Kentucky Transportation Cabinet - Multimodal Programs
Paul Utter	Kentucky Transportation Cabinet - Transportation Planning
Ed Whittaker	Kentucky Transportation Cabinet - Transportation Planning
Greg Witt	Kentucky Transportation Cabinet - Transportation Planning

Also, special thanks to Barry House (Kentucky Transportation Cabinet – Multimodal Programs) for his input into the KYTC VMT forecasting procedure found in Appendix A.

1.0 BACKGROUND AND OBJECTIVES

Traffic flow patterns and growth rates are necessary for many of the planning and design functions of the Kentucky Transportation Cabinet (KYTC), and an accurate estimation of those rates are needed for pavement design, air quality modeling, overall planning activities, and other highway infrastructure needs. Recent requirements of TEA-21 and the Environmental Protection Agency (EPA) have placed further restrictions on growth and new highway facility development, primarily based on the acceptable level of vehicle exhaust emissions. Estimates of future growth and the composition of that traffic are critical to these requirements.

Traffic growth rates have been tracked for many years and the patterns have varied significantly dependent upon the geographic area, the socioeconomic conditions, and proximity to growth areas. Highway Performance Monitoring System (HPMS) sample sites have been monitored for several years and the growth rates for Kentucky and 12 other states in the region were compared for the period 1980 through 1995. The annual growth rates for Kentucky during the most recent five years exceeded those of any other state. Kentucky=s annual traffic growth rate for the period 1991- 1995 was 4.09 percent, and was the only state with rates in excess of 4.0 percent. Some states were near 4.0 percent (Tennessee and Indiana), while others had rates near 2.0 percent (Ohio and West Virginia). A recent analysis of annual vehicle miles traveled for all vehicle types as compared to heavy trucks showed a relatively even pattern of growth for all traffic between the years 1988 and 1996; however, the pattern for heavy trucks was less uniform. The growth rate for all traffic was approximately 4.0 percent annually, while the rate of growth for heavy trucks was less than the rate for all traffic. The procedure used by the Kentucky Transportation Cabinet to produce the year 2020 VMT forecasts is presented in Appendix A.

The accuracy of measuring traffic growth is linked to the ability of highway planners to adequately monitor the patterns and trends of highway usage by various types of vehicles. This task is directly related to the selection of data collection sites, the reliability of data collection equipment, and the ability to extrapolate from short-term data collection periods to represent annual average data. These and other factors can significantly affect the estimated growth patterns and universal procedures are not in place to represent the variations which can be expected based on geographic area, type of road, socio-economic factors, and various demand generators.

The primary objectives of this study were to determine patterns of traffic flow and develop traffic growth rates by highway type for Kentucky=s system of highways. There were other subtasks associated with accomplishing the primary objectives including the following:

· Conducting a search of the literature to determine if there were new procedures being used to more accurately represent traffic growth rates
· Development of a random sampling procedure for collecting traffic count data on local roads and streets
· Development of a procedure for explaining the relationship between routes functionally classified as collectors and locals
· Development of county-level growth rates based on procedures to estimate or model trends in vehicle miles traveled (VMT) and average daily traffic (ADT)

2.0 REVIEW OF LITERATURE AND SURVEY OF STATES

2.1 Review of Literature

A literature search provided several reports as reference material on this project. These reports were reviewed to determine what socio-economic factors could be beneficial for this project and if there were new procedures being used to more accurately represent traffic growth rates. The following reports are listed and summarized below.

· "Estimation of Annual Average Daily Traffic for Non-State Roads in a Florida County" Xia, Qing; et al.; Department of Civil and Environmental Engineering, Florida International University, 1999

A multiple regression model was developed for estimating ADT on non-state roads in urbanized areas in Florida. A sample size of 450 counts was used and 12 initial independent variables were analyzed. Results indicated that the most important contributing predictors were roadway characteristics, such as the number or lanes, functional classification, and area types. Various socioeconomic variables including nearby population, dwelling units, automobile ownership, employment statistics, and school enrollment have insignificant impact on ADT. Additional analyses revealed deficiencies in traditional roadway functional classifications and a need to improve or revise the classification procedures.

· "Estimation of Traffic Volume on Local Roads" Chatterjee, Dr. Arun, et al.; Department of Civil and Environmental Engineering, University of Tennessee

In recent years, the need for reliable estimates of vehicle-miles of travel on local roads has been recognized for the analysis of air quality and also highway safety issues. In order to provide a better understanding of traffic volumes on local roads and to explore alternative methods for estimation, data from Georgia were analyzed using different statistical procedures. In order to develop a mathematical model, an attempt was made to correlate local road volumes with socioeconomic and geographic variables. Initially, eight categories with 45 variables were explored. These included population demographics, education, transportation, income, employment, agriculture, urbanization, and housing. The models developed had poor predictability for rural roads. The results suggested that there might be additional subgroups needed such as road type (paved or unpaved) and locations (outside or within metropolitan areas). Regression clustering analysis was then used. It appeared that it could play a useful role for certain subgroups of traffic volume on local roads but further research in needed.

· "Guidebook on Statewide Travel Forecasting" University of Wisconsin-Milwaukee, Center for Urban Transportation Studies and Wisconsin Department of Transportation, 1999

This guidebook reviews the state-of-the-practice of statewide travel forecasting. It focuses on those techniques that have been considered essential to good statewide travel forecasting. Emphasis is placed on practical methods. This book also makes a distinction between urban travel forecasting and statewide travel forecasting.

· "Assessment of Land-Use and Socioeconomic Forecasts in the Baltimore Region" Talvitie, Morris, and Anderson; Transportation Research Record 775, 1980

Accuracy of forecasts for population, labor force, employment, and car ownership from 1962 to 1975 in the Baltimore area are examined. Comparisons are made at three levels of zonal aggregation-city and suburbs, traffic districts, and traffic zones. The lack of information about household size and household income made inferences from the results incomplete. The results show that region-wide forecasts were accurate for all the variables except population. However, allocation of these forecasts between city and suburbs, to traffic districts, and to traffic zones was quite inaccurate. The results in the paper point toward large errors and uncertainties in the independent variables of traditional traveldemand models.

· "Factors that Affect Traffic Growth Rates and Projection of Traffic Volumes for Use in Highway Economic Models" J.L. Memmott; Transportation Research Record 912, 1983

The magnitude of potential highway user benefits and costs that result from proposed highway improvements must be estimated with a reasonable degree of accuracy for highway agencies to make rational decisions in the public interest. One of the important aspects of most highway economic analysis models is the assumed traffic growth-rate pattern, which is based on one or more projected traffic volumes. The effects of different growth-rate patterns on the estimate of future benefits from a proposed project, as well as the factors that affect traffic projection errors from data collected in Dallas County, Texas, are examined. These factors include the year the projection was made, the percentage of commercial and industrial land development, and changes in highway capacity. A simple model for projecting future traffic volume is also presented, which is based on a multiple regression analysis of historical traffic volume data and adjustments for capacity changes and land development. The model is tested against the traffic projections collected for the Dallas County study sites, with the model producing somewhat more accurate projections in this sample.

· "The Linkage Between Travel Demand Forecasting Models and Traffic Analysis Models" Ho-EPK; Institute of Transportation Engineers, 1992

There are two major types of models for transportation analysis: Travel Demand Forecasting Model (TDFM) and Traffic Operational Analysis Model (TOAM). In order to increase the efficiency and accuracy of the analysis, certain kinds of linkages between these two types of models need to be developed. The purpose of this paper is to investigate such linkages. It first provides a comparison of these two types of models, followed by the discussion of their possible linkages. Finally, this paper provides an integrated framework for the TDFM and TOAM analysis.

· "Relationships Between Highway Capacity and Induced Vehicle Travel" Noland, Robert B., U.S. Environmental Protection Agency, 1998

An analysis of US data on lane mileage and vehicle miles of travel (VMT) by state was conducted. The data were separated by road type (interstates, arterials, and collectors) as well as by urban and rural classifications. Various econometric specifications were tested using a fixed effect cross-sectional time series model and a set of equations by road type. Lane miles are found to generally have a statistically significant relationship with VMT.

· "Policy Options For Improving Air Quality- The Relationship Between Transport Policies and Air Quality" Henderson, Gordon, et al.; Ove Arup & Partners, 1996

The context and effectiveness of various transport proposals and strategies that aim to reduce traffic related emissions were addressed in this paper. This was due to the gradual change in emphasis in Government policies and initiatives towards the environmental impacts of transportation. Policies have started to reflect increasing concerns over the contribution of road traffic emissions to poor air quality, together with the associated effects on health. Much of the work undertaken had been based on theoretical studies, which often used hypothetical situations. The effects of these situations need to be better understood before appropriate solutions can be reached. It would be necessary to undertake a greater volume of practical research in order to achieve a high level of understanding. It is clear that there are no quick-fix solutions to the problem of urban traffic congestion and its related side effects, such as vehicle emissions.

· "Traffic Growth on Road System – A Review" Sarna, Dr. A.C. and I.C. Agrawal, Traffic and Transportation Division, Central Road Research Institute, New Delhi, 1990

This study was undertaken with the objective of reviewing the traffic growth rates developed and adopted for various studies conducted for road and highway projects and to suggest suitable growth rates for traffic projections.

· "Modeling Of Traffic Growth in Congested Urban Networks" Hounsell, N.B., University of Southampton, U.K.

The economic evaluation of new road and traffic management schemes in urban areas requires forecasts to be made of traffic demand for up to 30 years. This paper describes the results of recent research completed in which a methodology for deriving limits to traffic growth was produced. Network modeling was undertaken to monitor the relationships between traffic growth and a range of network performance measures to establish criteria for identifying effective network capacity.

· "Forecasts of Traffic Growth in South East England" Stokes, Gordon, University of Oxford, Transport Studies Unit, 1992

This paper has taken an exploratory look at traffic forecasts and their feasibility on a local level. It concludes that at the county level the implications of the national forecasts are attainable. However, at the county level some major changes would be required to accommodate the forecasts.

2.2 Survey of States

A survey of the states was conducted to determine: (1) how other states were predicting VMT on functionally classified local roads and (2) how other states predicted traffic growth rates on all functionally classified roads. This survey was sent to 45 of the 50 states and 29 responses were received. This yielded a 64% response rate. This satisfactory response rate may have been due to the convenience of the survey. The survey was sent through electronic mail. This allowed respondents to quickly complete and return the results. Of the 29 responses received, 21 of them, or 72%, responded by electronic mail.

In general, the survey showed that 85% of states did use traffic growth rates. Seventy percent of those growth rates were determined using historic data and regression analysis. The historic data included such parameters as traffic growth, population, land use characteristics, employment status, location, and many others. Twelve states had different traffic growth rates for each county, seventeen states had different traffic growth rates for each functional class of road, and one state had different traffic growth rates for each vehicle type.

Eighty-one percent of states collected ADT by counting some years and estimating others. When these counts were estimated, 43% were estimated using other local road counts. However, other count estimations were based on a higher functional class, proximity to other roads, population, or other parameters. Only 11% of the states counted all their local roads.

The survey results showed that the majority of other states were predicting VMT on local roads and traffic growth rates for all functionally classified roads similar to Kentucky. However, with this project, an improved local road estimation methodology will be generated and therefore advance Kentucky in this area.

The survey questions and answers are provided in Appendix B.

3.0 DEVELOPMENT OF A RANDOM SAMPLING PROCEDURE FOR LOCAL ROAD TRAFFIC COUNT LOCATIONS

3.1 Introduction

Traditionally, transportation agencies have conducted routine traffic volume counts on higher volume highway corridors. However, local roads are important and unique because of the fact that they account for a considerable amount of the total roadway mileage. For example, local roads make up 67% of the total roadway mileage and 12% of the VMT in Kentucky (1). Since traffic counts have typically only been conducted on local roads for events such as road improvement projects and specific developments, the counts are not random which creates problems for estimating total travel on this class of roads.

In September 1998, the need to estimate the overall travel on local roads was further motivated by the EPA who issued a mandate requiring 22 states (including Kentucky) and the District of Columbia to submit state implementation plans (SIPs) regarding the transport of ozone across state lines (2). Nitrous oxides contribute to ozone, or smog, which causes serious unfavorable impacts on the environment and human health such as damaged vegetation, water quality deterioration, acid rain, and respiratory and heart disease. Sources of NOx emissions include motor vehicles and electric utilities. The EPA requires state agencies to provide VMT by land-use classification, road-type, and vehicle-type in order to estimate the amount of vehicle emissions being produced on the county level.

VMT is most commonly estimated from average 24-hour traffic counts at points along roads or a subset of roads. The traffic count is adjusted for daily and seasonal factors and then multiplied by the length of the road section to get the VMT. For example, if 1000 vehicles travel a 2-mile section of road, the VMT is estimated to be 2000 vehicle-miles. Likewise, if there are a total of 100 miles of a particular road class in a county and the mean of a number of random traffic counts is 40,000 vehicles per day, then the county-wide VMT estimate is 4,000,000 vehicle-miles for that class of roads. VMT estimated from the existing non-random local road counts and total mileage would overestimate VMT given that the more heavily traveled local roads are the ones more often counted. These more heavily traveled local roads have traditionally been classified functionally as local, but are state maintained.

Now that air quality and not traffic management is the focus of local VMT determination and local traffic count efforts, the problem of determining random locations for local road traffic volume counts must be solved. One common source of random traffic counts is the HPMS established in 1978 by the Federal Highway Administration (FHWA). It is a data collection effort designed to provide current statistics on the condition, use, operating characteristics, and performance of the nation's major highways. This travel information is routinely available for major highway systems and given that it contains random statewide and national information it is useful for the estimation of VMT. In Kentucky, this information is used for estimating the total VMT for the entire arterial and collector road systems, even though the sample is not completely random. In order to get the HPMS sample for submittal to the FHWA, each state had to break the arterial and collector routes into logical roadway sections. Rural section lengths were to range from 3 to 10 miles. Urban access-controlled facility sections were not to exceed 5 miles. All other urban sections were to be between 1 and 3 miles. A random sample was then taken from this total set of road sections (3). What made the sample non-random was the various section lengths and the fact that there were no instructions for selecting the point on the section to take the traffic count. Some agencies may have counted at the busiest point or others at the center. Although some states count local roads as part of the HPMS, most do not.

It might seem that producing a spatially random sample could be easily accomplished by dividing the local roads into segments of a particular length (one tenth of a mile is common for other purposes) and selecting a random sample from this database. However, local road Geographic Information Systems (GIS) databases from which sample locations would be drawn are less developed than those for more major roadways. Given a tenth of a mile section it would be necessary to attribute every road segment in the database with starting points, ending points and mile point locations in order to produce maps of the count locations for field workers. One additional complication is the fact that many local roads, especially in urban areas, are shorter than the segment length that roads are normally divided into. This makes discretizing the routes complicated. Roads shorter than the segment length would always be a single segment and would have a higher chance per unit length of being selected.

It would be useful to have a procedure which selected random points on the roads directly or graphically, analogous to throwing a dart at a map blindfolded and counting at the road location that the dart hit. The objective of this study is to develop such a GIS-based random sampling procedure for the count locations on the functionally classified local roads. A subset of the Kentucky statewide sample that was generated through this procedure is used here to explore the bias issues that arise due to the grid-based nature of the procedure, the shorter length of some local roads, and the various directions or curves of individual roads. The following section of this paper describes other efforts to estimate VMT on local roads. The remainder of the paper describes the GIS grid-based procedure and the evaluation of the bias it creates. The results of the bias analysis are presented along with a description of a procedure to correct for the sampling bias. However, the sampling bias was considered small enough to recommend use of the straightforward sampling procedure without the more complicated bias correction procedure.

3.2 Other Efforts to Estimate Local Road VMT

Efforts have been made in several states to estimate the overall travel on local roads through random samples. Tennessee takes counts on local roads for specific highway projects, railroad crossing studies, and intersection analysis. These count locations are not typically selected randomly. Therefore, the Tennessee Department of Transportation (TDOT) sought other methods to get a random sample of count locations (4). In their study, a program that collects traffic count information for all bridges in the state whose span length is 24 feet or greater was analyzed for possible use. Crouch, Seaver, and Chatterjee (4) proposed a method to measure the randomness of these bridge counts for VMT estimation for rural local roads. The traffic counts at bridge locations were compared to a random sample of traffic counts at nonbridge locations on local roads in eight counties. The researchers developed the procedure used to collect the random sample for non-bridge locations. Each of the eight counties was divided into four square mile grids (the width and length were 2 miles), and a process of repeated systematic sampling was used. First, the grids throughout the county were sampled. Then, within each grid, the location of the actual count was chosen by randomly selecting x- and ycoordinates. Each grid cell consisted of a 10 by 10 matrix. From the randomly selected coordinates, the closest local road location was selected, and at this location, a traffic count was collected by TDOT. This is indeed a random procedure with one possible bias; shorter roads may be less likely to be closest to the 0.2 mile by 0.2 mile grid selected. When working with a large number of counties, the process could be labor intensive and time consuming. Using the random counts generated in this manner the researchers found the bridge counts to be an unrepresentative sample of all rural local roads in each county.

In a California study (5), vehicle miles traveled on dead-end unpaved roads were estimated on a random sample. Traffic counts were collected at random unpaved local road access points to paved roads. Because counting was conducted at the access points to prevent trespassing on the private roads, the issue of selecting the point along a road was not faced. Therefore, a random sample of whole roads was taken. The count locations were mapped using a GIS, so the sites could be easily found. The count provided an estimate of the number of trips generated on the unpaved road, and this was converted into VMT by assuming that there was a single destination on the road and that each vehicle entering or exiting the road traveled half the length of the segment. The assumption that the vehicle is traveling to or from the midpoint of the road may cause the VMT to be incorrectly estimated. For example, dead-end unpaved local

roads could have one origin/destination point at the end of the road. This method is random, but it is only suitable for local roads that dead-end and have very few origin/destination points.

As part of this research study, an email survey of 45 states was conducted using contact names provided by the FHWA division office. The 29 replies indicated various methods for obtaining local road volume counts and sample locations. In Oregon, sample locations are picked from a select group of local roads that a software package indicates are under-sampled. The most recent counts from the local roads that are frequently sampled are then added to the counts of the sampled roads. The total sample may be nonrandom because the local roads that are frequently sampled are usually selected based on where road improvement projects are to be located, developments are to be built, or traffic problems exist. These are historically the higher traveled areas. The random sample of the under-sampled road segments is built by aggregating the full dataset as if it was one continuous road. Microsoft Excel then randomly picks a mile point along the road segments, and each pick becomes a location for a traffic count. The urban sample segments are 0.1-miles in length, while the rural sample segments are 1 mile. The count is taken at the center of the segment.

Other states provided less detailed input in the email survey. Vermont, for instance, selects what they think are the most "important" local roads for the counts. This, of course, is not random. West Virginia does not sample roads that have an average daily traffic value of less than 50 vehicles per day. This nonrandom method would certainly cause the VMT to be inflated if total road length was used for the estimate. In Wisconsin, local roads get counted for special reasons, such as a traffic problem or new development. Again, this is not a random sample and, therefore, the VMT estimate for EPA purposes could be incorrect. Wisconsin proposed developing a random sample of locations on local roads, but it was considered cost prohibitive.

Until recently, VMT estimations were mainly used to determine if a road needed improvements or expansion. Now that VMT is needed by the EPA to predict total vehicle emissions for each county, the importance of an accurate estimation is much greater. The formerly sufficient non-random sampling methods used by many states are no longer adequate. Clearly, a random sampling procedure for the count locations to be used for estimating the VMT on all functionally local roads that is not extremely labor intensive is needed.

3.3 The GIS Grid-Based Sampling Methodology

3.3.1 The Challenges of Finding a Methodology

The location and alignment of roads in most jurisdictions are now usually stored in GIS databases. In addition to this factor, the desire to have maps to direct field workers to count locations makes proceeding with a GIS-based method logical. When roadways are stored in GIS they are usually divided into segments (and, therefore, individual GIS features) at all intersections and many other points, some unsystematic. In the road databases for the three counties used in this study, local road segments ranged in length from a few feet to 10 miles. ArcView, a Windows-based GIS produced by the Environmental Systems Research Institute (ESRI), has a built-in function that can select a random set of such features or in this case segments. However, a random sample taken from this form of road database would not be appropriate for several reasons. First, the exact location on the road needs to be chosen and two locations on the same road segment need to have the opportunity to be chosen. The reasoning for this is based on the non-uniform variation in traffic volume along a road segment especially for longer roads where different intersecting roads and land uses affect traffic levels. Another reason that the sample could not be taken from this line network is that short and long segments would have been weighted equally. If the sample were taken from the existing GIS line theme, the precise location on the selected segment would then have to be subsequently chosen. Therefore, an individual point on a short segment would have a greater opportunity of being selected than a point on a longer segment.

As discussed in the introduction, a logical approach to developing the random sample would involve picking a random mile point or distance measure along these roads and then mapping it for the people conducting the counts. Knowing the length of every local road in a particular county, a line or row in a spreadsheet program could represent each 1/10th of a mile section. Most spreadsheet programs are capable of taking a random sample from the whole set. However, once the sample is taken it is difficult to direct the people making the traffic counts to the place to count. On local roads, there are typically no mile-markers to indicate location as there are with more major or higher volume roads. Maps of the count locations made in ArcView could have solved this problem. However, limitations in the coding of local road databases present a further problem for this mapping.

Mapping a specific point on a road is very easy with many GIS road databases that have been attributed with a feature called dynamic segmentation. Using this process, every road segment has two "special" attributes in its descriptive attribute table. One indicates the beginning linear reference marker at the start of the segment and the second indicates the end reference. The GIS can then locate any mile point on the road segment based on this information. This allows the mile point reference system to span across adjacent segments. The system could span across an intersection, for example. However, the available GIS databases for local roads rarely contain dynamic segmentation. Therefore, use of a sampling procedure that required start and end mile points to allow mapping would become a labor-intensive process.

As an alternative to creating dynamic segmentation attributes in the database, each individual road segment (as opposed to the whole road) could have been coded automatically with a start mile point of zero and an ending mile point of its length. However, using discrete mile point demarcations such as one tenth in the spreadsheet listing and random sampling still presents another problem for very short local roads especially in urban areas. Therefore, selection of a random continuous number between zero and each segment's length would be necessary in a two-stage process like that used in Tennessee. In the first stage a weighted (by segment length) random sample, with replacement, of the road segments would be taken. In the second stage a point or points along the segment would be selected by random number generation. This procedure would require separate programming outside the GIS and the results would require subsequent transfer back into the GIS for mapping (because the mile points are not meaningful on a segment by segment basis or on local roads without field mile point markers).

The new methodology proposed here is also two-stage but involves use of standard builtin functions of the typical GIS: grid generation, database intersection and random sampling from a feature table. The product is already a line feature in the database and is immediately mapped. Essentially a GIS-grid is generated and used to cut the road segments into small point-like sections, making a new theme from which the random sample is drawn using the direct built-in random sample command. The procedure ensures that the sample locations are spread randomly throughout the study area and that each point-like section along all roads has an equal chance of being in the sample regardless of the total length of the road.

3.3.2 Creating the Point-like Sections for Three Study Areas

In this case the primary GIS used was ArcView. Because the procedure developed during this study involved cutting the roads into small sections using a grid, the shape and density of the local roads were considered potentially influencing and affected the selection of study areas. Since it was not feasible to include all 120 Kentucky counties, three study counties were used: Henderson, Pike and Fayette. Henderson County (440 square miles or 1140 km² ) was chosen because it is in the western part of the state where the flat plain topography results in grid-like roads (total of 601 miles (968 km) of local road). It includes the small city of Henderson, which has a population of approximately 27,000. Pike County (788 square miles or 2041 km² ) was chosen because it is in the eastern mountainous part of the state, had windy and curvy roads, and was considered a relatively rural county (total of 829 miles or 1335 km of local roads). Fayette County (284 square miles or 736 km² ), with a population of approximately 250,000, was selected to represent an urban county with a dense road network (total of 734 miles or 1182 km of local roads). The separate GIS themes for state-maintained, county-maintained and city-maintained local roads were combined for the three test counties to obtain three local road GIS databases.

Unfortunately, ArcView does not have the capability to create a grid (a set of adjacent polygon squares covering a certain area or extent). However, ArcInfo, a compatible ESRI GIS, does have a grid function. Grids were created in ArcInfo by specifying the extent of the area and the grid size. They can be directly used in ArcView. The use of the grid as a "cookie cutter" using the intersection function in ArcView is demonstrated in Figure 1 where the inset shows that the roads in the square are now in four separate pieces or features. Each separate tiny line feature in the output database has a record in the attribute table from which ArcView's sampling script draws the random sample. Note that the random point-like road segments are selected, not the squares.

One obstacle with the grid approach is that some bias can be introduced by virtue of the point-like segments not being of equal length as illustrated in Figure 1. The grid being used to cut the roads into small sections was at 90º North, so the roads were being cut at different angles. Some of the sections were considerably longer than others. If you have two roads of equal length and one is cut into several short pieces and the other is cut into a few long pieces, then the road that was cut into several short pieces would have a greater chance of being selected in the random sample. Given that the local road traffic volume was found to vary with original road segment length and between the rural and urban areas, in order to have no bias, the number of segments a particular road was divided into would have to be directly proportional to the length of that road. This means that a road with twice the length of another road should be divided into twice the number of sections.

Figure 1. A "Cookie Cutter" Grid on a Network of Roads

The objective then becomes determining the size of the largest grid square that brings an acceptably low bias to the sample. As the grid size approaches zero, the point-like sections approach true points of zero length, which would present absolutely no bias. The smaller the grid square size, the more computer space and time used for the spatial analysis that cuts the road segments. The three counties were analyzed with 0.2-mile, 0.15-mile, 0.1-mile, and 0.05-mile grid square sizes. Although the space issue needed to be considered (the grid for one county at the 0.05-mile size was 148 MB) when choosing the final grid square size, the computing time and ability of a personal computer to do the intersection (cutting) without crashing were the more critical issues.

3.4 Consideration of Bias in the Point-like Sections

The development of a method to measure the bias that would be present in an average traffic count from a sample drawn using this process is necessary in order to compare grid sizes and determine if the straightforward sampling procedure could be used without a more complicated weighting procedure to correct for the bias. Once the road segments were cut by the grid, the length of the original road section and the number of point-like segments into which it was divided were available for use in measuring bias. Figure 2 illustrates this data for one of the 0.2-mile grids in Pike County. (The lines and equations on this figure are described below.)

Figure 2. Bias Analysis for Pike County (0.20 mile grid)

The first of several indicators of bias considered was the coefficient on the X² variable in the equation for the best-fit quadratic curve (this curve is not represented on the figure). The value of the coefficient on the X variable is an indication of the curvature of the line and increasing values of the coefficient would indicate bias. A negative value would indicate that the line curved downward specifying that the longer roads were being cut into relatively fewer pieces and were, therefore, under-represented in the sample. A positive value would denote the opposite; longer roads were over-represented in the sample. The magnitude of the coefficient for the X term also provided an indication of whether it was appropriate to proceed using a linear regression-based representation of the relationship between road length and number of point-like segments.

Bias analysis graphs and equations such as that shown in Figure 2 were generated for each county and grid size analyzed. The coefficients on the X² variable in the equation for the best-fit quadratic line as generated by Excel are shown in Table 1. Bold values are statistically significant at the 0.05 level. Within an individual county, the value of the coefficient fluctuates. This alone is not insightful. It is the comparison between counties that provides some useful information. The magnitude of the coefficient is substantially greater for Fayette County than it is for Henderson and Pike Counties. This is evidence that the grid process works better for rural roads than for urban roads because they are longer and less dense. The low magnitude of these coefficients was considered justification to proceed with representing the relationship with a linear equation.

	Coefficient on the	y-intercept	r-squared
	2 X variable	(linear)	(linear)
Pike County
0.20 mile grid	-0.0007	1.071	0.97
0.15 mile grid	-0.0005	1.066	0.98
0.10 mile grid	0.00001	1.023	0.99
0.05-mile grid	-0.0016	1.026	0.99
Henderson County
0.20 mile grid	-0.0005	1.049	0.98
0.15 mile grid	-0.0011	1.11	0.98
0.10 mile grid	-0.0001	1.038	0.99
0.05-mile grid	-0.0013	1.036	0.99
Fayette County
0.20 mile grid	-0.0054	1.005	0.74
0.15 mile grid	-0.0120	1.012	0.81
0.10 mile grid	-0.0073	1.016	0.90
0.05-mile grid	-0.0123	1.032	0.97

Table 1. Y-Intercept, R-Squared, and X² Coefficient

However, it is important to note that the relationship could be linear (X² coefficient = zero) and bias could still exist. Therefore further consideration of the linear regression equation was undertaken. One factor considered in measuring this bias was the y-intercept of the best-fit line. On one hand, this value would ideally seem to be zero because a road of zero length should be divided into zero sections. However, a y-intercept of one would indicate that a road of very small length was divided into one section. But this indicates that very short roads will be automatically over-represented in the sample. As evident in Figure 2 some very short roads were divided into up to 3 or 4 segments. As shown in Table 1, the y-intercept value did not vary significantly as the grid size was changed. For all counties and grid sizes, it hovered just above 1, which is expected because very short segments would most often be cut into one piece or, at most, two pieces. This result illustrates that some bias will be present with all grid sizes given that short segments are over-represented.

The line corresponding to no sampling bias due to road length would be expected to have a certain slope referred to here as the target slope. The target slope is obtained by dividing the total number of segments in a county by the total length of local roadway in that county. For

example, if there are 5,000,000 distance units of local road in a particular county, and a specific grid size cut these roads into 7000 segments, the segments should be on average 714.29 distance units (5,000,000 distance units / 7000 segments) long. The target slope is the inverse of this number (divided by 1000 for the graph scale shown) and the line on Figure 2 was derived by using this slope with a y-intercept of one.

Comparison of the target slope to the actual slope first required consideration of the rsquared value. The r-squared values shown in Table 1 indicate that both the sampling procedure and the weighting procedure described below which is based on the linear slope are better suited to the non-urban areas. The variation in the number of segments decreases with the smaller grid square sizes as expected. However, the relatively high overall r-square values indicate that the best-fit line does indeed represent the data well. It provides legitimacy to the comparison of the actual and target slopes described below.

Table 2 includes the target slope, the actual slope of the best-fit line, and the percent difference between its slope and the target slope. The range included with the slope is the 95% confidence interval. The confidence interval was inspected for the inclusion of the target slope. None of the target slopes were included indicating bias was present.

In each county the percent error between the target slope and the actual slope decreased as the grid square size approached zero, as expected. The target slopes are greater than the actual slopes indicating that as road length increases the road becomes under-represented in the sample. Fayette County had percent errors that were greater than the other two counties. Again, this indicates that less dense roads are better suited to the grid process. Henderson County's grid-like roads have smaller error than Pike County where roads are curvier. Therefore, it can be inferred that the grid procedure works best for grid-like roads and rural roads. The grid size is more crucial in urban areas.

In order to consider the impact of the bias due to road length and the grid procedure, weights were developed based on the slope comparison for application to the traffic counts collected for these three counties by the Kentucky Transportation Cabinet. Counts were performed during the calendar year 2000 at points selected using the 0.2-mile grid procedure (a worst case scenario). The number of 24-hour counts performed in Henderson, Pike, and Fayette counties was 164, 243, and 337 respectively. Counts were corrected for seasonal and weekly factors using constants developed in Kentucky based on counts on all functionally classed roads over many years.

	Target		Percent
	Slope	Slope	Error
Pike County
0.20 mile grid	1.547	1.193 + 0.0100	22.9
0.15 mile grid	1.945	1.593 + 0.0103	18.1
0.10 mile grid	2.752	2.414 + 0.0116	12.3
0.05-mile grid	5.182	4.841 + 0.0137	6.6
Henderson County
0.20 mile grid	1.552	1.260 + 0.0120	18.8
0.15 mile grid	1.977	1.668 + 0.0143	15.6
0.10 mile grid	2.802	2.512 + 0.0161	10.3
0.05-mile grid	5.296	5.009 + 0.0245	5.4
Fayette County
0.20 mile grid	3.029	1.228 + 0.0170	59.5
0.15 mile grid	3.441	1.629 + 0.0183	52.7
0.10 mile grid	4.258	2.441 + 0.0190	42.7
0.05-mile grid	6.754	4.908 + 0.0206	27.3

Table 2. Slope Comparison

The best-fit line and the target line were known for each county for the 0.2-mile grid size. In other words for a road of a particular length, the number of segments into which it was divided and the number of segments into which it should have been divided were known. The weight was calculated as the ratio of the number of segments into which the road of a given length should have been divided if no bias by road length existed and the actual average number of segments into which the road was divided. This weight varied by road length as illustrated in Figure 3 for Pike County for all grid sizes. Using the weights for the 0.2-mile grid size a weighted average for the 24-hour traffic count, or ADT was calculated. Table 3 presents the sampled and weighted average ADT and the subsequent sampled and weighted VMT estimate for the local roads in each county based on the 0.2-mile grid process. The table demonstrates that without the weighted ADT, the VMT estimate for each county would be slightly overestimated. The greatest difference is in Fayette County. This is further evidence that the weighting procedure is more necessary in urban areas but also a function of the greater number of shorter roads in an urban area. However, the percent difference due to the sampling bias is small and deemed acceptably low for the modeling purposes in either planning or the air quality considerations described at the beginning of this paper. Based on the slope comparison the bias would be even less with the smaller grid sizes. It would not be useful to undertake the multistage weighting procedure calculations.

Figure 3. Pike County Weights

	Average ADT (veh/day)	Average weighted ADT Estimate (veh/day)	VMT (veh-miles)	Weighted VMT estimate (veh-miles)
Pike County	454.87	453.18	377232.79	375831.24
Henderson County	386.27	367.59	232105.78	220881.16
Fayette County	747.06	719.56	548177.69	527998.74

Table 3. Corrected and Uncorrected ADT and VMT Values (0.2 mile grid-based sample)

3.5 Conclusions

In summary, a straightforward sampling procedure has been developed and validated that will allow random sampling of traffic count locations on extensive local road systems. Due to use of built-in GIS commands, sampling does not require time-intensive processes and the results can be directly mapped for field use. The procedure offers a means to determine not only a random road but also the point along the road where counting should occur. Furthermore, it can handle the very short local roads without greatly biasing the sample.

The analysis presented here provides guidance to determine a recommended grid size for use in sampling that would balance computer time/space while ensuring acceptable randomness of sampling. Attempts to use grid sizes below 0.05-miles were not successful in ArcView for the study areas used. Although individuals should select a grid square size based on their computer capabilities and the characteristics of the roads in their study, these results indicated that a larger grid size can be used for rural roads and grid-like roads. The grid square size needs to be smaller for urban counties due to the dense, short roads. Since the 0.05-mile grid square size is very difficult to work with, the 0.1-mile size is recommended for urban counties. The recommendation for rural counties is to use the smallest grid square size that is feasible, but a 0.2-mile size would be sufficient especially if roads are in a grid-like pattern.

3.6 Local Road ADT Sample with Spatial Variables

This section describes the sample of local road ADTs that were collected during this project, a summary of the data, and the regression results of a GIS analysis to determine if spatial variables such as local road density and distance from cities or interstates would be useful in predicting ADT.

Using the sampling procedure described previously, the locations for 24-hour traffic volume counts on local roads throughout the state were selected. Samples were taken in the 27 counties shown in Figure 4. One random rural and one random urban county was selected from each of the 12 highway districts in Kentucky in order to provide a geographic representation for the whole state. Because a significant portion of the population of the state lives within the Louisville, Lexington, and Northern Kentucky triangle, three additional counties were selected in this area: Shelby, Fayette, and Grant. Shelby and Fayette have both an urban and rural area while Grant has only rural areas despite being on the outer fringe of the rapidly growing area of Northern Kentucky.

In order to define the population of local roads from which the random sample would be taken for counting, the local roads were combined from the state maintained GIS road database and the county/city maintained GIS database. In some cases, certain roads changed functional class along their length. These roads were manually edited to ensure only the local segments were included in the database.

Figure 4. Counties Used in Local Road ADT Data Collection

Once the full local road database had been established for all 27 counties, it was necessary to separate the database for each county into separate rural and urban datasets. This was conducted at the direction of the KYTC and relates to their tradition of handling VMT predictions separately for urban versus rural roads. There was a desire to ensure both types of roads were represented. The separation procedure was conducted in GIS on a county-by-county basis. Because incorporated city boundaries do not necessarily match the boundaries of the urbanized areas, the GIS polygons of incorporated city areas were augmented with the areas that were considered functionally urban by the KYTC. This information was contained on paper maps and was manually entered into the GIS. One exception was Fayette County, where all roads are considered urban due to the single city and county designation. In this case, the urban service boundary was used to classify the local roads as either urban or rural. The second column of Table 4 indicates which counties contained both rural and urban local roads and which contained only rural. Once boundaries had been established for the urbanized areas, they were overlaid with the local road database for each county to divide the databases in two.

County	Rural / Urban	Target	Final Sample
		Sample Size	Size
Allen	Rural	107	127
Barren	Rural /Urban	145/29	167/32
Boyd	Rural /Urban	45/86	47/99
Clark	Rural /Urban	N/A	63/63
Fayette	Rural /Urban	50/308	35/302
Franklin	Rural /Urban	42/52	48/56
Garrard	Rural	56	62
Grant	Rural	79	92
Green	Rural	97	102
Henderson	Rural /Urban	106/44	119/45
Henry	Rural	66	72
Laurel	Rural /Urban	163/25	176/24
Leslie	Rural	74	86
Lewis	Rural	99	105
Lyon	Rural	92	107
McCracken	Rural /Urban	45/91	50/88
McCreary	Rural	138	160
Mercer	Rural /Urban	62/25	72/27
Muhlenburg	Rural	142	150
Nelson	Rural /Urban	77/28	88/31
Owen	Rural	78	76
Perry	Rural /Urban	76/25	90/28
Pike	Rural /Urban	191/25	219/24
Shelby	Rural /Urban	62/25	67/24
Wayne	Rural /Urban	106/25	126/29
Wolfe	Rural	56	62
Total		3375	3801

Table 4. Local Road ADT Sample Locations by County

The total number of traffic volume counts to be taken was based on the budget available in the KYTC Division of Transportation Planning, which was separate from this project. The counts were to be conducted by Wilbur Smith Associates, some KYTC Divisions, and some Area Development Districts. Although the count locations were determined as part of this project, the count management was conducted by the KYTC. The total target number of counts for each rural and urban section of each county is shown in Table 4 column 3. The 3375 counts were allocated proportionally to the length of local road in the jurisdiction, but assuming that 25 was the minimum number that should be counted in any given rural or urban jurisdiction.

As shown in Table 4 column 4, the actual number of counts taken was different from that

intended. This occurred for several reasons. Clark County was added after the fact at the request of the KYTC thus increasing the overall total. In the random sampling procedure, an over sample of 15% was taken. These "extra" locations were used in several ways. First, extras were used to replace roads that could not be found, did not exist, or could not be counted for various reasons. Second, when two locations were adjacent to each other such that no intersection or traffic generation was present between them, another count was taken and the count at the one site was used as the count at the adjacent site. This ensured that the contractors still took the number of counts for which they had been hired, while effectively increasing the sample size.

At the direction of the KYTC, the ADT at some count locations was estimated by the contractor instead of actually being counted. The equipment and software package that were used to process counts rounds to the nearest 10 vehicles in each hour. Therefore when a traffic count in one hour was less than 5 it was recorded as 0. In very low volume locations where it was expected that most if not all hours would be less than 5 vehicles (such as a dead-end road); the ADT was estimated using the following guideline. A residential home was assumed to produce a total of 10 trips per day. A business was assumed to produce 25 trips per day. In general if the field workers felt that less than 200 trips per day would be counted on the road he or she undertook estimation instead of an actual count. In total, exactly one third of the ADT data was estimated.

The counts received from the field workers were adjusted for seasonal and weekly variation using standard programs at the KYTC.

3.7 Local Road Traffic Volume Summary

A summary by county of the ADT values for rural and urban local roads is shown in Tables 5 and 6. Chi-square test results indicate that the mean ADT varies between rural and urban areas as well as by county. The mean ADT in urban areas was 763 while in rural areas it was 212. A standard deviation for rural areas of 386 indicates that there is wide variation in ADT on local roads. However, a standard deviation of 1323 for urban areas indicates this variation is even higher in the urban areas. This unfortunately dictates that large samples are needed in order to get good confidence in mean ADT values for local roads.

	ADT	ADT			Deviation
Allen	2285	0	212.7	80	368.7	30	232
Barren	1156	0	152.6	99	185.2	40	189
Boyd	2094	8	335	160	507.1	64	256
Clark	1310	30	279.8	184	284.5	118	304
Fayette	1916	8	532	299	562.3	144	869
Franklin	2356	24	299.2	161	453.8	86	313
Garrard	2759	0	294.8	105	534.2	38	242.3
Grant	2094	0	222.1	130	357.2	60	227.5
Green	2290	0	118.2	48	269	23.5	119.2
Henderson	2420	0	162.8	60	337	19	178
Henry	1169	0	162.7	94	223.6	34	175
Kenton	300	40	154.3	145	83.6	77.5	227.5
Laurel	7325	0	305.6	168	630.6	41	335.8
Lawrence	1785	0	137.1	53	260.3	30	102.3
Leslie	2393	0	219.8	143	317	47.5	268
Lewis	2560	0	177.6	85	344.2	32	197
Lyon	2173	0	131.3	55	261.3	20	147
McCracken	1186	0	180	120	216.9	37	214.2
McCreary	2378	0	154.6	50	287.6	20	180.5
Mercer	620	0	165.7	138.5	157.3	47.7	204.5
Muhlenberg	4771	0	182.5	80	440.8	30	202
Nelson	1064	0	192.3	138.5	205.1	51.2	244
Owen	2294	0	170.5	51.5	382.8	16.5	183.8
Perry	1869	0	263.3	159	329.4	59	394.8
Pike	5592	0	391.3	180	630.2	90	463
Shelby	1107	9	221.6	156	212.8	92	267
Wayne	980	0	113.9	55	166.8	28	129
Wolfe	393	0	84.2	64	84.3	16	109.5

Table 5. Local Rural Road ADT Summary

County	Maximum	Minimum	Mean	Median	Standard	Quartile 1	Quartile 2
	ADT	ADT			Deviation
Barren	10069	10	962.219	400.5	1826.6	121	1070
Boyd	6862	0	425	175	1073	100	260
Clark	6612	0	1077	478	1376	228	1626
Fayette	10587	0	772	365	1271.2	137	861.2
Franklin	5495	0	858	349	1263	155	973
Henderson	7495	0	977	589	1397	158	1318
Kenton	14114	0	805	234	1536	150	627
Laurel	4919	20	902	362	1414	45	1149
McCracken	7238	0	535	228	969	113	504
Mercer	4402	0	636	132	1038	50	703
Nelson	6956	0	1114	488	1504	140	1659
Perry	1924	19	460	256	531	134	630
Pike	9794	0	1035	189	2140	90	1189
Shelby	1843	0	612	426	579	88	1161
Wayne	3455	0	587	261	732	165	709

Table 6. Local Urban Road ADT Summary

3.8 Regression Analysis to Predict Local Road ADT

A standard set of county level variables were available and used elsewhere in this project to predict VMT. These include: 1) population, 2) average per capita income, 3) employment, 4) county-wide total earnings, and 5) licensed drivers. Table 7 indicates the R-square results of linear regression models to predict the mean county-wide local road ADT from this dataset using each of these county-wide variables. The R-squared values were used as a measure of the amount of variance in the ADT that each variable could account for. None of the variables for the urban areas produced results that would be acceptable. The results for the rural areas are promising in terms of use of these variables to forecast VMT and ADT on local roads.

URBAN

Dependent Variable	Independent variables	R-Squared
Mean ADT	Population (P) Income (I)	0.002 0.001
	Employment (E) County-wide Total Earnings (C) Licensed Drivers (L)	0 0.001 0.002

RURAL

Dependent Variable	Independent variables	R-Squared

Mean ADT	Population (P) Income (I) Employment (E) County-wide Total Earnings (C) Licensed Drivers (L)	0.422 0.263 0.474 0.484 0.416

Table 7. Regression Results for County-Level Variables

In this phase of the project, the main objective was to generate and test spatial variables generated by GIS to determine their usefulness in predicting ADT on local roads. Six new variables were generated for each count point using ArcView. These had the advantage of not being county based but specified to the actual location of the road section. The first three variables were local road densities. The variables were equal to the number of miles of local road in a 1, 2, and 5-mile radius of the count location. This was considered a proxy measure of the surroundings of the road in that remote locations would have low road densities and city or congested areas would have higher road densities. The final three variables were the straightline distance between the count location and the nearest freeway or main highway, city, and state road respectively. The main highways used in this calculation are shown in Figure 5 while the cities used are shown in Figure 6.

Figure 5. Major Highways Used in Distance Calculation

Ashland Bowling Green Covington Danville Elizabethtown Erlanger Fern Creek Florence Fort Campbell North Fort Knox Louisville Madisonville Middlesborough Murray Newburg Newport Nicholasville Okolona Owensboro Paducah

Fort Thomas Pleasure Ridge Park

Frankfort Georgetown Glasgow Henderson Highview Hopkinsville Independence Jeffersontown Radcliff Richmond Shively Somerset St. Dennis St. Matthews Valley Station Winchester

Lexington-Fayette

Figure 6. Cities Used in Distance Calculation

The results of the regression models for each county with these spatial variables as predictors are shown in Tables 8 and 9. The regressions were also performed for all the data points together and this is indicated in the "all counties" column. Overall these variables on their own do not account for a significant portion of the variation in the ADT and could not be used to predict local road ADT. However, in the case of rural roads, the road density variable does explain up to 5% of the variation and could be used in combination with other predictors.

	Barren	Clark	Fayette	Franklin	Henderson	Kenton	Laurel
Independent variables
1 mile local road density	0.053	0.012	0.01	0.006	0.003	0.006	0.015
2 mile local road density	0.052	0.004	0.032	0.006	0.043	0.003	0.015
5 mile local road density	0.026	0.008	0.047	0.034	0	0	0.034
Distance from Major Road	0	0.028	0.018	0.069	0.007	0.006	0.406
Distance from City	0.036	0.008	0.022	0.003	0.075	0.005	0.389
Distance from State Road	0	0.028	0.036	0.056	0	0.002	0.072
	Mercer	Nelson	Perry	Pike	Shelby		Wayne All counties
Independent variables
1 mile local road density	0.111	0.274	0.065	0.011	0.069	0.008	0.016
2 mile local road density	0.005	0.198	0.064	0	0.022	0.002	0.011
5 mile local road density	0.076	0.036	0.008	0.005	0.11	0.002	0.003
Distance from Major Road	0.094	0.006	0.08	0.001	0.114	0.093	0.0005
Distance from City	0.109	0.028	0.004	0	0.001	0.029	0.0005
Distance from State Road	0.053	0.064	0.031	0.002	0.048	0.006	0.008

Table 8. Urban ADT R-Squared Regression Results for Spatial Variables

	Allen	Barren	Boyd	Clark	Fayette	Franklin	Garrard
Independent variables
1 mile local road density	0.149	0.017	0.226	0.058	0.116	0.215	0.317
2 mile local road density	0.173	0.005	0.277	0.396	0.365	0.242	0.065
5 mile local road density	0.149	0.005	0.138	0.209	0.326	0.126	0
Distance from Major Road	0.011	0.055	0	0.005	0.001	0.06	0.054
Distance from City	0.017	0.029	0.243	0.267	0.366	0.09	0.009
Distance from State Road	0.065	0.049	0.064	0.103	0.026	0.024	0.092
	Green	Henderson	Henry	Laurel	Lawrence	Leslie	Lewis
1 mile local road density	0.017	0.071	0.047	0.016	0.134	0.013	0.045
2 mile local road density	0.047	0.015	0.008	0.01	0.067	0.003	0.022
5 mile local road density	0.058	0.012	0.067	0.003	0	0.068	0.029
Distance from Major Road	0.003	0.057	0.007	0.078	0	0.013	0.028
Distance from City	0.009	0.02	0.022	0.013	0.004	0	0
Distance from State Road	0.034	0.044	0.033	0.024	0.073	0.053	0.006
	Lyon	McCracken	McCreary	Mercer	Muhlenberg	Nelson	Owen
1 mile local road density	0.018	0.027	0.071	0.023	0.022	0.062	0.031
2 mile local road density	0.03	0.053	0.06	0	0.041	0.242	0.005
5 mile local road density	0.05	0.015	0.048	0.054	0.035	0.299	0.005
Distance from Major Road	0.036	0.007	0.029	0.067	0.023	0.031	0.046
Distance from City	0.031	0.02	0	0.035	0.008	0.043	0.006
Distance from State Road	0.02	0.096	0.035	0.068	0.003	0.008	0.042
	Pike	Shelby	Wayne	Wolfe	All counties
1 mile local road density	0.054	0.048	0.026	0.006	0.026
2 mile local road density	0.021	0.066	0.009	0.018	0.024
5 mile local road density	0.003	0.018	0.029	0.014	0.19
Distance from Major Road	0.001	0.028	0.023	0.022	0.002
Distance from City	0.009	0.044	0.015	0.03	0.004
Distance from State Road	0.021	0.092	0.026	0.05	0.24

Table 9. Rural ADT R-Squared Regression Results for Spatial Variables

4.0 PREDICTION OF VMT BASED ON SOCIOECONOMIC DATA

4.1 Introduction

Traffic flow patterns and their likely growth trends are critical for planning, design, and maintenance of transportation facilities. Recent transportation authorization bills (TEA-21 and ISTEA) placed restrictions on construction of transportation facilities and tied that to meeting emission standards. This made the task of reliably estimating future travel more critical to the transportation planning process.

The KYTC has undertaken a number of studies to address different aspects of the traffic/travel growth issue. This study is one of several that are aimed at developing methods to estimate future travel levels in all counties of the State of Kentucky. This study develops models to predict VMT for interstate and non-interstate routes for each of the 120 counties. As predictors for VMT, the models use socioeconomic data such as income, employment, retail sales, etc.

This section documents all modeling attempts. Numerous modeling approaches were tried with varying degrees of success; the outcomes of all (successful and unsuccessful ones) are noted in this section. The unsuccessful ones still represent a valuable source of information for any future work in the area of traffic growth modeling.

4.2 Background

Reliable estimates of VMT are critical for highway planning and design. Because of enacted regulations by EPA and the requirements of recent transportation authorization bills (TEA-21), vehicle miles of travel play a critical role in determining the use of funds to construct and upgrade transportation facilities in areas that have been previously designated as being in non-attainment of the National Ambient Air Quality Standards (NAAQS). For these areas, EPA and the Kentucky Division for Air Quality are required to develop a State Implementation Plan (SIP) demonstrating how the area will achieve and maintain attainment of the NAAQS. Utilizing the EPA mobile source emission model to determine future emission rates, and taking into account forecasted county-level VMT, along with associated speed distributions, this SIP establishes maximum level (budgets) emissions that can be produced by highway mobile sources in future years. These emission calculations are performed periodically to determine if the area is in "conformance", i.e. that the calculated emissions are not greater than the budgeted emissions as set in the SIP. Against this background, this study was initiated to develop models to predict future VMT.

Currently, VMTs for a given county are estimated based on traffic volume counts, mileage of different road classes, and a measure of socioeconomic activity in the respective counties. Where actual traffic counts are not available, estimates based on modeled historical data are used.

The initial effort of this study aimed at developing VMT growth trends at the county level for each roadway functional class. Because of the limited number and coverage of traffic counting stations, this objective was later revised, and roadways were divided into only two classes: Interstate and non-Interstate. VMT growth models were to be developed for these two roadway classes.

4.3 Objective

The objective of this section is to develop models to predict VMT at the county level using socioeconomic data as "predictors". The predictors should be such that they, themselves, can be reliably predicted for future years. The resulting models should have a reasonable level of accuracy (a ±10% error was established as a goal). Yearly VMT growth should be within the KYTC's established "normal" ranges. Other appropriate statistical and data quality tests will have to be satisfied.

The underlying rationale is that VMT are generated because of socioeconomic data within an area, and that trends in socioeconomic data may be good indicators of how VMT will change. The approach to be developed in this chapter may be used in parallel to using actual ADT and road mileage.

4.4 Data Input

The 1993-1999 VMT database (Appendix C) developed by the Division of Planning at the KYTC was used to build the VMT prediction models. The county estimates of VMT are prepared by the KYTC each year for all counties in Kentucky. Furthermore, VMT are prepared by functional class and area type (i.e., rural vs. urban).

The socioeconomic data was based on the Woods and Poole published data (6). Appendix D lists the socioeconomic data for each county for each of the study years (1993-1999). A list of available socioeconomic variables is shown in Table 10. Data on these variables is available for past and current year. Projections are also available for future years (but not for licensed drivers).

Variables
Population
Earnings (includes individual but not corporate earnings)
Employment
Per Capita Income
Retail Sales
Number of Licensed Drivers (not shown in Appendix C)

Table 10. Socioeconomic Variables Used

4.4.1 Data Measures of Quality

Two sets of quality measures were used to ensure the usefulness of the developed models. First, specific tests on the inputs and outputs that are standard with each of the modeling approaches were used. The second set is one that was imposed by the KYTC. This measure specifically requires that errors in prediction do not exceed ±10% for a given county during a given year. Errors in prediction are calculated from existing (given) and modeled VMT values:

% Error = 100*(Given VMT-Predicted VMT)/Given VMT

Additionally, when models are tested in forecasting applications, yearly VMT variations are to be within general ranges. These rules-of-thumb were developed based on historic trends: 1-6% increase per year for non-interstate VMT and around 2-4% increase per year for interstate VMT.

4.5 Methodology

Two modeling approaches were used to achieve the above objectives: 1) Linear regression, and 2) Neural Networks. Initial modeling effort was confined to linear regression only. Neural Networks were used when linear regression did not produce acceptable models. The basic approach was to try to establish association between VMT estimates (dependent variable) and the independent variables.

4.5.1 Regression Modeling

Two independent approaches were used based on linear regression. In the first, regression models were developed using county-based VMTs. The second developed state-based VMT then apportioned it to each county based on selected county socioeconomic measures. Within each of these approaches, different variations were used (described in the Results section). Unless otherwise noted, regression models were generated using the VMT estimates that the KYTC has generated over the years. No homogeneity of variance and normality tests were conducted for this data. Preliminary scatter plots were examined to establish any trends between VMT and the independent variables.

4.5.2 Neural Networks (NNets) Modeling

Neural Networks (Nnets) are computational structures capable of learning from examples and quickly recognizing the patterns they have learned (7). Put differently, Neural Nets simulate human neuron functions and interconnections between neurons, and can be implemented on digital computers. An important feature of neural nets is their ability to interpolate and extrapolate from known cases and thus produce the best approximation to the desired result even if the patterns to be recognized were not in the set of patterns used for the training (7). NNets resemble the brain in two respects (8):

1. Knowledge is acquired by the network through a learning process
2. Inter-neuron connection strengths known as synaptic weights are used to store the knowledge

NNets operate in the two fundamental modes of learning (or training) and recognition (or testing). In the learning phase, a large set of example patterns (the training set) is presented to the input of the network. The outputs obtained are compared with the desired results and a set of internally stored parameters or "weights" of the network are modified according to a given learning algorithm. The process is iterated until the total error reaches an acceptably low level. In the recognition phase, the test data is fed to the network to produce the desired outputs under the effect of the weights.

A NNet consists of neuron, or nodes, and neuron synapses, or connections. The neurons are grouped together to form a layer of a NNet. Any NNet typically contains two or more layers, with interconnections between each two adjacent layers. NNets can handle highly nonlinear problems, which would be much more complex or impossible to solve using traditional analytical methods. They can solve these problems much more quickly as well. Put differently, NNets have the capability to detect causal relations between the data patterns that other techniques cannot. This derives from the unique structure and functioning of a NNet.

There are different structures for a NNet. The one used in this study is the Backpropagation Neural Network (BPN). A full description of this Net is beyond the scope of this report, and may be found in specialized texts (8).

The use of NNets for prediction is relatively new. NNets have been shown to have an advantage over traditional prediction techniques particularly when the data has some noise and where complex non-linear relationships exist between the dependent (predicted) and independent (predictors) variables. In these cases, NNets were shown to have a predicting advantage over traditional approaches such as regression.

For this study, Neural Networks were tried with different combinations (and forms) of variables.

4.6 Results

This section presents a brief account of the results from the different modeling approaches (and the variations within each). This summary accounts for both successful and unsuccessful trials. The account of unsuccessful trials is presented because of the valuable lessons and findings. This information is important for future work on this subject. A summary of the most promising models, henceforth called final models, is presented at the end of this chapter.

The results are presented in two parts: The first part is the regression models; the later part is the NNet models. Within each of these two modeling approaches, numerous combinations of models, levels of aggregation (state vs. county-based), class of dependent variables (interstate VMT, non-interstate VMT, and combined VMT), and form of independent variables (logarithmic and normal form) were tried.

The final models are presented separately for interstate and non-interstate VMT. These are the best models that could be obtained; they do not necessarily represent models that are ready to use (see section 4.8 Discussion).

4.6.1 Descriptive Summary of All Trials and Results

Valuable information was gained from all modeling trials regardless of the level of success attained. This section provides a summary of all (successful and unsuccessful) modeling trials. This is being provided as a resource for future VMT modeling effort.

4.6.1.1 Regression

This section provides a summary of all regression-based modeling. Initially, models were developed based on degree of aggregation: county-based and then state-based models. The general form of the model is:

$$VMT = Constant + a_1 X_1 + a_2 X_2 + \dots + a_n X_n$$

Where ai is a regression coefficient and Xi is an independent variable.

For county-based models, VMT is for a given county-year combination. For state-based models, VMT is that of the state for a given year.

4.6.1.1.1 County-Based Regression Models

Initially, effort was directed at forming "logical" groups of counties using common characteristics. The idea was to develop separate models for each group of "similar" counties. However, all efforts to logically group counties were unsuccessful. Hence, the county-based models described throughout this chapter treated each county as a unique entity. Note that a county-year data constitutes an observation. In the language of NNets, it is a pattern.

In this case, county-based VMT and socioeconomic variables were used to develop the regression models. The best models were then used to predict VMT based on the corresponding socioeconomic data to verify their predictive ability at the county level. The following section presents a summary of the resulting models, along with an assessment of their potential for application.

From the county-level prediction error statistics, the county-based regression models generally lacked the predictive capability desired by the KYTC. Examination of errors for individual counties-years showed that for many points (county/year combination), the model grossly underestimates and in some cases overestimates the VMT. At least for such cases, the variables in the models seem inadequate to reliably predict VMT. Hence, the county-based regression models are inadequate to predict VMT with reasonable accuracy. This is in spite of the fact that very high values for the coefficient of multiple correlation (R² ) were obtained.

Additionally, problems caused by correlations among independent variables limited the usefulness of the models even if errors were within the acceptable limits. Inclusion and removal of some variables changed the values of the regression coefficients and signs of remaining variables. The problem of correlation is addressed in a subsequent section. As a point of clarification, correlation among independent variables does not mean that the regression models cannot be used; rather the unique contribution or importance of each variable—as measured by the regression coefficient—cannot be uniquely determined. It changes depending on what variables are included in the model. Hence, on both accounts--large prediction error and correlation among independent variables--the county-based regression models were not useful.

Tests revealed that most of the independent variables are highly correlated (Table 11). This finding is consistent with similar efforts in other states (9). This restricts the ability to interpret the contribution of individual variables in the model.

Population	1
Per capita income	0.463111	1
Retail Sales	0.985234	0.489655838	1
Employment	0.991666	0.467511402	0.994508921	1
Earnings	0.987836	0.468945291	0.99284964	0.9988718	1

^* Exact values of correlation coefficients would be different if different subsets of data are used.

Table 11. Correlation Matrix (using data for all counties for 1993-1999)*

4.6.1.1.2 State-Based Regression Models

In this case, models are developed to predict VMT at the state level. Time-based models were found to accurately capture the VMT growth trend. The models were then used to allocate the statewide VMT to each county based on population or a combination of socioeconomic factors. A sample of the models is shown in Figure 7. It is seen that the above model closely captures the state VMT growth trend. Other variations of the above modeling approach produced comparable results.

Figure 7. Prediction of State VMT (combined interstate and non-interstate VMT)

Upon allocation to counties based on population, and based on the resulting error as measured at the county-level, this approach still lacks the predictive capability desired by the KYTC (see Table 12 and Figure 8). Figure 8 shows that approximately 75% of the predictions are more extreme that the ±10% limits. When compared to the county-based regression models, the state-based regression models have smaller average error--but not small enough to meet the KYTC's prediction accuracy constraint.

Maximum Percent Error	59.0072

| | Minimum Percent Error | -74.3602 | |--|-----------------------|----------| |--|-----------------------|----------|

Table 12. Percent Error at the County Level (based on state-wide models)

Figure 8. Distribution of Observation Error over the Error Range (interstate and non-interstate VMT combined)

4.6.1.1.3 Addressing the Problem of Correlation between Independent Variables (county-based models)

Two approaches were tried to reduce the impact of the correlation problem noted above:

1) Using reduced models (i.e., models with a limited number of not—highly-correlated variables) and,
2) Transforming the data (i.e., values of independent variables).

Although some of these attempts resulted in models that did pass standard statistical tests, they did not meet the KYTC's 10% error requirement. More specific information on these attempts is given in the following subsections.

4.6.1.1.3.1 Reduced Models

These are models with only limited number of independent variables (in some cases, only one independent variable). Variables were removed to help reduce the correlation problem. Separate models were developed to predict interstate and non-interstate VMT.

This step significantly reduced the problem of correlation. However, the predictive ability

of the models at the county level significantly diminished as well. The predictive accuracy at the county level was well below what the KYTC considered acceptable (See a sample of the models below). It is shown that the models meet important statistical quality tests; however, the models grossly under/overestimated the VMT for several counties.

Sample of Reduced Models

a) Non-Interstate VMT (county-based):

VMT(000) = 160.0720062 + 0.018500727(pop)
	R-Square =0.968283414

		Sum of
	df	Squares
Regression	1	1.24E+09
Residual	838	40543035
Total	839	1.28E+09

(Equation 1)

b) Interstate VMT (county-based)

VMT(000) = 140.0945954 + 0.010162643 (pop)
	R-Square = 0.912011105

		Sum of
	df	Squares
Regression	1	3.34E+08
Residual	278	32102339
Total	279	3.66E+08

(Equation 2)

Note that based on the R² value and the sum of squares, the above models are very good models (i.e., high overall predictive capability). For illustrative purposes, the distribution of the errors associated with the predictions of the above two models is shown in Figure 9 and 10.

Figure 9. Distribution of Non-Interstate VMT Prediction Error over Error Range

Figure 10. Distribution of Interstate VMT Prediction Error over Error Range

Figure 9 shows that only about 30% of the non-interstate VMT predictions were within the ±10% error limit. In the case of Interstate VMT (Figure 10) only 15% of predictions were within the ±10% error limit. The contrast between the quality of the models in Equations 1 and 2--as reflected by the corresponding statistics--and what Figures 9 and 10 show is striking. This demonstrates that the ±10% error constraint may be too stringent given the variation of VMT values in the KYTC database.

Models with other combinations of independent variables were also attempted. In all cases the models were statistically sound but failed to meet the ±10% prediction accuracy criterion.

4.6.1.1.3.2 Models with Transformed Data

Regression models with logarithmic forms of the data were also tried. The purpose of the transformations was to reduce the correlation problems. Because of the high correlation, the success of this attempt was only limited; the transformation of data did not eliminate the correlation problem. Specific model information is provided below.

4.6.1.1.3.2.1 County-Based Regression Models with Transformed Data

The R for several of the regression models were very high (above 0.9 in some cases). Although such high value signifies successful models, this measure by itself meant very little because of the high correlation issue noted above. Additionally, the prediction error was in many cases significantly higher than the thresholds set by the KYTC. For these two reasons, the regression models were deemed inadequate to meet the objectives of this study.

As a variation and potential remedial action, a time variable was included in the models to account for possible changes in driving and travel trends that are prompted by changes in demographic and socioeconomic characteristics.

a) Non-interstate VMT

LN(VMT) =	0.152	LN(POP) +	LN(TIME VARIABLE)

			R-square = 0.8727
		Sum of
	df	Squares
Regression		3 509.4516
Residual	837	73.32543
Total	840	582.777

(Equation 3)

b) Interstate VMT

LN(VMT) =	0.22	LN(POP) +	LN(TIME VARIABLE)
			R-square = 0.5240
		Sum of
	df	Squares
Regression		3 182.0666
Residual	277	160.7821
Total	280	342.8487
			(Equation 4)

The statistics on both models indicate that data transformation did not lead to much improvement. The time variable did not help.

4.6.1.1.3.2.2. State-Based Regression Models with Transformed Data

No state-based models were developed with transformed data. There was no need for this since the non-transformed data models met all quality requirements (when used at the state level).

4.6.1.1.3.3 Models with a K-Factor

This variation of regression models was one of the last attempts. In these models, explicit attempt was made to account for the "missing" variables. Models in this group showed more promise for predicting Interstate VMT--they were relatively more accurate than the rest. These models are described in more details in the Final Models section.

The modest success of the regression approach is apparent from the above results. The following section describes the result of the Neural Networks (NNets) approach. NNets use untraditional approach to prediction. They are presented in the following section.

4.6.1.2 NNets Results

4.6.1.2.1 Variations of NNet:

Firstly, all available independent variables were included in the Nnet. The NNet itself is used to determine the significant variables. In subsequent trials, NNets with only significant variables were used. As in the regression approach, several variations of NNet were tried:

1. NNets with log-transformed data. This was done because of the large differences in values of different independent variables. Very large difference can negatively impact the quality of the NNet's prediction ability.
2. NNets with percentage VMT change (instead of actual VMT values). This was done to avoid problems caused by similar (among different counties) percentage changes but significantly different actual VMT change.

3. NNets with an "adjustment" factor (labeled K-factor)

In brief, the NNet models resulted in significant improvements in prediction accuracy. However, they still fell short of the ±10% error limit for several of the counties except for the NNet with the K-factor (described in the Final Models section). The NNet with the K-factor produced significantly better results.

4.7 Final Models

From the initial results, it was very clear that some important independent variables were missing. The models presented in this section accounted for these missing variables using surrogate measures that capture the influence of those missing variables without having to explicitly determine the variables themselves. Different functional variations of the residual associated with each observation (county-year combination) were evaluated, and the form that showed the most potential was selected. The residual represent the difference between the actual and the predicted VMT. It results when a "first level" model is applied to predict VMT based on the independent variables.

4.7.1 Non-Interstate VMT

For non-interstate VMT, the K-factor model was NNet-based.

4.7.1.1 NNets with K-Factor

The NNet was developed through a two-level training and testing process as follows:

1) In the first level, a NNet was developed, and then tested on the entire database. (the residuals from the testing process were retained and treated as unique variables associated with the respective county-year observation).
2) In the second level, another independent NNet was developed through training on the 1993-1997 data and then tested on the 1998-1999 data.
3) The residual from the first Net was treated as an additional independent variable in the second level Net.

The above is not a standard practice when developing NNet, and should normally be used only as a last resort. The premise for this use is the following: The residual is a measure of the influence of the missing variables; if we can determine a suitable form of the residual then we may use it as a variable.

The above process was followed for both interstate and non-interstate VMT. The noninterstate VMT NNet (NIVMTNNet) generated predictions such that only one county had an error more extreme than ±10%. The exception was Jefferson County with errors of 26% and 18% for 1998 and 1999, respectively. The following variables were used in Non-I VMT NNet-K model (LN means Logarithmic form):

1. LN Urban mileage
2. LN Population
3. LN Employment
4. LN Earnings (includes individual but not corporate earnings)
5. LN of number of Interstate Interchanges
6. LN of number of Parkway Interchanges
7. Residual (K-factor)

| Average Error | | 2
R | | | |---------------|--------------|---------------|--------------|--| | Training Data | Testing Data | Training Data | Testing Data | | | 0.0104 | 0.0179 | 0.9997 | 0.9987 | |

The error and R² values indicate strong prediction ability with very low error. That is consistent for both training and testing data. It should be noted that the testing data is one that was not used in developing (or training) the model. The distribution of prediction error over the error range is shown in Figure 11.

Figure 11. Distribution of Prediction Error over the Error Range (non-interstate VMT)

The interstate VMT NNet (IVMTNNet) performed poorly, and therefore the results are not presented. A regression-based model with a K-factor performed better for interstate VMT (see Section 4.7.2).

4.7.1.2 Projecting the 2000 Non-Interstate VMT

In order to test the stability of the K-factor Nnet non-interstate VMT model as a forecasting tool, the model was used to project year 2000 non-interstate VMT. The results are shown in Figure 12. The numbers on the x-axis are those of the observations—an observation is a county-year combination. It is noted that for a significant number of counties the model projects a decline in VMT, which is contrary to expectation. For many other counties much higher than "normal" increase is projected. A "normal" change for non-interstate VMT, according to the KYTC, is in the range 1-6%.

The results of Figure 12 suggest that the non-interstate VMT predictions of the NNet Kfactor model are not within KYTC's "normal" range. Although this is not a calibrated/validated rule of thumb, for a large number of counties, the model projections deviate significantly from the "normal" range. More will be said on this in Section 4.8, Discussion.

Figure 12. Projected Increase in Non-Interstate VMT between 1999 and 2000 as a percentage of 1999 VMT

4.7.2 Interstate VMT

Two different sets of "final" models for predicting interstate VMT were developed. Both are regression-based. The first uses the K-factor approach and predicts VMT at the county level directly. The second is a corridor-based model that predicts VMT based on socioeconomic factors.

4.7.2.1 Interstate VMT, K-factor Model

This is a regression-based model with K-factor. The model is:

$$VMT (000) = b + a (population) + K$$ (Equation 5)

Where:

b: Constant

a: Regression coefficient

K: K-factor

Different functional forms of the K-factor were evaluated. The best results were obtained with the following form:

K= [(minimum (Std Res)+Range /6(Year x -93)],

Where:

Std Res: Standard residual obtained for the subject year obtained from the First-

Level regression models

Range: Range of standard residuals for the base years (1993-1999) obtained from

the First-level model

When the model in Equation 5 was applied to the 1993-1999 data, approximately 85% of the observations had errors with the ± 10% limit (see Figure 13).

Figure 13. Distribution of Prediction Error Over the Error Range (interstate VMT) (1993-1999 data)

4.7.2.2 Projecting the 2000 Interstate VMT

The K-factor interstate VMT model was used to project the 2000 interstate VMT. Because no residual is available for the projected year, the K factor value for 1999 is used. The results are shown in Figure 14. It is noted that for a few counties negative VMT change was projected and for some the projections are unrealistically high.

Application of the model to project the 2010 and 2020 VMT resulted in noticeably low projected VMT values. Although population did not grow--even declined--for some counties, that alone cannot account for the low VMT projections. The K factor is suspect in this case. The K-factor for future years is, by definition, not known. Use of such K-factor values may have introduced unrealistic biases.

Figure 14. Projected Increase in Interstate VMT between 1999 and 2000 as a Percentage of 1999 VMT

4.7.2.3 Corridor-Based Models

This model was developed according to the following steps. Regression and Neural Net models were tried:

1. For a given county and interstate route, determine an ADT growth factor by examining data for different count stations. If applicable, different growth rates may need to be generated for different sections of the interstate route.
2. Determine the VMT for respective sections of the interstate route for each year from the ADT and mileage of the section.
3. Develop a model for each corridor and regress VMT against appropriate socioeconomic variables for the counties comprising the corridor,.
4. Use models generated in Step 3 to project future VMT.

A model is developed for each corridor (as contrasted with previous models where one model was developed for all corridor/counties). Figure 15 shows a sample of the regression models for two corridors. Population and retail sales-based models are shown. Figure 16 shows a sample of the NNet models.

I-24 corridor y = 1741.1x + 283766 R = 0.2505 0 100000 200000 300000 400000 500000 600000 0 10 20 30 40 50 60 70 80 Population (000) VMT

Figure 15. A Sample of Corridor-Based VMT Models

4.7.2.3.1 Assessment of Regression Corridor-based Models

Figure 15 demonstrates clearly that the regression corridor-based models do not have sufficient predictive ability. The same conclusion holds true after the models were normalized for the length of interstate route sections in the respective counties.

4.7.2.3.2 Assessment of NNet Corridor-based Models

Figure 16 demonstrates that the NNet-based models are capable of predicting the general trend in VMT for the various counties. However, the level of accuracy is not sufficient given the accuracy constraints set for this study. Figures 16a and 16b show the training (1993-1997) and testing (1998 & 1999) data results, respectively. In both graphs, the x-axis shows observation numbers and the y-axis shows VMT values. While the match is overall close between the actual and predicted VMT, it is not close enough to be of sufficient accuracy.

Figure 16a. Comparison of Actual and Predicted VMT - Training Data (Interstate 75 Corridor)

Figure 16b. Comparison of Actual and Predicted VMT - Testing Data (Interstate 75 Corridor)

4.7.2.3.3 Corridor-based Models: Are Interstate-VMT and Socioeconomic Factors Related?

The above question became necessary to address after the corridor-based models did not bring in any improvement over the previous ones. The question was addressed by examining the relation between change in interstate VMT and socioeconomic factors. A sample of the results for I-24 and I-64 corridors are shown in Figures 17 and 18, respectively. The results are for yearly changes from 1993 to 1999.

Figures 17 and 18 demonstrate one important piece of information: the association between change in interstate-VMT and socioeconomic measures is very weak. In fact, one can assert based on the above two figures that there is no relationship between change in Interstate-VMT and the socioeconomic variables that are used in this study. It seems that other variables need to be identified, or a completely different basis should be explored.

Figure 17. Relationship between Change in Interstate VMT and Retail Sales (Interstate 24 Corridor)

Figure 18. Relationship between Change in Interstate VMT and Retail Sales (Interstate 64 Corridor)

4.8. Discussion

The limited success of the VMT modeling effort prompted an inquiry into the possible reasons. First, the VMT data was examined. Yearly trends and the magnitude of variation in a given county were evaluated. A sample of the results is shown in the following figures. In each case, the closest linear regression model is shown.

Figure 19 shows yearly interstate VMT variation for four representative counties. The wide variation between successive years is clearly demonstrated in the two figures.

Fayette-Interstate VMT y = 72.092x - 142330 R 2 = 0.9288 0 500 1000 1500 2000 1992 1993 1994 1995 1996 1997 1998 1999 2000 year VMT

Simpson County- Interstate VMT y = 2.3321x - 4189 R 2 = 0.0351 400 420 440 460 480 500 1992 1993 1994 1995 1996 1997 1998 1999 2000 year VMT

Figure 19. Variation of VMT over Time for Selected Counties (VMT Figures are in Thousands)

For interstate VMT, the normal growth should be around 3%¹ . Given this rule, approximately 25% of the observations had less than 0% growth and 50% had more than 5% growth (Figure 20). In other words, about 75% of the cases in the database have yearly growth outside the "normal" range.

Figure 20. Distribution of Yearly Interstate VMT

¹ Rule of thumb used by KYTC to check Interstate VMT growth.

The same general observation is true of non-interstate VMT as shown in Figure 21. In many cases growth was more than 6%, and in a significant number of cases the growth was less than – 3%. The normal expected yearly growth is 1-6%² . Given this rule, about 32% of the observations had less than 1% growth, and 15% had more than 6% growth. That is, over 47% of the yearly growths were outside the range.

Figure 21. Distribution of Yearly Non-Interstate VMT

Such wide variations indicate unsystematic ways of generating VMT. With such variations, modeling is almost impossible because yearly trends for different counties is significantly different and, at times, completely opposite. The following comments can be made with regard to the information shown in Figures 19 through 21:

1. Some yearly VMT changes are well beyond the established "normal" ranges
2. Unsystematic changes are not too uncommon
3. Approximately 47% of non-interstate VMT yearly changes, and 75% of interstate VMT yearly changes are OUTSIDE the "normal" range

It should not come as a surprise that no model could meet the stringent, but seemingly unsupported "normal" yearly growth ranges. In fact, it is completely unrealistic, given such wide VMT yearly variations, to expect the generated models to predict completely different and much lower variation. There was a mismatch between the type of VMT data used and the VMT error and yearly variation thresholds established for this study. Those thresholds were unattainable right from the onset. This, however, was not realized until the VMT data was diagnosed in the later stages of the project.

² Rule of thumb used by KYTC to check Non-Interstate VMT growth

4.9 Conclusion

The objective of this research was to develop models that predict VMT based on socioeconomic data inputs. Two modeling approaches were used to develop models for both interstate and non-interstate VMT: Linear Regression and Neural Networks (NNet). Models from both approaches were evaluated for soundness and usability. Although statistically sound models were developed, success to achieve the stated objective was only modest. The following specific conclusions can be drawn based on the aforementioned results.

· Predicting VMT based on socioeconomic data (independent variables) was not successful.
· Currently available socioeconomic data has the potential to predict non-interstate VMT for most counties. However, more data refinement and modeling is needed to develop more reliable and accurate models. Additional variables may also be necessary
· Neural Networks has shown significant potential for use as a modeling technique. Their flexibility and unconventional approach make them particularly suited when the patterns of the data that is to be modeled are complex.
· Results from the corridor-based interstate VMT models are not much different than earlier models; prediction accuracy is still not sufficient. For corridor-based models, using NNets produced better results than regression.
· Although NNets models were generally better than regression models, that should not be construed as a statement against regression. The nature and structure of the data determine which modeling approach is better.

4.10 Recommendations

Based on the results presented thus far, the following recommendations are made:

· More than one approach should be used to predict future VMT so as to minimize the effect of biases that are present in any one single modeling approach.
· Time series models were shown to have very good predictive capability. They should be considered when sufficient independent variables are either not available or incapable of producing models with sufficient predictive capabilities.
· Neural Nets should be seriously considered in future research where the objective is to learn complex relations. They have demonstrated great capability of interpolating and extrapolating from known cases, and then generalizing this knowledge to cases that were not used in the training.
· A refinement of the models developed in this study is recommended. As part of this exercise, only basic (not processed) data should be should be modeled. Based on the data available to the KYTC, this means that only traffic volumes may be predicted based on socioeconomic input variables. VMT can then be estimated based on road mileage and traffic volume.
· It is recommended that regional and national variables be used to predict interstate VMT. However, for such variables to be useful they have to be available for future years.
· Because of the high correlation among several of the socioeconomic data variables, it is important that regression-based models be used carefully particularly when several socioeconomic variable are significant.
· It is appropriate to suggest that improved procedures are needed for screening anomalies and errors out of the KYTC traffic count data because of the influence that an aberrant count can have on the change in county level VMT from year to year.

5.0 DEVELOPMENT OF RATIOS FOR RELATIONSHIP BETWEEN COLLECTORS AND LOCAL ROADS

5.1 Local to Collector Ratio Analysis

In the determination of local VMT, it is necessary to have county level local ADT and local road mileage. Historically, the KYTC Division of Planning has collected local road (both state maintained and non-state maintained) ADT on a periodic basis. A portion of these counts was collected for local HPMS sampling required by the FHWA on a regular basis.

The logistics of collecting local road ADT on a statewide basis can be difficult. Therefore a procedure was developed to relate functionally classified local roads and functionally classified collector roads. This relationship consisted of a ratio between local ADT and collector ADT. The numerator of the ratio was defined as the average ADT for a specified grouping of local roads, while the denominator was defined by the average collector ADT for the same grouping of roadways.

Separate ratios were determined for rural functional classifications (FC 09/FC 08) and for urban functional classifications (FC 19/FC 17). These relationships were developed for groupings of both urbanized and non-urbanized counties. The urbanized counties utilized in this analysis were defined as follows: Boone, Boyd, Bullitt, Campbell, Daviess, Fayette, Greenup, Henderson, Jefferson, Jessamine, Kenton, and Oldham.

It was determined that additional analysis was necessary to further evaluate this relationship. The remainder of this section will outline the procedures that were utilized to evaluate these ratios. The ratios previously utilized that were developed by the Division of Planning are contained in Table 13.

Highway Grouping	FC 9/FC 8	FC 19/ FC 17
Non-Urbanized Counties	0.33	0.12
Urbanized Counties	0.33	0.28

Table 13. Historical Functional Class Ratios

The ratios contained in Table 13 were developed from a single years worth of data. In the development of these ratios, some concern was expressed that these ratios may change over time. One of the objectives of this effort was to determine the behavior of this relationship.

5.2 Data Analysis

To evaluate the potential change in this relationship over time, the research effort utilized the data maintained in the Traffic Volume System (TVS) maintained by the Division of Planning. Historical data from 1980 through 2000 were utilized in the analysis. The typical schedule for traffic counting in Kentucky has been as follows: 1) interstates and parkways, every year; 2) HPMS samples, every 3 years; 3) coverage counts on the remaining sites every six years. This schedule was typically utilized for all state maintained routes within the state. Functionally classified local roads that were not state maintained were also counted on a periodic basis; however, this data is sporadic and may have more than six years between actual counts.

After review of the available data, it was determined that data used to calculate the numerator of the FC ratio should be obtained from historical local HPMS samples. While the data used to calculate the denominator should be from all collector routes (FC 08 or FC 17).

The TVS contains the historical record for every traffic monitoring station utilized in Kentucky; and as was previously mentioned, actual traffic counts are not obtained on all stations each year. The TVS provides estimates for years when actual data is not available. Therefore, a complete history of each traffic volume station is available. This estimating procedure is outlined in Appendix E. Since the estimating procedure utilizes counts that are defined as actual counts (meaning some type of field count was actually performed), stations to be used in the analysis were restricted to those that had at least two actual counts since 1980. TVS also identifies stations that have had significant traffic impact events in a given year. Only data from these impact years until the year 2000 were utilized in the analysis.

To determine the ratio for each of the groupings outlined in Table 13, the numerical average of the annual ADT for all stations within a grouping, FC 19 urbanized counties for example, was determined.

Similar annual averages were determined for the other groupings. The calculation of these annual average ADTs provided the necessary information to compute the following functional class ADT ratios:

FC 09/FC 08 Urbanized Counties FC 09/FC 08 Non-Urbanized Counties

FC 19/FC 17 Urbanized Counties FC 19/FC 17 Non-Urbanized Counties

To evaluate the relationship of this ratio with time, an individual FC ratio was determined for each year from 1980 – 2000. Due to an increase in the local sample size in 1991, only data from 1992 through 2000 are used in the final evaluation. These ratios calculated for the years 1992 – 2000 are plotted in Figure 22.

Figure 22. Comparison of Functional Class Ratios

It may be seen from Figure 22 that there is very little difference between the urbanized and non-urbanized FC 19/FC 17 ratios. Therefore it was determined that for the remaining analysis a single FC 19/FC 17 ratio would be determined for all counties.

Functional class ADT ratios were determined using both numerical average ADTs each year and ADTs calculated using a weighted average approach. The weighted average ADTs were determined using the length of the highway the traffic volume station represents as the weighting factor. Therefore, a station representing a longer length would have more of an impact on the resulting weighted average than that of a station representing a shorter length. The resulting functional class ratios for each grouping for both weighted and non-weighted ADT averages are given in Figure 23 – 25.

Figure 23. Non-Urbanized Counties Functional Class Ratio FC 09/08

Figure 24. Urbanized Counties Functional Class Ratio FC 09/08

Figure 25. All Counties Functional Class Ratio FC 19/17

It may be seen from these figures that the ratio between the ADT of local routes (FC 09 and 19) to that of collector routes (FC 08 and 17) appears to change with time. In addition, the use of weighted or arithmetic averages of functional class ADT also changes the magnitude of the ratio. This is due to the effects of station length on the resulting average. Stations representing short sections of roadway would have very little impact on the resulting weighted average, but would have equal impact as other stations on the arithmetic average. While stations representing long lengths would have a greater impact on the weighted average and equal impact on the arithmetic average.

There are many issues that could affect these ratios, such as the representation of the roadways that are sampled. The sample of the collector routes would be assumed to be complete, since all routes functionally classified as collectors would have traffic counts available. The local HPMS data utilized may not provide a representative sample of all local routes. The data available in the HPMS sample has primarily been obtained on state maintained local routes. There are many routes that are functionally classified as local routes but are maintained by other jurisdictions, and therefore are not included in the Transportation Cabinet's traffic monitoring system.

5.3 2000 Local Sample

As a means to address the situation outlined in the previous section regarding having representative samples of local roads, a number of counties were selected in the year 2000 to obtain a statistically based sample of all local roads. The selection of these sample locations has been addressed in Section 3.0 of this report.

After review of the historical data in conjunction with the 2000 local samples taken, it

was determined that the sampling of the previous local counts may not have included a representative sample of all local routes within a county. Therefore it was determined that the 2000 local sample would provide a more representative sample and therefore should be used for the analysis of a relationship between local and collector ADT.

The average ADT for the local roads sampled in each county and the corresponding collector ADT are contained in Table 14 for rural routes and Table 15 for urban routes.

County		Collector Routes		Local Routes
Name	ADT	Mileage	ADT	Mileage
Allen	672	87.6	213	450.5
Barren	584	127.5	153	604.3
Boyd	527	35.2	335	83.1
Clark	825	64.0	280	225.2
Franklin	884	75.8	299	227.2
Garrard	539	52.7	295	236.4
Grant	937	56.7	222	349.4
Green	348	91.2	118	381.2
Henderson	638	107.2	188	458.6
Henry	592	106.8	163	238.8
Kenton	569	28.5	154	106.7
Laurel	798	93.0	263	826.3
Lawrence	907	64.6	137	362.7
Leslie	870	72.0	220	349.8
Lewis	502	83.3	183	395.0
Lyon	550	57.7	103	452.9
McCracken	891	68.9	180	212.2
McCreary	878	96.0	164	542.2
Mercer	467	63.1	166	256.3
Muhlenberg	1,292	122.7	191	565.7
Nelson	696	105.4	192	355.6
Owen	423	65.7	180	276.2
Perry	1,031	94.7	272	338.8
Pike	1,659	178.5	395	690.4
Shelby	738	111.9	222	278.4
Wayne	540	103.3	117	405.6
Wolfe	349	49.1	86	251.3

Table 14. Average FC 08 and FC 09 ADT for 2000 Local Sample Counties

	Collector		Local Routes
	ADT	Mileage	ADT	Mileage

Barren	3,327	10.1	592	26.4
Boyd	3,773	31.9	313	334.2
Clark	2,815	11.1	676	54.3
Fayette	4,121	158.8	706	739.8
Franklin	3,675	19.9	584	114.1
Henderson	3,519	27.1	523	107.7
Kenton	4,976	66.1	600	564.4
Laurel	2,297	7.9	519	54.1
McCracken	3,928	29.1	390	236.3
Mercer	4,506	7.4	507	35.7
Nelson	2,255	6.6	919	41.9
Perry	4,078	4.5	472	31.7
Pike	2,551	6.0	655	15.2
Shelby	2,749	9.1	558	40.6
Wayne	1,367	6.5	438	15.2

Table 15. Average FC 17 and FC 19 ADT for 2000 Local Sample Counties

The data contained in these tables were utilized to develop a relationship between local road ADT and collector ADT. The data in Tables 14 and 15 is expressed graphically in Figure 26.

Figure 26. Comparison of Collector ADT and Local ADT

It may be see from this figure that there appears to be a relationship between collector ADT and local ADT for the sampled counties. Several different relationships were evaluated to determine the most appropriate model to characterize this relationship, including multivariate regression, simple ADT ratios, and several least squares regression models. Regression models were evaluated for the data set as a whole and by separating the local and urban functional classifications.

The multi-variant regression analysis utilized the following variables:

Rural Collector ADT Rural Local Road Mileage

Retail Sales Earnings

Rural Collector Mileage Licensed Drivers Population Employment

After evaluation of this model, it was determined that it did provide a means to predict local road ADT. However the ability to obtain accurate data for each of the independent variables for future years may be difficult. Therefore it was determined that a relationship utilizing collector ADT as the independent variable would be more appropriate. Therefore, the following types of relationships between functionally classified local road ADT and functionally classified collector ADT were utilized.

Linear Local ADT = A x (Collector ADT) + B Logarithmic Local ADT = A x (LN(Collector ADT) + B

Power Local ADT = A x (Collector ADT) ^B

Average Ratio Local ADT/Collector ADT

The linear relationship and the average ratio procedure were evaluated for both rural and urban classifications separately and for all local sample counties combined. The logarithmic and power relationships were evaluated for the entire 2000 local sample combined. The results of each of these models are presented in Figure 27.

Figure 27. Comparison of Predictive Relationships

Each of these models was evaluated for both goodness of fit and the ability to predict local ADT beyond the limits of the 2000 local sample data. Based on these criteria, it was determined that the power equation best represents the relationship between local and collector ADT. The relationship provides a good fit of the collected data with a R of 0.73 and has the ability to predict reasonable local ADT even at very low levels of collector ADT. The resulting equation of the best-fit line is as follows:

Local ADT = 3.3439 x (Collector ADT)0.6248

6.0 DEVELOPMENT OF COUNTY-LEVEL GROWTH RATES

6.1 Growth Rate Development

In many instances in traffic planning, it is necessary to assess the historical growth of vehicle traffic. This traffic growth may be obtained from historical data for individual traffic monitoring stations or groupings of stations. Data obtained from the TVS was again utilized to analyze the traffic growth characteristics of various groupings of highways. The same procedures were followed in the selection of data for the analysis of traffic growth as were utilized in selection of stations used for the analysis of the functional class ratio procedure. The stations utilized were required to have had at least two actual counts since 1980 or since a traffic impact year was identified. Both actual traffic counts and computer estimates were utilized in developing the database for analysis. The analysis of traffic growth was limited to data from 1991 through 2000 for all functional classes with the exception of FC 09 and 19. The analysis of these functional classes was limited to 1992 through 2000 for the reasons previously stated in 5.0.

In the analysis of the growth of a specific grouping of stations, data were obtained for each station within the group for the years 1991 through 2000 (1992 – 2000 for FC 09 and 19). The average ADT, both weighted and unweighted, was determined for all stations within the grouping for each year. The weighted ADT is determined using the length which the station represents as the weighting factor while the unweighted ADT is a simple arithmetic average of all the stations. This resulted in a single average historical ADT for this group for every year. This is illustrated in Figure 28 for functional class 06 in Allen County. It may be seen from this figure that there were four functional class 06 stations in Allen County. The resulting blue line with round symbols indicates the annual unweighted average for these stations. A similar plot could be generated for weighted annual ADT. A linear regression analysis may then be performed on the average annual ADT. This is illustrated in Figure 29.

Figure 28. Allen County Functional Class 06 Traffic Monitoring Stations

Figure 29. Average Annual Functional Class 06 ADT for Allen County

The equation has the form of a straight line, y = mx + b, where "y" is the resulting ADT and "x" is the year of prediction, and "m" and "b" are regression coefficients. The slope term of the linear regression "m" may be utilized to calculate a growth rate for the grouping of stations. The slope represents the average change in ADT between each year throughout the history of the station. This slope may be utilized in conjunction with a single years ADT to determine an individual year's growth rate. For the purpose of this project, growth was defined as the slope of the regression line divided by the predicted year 2000 ADT multiplied by 100. The resulting growth rates may then be expressed as a percent. For the example given in Figure 28 and 29, the growth rate would be determined as follows:

$$\frac{\text{Regression Slope}}{\text{Year 2000 ADT}} \$100 = \text{Growth Rate (\%)} \quad \text{fi} \quad \frac{262.08}{4,853} \$100 = 5.4\%$$

These types of calculations were carried out for each functional class in each county. The results of this analysis are given for both weighted and unweighted ADT in Appendix F and Appendix G, respectively.

A statewide summary of these results is given in Table 16. This table shows the numerical average of all the individual county level growth rates for both weighted and unweighted ADT.

						Functional Class
	01	02	06	07	08	09	11	12	14	16	17	19
						Growth Rate (%)
Unweighted ADT	3.40	3.01	2.09	1.71	1.64	1.95	3.34	2.63	2.02	1.48	1.01	1.95
Weighted ADT	3.32	3.15	2.18	1.80	1.79	2.31	3.37	2.81	2.19	1.54	1.35	2.08

Table 16. Summary of Statewide County Level Functional Classification Growth Rates

It may be seen from the tables in Appendix F and Appendix G that counties actually have negative growth rates. These negative growth rates were included in the above averages.

6.2 Statewide Functional Class Averages

The statewide unweighted average annual ADT for FC 01 and 11 are given in Figure 30. These averages were determined by averaging all FC 01 or FC 11 stations for a given year. A linear regression line is also provided in the figure. The results of this regression yield growth rates of 3.53% and 3.07% for functional class 01 and 11 respectively.

Figure 30. Statewide Unweighted Average ADT for Functional Class 01 and 11

Similar plots for the rural functional classifications and urban functional classifications are given in Figures 31 and 32 respectively. The results of the linear regression for each of these ADT histories are given in Table 17 along with the results obtained from weighted average ADT.

Figure 31. Statewide Unweighted Average ADT for Rural Functional Classes

Figure 32. Statewide Unweighted Average ADT for Urban Functional Classes

						Functional Class
	01	02	06	07	08	09	11	12	14	16	17	19
						Growth Rate (%)
Unweighted ADT	3.53	2.67	2.20	1.67	1.73	2.05	3.07	2.22	1.32	1.48	1.44	2.76
Weighted ADT	3.51	3.21	2.39	1.82	1.89	2.67	2.94	2.65	1.58	1.77	1.95	3.44

Table 17. Summary of Statewide Functional Class Growth Rates

6.3 Interstate Corridor Analysis

Interstate functional class growth rates based on weighted ADT have been determined for each individual interstate corridor. These results are given in Table 18. Interstate corridors for I-64, I-71, and I-75 have been further broken down into urban and rural counties. These county groupings do not necessarily correspond to the standard functional class groupings of FC 01 and 11. These results are given in Appendix H.

Interstate Route	Average ADT	Weighted Average ADT	Predicted Weighted Average ADT	2000 Weighted ADT Growth Rate (%)	Regression Slope	Regression Constant
24	23,543	22,019	21,790	4.22	919	-1,817,117
64	42,767	31,560	32,032	2.95	945	-1,857,131
65	61,502	50,208	50,619	2.48	1,253	-2,455,654
71	37,062	34,904	34,599	3.46	1,196	-2,357,128
75	68,025	50,532	51,707	3.70	1,911	-3,770,625
264	113,095	100,494	108,877	4.24	4,618	-9,127,772
265	52,292	50,953	50,629	2.47	1,251	-2,452,021
275	78,350	68,291	68,199	4.74	3,233	-6,398,578
471	98,740	95,656	96,142	1.86	1,786	-3,476,124

Table 18. Interstate Corridor Weighted ADT Growth

7.0 REFERENCES

1. Traffic Characteristics of Kentucky Highways 1997. Frankfort, KY: Commonwealth of Kentucky Transportation Cabinet, Division of Transportation Planning.
2. "The Regional Transport of Ozone: New EPA Rulemaking on Nitrogen Oxide Emissions." http://www.epa.gov/ttn/otag/about_1.html (April 22, 1999).
3. "HPMS Field Manual-December 1999: Chapter 7: Sample Selection and Maintenance" http://www.fhwa.dot.gov/ohim/hpmsmanl/chap7.pdf (February 28, 2001).
4. Crouch, J.A., Seaver, W.L., and Chatterjee, A. "Estimation of Traffic Volumes on Rural Local Roads in Tennessee." Transportation Research Board 80th Annual Meeting. CD-ROM. 2001.
5. Niemeier, Debbie, J. Morey, J. Franklin, T. Limanond, K. Lakshminarayanan, An Exploratory Study: A New Methodology for Estimating Unpaved Road Miles and Vehicle Activity on Unpaved Roads, February 1999, pp. 55, ITS-Davis Pub # RR-99-2.
6. Woods and Poole Economics, Inc. 1740 Columbia Road NW, Suite 4, Washington, D.C. 20009.
7. Sartori, A.: "Applied Thought", Traffic Technology International 2001, pp 146-149, 2001.
8. Haykin, S.: Neural Networks: A Comprehensive Foundation. IEEE Press, 1994.
9. Iskander, W., Jaraiedi, M., Thomas, T., & Martinelli, D.: "Traffic Volume Projections in West Virginia and the I-81 Corridor". Final Report, West Virginia Department of Transportation, 1996.
10. Fricker, J.D. & Saha, S.K.: "Traffic Volume Forecasting Methods for Rural State Highways". Final Report, FHWA/IN/JHRP-86/20.

8.0 APPENDICES

8.1 Appendix A – Kentucky Year 2020 VMT Forecast Procedures

KYTC VMT Forecasting Procedure Overview

Given the significant and non-demographic based influence of Interstate highway travel on vehicle miles traveled (VMT) in many counties, the KYTC's VMT forecasting procedure splits total VMT into two categories for forecasting purposes - Interstate and non-Interstate. Growth in VMT is forecasted first at the statewide level and then allocated to the county level. Non-Interstate VMT is then split into the functional class categories (including local) based on historical, county-specific percentages.

For non-Interstate travel, VMT growth is linked to population growth. Statewide VMT growth is forecasted using a population based linear model and a forecast of Kentucky's future population for the desired target year. This model is revised each year with the incorporation of an additional year of historical data. Because of a strong linear relationship between statewide population and statewide VMT and because statewide (and county-level) population estimates and forecasts exist and are widely accepted, the use of population change makes for a convenient and defensible method for forecasting (and subsequently allocating) statewide VMT growth. The difference between the base year (the most recent year for which estimates are available) estimate and the target year forecast establishes a statewide control total for non-Interstate VMT growth. This growth is then allocated to the counties based on a combination of county population change and the model-derived projected increase in VMT per person per year.

For Interstate travel, it is felt that the relationships between county level demographic variables and VMT are much less cause-and-effect in nature than for non-Interstate travel. Therefore, VMT growth is projected using growth rates determined from historical trends and assumed changes in the growth rate over time. Changes in the growth rate over time are based on the expectation that Interstate travel will continue to increase at a higher rate than non-Interstate travel, but that the rate of increase will likely flatten out somewhat due to various reasons (capacity restraint and the mathematics of compound interest being two of the primary reasons).

Kentucky Year 2020 VMT Forecasts

This appendix describes the methodology and assumptions used to develop year 2020 forecasts of vehicle miles of travel (VMT) for Kentucky's 120 counties.

Data Available:

1. Statewide VMT from HPMS for the years 1980 1998.
2. VMT by county and functional class for the years 1993 1998.
3. Statewide population estimates for the years 1980 1998.
4. County level population estimates for 1998.
5. County level population forecasts for 2020.

Data Adjustments

The need to create county level VMT estimates required improved procedures for estimating local VMT. Prior to 1998, local VMT had been estimated based on a statewide average local ADT and local mileage. This procedure was insensitive to differences in the magnitude of travel on the local system from county to county. In 1998, a procedure (Collector Ratio Method) was developed to estimate county level differences in local travel based on travel on the collector system. This procedure, which produced significantly improved estimates of county level local VMT, also produced a slight increase (2.57%) in the total estimated statewide VMT. Because of file format changes over time, it was practical to apply the revised procedure to historical VMT estimates back to 1993 only. For consistency purposes, statewide total VMT estimates for the years 1980 - 1992 were increased by 2.57%. These calculations are documented in Table 1 of the KYTC VMT forecasting spreadsheet. This spreadsheet is summarized and illustrated on pages (p. 74-86).

Basic Procedure

Given the significant and non-demographic based influence of Interstate travel on VMT in many counties, the decision was made to split VMT into two categories for forecasting purposes - Interstate and non-Interstate. Growth in VMT was forecasted first at the statewide level and then allocated to the county level.

For non-Interstate travel, VMT growth was linked to population growth. Statewide VMT growth was forecasted using a population based linear model and a forecast of Kentucky's year 2020 population. The growth in statewide VMT was then allocated to the county level VMT based on county level population growth and the model estimated increase in VMT/person/year.

For Interstate travel, the relationships between county level demographic variables and VMT are of a much less cause-and-effect nature. Therefore, VMT growth was based on historical trends and assumed changes in the growth rate over time.

Non-Interstate VMT Forecast Details

Table 2 shows statewide population and VMT estimates for the years 1980 - 1998 and a forecast of Kentucky's year 2020 population. The source of the population data was the Kentucky State Data Center (see http://cbpa.louisville.edu/ksdc/). The source of the VMT data was the Highway Performance Monitoring System (HPMS), and the estimates for 1980 - 1992 were factored as described above under Data Adjustments. The Excel forecast function was used to determine a linear model to predict statewide VMT from statewide population based on the 18 years of data. As can be seen in Table 2, a strong linear relationship exists between statewide population and VMT (R²= 0.84). Furthermore, because statewide and county-level population estimates and forecasts exist and are widely accepted, the use of population change makes for a convenient and defensible method of forecasting VMT. A year 2020 forecast of daily VMT of 149,929,849 resulted. This represents an annual growth rate of 2.05% over the 22-year forecast period, which is felt to be somewhat conservative - but nevertheless very reasonable.

The statewide growth in non-Interstate VMT between 1998 and 2020 was allocated to the counties based on a combination of county population change and the projected increase in VMT per person. Non-Interstate VMT per person per year estimates are shown in Table 2 along with the forecasted value of this parameter for the year 2020. To account for a continued increasing trend in VMT per person, a base level growth of 1.65% per year was applied to each county over 1998 county level estimates. These calculations are shown in Table 3 (Column I). The difference between the resulting statewide total (137,538,035) and the forecasted statewide total discussed above (149,929,849) was then allocated to the county level based on each county's proportion of statewide population change between 1998 and 2020 (See Table 4). This step added VMT above the 1.65% base level growth for counties that are expected to increase in population, and it subtracted VMT from the base level growth for counties that are expected to decrease in population.

Interstate VMT Forecast Details

Table 2 shows Interstate VMT estimates for the years 1980 - 1998. Illustrative historical annual growth rates calculated for selected periods are also shown in this table. This analysis shows that Interstate VMT has increased at an annual rate of 4% or more over this 18-year period and that Interstate VMT has increased at a higher annual rate (by 1% or more) than non-Interstate VMT. In the absence of a more rigorous model, it was decided to use this historical trend, measured in terms of an annual growth rate, as the means to arrive at a year 2020 forecast of Interstate VMT.

It is expected that Interstate travel will continue to increase at a higher rate than non-Interstate travel, but that the rate of increase will likely flatten out somewhat due to various reasons (capacity restraint and the mathematics of compound interest, primary among them). An annual growth rate - starting at 4% per year and decreasing in 0.5% increments over various intervals of time to 2.5% per year - was assumed. The details of this calculation are documented in Table 2. A year 2020 forecast of daily VMT of 65,335,032 resulted. This represents an annual growth rate of 3.36% over the 22-year forecast period, which is felt to be reasonable.

County and Statewide Total VMT Summary

Year 2020 forecasts of total VMT for Kentucky's 120 counties are shown in Table 5. This table also shows an annual VMT growth rate for each county calculated over this 22-year period. This procedure produces an annual growth rate of 2.41% for the state as a whole.

Index

Table 1 This table compares statewide totals for non-Interstate VMT, as originally submitted by HPMS, with revised estimates based on the 2001 procedure for estimating local VMT. The comparison is made for the years 1993-1997 (the only years possible), and concludes that HPMS numbers prior to 1993 do not need to be factored (previous versions of this procedure did apply a slight factoring to the 1980-1992 original HPMS estimates). Since factoring is no longer needed, this table can be omitted from future versions of this procedure. It is presented here primarily for the sake of continuity from previous versions.

Table 2(a) This table presents the results of a straight-line trend equations based on historical data for the period 1980- 2000 for Interstate VMT (using year as the independent varialble) and non-Interstate VMT (using population as the independent variable). Questionable results are highlighted and noted in the table. This table also calculates projected growth rates in VMT per capita.

Chart 1 This chart graphs historical Interstate VMT and shows the results of three alternative forecasting equations (based on the period used for the trend extrapolation). It is concluded that the most proper period to use for trend extrapolation is 1989-1999. As indicated by the historical VMT curve, VMT growth prior to 1989 was flatter than the years since 1989 (which follow a very straight line). Year 2000 was not used for forecasting purposes due to its much lower growth. Additional years of future data will be needed to determine if this year represents a trend shift or if it is an abnormality (as suspected) due to aberrant data or short term travel constraints.

Table 2(b)

This table is a modifcation of Table 2(a). For non-Interstate VMT the year 2005 forecast is modified by using a straight-line interpolation between 2000 and the forecasted value for 2010. This was done because it was felt that the equation produced a growth rate for this forecast period that was slightly high. For Interstate VMT the revised statewide totals are based on the 1989-1999 trend, as discussed above. It should be noted, though, that these totals (and the growth rates that result) are used only for cross-checking purposes. The county/corridor growth rates presented in Table 3 control the Interstate VMT forecasting procedure.

Table 3 This table presents assumed annual growth rates for each county containing Interstate mileage for five-year increment periods from 2000 - 2020. These growth rates are generally applied on a corridor basis and are based on an analysis of historical traffic data (not presented here). This table also calculates growth factors for the forecast periods.

Table 4 This table presents 2000 Census population data for each county and population projections for the years 2005, 2010, 2015, and 2020. The porportion of the state's population change attributable to each county is calculated for the forecast years.

Table 5

These tables (one for each forecast year) calculate the VMT forecasts. For Interstate VMT the growth factors developed in Table 3 are applied. For non-Interstate VMT the forecast is developed in two stages. In Column I an increased VMT based on increasing VMT per capita is calculated. The calculations in Column J add or subtract a proportion of the remaining statewide increase (the difference between the statewide total forecasted in Table 2(b) and the statewide total after accounting for increased VMT per capita) based on the proportion of the statewide population change attributable to each county (from Table 4).

Table 6 This table summarizes the total VMT forecasts for each of the Table 5 forecast years and computes an annual VMT growth rate for each county. These rates, particularly the extremes, are subjectively evaluated for reasonableness.

Comparison of Statewide Total VMT for Years 1993 - 1997

(Original HPMS vs. Revised Local VMT Procedure)

	Original	Original Daily VMT Interstate VMT	Original Non-Int VMT	Revised Non-Int VMT	Ratio
1980	69,131,507	15,589,000	53,542,507
1981	69,027,397	16,263,000	52,764,397
1982	70,210,959	15,904,000	54,306,959
1983	73,202,740	17,287,000	55,915,740
1984	76,578,082	17,961,000	58,617,082
1985	78,136,986	17,526,000	60,610,986
1986	80,142,466	17,830,000	62,312,466
1987	83,068,493	18,707,000	64,361,493
1988	86,613,699	20,550,000	66,063,699
1989	88,123,288	20,945,000	67,178,288
1990	92,161,644	22,019,000	70,142,644
1991	96,473,973	23,216,000	73,257,973
1992	104,279,452	24,989,000	79,290,452
1993	108,487,671	25,703,000	82,784,671	81,744,541	0.9874
1994	109,101,370	26,395,000	82,706,370	82,539,710	0.9980
1995	112,589,041	27,628,000	84,961,041	84,160,441	0.9906
1996	116,358,904	28,747,000	87,611,904	86,669,117	0.9892
1997	122,914,000	29,928,000	92,986,000	89,100,695	0.9582

0.9847

Appendix A - Table 2(a)

Population & VMT Trends and 2020 Projections

*Note*

					Non-Interstate	Illustrative Observed & Forecasted Annual Growth Rates
Year	Population	Daily VMT	Interstate VMT	Non-Inter VMT	VMT/person/yr	Interstate VMT
1980	3,660,334	69,131,507	15,589,000	53,542,507	5,339	1980-2000 Annual Growth Rate =	3.81%
1981	3,670,395	69,027,397	16,263,000	52,764,397	5,247	1990-2000 Annual Growth Rate =	4.10%
1982	3,683,449	70,210,959	15,904,000	54,306,959	5,381	1995-2000 Annual Growth Rate =	3.56%
1983	3,694,469	73,202,740	17,287,000	55,915,740	5,524	1999-2000 Annual Growth Rate =	0.31%
1984	3,695,459	76,578,082	17,961,000	58,617,082	5,790	Projected Annual Growth Rate (2000-2005) =	2.40%
1985	3,694,816	78,136,986	17,526,000	60,610,986	5,988	Projected Annual Growth Rate (2000-2010) =	2.40%
1986	3,687,805	80,142,466	17,830,000	62,312,466	6,167	Projected Annual Growth Rate (2000-2015) =	2.31%
1987	3,683,330	83,068,493	18,707,000	64,361,493	6,378	Projected Annual Growth Rate (2000-2020) =	2.22%
1988	3,680,002	86,613,699	20,550,000	66,063,699	6,553
1989	3,677,318	88,123,288	20,945,000	67,178,288	6,668	Non-Interstate VMT
1990	3,686,892	92,161,644	22,019,000	70,142,644	6,944	1980-2000 Annual Growth Rate =	2.93%
1991	3,714,685	96,473,973	23,216,000	73,257,973	7,198	1990-2000 Annual Growth Rate =	3.12%
1992	3,751,866	104,279,452	24,989,000	79,290,452	7,714	1995-2000 Annual Growth Rate =	2.53%
1993	3,792,623	107,447,541 (2)	25,703,000	81,744,541	7,867	1999-2000 Annual Growth Rate =	0.96%
1994	3,823,954	108,934,710	26,395,000	82,539,710	7,878	Projected Annual Growth Rate (2000-2005) =	4.11%
1995	3,856,212	111,788,441	27,628,000	84,160,441	7,966	Projected Annual Growth Rate (1995-2005) =	3.32%
1996	3,882,071	115,416,117	28,747,000	86,669,117	8,149	Projected Annual Growth Rate (2000-2010) =	2.78%
1997	3,908,124	119,028,695	29,928,000	89,100,695	8,322	Projected Annual Growth Rate (2000-2015) =	2.21%
1998	3,936,499	122,899,633	31,566,000	91,333,633	8,469	Projected Annual Growth Rate (2000-2020) =	1.89%
1999	3,960,825	127,267,666	32,807,303	94,460,363	8,705	Non-Inter VMT/person/yr
2000	4,041,769	128,277,992	32,909,866	95,368,126	8,612	2000-2005 Annual Growth Rate =	3.52%
2005	4,156,300 (1)		37,047,542	116,617,153	10,241	2000-2010 Annual Growth Rate =	2.30%
2010	4,233,231		41,706,719	125,405,210	10,813	2000-2015 Annual Growth Rate =	1.80%
2015	4,293,852		46,365,897	132,330,127	11,249	2000-2020 Annual Growth Rate =	1.51%
2020	4,348,306		51,025,075	138,550,569	11,630

⁽¹⁾ Kentucky State Data Center 1999 projections for 2005 - 2020 revised for consistency with 2000 Census

⁽²⁾ VMT estimates for 1993 - 1999 represent slight modification of numbers originally submitted with HPMS. Modification involves enchanced procedure for estimating VMT on local functional systems. Developed 2001.

| Linear Fit Equation Statistics | | |--------------------------------|--| |--------------------------------|--|

(A) Interstate VMT
931835.5706	(1,831,282,777)	VMT= 931,835.57xYear - 1,831,282,777
36359.13889	72355021.35
0.9719	1008925.158	R2 = 0.9719
656.8275961	19
6.68604E+14	1.93407E+13


(B)Non-Interstate VMT
	114.2330 (358,169,359)	VMT = 114.2330xPOP - 358,169,359
11.2190	42321483.34
		R2 = 0.8451
0.8451	5808857.275
103.6753 3.4983E+15	19 6.4111E+14

Appendix A - Chart 1

Interstate VMT: 1980 - 2000 Data and Various Trend Extrpolations

See Equations Below

			Equation Values
Year	HPMS Estimates	1980-2000 Trend	1990-2000 Trend	1989-1999 Trend	1980-2000 Trend Equation
1980	15,589,000	13,751,652	10,989,032	10,548,744	931,835.57	(1,831,282,777)	VMT= 931,835.57xYear - 1,831,282,777
1981	16,263,000	14,683,488	12,110,419	11,703,985	36359.13889	72355021.35
1982	15,904,000	15,615,323	13,231,806	12,859,226	0.9719	1008925.158	2 = 0.9719 R
1983	17,287,000	16,547,159	14,353,192	14,014,467	656.8275961	19
1984	17,961,000	17,478,995	15,474,579	15,169,708	6.68604E+14	1.93407E+13
1985	17,526,000	18,410,830	16,595,966	16,324,949
1986	17,830,000	19,342,666	17,717,353	17,480,190
1987	18,707,000	20,274,501	18,838,740	18,635,431	1990-2000 Trend Equation
1988	20,550,000	21,206,337	19,960,126	19,790,672	1,121,386.76	(2,209,356,746)	VMT= 1,121,386.76xYear - 2,209,356,746
1989	20,945,000	22,138,172	21,081,513	20,945,913	34823.49691	69472963.62
1990	22,019,000	23,070,008	22,202,900	22,101,154	0.9914	365231.9169	2 = 0.9914 R
1991	23,216,000	24,001,844	23,324,287	23,256,395	1036.969745	9
1992	24,989,000	24,933,679	24,445,673	24,411,636	1.38326E+14	1.20055E+12
1993	25,703,000	25,865,515	25,567,060	25,566,877
1994	26,395,000	26,797,350	26,688,447	26,722,118
1995	27,628,000	27,729,186	27,809,834	27,877,359	1989-1999 Trend Equation
1996	28,747,000	28,661,021	28,931,220	29,032,601	1,155,241.04	(2,276,828,519)	VMT= 1,155,241.04xYear - 2,276,828,519
1997	29,928,000	29,592,857	30,052,607	30,187,842	28844.62656	57516257.69
1998	31,566,000	30,524,693	31,173,994	31,343,083	0.9944	302524.9956	2 = 0.9944 R
1999	32,807,303	31,456,528	32,295,381	32,498,324	1604.040678	9
2000	32,909,866	32,388,364	33,416,767	33,653,565	1.46804E+14	8.23692E+11
2001		33,320,199	34,538,154	34,808,806
2002		34,252,035	35,659,541	35,964,047
2003		35,183,870	36,780,928	37,119,288
2004		36,115,706	37,902,314	38,274,529
2005		37,047,542	39,023,701	39,429,770
2006		37,979,377	40,145,088	40,585,011
2007		38,911,213	41,266,475	41,740,252
2008		39,843,048	42,387,861	42,895,493
2009		40,774,884	43,509,248	44,050,734
2010		41,706,719	44,630,635	45,205,975
2011		42,638,555	45,752,022	46,361,216
2012		43,570,391	46,873,408	47,516,457
2013		44,502,226	47,994,795	48,671,698

Appendix A - Table 2(b)

Population & VMT Trends and 2020 Projections

					Non-Interstate	Illustrative Observed & Forecasted Annual Growth Rates
Year	Population	Daily VMT	Interstate VMT	Non-Inter VMT	VMT/person/yr	Interstate VMT
1980	3,660,334	69,131,507	15,589,000	53,542,507	5,339	1980-2000 Annual Growth Rate =	3.81%
1981	3,670,395	69,027,397	16,263,000	52,764,397	5,247	1990-2000 Annual Growth Rate =	4.10%
1982	3,683,449	70,210,959	15,904,000	54,306,959	5,381	1995-2000 Annual Growth Rate =	3.56%
1983	3,694,469	73,202,740	17,287,000	55,915,740	5,524	1999-2000 Annual Growth Rate =	0.31%
1984	3,695,459	76,578,082	17,961,000	58,617,082	5,790	Projected Annual Growth Rate (2000-2005) =	3.68%
1985	3,694,816	78,136,986	17,526,000	60,610,986	5,988	Projected Annual Growth Rate (2000-2010) =	3.23%
1986	3,687,805	80,142,466	17,830,000	62,312,466	6,167	Projected Annual Growth Rate (2000-2015) =	2.96%
1987	3,683,330	83,068,493	18,707,000	64,361,493	6,378	Projected Annual Growth Rate (2000-2020) =	2.76%
1988	3,680,002	86,613,699	20,550,000	66,063,699	6,553
1989	3,677,318	88,123,288	20,945,000	67,178,288	6,668	Non-Interstate VMT
1990	3,686,892	92,161,644	22,019,000	70,142,644	6,944	1980-2000 Annual Growth Rate =	2.93%
1991	3,714,685	96,473,973	23,216,000	73,257,973	7,198	1990-2000 Annual Growth Rate =	3.12%
1992	3,751,866	104,279,452	24,989,000	79,290,452	7,714	1995-2000 Annual Growth Rate =	2.53%
1993	3,792,623	107,447,541 (2)	25,703,000	81,744,541	7,867	1999-2000 Annual Growth Rate =	0.96%
1994	3,823,954	108,934,710	26,395,000	82,539,710	7,878	Projected Annual Growth Rate (2000-2005) =	2.97%
1995	3,856,212	111,788,441	27,628,000	84,160,441	7,966	Projected Annual Growth Rate (1995-2005) =	2.75%
1996	3,882,071	115,416,117	28,747,000	86,669,117	8,149	Projected Annual Growth Rate (2000-2010) =	2.78%
1997	3,908,124	119,028,695	29,928,000	89,100,695	8,322	Projected Annual Growth Rate (2000-2015) =	2.21%
1998	3,936,499	122,899,633	31,566,000	91,333,633	8,469	Projected Annual Growth Rate (2000-2020) =	1.89%
1999	3,960,825	127,267,666	32,807,303	94,460,363	8,705	Non-Inter VMT/person/yr
2000	4,041,769	128,277,992	32,909,866	95,368,126	8,612	2000-2005 Annual Growth Rate =	2.39%
2005	4,156,300		39,429,770	110,386,668	9,694	2000-2010 Annual Growth Rate =	2.30%
2010	4,233,231		45,205,975	125,405,210	10,813	2000-2015 Annual Growth Rate =	1.80%
2015	4,293,852		50,982,180	132,330,127	11,249	2000-2020 Annual Growth Rate =	1.51%
2020	4,348,306 (1)		56,758,386	138,550,569	11,630

⁽¹⁾ Kentucky State Data Center 1999 projections for 2005 - 2020 revised for consistency with 2000 Census

Linear Fit Equation Statistics (A) Interstate VMT

		1155241.0418 (2,276,828,519)	VMT = 1,155,241.04xYear - 2,276,828,519
	28844.6266	57516257.69
	0.9944	302524.9956	R2 = 0.9944
	1604.0407	9
1.46804E+14		8.2369E+11
(B)Non-Interstate VMT
		114.2330 (358,169,359)	VMT = 114.2330xPOP - 358,169,360
	11.2190	42321483.34
	0.8451	5808857.275	R2 = 0.8451
	103.6753	19
	3.4983E+15	6.4111E+14

Interstate Growth Rates

		2000 to 2005	Annual Growth Rates 2005 to 2010	2010 to 2015	2015 to 2020	2000 to 2005	Growth Factors 2000 to 2010	2000 to 2015	2000 to 2020

001	Adair	0	0	0	0	1.0000	1.0000	1.0000	1.0000
003	Allen	0	0	0	0	1.0000	1.0000	1.0000	1.0000
005	Anderson	0	0	0	0	1.0000	1.0000	1.0000	1.0000
007	Ballard	0	0	0	0	1.0000	1.0000	1.0000	1.0000
009 011	Barren Bath	2.25 3.50	2.25 3.00	2.00 2.50	1.75 2.00	1.1177 1.1877	1.2492 1.3769	1.3792 1.5578	1.5042 1.7199
013	Bell	0	0	0	0	1.0000	1.0000	1.0000	1.0000
015	Boone	4.50	4.25	4.00	3.50	1.2462	1.5345	1.8669	2.2173
017	Bourbon	0	0	0	0	1.0000	1.0000	1.0000	1.0000
019	Boyd	3.50	3.00	2.50	2.00	1.1877	1.3769	1.5578	1.7199
021	Boyle	0	0	0	0	1.0000	1.0000	1.0000	1.0000
023	Bracken	0	0	0	0	1.0000	1.0000	1.0000	1.0000
025	Breathitt	0	0	0	0	1.0000	1.0000	1.0000	1.0000
027	Breckinridge	0	0	0	0	1.0000	1.0000	1.0000	1.0000
029	Bullitt	3.75	3.25	2.75	2.50	1.2021	1.4106	1.6155	1.8278
031 033	Butler Caldwell	0 4.50	0 4.00	0 3.50	0 3.00	1.0000 1.2462	1.0000 1.5162	1.0000 1.8007	1.0000 2.0875
035	Calloway	0	0	0	0	1.0000	1.0000	1.0000	1.0000
037	Campbell	3.00	3.00	2.75	2.50	1.1593	1.3439	1.5392	1.7414
039	Carlisle	0	0	0	0	1.0000	1.0000	1.0000	1.0000
041	Carroll	4.00	3.50	3.00	2.50	1.2167	1.4450	1.6752	1.8953
043	Carter	3.50	3.00	2.50	2.00	1.1877	1.3769	1.5578	1.7199
045	Casey	0	0	0	0	1.0000	1.0000	1.0000	1.0000
047	Christian	4.50	4.00	3.50	3.00	1.2462	1.5162	1.8007	2.0875
049	Clark	3.50	3.00	2.50	2.00	1.1877	1.3769	1.5578	1.7199
051 053	Clay Clinton	0 0	0 0	0 0	0 0	1.0000 1.0000	1.0000 1.0000	1.0000 1.0000	1.0000 1.0000
055	Crittenden	0	0	0	0	1.0000	1.0000	1.0000	1.0000
057	Cumberland	0	0	0	0	1.0000	1.0000	1.0000	1.0000
059	Daviess	0	0	0	0	1.0000	1.0000	1.0000	1.0000
061	Edmonson	2.25	2.25	2.00	1.75	1.1177	1.2492	1.3792	1.5042
063	Elliott	0	0	0	0	1.0000	1.0000	1.0000	1.0000
065	Estill	0	0	0	0	1.0000	1.0000	1.0000	1.0000
067	Fayette	3.25	3.00	2.75	2.50	1.1734	1.3603	1.5579	1.7626
069	Fleming	0	0	0	0	1.0000	1.0000	1.0000	1.0000
071 073	Floyd Franklin	0 3.50	0 3.25	0 3.00	0 2.50	1.0000 1.1877	1.0000 1.3936	1.0000 1.6156	1.0000 1.8279
075	Fulton	0	0	0	0	1.0000	1.0000	1.0000	1.0000
077	Gallatin	4.00	3.50	3.00	2.50	1.2167	1.4450	1.6752	1.8953
079	Garrard	0	0	0	0	1.0000	1.0000	1.0000	1.0000
081	Grant	4.50	4.25	4.00	3.50	1.2462	1.5345	1.8669	2.2173
083	Graves	0	0	0	0	1.0000	1.0000	1.0000	1.0000
085	Grayson	0	0	0	0	1.0000	1.0000	1.0000	1.0000
087	Green	0	0	0	0	1.0000	1.0000	1.0000	1.0000
089	Greenup	0	0	0	0	1.0000	1.0000	1.0000	1.0000
091 093	Hancock Hardin	0 4.25	0 4.00	0 3.50	0 3.00	1.0000 1.2313	1.0000 1.4981	1.0000 1.7793	1.0000 2.0627
095	Harlan	0	0	0	0	1.0000	1.0000	1.0000	1.0000
097	Harrison	0	0	0	0	1.0000	1.0000	1.0000	1.0000
099	Hart	2.25	2.25	2.00	1.75	1.1177	1.2492	1.3792	1.5042
101	Henderson	0	0	0	0	1.0000	1.0000	1.0000	1.0000
103	Henry	4.00	3.50	3.00	2.50	1.2167	1.4450	1.6752	1.8953
105	Hickman	0	0	0	0	1.0000	1.0000	1.0000	1.0000
107	Hopkins	0	0	0	0	1.0000	1.0000	1.0000	1.0000
109	Jackson	0	0	0	0	1.0000	1.0000	1.0000	1.0000
111 113	Jefferson Jessamine	3.25 0	3.00 0	2.75 0	2.50 0	1.1734 1.0000	1.3603 1.0000	1.5579 1.0000	1.7626 1.0000
115	Johnson	0	0	0	0	1.0000	1.0000	1.0000	1.0000
117	Kenton	3.25	3.00	2.75	2.50	1.1734	1.3603	1.5579	1.7626
119	Knott	0	0	0	0	1.0000	1.0000	1.0000	1.0000
121	Knox	0	0	0	0	1.0000	1.0000	1.0000	1.0000
123	Larue	2.25	2.25	2.00	1.75	1.1177	1.2492	1.3792	1.5042
125	Laurel	3.50	3.00	2.50	2.00	1.1877	1.3769	1.5578	1.7199
127	Lawrence	0	0	0	0	1.0000	1.0000	1.0000	1.0000
129	Lee	0	0	0	0	1.0000	1.0000	1.0000	1.0000
131 133	Leslie Letcher	0 0	0 0	0 0	0 0	1.0000 1.0000	1.0000 1.0000	1.0000 1.0000	1.0000 1.0000
135	Lewis	0	0	0	0	1.0000	1.0000	1.0000	1.0000

				Annual Growth Rates			Growth Factors
		2000 to 2005	2005 to 2010	2010 to 2015	2015 to 2020	2000 to 2005	2000 to 2010	2000 to 2015	2000 to 2020
137	Lincoln	0	0	0	0	1.0000	1.0000	1.0000	1.0000
139	Livingston	4.50	4.00	3.50	3.00	1.2462	1.5162	1.8007	2.0875
141	Logan	0	0	0	0	1.0000	1.0000	1.0000	1.0000
143	Lyon	4.50	4.00	3.50	3.00	1.2462	1.5162	1.8007	2.0875
145	McCracken	4.50	4.00	3.50	3.00	1.2462	1.5162	1.8007	2.0875
147	McCreary	0	0	0	0	1.0000	1.0000	1.0000	1.0000
149	McLean	0	0	0	0	1.0000	1.0000	1.0000	1.0000
151	Madison	3.50	3.00	2.50	2.00	1.1877	1.3769	1.5578	1.7199
153	Magoffin	0	0	0	0	1.0000	1.0000	1.0000	1.0000
155	Marion	0	0	0	0	1.0000	1.0000	1.0000	1.0000
157	Marshall	4.50	4.00	3.50	3.00	1.2462	1.5162	1.8007	2.0875
159	Martin	0	0	0	0	1.0000	1.0000	1.0000	1.0000
161	Mason	0	0	0	0	1.0000	1.0000	1.0000	1.0000
163	Meade	0	0	0	0	1.0000	1.0000	1.0000	1.0000
165	Menifee	0	0	0	0	1.0000	1.0000	1.0000	1.0000
167	Mercer	0	0	0	0	1.0000	1.0000	1.0000	1.0000
169	Metcalfe	0	0	0	0	1.0000	1.0000	1.0000	1.0000
171	Monroe	0	0	0	0	1.0000	1.0000	1.0000	1.0000
173	Montgomery	3.50	3.00	2.50	2.00	1.1877	1.3769	1.5578	1.7199
175	Morgan	0	0	0	0	1.0000	1.0000	1.0000	1.0000
177	Muhlenberg	0	0	0	0	1.0000	1.0000	1.0000	1.0000
179	Nelson	0	0	0	0	1.0000	1.0000	1.0000	1.0000
181	Nicholas	0	0	0	0	1.0000	1.0000	1.0000	1.0000
183	Ohio	0	0	0	0	1.0000	1.0000	1.0000	1.0000
185	Oldham	4.00	3.50	3.00	2.50	1.2167	1.4450	1.6752	1.8953
187	Owen	0	0	0	0	1.0000	1.0000	1.0000	1.0000
189	Owsley	0	0	0	0	1.0000	1.0000	1.0000	1.0000
191	Pendleton	0	0	0	0	1.0000	1.0000	1.0000	1.0000
193	Perry	0	0	0	0	1.0000	1.0000	1.0000	1.0000
195	Pike	0	0	0	0	1.0000	1.0000	1.0000	1.0000
197	Powell	0	0	0	0	1.0000	1.0000	1.0000	1.0000
199	Pulaski	0	0	0	0	1.0000	1.0000	1.0000	1.0000
201	Robertson	0	0	0	0	1.0000	1.0000	1.0000	1.0000
203	Rockcastle	3.50	3.00	2.50	2.00	1.1877	1.3769	1.5578	1.7199
205	Rowan	3.50	3.00	2.50	2.00	1.1877	1.3769	1.5578	1.7199
207	Russell	0	0	0	0	1.0000	1.0000	1.0000	1.0000
209	Scott	5.00	4.75	4.50	4.00	1.2763	1.6096	2.0058	2.4404
211	Shelby	3.50	3.25	3.00	2.50	1.1877	1.3936	1.6156	1.8279
213	Simpson	2.25	2.25	2.00	1.75	1.1177	1.2492	1.3792	1.5042
215	Spencer	0	0	0	0	1.0000	1.0000	1.0000	1.0000
217	Taylor	0	0	0	0	1.0000	1.0000	1.0000	1.0000
219	Todd	0	0	0	0	1.0000	1.0000	1.0000	1.0000
221	Trigg	4.50	4.00	3.50	3.00	1.2462	1.5162	1.8007	2.0875
223	Trimble	4.00	3.50	3.00	2.50	1.2167	1.4450	1.6752	1.8953
225	Union	0	0	0	0	1.0000	1.0000	1.0000	1.0000
227	Warren	2.25	2.25	2.00	1.75	1.1177	1.2492	1.3792	1.5042
229	Washington	0	0	0	0	1.0000	1.0000	1.0000	1.0000
231	Wayne	0	0	0	0	1.0000	1.0000	1.0000	1.0000
233	Webster	0	0	0	0	1.0000	1.0000	1.0000	1.0000
235	Whitley	3.50	3.00	2.50	2.00	1.1877	1.3769	1.5578	1.7199
237	Wolfe	0	0	0	0	1.0000	1.0000	1.0000	1.0000
239	Woodford	3.50	3.25	3.00	2.50	1.1877	1.3936	1.6156	1.8279

Population Change: 2000 - 2020

				% of State			% of State			% of State			% of State
	2000	2005	2005-2000	Increase	2010	2010-2000	Increase	2015	2015-2000	Increase	2020	2020-2000	Increase
Kentucky 1 Adair	4,041,769 17,244	4,156,300 17,840	114,531 596	0.52%	4,233,231 18,153	191,462 909	0.47%	4,293,852 18,383	252,083 1,139	0.45%	4,348,306 18,630	306,537 1,386	0.45%
2 Allen	17,800	18,977	1,177	1.03%	19,636	1,836	0.96%	20,215	2,415	0.96%	20,894	3,094	1.01%
3 Anderson	19,111	21,078	1,967	1.72%	22,384	3,273	1.71%	23,605	4,494	1.78%	25,010	5,899	1.92%
4 Ballard	8,286	8,612	326	0.28%	8,713	427	0.22%	8,779	493	0.20%	8,896	610	0.20%
5 Barren 6 Bath	38,033 11,085	39,915 11,306	1,882 221	1.64% 0.19%	40,940 11,415	2,907 330	1.52% 0.17%	41,758 11,487	3,725 402	1.48% 0.16%	42,682 11,541	4,649 456	1.52% 0.15%
7 Bell	30,060	29,299	-761	-0.66%	28,929	-1,131	-0.59%	28,513	-1,547	-0.61%	27,850	-2,210	-0.72%
8 Boone	85,991	99,686	13,695	11.96%	109,392	23,401	12.22%	118,647	32,656	12.95%	129,784	43,793	14.29%
9 Bourbon 10 Boyd	19,360 49,752	19,310 48,648	-50 -1,104	-0.04% -0.96%	19,266 47,765	-94 -1,987	-0.05% -1.04%	19,173 46,749	-187 -3,003	-0.07% -1.19%	18,973 45,421	-387 -4,331	-0.13% -1.41%
11 Boyle	27,697	28,237	540	0.47%	28,516	819	0.43%	28,698	1,001	0.40%	28,830	1,133	0.37%
12 Bracken	8,279	8,410	131	0.11%	8,475	196	0.10%	8,508	229	0.09%	8,520	241	0.08%
13 Breathitt 14 Breckinridge	16,100 18,648	16,524 19,405	424 757	0.37% 0.66%	16,835 19,815	735 1,167	0.38% 0.61%	17,095 20,156	995 1,508	0.39% 0.60%	17,306 20,542	1,206 1,894	0.39% 0.62%
15 Bullitt	61,236	66,838	5,602	4.89%	70,779	9,543	4.98%	74,197	12,961	5.14%	77,658	16,422	5.36%
16 Butler	13,010	13,358	348	0.30%	13,565	555	0.29%	13,730	720	0.29%	13,898	888	0.29%
17 Caldwell	13,060	13,303	243	0.21%	13,344	284	0.15%	13,361	301	0.12%	13,411	351	0.11%
18 Calloway 19 Campbell	34,177 88,616	34,113 90,207	-64 1,591	-0.06% 1.39%	33,769 91,316	-408 2,700	-0.21% 1.41%	33,312 92,078	-865 3,462	-0.34% 1.37%	32,798 92,549	-1,379 3,933	-0.45% 1.28%
20 Carlisle	5,351	5,380	29	0.03%	5,331	-20	-0.01%	5,271	-80	-0.03%	5,197	-154	-0.05%
21 Carroll	10,155	10,242	87	0.08%	10,299	144	0.08%	10,343	188	0.07%	10,353	198	0.06%
22 Carter	26,889	28,077	1,188	1.04%	28,821	1,932	1.01%	29,399	2,510	1.00%	29,938	3,049	0.99%
23 Casey 24 Christian	15,447 72,265	15,409 75,539	-38 3,274	-0.03% 2.86%	15,394 78,025	-53 5,760	-0.03% 3.01%	15,368 80,315	-79 8,050	-0.03% 3.19%	15,273 82,467	-174 10,202	-0.06% 3.33%
25 Clark	33,144	34,417	1,273	1.11%	35,243	2,099	1.10%	35,892	2,748	1.09%	36,468	3,324	1.08%
26 Clay	24,556	24,599	43	0.04%	24,896	340	0.18%	25,163	607	0.24%	25,256	700	0.23%
27 Clinton 28 Crittenden	9,634 9,384	9,677 9,344	43 -40	0.04% -0.03%	9,667 9,230	33 -154	0.02% -0.08%	9,635 9,107	1 -277	0.00% -0.11%	9,566 8,983	-68 -401	-0.02% -0.13%
29 Cumberland	7,147	7,143	-4	0.00%	7,085	-62	-0.03%	7,009	-138	-0.05%	6,929	-218	-0.07%
30 Daviess	91,545	93,074	1,529	1.34%	94,166	2,621	1.37%	94,894	3,349	1.33%	95,229	3,684	1.20%
31 Edmonson	11,644	12,433	789	0.69%	12,866	1,222	0.64%	13,197	1,553	0.62%	13,544	1,900	0.62%
32 Elliott 33 Estill	6,748 15,307	6,683 15,365	-65 58	-0.06% 0.05%	6,658 15,362	-90 55	-0.05% 0.03%	6,646 15,324	-102 17	-0.04% 0.01%	6,604 15,210	-144 -97	-0.05% -0.03%
34 Fayette	260,512	264,013	3,501	3.06%	266,560	6,048	3.16%	267,987	7,475	2.97%	268,230	7,718	2.52%
35 Fleming	13,792	14,250	458	0.40%	14,455	663	0.35%	14,628	836	0.33%	14,815	1,023	0.33%
36 Floyd	42,441	42,090	-351	-0.31%	42,097	-344	-0.18%	42,067	-374	-0.15%	41,728	-713	-0.23%
37 Franklin 38 Fulton	47,687 7,752	48,382 7,655	695 -97	0.61% -0.08%	48,676 7,581	989 -171	0.52% -0.09%	48,657 7,494	970 -258	0.38% -0.10%	48,410 7,405	723 -347	0.24% -0.11%
39 Gallatin	7,870	9,019	1,149	1.00%	9,822	1,952	1.02%	10,644	2,774	1.10%	11,669	3,799	1.24%
40 Garrard	14,792	16,242	1,450	1.27%	17,033	2,241	1.17%	17,695	2,903	1.15%	18,506	3,714	1.21%
41 Grant	22,384	25,419	3,035	2.65%	27,587	5,203	2.72%	29,686	7,302	2.90%	32,166	9,782	3.19%
42 Graves 43 Grayson	37,028 24,053	37,994 25,169	966 1,116	0.84% 0.97%	38,487 25,865	1,459 1,812	0.76% 0.95%	38,908 26,464	1,880 2,411	0.75% 0.96%	39,448 27,043	2,420 2,990	0.79% 0.98%
44 Green	11,518	11,592	74	0.06%	11,554	36	0.02%	11,475	-43	-0.02%	11,392	-126	-0.04%
45 Greenup	36,891	36,815	-76	-0.07%	36,627	-264	-0.14%	36,269	-622	-0.25%	35,661	-1,230	-0.40%
46 Hancock 47 Hardin	8,392 94,174	9,303 94,484	911 310	0.80% 0.27%	9,914 95,680	1,522 1,506	0.79% 0.79%	10,487 96,719	2,095 2,545	0.83% 1.01%	11,142 97,066	2,750 2,892	0.90% 0.94%
48 Harlan	33,202	32,609	-593	-0.52%	32,423	-779	-0.41%	32,207	-995	-0.39%	31,702	-1,500	-0.49%
49 Harrison	17,983	18,534	551	0.48%	18,813	830	0.43%	19,047	1,064	0.42%	19,333	1,350	0.44%
50 Hart	17,445	18,346	901	0.79%	18,871	1,426	0.74%	19,292	1,847	0.73%	19,736	2,291	0.75%
51 Henderson 52 Henry	44,829 15,060	45,032 16,146	203 1,086	0.18% 0.95%	45,059 16,807	230 1,747	0.12% 0.91%	44,949 17,377	120 2,317	0.05% 0.92%	44,649 17,999	-180 2,939	-0.06% 0.96%
53 Hickman	5,262	4,904	-358	-0.31%	4,646	-616	-0.32%	4,418	-844	-0.33%	4,190	-1,072	-0.35%
54 Hopkins	46,519	46,365	-154	-0.13%	46,223	-296	-0.15%	46,018	-501	-0.20%	45,656	-863	-0.28%
55 Jackson	13,495	14,014	519	0.45%	14,352	857	0.45%	14,642	1,147	0.46%	14,925	1,430	0.47%
56 Jefferson 57 Jessamine	693,604 39,041	692,873 42,210	-731 3,169	-0.64% 2.77%	693,303 44,436	-301 5,395	-0.16% 2.82%	690,635 46,411	-2,969 7,370	-1.18% 2.92%	683,742 48,511	-9,862 9,470	-3.22% 3.09%
58 Johnson	23,445	23,596	151	0.13%	23,712	267	0.14%	23,749	304	0.12%	23,667	222	0.07%
59 Kenton	151,464	153,242	1,778	1.55%	155,260	3,796	1.98%	156,716	5,252	2.08%	157,455	5,991	1.95%
60 Knott	17,649	17,569	-80	-0.07%	17,611	-38	-0.02%	17,636	-13	-0.01%	17,537	-112	-0.04%
61 Knox 62 Larue	31,795 13,373	32,860 13,992	1,065 619	0.93% 0.54%	33,633 14,329	1,838 956	0.96% 0.50%	34,306 14,589	2,511 1,216	1.00% 0.48%	34,933 14,878	3,138 1,505	1.02% 0.49%
63 Laurel	52,715	56,741	4,026	3.52%	59,633	6,918	3.61%	62,266	9,551	3.79%	65,045	12,330	4.02%
64 Lawrence	15,569	16,308	739	0.65%	16,761	1,192	0.62%	17,131	1,562	0.62%	17,504	1,935	0.63%
65 Lee	7,916	8,049	133	0.12%	8,099	183	0.10%	8,136	220	0.09%	8,140	224	0.07%
66 Leslie 67 Letcher	12,401 25,277	12,244 25,139	-157 -138	-0.14% -0.12%	12,191 25,082	-210 -195	-0.11% -0.10%	12,110 24,955	-291 -322	-0.12% -0.13%	11,903 24,629	-498 -648	-0.16% -0.21%
68 Lewis	14,092	14,532	440	0.38%	14,860	768	0.40%	15,157	1,065	0.42%	15,422	1,330	0.43%
69 Lincoln	23,361	24,615	1,254	1.09%	25,389	2,028	1.06%	26,016	2,655	1.05%	26,645	3,284	1.07%

				% of State			% of State			% of State			% of State
	2000	2005	2005-2000	Increase	2010	2010-2000	Increase	2015	2015-2000	Increase	2020	2020-2000	Increase
70 Livingston	9,804	9,937	133	0.12%	9,898	94	0.05%	9,808	4	0.00%	9,698	-106	-0.03%
71 Logan	26,573	27,509	936	0.82%	28,050	1,477	0.77%	28,516	1,943	0.77%	28,989	2,416	0.79%
72 Lyon	8,080	8,456	376	0.33%	8,542	462	0.24%	8,551	471	0.19%	8,576	496	0.16%
73 McCracken	65,514	65,687	173	0.15%	65,653	139	0.07%	65,452	-62	-0.02%	65,105	-409	-0.13%
74 McCreary	17,080	17,650	570	0.50%	18,084	1,004	0.52%	18,456	1,376	0.55%	18,750	1,670	0.54%
75 McLean	9,938	10,000	62	0.05%	9,946	8	0.00%	9,862	-76	-0.03%	9,765	-173	-0.06%
76 Madison	70,872	75,161	4,289	3.74%	77,871	6,999	3.66%	80,045	9,173	3.64%	82,268	11,396	3.72%
77 Magoffin	13,332	13,746	414	0.36%	14,086	754	0.39%	14,391	1,059	0.42%	14,633	1,301	0.42%
78 Marion	18,212	18,529	317	0.28%	18,778	566	0.30%	19,008	796	0.32%	19,209	997	0.33%
79 Marshall	30,125	31,227	1,102	0.96%	31,567	1,442	0.75%	31,696	1,571	0.62%	31,863	1,738	0.57%
80 Martin	12,578	12,077	-501	-0.44%	11,917	-661	-0.35%	11,754	-824	-0.33%	11,432	-1,146	-0.37%
81 Mason	16,800	16,550	-250	-0.22%	16,359	-441	-0.23%	16,124	-676	-0.27%	15,823	-977	-0.32%
82 Meade	26,349	30,364	4,015	3.51%	33,076	6,727	3.51%	35,593	9,244	3.67%	38,633	12,284	4.01%
83 Menifee	6,556	6,931	375	0.33%	7,130	574	0.30%	7,291	735	0.29%	7,464	908	0.30%
84 Mercer	20,817	21,627	810	0.71%	22,084	1,267	0.66%	22,443	1,626	0.65%	22,794	1,977	0.64%
85 Metcalfe	10,037	10,336	299	0.26%	10,488	451	0.24%	10,599	562	0.22%	10,689	652	0.21%
86 Monroe	11,756	11,396	-360	-0.31%	11,152	-604	-0.32%	10,898	-858	-0.34%	10,575	-1,181	-0.39%
87 Montgomery	22,554	23,436	882	0.77%	24,000	1,446	0.76%	24,479	1,925	0.76%	24,938	2,384	0.78%
88 Morgan	13,948	14,204	256	0.22%	14,350	402	0.21%	14,467	519	0.21%	14,567	619	0.20%
89 Muhlenberg	31,839	32,622	783	0.68%	33,050	1,211	0.63%	33,413	1,574	0.62%	33,764	1,925	0.63%
90 Nelson	37,477	41,228	3,751	3.28%	43,876	6,399	3.34%	46,381	8,904	3.53%	49,235	11,758	3.84%
91 Nicholas	6,813	6,949	136	0.12%	7,016	203	0.11%	7,060	247	0.10%	7,092	279	0.09%
92 Ohio	22,916	23,479	563	0.49%	23,759	843	0.44%	24,001	1,085	0.43%	24,241	1,325	0.43%
93 Oldham	46,178	49,769	3,591	3.14%	51,981	5,803	3.03%	54,089	7,911	3.14%	56,921	10,743	3.50%
94 Owen	10,547	11,142	595	0.52%	11,454	907	0.47%	11,714	1,167	0.46%	12,010	1,463	0.48%
95 Owsley	4,858	4,957	99	0.09%	5,014	156	0.08%	5,047	189	0.07%	5,074	216	0.07%
96 Pendleton	14,390	15,458	1,068	0.93%	16,158	1,768	0.92%	16,789	2,399	0.95%	17,468	3,078	1.00%
97 Perry	29,390	29,660	270	0.24%	30,039	649	0.34%	30,319	929	0.37%	30,357	967	0.32%
98 Pike	68,736	68,453	-283	-0.25%	68,529	-207	-0.11%	68,282	-454	-0.18%	67,365	-1,371	-0.45%
99 Powell	13,237	13,938	701	0.61%	14,434	1,197	0.63%	14,896	1,659	0.66%	15,335	2,098	0.68%
100 Pulaski	56,217	59,779	3,562	3.11%	61,703	5,486	2.87%	63,152	6,935	2.75%	64,620	8,403	2.74%
101 Robertson	2,266	2,243	-23	-0.02%	2,213	-53	-0.03%	2,176	-90	-0.04%	2,136	-130	-0.04%
102 Rockcastle	16,582	17,084	502	0.44%	17,346	764	0.40%	17,531	949	0.38%	17,686	1,104	0.36%
103 Rowan	22,094	23,015	921	0.80%	23,535	1,441	0.75%	23,927	1,833	0.73%	24,231	2,137	0.70%
104 Russell	16,315	16,991	676	0.59%	17,323	1,008	0.53%	17,525	1,210	0.48%	17,709	1,394	0.45%
105 Scott	33,061	37,413	4,352	3.80%	40,346	7,285	3.80%	43,131	10,070	3.99%	46,541	13,480	4.40%
106 Shelby	33,337	36,032	2,695	2.35%	37,682	4,345	2.27%	39,109	5,772	2.29%	40,731	7,394	2.41%
107 Simpson	16,405	16,939	534	0.47%	17,259	854	0.45%	17,508	1,103	0.44%	17,746	1,341	0.44%
108 Spencer	11,766	14,178	2,412	2.11%	15,910	4,144	2.16%	17,681	5,915	2.35%	19,972	8,206	2.68%
109 Taylor	22,927	23,515	588	0.51%	23,773	846	0.44%	23,943	1,016	0.40%	24,092	1,165	0.38%
110 Todd	11,971	12,007	36	0.03%	12,001	30	0.02%	11,989	18	0.01%	11,953	-18	-0.01%
111 Trigg	12,597	13,896	1,299	1.13%	14,566	1,969	1.03%	15,115	2,518	1.00%	15,783	3,186	1.04%
112 Trimble	8,125	8,646	521	0.45%	8,979	854	0.45%	9,271	1,146	0.45%	9,605	1,480	0.48%
113 Union	15,637	16,103	466	0.41%	16,713	1,076	0.56%	17,373	1,736	0.69%	18,041	2,404	0.78%
114 Warren	92,522	96,623	4,101	3.58%	99,247	6,725	3.51%	101,333	8,811	3.50%	103,254	10,732	3.50%
115 Washington	10,916	11,191	275	0.24%	11,321	405	0.21%	11,439	523	0.21%	11,587	671	0.22%
116 Wayne	19,923	20,632	709	0.62%	21,058	1,135	0.59%	21,397	1,474	0.58%	21,689	1,766	0.58%
117 Webster	14,120	13,941	-179	-0.16%	13,801	-319	-0.17%	13,668	-452	-0.18%	13,494	-626	-0.20%
118 Whitley	35,865	36,733	868	0.76%	37,351	1,486	0.78%	37,879	2,014	0.80%	38,297	2,432	0.79%
119 Wolfe	7,065	7,432	367	0.32%	7,711	646	0.34%	7,971	906	0.36%	8,225	1,160	0.38%
120 Woodford	23,208	24,634	1,426	1.25%	25,571	2,363	1.23%	26,360	3,152	1.25%	27,189	3,981	1.30%
	4,041,769	4,156,300	114,531	100.00%	4,233,231	191,462	100.00%	4,293,852	252,083	100.00%	4,348,306	306,537	100.00%

2005 VMT Forecasts

		2000 Total DVMT	2000 Interstate DVMT	2000 Non-Inter. DVMT	2005 Interstate DVMT	2000 Non-Inter. +2.39%/yr.	2005 Non-Inter. DVMT	2005 Total DVMT
001	Adair	499,690	0	499,690	0	562,326	578,271	578,271
003	Allen	429,261	0	429,261	0	483,069	514,558	514,558
005	Anderson	574,215	0	574,215	0	646,193	698,817	698,817
007	Ballard	272,170	0	272,170	0	306,286	315,008	315,008
009 011	Barren Bath	1,332,171 485,692	260,874 248,370	1,071,297 237,322	291,573 294,985	1,205,584 267,071	1,255,935 272,983	1,547,508 567,968
013	Bell	889,500	0	889,500	0	1,000,999	980,639	980,639
015	Boone	3,714,615	2,082,005	1,632,610	2,594,557	1,837,258	2,203,650	4,798,207
017	Bourbon	576,998	0	576,998	0	649,325	647,987	647,987
019	Boyd	1,348,719	189,505	1,159,215	225,072	1,304,522	1,274,986	1,500,059
021	Boyle	716,728	0	716,728	0	806,570	821,017	821,017
023 025	Bracken Breathitt	294,198 431,986	0 0	294,198 431,986	0 0	331,075 486,135	334,580 497,479	334,580 497,479
027	Breckinridge	443,317	0	443,317	0	498,887	519,140	519,140
029	Bullitt	2,077,887	1,214,990	862,897	1,460,540	971,061	1,120,935	2,581,474
031	Butler	480,073	0	480,073	0	540,250	549,561	549,561
033	Caldwell	521,369	37,910	483,460	47,242	544,062	550,563	597,805
035	Calloway	817,228	0	817,228	0	919,668	917,956	917,956
037 039	Campbell Carlisle	2,143,995 159,375	824,757 0	1,319,237 159,375	956,120 0	1,484,604 179,352	1,527,169 180,128	2,483,289 180,128
041	Carroll	624,249	370,203	254,046	450,408	285,891	288,218	738,626
043	Carter	1,093,167	474,248	618,920	563,258	696,501	728,285	1,291,542
045	Casey	394,825	0	394,825	0	444,317	443,300	443,300
047	Christian	2,302,526	466,406	1,836,121	581,226	2,066,279	2,153,870	2,735,097
049	Clark	1,229,322	460,383	768,938	546,791	865,325	899,382	1,446,173
051	Clay	701,215	0	701,215	0	789,112	790,263	790,263
053 055	Clinton Crittenden	272,191 236,572	0 0	272,191 236,572	0 0	306,310 266,227	307,461 265,157	307,461 265,157
057	Cumberland	219,322	0	219,322	0	246,815	246,708	246,708
059	Daviess	2,110,068	0	2,110,068	0	2,374,565	2,415,471	2,415,471
061	Edmonson	332,216	78,314	253,901	87,530	285,728	306,837	394,367
063	Elliott	140,399	0	140,399	0	157,998	156,259	156,259
065	Estill	337,380	0	337,380	0	379,671	381,223	381,223
067 069	Fayette Fleming	7,342,753 357,213	1,792,108 0	5,550,644 357,213	2,102,880 0	6,246,419 401,990	6,340,084 414,243	8,442,964 414,243
071	Floyd	1,493,891	0	1,493,891	0	1,681,150	1,671,760	1,671,760
073	Franklin	1,504,839	444,473	1,060,366	527,895	1,193,283	1,211,877	1,739,772
075	Fulton	206,673	0	206,673	0	232,580	229,985	229,985
077	Gallatin	620,913	445,524	175,389	542,048	197,374	228,114	770,162
079 081	Garrard Grant	360,746 1,262,752	0 902,674	360,746 360,078	0 1,124,896	405,966 405,213	444,759 486,411	444,759 1,611,307
083	Graves	1,207,177	0	1,207,177	0	1,358,497	1,384,342	1,384,342
085	Grayson	880,393	0	880,393	0	990,751	1,020,608	1,020,608
087	Green	270,461	0	270,461	0	304,364	306,343	306,343
089	Greenup	908,623	0	908,623	0	1,022,519	1,020,486	1,020,486
091	Hancock	282,667	0	282,667	0	318,099	342,471	342,471
093 095	Hardin Harlan	3,673,512 843,751	1,052,169 0	2,621,343 843,751	1,295,584 0	2,949,930 949,516	2,958,223 933,651	4,253,807 933,651
097	Harrison	381,630	0	381,630	0	429,468	444,209	444,209
099	Hart	1,060,312	675,089	385,224	754,532	433,511	457,617	1,212,148
101	Henderson	1,603,327	0	1,603,327	0	1,804,305	1,809,736	1,809,736
103	Henry	690,546	372,301	318,245	452,961	358,137	387,192	840,153
105	Hickman	197,484	0	197,484	0	222,239	212,661	212,661
107 109	Hopkins Jackson	1,747,339 313,765	0 0	1,747,339 313,765	0 0	1,966,368 353,096	1,962,248 366,981	1,962,248 366,981
111	Jefferson	19,084,449	7,955,972	11,128,476	9,335,628	12,523,434	12,503,877	21,839,505
113	Jessamine	939,376	0	939,376	0	1,057,127	1,141,909	1,141,909
115	Johnson	668,852	0	668,852	0	752,693	756,733	756,733
117	Kenton	3,919,090	2,129,239	1,789,850	2,498,474	2,014,209	2,061,777	4,560,251
119	Knott	504,967	0	504,967	0	568,265	566,125	566,125
121 123	Knox Larue	891,148 503,382	0 135,630	891,148 367,752	0 151,590	1,002,854 413,850	1,031,346 430,410	1,031,346 582,000
125	Laurel	1,996,640	808,146	1,188,494	959,824	1,337,472	1,445,182	2,405,006
127	Lawrence	604,878	0	604,878	0	680,699	700,470	700,470
129	Lee	197,355	0	197,355	0	222,094	225,652	225,652
131	Leslie	404,219	0	404,219	0	454,887	450,687	450,687
133	Letcher	711,210	0	711,210	0	800,361	796,669	796,669
135	Lewis	435,920	0	435,920	0	490,562	502,334	502,334

		2000 Total DVMT	2000 Interstate DVMT	2000 Non-Inter. DVMT	2005 Interstate DVMT	2000 Non-Inter. +2.39%/yr.	2005 Non-Inter. DVMT	2005 Total DVMT
137	Lincoln	710,692	0	710,692	0	799,777	833,326	833,326
139	Livingston	389,452	111,588	277,864	139,059	312,694	316,252	455,312
141	Logan	860,098	0	860,098	0	967,912	992,953	992,953
143	Lyon	676,173	378,142	298,030	471,234	335,388	345,448	816,682
145	McCracken	2,133,941	562,163	1,571,777	700,558	1,768,800	1,773,428	2,473,986
147	McCreary	472,284	0	472,284	0	531,485	546,734	546,734
149	McLean	307,586	0	307,586	0	346,142	347,801	347,801
151	Madison	2,422,949	1,095,608	1,327,341	1,301,238	1,493,724	1,608,470	2,909,709
153	Magoffin	421,918	0	421,918	0	474,806	485,882	485,882
155	Marion	430,566	0	430,566	0	484,537	493,018	493,018
157	Marshall	1,259,674	317,265	942,409	395,370	1,060,540	1,090,023	1,485,393
159	Martin	340,828	0	340,828	0	383,551	370,147	370,147
161	Mason	678,811	0	678,811	0	763,900	757,211	757,211
163	Meade	663,413	0	663,413	0	746,572	853,988	853,988
165	Menifee	143,467	0	143,467	0	161,450	171,483	171,483
167	Mercer	618,651	0	618,651	0	696,200	717,870	717,870
169	Metcalfe	340,816	0	340,816	0	383,538	391,537	391,537
171	Monroe	276,823	0	276,823	0	311,523	301,892	301,892
173	Montgomery	713,101	228,347	484,754	271,205	545,518	569,115	840,320
175	Morgan	358,763	0	358,763	0	403,735	410,583	410,583
177	Muhlenberg	1,038,484	0	1,038,484	0	1,168,658	1,189,606	1,189,606
179	Nelson	1,192,457	0	1,192,457	0	1,341,932	1,442,285	1,442,285
181	Nicholas	171,513	0	171,513	0	193,012	196,651	196,651
183	Ohio	930,782	0	930,782	0	1,047,456	1,062,518	1,062,518
185	Oldham	1,278,095	616,475	661,620	750,036	744,554	840,626	1,590,662
187	Owen	231,732	0	231,732	0	260,780	276,699	276,699
189	Owsley	124,675	0	124,675	0	140,303	142,952	142,952
191	Pendleton	338,493	0	338,493	0	380,924	409,497	409,497
193	Perry	958,955	0	958,955	0	1,079,160	1,086,384	1,086,384
195	Pike	2,177,247	0	2,177,247	0	2,450,165	2,442,594	2,442,594
197	Powell	497,464	0	497,464	0	559,821	578,575	578,575
199	Pulaski	1,723,396	0	1,723,396	0	1,939,424	2,034,720	2,034,720
201	Robertson	44,126	0	44,126	0	49,658	49,042	49,042
203	Rockcastle	1,204,077	804,632	399,445	955,651	449,515	462,946	1,418,596
205	Rowan	830,467	278,974	551,493	331,333	620,623	645,263	976,597
207	Russell	514,862	0	514,862	0	579,400	597,486	597,486
209	Scott	1,991,532	1,268,943	722,589	1,619,529	813,165	929,597	2,549,126
211	Shelby	1,533,323	856,296	677,028	1,017,011	761,893	833,994	1,851,005
213	Simpson	896,853	495,968	400,886	554,332	451,137	465,423	1,019,755
215	Spencer	300,410	0	300,410	0	338,066	402,596	402,596
217	Taylor	587,402	0	587,402	0	661,033	676,765	676,765
219	Todd	330,629	0	330,629	0	372,073	373,036	373,036
221	Trigg	536,766	182,482	354,284	227,406	398,694	433,447	660,853
223	Trimble	209,771	19,350	190,421	23,542	214,291	228,229	251,771
225	Union	428,872	0	428,872	0	482,631	495,099	495,099
227	Warren	3,352,540	1,187,538	2,165,002	1,327,284	2,436,386	2,546,102	3,873,387
229	Washington	348,934	0	348,934	0	392,673	400,031	400,031
231	Wayne	474,610	0	474,610	0	534,103	553,071	553,071
233	Webster	559,244	0	559,244	0	629,345	624,556	624,556
235	Whitley	1,638,010	864,748	773,262	1,027,049	870,190	893,412	1,920,462
237	Wolfe	319,037	0	319,037	0	359,028	368,846	368,846
239	Woodford	1,018,333	220,058	798,275	261,359	898,339	936,490	1,197,850
	Totals	128,277,992	32,909,866	95,368,126	39,218,812	107,322,545	110,386,668	149,605,480

Annual Growth Rates 3.57% 2.97%

VMT Annual Growth Rates: 2000 - 2020

		2000 Total DVMT	2005 Total DVMT	2000-2005 Annual Growth %	2010 Total DVMT	2000-2010 Annual Growth %	2015 Total DVMT	2000-2015 Annual Growth %	2020 Total DVMT	2000-2020 Annual Growth %
001	Adair	499,690	578,271	2.46%	654,274	2.48%	687,801	2.02%	718,874	1.75%
003	Allen	429,261	514,558	3.07%	593,399	2.99%	634,744	2.47%	678,715	2.21%
005	Anderson	574,215	698,817	3.33%	818,047	3.27%	887,684	2.76%	964,467	2.50%
007	Ballard	272,170	315,008	2.47%	354,345	2.43%	370,738	1.95%	386,898	1.69%
009	Barren	1,332,171	1,547,508	2.53%	1,757,060	2.55%	1,873,593	2.15%	1,987,525	1.92%
011	Bath	485,692	567,968	2.64%	649,688	2.68%	709,325	2.40%	762,098	2.17%
013	Bell	889,500	980,639	1.64%	1,083,017	1.81%	1,115,159	1.42%	1,129,375	1.14%
015	Boone	3,714,615	4,798,207	4.36%	5,939,358	4.36%	7,018,104	4.06%	8,226,953	3.86%
017	Bourbon	576,998	647,987	1.95%	721,528	2.05%	748,321	1.64%	766,230	1.36%
019	Boyd	1,348,719	1,500,059	1.79%	1,657,090	1.89%	1,718,357	1.53%	1,751,135	1.25%
021	Boyle	716,728	821,017	2.29%	924,055	2.34%	967,216	1.89%	1,003,641	1.62%
023 025	Bracken Breathitt	294,198 431,986	334,580 497,479	2.17% 2.38%	375,136 564,115	2.23% 2.46%	391,460 594,925	1.80% 2.02%	404,767 621,723	1.53% 1.75%
027	Breckinridge	443,317	519,140	2.67%	591,172	2.65%	625,405	2.17%	659,123	1.91%
029	Bullitt	2,077,887	2,581,474	3.68%	3,080,494	3.64%	3,486,384	3.29%	3,912,901	3.06%
031	Butler	480,073	549,561	2.28%	619,134	2.34%	649,366	1.91%	676,399	1.65%
033	Caldwell	521,369	597,805	2.31%	672,813	2.35%	709,257	1.94%	742,851	1.70%
035	Calloway	817,228	917,956	1.96%	1,013,768	1.98%	1,041,548	1.53%	1,058,547	1.24%
037	Campbell	2,143,995	2,483,289	2.48%	2,844,678	2.60%	3,099,197	2.33%	3,342,948	2.14%
039	Carlisle	159,375	180,128	2.06%	199,473	2.06%	205,830	1.61%	210,129	1.33%
041	Carroll	624,249	738,626	2.84%	858,131	2.94%	957,882	2.71%	1,050,839	2.51%
043	Carter	1,093,167	1,291,542	2.82%	1,487,303	2.84%	1,624,272	2.51%	1,748,883	2.26%
045	Casey	394,825	443,300	1.95%	494,060	2.06%	513,553	1.66%	527,230	1.39%
047	Christian	2,302,526	2,735,097	2.91%	3,183,174	2.99%	3,485,277	2.62%	3,779,338	2.39%
049	Clark	1,229,322	1,446,173	2.74%	1,661,497	2.78%	1,805,993	2.43%	1,936,326	2.19%
051	Clay	701,215	790,263	2.01%	890,352	2.19%	934,907	1.81%	968,791	1.55%
053	Clinton	272,191	307,461	2.05%	342,669	2.12%	355,736	1.69%	365,140	1.41%
055 057	Crittenden Cumberland	236,572 219,322	265,157 246,708	1.92% 1.98%	292,401 273,479	1.94% 2.03%	300,696 282,400	1.51% 1.59%	306,371 288,973	1.24% 1.32%
059	Daviess	2,110,068	2,415,471	2.28%	2,726,676	2.36%	2,859,793	1.92%	2,965,941	1.63%
061	Edmonson	332,216	394,367	2.90%	452,858	2.86%	487,260	2.42%	521,498	2.17%
063	Elliott	140,399	156,259	1.80%	173,573	1.95%	180,360	1.58%	184,842	1.32%
065	Estill	337,380	381,223	2.06%	425,156	2.12%	441,415	1.69%	452,181	1.40%
067	Fayette	7,342,753	8,442,964	2.35%	9,585,329	2.45%	10,274,026	2.12%	10,897,517	1.90%
069	Fleming	357,213	414,243	2.50%	468,112	2.49%	492,353	2.03%	514,936	1.76%
071	Floyd	1,493,891	1,671,760	1.89%	1,865,101	2.04%	1,940,825	1.65%	1,993,111	1.38%
073	Franklin	1,504,839	1,739,772	2.45%	1,979,919	2.53%	2,133,441	2.21%	2,266,671	1.97%
075	Fulton	206,673	229,985	1.80%	254,363	1.91%	262,204	1.50%	267,758	1.24%
077	Gallatin	620,913	770,162	3.66%	921,935	3.66%	1,060,266	3.40%	1,203,159	3.20%
079	Garrard	360,746	444,759	3.55%	519,421	3.37%	560,115	2.79%	606,176	2.50%
081 083	Grant Graves	1,262,752 1,207,177	1,611,307 1,384,342	4.15% 2.31%	1,991,701 1,558,739	4.23% 2.35%	2,378,858 1,634,999	4.04% 1.91%	2,801,787 1,706,864	3.87% 1.66%
085	Grayson	880,393	1,020,608	2.49%	1,159,004	2.53%	1,224,172	2.08%	1,284,181	1.81%
087	Green	270,461	306,343	2.10%	340,586	2.12%	352,131	1.66%	360,942	1.38%
089	Greenup	908,623	1,020,486	1.95%	1,132,776	2.02%	1,168,408	1.58%	1,186,673	1.28%
091	Hancock	282,667	342,471	3.25%	400,048	3.21%	433,395	2.71%	469,830	2.45%
093	Hardin	3,673,512	4,253,807	2.47%	4,911,650	2.68%	5,375,500	2.41%	5,800,766	2.20%
095	Harlan	843,751	933,651	1.70%	1,036,043	1.88%	1,072,237	1.51%	1,090,451	1.23%
097	Harrison	381,630	444,209	2.56%	503,724	2.56%	531,227	2.09%	558,395	1.83%
099	Hart	1,060,312	1,212,148	2.26%	1,369,262	2.35%	1,490,940	2.15%	1,608,952	2.01%
101	Henderson	1,603,327	1,809,736	2.04%	2,019,529	2.12%	2,098,930	1.70%	2,157,924	1.42%
103	Henry	690,546	840,153	3.32%	989,369	3.32%	1,110,333	3.01%	1,229,532	2.79%
105	Hickman	197,484	212,661	1.24%	229,610	1.38%	232,294	1.02%	232,060	0.77%
107	Hopkins	1,747,339	1,962,248	1.95%	2,184,687	2.05%	2,268,157	1.64%	2,330,322	1.38%
109	Jackson	313,765	366,981	2.65%	419,333	2.67%	445,075	2.21%	469,381	1.94%
111 113	Jefferson Jessamine	19,084,449 939,376	21,839,505 1,141,909	2.27% 3.31%	24,783,471 1,339,475	2.40% 3.28%	26,847,026 1,452,744	2.16% 2.76%	28,724,658 1,572,006	1.97% 2.48%
115	Johnson	668,852	756,733	2.08%	847,558	2.18%	883,358	1.75%	909,757	1.48%
117	Kenton	3,919,090	4,560,251	2.56%	5,256,017	2.70%	5,816,649	2.50%	6,361,031	2.33%
119	Knott	504,967	566,125	1.92%	632,770	2.07%	659,506	1.68%	677,860	1.41%
121	Knox	891,148	1,031,346	2.47%	1,173,277	2.53%	1,241,281	2.09%	1,303,450	1.83%
123	Larue	503,382	582,000	2.45%	659,474	2.49%	704,798	2.13%	748,661	1.91%
125	Laurel	1,996,640	2,405,006	3.15%	2,810,137	3.16%	3,103,841	2.80%	3,390,041	2.55%
127	Lawrence	604,878	700,470	2.48%	794,726	2.51%	838,186	2.06%	878,469	1.79%
129	Lee	197,355	225,652	2.26%	253,181	2.29%	264,629	1.85%	273,531	1.57%
131	Leslie	404,219	450,687	1.83%	501,188	1.97%	519,352	1.58%	529,495	1.29%
133	Letcher	711,210	796,669	1.91%	887,008	2.03%	919,589	1.62%	938,964	1.33%

		2000	2005	2000-2005 Annual	2010	2000-2010 Annual	2015	2000-2015 Annual	2020	2000-2020 Annual
		Total DVMT	Total DVMT	Growth %	Total DVMT	Growth %	Total DVMT	Growth %	Total DVMT	Growth %
135	Lewis	435,920	502,334	2.39%	570,034	2.47%	602,204	2.04%	631,016	1.78%
137	Lincoln	710,692	833,326	2.69%	952,389	2.70%	1,009,856	2.22%	1,064,614	1.94%
139	Livingston	389,452	455,312	2.64%	520,789	2.68%	564,182	2.34%	604,520	2.12%
141	Logan	860,098	992,953	2.42%	1,123,576	2.46%	1,183,353	2.01%	1,238,348	1.75%
143	Lyon	676,173	816,682	3.20%	961,177	3.25%	1,084,796	3.00%	1,207,523	2.80%
145 147	McCracken McCreary	2,133,941 472,284	2,473,986 546,734	2.49% 2.47%	2,829,557 622,693	2.60% 2.55%	3,064,448 659,227	2.29% 2.11%	3,281,531 691,016	2.07% 1.83%
149	McLean	307,586	347,801	2.07%	386,359	2.09%	399,639	1.65%	409,532	1.37%
151	Madison	2,422,949	2,909,709	3.10%	3,382,635	3.08%	3,721,546	2.72%	4,041,816	2.47%
153	Magoffin	421,918	485,882	2.38%	552,041	2.47%	583,724	2.05%	611,189	1.78%
155	Marion	430,566	493,018	2.28%	557,313	2.37%	586,990	1.96%	613,091	1.70%
157	Marshall	1,259,674	1,485,393	2.79%	1,706,891	2.80%	1,850,865	2.43%	1,989,946	2.20%
159	Martin	340,828	370,147	1.38%	408,216	1.65%	420,230	1.32%	423,126	1.04%
161	Mason	678,811	757,211	1.84%	839,029	1.95%	866,434	1.54%	884,668	1.27%
163	Meade	663,413	853,988	4.30%	1,032,618	4.10%	1,149,359	3.49%	1,290,015	3.22%
165	Menifee	143,467	171,483	3.02%	197,147	2.93%	209,939	2.41%	222,787	2.12%
167	Mercer	618,651	717,870	2.51%	814,244	2.53%	858,140	2.07%	898,406	1.79%
169	Metcalfe	340,816	391,537	2.34%	441,232	2.38%	462,555	1.93%	480,887	1.65%
171	Monroe	276,823	301,892	1.46%	329,562	1.60%	335,548	1.21%	335,626	0.92%
173 175	Montgomery Morgan	713,101 358,763	840,320 410,583	2.77% 2.27%	965,877 462,306	2.80% 2.33%	1,048,010 484,695	2.44% 1.90%	1,123,527 504,046	2.19% 1.63%
177	Muhlenberg	1,038,484	1,189,606	2.29%	1,339,606	2.34%	1,405,198	1.91%	1,463,303	1.65%
179	Nelson	1,192,457	1,442,285	3.22%	1,686,997	3.20%	1,830,339	2.71%	1,987,063	2.46%
181	Nicholas	171,513	196,651	2.31%	221,335	2.35%	231,683	1.90%	240,424	1.62%
183	Ohio	930,782	1,062,518	2.23%	1,193,475	2.29%	1,249,513	1.86%	1,298,678	1.60%
185	Oldham	1,278,095	1,590,662	3.71%	1,893,728	3.64%	2,138,983	3.27%	2,406,471	3.06%
187	Owen	231,732	276,699	3.00%	317,841	2.91%	338,484	2.40%	359,737	2.12%
189	Owsley	124,675	142,952	2.31%	161,142	2.36%	168,702	1.91%	175,192	1.63%
191	Pendleton	338,493	409,497	3.22%	477,436	3.18%	515,638	2.67%	555,709	2.39%
193	Perry	958,955	1,086,384	2.10%	1,223,079	2.24%	1,281,564	1.83%	1,325,194	1.55%
195	Pike	2,177,247	2,442,594	1.94%	2,727,004	2.07%	2,831,406	1.66%	2,894,164	1.36%
197	Powell	497,464	578,575	2.55%	660,035	2.60%	700,778	2.16%	738,750	1.90%
199	Pulaski	1,723,396	2,034,720	2.81%	2,326,378	2.76%	2,464,030	2.26%	2,595,762	1.97%
201 203	Robertson Rockcastle	44,126 1,204,077	49,042 1,418,596	1.78% 2.77%	53,819 1,631,988	1.82% 2.80%	54,916 1,804,438	1.38% 2.56%	55,372 1,958,434	1.09% 2.34%
205	Rowan	830,467	976,597	2.74%	1,119,213	2.75%	1,211,281	2.39%	1,292,729	2.13%
207	Russell	514,862	597,486	2.51%	676,261	2.51%	709,798	2.03%	739,607	1.74%
209	Scott	1,991,532	2,549,126	4.20%	3,165,962	4.30%	3,797,232	4.12%	4,505,061	3.96%
211	Shelby	1,533,323	1,851,005	3.19%	2,172,325	3.22%	2,444,529	2.96%	2,716,495	2.76%
213	Simpson	896,853	1,019,755	2.16%	1,148,174	2.27%	1,241,631	2.05%	1,330,127	1.89%
215	Spencer	300,410	402,596	5.00%	500,205	4.74%	573,280	4.12%	669,096	3.89%
217	Taylor	587,402	676,765	2.39%	762,511	2.40%	798,669	1.94%	830,142	1.66%
219	Todd	330,629	373,036	2.03%	415,938	2.11%	432,623	1.69%	445,609	1.43%
221	Trigg	536,766	660,853	3.53%	779,903	3.45%	868,511	3.05%	961,428	2.81%
223	Trimble	209,771	251,771	3.09%	292,368	3.06%	316,270	2.60%	341,207	2.34%
225	Union	428,872	495,099	2.42%	570,336	2.63%	613,493	2.26%	656,017	2.04%
227	Warren	3,352,540	3,873,387	2.44%	4,401,017	2.50%	4,736,319	2.18%	5,052,851	1.97%
229 231	Washington Wayne	348,934 474,610	400,031 553,071	2.30% 2.58%	450,056 629,504	2.34% 2.60%	471,973 665,261	1.91% 2.13%	492,453 697,240	1.65% 1.85%
233	Webster	559,244	624,556	1.86%	692,557	1.96%	717,024	1.57%	734,590	1.31%
235	Whitley	1,638,010	1,920,462	2.69%	2,205,467	2.74%	2,419,132	2.47%	2,608,972	2.24%
237	Wolfe	319,037	368,846	2.45%	419,683	2.52%	444,602	2.10%	467,819	1.84%
239	Woodford	1,018,333	1,197,850	2.74%	1,378,968	2.79%	1,495,024	2.43%	1,607,454	2.20%
		128,277,992	149,605,480	2.60%	171,502,490	2.68%	185,672,867	2.34%	199,197,341	2.12%

8.2 Appendix B – State Survey Results

General Survey Responses

Question # 1

a. Does your agency develop and use traffic growth rates?

Yes (AL, AZ, DE, IL, IN, IA, KY, MA, NH, NJ, NM, OR, PA, SC, SD, TN, UT, VT, VA, WV, WI, WY)

No (AK, CT, MN, NE)

b. If yes, what are the values?

(IA, MA, NM, PA, SC, VT)

Question # 2

a. What type of methodology is used in forecasting your traffic growth rates?

Regression (AZ, DE, KY, MN, NJ, NM, SD, TN, UT, VT, VA, WV, WI, WY)

Time Series (MN, NH) Other (CT, IL, MA, PA)

b. 22 Historic traffic growth extrapolation (AL, AK, AZ, DE, IL, IN, IA, KY, MA, MN, NE, NM, OR, PA, SC, TN, UT, VT, VA, WV, WI, WY)

Question # 3

Can documentation of the methodology or modeling be obtained?

Yes (AL, CT, DE, IL, IN, IA, KY, MN, NH, NJ, NM, OR, PA, UT, VA, WV, WI, WY)

No (AK, AZ, IN, MA, NE, SC, SD, TN)

Question # 4

What parameters/variables are used in the methodology?

Employment status (CT, DE, MA, NJ, NM, PA, TN, UT, VA, WV)

Income (CT, DE, IN, MA, OR, PA, UT, VA, WI)

Number of registered vehicles (DE, IN, MA, OR, TN, WV)

Population (CT, DE, IN, IA, KY, NE, NJ, OR, PA, UT, VA, WV, WI, WY)

Land use characteristics (CT, DE, IL, IN, MA, MN, NJ, NM, OR, PA, TN, VA, WY)

Location (CT, DE, IN, IA, MA, MN, TN, WV, WI, WY)

Historic traffic growth (AL, AZ, DE, IL, IA, KY, MA, MN, NE, NH, OR, PA, SC, TN, VT, VA, WV, WI, WY)

Other (CT, MA, NE, NM, OR, UT, VA, WI)

Question # 5

What are the sources of your agency's future forecasting input parameters/variables?

Census Bureau (AL, CT, DE, IN, IA, MA, NE, NM, OR, UT, VA, WV)

Bureau of Economic Analysis (DE, NJ, OR) Bureau of Labor Statistics (DE OR, WV)

IRS - Statistics of Income Division

Other (CT, DE, IL, IA, KY, MA, NJ, OR, PA, SC, UT, VA, WV, WI)

Question # 6

a. Are there different traffic growth rates for each county or groups of counties?

Yes, each county (DE, IL, IN, IA, KY, MA, MN, NJ, TN, VA, WV, WI)

Yes, groups of counties (IL, IA, NH, PA, VA, WY) No (AL, AZ, NE, NM, SC, UT, VT)

b. How did you group the counties?

Population (IA)

Geographic location (IL, IA, PA, VA, WY)

Other (MA, NH, VA)

Question # 7

a. Are the traffic growth rates different for each functional class of road?

Yes (AL, DE, IL, IN, IA, KY, NE, NH, NJ, NM, PA, SC, UT, VA, WV, WI, WY)

No (AK, AZ, MA, MN, VT)

b. Are the traffic growth rates different for each vehicle type?

Yes (MN)

No (AL, AK, AZ, DE, IN, IA, KY, MA, NE, NH, NJ, NM, PA, SC, UT, VT, VA, WV, WI, WY)

Question # 8

What scheme/categorization is used for the vehicle classification?

FHWA scheme F (AL, AK, AZ, CT, DE, IN, KY, MA, MN, NE, NH, NJ, NM, OR, SC, SD, TN, UT, VT, VA, WV, WY)

EPA scheme (WI) Other (IL, PA)

Question # 9

a. Does your agency have a methodology for converting between FHWA and EPA vehicle classification systems?

Yes (AL, NH, WV, WI)

No (AK, AZ, CT, DE, IL, IN, KY, MA, MN, NE, NJ, NM, OR, PA, SC, SD, TN, UT, VT, VA, WY)

b. If yes, please describe or provide documentation.

(AL, NH, WV, WI)

Question # 10

a. How is ADT collected for local roads?

Always counted (NJ)

Always estimated (AZ, MA, NE, NH)

Counted some years, estimated other years (AL, AK, CT, DE, IL, IN, IA, KY, MN, NM, OR, PA, SC, SD, TN, UT, VT, VA, WV, WI, WY)

b. If estimated, what is the estimation based on?

Higher functional class (AK, CT, KY, NE, TN, VT)

Population (AZ, TN, WI)

Proximity to other roads (AL, NM, TN, WV, WI, WY)

Other local road counts (AK, DE, MA, NM, OR, PA, SD, TN, VA,WY)

Other (IL, IN, MA, MN, NH, PA, WV, WI)

Question # 11

a. How often are local roads counted?

Annually

Three-year cycle (CT,DE, IN, NJ, NM, OR, UT, VA, WV, WY)

Six-year cycle (KY, SD, VA)

Other (AL, AK, IL, IA, MA, MN, NE, NH, PA, SC, TN, VT, VA, WI)

b. Are all local roads counted?

Yes (DE, SD, VA)

No (AL, AK, CT, IL, IN, IA, KY, MA, MN, NE, NH, NJ, NM, OR, PA, SC, TN, UT, VT, VA, WV, WI, WY)

c. If no, what is the sampling scheme?

Described on page 15 (AL, CT, IL, KY, NH, NJ, NM, OR, PA, TN, UT, VT, VA, WV)

No sampling scheme (AK, MA, NE, SC, WI)

HPMS (IN)

Question # 12

a. What is your local road VMT estimation methodology based on?

HPMS (AK, DE, IN, KY, MA, MN, NE, NJ, OR, PA, SD, UT, VT, VA, WY)

Other (AL, AZ, CT, IL, IA, NH, NM, NC, SC, TN, WV, WI)

b. Is the VMT county-based?

Yes (AZ, DE, IL, KY, NE, PA, SD, WV, WI, WY)

No (AL, AK, CT, IN, MN, MA, NH, NJ, NM, NC, OR, SC, TN, UT, VT, VA)

Question # 13

Additional comments.

(CO, CT, FL, KY, MA, NE, NJ, NC, SD, TN, WI)

Detailed Survey Responses

	Does your agency develop and use traffic growth rates?	Values provided?
Alabama	Yes	By route.
Alaska	No	N/A
Arizona	Yes	Annual traffic growth rates are developed from our network of automatic traffic recorders (ATRs). These ATR based growth factors are used to adjust older AADT volumes on highways which do not receive a 24 or 48 hour raw coverage count for a given year. These growth factors are mean values from all ATR stations within a specific statewide factor grouping. This grouping is geographically based rather than by highway functional class. Any traffic forecasting that Arizona DOT performs does not use any of these factors as inputs.
Connecticut	No	N/A
Delaware	Yes	-
Florida	-	-
Illinois	Yes	-
Indiana	Yes	-
Iowa	Yes	Enclosed is a copy of the old growth rates. New values are being generated and will be available March 1, 2000.
Kentucky	Yes	Provided by functional class and county statewide.
Massachusetts	Yes	Varies, usually between 1/2 to 1 1/2 % per year based on the area and regional planning models.
Minnesota	No	N/A
Nebraska	No	N/A
New Hampshire	Yes	Short term growth rates (1 to 3 years) are based on functional class. Long term (20 years) growth rates are determined for various regions of the state based on historical traffic data from permanent counters.
New Jersey	Yes	Values are provided to the county level by functional class. More details can be provided in documentation (q#3).
New Mexico	Yes	See attached sheet for values.
North Carolina	-	-
Oregon	Yes	Our traffic growth rates are based on location (i.e. by highway and milepost). For every location where traffic counts have been taken (locations described in ODOT's Traffic Volume Tables), we have a unique future traffic volume (future AADT for specific year), which changes all along the given roadway (particularly as the highway moves in and out of urban areas). These future volumes are calculated using an extrapolated method, based on historical traffic counts. In general, it's one thing to say that 25% of Oregon's traffic growth is on the interstate system. But, it's totally different to say where that growth is on any one of the five interstate roadways. For Planning, we are interested in how the system changes, whether we use HPMSAP, HERS, or some other model. Rather than using the Functional Classification (FC), most of ODOT uses the State Classification System (SCS), to designate the roadway (interstate, statewide, regional, or district). The SCS is much more generic than FC.
Pennsylvania	Yes	See Table #371 and FC Group Designation Table in 1998 Traffic Data report at www.dot.state.pa.us (Select: Programs and Services, select: Traffic Information, select: 1998 Traffic Data Report).
South Carolina	Yes	See attached sheet.
South Dakota	Yes	-
Tennessee	Yes	We figure growth rates, count by count, project by project.
Utah	Yes	Permanent traffic counters are used for cluster analysis to factor 48 hour counts, and these vary each year. Typical clusters are urban/rural interstate, urban/rural arterials, recreational, special construction
Vermont	Yes	These are based on individual continuous count stations, or by group: interstate highways (five year growth factor = 1.13), urban highways (five year growth factor = 1.03), primary and secondary highways (five year growth factor = 1.09).
Virginia	Yes	Values are not available.
West Virginia	Yes	Growth factors correspond to individual county and FC within each county.
Wisconsin	Yes	By two aggregate functional class groupings, principal arterials and others. We don't have the data needed to develop separate growth rates for all functional classes, given a lack of data on local roads and numerous changes in urban areas.
Wyoming	Yes	-

2	What type of methodology is used in forecasting your traffic growth rates?
Alabama	Historic traffic growth extrapolation
Alaska	Historic traffic growth extrapolation
Arizona	Historic traffic growth extrapolation (It is a straight-line linear regression simply using a feature available in Excel spreadsheet software and all of our historic AADT volumes as far back as 1974.)
Connecticut	Entire state is modeled using a traditional 4 step network model.
Delaware	Regression, historic traffic growth extrapolation
Florida	-
Illinois	Historic traffic growth extrapolation, (Illinois DOT, Division of Highways is divided into nine highway districts, each district being responsible for specific counties. With certain exceptions, traffic forecasting is performed by district personnel familiar with the highway systems within their jurisdictions, and their methodologies vary. Historical trends are the basis for most forecasts, but other factors such as residential or commercial development are considered and frequent use is made of the ITE Trip Generation manual. Exceptions are the forecasts made with traffic models for Chicago and eight other urbanized areas by the metropolitan planning organization (MPO) for each area.)
Indiana	Historic traffic growth extrapolation
Iowa	Historic traffic growth extrapolation
Kentucky	Regression, historic traffic growth extrapolation
Massachusetts	Regional traffic modeling, future year networks, historic traffic growth extrapolation
Minnesota	Regression, time series, historic traffic growth extrapolation
Nebraska	Historic traffic growth extrapolation (extrapolated as a volume, not as a rate)
New Hampshire	Time series
New Jersey	Regression
New Mexico	T-Model regression, historic traffic growth extrapolation
North Carolina	-
Oregon	Historic traffic growth extrapolation (the VMT methodology from Financial Services involves both regression and time series techniques (part of Mazen's revenue forecast model). The VMT methodology from Policy Section involves traffic growth extrapolation and analysis of fuel tax, WM tax, vehicle statistics, and other demographic variables.) Historic traffic growth extrapolation, some counties have travel demand models which forecast growth based on input
Pennsylvania	population and employment information
South Carolina	Historic traffic growth extrapolation
South Dakota	Regression
Tennessee	Regression, historic traffic growth extrapolation
Utah	Regression (our MPO's model in the urbanized areas with MINUTP), historic traffic growth extrapolation (our statewide planning section mostly uses this for the rural areas).
Vermont	Historic traffic growth extrapolation (We do linear regression based on the historic traffic data. We are looking into changing from a straight line regression for all years available to a more trend sensitive evaluation.)
Virginia	Regression, historic traffic growth extrapolation
West Virginia	Regression, historic traffic growth extrapolation
Wisconsin	We use a causally based model to develop long-range forecasts both personal and commercial VMT on a statewide basis. In a nutshell, we forecast personal VMT as a function of annual average miles driven per licensed driver by sex and six age groups. These miles driven per licensed driver increase as a function of income and an underlying time-trend factor. The changing age structure of the population over time also drives the forecasts. We forecast commercial VMT as a function of the Index of Industrial Production (excluding computers and office equipment) using a difference-stationary regression model over a 25-year historical time period. To get county specific growth rates, we use the results of the above described procedure to serve as a statewide control total and develop specific growth rates for each county based on each counties projected rate of population growth. We then get the two broad functional class groupings based on observed historical differences (from HPMS and other traffic count data).
Wyoming	Regression, historic traffic growth extrapolation

3	Can documentation of the modeling/methodology be obtained?
Alabama	Yes
Alaska	No
Arizona	No
Connecticut	Yes
Delaware	Yes
Florida	-
Illinois	Yes (No statewide "cookbook" for forecasting based on historical trends and future land use has been developed by IDOT because no single approach is appropriate for every area or every project. Experienced personnel apply the appropriate methodology in each situation. Documentation of traffic modeling may be obtained through the MPO.)
Indiana	Yes (historic data) / No (modeling)
Iowa	Yes (will be available later this year).
Kentucky	Yes
Massachusetts	No
Minnesota	Yes
Nebraska	No
New Hampshire	Yes (We use a commercial program called Smart Forecast. Documentation can be obtained from the vendor.)
New Jersey	Yes
New Mexico	Yes
North Carolina	-
Oregon	ODOT's VMT methods are being evaluated by a consultant and a final report will be available after July 2000. As a by product of the RFP, we have a very brief description of the 4 VMT methods. I am attaching a copy in case it is useful.
Pennsylvania	Yes (Historic growths are summarized in the 1998 Traffic Data Report.)
South Carolina	No
South Dakota	No (We are currently conducting a research project to review, and if necessary change, our methodology; therefore, I can't accurately answer the next 4 questions at this time.)
Tennessee	No (We don't have a manual.)
Utah	Yes (through the MPOs)
Vermont	-
Virginia	Yes
West Virginia	Yes
Wisconsin	Yes (but not much more specific documentation exists than what I have already stated).
Wyoming	Yes

| | What parameters/variables are used in the methodology? | | | | | |----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--|--|--|--| | Alabama | Historic traffic growth | | | | | | Alaska | N/A | | | | | | Arizona | Historic traffic growth (It is Arizona DOT's current policy not to prepare traffic forecasts for State System Highways
that are within large urbanized areas and that are influenced by Transportation Management Areas (TMA's) and/or
Metropolitan Planning Organizations (MPO's). We use their forecasts for those roads and streets under our
jurisdiction within these large urbanized areas. MPO and TMA forecasts are produced via modeling. Inputs to these
models are not known.) | | | | | | Connecticut | Employment status, income, population, land use characteristics, location, vehicle availability | | | | | | Delaware | Employment status, income, no. of reg. vehicles, population, land use char., location, historic traffic growth | | | | | | Florida | - | | | | | | Illinois | Land use characteristics, historic traffic growth | | | | | | Indiana | Income, no. of reg. vehicles, population, land use characteristics, location | | | | | | Iowa | Population, location, historic traffic growth | | | | | | Kentucky | Population, historic traffic growth | | | | | | Massachusetts | Employment status, income, no. of reg. vehicles, land use characteristics, location, historic traffic growth, households | | | | | | Minnesota | Land use characteristics, location, historic traffic growth | | | | | | Nebraska | Population, historical VMT | | | | | | New Hampshire | Historic traffic growth | | | | | | New Jersey | Employment status, population, land use characteristics | | | | | | New Mexico | Employment status, land use characteristics, network characteristics (links and nodes, speed, capacities, link lengths,
lanes, directional) | | | | | | North Carolina | - | | | | | | Oregon | Historic traffic growth, metropolitan models, (A combination of income, no. of registered vehicles, population, and
historic traffic growth are included in the Finance and Policy methods. Within specific metropolitan areas, where
transportation models have been developed, we use growth rates from the models. ODOT is currently working on a
statewide model which will be projecting future growth rates based on anticipated land use (which includes most of
the above).) | | | | | | Pennsylvania | Employment status, income, population, land use characteristics, historic traffic growth | | | | | | South Carolina | Historic traffic growth | | | | | | South Dakota | see Q#3 | | | | | | Tennessee | Employment status, no. of reg. vehicles, land use characteristics, location, historic traffic growth | | | | | | Utah | Employment status, income, population, vehicles per household, validated by length & number of trips by trip purpose | | | | | | Vermont | Historic traffic growth | | | | | | Virginia | Employment status, income, population, land use characteristics, historic traffic growth, school enrollment, trucks | | | | | | West Virginia | Employment status, no. of reg. vehicles, population, location, historic traffic growth | | | | | | Wisconsin | Income, population, location, historic traffic growth, miles driven per licensed driver, licensed drivers, sex, six age
group cohorts (16-19, 20-34, 35-59, 60-69, 70-79, 80+), Index of Industrial Production (excluding computers and
office equipment) | | | | | | Wyoming | Population, land use characteristics, location, historic traffic growth | | | | |

	What are the sources of your agency's future forecasting input parameters/variables?
Alabama	Census Bureau
Alaska	N/A
Arizona	None of these apply.
Connecticut	Census Bureau, Conn. Dept. of Labor, current traffic counts and transit ridership
Delaware	Census Bureau, Bureau of Economic Analysis, Bureau of Labor Statistics., building permit/land development tracking process inputs to statewide local area demographic forecasting
Florida	-
Illinois	The Department maintains an extensive history of annual vehicle miles traveled (AVMT) tabulated by functional class, by county, urbanized area, etc. An annual traffic monitoring program provides current AADT at approximately 20,000 locations statewide, which are used to calculate AVMT and to publish a variety of traffic maps. These maps provide a reliable basis for historical trend analysis for specific roadway sections.
Indiana	Census Bureau
Iowa	Census Bureau, historic data from automatic traffic
Kentucky	Kentucky State Data Center (population), KY Transportation Cabinet's Division of Planning (traffic data)
Massachusetts	Census Bureau, Mass. Institute of Social and Economic Research-MISER, Regional Economic Models IncREMI
Minnesota	-
Nebraska	Census Bureau
New Hampshire	-
New Jersey	Bureau of Economic Analysis, Consultant Economic Analysis
New Mexico	Census Bureau
North Carolina	-
Oregon	Census Bureau, Bureau of Economic Analysis, Bureau of Labor Statistics, Standard & Poor's DRI, Office of Economic Analysis of DAS
Pennsylvania	Metropolitan Planning Organization forecasts
South Carolina	Department Traffic Count Data
South Dakota	see Q#3
Tennessee	-
Utah	Census Bureau, State Office of Planning & Budget
Vermont	-
Virginia	Census Bureau, state agencies-local govt., school boards
West Virginia	Census Bureau, Bureau of Labor Statistics, DMV vehicle registrations
Wisconsin	The Demographic Services section of our state Department of Administration, Data Resources Inc. for the income forecasts
Wyoming	-

| | Are there different traffic growth rates for
each county or groups of counties? | How did you group the counties? | | | |----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--|--| | Alabama | No | N/A | | | | Alaska | N/A | N/A | | | | Arizona | No | By geographic location but largely defined by principle
highway routes and urban areas. But these growth rates
are for adjusting dated AADT volumes - NOT FOR
CALCULATING FORECASTS!!! | | | | Connecticut | N/A | N/A | | | | Delaware | Yes, each county. | N/A | | | | Florida | - | - | | | | Illinois | Yes, each county and groups of counties (historical
AVMT growth rates for specific geographical areas
(individual counties, groups of counties, urbanized
areas, etc.) or specific routes are developed on an ad hoc
basis for use by forecasters in the districts.) | Geographic location | | | | Indiana | Yes, each county. | N/A | | | | Iowa | Yes, each county and groups of counties. | Population, geographic location | | | | Kentucky | Yes, each county. | N/A | | | | Massachusetts | Yes, each county (or regional planning district). | Regional planning district, urbanized areas | | | | Minnesota | Yes, each county. | N/A | | | | Nebraska | No | N/A | | | | New Hampshire | Yes, groups of counties. | Based on historical traffic growth | | | | New Jersey | Yes, each county. | N/A | | | | New Mexico | No | N/A | | | | North Carolina | - | - | | | | Oregon | For Planning, we have defined different growth rates all
along any given highway. It could be rolled up to a
district, county, or regional level, but we don't need it
that way. | N/A | | | | Pennsylvania | Yes, groups of counties. | Geographic location | | | | South Carolina | No (We are currently working on a project to develop
rates for each county by FC.) | N/A | | | | South Dakota | see Q#3 | see Q#3 | | | | Tennessee | Yes, each county (We have a program that calculates
growth rates for each county.) | N/A | | | | Utah | No | N/A | | | | Vermont | No | N/A | | | | Virginia | Yes, each county (and city) and groups of counties (by
district). | Geographic location, individual cities | | | | West Virginia | Yes, each county. | N/A | | | | Wisconsin | Yes, each county. | N/A | | | | Wyoming | Yes, groups of counties. | Geographic location | | |

| Alabama
Yes
No
|------------------------- | | | | | | Florida
-
- | Illinois
Yes
- | Indiana
Yes
No | Iowa
Yes
No | Kentucky
Yes
No | Massachusetts
No
No | Minnesota
No
Yes | Nebraska
Yes
No | New Hampshire
Yes | New Jersey
Yes
No | New Mexico
Yes
No | North Carolina
-
- | Oregon
N/A
N/A | Pennsylvania
Yes
No | South Carolina
Yes
No | South Dakota
see Q#3
see Q#3 | Tennessee
-
- | Utah
Yes (but | No (although the | Virginia
Yes
No | West Virginia
Yes
No | Wisconsin
Yes | No (unless we have Alaska
No
No
Arizona
No
No
Connecticut
N/A
N/A
Delaware
Yes
No | | | | --------------------------------------------------------------------------------------------------------------------|--|--|--| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | (insufficient data) | | | | (for short term growth (1-3 years))
No | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | not all classes)
No | | | | groups can be related to FC: interstates are
Vermont
No
1,11; urban are 12-19; and primary and secondary are 2-9) | | | | | | | | | | | | (but only for 2 groups: prin. arterials and all lower FC)
No | | | | information on a vehicle type that is
Wyoming
Yes
going to grow different) | | | |

8	What scheme is used for the vehicle classification?
Alabama	FHWA scheme F
Alaska	FHWA scheme F
Arizona	FHWA scheme F
Connecticut	FHWA scheme F
Delaware	FHWA scheme F
Florida	-
Illinois	Trend analysis is done for three vehicle categories; passenger vehicles, single-unit and multiple-unit trucks.
Indiana	FHWA scheme F
Iowa	N/A
Kentucky	FHWA scheme F
Massachusetts	FHWA scheme F
Minnesota	FHWA scheme F
Nebraska	FHWA scheme F
New Hampshire	FHWA scheme F
New Jersey	FHWA scheme F
New Mexico	FHWA scheme F
North Carolina	-
Oregon	FHWA scheme F
Pennsylvania	9 vehicle types.
South Carolina	FHWA scheme F
South Dakota	FHWA scheme F
Tennessee	FHWA scheme F
Utah	FHWA scheme F
Vermont	FHWA scheme F
Virginia	FHWA scheme F
West Virginia	FHWA scheme F
Wisconsin	EPA scheme
Wyoming	FHWA scheme F

| 9 | Does your agency have a methodology for
converting between FHWA and EPA vehicle
classification systems? | Provided documentation or description? | | | |----------------|---------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--|--| | Alabama | Yes | Use of EPA's default values. | | | | Alaska | No | N/A | | | | Arizona | No | N/A | | | | Connecticut | No | N/A | | | | Delaware | No | N/A | | | | Florida | - | - | | | | Illinois | No | N/A | | | | Indiana | No | N/A | | | | Iowa | N/A | N/A | | | | Kentucky | No | N/A | | | | Massachusetts | No | N/A | | | | Minnesota | No | N/A | | | | Nebraska | No | N/A | | | | New Hampshire | Yes | We use the conversion required for input into Mobile 5. Mobile 5
documentation explains the process. | | | | New Jersey | No | N/A | | | | New Mexico | No | N/A | | | | North Carolina | - | - | | | | Oregon | No | N/A | | | | Pennsylvania | No | N/A | | | | South Carolina | No | N/A | | | | South Dakota | No | N/A | | | | Tennessee | No | N/A | | | | Utah | No | N/A | | | | Vermont | No | N/A | | | | Virginia | No | N/A | | | | West Virginia | Yes | 2-axle dual tire vehicles, including buses, are "Medium Trucks",
all others are "Heavy Trucks". | | | | Wisconsin | Yes | We essentially use the EPA conversion methodology, except that
we use state specific data where we have it (from our I/M
stations) to estimate the split between the combined FHWA
classes 2 and 3 to the EPA classes LDGV, LDGT1, LDGT2
(which we estimate at the following proportions, .554, .327, .118,
respectively. | | | | Wyoming | No | N/A | | |

| Alabama
Counted some years, estimated other years.
Proximity to other roads.
Counted some years, estimated other years (some always
Alaska
Higher functional class, other local road counts.
estimated).
Arizona
Always estimated.
own them - not the State DOT.)
Connecticut
Counted some years, estimated other years.
Higher functional class.
Delaware
Counted some years, estimated other years.
Other local road counts.
Florida
-
-
Illinois
Counted some years, estimated other years.
Historical trend extrapolation.
Indiana
Counted some years, estimated other years.
County growth.
Iowa
Counted some years, estimated other years.
-
Kentucky
Counted some years, estimated other years.
Higher functional class (collector to local ratios).
Massachusetts
Always estimated.
local roads), mileage, default values for ADT.
Minnesota
Counted some years, estimated other years.
ATR growth factors for similar roads.
Nebraska
Always estimated.
Higher functional class.
New Hampshire
Always estimated.
-
New Jersey
Always counted.
N/A
New Mexico
Counted some years, estimated other years.
Proximity to other roads, other local road counts.
North Carolina
-
-
Oregon
ADT is counted on a sample of local roads.
Other local road counts (partial count of local roads).
Pennsylvania
Counted some years, estimated other years.
Other local road counts, total annual system growth.
South Carolina
Counted some years, estimated other years.
-
South Dakota
Counted some years, estimated other years.
Other local road counts.
Tennessee
Counted some years, estimated other years.
other local road counts. | 10 | How is ADT collected for local roads? | If estimated, what is estimation based on? | | | |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------|--------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--|--| | | | | | | | | | | | | | | | | | | Population (Local functional system travel estimates are
prepared and submitted by the local government agencies who | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Other local road counts (regional planning agencies count some | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Higher functional class, population, proximity to other roads, | | | | | Utah | Counted some years, estimated other years. | - | | | | Counted some years, estimated other years (some roads are
Vermont
counted in our coverage count schedule, and others are never
Higher functional class.
counted). | | | | | | | Virginia
Counted some years, estimated other years.
Other local road counts. | | | | | | | Proximity to other roads, previous counts, number of
West Virginia
Counted some years, estimated other years.
residences on route. | | | | | | | Wisconsin
Counted some years, estimated other years.
vehicle trips per household) and others just make
proximity to other roads. | | | Depends on the transportation district office. Some use crude
traffic generation rates derived from land-use (such as 10
"guesstimates" based on local knowledge, population, or | | | | Wyoming
Counted some years, estimated other years.
Proximity to other roads, other local road counts. | | | | | |

| Alabama
As requested.
No
As requested.
Some counted 3-5 years, others
Alaska
No
None
not counted.
Arizona
Unknown.
Unknown
-
Connecticut
Three-year cycle.
No
VMT.
Delaware
Three-year cycle.
Yes
N/A
Florida
-
-
-
5-yr cycle downstate, 4-yr cycle in
Illinois
No
Chicago area counties.
on township roads and municipal streets.
Indiana
Three-year cycle.
No
HPMS
Iowa
Four-year cycle.
No
-
Kentucky
Six-year cycle.
No
Random samples developed for HPMS in 1979.
By regional agencies upon
Massachusetts
No
None
municipal requests.
Minnesota
4 years.
No
-
Never as an entire system, only a
Nebraska
No
None
handful of locations each year.
New Hampshire
Always estimated.
No
We estimate VMT for local roads.
New Jersey
Three-year cycle.
No
streets were randomly selected within randomized grid
clusters. Additional samples were added for tims/h.
New Mexico
Three-year cycle.
No
Random selection or as needed.
North Carolina
-
-
-
Three-year cycle (and then only
Oregon
No
part of them).
FHWA HPMS Manual-Appendix F.
Locally owned roads are estimated yearly from known
Pennsylvania
10-yr cycle - state owned only.
No
system growth.
South Carolina
As time permits.
No
None
South Dakota
Six-year cycle.
Yes
N/A
Tennessee
-
No
intersection analysis, high hazard locations, etc.
Utah
Three-year cycle.
No
zone.
Vermont
Four-year cycle.
No
Yes/No (all local roads
Virginia
3-,6- and 12-year cycles.
are counted that are
miles of road.
VDOT maintained).
West Virginia
Three-year cycle.
No
Do not sample roads with < 50 ADT.
No fixed cycle. Some local roads
Wisconsin
get counted for special purposes,
No
them as part of the regular 3-yr count cycle, but mgmt
but most never get counted.
considered it cost prohibitive. | 11 | | How often are local roads
counted? | Are all local roads
counted? | If no, is sampling scheme provided? | | |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----|---------|---------------------------------------|---------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Random sample for each urbanized area and rural area 1/3
counted each year-factors developed to estimate total local | | | | | | | | | | | | | | | | | | | | | | | | Seventy to 100 percent coverage on local systems down to
and including collectors. Count structures and RR crossings | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The original sample was selected for HPMS. Five local | | | | | | | | | | | | | | | | | | | | | | | | Samples are picked at random. The idea is to have a certain
number per volume group. Volume groups are based on
ADT ranges within each functional class as outlined in the | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | As requested for project projections from Design Division
and others, for bridge counts, for railroad crossing studies, | | | | | | | | Local roads are grouped into zones and then sampled in each | | | | | | | | We have selected what we think are the most important local
roads for our coverage counts. It is not a random sample. | | | | | | | | In urban areas, the sample is one count made for each ten | | | | | | | | | | | | | | | | We don't sample count local roads. We proposed developing
a random sample of locations on local roads and counting | | | | | Wyoming | Three-year cycle. | No | - | |

| | What is your local road VMT estimation
methodology based on? | Is the VMT county-based? | | |----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--| | Alabama | Counted +2% growth factor. | No | | | Alaska | HPMS | No | | | Arizona | Provided by local govt. agencies and typically based on
population growth. | Yes (Total VMT over all functional systems by each county is
originally based on fuel sales and then adjusted by statewide functional
system VMT after the HPMS data submittal.) | | | Connecticut | Network model factored to HPMS control totals by
functional classification. | No | | | Delaware | HPMS | Yes | | | Florida | - | - | | | Illinois | Representative samples on county level. | Yes | | | Indiana | HPMS | No | | | Iowa | - | - | | | Kentucky | HPMS | Yes | | | Massachusetts | HPMS | No, based on regional planning districts, only a few are contiguous
with county boundaries (common in New England). Results are
routinely summed to non-attainment areas for conformity purposes,
and occasionally extrapolated to county-by-county reporting for DEP
tracking purposes (reasonable further progress, etc.). | | | Minnesota | HPMS | No | | | Nebraska | HPMS | Yes | | | New Hampshire | - | No | | | New Jersey | HPMS | No | | | New Mexico | - | No | | | North Carolina | Based on growth trends of other FC of roads. | No | | | Oregon | HPMS | No | | | Pennsylvania | HPMS | Yes | | | South Carolina | Statewide average. | No | | | South Dakota | HPMS | Yes | | | Tennessee | TN conducts short machine counts (24 hours) on 14,000
locations each year as well as hundreds of other machine,
WIM, and turning movement counts. Most of these counts
are on functional classified routes. TN has a database that
develops VMT for each route, log mile by log mile, for all
of the classified routes. (This is furnished to HPMS.) The
local routes are estimated based on the classified routes. | No | | | Utah | HPMS | No (by FC and jurisdiction). | | | Vermont | HPMS | No (the data is collected by Town, then we can do a county total if we
wish). | | | Virginia | HPMS | No | | | West Virginia | Road inventory log of state system with ADT count or
estimate on each link. | Yes | | | Wisconsin | We back into the estimates of local road VMT. We develop
statewide estimates of VMT, arrived at based on three
independent approaches: 1) an estimate based on gasoline
and diesel fuel consumption in the state multiplied by auto
and truck fleet fuel efficiency (MPG) estimates; 2) the
percent change in functional class system weighted changes
in AADT levels based on over 100 automatic traffic
recorders (ATRs) statewide (but none on local roads and
few on the lower level functional systems); 3) the change in
the HPMS VMT estimates for arterial and collector
highways in the state. From this final statewide estimate of
VMT, we subtract out the HPMS VMT as given from the
arterial and collector highways and the remainder gets
allocated to the local roads (given that we don't have the
actual traffic counts or samples to estimate local road VMT | No (not originally, but in the end it gets allocated on a county basis). | | | | directly. | | |

| | Additional comments provided? | | | | | |----------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--|--|--|--| | Alabama | - | | | | | | Alaska | - | | | | | | Arizona | - | | | | | | Connecticut | CONNDOT uses a network based travel model factored to our HPMS submittal to calculate the VMTs by speed range that are
input to the mobile emission model. CONNDOT doesn't use growth rates-HPMS VMT for local roads is based on a sample of
local road ADTs. | | | | | | Delaware | - | | | | | | Florida | In Florida, all areas are in maintenance status; however, may go into attainment status in the next year or so. Historically, FDOT
has used a transportation model (FSUTMS, Transplan derivation) to forecast future volumes. These volumes are used to estimate
air quality emissions. FDOT does tie the base year model (as required by EPA) back to HPMS based mileage. FDOT doesn't go
to any extra effort to deal with local roads. FDOT uses centroid connectors within the model as local road elements. | | | | | | Illinois | - | | | | | | Indiana | - | | | | | | Iowa | - | | | | | | Kentucky | Ongoing study will develop new HPMS local sample and will generate an improved local road estimation methodology.
Alternatives include improved collector/local ratios derived from historical count data and a regression procedure derived from
other variables (e.g. population, employment, proximity, etc.). | | | | | | Massachusetts | Local road VMT estimation for HPMS reporting is based on mileage and default values for ADT. As for NOx and other
pollutants, we use the VMT results of the regional travel demand models, which include some local roads, for reporting emission
results to DEP and EPA. These results are factored to HPMS before emissions are calculated. A possible aim of MassHighway is
to eventually convince EPA that the VMT results of the regional models (and within a few years the statewide model), are more
accurate than HPMS factoring because of better coverage of all roads, including local (HPMS is based on samples). | | | | | | Minnesota | - | | | | | | Nebraska | Nebraska has no air quality non-attainment areas. | | | | | | New Hampshire | - | | | | | | New Jersey | We have asked our universities for a statistical analysis to develop a countywide VMT estimation process within or parallel to
HPMS. | | | | | | New Mexico | - | | | | | | North Carolina | | | | | | | Oregon | - | | | | | | Pennsylvania | - | | | | | | South Carolina | - | | | | | | South Dakota | Our research project is scheduled to be completed in late spring. | | | | | | Tennessee | Comparisons are made on rural, small urban, urbanized areas. | | | | | | Utah | - | | | | | | Vermont | - | | | | | | Virginia | - | | | | | | West Virginia | - | | | | | | Wisconsin | We know we need a better method to estimate local road VMT, such as some type of sampling or estimated local road VMT at the
county level based population. But thus far we have not gotten the resources committed to develop a better approach and the ideas
remain on the drawing board only. | | | | | | Wyoming | - | | | | |

	Respondent		Respondent
	Charles Turney		Jim Carl
Alabama	(334) 242-6393	New Jersey	(609) 530-3510
	turneyc@dot.state.al.us		jimcarl@dot.state.nj.us
	Mary Ann Dierckman		Alvaro Vigil
Alaska	(907) 465-6993	New Mexico	(505) 827-5665
	maryann_dierckman@dot.state.ak.us
	Mark Catchpole		L.C. Smith
Arizona	(602) 712-8596	North Carolina	(919) 250-4188
	mcatchpole@dot.state.az.us		lcsmith@dot.state.nc.us
	Stuart Leland		David Canfield
Connecticut	(860) 594-2020	Oregon	(503) 986-4149
	stu.leland@po.state.ct.us
	Michael DuRoss		Gaye Liddick
Delaware	(302) 760-2110	Pennsylvania	(717) 787-5983
	mduross@mail.dot.state.de.us		liddick_padot@yahoo.com
	Harry Gramling		John Gardner
Florida	(850) 414-4928	South Carolina	(803) 737-1444
	harry.gramling@dot.state.fl.us		gardnerjf@dot.state.sc.us
	James Hall		Jeff Brosz
Illinois	(217) 785-2998	South Dakota	(605) 773-3278
	halljp@nt.dot.state.il.us		jeff.brosz@state.sd.us
	Scott MacArthur		Bonnie Brothers
Indiana	(317) 233-1166	Tennessee	(615) 741-2208
			bbrothers@mail.state.tn.us
	Craig Marvick		Gary Kuhl
Iowa	(515) 239-1369	Utah	(801) 964-4552
	cmarvic@iadot.e-mail.com		gkuhl@dot.state.ut.us
	Rob Bostrom		Mary Godin
Kentucky	(502) 564-7686	Vermont	(802) 828-2681
	rbostrom@mail.kytc.state.ky.us		mary.godin@state.vt.us
	Bob Frey		Tom Schinkel
Massachusetts	(617) 973-7449	Virginia	(804) 225-3123
	bob.frey@state.ma.us		schinkel_to@vdot.state.va.us
	George Cepress		Jerry Legg
Minnesota	(651) 296-0217	West Virginia	(304) 558-2864
	george.cepress@dot.state.mn.us		jllegg@dot.state.wv.us
	Rick Ernstmeyer		Bruce Aunet
Nebraska	(402) 479-4520	Wisconsin	(608) 266-9990
	rernstme@dor.state.ne.us		bruce.aunet@dot.state.wi.us
	Subramanian Sharma		David Birge
New Hampshire	(603) 271-1625	Wyoming	(307) 777-4190
	ssharma@dot.state.nh.us		dbirge@missc.state.wy.us

Charles Turney Jim Carl New Jersey turneyc@dot.state.al.us jimcarl@dot.state.nj.us Mary Ann Dierckman Alvaro Vigil New Mexico alvaro.vigil@nmshtd.state.nm.us Mark Catchpole L.C. Smith North Carolina lcsmith@dot.state.nc.us Stuart Leland David Canfield Oregon david.l.canfield@odot.state.or.us Michael DuRoss Gaye Liddick Pennsylvania liddick_padot@yahoo.com Harry Gramling John Gardner South Carolina gardnerjf@dot.state.sc.us James Hall Jeff Brosz South Dakota jeff.brosz@state.sd.us Scott MacArthur Bonnie Brothers bbrothers@mail.state.tn.us Craig Marvick Gary Kuhl Utah (515) 239-1369 (801) 964-4552 gkuhl@dot.state.ut.us Rob Bostrom Mary Godin Vermont mary.godin@state.vt.us Bob Frey Tom Schinkel Virginia bob.frey@state.ma.us schinkel_to@vdot.state.va.us George Cepress Jerry Legg West Virginia jllegg@dot.state.wv.us Rick Ernstmeyer Bruce Aunet Wisconsin bruce.aunet@dot.state.wi.us Subramanian Sharma David Birge Wyoming
	Respondent	Respondent





	maryann_dierckman@dot.state.ak.us


	mcatchpole@dot.state.az.us


	stu.leland@po.state.ct.us


	mduross@mail.dot.state.de.us


	harry.gramling@dot.state.fl.us


	halljp@nt.dot.state.il.us





	cmarvic@iadot.e-mail.com


	rbostrom@mail.kytc.state.ky.us





	george.cepress@dot.state.mn.us


	rernstme@dor.state.ne.us


	ssharma@dot.state.nh.us	dbirge@missc.state.wy.us

8.3 Appendix C – VMT from Historical KYTC Data Files (1993-1999)

				Non-Interstate VMT (x 1000)
County	1993	1994	1995	1996	1997	1998	1999
Adair	425	410	417	424	480	497	489
Allen	360	360	366	366	383	402	423
Anderson	462	462	465	500	522	524	570
Ballard	248	236	237	244	245	242	251
Barren	877	881	910	953	966	992	1037
Bath	181	183	202	204	207	213	223
Bell	722	712	729	733	767	785	856
Boone	1958	1968	2139	2234	2484	2579	2376
Bourbon	489	495	499	502	518	546	569
Boyd	1169	1166	1204	1241	1344	1385	1246
Boyle	566	565	569	612	622	642	712
Bracken Breathitt	204 387	205 388	203 399	202 408	204 419	247 415	274 422
Breckinridge	364	358	378	387	401	408	415
Bullitt	720	739	755	805	864	863	1025
Butler	377	379	388	391	489	426	446
Caldwell	412	375	397	402	460	449	486
Calloway	696	690	694	709	768	779	819
Campbell	1619	1578	1607	1589	1622	1631	1568
Carlisle	136	136	145	147	143	144	150
Carroll	203	201	211	212	220	225	245
Carter	513	514	538	566	593	593	604
Casey	344	346	355	363	359	368	380
Christian	1484	1668	1586	1679	1582	1682	1832
Clark	629	615	637	677	672	694	743
Clay	607	605	601	612	625	646	710
Clinton	221	226	229	233	249	259	270
Crittenden	203	218	223	224	225	229	221
Cumberland	161	162	193	191	193	192	205
Daviess	1988	1959	1974	2045	2153	2185	2185
Edmonson	217	223	216	217	216	217	244
Elliott	113	114	116	114	115	119	124
Estill	325	306	312	319	330	337	335
Fayette	4680	5045	5100	5216	5371	5499	5476
Fleming Floyd	276 1412	296 1405	311 1457	319 1452	325 1485	334 1510	342 1549
Franklin	877	917	912	936	971	1024	1088
Fulton	163	172	188	191	194	207	205
Gallatin	125	124	125	125	135	141	166
Garrard	283	283	291	298	317	341	351
Grant	291	291	336	342	355	360	375
Graves	1036	1039	1039	1077	1126	1147	1173
Grayson	751	722	813	868	864	887	964
Green	223	225	230	231	241	248	255
Greenup	906	854	857	875	919	936	1003
Hancock	209	209	194	229	240	245	251
Hardin	2142	2497	2325	2413	2463	2536	2667
Harlan	764	771	772	781	825	866	883
Harrison	341	320	321	325	315	328	351
Hart	278	279	343	345	345	354	356
Henderson	1397	1421	1454	1507	1626	1610	1775
Henry	380	380	405	443	447	470	350
Hickman	149	157	160	166	171	181	175
Hopkins	1555	1558	1705	1668	1752	1790	1828
Jackson	224	224	246	253	257	280	297
Jefferson	11980	11618	11664	12329	12551	12864	12103
Jessamine	725	734	753	800	879	933	1014
Johnson Kenton	588 2000	596 2165	611 2161	610 2129	559 2097	651 2114	667 1967
Knott	504	504	520	521	546	556	503

				Non-Interstate VMT (x 1000)
County	1993	1994	1995	1996	1997	1998	1999
Knox	688	678	690	701	758	794	850
Larue	352	348	348	358	333	355	360
Laurel	1016	1019	1011	1029	1114	1169	1213
Lawrence	485	476	527	545	545	583	606
Lee	169	169	173	177	176	182	184
Leslie	357	359	352	375	409	419	429
Letcher	562	641	646	651	694	705	742
Lewis	382	418	379	394	408	394	413
Lincoln	515	537	550	593	601	612	668
Livingston	244	244	259	263	263	264	269
Logan	627	641	664	674	699	753	825
Lyon	246	310	268	265	301	316	323
Madison	1045	1084	1100	1154	1217	1277	1344
Magoffin	376	377	388	369	370	378	385
Marion	379	379	392	392	364	376	388
Marshall	1032	1024	1006	923	915	942	946
Martin	359	385	394	417	431	445	402
Mason McCracken	476 1316	488 1351	514 1405	568 1433	606 1456	621 1465	636 1579
McCreary	396	402	413	425	425	449	475
McLean	258	258	266	269	289	290	293
Meade	533	556	595	638	625	662	662
Menifee	121	120	120	125	127	134	128
Mercer	459	469	490	520	533	534	573
Metcalfe	329	329	332	339	342	362	321
Monroe	272	269	267	271	274	277	270
Montgomery	415	403	410	435	435	443	469
Morgan	288	288	292	310	317	319	331
Muhlenberg	954	953	1039	1049	1074	1057	1145
Nelson	896	898	960	996	1025	1119	1190
Nicholas	135	147	144	146	152	158	171
Ohio	818	818	865	896	930	890	1037
Oldham	557	561	583	590	631	649	871
Owen	175	178	184	191	210	218	221
Owsley	105	105	107	109	107	109	112
Pendleton	265	270	275	280	280	287	328
Perry	864	865	896	897	922	954	965
Pike	2111	2089	2127	2159	2215	2217	2267
Powell	412	410	432	439	456	478	479
Pulaski	1477	1454	1518	1540	1578	1605	1605
Robertson	36	36	38	38	38	39	40
Rockcastle	311	322	338	352	345	355	393
Rowan	411	427	442	462	506	527	545
Russell	455	433	442	490	502	514	523
Scott Shelby	509 505	520 503	594 497	637 505	689 527	735 549	752 667
Simpson	333	328	346	353	371	362	400
Spencer	192	188	198	201	199	218	285
Taylor	505	480	497	506	536	542	563
Todd	276	276	275	277	270	305	347
Trigg	298	346	290	328	353	371	368
Trimble	164	164	166	166	169	165	181
Union	445	448	424	450	447	441	446
Warren	1712	1739	1812	1903	2075	2079	2265
Washington	295	295	312	321	323	341	354
Wayne	397	398	381	395	433	447	454
Webster	523	498	506	512	517	533	590
Whitley	674	714	715	770	783	753	782
Wolfe	272	269	293	283	310	318	309
Woodford	672	704	697	710	734	771	808

				Interstate VMT (x 1000)
County	1993	1994	1995	1996	1997	1998	1999
Barren	249	215	234	226	227	249	270
Bath	180	200	198	194	222	228	240
Boone	1469	1554	1588	1682	1768	2016	2067
Boyd	153	172	156	156	168	187	195
Bullitt	906	910	945	1106	1190	1211	1201
Caldwell	28	35	32	27	29	34	39
Campbell	658	758	725	718	711	695	848
Carroll	285	286	296	303	326	337	385
Carter	399	438	435	415	441	441	477
Christian	310	469	336	366	310	413	411
Clark	358	376	407	476	462	506	488
Edmonson	71	65	69	66	68	75	83
Fayette	1395	1406	1485	1552	1594	1672	1854
Franklin	333	313	374	384	394	430	475
Gallatin	356	325	327	339	366	385	429
Grant	667	694	718	811	899	842	917
Hardin	728	820	833	908	955	1036	1051
Hart	548	540	624	573	634	604	706
Henry	288	294	327	321	344	376	363
Jefferson	6465	6750	7021	7404	7498	7848	7883
Kenton	1680	1698	1863	1940	1958	2020	2079
LaRue	105	104	132	116	132	132	138
Laurel	671	675	720	704	732	754	794
Livingston	85	88	87	89	94	107	115
Lyon	287	289	302	298	300	348	389
Madison	879	860	935	1003	1126	1116	1155
Marshall	231	238	244	255	247	307	319
McCracken	408	425	402	479	467	533	546
Montgomery	176	172	190	221	217	225	226
Oldham	452	472	197	387	524	600	596
Rockcastle	642	614	720	705	706	782	840
Rowan	221	236	216	213	252	247	281
Scott	720	869	823	886	1033	1184	1192
Shelby	654	676	803	772	818	796	840
Simpson	484	411	473	471	479	489	453
Trigg	137	167	148	140	149	164	185
Trimble	14	15	16	17	18	18	19
Warren	1114	1005	1041	1076	1097	1132	1119
Whitley	721	602	706	744	802	803	887
Woodford	175	159	180	204	171	224	252

8.4 Appendix D – Socioeconomic Census-Based Data

8.5 Appendix E – Traffic Volume (TVS) Estimating Procedure

TVS Estimating Procedure, Historical Estimates

No Actual Data	Functional Classification (FC) or Statewide Average (SW)
One Actual Count	Ratio of Data to Functional Classification or Statewide Average
Two or More Actual Counts	Piecewise estimate from current year to last actual data point.
	If slope from first actual data point to current year is negative, a ratio of FC or SW is used from most recent actual county to current.
	If Five or more actual data points are available an exponential fit will be used from the last actual data point to the end of the record.
	If fewer than five data points are available a ratio to FC or SW to the last actual data point will be used.
Bypass or New Road	If a bypass year or new road year is indicated, estimates will be made from the year indicated to the current year, using only data from that same period. Estimates made before the Bypass year will remain unchanged.

TVS Estimating Procedure, 20-year Estimates

No Actual Data	Same as Historical Estimates
One Through Four Data Points in the Last	Ratio of latest actual data to FC or SW twenty
Twenty Years	Year Estimate
Five or More Data points in the Last Twenty	A linear regression will be used , If the slope is
Years	negative, a ratio of latest actual to FC or SW

8.6 Appendix F – Weighted County Level Functional Class Growth Rates

Functional Class 01, Weighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Weighted Average ADT	Predicted Weighted Average ADT	2000 Weighted ADT Growth Rate (%)	Weighted Analysis Regression Constant	Weighted Analysis Regression Slope
5	BARREN	1	2000	32,350	31,013	29,940	1.02	-581,703	305.82
6	BATH	1	2000	18,667	18,663	18,310	2.91	-1,049,093	533.70
8	BOONE	1	2000	44,629	41,781	41,845	4.23	-3,497,897	1,769.87
10	BOYD	1	2000	18,300	17,719	17,932	2.89	-1,017,341	517.64
15	BULLITT	1	2000	56,375	58,114	59,884	2.96	-3,490,880	1,775.38
17	CALDWELL	1	2000	14,750	14,884	14,795	3.97	-1,158,736	586.77
21	CARROLL	1	2000	26,367	25,313	25,581	3.71	-1,872,601	949.09
22	CARTER	1	2000	14,325	14,753	14,956	2.61	-766,714	390.83
24	CHRISTIAN	1	2000	19,533	18,963	17,586	4.58	-1,592,153	804.87
25	CLARK	1	2000	30,050	29,597	30,137	3.25	-1,929,521	979.83
31	EDMONSON	1	2000	29,800	29,800	28,589	1.20	-659,896	344.24
37	FRANKLIN	1	2000	33,100	33,857	34,614	3.41	-2,328,591	1,181.60
39	GALLATIN	1	2000	26,067	27,072	25,950	3.52	-1,800,636	913.29
41	GRANT	1	2000	41,580	39,205	40,774	3.86	-3,104,910	1,572.84
47	HARDIN	1	2000	40,580	41,980	43,176	4.67	-3,988,367	2,015.77
50	HART	1	2000	32,525	32,667	33,385	3.20	-2,102,095	1,067.74
52	HENRY	1	2000	28,467	27,869	28,341	3.17	-1,769,629	898.98
56	JEFFERSON	1	2000	43,600	43,600	44,556	2.36	-2,056,050	1,050.30
59	KENTON	1	2000	44,700	44,700	49,075	4.22	-4,095,168	2,072.12
62	LARUE	1	2000	33,100	33,580	35,141	3.59	-2,488,705	1,261.92
63	LAUREL	1	2000	34,567	35,304	34,909	2.39	-1,636,355	835.63
70	LIVINGSTON	1	2000	25,000	24,644	25,104	4.24	-2,104,237	1,064.67
72	LYON	1	2000	19,225	18,039	18,013	4.00	-1,424,767	721.39
76	MADISON	1	2000	47,320	47,398	49,460	3.44	-3,355,894	1,702.68
79	MARSHALL	1	2000	26,567	26,368	26,532	4.40	-2,309,680	1,168.11
73	MCCRACKEN	1	2000	27,450	28,175	28,560	4.12	-2,327,278	1,177.92
87	MONTGOMERY	1	2000	19,767	20,054	20,781	3.64	-1,490,456	755.62
93	OLDHAM	1	2000	44,920	45,965	45,901	3.90	-3,538,122	1,792.01
102	ROCKCASTLE	1	2000	34,467	35,539	36,341	3.21	-2,295,497	1,165.92
103	ROWAN	1	2000	15,633	14,154	13,840	3.38	-921,796	467.82
105	SCOTT	1	2000	43,717	48,330	47,404	5.62	-5,280,799	2,664.10
106	SHELBY	1	2000	38,520	38,349	39,177	3.13	-2,411,351	1,225.26
107	SIMPSON	1	2000	35,833	36,173	35,196	0.44	-271,831	153.51
111	TRIGG	1	2000	14,500	14,668	14,682	3.94	-1,140,908	577.79
112	TRIMBLE	1	2000	26,800	26,800	27,235	3.79	-2,037,008	1,032.12
114	WARREN	1	2000	41,040	39,830	39,027	1.24	-929,382	484.20
118	WHITLEY	1	2000	30,767	30,461	30,514	2.58	-1,545,423	787.97
120	WOODFORD	1	2000	29,950	28,672	30,386	3.47	-2,080,718	1,055.55

Functional Class 02, Weighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Weighted Average ADT	Predicted Weighted Average ADT	2000 Weighted ADT Growth Rate (%)	Weighted Analysis Regression	Weighted Analysis Regression
								Constant	Slope
1 2	ADAIR ALLEN	2 2	2000 2000	14,041 5,298	5,526 4,952	5,972 5,061	4.31 3.31	-509,055 -330,265	257.51 167.66
3	ANDERSON	2	2000	12,752	12,644	12,329	3.13	-758,425	385.38
4	BALLARD	2	2000	5,723	5,623	5,508	2.13	-229,315	117.41
5	BARREN	2	2000	6,615	5,519	6,014	4.22	-501,094	253.55
7	BELL	2	2000	15,312	13,037	12,655	2.90	-721,667	367.16
9	BOURBON	2	2000	10,181	10,081	10,130	2.17	-429,411	219.77
10	BOYD	2	2000	11,651	11,131	11,068	0.75	-156,015	83.54
11	BOYLE	2	2000	11,131	10,545	10,545	2.87	-595,341	302.94
12	BRACKEN	2	2000	7,293	7,355	7,311	7.40	-1,074,893	541.10
13	BREATHITT	2	2000	10,353	7,692	7,701	1.95	-292,146	149.92
14	BRECKINRIDGE	2	2000	5,434	4,854	4,888	2.62	-250,951	127.92
16	BUTLER	2	2000	8,492	8,732	8,611	5.21	-888,388	448.50
17	CALDWELL	2	2000	8,850	8,484	9,515	4.58	-862,894	436.20
18	CALLOWAY	2	2000	8,265	8,539	8,748	2.60	-445,577	227.16
19	CAMPBELL	2	2000	9,623	10,121	10,248	6.47	-1,316,182	663.21
20	CARLISLE	2	2000	3,325	2,927	2,950	0.39	-20,245	11.60
22 23	CARTER CASEY	2 2	2000 2000	4,690 6,154	4,361 4,402	4,286 4,436	4.29 2.55	-363,716 -221,485	184.00 112.96
24	CHRISTIAN	2	2000	9,372	10,541	10,360	3.08	-626,823	318.59
25	CLARK	2	2000	9,928	12,253	11,951	3.12	-734,138	373.04
26	CLAY	2	2000	5,640	6,524	7,627	5.70	-862,059	434.84
27	CLINTON	2	2000	9,579	4,224	4,308	3.07	-259,995	132.15
30	DAVIESS	2	2000	9,283	7,751	8,647	3.46	-590,519	299.58
35	FLEMING	2	2000	3,115	2,991	2,990	2.31	-135,348	69.17
36	FLOYD	2	2000	14,605	15,527	15,579	2.72	-831,499	423.54
37	FRANKLIN	2	2000	18,450	18,053	18,586	3.49	-1,277,216	647.90
38	FULTON	2	2000	5,736	4,791	4,974	4.83	-475,277	240.13
40	GARRARD	2	2000	11,039	10,498	10,543	2.67	-553,425	281.98
42	GRAVES	2	2000	9,578	9,369	8,999	2.69	-474,473	241.74
43	GRAYSON	2	2000	8,893	8,772	9,862	4.26	-830,934	420.40
45	GREENUP	2	2000	8,513	7,680	7,612	1.80	-266,349	136.98
46	HANCOCK	2	2000	9,445	9,810	9,753	3.64	-700,537	355.15
47	HARDIN	2	2000	8,857	12,100	11,826	2.66	-618,016	314.92
48	HARLAN	2	2000	7,140	6,073	6,157	1.55	-184,854	95.51
51	HENDERSON	2	2000	8,836	9,534	10,775	5.16	-1,101,878	556.33
53	HICKMAN	2	2000	4,251	4,539	4,484	4.39	-389,369	196.93
54	HOPKINS	2	2000	14,612	13,613	15,120	6.02	-1,804,955	910.04
56	JEFFERSON	2	2000	23,420	23,916	24,600	1.85	-885,248	454.92
57	JESSAMINE	2	2000	35,267	32,796	33,504	4.21	-2,789,571	1,411.54
58	JOHNSON	2	2000	8,608	8,178	7,753	0.17	-18,645	13.20
60 61	KNOTT KNOX	2 2	2000 2000	6,402 17,563	6,228 16,238	6,330 16,316	1.23 3.42	-148,908 -1,098,922	77.62 557.62
63	LAUREL	2	2000	8,508	7,860	8,133	2.38	-378,452	193.29
64	LAWRENCE	2	2000	8,857	8,983	8,873	1.11	-188,651	98.76
66	LESLIE	2	2000	5,090	5,065	6,160	3.53	-429,366	217.76
67	LETCHER	2	2000	6,432	5,956	6,305	2.12	-260,713	133.51
68	LEWIS	2	2000	4,594	4,115	4,035	4.51	-359,578	181.81
69	LINCOLN	2	2000	11,713	10,177	9,651	4.00	-762,556	386.10
72	LYON	2	2000	8,130	8,200	9,178	5.02	-912,088	460.63
76	MADISON	2	2000	10,158	10,629	10,774	2.67	-564,847	287.81
77	MAGOFFIN	2	2000	8,109	6,523	6,120	1.40	-165,734	85.93
78	MARION	2	2000	6,770	6,252	6,198	3.03	-369,696	187.95
79	MARSHALL	2	2000	7,629	7,095	7,572	3.49	-520,798	264.19
80	MARTIN	2	2000	7,345	7,172	7,519	0.78	-110,025	58.77
81	MASON	2	2000	7,164	7,632	7,651	5.20	-787,387	397.52
73	MCCRACKEN	2	2000	12,481	11,005	11,102	1.81	-389,883	200.49
74	MCCREARY	2	2000	9,539	7,873	7,944	2.90	-452,447	230.20
82	MEADE	2	2000	11,510	8,761	9,009	2.78	-492,079	250.54
84	MERCER	2	2000	13,425	13,113	12,934	2.40	-608,576	310.76
85	METCALFE	2	2000	4,535	4,528	4,839	3.82	-364,412	184.63
88 89	MORGAN MUHLENBERG	2 2	2000 2000	5,750 8,990	5,750 9,091	4,963 10,973	1.49 5.02	-143,158 -1,090,271	74.06 550.62
90	NELSON	2	2000	9,483	9,383	9,876	4.75	-928,281	469.08
91	NICHOLAS	2	2000	4,600	4,325	4,367	2.09	-178,185	91.28
92	OHIO	2	2000	7,723	7,356	8,025	4.50	-714,435	361.23
96	PENDLETON	2	2000	7,630	7,630	7,581	5.53	-831,329	419.45
97	PERRY	2	2000	10,268	9,477	9,517	2.37	-442,264	225.89
98	PIKE	2	2000	12,245	10,474	10,426	2.43	-497,078	253.75
99	POWELL	2	2000	11,322	10,529	10,707	2.30	-482,442	246.57
100	PULASKI	2	2000	9,631	7,542	7,652	2.35	-351,647	179.65
101	ROBERTSON	2	2000	3,070	3,070	2,937	1.86	-106,517	54.73
102	ROCKCASTLE	2	2000	8,193	7,307	7,474	3.95	-583,308	295.39
104	RUSSELL	2	2000	6,107	4,231	4,480	2.95	-259,685	132.08
109	TAYLOR	2	2000	7,987	7,478	7,515	3.14	-465,034	236.27
110	TODD	2	2000	6,860	6,860	7,323	-4.28	634,233	-313.45
111	TRIGG	2	2000	4,439	3,752	3,869	2.22	-167,764	85.82
113	UNION	2	2000	6,801	4,918	4,959	1.18	-111,882	58.42
114	WARREN	2	2000	11,001	8,901	9,401	4.30	-798,704	404.05
115	WASHINGTON	2	2000	5,548	5,086	5,727	3.78	-427,115	216.42
117	WEBSTER	2	2000	13,800	14,137	14,505	10.70	-3,089,976	1,552.24
119 120	WOLFE WOODFORD	2 2	2000 2000	6,063 25,557	5,593 20,181	5,656 19,163	2.23 2.10	-246,346 -787,072	126.00 403.12

Functional Class 06, Weighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Weighted Average ADT	Predicted Weighted Average ADT	2000 Weighted ADT Growth Rate (%)	Weighted Analysis Regression Constant	Weighted Analysis Regression Slope
1	ADAIR	6	2000	7,872	2,324	2,388	0.78	-34,906	18.65
2	ALLEN	6	2000	4,875	4,594	4,569	5.04	-455,619	230.09
3 4	ANDERSON BALLARD	6 6	2000 2000	5,783 2,644	5,252 1,873	5,167 1,881	0.59 1.21	-55,739 -43,564	30.45 22.72
5	BARREN	6	2000	7,298	6,362	6,344	2.27	-281,628	143.99
6	BATH	6	2000	2,948	2,615	2,580	2.78	-141,083	71.83
9 11	BOURBON BOYLE	6 6	2000 2000	3,163 4,684	3,206 5,231	3,234 5,224	2.65 2.51	-167,989 -256,673	85.61 130.95
14	BRECKINRIDGE	6	2000	2,491	2,484	2,501	1.91	-92,875	47.69
15	BULLITT	6	2000	12,038	9,501	9,637	2.50	-472,503	241.07
17	CALDWELL	6	2000	3,477	3,421	3,499	3.00	-206,160	104.83
18 19	CALLOWAY CAMPBELL	6 6	2000 2000	6,238 5,697	4,926 6,596	5,025 6,617	2.59 2.14	-254,954 -276,020	129.99 141.32
20	CARLISLE	6	2000	1,214	1,296	1,296	2.00	-50,440	25.87
22	CARTER	6	2000	8,786	5,367	5,358	2.07	-216,480	110.92
24 25	CHRISTIAN CLARK	6 6	2000 2000	4,310 5,905	4,583 3,799	4,682 3,753	5.10 3.07	-473,093 -226,620	238.89 115.19
26	CLAY	6	2000	11,241	7,620	7,685	2.49	-375,301	191.49
27	CLINTON	6	2000	3,626	3,740	3,659	3.70	-267,383	135.52
28 29	CRITTENDEN CUMBERLAND	6 6	2000 2000	6,465 5,028	3,893 3,372	3,907 3,449	1.49 2.70	-112,492 -182,854	58.20 93.15
30	DAVIESS	6	2000	6,945	6,937	6,956	2.62	-357,685	182.32
31	EDMONSON	6	2000	5,526	4,201	4,102	1.54	-122,025	63.06
32 35	ELLIOTT FLEMING	6 6	2000 2000	3,150 4,349	2,354 3,850	2,376 3,932	1.74 2.83	-80,481 -218,343	41.43 111.14
36	FLOYD	6	2000	2,090	2,090	2,224	-9.76	436,163	-216.97
37	FRANKLIN	6	2000	3,902	3,558	3,639	2.60	-185,560	94.60
38	FULTON	6	2000	1,916	1,170	1,214	1.80	-42,588	21.90
39 40	GALLATIN GARRARD	6 6	2000 2000	2,367 3,726	2,304 2,238	2,323 2,269	3.77 2.18	-172,890 -96,629	87.61 49.45
42	GRAVES	6	2000	3,915	3,452	3,452	1.38	-91,684	47.57
43	GRAYSON	6	2000	10,248	4,228	4,278	2.71	-227,190	115.73
44 47	GREEN HARDIN	6 6	2000 2000	5,621 8,955	3,594 9,639	3,599 9,573	1.10 1.31	-75,670 -241,077	39.63 125.32
48	HARLAN	6	2000	10,681	6,296	6,254	1.80	-218,636	112.45
49	HARRISON	6	2000	5,073	4,031	3,955	2.18	-168,181	86.07
51 52	HENDERSON HENRY	6 6	2000 2000	5,402 5,031	5,275 3,350	5,382 3,384	2.63 2.61	-277,410 -173,468	141.40 88.43
53	HICKMAN	6	2000	555	582	575	1.70	-18,922	9.75
54	HOPKINS	6	2000	6,798	6,495	6,554	1.76	-223,634	115.09
55 56	JACKSON JEFFERSON	6 6	2000 2000	3,935 9,780	3,183 10,221	3,267 10,317	2.57 3.90	-164,954 -794,421	84.11 402.37
57	JESSAMINE	6	2000	8,140	8,792	8,883	3.94	-690,858	349.87
58	JOHNSON	6	2000	4,290	4,290	4,364	2.23	-190,666	97.52
62 63	LARUE LAUREL	6 6	2000 2000	5,350 7,407	5,317 5,659	5,327 5,519	2.55 0.77	-266,253 -79,224	135.79 42.37
65	LEE	6	2000	5,377	3,932	3,953	2.72	-211,449	107.70
66	LESLIE	6	2000	5,993	3,493	3,557	1.47	-100,907	52.23
69 70	LINCOLN LIVINGSTON	6 6	2000 2000	3,940 4,788	3,910 4,464	3,947 4,499	2.08 1.06	-160,248 -90,446	82.10 47.47
71	LOGAN	6	2000	4,975	4,809	4,819	2.56	-241,853	123.34
72	LYON	6	2000	5,972	5,457	5,616	1.19	-127,811	66.71
76	MADISON	6	2000	4,423	4,319	4,339	2.80	-238,244	121.29
77 79	MAGOFFIN MARSHALL	6 6	2000 2000	7,403 4,678	4,074 4,029	4,231 4,023	2.63 1.26	-218,208 -97,331	111.22 50.68
80	MARTIN	6	2000	5,603	5,580	5,998	0.60	-66,386	36.19
81	MASON	6	2000	5,440	5,381	5,374	1.18	-121,170	63.27
74 75	MCCREARY MCLEAN	6 6	2000 2000	1,218 6,249	1,186 5,907	1,077 5,942	-1.76 1.84	39,055 -212,478	-18.99 109.21
82	MEADE	6	2000	8,550	6,804	7,134	3.25	-456,637	231.89
83	MENIFEE	6	2000	3,939	3,207	3,252	3.07	-196,119	99.69
84 85	MERCER METCALFE	6 6	2000 2000	2,888 3,438	2,884 3,311	2,856 3,313	1.89 1.73	-104,940 -111,087	53.90 57.20
87	MONTGOMERY	6	2000	6,136	5,767	5,702	2.94	-329,080	167.39
88	MORGAN	6	2000	4,853	2,775	2,771	1.56	-83,540	43.16
89 90	MUHLENBERG NELSON	6 6	2000 2000	6,657 6,211	4,891 6,751	4,907 7,019	1.57 4.04	-149,399 -559,848	77.15 283.43
93	OLDHAM	6	2000	10,634	8,137	8,145	2.42	-385,389	196.77
94	OWEN	6	2000	4,517	2,737	2,676	3.14	-165,307	83.99
95 96	OWSLEY PENDLETON	6 6	2000 2000	1,143 6,771	1,286 6,090	1,270 6,116	1.23 2.57	-29,968 -308,702	15.62 157.41
99	POWELL	6	2000	3,005	2,879	2,753	4.06	-220,864	111.81
100	PULASKI	6	2000	10,868	9,771	9,877	2.90	-563,386	286.63
102 103	ROCKCASTLE ROWAN	6 6	2000 2000	7,600 8,693	6,495 8,267	6,524 8,489	1.77 3.40	-224,116 -569,381	115.32 288.94
104	RUSSELL	6	2000	10,600	10,600	10,529	1.93	-396,017	203.27
105	SCOTT	6	2000	6,628	6,546	6,612	3.89	-507,965	257.29
106	SHELBY	6	2000	6,749	6,409	6,532	3.55	-457,056	231.79
107 108	SIMPSON SPENCER	6 6	2000 2000	8,993 8,010	8,466 8,514	8,500 8,463	3.28 4.95	-549,140 -829,463	278.82 418.96
110	TODD	6	2000	2,975	2,358	2,559	3.56	-179,692	91.13
112	TRIMBLE	6	2000	5,286	4,309	4,277	3.31	-278,461	141.37
113 114	UNION WARREN	6 6	2000 2000	5,580 3,081	4,630 2,424	4,688 2,769	0.48 -0.07	-40,138 6,921	22.41 -2.08
115	WASHINGTON	6	2000	7,134	4,501	4,604	2.16	-194,708	99.66
116	WAYNE	6	2000	6,883	6,872	7,072	3.94	-550,155	278.61
117 118	WEBSTER WHITLEY	6 6	2000 2000	4,323 6,100	4,159 4,704	4,215 4,800	-0.07 3.54	10,365 -335,013	-3.08 169.91
119	WOLFE	6	2000	3,928	2,020	2,033	1.58	-62,386	32.21

Functional Class 07, Weighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Weighted Average ADT	Predicted Weighted Average ADT	2000 Weighted ADT Growth Rate (%)	Weighted Analysis Regression Constant	Weighted Analysis Regression Slope
1	ADAIR	7	2000	2,639	2,028	2,074	2.65	-107,784	54.93
2	ALLEN	7	2000	3,552	2,068	2,092	1.85	-75,295	38.69
3	ANDERSON	7	2000	1,745	1,526	1,514	1.44	-42,047	21.78
4	BALLARD	7	2000	1,534	1,446	1,461	0.75	-20,366	10.91
5	BARREN	7	2000	3,094	2,256	2,285	1.51	-66,790	34.54
6	BATH	7	2000	3,340	1,957	1,991	2.19	-85,251	43.62
7	BELL	7	2000	2,264	1,588	1,594	2.29	-71,563	36.58
8	BOONE	7	2000	4,547	3,105	3,111	2.71	-165,716	84.41
9	BOURBON	7	2000	1,794	1,571	1,573	2.28	-70,227	35.90
10	BOYD	7	2000	3,130	2,990	3,037	2.27	-134,748	68.89
11	BOYLE	7	2000	2,722	2,128	2,101	1.83	-74,846	38.47
12	BRACKEN	7	2000	1,633	1,225	1,226	1.19	-27,853	14.54
13	BREATHITT	7	2000	2,904	1,325	1,305	0.32	-7,006	4.16
14	BRECKINRIDGE	7	2000	1,724	1,219	1,229	1.86	-44,391	22.81
15	BULLITT	7	2000	7,494	5,441	5,425	1.71	-180,541	92.98
16	BUTLER	7	2000	3,255	2,149	2,159	1.78	-74,792	38.48
17	CALDWELL	7	2000	1,675	1,584	1,615	1.51	-47,089	24.35
18	CALLOWAY	7	2000	2,238	2,177	2,188	2.25	-96,182	49.19
19	CAMPBELL	7	2000	1,295	1,235	1,222	0.51	-11,215	6.22
20	CARLISLE	7	2000	1,531	1,560	1,558	1.36	-40,796	21.18
21	CARROLL	7	2000	4,356	3,343	3,369	2.16	-142,269	72.82
22	CARTER	7	2000	4,785	3,026	3,064	1.97	-117,940	60.50
23	CASEY	7	2000	2,334	1,449	1,423	0.94	-25,309	13.37
24	CHRISTIAN	7	2000	1,880	1,905	1,933	0.88	-32,090	17.01
25	CLARK	7	2000	3,307	2,967	2,980	1.71	-98,736	50.86
26	CLAY	7	2000	2,467	2,018	2,035	2.60	-103,827	52.93
27	CLINTON	7	2000	2,932	2,017	2,021	2.25	-88,973	45.50
28	CRITTENDEN	7	2000	1,221	798	809	0.33	-4,532	2.67
29	CUMBERLAND	7	2000	2,702	1,193	1,222	2.64	-63,319	32.27
30	DAVIESS	7	2000	4,018	3,993	3,963	1.68	-129,239	66.60
31	EDMONSON	7	2000	1,177	1,259	1,281	1.71	-42,439	21.86
32	ELLIOTT	7	2000	697	592	595	0.63	-6,940	3.77
33	ESTILL	7	2000	6,448	3,420	3,401	1.28	-83,844	43.62
35	FLEMING	7	2000	2,746	1,960	1,965	1.54	-58,434	30.20
36	FLOYD	7	2000	4,745	3,361	3,206	0.44	-25,249	14.23
37	FRANKLIN	7	2000	3,003	4,014	3,926	0.83	-60,865	32.40
38	FULTON	7	2000	3,326	1,912	1,901	0.59	-20,704	11.30
39	GALLATIN	7	2000	3,197	3,072	3,116	3.87	-237,868	120.49
40	GARRARD	7	2000	1,397	1,186	1,191	1.86	-43,052	22.12
41	GRANT	7	2000	3,632	3,062	3,111	2.34	-142,618	72.86
42	GRAVES	7	2000	1,900	1,842	1,838	1.73	-61,583	31.71
43	GRAYSON	7	2000	3,737	2,271	2,306	2.29	-103,098	52.70
44	GREEN	7	2000	1,939	2,287	2,315	2.59	-117,822	60.07
45	GREENUP	7	2000	1,956	1,785	1,791	1.25	-42,875	22.33
46	HANCOCK	7	2000	1,605	1,384	1,373	1.77	-47,318	24.35
47	HARDIN	7	2000	2,678	2,703	2,679	1.79	-93,482	48.08
48	HARLAN	7	2000	4,436	2,473	2,565	2.19	-109,731	56.15
49	HARRISON	7	2000	2,217	2,249	2,251	2.66	-117,498	59.87
50	HART	7	2000	3,444	2,183	2,245	2.12	-92,870	47.56
51	HENDERSON	7	2000	2,524	2,518	2,543	0.61	-28,299	15.42
52	HENRY	7	2000	2,963	2,032	2,101	2.20	-90,266	46.18
53	HICKMAN	7	2000	1,398	1,192	1,185	1.14	-25,754	13.47
54	HOPKINS	7	2000	4,095	3,038	3,040	0.93	-53,426	28.23
55	JACKSON	7	2000	1,208	825	845	2.57	-42,609	21.73
57	JESSAMINE	7	2000	3,396	2,731	2,722	2.62	-139,998	71.36
58	JOHNSON	7	2000	6,358	4,043	3,958	0.48	-34,246	19.10
59	KENTON	7	2000	2,991	2,932	2,962	2.39	-138,752	70.86
60	KNOTT	7	2000	3,638	2,534	2,519	0.77	-36,381	19.45
61	KNOX	7	2000	2,962	2,112	2,068	2.82	-114,518	58.29
62	LARUE	7	2000	2,620	2,410	2,301	0.22	-7,649	4.97
63	LAUREL	7	2000	5,260	3,770	3,816	2.69	-201,440	102.63
64	LAWRENCE	7	2000	3,374	1,226	1,244	2.25	-54,786	28.01
65	LEE	7	2000	2,778	1,422	1,416	0.75	-19,853	10.63
66	LESLIE	7	2000	2,510	1,964	1,966	0.96	-35,679	18.82
67	LETCHER	7	2000	3,254	2,171	2,170	1.60	-67,329	34.75
68	LEWIS	7	2000	2,468	1,366	1,365	1.35	-35,441	18.40
69	LINCOLN	7	2000	2,128	1,541	1,527	2.56	-76,523	39.03
70	LIVINGSTON	7	2000	1,478	1,398	1,398	1.51	-40,689	21.04
71	LOGAN	7	2000	1,153	1,161	1,161	1.86	-42,090	21.63
72	LYON	7	2000	1,801	1,528	1,521	-0.53	17,529	-8.00
76	MADISON	7	2000	4,257	4,586	4,598	2.16	-194,458	99.53
77	MAGOFFIN	7	2000	2,101	1,509	1,496	1.33	-38,216	19.86
78	MARION	7	2000	1,815	1,560	1,588	2.16	-67,110	34.35
79	MARSHALL	7	2000	3,639	3,305	3,299	0.20	-9,963	6.63
80	MARTIN	7	2000	2,084	1,573	1,707	-1.50	52,873	-25.58
81	MASON	7	2000	1,404	1,202	1,195	1.32	-30,397	15.80
73	MCCRACKEN	7	2000	3,029	2,724	2,760	1.88	-101,053	51.91
74	MCCREARY	7	2000	2,369	1,529	1,555	2.38	-72,599	37.08
75	MCLEAN	7	2000	2,737	2,237	2,251	1.96	-86,104	44.18
82	MEADE	7	2000	3,396	3,220	3,306	2.61	-169,291	86.30
83	MENIFEE	7	2000	1,288	754	752	2.16	-31,709	16.23
84	MERCER	7	2000	2,120	1,786	1,795	1.82	-63,696	32.75
85	METCALFE	7	2000	3,456	2,276	2,278	1.53	-67,280	34.78
86	MONROE	7	2000	3,441	1,921	1,899	0.78	-27,686	14.79
87	MONTGOMERY	7	2000	3,356	2,812	2,866	3.15	-177,496	90.18
88	MORGAN	7	2000	1,811	1,921	1,966	2.77	-107,059	54.51
89	MUHLENBERG	7	2000	6,128	4,064	4,083	1.63	-128,888	66.49
90	NELSON	7	2000	3,541	2,803	2,850	3.04	-170,299	86.57
91	NICHOLAS	7	2000	4,539	2,360	2,363	3.08	-143,420	72.89
92	OHIO	7	2000	5,682	3,261	3,228	1.20	-73,929	38.58
93	OLDHAM	7	2000	3,560	2,881	2,868	2.77	-155,927	79.40
94	OWEN	7	2000	1,394	961	976	2.31	-44,112	22.54
95	OWSLEY	7	2000	2,904	1,450	1,446	2.33	-65,862	33.65
96	PENDLETON	7	2000	1,727	1,071	1,076	1.71	-35,776	18.43
97	PERRY	7	2000	3,171	2,462	2,456	1.63	-77,849	40.15
98	PIKE	7	2000	3,590	2,837	2,824	-0.05	5,683	-1.43
99	POWELL	7	2000	5,482	3,013	3,024	2.05	-121,009	62.02
100	PULASKI	7	2000	2,460	2,333	2,302	1.82	-81,462	41.88
101	ROBERTSON	7	2000	1,122	743	750	1.61	-23,408	12.08
102	ROCKCASTLE	7	2000	1,754	1,788	1,821	1.93	-68,597	35.21
103	ROWAN	7	2000	3,163	2,828	2,868	2.40	-134,599	68.73
104	RUSSELL	7	2000	3,417	2,457	2,469	1.85	-88,655	45.56
105	SCOTT	7	2000	3,801	3,299	3,378	3.23	-215,106	109.24
106	SHELBY	7	2000	2,422	2,432	2,469	3.52	-171,202	86.84
107	SIMPSON	7	2000	2,129	2,488	2,443	3.52	-169,446	85.94
108	SPENCER	7	2000	3,077	2,567	2,539	4.12	-206,506	104.52
109	TAYLOR	7	2000	2,750	2,717	2,742	2.15	-115,023	58.88
110	TODD	7	2000	2,402	1,732	1,706	1.05	-34,075	17.89
111	TRIGG	7	2000	2,007	1,176	1,181	1.35	-30,759	15.97
112	TRIMBLE	7	2000	3,541	3,079	3,066	3.95	-238,879	120.97
113	UNION	7	2000	2,138	1,593	1,604	0.86	-26,069	13.84
114	WARREN	7	2000	4,562	4,140	4,182	2.25	-183,632	93.91
115	WASHINGTON	7	2000	1,360	949	959	1.60	-29,650	15.30
116	WAYNE	7	2000	1,414	1,064	1,065	2.42	-50,559	25.81
117	WEBSTER	7	2000	2,822	2,186	2,164	0.94	-38,658	20.41
118	WHITLEY	7	2000	3,619	2,990	3,043	2.23	-132,647	67.84
119	WOLFE	7	2000	1,292	1,040	1,042	2.03	-41,288	21.17
120	WOODFORD	7	2000	3,533	3,196	3,229	3.00	-190,394	96.81

Functional Class 08, Weighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Weighted Average	Predicted Weighted	2000 Weighted ADT	Weighted Analysis	Weighted Analysis
					ADT	Average ADT	Growth Rate (%)	Regression Constant	Regression Slope
1 2	ADAIR ALLEN	8 8	2000 2000	770 969	426 673	431 671	1.06 1.32	-8,714 -17,073	4.57 8.87
3	ANDERSON	8	2000	784	676	673	1.27	-16,477	8.57
4 5	BALLARD BARREN	8 8	2000 2000	364 584	322 594	318 598	0.65 1.93	-3,826 -22,463	2.07 11.53
6	BATH	8	2000	543	505	508	2.10	-20,862	10.68
7 8	BELL BOONE	8 8	2000 2000	1,050 1,248	842 1,115	860 1,120	3.11 2.19	-52,630 -47,867	26.74 24.49
9	BOURBON	8	2000	952	887	881	1.97	-33,834	17.36
10	BOYD	8	2000	568	537	540	-2.02	22,381	-10.92
11 12	BOYLE BRACKEN	8 8	2000 2000	751 478	611 451	602 457	1.45 3.43	-16,847 -30,873	8.72 15.67
13	BREATHITT	8	2000	526	487	491	1.71	-16,318	8.40
14 15	BRECKINRIDGE BULLITT	8 8	2000 2000	549 1,382	417 1,262	417 1,272	1.31 3.02	-10,541 -75,568	5.48 38.42
16	BUTLER	8	2000	468	425	423	1.34	-10,922	5.67
17 18	CALDWELL CALLOWAY	8 8	2000 2000	358 696	363 643	363 646	0.85 1.89	-5,798 -23,767	3.08 12.21
19	CAMPBELL	8	2000	764	543	561	3.01	-33,263	16.91
20 21	CARLISLE CARROLL	8 8	2000 2000	271 479	266 471	262 480	-0.26 3.57	1,646 -33,784	-0.69 17.13
22	CARTER	8	2000	875	777	761	0.58	-7,995	4.38
23 24	CASEY CHRISTIAN	8 8	2000 2000	593 588	564 572	561 569	1.38 1.43	-14,914 -15,684	7.74 8.13
25	CLARK	8	2000	872	832	843	1.93	-31,667	16.25
26 27	CLAY CLINTON	8 8	2000 2000	1,121 797	886 614	877 618	2.33 1.39	-40,063 -16,530	20.47 8.57
28	CRITTENDEN	8	2000	383	271	270	0.11	-333	0.30
29 30	CUMBERLAND DAVIESS	8 8	2000 2000	397 744	357 684	358 689	1.95 2.09	-13,611 -28,122	6.98 14.41
31	EDMONSON	8	2000	614	602	568	2.34	-25,973	13.27
32	ELLIOTT	8	2000	390	372	378	2.52	-18,672	9.53
33 35	ESTILL FLEMING	8 8	2000 2000	934 667	821 547	823 555	2.26 2.50	-36,307 -27,250	18.57 13.90
36	FLOYD	8	2000	2,857	1,714	1,723	1.38	-45,852	23.79
37 38	FRANKLIN FULTON	8 8	2000 2000	889 1,050	799 396	816 401	1.40 0.34	-21,990 -2,361	11.40 1.38
39	GALLATIN	8	2000	708	721	713	4.25	-59,880	30.30
40 41	GARRARD GRANT	8 8	2000 2000	521 1,237	521 959	534 980	3.34 2.94	-35,142 -56,639	17.84 28.81
42	GRAVES	8	2000	885	844	845	1.12	-18,126	9.49
43 44	GRAYSON GREEN	8 8	2000 2000	1,138 376	947 346	956 352	2.52 1.22	-47,149 -8,199	24.05 4.28
45	GREENUP	8	2000	665	495	495	0.70	-6,441	3.47
46 47	HANCOCK HARDIN	8 8	2000 2000	684 1,598	588 1,366	584 1,357	2.46 2.97	-28,196 -79,401	14.39 40.38
48	HARLAN	8	2000	2,489	1,424	1,440	1.16	-31,941	16.69
49 50	HARRISON HART	8 8	2000 2000	687 492	662 486	662 493	2.70 2.35	-35,141 -22,679	17.90 11.59
51	HENDERSON	8	2000	576	630	640	1.64	-20,326	10.48
52	HENRY	8	2000	693	599	607	2.07	-24,451	12.53
53 54	HICKMAN HOPKINS	8 8	2000 2000	317 1,195	243 964	243 961	-0.04 1.06	435 -19,440	-0.10 10.20
55	JACKSON	8	2000	537	455	455	2.97	-26,546	13.50
56 57	JEFFERSON JESSAMINE	8 8	2000 2000	1,385 1,470	1,503 1,242	1,477 1,228	4.39 1.33	-128,071 -31,507	64.77 16.37
58	JOHNSON	8	2000	1,428	933	939	2.01	-36,740	18.84
59 60	KENTON KNOTT	8 8	2000 2000	605 1,152	591 915	579 911	2.38 0.58	-26,994 -9,678	13.79 5.29
61	KNOX	8	2000	1,481	1,157	1,141	3.57	-80,333	40.74
62 63	LARUE LAUREL	8 8	2000 2000	1,092 927	647 812	658 824	1.15 2.92	-14,530 -47,248	7.59 24.04
64	LAWRENCE	8	2000	978	712	722	3.42	-48,633	24.68
65 66	LEE LESLIE	8 8	2000 2000	759 959	487 888	486 901	2.27 2.64	-21,577 -46,700	11.03 23.80
67	LETCHER	8	2000	1,468	1,218	1,239	2.52	-61,152	31.20
68 69	LEWIS LINCOLN	8 8	2000 2000	711 1,496	415 858	420 866	0.44 2.68	-3,292 -45,632	1.86 23.25
70	LIVINGSTON	8	2000	520	412	413	1.51	-12,090	6.25
71	LOGAN	8	2000	583	576	578	1.53	-17,143	8.86
72 76	LYON MADISON	8 8	2000 2000	547 1,145	543 1,116	529 1,143	1.00 3.18	-10,021 -71,639	5.27 36.39
77	MAGOFFIN	8	2000	541	517	504	1.50	-14,567	7.54
78 79	MARION MARSHALL	8 8	2000 2000	991 896	807 871	790 868	0.97 0.83	-14,594 -13,500	7.69 7.18
80	MARTIN	8	2000	1,453	1,203	1,241	0.52	-11,623	6.43
81 73	MASON MCCRACKEN	8 8	2000 2000	530 970	474 912	493 922	1.30 1.74	-12,321 -31,259	6.41 16.09
74	MCCREARY	8	2000	1,508	905	894	1.65	-28,530	14.71
75 82	MCLEAN MEADE	8 8	2000 2000	554 1,045	521 915	525 914	1.82 2.15	-18,550 -38,495	9.54 19.70
83	MENIFEE	8	2000	449	411	398	0.07	-130	0.26
84 85	MERCER METCALFE	8 8	2000 2000	463 622	476 554	479 560	1.73 -0.84	-16,084 10,003	8.28 -4.72
86	MONROE	8	2000	799	576	576	1.01	-11,028	5.80
87 88	MONTGOMERY MORGAN	8 8	2000 2000	925 376	810 387	797 385	1.98 1.95	-30,778 -14,636	15.79 7.51
89	MUHLENBERG	8	2000	1,872	1,329	1,326	1.31	-33,404	17.36
90 91	NELSON NICHOLAS	8 8	2000 2000	764 465	695 406	702 409	2.97 1.28	-40,997 -10,062	20.85 5.24
92	OHIO	8	2000	781	637	645	1.29	-16,019	8.33
93 94	OLDHAM OWEN	8 8	2000 2000	1,695 448	1,515 430	1,493 438	3.97 2.20	-117,178 -18,835	59.34 9.64
95	OWSLEY	8	2000	344	315	305	0.37	-1,953	1.13
96	PENDLETON	8	2000	946	830	832	2.99	-48,924	24.88
97 98	PERRY PIKE	8 8	2000 2000	1,134 1,944	1,049 1,671	1,048 1,661	1.78 0.97	-36,191 -30,495	18.62 16.08
99	POWELL	8	2000	875	635	631	2.32	-28,715	14.67
100 101	PULASKI ROBERTSON	8 8	2000 2000	805 218	689 227	674 228	1.64 1.22	-21,496 -5,347	11.09 2.79
102	ROCKCASTLE	8	2000	987	655	660	3.10	-40,217	20.44
103 104	ROWAN RUSSELL	8 8	2000 2000	853 1,477	853 1,043	878 1,058	3.07 2.40	-52,970 -49,725	26.92 25.39
105	SCOTT	8	2000	1,223	1,268	1,298	4.01	-102,657	51.98
106 107	SHELBY SIMPSON	8 8	2000 2000	742 630	743 565	757 576	2.73 2.13	-40,621 -23,941	20.69 12.26
108	SPENCER	8	2000	678	704	684	3.75	-50,628	25.66
109 110	TAYLOR TODD	8 8	2000 2000	736 592	665 566	676 567	2.13 1.89	-28,106 -20,821	14.39 10.69
111	TRIGG	8	2000	604	556	553	1.09	-11,511	6.03
112	TRIMBLE	8	2000	554	435	415	-3.07	25,963	-12.77
113 114	UNION WARREN	8 8	2000 2000	663 1,298	705 1,116	698 1,111	0.70 2.73	-9,019 -59,561	4.86 30.34
115	WASHINGTON	8	2000	940	496	505	1.98	-19,503	10.00
116 117	WAYNE WEBSTER	8 8	2000 2000	560 683	549 624	555 628	1.53 0.79	-16,423 -9,257	8.49 4.94
118	WHITLEY	8	2000	1,209	1,078	1,073	2.16	-45,343	23.21
119 120	WOLFE WOODFORD	8 8	2000 2000	468 836	345 909	343 910	0.05 1.21	11 -21,049	0.17 10.98

Functional Class 09, Weighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Weighted Average ADT	Predicted Weighted Average ADT	2000 Weighted ADT Growth Rate (%)	Weighted Analysis Regression Constant	Weighted Analysis Regression Slope
1	ADAIR	9	2000	406	289	287	5.55	-31,611	15.95
2	ALLEN	9	2000	356	255	249	0.15	-511	0.38
3	ANDERSON	9	2000	290	282	277	5.09	-27,955	14.12
4	BALLARD	9	2000	306	288	290	0.54	-2,851	1.57
5	BARREN	9	2000	631	554	555	0.89	-9,293	4.92
6	BATH	9	2000	399	474	480	3.53	-33,433	16.96
7	BELL	9	2000	1,090	911	910	3.30	-59,151	30.03
8	BOONE	9	2000	854	541	543	1.19	-12,432	6.49
9	BOURBON	9	2000	367	337	334	2.66	-17,421	8.88
10	BOYD	9	2000	937	754	751	1.20	-17,288	9.02
11	BOYLE	9	2000	393	274	273	2.15	-11,459	5.87
12	BRACKEN	9	2000	199	210	206	3.97	-16,193	8.20
13	BREATHITT	9	2000	538	280	271	1.19	-6,171	3.22
14	BRECKINRIDGE	9	2000	1,719	489	494	1.22	-11,568	6.03
15	BULLITT	9	2000	1,511	1,032	1,014	0.67	-12,590	6.80
16	BUTLER	9	2000	395	174	166	1.39	-4,449	2.31
17	CALDWELL	9	2000	127	133	134	0.03	64	0.04
18	CALLOWAY	9	2000	520	446	453	3.09	-27,541	14.00
19	CAMPBELL	9	2000	1,600	987	990	2.34	-45,329	23.16
20	CARLISLE	9	2000	192	170	169	0.88	-2,808	1.49
21	CARROLL	9	2000	478	309	305	2.65	-15,871	8.09
22	CARTER	9	2000	465	306	303	3.43	-20,464	10.38
23	CASEY	9	2000	553	378	381	2.56	-19,105	9.74
24	CHRISTIAN	9	2000	363	232	236	0.84	-3,717	1.98
25	CLARK	9	2000	1,390	1,011	1,010	1.01	-19,398	10.20
26	CLAY	9	2000	884	471	460	2.76	-24,906	12.68
27	CLINTON	9	2000	185	177	177	1.47	-5,040	2.61
28	CRITTENDEN	9	2000	139	134	134	2.14	-5,592	2.86
29	CUMBERLAND	9	2000	214	133	128	-0.91	2,463	-1.17
30	DAVIESS	9	2000	605	533	530	2.44	-25,329	12.93
31	EDMONSON	9	2000	287	324	326	1.58	-9,940	5.13
32	ELLIOTT	9	2000	175	173	171	-1.58	5,554	-2.69
33	ESTILL	9	2000	680	345	346	1.25	-8,279	4.31
34	FAYETTE	9	2000	1,498	1,416	1,417	2.21	-61,085	31.25
35	FLEMING	9	2000	404	279	281	3.32	-18,388	9.33
36	FLOYD	9	2000	1,143	841	832	1.46	-23,452	12.14
37	FRANKLIN	9	2000	587	351	355	0.84	-5,636	3.00
38	FULTON	9	2000	457	211	208	1.07	-4,254	2.23
39	GALLATIN	9	2000	430	374	375	4.15	-30,744	15.56
40	GARRARD	9	2000	449	494	484	4.76	-45,585	23.03
41	GRANT	9	2000	671	534	523	5.63	-58,290	29.41
42	GRAVES	9	2000	426	406	410	2.48	-19,927	10.17
43	GRAYSON	9	2000	315	365	360	3.73	-26,489	13.42
44	GREEN	9	2000	648	373	374	0.35	-2,234	1.30
45	GREENUP	9	2000	952	681	689	-0.98	14,226	-6.77
46	HANCOCK	9	2000	1,074	690	685	1.00	-13,027	6.86
47	HARDIN	9	2000	534	522	515	1.31	-12,936	6.73
48	HARLAN	9	2000	1,265	976	984	3.38	-65,494	33.24
49	HARRISON	9	2000	432	436	440	6.16	-53,785	27.11
50	HART	9	2000	389	300	302	1.86	-10,908	5.60
51	HENDERSON	9	2000	393	329	336	0.44	-2,601	1.47
52	HENRY	9	2000	334	316	317	1.96	-12,113	6.21
53	HICKMAN	9	2000	331	249	251	2.16	-10,584	5.42
54	HOPKINS	9	2000	761	689	676	3.95	-52,716	26.70
55	JACKSON	9	2000	408	349	344	3.94	-26,788	13.57
56	JEFFERSON	9	2000	1,820	1,820	1,820	0.55	-18,180	10.00
57	JESSAMINE	9	2000	1,809	2,044	2,091	4.90	-202,776	102.43
58	JOHNSON	9	2000	460	474	465	2.30	-20,941	10.70
59	KENTON	9	2000	476	476	473	2.92	-27,117	13.79
60	KNOTT	9	2000	616	613	606	6.47	-77,777	39.19
61	KNOX	9	2000	2,542	1,535	1,540	2.44	-73,682	37.61
62	LARUE	9	2000	218	200	198	0.59	-2,127	1.16
63	LAUREL	9	2000	666	625	622	3.92	-48,060	24.34
64	LAWRENCE	9	2000	764	352	349	4.13	-28,481	14.42
65	LEE	9	2000	208	203	201	2.48	-9,762	4.98
66	LESLIE	9	2000	481	451	455	4.62	-41,572	21.01
67	LETCHER	9	2000	822	481	484	0.19	-1,329	0.91
68	LEWIS	9	2000	255	246	261	1.18	-5,916	3.09
69	LINCOLN	9	2000	841	494	495	-0.06	1,123	-0.31
70	LIVINGSTON	9	2000	229	195	197	2.26	-8,670	4.43
71	LOGAN	9	2000	310	220	221	2.14	-9,234	4.73
72	LYON	9	2000	227	218	213	2.79	-11,668	5.94
76	MADISON	9	2000	957	935	938	5.63	-104,711	52.82
77	MAGOFFIN	9	2000	496	292	291	1.42	-7,969	4.13
78	MARION	9	2000	214	229	222	2.53	-10,985	5.60
79	MARSHALL	9	2000	970	871	868	1.05	-17,381	9.12
80	MARTIN	9	2000	644	910	910	4.25	-76,412	38.66
81	MASON	9	2000	249	208	211	-1.11	4,902	-2.35
73	MCCRACKEN	9	2000	599	627	621	3.18	-38,852	19.74
74	MCCREARY	9	2000	518	403	401	1.93	-15,074	7.74
75	MCLEAN	9	2000	298	227	227	1.61	-7,056	3.64
82	MEADE	9	2000	393	271	274	1.61	-8,564	4.42
83	MENIFEE	9	2000	237	244	241	5.59	-26,716	13.48
84	MERCER	9	2000	516	459	445	0.10	-416	0.43
85	METCALFE	9	2000	297	315	306	-0.65	4,306	-2.00
86	MONROE	9	2000	485	333	323	4.85	-31,022	15.67
87	MONTGOMERY	9	2000	686	313	315	2.69	-16,683	8.50
88	MORGAN	9	2000	649	374	375	2.62	-19,273	9.82
89	MUHLENBERG	9	2000	984	756	766	1.54	-22,754	11.76
90	NELSON	9	2000	434	674	675	2.45	-32,421	16.55
91	NICHOLAS	9	2000	232	204	205	2.18	-8,723	4.46
92	OHIO	9	2000	927	407	408	1.20	-9,390	4.90
93	OLDHAM	9	2000	1,950	1,306	1,306	2.37	-60,675	30.99
94	OWEN	9	2000	423	250	261	2.45	-12,501	6.38
95	OWSLEY	9	2000	231	139	139	2.45	-6,641	3.39
96	PENDLETON	9	2000	418	396	396	3.55	-27,712	14.05
97	PERRY	9	2000	598	546	548	2.93	-31,613	16.08
98	PIKE	9	2000	1,098	808	809	0.70	-10,502	5.66
99	POWELL	9	2000	730	362	365	1.94	-13,809	7.09
100	PULASKI	9	2000	506	363	359	3.70	-26,215	13.29
101	ROBERTSON	9	2000	118	113	113	1.57	-3,438	1.78
102	ROCKCASTLE	9	2000	434	288	287	2.72	-15,341	7.81
103	ROWAN	9	2000	262	255	256	2.95	-14,859	7.56
104	RUSSELL	9	2000	366	322	323	4.36	-27,819	14.07
105	SCOTT	9	2000	356	287	293	2.71	-15,569	7.93
106	SHELBY	9	2000	568	517	522	4.33	-44,668	22.60
107	SIMPSON	9	2000	260	253	247	6.42	-31,429	15.84
108	SPENCER	9	2000	422	303	302	3.06	-18,200	9.25
109	TAYLOR	9	2000	350	329	328	2.87	-18,498	9.41
110	TODD	9	2000	411	387	385	1.54	-11,519	5.95
111	TRIGG	9	2000	312	307	306	2.76	-16,582	8.44
112	TRIMBLE	9	2000	287	221	220	-0.15	865	-0.32
113	UNION	9	2000	305	283	283	-1.98	11,485	-5.60
114	WARREN	9	2000	1,069	895	908	3.01	-53,746	27.33
115	WASHINGTON	9	2000	225	220	220	2.92	-12,645	6.43
116	WAYNE	9	2000	735	679	684	1.86	-24,800	12.74
117	WEBSTER	9	2000	486	454	458	1.78	-15,834	8.15
118	WHITLEY	9	2000	543	450	428	-0.87	7,842	-3.71
119	WOLFE	9	2000	488	315	304	6.17	-37,183	18.74
120	WOODFORD	9	2000	1,202	1,076	1,097	4.94	-107,416	54.26

Functional Class 11, Weighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Weighted Average ADT	Predicted Weighted Average ADT	2000 Weighted ADT Growth Rate (%)	Weighted Analysis Regression Constant	Weighted Analysis Regression Slope
8	BOONE	11	2000	101,025	91,184	92,811	4.77	-8,761,835	4,427.32
15	BULLITT	11	2000	79,700	80,608	79,601	3.08	-4,829,833	2,454.72
19	CAMPBELL	11	2000	90,767	86,507	85,496	1.81	-3,003,789	1,544.64
24	CHRISTIAN	11	2000	25,300	25,300	24,657	4.52	-2,203,914	1,114.29
25	CLARK	11	2000	41,000	41,000	42,278	3.35	-2,788,025	1,415.15
34	FAYETTE	11	2000	54,338	50,595	51,108	2.96	-2,972,068	1,511.59
47	HARDIN	11	2000	47,050	47,947	49,964	4.19	-4,136,218	2,093.09
56	JEFFERSON	11	2000	94,464	84,301	85,913	2.70	-4,545,856	2,315.88
59	KENTON	11	2000	125,864	122,309	124,337	3.16	-7,725,200	3,924.77
63	LAUREL	11	2000	35,550	35,996	37,562	3.25	-2,404,105	1,220.83
76	MADISON	11	2000	44,000	44,143	44,763	2.77	-2,431,458	1,238.11
73	MCCRACKEN	11	2000	34,400	34,896	35,050	3.96	-2,741,632	1,388.34
105	SCOTT	11	2000	42,100	42,100	43,765	4.25	-3,678,659	1,861.21
114	WARREN	11	2000	44,500	44,500	42,620	2.24	-1,864,047	953.33
118	WHITLEY	11	2000	34,600	34,600	38,131	3.52	-2,646,718	1,342.42

Functional Class 12, Weighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Weighted Average ADT	Predicted Weighted Average ADT	2000 Weighted ADT Growth Rate (%)	Weighted Analysis Regression Constant	Weighted Analysis Regression Slope
5	BARREN	12	2000	6,920	6,920	7,173	3.46	-488,706	247.94
19	CAMPBELL	12	2000	48,800	48,800	40,049	1.23	-941,769	490.91
24	CHRISTIAN	12	2000	14,967	14,475	15,262	3.03	-910,037	462.65
30	DAVIESS	12	2000	18,814	18,985	19,281	3.23	-1,225,230	622.26
34	FAYETTE	12	2000	63,689	63,439	62,987	3.00	-3,714,172	1,888.58
42	GRAVES	12	2000	15,033	14,854	14,762	3.75	-1,092,305	553.53
47	HARDIN	12	2000	20,850	18,953	19,212	2.29	-861,889	440.55
51	HENDERSON	12	2000	25,300	29,168	28,934	1.98	-1,119,609	574.27
54	HOPKINS	12	2000	19,633	17,702	19,629	1.72	-656,786	338.21
56	JEFFERSON	12	2000	33,700	34,371	34,332	3.79	-2,570,527	1,302.43
90	NELSON	12	2000	9,590	9,590	10,237	4.89	-991,096	500.67
100	PULASKI	12	2000	10,400	10,400	9,824	-0.28	64,854	-27.52
114	WARREN	12	2000	11,740	11,727	11,639	4.45	-1,023,873	517.76

Functional Class 14, Weighted County Level Growth Rates

County	County Name	Functional	Year	Average	Weighted	Predicted	2000	Weighted	Weighted
Number		Class		ADT	Average	Weighted	Weighted	Analysis	Analysis
					ADT	Average ADT	ADT Growth Rate (%)	Regression Constant	Regression Slope
3	ANDERSON	14	2000	16,200	16,288	16,643	3.28	-1,075,374	546.01
5	BARREN	14	2000	15,311	14,842	14,982	2.64	-775,480	395.23
7	BELL	14	2000	26,225	25,829	25,347	2.30	-1,141,584	583.47
9	BOURBON	14	2000	8,743	11,190	11,017	2.60	-561,676	286.35
10	BOYD	14	2000	21,286	22,140	22,133	1.04	-436,878	229.51
11	BOYLE	14	2000	14,009	15,409	15,383	3.35	-1,014,994	515.19
15	BULLITT	14	2000	19,200	19,263	18,643	3.42	-1,257,863	638.25
18	CALLOWAY	14	2000	17,117	16,700	16,929	2.18	-722,641	369.78
19	CAMPBELL	14	2000	15,586	18,268	18,257	1.92	-684,121	351.19
24	CHRISTIAN	14	2000	17,047	15,593	15,179	2.50	-743,882	379.53
25	CLARK	14	2000	19,600	19,161	18,978	2.94	-1,095,349	557.16
30	DAVIESS	14	2000	14,713	17,404	17,478	1.06	-351,399	184.44
34	FAYETTE	14	2000	29,879	30,586	30,699	1.36	-806,341	418.52
37	FRANKLIN	14	2000	23,122	21,885	22,264	2.78	-1,215,079	618.67
42	GRAVES	14	2000	9,434	9,502	9,534	0.33	-53,072	31.30
45	GREENUP	14	2000	20,988	19,536	19,705	1.63	-621,287	320.50
47	HARDIN	14	2000	27,838	26,739	27,183	1.33	-695,839	361.51
49	HARRISON	14	2000	10,355	11,166	11,433	1.05	-229,184	120.31
51	HENDERSON	14	2000	26,767	21,672	21,717	1.28	-535,710	278.71
54	HOPKINS	14	2000	13,924	12,678	13,179	1.57	-399,445	206.31
56	JEFFERSON	14	2000	23,989	26,009	26,103	1.11	-553,342	289.72
57	JESSAMINE	14	2000	22,200	23,297	22,927	3.18	-1,435,306	729.12
59	KENTON	14	2000	12,060	16,083	15,586	-0.19	73,969	-29.19
61	KNOX	14	2000	26,700	25,692	26,189	4.55	-2,358,619	1,192.40
63	LAUREL	14	2000	20,000	17,812	17,761	2.89	-1,010,210	513.99
71	LOGAN	14	2000	8,785	8,522	8,502	2.07	-342,806	175.65
76	MADISON	14	2000	17,310	16,195	16,578	1.78	-574,858	295.72
78	MARION	14	2000	12,600	13,260	13,169	2.40	-618,715	315.94
81	MASON	14	2000	14,229	12,299	12,458	0.88	-207,246	109.85
73	MCCRACKEN	14	2000	16,207	16,705	17,021	0.29	-81,620	49.32
82	MEADE	14	2000	14,390	13,483	13,693	2.42	-649,709	331.70
84	MERCER	14	2000	17,818	18,196	18,403	2.26	-814,605	416.50
87	MONTGOMERY	14	2000	18,050	16,382	16,387	3.74	-1,209,550	612.97
90	NELSON	14	2000	14,942	13,877	13,903	2.13	-578,248	296.08
97 98	PERRY PIKE	14 14	2000 2000	18,986 27,722	17,998 27,045	17,981 27,180	2.49 2.72	-878,856 -1,454,061	448.42 740.62
100	PULASKI	14	2000	24,020	22,375	22,436	0.73	-304,639	163.54
103	ROWAN	14	2000	19,691	19,800	19,782	2.16	-835,686	427.73
105	SCOTT	14	2000	12,313	10,790	11,123	3.93	-864,073	437.60
106	SHELBY	14	2000	18,985	17,520	17,623	3.30	-1,146,838	582.23
109	TAYLOR	14	2000	17,991	16,023	16,063	2.42	-761,565	388.81
114	WARREN	14	2000	19,581	23,631	23,764	2.27	-1,055,135	539.45
116	WAYNE	14	2000	10,455	10,484	10,667	5.41	-1,143,788	577.23
120	WOODFORD	14	2000	23,075	23,337	22,965	0.70	-297,666	160.32

Functional Class 16, Weighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Weighted Average ADT	Predicted Weighted Average ADT	2000 Weighted ADT Growth	Weighted Analysis Regression Constant	Weighted Analysis Regression Slope
							Rate (%)
3	ANDERSON	16	2000	9,165	9,286	9,240	1.42	-252,669	130.95
5	BARREN	16	2000	7,412	7,471	7,570	2.04	-301,604	154.59
7	BELL	16	2000	8,489	8,900	8,840	0.94	-157,100	82.97
8	BOONE	16	2000	18,570	15,637	15,432	2.46	-744,188	379.81
9	BOURBON	16	2000	7,635	7,103	7,154	1.81	-251,954	129.55
10	BOYD	16	2000	8,835	7,982	8,040	0.78	-116,874	62.46
11	BOYLE	16	2000	6,769	5,858	5,743	1.75	-195,554	100.65
15	BULLITT	16	2000	13,071	9,584	9,668	2.72	-516,114	262.89
17	CALDWELL	16	2000	6,181	6,338	6,503	3.15	-403,818	205.16
18	CALLOWAY	16	2000	9,334	8,914	9,042	1.90	-334,340	171.69
19	CAMPBELL	16	2000	5,982	4,724	4,718	0.78	-68,552	36.63
24	CHRISTIAN	16	2000	8,688	8,054	7,993	1.06	-162,006	85.00
25 30	CLARK DAVIESS	16 16	2000 2000	10,669 8,279	10,928 8,341	11,114 8,233	2.24 0.88	-487,860 -136,429	249.49 72.33
34	FAYETTE	16	2000	13,683	12,263	12,304	2.78	-670,573	341.44
37	FRANKLIN	16	2000	9,922	10,934	10,991	0.74	-150,711	80.85
42	GRAVES	16	2000	3,933	4,347	4,460	1.10	-93,867	49.16
45	GREENUP	16	2000	8,720	9,217	9,075	2.06	-364,828	186.95
47	HARDIN	16	2000	11,560	10,588	10,658	2.94	-616,727	313.69
49	HARRISON	16	2000	5,310	5,179	5,218	1.62	-163,732	84.48
51	HENDERSON	16	2000	8,952	7,589	7,577	1.60	-234,276	120.93
54	HOPKINS	16	2000	12,781	11,727	11,656	1.39	-313,179	162.42
56	JEFFERSON	16	2000	15,342	14,929	14,900	1.26	-359,924	187.41
57	JESSAMINE	16	2000	12,065	11,712	11,912	3.48	-816,220	414.07
59	KENTON	16	2000	13,466	11,799	11,562	1.02	-224,627	118.09
61	KNOX	16	2000	8,920	8,469	8,375	0.26	-34,547	21.46
63	LAUREL	16	2000	10,274	9,859	9,837	0.75	-137,652	73.74
71	LOGAN	16	2000	7,170	5,858	5,918	2.44	-282,594	144.26
76	MADISON	16	2000	8,822	8,365	8,529	2.02	-335,541	172.03
78	MARION	16	2000	5,458	5,526	5,298	-0.12	17,716	-6.21
81	MASON	16	2000	5,160	3,715	3,785	-0.25	22,487	-9.35
73	MCCRACKEN	16	2000	7,155	7,201	7,277	0.83	-113,706	60.49
82	MEADE	16	2000	3,780	2,342	2,485	2.07	-100,288	51.39
84	MERCER	16	2000	4,641	4,362	4,394	1.73	-147,802	76.10
87	MONTGOMERY	16	2000	7,078	6,769	6,870	2.47	-332,060	169.47
90	NELSON	16	2000	9,309	11,124	11,256	3.82	-849,323	430.29
93	OLDHAM	16	2000	11,492	10,073	9,988	3.11	-610,549	310.27
97	PERRY	16	2000	8,043	7,165	7,304	1.22	-171,603	89.45
98	PIKE	16	2000	9,558	7,067	7,033	-0.61	92,904	-42.94
100	PULASKI	16	2000	6,885	6,730	6,813	1.02	-132,727	69.77
103	ROWAN	16	2000	7,290	7,770	7,975	2.49	-388,882	198.43
105	SCOTT	16	2000	11,228	9,099	9,077	1.07	-184,560	96.82
106	SHELBY	16	2000	8,590	4,542	4,974	-1.78	182,482	-88.75
107	SIMPSON	16	2000	6,367	6,292	6,230	0.47	-51,855	29.04
109	TAYLOR	16	2000	8,371	7,672	7,636	1.24	-181,101	94.37
114	WARREN	16	2000	11,580	11,934	11,955	2.24	-522,966	267.46
116	WAYNE	16	2000	12,137	12,464	12,335	1.74	-416,440	214.39
118	WHITLEY	16	2000	10,569	10,710	10,792	2.05	-432,090	221.44
120	WOODFORD	16	2000	9,474	9,621	9,535	1.27	-231,922	120.73

Functional Class 17, Weighted County Level Growth Rates

County	County Name	Functional	Year	Average	Weighted	Predicted	2000	Weighted	Weighted
Number		Class		ADT	Average	Weighted	Weighted	Analysis	Analysis
					ADT	Average ADT	ADT Growth	Regression Constant	Regression Slope
							Rate (%)
3	ANDERSON	17	2000	5,554	5,390	5,585	2.47	-269,787	137.69
5	BARREN	17	2000	2,812	2,452	2,451	-0.53	28,668	-13.11
7	BELL	17	2000	3,674	3,590	3,599	2.57	-181,194	92.40
8	BOONE	17	2000	11,763	10,801	10,920	4.38	-946,178	478.55
9	BOURBON	17	2000	2,331	2,450	2,457	1.59	-75,696	39.08
10	BOYD	17	2000	4,199	3,905	3,879	1.64	-123,603	63.74
11	BOYLE	17	2000	3,783	3,351	3,477	1.06	-69,897	36.69
15	BULLITT	17	2000	5,066	5,583	5,611	3.11	-343,097	174.35
17	CALDWELL	17	2000	1,986	1,696	1,660	-0.48	17,493	-7.92
18	CALLOWAY	17	2000	3,508	3,783	3,875	1.94	-146,541	75.21
19	CAMPBELL	17	2000	6,989	5,340	5,325	1.23	-125,710	65.52
24	CHRISTIAN	17	2000	3,842	4,534	4,597	1.81	-161,761	83.18
25	CLARK	17	2000	2,680	2,897	2,947	1.40	-79,304	41.13
30	DAVIESS	17	2000	4,046	3,958	3,922	1.67	-127,055	65.49
34	FAYETTE	17	2000	4,992	3,980	3,969	2.06	-159,187	81.58
37	FRANKLIN	17	2000	3,507	3,960	3,960	2.23	-172,622	88.29
38	FULTON	17	2000	469	493	491	-0.33	3,699	-1.60
42	GRAVES	17	2000	3,047	3,235	3,208	0.81	-48,671	25.94
45	GREENUP	17	2000	4,653	4,402	4,394	1.66	-141,402	72.90
47	HARDIN	17	2000	4,586	4,493	4,599	2.16	-193,857	99.23
49	HARRISON	17	2000	3,295	3,201	3,197	1.34	-82,419	42.81
51	HENDERSON	17	2000	2,907	3,082	3,140	2.02	-123,869	63.50
54	HOPKINS	17	2000	3,921	4,011	3,993	0.37	-25,864	14.93
56	JEFFERSON	17	2000	7,014	6,417	6,380	1.77	-219,351	112.87
57	JESSAMINE	17	2000	3,047	3,115	3,123	2.44	-149,427	76.27
59	KENTON	17	2000	5,738	4,920	4,905	2.03	-194,236	99.57
61	KNOX	17	2000	1,978	1,521	1,517	2.45	-72,915	37.22
63	LAUREL	17	2000	2,365	2,755	2,737	1.31	-68,743	35.74
71	LOGAN	17	2000	1,579	1,995	2,014	-3.96	161,473	-79.73
76	MADISON	17	2000	6,440	6,142	6,072	1.49	-175,292	90.68
78	MARION	17	2000	2,333	2,272	2,199	0.60	-24,318	13.26
81	MASON	17	2000	2,391	2,356	2,329	2.07	-94,227	48.28
73	MCCRACKEN	17	2000	4,558	4,014	4,035	2.25	-177,413	90.72
82	MEADE	17	2000	5,367	7,041	7,825	-1.76	283,352	-137.76
84	MERCER	17	2000	4,223	4,029	4,131	2.84	-230,102	117.12
87	MONTGOMERY	17	2000	2,045	1,686	1,720	0.04	473	0.62
90	NELSON	17	2000	2,215	2,290	2,297	2.93	-132,227	67.26
93	OLDHAM	17	2000	3,050	2,742	2,729	2.74	-146,589	74.66
97	PERRY	17	2000	4,800	5,438	5,781	4.86	-556,475	281.13
98 100	PIKE PULASKI	17 17	2000 2000	3,612 6,387	2,954 6,877	2,913 7,041	2.38 2.94	-135,944 -407,656	69.43 207.35
103	ROWAN	17	2000	3,717	2,857	2,803	0.66	-33,915	18.36
105	SCOTT	17	2000	2,405	2,678	2,636	0.45	-21,095	11.87
106	SHELBY	17	2000	3,728	3,067	3,056	2.16	-128,724	65.89
107	SIMPSON	17	2000	2,896	3,566	3,585	1.44	-99,852	51.72
109	TAYLOR	17	2000	3,761	3,480	3,483	-0.02	4,539	-0.53
114	WARREN	17	2000	4,859	5,170	5,382	1.44	-149,529	77.46
116	WAYNE	17	2000	1,174	1,180	1,146	-7.94	183,121	-90.99
118	WHITLEY	17	2000	3,273	2,253	2,256	0.87	-37,008	19.63
120	WOODFORD	17	2000	4,494	4,771	4,838	3.01	-286,865	145.85

Functional Class 19, Weighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Weighted Average ADT	Predicted Weighted Average ADT	2000 Weighted ADT Growth Rate (%)	Weighted Analysis Regression Constant	Weighted Analysis Regression Slope
5	BARREN	19	2000	906	906	902	-5.03	91,646	-45.37
7	BELL	19	2000	1,713	1,572	1,654	5.11	-167,379	84.52
8	BOONE	19	2000	2,411	4,277	4,272	6.55	-555,250	279.76
9	BOURBON	19	2000	1,096	1,096	1,112	3.71	-81,388	41.25

10	BOYD	19	2000	1,990	2,631	2,617	0.59	-28,197	15.41
11	BOYLE	19	2000	745	622	601	4.27	-50,723	25.66
15	BULLITT	19	2000	4,790	4,790	4,633	2.28	-206,367	105.50
16	BUTLER	19	2000	1,320	1,320	1,320	0.76	-18,680	10.00
17	CALDWELL	19	2000	1,097	829	841	1.18	-19,032	9.94
19	CAMPBELL	19	2000	1,062	1,146	1,226	4.40	-106,591	53.91
21	CARROLL	19	2000	2,850	2,850	2,844	2.92	-163,156	83.00
24	CHRISTIAN	19	2000	1,687	2,746	2,780	5.02	-276,263	139.52
25	CLARK	19	2000	1,142	1,679	1,641	2.07	-66,459	34.05
26	CLAY	19	2000	1,255	1,255	1,259	1.59	-38,808	20.03
27	CLINTON	19	2000	426	426	426	-2.61	22,682	-11.13
30	DAVIESS	19	2000	1,744	2,092	2,064	2.82	-114,334	58.20
33	ESTILL	19	2000	848	848	849	2.58	-43,018	21.93
34	FAYETTE	19	2000	2,110	1,546	1,549	2.68	-81,532	41.54
37	FRANKLIN	19	2000	680	815	785	6.86	-106,887	53.84
38	FULTON	19	2000	1,255	1,186	1,186	1.20	-27,336	14.26
40	GARRARD	19	2000	1,885	2,002	1,976	-5.28	210,658	-104.34
41	GRANT	19	2000	2,615	3,095	3,365	12.09	-810,182	406.77
42	GRAVES	19	2000	390	469	461	-0.81	7,922	-3.73
43	GRAYSON	19	2000	526	375	390	3.63	-27,933	14.16
44	GREEN	19	2000	1,360	1,286	1,249	0.27	-5,471	3.36
45	GREENUP	19	2000	602	1,491	1,487	1.44	-41,475	21.48
47	HARDIN	19	2000	2,560	1,356	1,541	0.19	-4,289	2.92
48	HARLAN	19	2000	143	143	142	-6.84	19,508	-9.68
49	HARRISON	19	2000	1,447	1,073	1,066	2.70	-56,422	28.74
51	HENDERSON	19	2000	1,146	1,146	1,135	3.91	-87,635	44.38
52	HENRY	19	2000	1,260	1,260	1,258	-1.19	31,258	-15.00
54	HOPKINS	19	2000	4,370	5,167	5,189	2.32	-235,155	120.17
56	JEFFERSON	19	2000	1,126	1,723	1,722	1.54	-51,362	26.54
57	JESSAMINE	19	2000	1,783	1,470	1,482	2.78	-80,985	41.23
59	KENTON	19	2000	1,510	1,743	1,743	4.40	-151,673	76.71
61	KNOX	19	2000	808	1,183	1,180	5.55	-129,764	65.47
63	LAUREL	19	2000	1,078	1,209	1,227	2.60	-62,516	31.87
69	LINCOLN	19	2000	782	782	781	0.69	-9,977	5.38
71	LOGAN	19	2000	551	551	559	0.85	-8,974	4.77
76	MADISON	19	2000	969	884	881	4.57	-79,659	40.27
81	MASON	19	2000	878	1,273	1,275	4.64	-117,067	59.17
73	MCCRACKEN	19	2000	900	1,291	1,300	4.34	-111,613	56.46
74	MCCREARY	19	2000	194	194	204	0.17	-496	0.35
84	MERCER	19	2000	765	803	798	3.02	-47,396	24.10
87	MONTGOMERY	19	2000	1,578	2,841	2,848	2.30	-128,291	65.57
89	MUHLENBERG	19	2000	703	644	641	3.06	-38,588	19.61
90	NELSON	19	2000	425	522	505	-0.40	4,525	-2.01
97	PERRY	19	2000	486	712	688	-0.79	11,568	-5.44
98	PIKE	19	2000	2,866	1,647	1,657	-0.92	32,024	-15.18
100	PULASKI	19	2000	3,234	3,399	3,367	4.43	-295,204	149.29
103	ROWAN	19	2000	693	709	696	2.52	-34,426	17.56
105	SCOTT	19	2000	1,819	2,893	2,822	3.80	-211,387	107.10
107	SIMPSON	19	2000	621	564	554	4.78	-52,411	26.48
109	TAYLOR	19	2000	433	667	658	3.11	-40,300	20.48
114	WARREN	19	2000	1,275	1,878	1,843	6.18	-225,737	113.79
118	WHITLEY	19	2000	861	837	830	2.22	-36,087	18.46
120	WOODFORD	19	2000	180	180	197	-8.04	31,864	-15.83

8.7 Appendix G – Unweighted County Level Functional Class Growth Rates

Functional Class 01, Unweighted County Level Growth Rates

County Number	County Name	Function al Class	Year	Average ADT	Predicted 2000 ADT	2000 Growth (%)	Regression Slope	Regression Constant
5	BARREN	1	2000	32,350	31,714	1.65	522.58	-1,013,437.42
6	BATH	1	2000	18,667	18,502	3.04	562.02	-1,105,537.98
8	BOONE	1	2000	44,629	45,495	4.27	1,944.24	-3,842,990.04
10	BOYD	1	2000	18,300	18,931	2.92	552.42	-1,085,917.58
15	BULLITT	1	2000	56,375	58,916	3.54	2,083.13	-4,107,346.87
17	CALDWELL	1	2000	14,750	14,659	3.97	581.39	-1,148,128.61
21	CARROLL	1	2000	26,367	26,459	3.93	1,038.99	-2,051,521.01
22	CARTER	1	2000	14,325	14,475	2.42	350.45	-686,434.55
24	CHRISTIAN	1	2000	19,533	19,119	4.61	880.95	-1,742,780.38
25	CLARK	1	2000	30,050	30,617	3.39	1,039.39	-2,048,170.61
31	EDMONSON	1	2000	29,800	28,589	1.20	344.24	-659,895.76
37	FRANKLIN	1	2000	33,100	34,072	3.34	1,139.39	-2,244,715.61
39	GALLATIN	1	2000	26,067	25,423	3.48	885.86	-1,746,294.14
41	GRANT	1	2000	41,580	44,192	4.53	2,001.70	-3,959,202.30
47	HARDIN	1	2000	40,580	42,410	4.35	1,845.24	-3,648,074.76
50	HART	1	2000	32,525	33,379	3.21	1,072.58	-2,111,772.42
52	HENRY	1	2000	28,467	28,705	2.92	839.60	-1,650,487.07
56	JEFFERSON	1	2000	43,600	44,556	2.36	1,050.30	-2,056,049.70
59	KENTON	1	2000	44,700	49,075	4.22	2,072.12	-4,095,167.88
62	LARUE	1	2000	33,100	34,635	3.49	1,208.79	-2,382,941.21
63	LAUREL	1	2000	34,567	34,161	2.11	721.62	-1,409,071.72
70	LIVINGSTON	1	2000	25,000	25,452	4.45	1,131.52	-2,237,578.48
72	LYON	1	2000	19,225	19,155	4.03	771.02	-1,522,874.98
76	MADISON	1	2000	47,320	49,120	3.32	1,631.03	-3,212,940.97
79	MARSHALL	1	2000	26,567	26,808	4.78	1,281.01	-2,535,212.32
73	MCCRACKEN	1	2000	27,450	27,705	4.07	1,128.79	-2,229,871.21
87	MONTGOMERY	1	2000	19,767	20,478	3.72	762.42	-1,504,370.91
93	OLDHAM	1	2000	44,920	45,122	3.62	1,631.48	-3,217,847.52
102	ROCKCASTLE	1	2000	34,467	35,145	3.05	1,070.71	-2,106,269.29
103	ROWAN	1	2000	15,633	15,255	4.05	617.23	-1,219,209.43
105	SCOTT	1	2000	43,717	43,178	4.98	2,148.73	-4,254,276.61
106	SHELBY	1	2000	38,520	39,532	3.10	1,227.03	-2,414,528.97
107	SIMPSON	1	2000	35,833	35,587	1.39	493.84	-952,089.49
111	TRIGG	1	2000	14,500	14,553	3.92	570.94	-1,127,326.06
112	TRIMBLE	1	2000	26,800	27,235	3.79	1,032.12	-2,037,007.88
114	WARREN	1	2000	41,040	40,368	1.45	586.67	-1,132,965.33
118	WHITLEY	1	2000	30,767	31,125	2.80	870.71	-1,710,289.29
120	WOODFORD	1	2000	29,950	30,655	3.88	1,188.79	-2,346,921.21

Functional Class 02, Unweighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Predicted 2000 ADT	2000 Growth (%)	Regression Slope	Regression Constant
1	ADAIR	2	2000	14,041	14,295	2.15	307.22	-600,146.64
2	ALLEN	2	2000	5,298	5,378	3.25	174.77	-344,152.73
3	ANDERSON	2	2000	12,752	12,617	3.14	395.80	-778,983.00
4	BALLARD	2	2000	5,723	5,674	1.56	88.61	-171,544.92
5	BARREN	2	2000	6,615	7,108	4.79	340.18	-673,255.82
7	BELL	2	2000	15,312	15,098	3.24	488.99	-962,876.44
9	BOURBON	2	2000	10,181	10,298	2.21	227.76	-445,217.39
10	BOYD	2	2000	11,651	11,570	0.62	71.68	-131,793.32
11	BOYLE	2	2000	11,131	11,183	2.85	318.43	-625,677.97
12	BRACKEN	2	2000	7,293	7,251	7.55	547.35	-1,087,455.98
13	BREATHITT	2	2000	10,353	10,407	2.12	221.06	-431,706.32
14	BRECKINRIDGE	2	2000	5,434	5,475	3.17	173.55	-341,615.74
16	BUTLER	2	2000	8,492	8,325	5.09	423.49	-838,664.77
17	CALDWELL	2	2000	8,850	10,120	5.15	521.01	-1,031,899.99
18	CALLOWAY	2	2000	8,265	8,535	2.49	212.81	-417,083.01
19	CAMPBELL	2	2000	9,623	9,648	6.78	653.72	-1,297,796.22
20	CARLISLE	2	2000	3,325	3,327	-0.54	-18.04	39,411.34
22	CARTER	2	2000	4,690	4,561	5.19	236.70	-468,833.30
23	CASEY	2	2000	6,154	6,171	2.38	146.66	-287,147.69
24	CHRISTIAN	2	2000	9,372	9,415	3.59	338.08	-666,746.59
25	CLARK	2	2000	9,928	9,543	3.71	354.15	-698,760.52
26	CLAY	2	2000	5,640	6,594	4.35	286.71	-566,820.63
27	CLINTON	2	2000	9,579	9,650	3.09	298.63	-587,610.44
30	DAVIESS	2	2000	9,283	9,701	2.64	256.40	-503,107.26
35	FLEMING	2	2000	3,115	3,080	2.74	84.24	-165,404.76
36	FLOYD	2	2000	14,605	14,645	2.68	391.81	-768,972.14
37	FRANKLIN	2	2000	18,450	18,983	3.42	648.45	-1,277,926.55
38	FULTON	2	2000	5,736	5,865	4.76	279.19	-552,513.81
40	GARRARD	2	2000	11,039	11,114	2.57	286.11	-561,105.89
42	GRAVES	2	2000	9,578	9,341	2.51	234.72	-460,098.53
43	GRAYSON	2	2000	8,893	9,943	4.33	430.16	-850,380.34
45	GREENUP	2	2000	8,513	8,505	1.50	127.63	-246,747.37
46	HANCOCK	2	2000	9,445	9,400	3.04	285.34	-561,285.27
47	HARDIN	2	2000	8,857	8,882	2.90	257.19	-505,496.45
48	HARLAN	2	2000	7,140	7,319	2.51	183.75	-360,177.55
51	HENDERSON	2	2000	8,836	9,678	4.33	419.17	-828,656.26
53	HICKMAN	2	2000	4,251	4,256	1.99	84.78	-165,311.81
54	HOPKINS	2	2000	14,612	15,658	6.46	1,012.29	-2,008,927.57
56	JEFFERSON	2	2000	23,420	23,813	2.16	514.55	-1,005,277.45
57	JESSAMINE	2	2000	35,267	35,533	4.29	1,523.94	-3,012,346.06
58	JOHNSON	2	2000	8,608	8,319	1.00	82.80	-157,271.70
60	KNOTT	2	2000	6,402	6,605	1.14	75.28	-143,961.05
61	KNOX	2	2000	17,563	17,552	3.31	580.18	-1,142,809.21
63	LAUREL	2	2000	8,508	8,677	2.52	218.82	-428,959.52
64	LAWRENCE	2	2000	8,857	8,709	0.78	68.21	-127,714.95
66	LESLIE	2	2000	5,090	6,170	3.59	221.33	-436,496.67
67	LETCHER	2	2000	6,432	6,645	1.50	99.38	-192,108.88
68	LEWIS	2	2000	4,594	4,420	5.15	227.67	-450,916.20
69	LINCOLN	2	2000	11,713	11,249	4.09	459.69	-908,124.89
72	LYON	2	2000	8,130	9,117	4.93	449.82	-890,519.18
76	MADISON	2	2000	10,158	10,226	2.41	246.72	-483,208.62
77	MAGOFFIN	2	2000	8,109	7,943	1.56	123.73	-239,524.07
78	MARION	2	2000	6,770	6,723	2.75	184.65	-362,586.15
79	MARSHALL	2	2000	7,629	7,784	1.39	108.23	-208,682.31
80	MARTIN	2	2000	7,345	7,686	0.63	48.51	-89,331.89
81	MASON	2	2000	7,164	7,158	5.24	374.92	-742,684.88
73	MCCRACKEN	2	2000	12,481	12,610	0.84	106.32	-200,028.35
74	MCCREARY	2	2000	9,539	9,713	3.13	304.32	-598,930.60
82	MEADE	2	2000	11,510	11,792	2.47	290.95	-570,107.38
84	MERCER	2	2000	13,425	13,206	2.40	316.70	-620,187.64
85	METCALFE	2	2000	4,535	4,886	3.75	183.18	-361,477.82
88	MORGAN	2	2000	5,750	4,963	1.49	74.06	-143,157.94
89	MUHLENBERG	2	2000	8,990	10,955	4.97	544.36	-1,077,772.30
90	NELSON	2	2000	9,483	9,825	4.27	419.67	-829,508.83
91	NICHOLAS	2	2000	4,600	4,642	2.03	94.28	-183,912.29
92	OHIO	2	2000	7,723	8,292	4.64	384.73	-761,162.94
96	PENDLETON	2	2000	7,630	7,581	5.53	419.45	-831,328.55
97	PERRY	2	2000	10,268	10,496	2.40	251.58	-492,664.42
98	PIKE	2	2000	12,245	12,229	2.37	289.68	-567,134.34
99	POWELL	2	2000	11,322	11,108	2.45	271.73	-532,358.67
100	PULASKI	2	2000	9,631	9,740	2.14	208.70	-407,664.78
101	ROBERTSON	2	2000	3,070	2,937	1.86	54.73	-106,517.27
102	ROCKCASTLE	2	2000	8,193	8,405	3.47	291.45	-574,487.13
104	RUSSELL	2	2000	6,107	6,134	2.16	132.50	-258,875.22
109	TAYLOR	2	2000	7,987	8,026	3.01	241.98	-475,925.22
110	TODD	2	2000	6,860	7,323	-4.28	-313.45	634,232.55
111	TRIGG	2	2000	4,439	4,605	3.03	139.34	-274,072.93
113	UNION	2	2000	6,801	6,869	1.37	93.87	-180,869.50
114	WARREN	2	2000	11,001	11,278	2.81	317.22	-623,159.67
115 117	WASHINGTON WEBSTER	2 2	2000 2000	5,548 13,800	6,032 14,225	3.87 9.15	233.18 1,301.61	-460,334.63 -2,588,989.29
119	WOLFE	2	2000	6,063	6,176	2.41	148.96	-291,743.37
120	WOODFORD	2	2000	25,557	24,951	1.67	416.63	-808,313.37

						Functional Class 06, Unweighted County Level Growth Rates
County		Functional		Average	Predicted	2000 Growth	Regression	Regression
Number	County Name	Class	Year	ADT	2000 ADT	(%)	Slope	Constant
1	ADAIR	6	2000	7,872	7,905	1.66	130.88	-253,859.56
2	ALLEN	6	2000	4,875	4,845	5.41	262.08	-519,306.92
3 4	ANDERSON BALLARD	6 6	2000 2000	5,783 2,644	5,612 2,666	0.07 0.93	4.08 24.70	-2,549.25 -46,740.10
5	BARREN	6	2000	7,298	7,143	2.31	164.84	-322,535.62
6	BATH	6	2000	2,948	2,883	2.23	64.41	-125,935.09
9 11	BOURBON BOYLE	6 6	2000 2000	3,163 4,684	3,180 4,684	2.56 2.23	81.34 104.59	-159,490.33 -204,497.41
14	BRECKINRIDGE	6	2000	2,491	2,511	1.94	48.78	-95,055.72
15	BULLITT	6	2000	12,038	12,089	2.65	320.94	-629,789.56
17 18	CALDWELL CALLOWAY	6 6	2000 2000	3,477 6,238	3,567 6,342	3.13 2.66	111.68 168.58	-219,786.32 -330,809.09
19	CAMPBELL	6	2000	5,697	5,724	2.37	135.93	-266,137.50
20	CARLISLE	6	2000	1,214	1,215	1.95	23.69	-46,161.03
22 24	CARTER CHRISTIAN	6 6	2000 2000	8,786 4,310	8,712 4,444	1.46 4.93	127.10 218.91	-245,497.36 -433,373.76
25	CLARK	6	2000	5,905	5,815	3.71	215.79	-425,761.21
26 27	CLAY CLINTON	6 6	2000 2000	11,241 3,626	11,278 3,550	1.75 3.84	197.25 136.27	-383,215.18 -268,983.33
28	CRITTENDEN	6	2000	6,465	6,404	1.73	110.47	-214,542.96
29	CUMBERLAND	6	2000	5,028	5,073	0.49	24.69	-44,314.53
30 31	DAVIESS EDMONSON	6 6	2000 2000	6,945 5,526	6,945 5,281	2.32 2.27	160.94 119.84	-314,942.06 -234,400.41
32	ELLIOTT	6	2000	3,150	3,202	1.96	62.74	-122,267.58
35	FLEMING	6	2000	4,349	4,433	2.97	131.45	-258,476.16
36 37	FLOYD FRANKLIN	6 6	2000 2000	2,090 3,902	2,224 4,009	-9.76 2.45	-216.97 98.14	436,163.03 -192,278.55
38	FULTON	6	2000	1,916	1,976	1.64	32.36	-62,747.88
39	GALLATIN	6	2000	2,367	2,365	3.22	76.26	-150,160.40
40 42	GARRARD GRAVES	6 6	2000 2000	3,726 3,915	3,688 3,923	0.93 1.36	34.42 53.54	-65,158.70 -103,158.13
43	GRAYSON	6	2000	10,248	10,478	3.10	324.88	-639,291.82
44	GREEN	6	2000	5,621	5,654	1.27	71.89	-138,127.51
47 48	HARDIN HARLAN	6 6	2000 2000	8,955 10,681	8,905 10,721	0.54 2.50	48.48 268.48	-88,064.52 -526,234.70
49	HARRISON	6	2000	5,073	5,012	1.84	92.24	-179,473.16
51	HENDERSON	6	2000	5,402	5,518	2.49	137.59	-269,667.84
52 53	HENRY HICKMAN	6 6	2000 2000	5,031 555	5,079 547	2.88 1.62	146.24 8.86	-287,393.26 -17,176.50
54	HOPKINS	6	2000	6,798	6,858	1.24	85.25	-163,639.15
55 56	JACKSON JEFFERSON	6 6	2000 2000	3,935 9,780	4,038 9,741	2.83 4.38	114.46 426.76	-224,889.59 -843,774.24
57	JESSAMINE	6	2000	8,140	8,201	4.38	358.82	-709,447.84
58	JOHNSON	6	2000	4,290	4,364	2.23	97.52	-190,666.48
62 63	LARUE LAUREL	6 6	2000 2000	5,350 7,407	5,382 7,257	1.75 0.66	94.39 47.72	-183,406.01 -88,177.28
65	LEE	6	2000	5,377	5,356	2.16	115.75	-226,149.08
66	LESLIE	6	2000	5,993	6,170	1.32	81.72	-157,274.11
69 70	LINCOLN LIVINGSTON	6 6	2000 2000	3,940 4,788	3,965 4,823	2.08 0.87	82.51 41.76	-161,045.49 -78,706.71
71	LOGAN	6	2000	4,975	4,995	2.00	99.84	-194,684.16
72	LYON	6	2000	5,972	6,132	1.02	62.71	-119,278.39
76 77	MADISON MAGOFFIN	6 6	2000 2000	4,423 7,403	4,449 7,572	3.11 2.22	138.56 167.88	-272,678.44 -328,185.76
79	MARSHALL	6	2000	4,678	4,675	0.51	23.70	-42,718.97
80 81	MARTIN MASON	6 6	2000 2000	5,603 5,440	6,000 5,422	0.89 1.56	53.13 84.70	-100,262.20 -163,972.30
74	MCCREARY	6	2000	1,218	1,114	-1.32	-14.68	30,465.57
75	MCLEAN	6	2000	6,249	6,201	1.28	79.14	-152,072.03
82 83	MEADE MENIFEE	6 6	2000 2000	8,550 3,939	9,008 3,982	3.61 2.91	325.12 115.94	-641,222.08 -227,897.06
84	MERCER	6	2000	2,888	2,848	1.73	49.37	-95,895.83
85	METCALFE	6	2000	3,438	3,444	1.41	48.71	-93,969.96
87 88	MONTGOMERY MORGAN	6 6	2000 2000	6,136 4,853	6,078 4,885	3.01 1.96	182.67 95.65	-359,271.97 -186,408.92
89	MUHLENBERG	6	2000	6,657	6,717	1.41	94.90	-183,082.80
90	NELSON	6	2000	6,211	6,402	3.41	218.29	-430,174.09
93 94	OLDHAM OWEN	6 6	2000 2000	10,634 4,517	10,587 4,486	2.59 2.12	273.84 95.28	-537,085.24 -186,078.26
95	OWSLEY	6	2000	1,143	1,125	1.07	12.07	-23,011.90
96 99	PENDLETON POWELL	6 6	2000 2000	6,771 3,005	6,817 2,929	2.69 4.31	183.16 126.18	-359,511.98 -249,434.82
100	PULASKI	6	2000	10,868	10,958	3.02	330.71	-650,465.79
102	ROCKCASTLE	6	2000	7,600	7,722	2.14	165.00	-322,287.42
103 104	ROWAN RUSSELL	6 6	2000 2000	8,693 10,600	8,843 10,529	2.96 1.93	261.36 203.27	-513,884.14 -396,016.73
105	SCOTT	6	2000	6,628	6,664	3.90	259.87	-513,068.93
106	SHELBY	6	2000	6,749	6,868	3.62	248.48	-490,100.63
107 108	SIMPSON SPENCER	6 6	2000 2000	8,993 8,010	9,052 7,989	3.30 4.48	299.13 357.55	-589,210.20 -707,110.16
110	TODD	6	2000	2,975	3,189	3.49	111.36	-219,533.58
112	TRIMBLE	6	2000	5,286	5,250	3.24	169.92	-334,581.93
113 114	UNION WARREN	6 6	2000 2000	5,580 3,081	5,646 3,278	0.35 0.72	19.94 23.55	-34,239.47 -43,816.85
115	WASHINGTON	6	2000	7,134	7,179	2.01	144.35	-281,526.47
116 117	WAYNE WEBSTER	6 6	2000 2000	6,883 4,323	7,030 4,363	3.72 -0.49	261.54 -21.55	-516,040.46 47,470.90
118	WHITLEY	6	2000	6,100	6,206	3.92	243.41	-480,616.30
119	WOLFE	6	2000	3,928	3,858	0.84	32.52	-61,179.70

Functional Class 07, Unweighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Predicted 2000 ADT	2000 Growth (%)	Regression Slope	Regression Constant
1	ADAIR	7	2000	2,639	2,695	1.97	52.98	-103,263.11
2 3	ALLEN ANDERSON	7 7	2000 2000	3,552 1,745	3,567 1,734	1.16 1.47	41.33 25.57	-79,090.79 -49,415.33
4	BALLARD	7	2000	1,534	1,548	0.91	14.16	-26,774.22
5	BARREN	7	2000	3,094	3,127	1.62	50.66	-98,191.49
6	BATH	7	2000	3,340	3,404	2.49	84.86	-166,309.61
7	BELL	7	2000	2,264	2,279	2.59	59.13	-115,985.68
8	BOONE	7	2000	4,547	4,615	2.92	134.98	-265,339.16
9 10	BOURBON BOYD	7 7	2000 2000	1,794 3,130	1,828 3,185	2.66 2.16	48.68 68.96	-95,542.08 -134,741.79
11	BOYLE	7	2000	2,722	2,749	2.23	61.44	-120,134.90
12	BRACKEN	7	2000	1,633	1,638	1.36	22.24	-42,849.01
13	BREATHITT	7	2000	2,904	2,850	-0.33	-9.29	21,421.59
14	BRECKINRIDGE	7	2000	1,724	1,718	1.90	32.58	-63,443.23
15	BULLITT	7	2000	7,494	7,414	1.56	115.65	-223,885.08
16	BUTLER	7	2000	3,255	3,289	2.00	65.89	-128,489.17
17	CALDWELL	7	2000	1,675	1,696	1.35	22.89	-44,076.61
18 19	CALLOWAY CAMPBELL	7 7	2000 2000	2,238 1,295	2,247 1,265	1.98 0.22	44.39 2.78	-86,525.44 -4,304.27
20	CARLISLE	7	2000	1,531	1,528	0.96	14.66	-27,793.02
21	CARROLL	7	2000	4,356	4,351	1.50	65.06	-125,774.50
22	CARTER	7	2000	4,785	4,840	1.67	80.76	-156,681.74
23	CASEY	7	2000	2,334	2,273	0.39	8.77	-15,263.48
24	CHRISTIAN	7	2000	1,880	1,923	0.97	18.70	-35,475.09
25	CLARK	7	2000	3,307	3,325	1.72	57.30	-111,277.60
26	CLAY	7	2000	2,467	2,483	2.33	57.92	-113,350.96
27 28	CLINTON CRITTENDEN	7 7	2000 2000	2,932 1,221	2,950 1,240	1.91 0.23	56.33 2.83	-109,703.84 -4,420.22
29	CUMBERLAND	7	2000	2,702	2,750	2.08	57.28	-111,802.50
30	DAVIESS	7	2000	4,018	3,997	1.86	74.18	-144,357.86
31	EDMONSON	7	2000	1,177	1,208	1.60	19.34	-37,462.10
32	ELLIOTT	7	2000	697	701	0.75	5.23	-9,751.77
33	ESTILL	7	2000	6,448	6,410	1.26	81.05	-155,693.32
35	FLEMING	7	2000	2,746	2,760	1.13	31.16	-59,564.58
36	FLOYD	7	2000	4,745	4,573	1.05	48.24	-91,908.90
37 38	FRANKLIN FULTON	7 7	2000 2000	3,003 3,326	2,942 3,308	1.13 0.33	33.28 10.93	-63,623.65 -18,548.35
39	GALLATIN	7	2000	3,197	3,193	3.42	109.28	-215,359.52
40	GARRARD	7	2000	1,397	1,386	1.22	16.96	-32,526.94
41	GRANT	7	2000	3,632	3,669	2.82	103.63	-203,584.28
42	GRAVES	7	2000	1,900	1,896	1.53	28.92	-55,936.41
43	GRAYSON	7	2000	3,737	3,797	2.53	96.00	-188,198.88
44	GREEN	7	2000	1,939	1,951	2.57	50.20	-98,454.68
45	GREENUP	7	2000	1,956	1,969	1.12	22.03	-42,091.00
46	HANCOCK	7	2000	1,605	1,600	0.95	15.18	-28,769.23
47 48	HARDIN HARLAN	7 7	2000 2000	2,678 4,436	2,644 4,530	2.21 2.06	58.54 93.26	-114,426.49 -181,987.46
49	HARRISON	7	2000	2,217	2,232	2.42	53.99	-105,743.54
50	HART	7	2000	3,444	3,545	2.30	81.64	-159,738.92
51	HENDERSON	7	2000	2,524	2,549	0.61	15.45	-28,360.09
52	HENRY	7	2000	2,963	3,061	2.71	83.02	-162,970.58
53	HICKMAN	7	2000	1,398	1,389	1.51	20.96	-40,521.48
54	HOPKINS	7	2000	4,095	4,105	0.88	36.15	-68,187.48
55	JACKSON	7	2000	1,208	1,229	2.15	26.45	-51,680.36
57 58	JESSAMINE JOHNSON	7 7	2000 2000	3,396 6,358	3,439 6,289	2.69 0.51	92.68 31.90	-181,916.04 -57,511.25
59	KENTON	7	2000	2,991	2,993	2.46	73.57	-144,155.98
60	KNOTT	7	2000	3,638	3,649	1.14	41.47	-79,288.53
61	KNOX	7	2000	2,962	2,955	1.75	51.64	-100,320.23
62	LARUE	7	2000	2,620	2,593	0.80	20.63	-38,667.64
63	LAUREL	7	2000	5,260	5,396	3.62	195.53	-385,661.44
64	LAWRENCE	7	2000	3,374	3,384	2.17	73.55	-143,711.28
65	LEE	7	2000	2,778	2,747	-0.21	-5.80	14,348.69
66 67	LESLIE LETCHER	7 7	2000 2000	2,510 3,254	2,519 3,275	1.26 0.99	31.64 32.49	-60,752.25 -61,711.99
68	LEWIS	7	2000	2,468	2,444	0.82	20.12	-37,803.28
69	LINCOLN	7	2000	2,128	2,105	1.07	22.52	-42,934.96
70	LIVINGSTON	7	2000	1,478	1,474	1.31	19.28	-37,086.73
71	LOGAN	7	2000	1,153	1,156	1.96	22.71	-44,272.22
72	LYON	7	2000	1,801	1,817	0.96	17.42	-33,013.98
76	MADISON	7	2000	4,257	4,274	2.10	89.86	-175,450.74
77 78	MAGOFFIN MARION	7 7	2000 2000	2,101 1,815	2,093 1,820	1.34 1.56	28.13 28.41	-54,176.87 -54,997.40
79	MARSHALL	7	2000	3,639	3,640	0.25	9.22	-14,801.63
80	MARTIN	7	2000	2,084	2,183	0.01	0.20	1,785.27
81	MASON	7	2000	1,404	1,384	1.83	25.32	-49,247.33
73	MCCRACKEN	7	2000	3,029	3,070	1.81	55.70	-108,330.16
74	MCCREARY	7	2000	2,369	2,402	2.37	57.02	-111,643.73
75	MCLEAN	7	2000	2,737	2,732	1.38	37.84	-72,943.93
82 83	MEADE MENIFEE	7 7	2000 2000	3,396 1,288	3,452 1,291	1.99 2.17	68.56 27.97	-133,676.33 -54,651.33
84	MERCER	7	2000	2,120	2,127	1.82	38.81	-75,487.13
85	METCALFE	7	2000	3,456	3,440	1.41	48.61	-93,779.68
86	MONROE	7	2000	3,441	3,403	0.32	10.80	-18,206.36
87	MONTGOMERY	7	2000	3,356	3,417	3.15	107.65	-211,877.17
88 89	MORGAN MUHLENBERG	7 7	2000 2000	1,811 6,128	1,846 6,151	2.31 1.00	42.63 61.71	-83,405.17 -117,262.71
90	NELSON	7	2000	3,541	3,597	3.03	109.05	-214,509.43
91	NICHOLAS	7	2000	4,539	4,553	2.21	100.53	-196,498.89
92	OHIO	7	2000	5,682	5,627	1.06	59.69	-113,748.49
93	OLDHAM	7	2000	3,560	3,562	2.33	83.07	-162,587.75
94	OWEN	7	2000	1,394	1,414	2.04	28.81	-56,206.71
95	OWSLEY	7	2000	2,904	2,902	1.68	48.81	-94,708.47
96 97	PENDLETON PERRY	7 7	2000 2000	1,727 3,171	1,731 3,163	1.75 1.72	30.23 54.44	-58,721.49 -105,722.99
98	PIKE	7	2000	3,590	3,580	-0.16	-5.78	15,130.56
99	POWELL	7	2000	5,482	5,461	2.04	111.32	-217,187.09
100	PULASKI	7	2000	2,460	2,449	1.81	44.26	-86,081.12
101	ROBERTSON	7	2000	1,122	1,123	1.92	21.51	-41,896.72
102	ROCKCASTLE	7	2000	1,754	1,791	1.99	35.59	-69,389.45
103	ROWAN	7	2000	3,163	3,203	2.26	72.38	-141,550.86
104 105	RUSSELL SCOTT	7 7	2000 2000	3,417 3,801	3,395 3,889	1.38 2.77	46.89 107.73	-90,381.12 -211,571.71
106	SHELBY	7	2000	2,422	2,459	3.53	86.84	-171,219.10
107	SIMPSON	7	2000	2,129	2,105	2.95	62.12	-122,130.31
108	SPENCER	7	2000	3,077	3,056	3.94	120.45	-237,850.44
109	TAYLOR	7	2000	2,750	2,770	2.02	55.89	-109,006.37
110	TODD	7	2000	2,402	2,361	0.88	20.69	-39,016.38
111	TRIGG	7	2000	2,007	2,024	1.59	32.13	-62,232.71
112 113	TRIMBLE UNION	7 7	2000 2000	3,541 2,138	3,535 2,151	3.98 0.72	140.72 15.47	-277,913.26 -28,797.79
114	WARREN	7	2000	4,562	4,605	2.10	96.78	-188,960.15
115	WASHINGTON	7	2000	1,360	1,386	1.34	18.50	-35,620.80
116	WAYNE	7	2000	1,414	1,412	2.29	32.29	-63,160.57
117	WEBSTER	7	2000	2,822	2,822	0.34	9.46	-16,100.77
118	WHITLEY	7	2000	3,619	3,668	2.33	85.34	-167,013.21
119	WOLFE	7	2000	1,292	1,305	1.98	25.80	-50,298.42
120	WOODFORD	7	2000	3,533	3,574	3.19	114.10	-224,620.79

Functional Class 08, Unweighted County Level Growth Rates


County Number	County Name	Functional Class	Year	Average ADT	Predicted 2000 ADT	2000 Growth (%)	Regression Slope	Regression Constant

Functional Class 09, Unweighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Predicted 2000 ADT	2000 Growth (%)	Regression Slope	Regression Constant
1	ADAIR	9	2000	406	410	4.47	18.34	-36,263.48
2	ALLEN	9	2000	356	352	0.50	1.77	-3,185.38
3	ANDERSON	9	2000	290	286	4.96	14.22	-28,144.97
4	BALLARD	9	2000	306	304	-2.59	-7.87	16,044.97
5	BARREN	9	2000	631	633	0.37	2.33	-4,021.78
6	BATH	9	2000	399	405	3.59	14.53	-28,649.51
7	BELL	9	2000	1,090	1,090	0.90	9.85	-18,601.41
8	BOONE	9	2000	854	858	2.88	24.69	-48,516.25
9	BOURBON	9	2000	367	365	2.50	9.11	-17,858.71
10	BOYD	9	2000	937	935	0.87	8.12	-15,303.40
11	BOYLE	9	2000	393	401	1.90	7.61	-14,826.98
12	BRACKEN	9	2000	199	195	3.66	7.12	-14,043.01
13	BREATHITT	9	2000	538	535	1.52	8.13	-15,729.49
14	BRECKINRIDGE	9	2000	1,719	1,735	0.62	10.69	-19,648.96
15	BULLITT	9	2000	1,511	1,490	-0.71	-10.62	22,733.53
16	BUTLER	9	2000	395	385	0.67	2.60	-4,809.30
17	CALDWELL	9	2000	127	128	-1.29	-1.64	3,416.89
18	CALLOWAY	9	2000	520	536	2.45	13.16	-25,778.44
19	CAMPBELL	9	2000	1,600	1,600	2.20	35.23	-68,850.11
20	CARLISLE	9	2000	192	192	0.34	0.65	-1,099.58
21	CARROLL	9	2000	478	471	0.99	4.65	-8,822.47
22	CARTER	9	2000	465	462	1.95	9.03	-17,598.79
23	CASEY	9	2000	553	534	-1.17	-6.23	13,002.26
24	CHRISTIAN	9	2000	363	367	1.04	3.83	-7,295.60
25	CLARK	9	2000	1,390	1,377	-0.85	-11.65	24,670.35
26	CLAY	9	2000	884	879	2.29	20.14	-39,400.78
27	CLINTON	9	2000	185	186	2.56	4.76	-9,342.26
28	CRITTENDEN	9	2000	139	138	0.22	0.30	-465.94
29	CUMBERLAND	9	2000	214	213	0.63	1.33	-2,453.89
30	DAVIESS	9	2000	605	596	1.07	6.39	-12,193.21
31	EDMONSON	9	2000	287	286	1.21	3.47	-6,659.86
32	ELLIOTT	9	2000	175	173	0.13	0.22	-270.27
33	ESTILL	9	2000	680	684	0.91	6.22	-11,752.15
34	FAYETTE	9	2000	1,498	1,498	1.18	17.73	-33,953.72
35	FLEMING	9	2000	404	408	2.41	9.81	-19,219.72
36	FLOYD	9	2000	1,143	1,147	2.06	23.64	-46,129.48
37	FRANKLIN	9	2000	587	588	2.13	12.51	-24,436.22
38	FULTON	9	2000	457	454	0.94	4.26	-8,064.90
39	GALLATIN	9	2000	430	430	4.14	17.81	-35,188.28
40	GARRARD	9	2000	449	442	4.49	19.84	-39,239.62
41	GRANT	9	2000	671	659	5.99	39.51	-78,364.18
42	GRAVES	9	2000	426	430	2.43	10.43	-20,435.78
43	GRAYSON	9	2000	315	308	3.46	10.67	-21,023.90
44	GREEN	9	2000	648	639	-1.21	-7.76	16,155.17
45	GREENUP	9	2000	952	967	-0.68	-6.62	14,200.37
46	HANCOCK	9	2000	1,074	1,064	1.51	16.07	-31,080.57
47	HARDIN	9	2000	534	526	1.22	6.44	-12,344.17
48	HARLAN	9	2000	1,265	1,244	2.80	34.79	-68,332.16
49	HARRISON	9	2000	432	434	3.42	14.85	-29,267.58
50	HART	9	2000	389	391	1.92	7.52	-14,646.45
51	HENDERSON	9	2000	393	397	2.29	9.09	-17,778.68
52	HENRY	9	2000	334	337	1.90	6.41	-12,491.06
53	HICKMAN	9	2000	331	328	-0.18	-0.59	1,514.74
54	HOPKINS	9	2000	761	746	4.32	32.20	-63,647.17
55	JACKSON	9	2000	408	403	4.69	18.88	-37,356.24
56	JEFFERSON	9	2000	1,820	1,820	0.55	10.00	-18,180.00
57	JESSAMINE	9	2000	1,809	1,854	4.77	88.40	-174,953.89
58	JOHNSON	9	2000	460	451	3.02	13.61	-26,777.71
59	KENTON	9	2000	476	469	2.65	12.46	-24,441.82
60	KNOTT	9	2000	616	605	6.38	38.58	-76,563.80
61	KNOX	9	2000	2,542	2,560	0.85	21.82	-41,078.46
62	LARUE	9	2000	218	215	1.00	2.15	-4,085.23
63	LAUREL	9	2000	666	664	3.74	24.81	-48,964.10
64	LAWRENCE	9	2000	764	769	2.65	20.40	-40,023.17
65	LEE	9	2000	208	206	2.53	5.22	-10,226.93
66	LESLIE	9	2000	481	480	4.70	22.56	-44,631.12
67	LETCHER	9	2000	822	827	-0.75	-6.19	13,205.86
68	LEWIS	9	2000	255	271	0.98	2.66	-5,041.33
69	LINCOLN	9	2000	841	844	-0.58	-4.90	10,650.66
70	LIVINGSTON	9	2000	229	230	0.71	1.62	-3,018.87
71	LOGAN	9	2000	310	312	1.73	5.40	-10,491.14
72	LYON	9	2000	227	220	2.08	4.59	-8,961.41
76	MADISON	9	2000	957	956	5.44	52.02	-103,081.52
77	MAGOFFIN	9	2000	496	494	0.03	0.17	158.23
78	MARION	9	2000	214	208	2.86	5.95	-11,696.52
79	MARSHALL	9	2000	970	966	0.49	4.76	-8,557.32
80	MARTIN	9	2000	644	647	4.00	25.91	-51,165.67
81	MASON	9	2000	249	252	0.35	0.89	-1,525.14
73	MCCRACKEN	9	2000	599	597	2.68	16.02	-31,447.21
74	MCCREARY	9	2000	518	518	0.09	0.45	-383.08
75	MCLEAN	9	2000	298	296	1.20	3.56	-6,825.77
82	MEADE	9	2000	393	400	1.06	4.22	-8,049.08
83	MENIFEE	9	2000	237	236	4.31	10.20	-20,163.54
84	MERCER	9	2000	516	502	0.18	0.90	-1,305.05
85	METCALFE	9	2000	297	288	-0.42	-1.21	2,711.60
86	MONROE	9	2000	485	473	1.41	6.68	-12,884.67
87	MONTGOMERY	9	2000	686	693	2.81	19.47	-38,250.75
88	MORGAN	9	2000	649	640	0.64	4.09	-7,533.77
89	MUHLENBERG	9	2000	984	988	1.13	11.14	-21,284.26
90	NELSON	9	2000	434	427	2.99	12.75	-25,080.68
91	NICHOLAS	9	2000	232	232	1.53	3.55	-6,878.12
92	OHIO	9	2000	927	932	2.39	22.22	-43,504.34
93	OLDHAM	9	2000	1,950	1,950	3.06	59.73	-117,509.04
94	OWEN	9	2000	423	438	1.74	7.61	-14,782.64
95	OWSLEY	9	2000	231	247	3.69	9.10	-17,951.85
96	PENDLETON	9	2000	418	419	3.85	16.12	-31,827.34
97	PERRY	9	2000	598	604	2.57	15.51	-30,408.43
98	PIKE	9	2000	1,098	1,090	1.01	10.98	-20,878.14
99	POWELL	9	2000	730	724	1.42	10.29	-19,847.72
100	PULASKI	9	2000	506	503	4.10	20.62	-40,729.73
101	ROBERTSON	9	2000	118	118	1.66	1.95	-3,782.18
102	ROCKCASTLE	9	2000	434	436	3.12	13.57	-26,714.25
103	ROWAN	9	2000	262	262	2.13	5.57	-10,875.75
104	RUSSELL	9	2000	366	367	3.61	13.25	-26,130.44
105	SCOTT	9	2000	356	363	2.05	7.43	-14,493.76
106	SHELBY	9	2000	568	572	3.85	22.04	-43,499.14
107	SIMPSON	9	2000	260	253	7.15	18.07	-35,890.22
108	SPENCER	9	2000	422	418	4.66	19.51	-38,591.74
109	TAYLOR	9	2000	350	349	2.87	10.01	-19,662.53
110	TODD	9	2000	411	413	2.04	8.41	-16,398.49
111	TRIGG	9	2000	312	311	2.17	6.76	-13,202.36
112	TRIMBLE	9	2000	287	286	0.61	1.75	-3,218.44
113	UNION	9	2000	305	306	-2.43	-7.44	15,180.51
114	WARREN	9	2000	1,069	1,078	2.39	25.76	-50,438.57
115	WASHINGTON	9	2000	225	225	2.78	6.24	-12,251.86
116	WAYNE	9	2000	735	739	2.38	17.56	-34,377.89
117	WEBSTER	9	2000	486	489	1.53	7.47	-14,456.52
118	WHITLEY	9	2000	543	534	0.32	1.71	-2,893.10
119	WOLFE	9	2000	488	479	1.84	8.79	-17,110.74
120	WOODFORD	9	2000	1,202	1,220	3.57	43.53	-85,835.17

Functional Class 11, Unweighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Predicted 2000 ADT	2000 Growth (%)	Regression Slope	Regression Constant
8	BOONE	11	2000	101,025	103,537	4.29	4,444.66	-8,785,792.48
15	BULLITT	11	2000	79,700	77,913	3.00	2,340.61	-4,603,299.39
19	CAMPBELL	11	2000	90,767	89,767	1.93	1,736.04	-3,382,311.26
24	CHRISTIAN	11	2000	25,300	24,657	4.52	1,114.29	-2,203,914.29
25	CLARK	11	2000	41,000	42,278	3.35	1,415.15	-2,788,024.85
34	FAYETTE	11	2000	54,338	55,047	3.14	1,730.15	-3,405,255.02
47	HARDIN	11	2000	47,050	48,715	4.10	1,997.88	-3,947,042.12
56	JEFFERSON	11	2000	94,464	97,740	3.07	3,002.49	-5,907,234.27
59	KENTON	11	2000	125,864	128,871	2.78	3,583.45	-7,038,022.27
63	LAUREL	11	2000	35,550	37,744	3.34	1,259.70	-2,481,650.30
76	MADISON	11	2000	44,000	44,594	2.77	1,233.03	-2,421,466.97
73	MCCRACKEN	11	2000	34,400	34,835	3.76	1,308.33	-2,581,831.67
105	SCOTT	11	2000	42,100	43,765	4.25	1,861.21	-3,678,658.79
114	WARREN	11	2000	44,500	42,620	2.24	953.33	-1,864,046.67
118	WHITLEY	11	2000	34,600	38,131	3.52	1,342.42	-2,646,717.58

Functional Class 12, Unweighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Predicted 2000 ADT	2000 Growth (%)	Regression Slope	Regression Constant
5	BARREN	12	2000	6,920	7,173	3.46	247.94	-488,706.06
19	CAMPBELL	12	2000	48,800	40,049	1.23	490.91	-941,769.09
24	CHRISTIAN	12	2000	14,967	15,097	2.41	364.40	-713,712.86
30	DAVIESS	12	2000	18,814	18,930	3.15	597.03	-1,175,133.11
34	FAYETTE	12	2000	63,689	63,617	3.15	2,003.34	-3,943,066.94
42	GRAVES	12	2000	15,033	14,931	3.84	573.49	-1,132,053.57
47	HARDIN	12	2000	20,850	21,462	2.82	605.66	-1,189,851.01
51	HENDERSON	12	2000	25,300	24,356	0.67	162.02	-299,684.65
54	HOPKINS	12	2000	19,633	21,278	0.95	203.17	-385,071.43
56	JEFFERSON	12	2000	33,700	33,621	3.63	1,219.09	-2,404,560.91
90	NELSON	12	2000	9,590	10,237	4.89	500.67	-991,096.22
100	PULASKI	12	2000	10,400	9,824	-0.28	-27.52	64,854.48
114	WARREN	12	2000	11,740	11,697	4.29	501.43	-991,171.90

Functional Class 14, Unweighted County Level Growth Rates

County	County Name	Functional	Year	Average	Predicted	2000	Regression	Regression
Number		Class		ADT	2000 ADT	Growth (%)	Slope	Constant
3	ANDERSON	14	2000	16,200	16,532	3.22	531.52	-1,046,498.48
5	BARREN	14	2000	15,311	15,397	2.04	313.37	-611,334.16
7	BELL	14	2000	26,225	25,593	2.38	608.48	-1,191,376.52
9	BOURBON	14	2000	8,743	8,600	1.62	138.94	-269,272.96
10	BOYD	14	2000	21,286	21,305	0.88	187.45	-353,595.48
11	BOYLE	14	2000	14,009	14,097	2.52	354.59	-695,079.74
15	BULLITT	14	2000	19,200	18,848	3.33	627.68	-1,236,505.66
18	CALLOWAY	14	2000	17,117	17,670	2.13	375.55	-733,429.74
19	CAMPBELL	14	2000	15,586	15,464	0.46	71.69	-127,922.58
24	CHRISTIAN	14	2000	17,047	16,820	2.36	397.48	-778,137.99
25	CLARK	14	2000	19,600	19,332	2.89	557.79	-1,096,244.21
30	DAVIESS	14	2000	14,713	14,765	0.91	134.61	-254,446.92
34	FAYETTE	14	2000	29,879	29,936	1.33	398.40	-766,868.92
37	FRANKLIN	14	2000	23,122	23,514	3.00	706.07	-1,388,625.70
42	GRAVES	14	2000	9,434	9,572	0.40	38.16	-66,754.84
45	GREENUP	14	2000	20,988	21,183	1.52	321.21	-621,241.29
47	HARDIN	14	2000	27,838	28,242	1.53	430.72	-833,198.90
49	HARRISON	14	2000	10,355	10,582	0.94	99.38	-188,175.87
51	HENDERSON	14	2000	26,767	26,910	0.91	246.06	-465,217.94
54	HOPKINS	14	2000	13,924	14,578	1.58	230.43	-446,288.14
56	JEFFERSON	14	2000	23,989	24,103	0.55	132.30	-240,494.31
57	JESSAMINE	14	2000	22,200	21,880	2.88	630.36	-1,238,847.64
59	KENTON	14	2000	12,060	11,812	-0.03	-3.85	19,514.09
61	KNOX	14	2000	26,700	27,178	4.63	1,257.19	-2,487,206.14
63	LAUREL	14	2000	20,000	19,946	2.82	561.74	-1,103,536.26
71	LOGAN	14	2000	8,785	8,815	1.96	172.53	-336,245.80
76	MADISON	14	2000	17,310	17,643	1.69	297.92	-578,207.22
78	MARION	14	2000	12,600	12,489	2.14	267.70	-522,918.07
81	MASON	14	2000	14,229	14,462	0.90	129.55	-244,632.34
73	MCCRACKEN	14	2000	16,207	16,443	0.28	45.80	-75,159.65
82	MEADE	14	2000	14,390	14,624	2.41	351.73	-688,830.27
84	MERCER	14	2000	17,818	18,014	2.05	369.83	-721,652.50
87	MONTGOMERY	14	2000	18,050	18,038	4.22	760.61	-1,503,173.64
90	NELSON	14	2000	14,942	14,873	1.58	235.61	-456,338.69
97	PERRY	14	2000	18,986	19,012	2.54	482.08	-945,143.64

98	PIKE	14	2000	27,722	27,857	2.66	741.89	-1,455,913.67
100	PULASKI	14	2000	24,020	23,614	0.29	67.39	-111,162.23
103	ROWAN	14	2000	19,691	18,891	2.15	405.77	-792,657.50
105	SCOTT	14	2000	12,313	12,640	4.17	527.68	-1,042,713.88
106	SHELBY	14	2000	18,985	19,200	3.01	577.14	-1,135,075.17
109	TAYLOR	14	2000	17,991	18,116	2.01	363.53	-708,941.14
114	WARREN	14	2000	19,581	19,618	2.03	399.01	-778,392.79
116	WAYNE	14	2000	10,455	10,628	5.23	555.76	-1,100,886.74
120	WOODFORD	14	2000	23,075	22,864	0.95	218.03	-413,196.97

Functional Class 16, Unweighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Predicted 2000 ADT	2000 Growth (%)	Regression Slope	Regression Constant
3	ANDERSON	16	2000	9,165	9,110	1.28	116.87	-224,623.73
5	BARREN	16	2000	7,412	7,478	1.35	101.28	-195,091.10
7	BELL	16	2000	8,489	8,558	1.82	156.04	-303,520.04
8	BOONE	16	2000	18,570	18,316	2.16	395.48	-772,653.64
9	BOURBON	16	2000	7,635	7,653	1.33	102.03	-196,409.77
10	BOYD	16	2000	8,835	8,864	0.74	65.48	-122,104.25
11	BOYLE	16	2000	6,769	6,680	0.74	49.22	-91,769.84
15	BULLITT	16	2000	13,071	13,227	3.05	404.04	-794,853.96
17	CALDWELL	16	2000	6,181	6,268	1.19	74.30	-142,324.12
18	CALLOWAY	16	2000	9,334	9,510	1.99	189.14	-368,775.08
19	CAMPBELL	16	2000	5,982	5,903	0.05	2.69	524.16
24	CHRISTIAN	16	2000	8,688	8,633	0.91	78.96	-149,294.36
25	CLARK	16	2000	10,669	10,835	2.00	216.46	-422,093.29
30	DAVIESS	16	2000	8,279	8,197	1.21	98.81	-189,425.00
34	FAYETTE	16	2000	13,683	13,737	2.41	330.71	-647,689.55
37	FRANKLIN	16	2000	9,922	9,976	0.76	75.50	-141,018.34
42	GRAVES	16	2000	3,933	4,076	0.78	31.84	-59,600.73
45	GREENUP	16	2000	8,720	8,635	1.89	163.04	-317,448.14
47	HARDIN	16	2000	11,560	11,694	2.25	263.68	-515,675.77
49	HARRISON	16	2000	5,310	5,325	1.37	72.79	-140,260.44
51	HENDERSON	16	2000	8,952	8,919	1.76	157.17	-305,412.96
54	HOPKINS	16	2000	12,781	12,790	0.99	126.71	-240,626.42
56	JEFFERSON	16	2000	15,342	15,333	1.21	185.59	-355,846.34
57	JESSAMINE	16	2000	12,065	12,168	2.74	333.02	-653,874.35
59 61	KENTON KNOX	16 16	2000 2000	13,466 8,920	13,152 8,796	0.92 0.27	121.65 23.58	-230,155.11 -38,355.22
63	LAUREL	16	2000	10,274	10,258	0.77	78.64	-147,023.03
71	LOGAN	16	2000	7,170	7,430	1.65	122.55	-237,671.32
76	MADISON	16	2000	8,822	9,016	1.91	171.91	-334,801.49
78	MARION	16	2000	5,458	5,278	-0.48	-25.23	55,746.66
81	MASON	16	2000	5,160	5,166	-0.67	-34.58	74,325.99
73	MCCRACKEN	16	2000	7,155	7,190	0.67	48.13	-89,073.58
82	MEADE	16	2000	3,780	4,325	6.10	263.70	-523,069.30
84	MERCER	16	2000	4,641	4,731	1.91	90.50	-176,258.98
87	MONTGOMERY	16	2000	7,078	7,172	1.23	88.16	-169,149.31
90	NELSON	16	2000	9,309	9,375	3.25	304.68	-599,983.85
93	OLDHAM	16	2000	11,492	11,273	2.56	288.87	-566,472.73
97	PERRY	16	2000	8,043	8,171	1.10	89.99	-171,814.17
98	PIKE	16	2000	9,558	9,541	0.55	52.80	-96,049.70
100	PULASKI	16	2000	6,885	6,930	0.41	28.57	-50,209.11
103	ROWAN	16	2000	7,290	7,500	2.67	200.02	-392,548.38
105	SCOTT	16	2000	11,228	11,250	0.94	105.40	-199,542.36
106	SHELBY	16	2000	8,590	8,688	3.36	291.76	-574,827.58
107	SIMPSON	16	2000	6,367	6,262	-0.10	-5.98	18,225.53
109	TAYLOR	16	2000	8,371	8,354	1.29	107.75	-207,151.16
114	WARREN	16	2000	11,580	11,565	1.87	216.29	-421,019.57
116	WAYNE	16	2000	12,137	12,065	1.81	218.52	-424,974.85
118	WHITLEY	16	2000	10,569	10,594	1.87	198.46	-386,333.87
120	WOODFORD	16	2000	9,474	9,377	0.89	83.51	-157,636.21

Functional Class 17, Unweighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Predicted 2000 ADT	2000 Growth (%)	Regression Slope	Regression Constant
3	ANDERSON	17	2000	5,554	5,740	2.42	138.82	-271,904.61
5	BARREN	17	2000	2,812	2,819	0.32	8.98	-15,150.06
7	BELL	17	2000	3,674	3,674	2.52	92.66	-181,644.91
8	BOONE	17	2000	11,763	11,834	4.22	499.47	-987,110.74
9	BOURBON	17	2000	2,331	2,349	1.49	34.91	-67,466.30
10	BOYD	17	2000	4,199	4,166	1.91	79.58	-154,997.17
11	BOYLE	17	2000	3,783	3,840	1.19	45.82	-87,802.32
15	BULLITT	17	2000	5,066	5,096	2.95	150.47	-295,843.53
17	CALDWELL	17	2000	1,986	1,956	-0.34	-6.64	15,228.20
18	CALLOWAY	17	2000	3,508	3,601	2.01	72.49	-141,380.43
19	CAMPBELL	17	2000	6,989	6,970	1.17	81.45	-155,923.31
24	CHRISTIAN	17	2000	3,842	3,865	1.11	42.83	-81,788.37
25	CLARK	17	2000	2,680	2,737	1.06	28.97	-55,205.09
30	DAVIESS	17	2000	4,046	4,003	0.81	32.41	-60,826.02
34	FAYETTE	17	2000	4,992	4,982	2.12	105.53	-206,076.22
37	FRANKLIN	17	2000	3,507	3,507	1.32	46.32	-89,135.04
38	FULTON	17	2000	469	467	-0.18	-0.82	2,103.54
42	GRAVES	17	2000	3,047	3,050	0.59	18.13	-33,218.03
45	GREENUP	17	2000	4,653	4,673	1.92	89.63	-174,596.81
47	HARDIN	17	2000	4,586	4,652	1.90	88.28	-171,907.75
49	HARRISON	17	2000	3,295	3,311	0.40	13.30	-23,296.54
51	HENDERSON	17	2000	2,907	2,942	1.63	48.00	-93,056.76
54	HOPKINS	17	2000	3,921	3,921	0.57	22.28	-40,645.05
56	JEFFERSON	17	2000	7,014	6,972	1.49	103.71	-200,453.55
57	JESSAMINE	17	2000	3,047	3,050	1.71	52.13	-101,212.45
59	KENTON	17	2000	5,738	5,630	0.99	55.52	-105,416.34
61	KNOX	17	2000	1,978	1,976	1.62	32.05	-62,132.58
63	LAUREL	17	2000	2,365	2,391	1.21	29.03	-55,675.28
71	LOGAN	17	2000	1,579	1,663	-2.22	-36.96	75,590.84
76	MADISON	17	2000	6,440	6,372	1.41	89.54	-172,705.66
78	MARION	17	2000	2,333	2,260	0.30	6.77	-11,279.04
81	MASON	17	2000	2,391	2,352	1.90	44.73	-87,107.79
73	MCCRACKEN	17	2000	4,558	4,569	1.19	54.56	-104,556.00
82	MEADE	17	2000	5,367	5,878	-1.29	-76.12	158,120.55
84	MERCER	17	2000	4,223	4,394	3.64	160.06	-315,729.21
87	MONTGOMERY	17	2000	2,045	2,066	-0.25	-5.10	12,265.43
90	NELSON	17	2000	2,215	2,206	1.80	39.72	-77,236.84
93	OLDHAM	17	2000	3,050	3,004	2.32	69.78	-136,564.70
97	PERRY	17	2000	4,800	4,976	2.47	122.98	-240,988.93
98	PIKE	17	2000	3,612	3,521	-1.11	-39.13	81,788.08
100	PULASKI	17	2000	6,387	6,472	2.56	165.54	-324,600.10
103	ROWAN	17	2000	3,717	3,668	0.66	24.23	-44,793.59
105	SCOTT	17	2000	2,405	2,374	0.17	3.95	-5,530.89
106	SHELBY	17	2000	3,728	3,718	1.41	52.40	-101,085.10
107	SIMPSON	17	2000	2,896	2,874	0.28	8.05	-13,216.94
109	TAYLOR	17	2000	3,761	3,730	-0.11	-4.23	12,196.86
114	WARREN	17	2000	4,859	5,016	0.99	49.45	-93,885.80
116	WAYNE	17	2000	1,174	1,047	-10.84	-113.50	228,047.90
118	WHITLEY	17	2000	3,273	3,297	1.85	61.03	-118,760.87
120	WOODFORD	17	2000	4,494	4,561	3.24	147.62	-290,671.07

Functional Class 19, Unweighted County Level Growth Rates

County Number	County Name	Functional Class	Year	Average ADT	Predicted 2000 ADT	2000 Growth (%)	Regression Slope	Regression Constant
5	BARREN	19	2000	906	902	-5.03	-45.37	91,645.96
7	BELL	19	2000	1,713	1,789	3.81	68.21	-134,629.64
8	BOONE	19	2000	2,411	2,393	4.90	117.34	-232,287.19
9	BOURBON	19	2000	1,096	1,112	3.71	41.25	-81,387.58
10	BOYD	19	2000	1,990	1,960	0.50	9.74	-17,525.43
11	BOYLE	19	2000	745	728	3.71	27.00	-53,262.36
15	BULLITT	19	2000	4,790	4,633	2.28	105.50	-206,366.89
16	BUTLER	19	2000	1,320	1,320	0.76	10.00	-18,680.00
17	CALDWELL	19	2000	1,097	1,116	0.76	8.52	-15,917.36
19	CAMPBELL	19	2000	1,062	1,090	3.25	35.42	-69,744.58
21 24	CARROLL CHRISTIAN	19 19	2000 2000	2,850 1,687	2,844 1,692	2.92 5.70	83.00 96.40	-163,155.78 -191,108.03
25	CLARK	19	2000	1,142	1,114	3.07	34.21	-67,305.70
26	CLAY	19	2000	1,255	1,259	1.59	20.03	-38,807.98
27	CLINTON	19	2000	426	426	-2.61	-11.13	22,681.97
30	DAVIESS	19	2000	1,744	1,729	1.78	30.83	-59,933.33
33	ESTILL	19	2000	848	849	2.58	21.93	-43,018.16
34	FAYETTE	19	2000	2,110	2,122	2.27	48.15	-94,177.32
37	FRANKLIN	19	2000	680	658	5.71	37.59	-74,522.53
38	FULTON	19	2000	1,255	1,256	1.21	15.23	-29,197.56
40	GARRARD	19	2000	1,885	1,859	-3.18	-59.18	120,225.96
41	GRANT	19	2000	2,615	2,789	10.52	293.47	-584,144.19
42	GRAVES	19	2000	390	384	-1.59	-6.13	12,644.36
43	GRAYSON	19	2000	526	546	3.88	21.17	-41,787.39
44	GREEN	19	2000	1,360	1,327	0.45	5.96	-10,589.56
45	GREENUP	19	2000	602	597	1.31	7.85	-15,104.54
47 48	HARDIN HARLAN	19 19	2000 2000	2,560 143	2,757 142	-1.44 -6.84	-39.69 -9.68	82,142.31 19,508.27
49	HARRISON	19	2000	1,447	1,441	2.14	30.81	-60,169.50
51	HENDERSON	19	2000	1,146	1,136	4.17	47.34	-93,551.32
52	HENRY	19	2000	1,260	1,258	-1.19	-15.00	31,257.78
54	HOPKINS	19	2000	4,370	4,391	2.69	117.89	-231,392.79
56	JEFFERSON	19	2000	1,126	1,117	1.50	16.72	-32,314.45
57	JESSAMINE	19	2000	1,783	1,795	1.89	34.00	-66,205.36
59	KENTON	19	2000	1,510	1,496	2.79	41.78	-82,064.51
61	KNOX	19	2000	808	807	7.16	57.78	-114,759.48
63	LAUREL	19	2000	1,078	1,074	3.74	40.18	-79,290.85
69	LINCOLN	19	2000	782	781	0.69	5.38	-9,976.93
71	LOGAN	19	2000	551	559	0.85	4.77	-8,974.16
76	MADISON	19	2000	969	961	4.58	43.98	-86,999.18
81	MASON	19	2000	878	879	3.14	27.58	-54,271.14
73 74	MCCRACKEN MCCREARY	19 19	2000 2000	900 194	894 204	4.41 0.17	39.45 0.35	-78,003.49 -496.27
84	MERCER	19	2000	765	763	3.76	28.69	-56,617.73
87	MONTGOMERY	19	2000	1,578	1,583	1.94	30.74	-59,902.02
89	MUHLENBERG	19	2000	703	700	1.79	12.51	-24,325.46
90	NELSON	19	2000	425	406	-2.50	-10.14	20,695.24
97	PERRY	19	2000	486	469	1.21	5.65	-10,838.45
98	PIKE	19	2000	2,866	2,868	1.41	40.56	-78,260.84
100	PULASKI	19	2000	3,234	3,235	2.14	69.08	-134,931.24
103	ROWAN	19	2000	693	687	4.40	30.18	-59,674.07
105	SCOTT	19	2000	1,819	1,798	3.47	62.41	-123,018.61
107	SIMPSON	19	2000	621	606	6.58	39.88	-79,159.52
109	TAYLOR	19	2000	433	423	3.57	15.08	-29,737.83
114	WARREN	19	2000	1,275	1,233	3.89	47.94	-94,639.17
118	WHITLEY	19	2000	861	852	2.95	25.17	-49,486.72
120	WOODFORD	19	2000	180	197	-8.04	-15.83	31,863.56

8.8 Appendix H – Corridor Interstate Growth Rates

I-64 Detailed Corridor Weighted ADT Growth Analysis

Year	Average	Weighted	Predicted Weighted	2000 Weighted ADT	Regressio	Regression
	ADT	Average ADT	Average ADT	Growth Rate (%)	n Slope	Constant
	Jefferson County MP 0.0 - 23.974			2.83	2,320	-4,558,472
1991	62,817	58,358	61,056
1992	68,325	64,749	63,376
1993	75,023	66,486	65,697
1994	76,462	68,979	68,017
1995	77,229	70,610	70,337
1996	76,393	70,772	72,657
1997	82,757	76,621	74,977
1998	87,229	79,312	77,298
1999	86,321	78,837	79,618
2000	87,386	80,247	81,938
	Shelby to Scott MP 23.974 - 71.0			3.24	1,158	-2,280,680
1991	25,642	25,874	25,329
1992	26,300	26,980	26,487
1993	26,483	27,503	27,646
1994	25,792	26,385	28,804
1995	30,242	30,976	29,962
1996	30,569	31,124	31,120
1997	31,785	32,489	32,279
1998	32,923	33,291	33,437
1999	34,969	35,842	34,595
2000	34,462	34,949	35,753
	Fayette County MP 71.0 - 89.48			2.58	847	-1,661,174
1991	26,050	25,443	25,143
1992	25,550	25,524	25,990
1993	26,850	26,665	26,837
1994	26,900	26,636	27,684
1995	32,500	33,292	28,531
1996	28,767	27,853	29,378
1997	27,867	26,559	30,225
1998	31,667	30,999	31,072
1999	33,400	33,898	31,919
2000	32,867 Clark to Boyd MP 89.48 - 191.507	32,680	32,766	2.97	551	-1,083,719

1991	15,236	13,474	13,579
1992 1993	15,440 16,360	14,000 14,767	14,131 14,682
1994	17,660	15,836	15,233
1995	17,686	15,669	15,784
1996	18,050	15,998	16,335
1997	18,714	16,689	16,886
1998	19,723	17,419	17,437
1999	20,614	18,319	17,988
2000	20,632	18,425	18,540
	All Counties MP 0.0 - 194.507			2.95	945	-1,857,131
1991	30,833	23,241	23,531
1992	32,509	24,662	24,475
1993	35,617	25,630	25,420
1994	36,394	26,247	26,364
1995	37,964	27,676	27,309
1996	37,506	27,708	28,254
1997	39,752	29,108	29,198
1998	41,887	30,354	30,143
1999	42,631	31,627	31,087
2000	42,767	31,560	32,032

I-65 Detailed Corridor Weighted ADT Growth Analysis

Year	Average ADT	Weighted	Predicted	2000	Regression Slope	Regression
		Average ADT	Weighted Average	Weighted		Constant
			ADT	ADT Growth Rate (%)
	Simpson to Larue MP 0.0 - 78.661		1.85	660	-1,284,094
1991	26,968	27,374	29,747
1992	30,558	31,878	30,407
1993	33,332	34,646	31,067
1994	29,468	29,686	31,727
1995	32,260	32,724	32,386
1996	31,730	32,121	33,046
1997	32,995	33,500	33,706
1998	33,715	33,977	34,366
1999	35,105	35,201	35,026
2000	35,635	36,055	35,686
	Hardin to Bullitt MP 78.661 - 123.18			3.75	1,982	-3,911,735
1991	34,420	34,658	34,979
1992	37,070	36,614	36,962
1993	38,373	39,881	38,944
1994	40,782	39,650	40,926
1995	43,958	41,841	42,908
1996	47,083	46,185	44,891
1997	48,992	48,183	46,873
1998	51,569	50,493	48,855
1999 2000	52,177 52,454	50,567 50,926	50,838 52,820
	Jefferson MP 123.18 - 137.18			2.64	3,491	-6,850,012
1991	95,644	96,525	101,037
1992	100,233	101,424	104,529
1993	110,556	110,779	108,020
1994	114,900	113,971	111,511
1995	119,844	118,701	115,002
1997	123,467	122,440	121,985
1996	123,767	122,590	118,494
1998	125,656	124,323	125,476
1999	128,222	126,405	128,967
2000	132,056	130,322	132,459
	All Counties MP 0.0 - 137.18			2.49	1,268	-2,485,264
1991	45,195	37,496	39,610
1992	48,774	41,439	40,878
1993	52,574	45,077	42,146
1994	52,374	42,258	43,414
1995	54,910	44,708	44,683
1996	56,427	45,982	45,951
1997	57,333	47,417	47,219
1998	58,943	48,634	48,487
1999	60,343	49,573	49,755
2000	61,502	50,582	51,023

I-71 Detailed Corridor Weighted ADT Growth Analysis

Year	Average ADT	Weighted Average ADT	Predicted Weighted Average ADT	2000 Weighted ADT Growth Rate (%)	Regression Slope	Regression Constant
	Jefferson MP 0.0 - 11.315			2.50	1,421	-2,786,206
1991	43,300	41,961	43,957
1992	51,900	49,387	45,378
1993	50,100	48,972	46,800
1994	46,750	44,519	48,221
1995	49,900	47,967	49,643
1996	51,850	51,568	51,064
1997	50,475	49,749	52,486
1998	58,000	57,625	53,907
1999	55,550	55,362	55,329
2000	55,300	56,424	56,750
	Oldham to Gallatin MP 11.315 - 69.89			3.90	1,208	-2,385,350
1991	22,036	20,272	20,123
1992	23,657	21,723	21,331
1994	25,071	22,994	23,747
1993	24,386	23,802	22,539
1996	26,029	24,327	26,164
1995	26,221	24,382	24,955
1997	29,140	27,107	27,372
1998	31,673	29,304	28,580
1999	32,307	30,548	29,788
2000	32,940	31,137	30,996
	Boone MP 69.89 - 77.724			3.41	1,010	-1,989,547
1991	20,550	21,318	20,537
1992	19,350	19,811	21,546
1993	26,900	27,471	22,556
1994	22,050	22,643	23,566
1995	22,000	22,571	24,575
1996	23,150	23,743	25,585
1997	24,950	25,587	26,594
1998	25,950	26,543	27,604
1999	28,450	29,131	28,614
2000	31,500	31,983	29,623
	All Counties MP 0.0 - 77.724			3.46	1,196	-2,357,128
1991	26,140	23,683	23,836
1992	28,875	25,731	25,032
1994	29,105	26,233	27,424
1995	30,535	27,780	28,620
1993	29,780	28,019	26,228
1996	30,905	28,411	29,816
1997	32,805	30,250	31,011
1998	36,143	33,149	32,207
1999	36,367	34,017	33,403
2000	37,062	34,904	34,599

I-75 Detailed Corridor Weighted ADT Growth Analysis

| | I-75 Detailed Corridor Weighted ADT Growth Analysis | | | | | | | | | |--------------|-----------------------------------------------------|-------------------------|-------------------------------|-----------------------------|---------------------|------------------------|--|--|--| | Year | Average ADT | Weighted Average
ADT | Predicted
Weighted Average | 2000 Weighted
ADT Growth | Regression
Slope | Regression
Constant | | | | | | | | ADT | Rate (%) | | | | | | | | Whitley to Madison MP 0.0 - 97.543 | | | 2.94 | 1,090 | -2,143,117 | | | | | 1991 | 28,811 | 27,498 | 27,249 | | | | | | | | 1992 | 30,705 | 29,162 | 28,339 | | | | | | | | 1993 | 31,105 | 29,423 | 29,429 | | | | | | | | 1994 | 31,168 | 29,238 | 30,519 | | | | | | | | 1995 | 33,526 | 31,527 | 31,609 | | | | | | | | 1996 | 34,295 | 31,925 | 32,699 | | | | | | | | 1997 | 35,979 | 33,544 | 33,790 | | | | | | | | 1998 | 38,721 | 36,060 | 34,880 | | | | | | | | 1999 | 38,637 | 36,767 | 35,970 | | | | | | | | 2000 | 38,405 | 36,401 | 37,060 | | | | | | | | | Fayette MP 97.443 - 120.792 | | | 3.38 | 2,054 | -4,047,406 | | | | | 1991 | 43,322 | 43,639 | 42,309 | | | | | | | | 1992 | 45,756 | 45,659 | 44,363 | | | | | | | | 1993 | 46,350 | 46,304 | 46,417 | | | | | | | | 1994 | 46,830 | 46,664 | 48,471 | | | | | | | | 1995 | 48,440 | 48,208 | 50,525 | | | | | | | | 1996 | 52,410 | 52,253 | 52,580 | | | | | | | | 1997 | 55,560 | 54,636 | 54,634 | | | | | | | | 1998 | 57,100 | 56,114 | 56,688 | | | | | | | | 1999 | 63,120 | 62,074 | 58,742 | | | | | | | | 2000 | 60,780 | 59,974 | 60,796 | | | | | | | | | Scott to Grant MP 120.792 - 166.263 | | | 4.97 | 2,257 | -4,468,205 | | | | | 1991 | 27,570 | 27,263 | 25,131 | | | | | | | | 1992 | 28,390 | 27,967 | 27,388 | | | | | | | | 1993 | 28,640 | 28,683 | 29,644 | | | | | | | | 1994 | 30,730 | 29,773 | 31,901 | | | | | | | | 1995 | 33,400 | 32,531 | 34,158 | | | | | | | | 1996 | 36,610 | 35,412 | 36,415 | | | | | | | | 1997 | 41,000 | 40,349 | 38,672 | | | | | | | | 1998 | 42,950 | 42,001 | 40,929 | | | | | | | | 1999 | 45,160 | 43,824 | 43,185 | | | | | | | | 2000 | 44,920 | 45,061 | 45,442 | | | | | | | | | Boone to Kenton MP 166.263 - 191.777 | | | 3.24 | 3,473 | -6,838,748 | | | | | 1991 | 91,788 | 76,463 | 75,851 | | | | | | | | 1992 | 95,794 | 79,274 | 79,324 | | | | | | | | 1993 | 90,794 | 77,579 | 82,797 | | | | | | | | 1994 | 96,112 | 82,535 | 86,269 | | | | | | | | 1995 | 107,811 | 91,581 | 89,742 | | | | | | | | 1996
1997 | 115,624
111,250 | 104,236
96,451 | 93,215
96,688 | | | | | | | | 1998 | 115,444 | 101,507 | 100,161 | | | | | | | | 1999 | 116,811 | 102,189 | 103,634 | | | | | | | | 2000 | 116,150 | 102,972 | 107,107 | | | | | | | | | I-75 All Data MP 0.0 - 191.777 | | | 3.70 | 1,911 | -3,770,625 | | | | | 1991 | 49,659 | 35,246 | 34,507 | | | | | | | | 1992 | 52,070 | 36,846 | 36,418 | | | | | | | | 1993 | 51,507 | 37,715 | 38,329 | | | | | | | | 1994 | 53,602 | 38,589 | 40,240 | | | | | | | | 1995 | 59,579 | 42,047 | 42,151 | | | | | | | | 1996 | 62,330 | 44,177 | 44,063 | | | | | | | | 1997 | 64,065 | 46,414 | 45,974 | | | | | | | | 1998 | 66,916 | 48,938 | 47,885 | | | | | | | | 1999 | 68,763 | 50,567 | 49,796 | | | | | | | | 2000 | 68,025 | 50,532 | 51,707 | | | | | | | | | | | | | | | | | |

barrettanalysistrafficgrowthrates2001.md 1.7 MB Постоянна връзка История Директен файл

status: converted

K T C ENTUCKY RANSPORTATION ENTER

ANALYSIS OF TRAFFIC GROWTH RATES

Our Mission

We Value...

KENTUCKY TRANSPORTATION CENTER

Research Report KTC-01-15/SPR213-00-1F

ANALYSIS OF TRAFFIC GROWTH RATES

15. Supplementary Notes

16. Abstract

LIST OF FIGURES

LIST OF TABLES

EXECUTIVE SUMMARY

ACKNOWLEDGMENTS

1.0 BACKGROUND AND OBJECTIVES

2.0 REVIEW OF LITERATURE AND SURVEY OF STATES

2.1 Review of Literature

2.2 Survey of States

3.0 DEVELOPMENT OF A RANDOM SAMPLING PROCEDURE FOR LOCAL ROAD TRAFFIC COUNT LOCATIONS

3.1 Introduction

3.2 Other Efforts to Estimate Local Road VMT

3.3 The GIS Grid-Based Sampling Methodology

3.3.1 The Challenges of Finding a Methodology

3.3.2 Creating the Point-like Sections for Three Study Areas

3.4 Consideration of Bias in the Point-like Sections

3.5 Conclusions

3.6 Local Road ADT Sample with Spatial Variables

3.7 Local Road Traffic Volume Summary

3.8 Regression Analysis to Predict Local Road ADT

URBAN

RURAL

4.0 PREDICTION OF VMT BASED ON SOCIOECONOMIC DATA

4.1 Introduction

4.2 Background

4.3 Objective

4.4 Data Input

4.4.1 Data Measures of Quality

4.5 Methodology

4.5.1 Regression Modeling

4.5.2 Neural Networks (NNets) Modeling

4.6 Results

4.6.1 Descriptive Summary of All Trials and Results

4.6.1.1 Regression

4.6.1.1.1 County-Based Regression Models

4.6.1.1.2 State-Based Regression Models

4.6.1.1.3 Addressing the Problem of Correlation between Independent Variables (county-based models)

4.6.1.1.3.1 Reduced Models

Sample of Reduced Models

a) Non-Interstate VMT (county-based):

b) Interstate VMT (county-based)

4.6.1.1.3.2 Models with Transformed Data

4.6.1.1.3.2.1 County-Based Regression Models with Transformed Data

a) Non-interstate VMT

b) Interstate VMT

4.6.1.1.3.2.2. State-Based Regression Models with Transformed Data

4.6.1.1.3.3 Models with a K-Factor

4.6.1.2 NNets Results

4.6.1.2.1 Variations of NNet:

3. NNets with an "adjustment" factor (labeled K-factor)

4.7 Final Models

4.7.1 Non-Interstate VMT

4.7.1.1 NNets with K-Factor

4.7.1.2 Projecting the 2000 Non-Interstate VMT

4.7.2 Interstate VMT

4.7.2.1 Interstate VMT, K-factor Model

4.7.2.2 Projecting the 2000 Interstate VMT

4.7.2.3 Corridor-Based Models

4.7.2.3.1 Assessment of Regression Corridor-based Models

4.7.2.3.2 Assessment of NNet Corridor-based Models

4.7.2.3.3 Corridor-based Models: Are Interstate-VMT and Socioeconomic Factors Related?

4.8. Discussion

4.9 Conclusion

4.10 Recommendations

5.0 DEVELOPMENT OF RATIOS FOR RELATIONSHIP BETWEEN COLLECTORS AND LOCAL ROADS

5.1 Local to Collector Ratio Analysis

5.2 Data Analysis

5.3 2000 Local Sample

6.0 DEVELOPMENT OF COUNTY-LEVEL GROWTH RATES

6.1 Growth Rate Development

barrettanalysistrafficgrowthrates2001.md 1.7 MB

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