--- 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" status: converted --- # K T C ENTUCKY RANSPORTATION ENTER *College of Engineering* ### **ANALYSIS OF TRAFFIC GROWTH RATES** ![](_page_0_Picture_4.jpeg) ![](_page_0_Picture_5.jpeg) ![](_page_1_Picture_0.jpeg) ## *Our Mission* We provide services to the transportation community through research, technology transfer and education. We create and participate in partnerships to promote safe and effective transportation systems. ## *We Value...* Teamwork -- Listening and Communicating, Along with Courtesy and Respect for Others Honesty and Ethical Behavior Delivering the Highest Quality Products and Services Continuous Improvement in All That We Do *For more information or a complete publication list, contact us at:* ### **KENTUCKY TRANSPORTATION CENTER** 176 Raymond Building University of Kentucky Lexington, Kentucky 40506-0281 > (859) 257-4513 (859) 257-1815 (FAX) 1-800-432-0719 www.engr.uky.edu/ktc ktc@engr.uky.edu ### Research Report KTC-01-15/SPR213-00-1F ### **ANALYSIS OF TRAFFIC GROWTH RATES** by 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 | | |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------|--------------------|----| | | | | v | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | | | | | 11 | | | | | 17 | | | | | 17 | | | | | 20 | | | | | 22 | | | | | 26 | | | | | 26 | | | | | 27 | | | | | 27 | | | | | 28 | | | | | 28 | | | | | 29 | | | | | 29 | | | | | 29 | | | | | 30 | | | | | 30 | | | | | 38 | | | | | 38 | | | | | 40 | | | | | 47 | | | | | 51 | | | 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. ![](_page_13_Picture_0.jpeg) ### **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. ![](_page_15_Picture_0.jpeg) ### **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. ![](_page_26_Picture_0.jpeg) **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.) ![](_page_27_Figure_1.jpeg) **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. ![](_page_31_Figure_0.jpeg) **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. ![](_page_33_Picture_0.jpeg) **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** 21 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. ![](_page_38_Figure_0.jpeg) **Figure 5. Major Highways Used in Distance Calculation** ![](_page_39_Picture_0.jpeg) 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** 24 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. ![](_page_47_Figure_5.jpeg) **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)** ![](_page_48_Figure_2.jpeg) **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. ![](_page_50_Figure_0.jpeg) **Figure 9. Distribution of Non-Interstate VMT Prediction Error over Error Range** ![](_page_50_Figure_2.jpeg) **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) + | 0.527 | LN(EMP) + | 2.656 | 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) 36 ### **b) Interstate VMT** | LN(VMT) = | 0.22 | LN(POP) + | 0.411 | LN(EMP) + | 1.098 | 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. ![](_page_54_Figure_9.jpeg) **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. ![](_page_55_Figure_3.jpeg) **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). ![](_page_56_Figure_15.jpeg) **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. ![](_page_57_Figure_3.jpeg) **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. ![](_page_58_Figure_0.jpeg) a) b) c) 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 ![](_page_58_Figure_2.jpeg) **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. ![](_page_59_Figure_4.jpeg) **Figure 16a. Comparison of Actual and Predicted VMT - Training Data (Interstate 75 Corridor)** ![](_page_60_Figure_0.jpeg) **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. ![](_page_61_Figure_0.jpeg) ![](_page_61_Figure_1.jpeg) **Figure 17. Relationship between Change in Interstate VMT and Retail Sales (Interstate 24 Corridor)** ![](_page_62_Figure_0.jpeg) **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. ![](_page_63_Figure_0.jpeg) a) b) c) 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 ![](_page_64_Figure_0.jpeg) **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. ![](_page_64_Figure_3.jpeg) **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. ![](_page_65_Figure_1.jpeg) **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. ![](_page_69_Figure_1.jpeg) **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. ![](_page_70_Figure_0.jpeg) **Figure 23. Non-Urbanized Counties Functional Class Ratio FC 09/08** ![](_page_70_Figure_2.jpeg) **Figure 24. Urbanized Counties Functional Class Ratio FC 09/08** ![](_page_71_Figure_0.jpeg) **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. ![](_page_73_Figure_3.jpeg) **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. ![](_page_75_Figure_0.jpeg) **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. ![](_page_77_Figure_0.jpeg) **Figure 28. Allen County Functional Class 06 Traffic Monitoring Stations** ![](_page_77_Figure_2.jpeg) **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. ![](_page_79_Figure_0.jpeg) **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. ![](_page_80_Figure_0.jpeg) **Figure 31. Statewide Unweighted Average ADT for Rural Functional Classes** ![](_page_80_Figure_2.jpeg) **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 ![](_page_92_Figure_3.jpeg) | | | | 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 | | | | | | | (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. #### **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 | 1 | | 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 | - | | | | **5** | | 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
Alaska
No
No
Arizona
No
No
Connecticut
N/A
N/A
Delaware
Yes
No | | | | |---------------------------------------------------------------------------------------------------------------------------------------------|--|--|--| | | | | | | | | | | | | | | | | | | | | | | | | | | Florida
-
- | | | | | Illinois
Yes
- | | | | | Indiana
Yes
No | | | | | Iowa
Yes
No | | | | | Kentucky
Yes
No | | | | | Massachusetts
No
No | | | | | Minnesota
No
Yes | | | | | Nebraska
Yes
No (insufficient data) | | | | | New Hampshire
Yes (for short term growth (1-3 years))
No | | | | | 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 not all classes)
No | | | | | No (although the groups can be related to FC: interstates are
Vermont
No
1,11; urban are 12-19; and primary and secondary are 2-9) | | | | | Virginia
Yes
No | | | | | West Virginia
Yes
No | | | | | Wisconsin
Yes (but only for 2 groups: prin. arterials and all lower FC)
No | | | | | No (unless we have 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 | 1
ADAIR
8
2000
770
780
1.25
9.74
-18,699.39
2
ALLEN
8
2000
969
1,016
0.31
3.17
-5,320.59
3
ANDERSON
8
2000
784
786
1.57
12.30
-23,813.88
4
BALLARD
8
2000
364
362
0.32
1.17
-1,974.62
5
BARREN
8
2000
584
588
1.87
11.02
-21,460.50
6
BATH
8
2000
543
543
1.95
10.61
-20,679.88
7
BELL
8
2000
1,050
1,071
2.74
29.33
-57,584.82
8
BOONE
8
2000
1,248
1,264
2.63
33.20
-65,143.74
9
BOURBON
8
2000
952
953
2.38
22.70
-44,447.94
10
BOYD
8
2000
568
588
-3.72
-21.92
44,428.28
11
BOYLE
8
2000
751
742
1.79
13.29
-25,847.92
12
BRACKEN
8
2000
478
484
3.17
15.33
-30,179.10
13
BREATHITT
8
2000
526
525
1.18
6.19
-11,863.24
14
BRECKINRIDGE
8
2000
549
549
0.92
5.06
-9,578.36
15
BULLITT
8
2000
1,382
1,392
3.57
49.67
-97,949.37
16
BUTLER
8
2000
468
467
1.48
6.91
-13,343.36
17
CALDWELL
8
2000
358
358
0.95
3.41
-6,463.10
18
CALLOWAY
8
2000
696
702
1.84
12.95
-25,200.32
19
CAMPBELL
8
2000
764
795
3.98
31.65
-62,511.45
20
CARLISLE
8
2000
271
267
-0.08
-0.23
717.49
21
CARROLL
8
2000
479
489
3.70
18.08
-35,676.15
22
CARTER
8
2000
875
864
0.81
7.01
-13,147.30
23
CASEY
8
2000
593
590
1.40
8.25
-15,901.90
24
CHRISTIAN
8
2000
588
583
1.46
8.51
-16,430.78
25
CLARK
8
2000
872
881
1.97
17.40
-33,913.70
26
CLAY
8
2000
1,121
1,089
1.66
18.09
-35,088.88
27
CLINTON
8
2000
797
802
1.71
13.70
-26,598.52
28
CRITTENDEN
8
2000
383
382
0.22
0.85
-1,308.33
29
CUMBERLAND
8
2000
397
398
1.94
7.73
-15,059.75
30
DAVIESS
8
2000
744
746
2.01
14.97
-29,201.75
31
EDMONSON
8
2000
614
580
2.45
14.18
-27,786.69
32
ELLIOTT
8
2000
390
395
2.46
9.72
-19,041.13
33
ESTILL
8
2000
934
937
2.49
23.33
-45,728.54
35
FLEMING
8
2000
667
680
2.51
17.04
-33,397.89
36
FLOYD
8
2000
2,857
2,865
1.49
42.76
-82,655.59
37
FRANKLIN
8
2000
889
902
1.83
16.54
-32,172.38
38
FULTON
8
2000
1,050
1,063
0.31
3.32
-5,574.05
39
GALLATIN
8
2000
708
702
4.35
30.51
-60,328.01
40
GARRARD
8
2000
521
536
3.39
18.20
-35,855.34
41
GRANT
8
2000
1,237
1,264
3.01
38.03
-74,798.90
42
GRAVES
8
2000
885
888
1.49
13.22
-25,550.35
43
GRAYSON
8
2000
1,138
1,150
2.66
30.59
-60,026.94
44
GREEN
8
2000
376
381
1.32
5.02
-9,661.17
45
GREENUP
8
2000
665
666
1.00
6.63
-12,601.52
46
HANCOCK
8
2000
684
679
1.89
12.81
-24,942.74
47
HARDIN
8
2000
1,598
1,585
2.92
46.32
-91,047.75
48
HARLAN
8
2000
2,489
2,559
0.99
25.36
-48,164.23
49
HARRISON
8
2000
687
683
2.84
19.39
-38,104.07
50
HART
8
2000
492
498
2.22
11.04
-21,581.16
51
HENDERSON
8
2000
576
586
1.51
8.83
-17,066.86
52
HENRY
8
2000
693
696
1.97
13.72
-26,749.28
53
HICKMAN
8
2000
317
317
0.48
1.51
-2,701.33
54
HOPKINS
8
2000
1,195
1,190
0.57
6.73
-12,278.69
55
JACKSON
8
2000
537
541
3.27
17.68
-34,814.05
56
JEFFERSON
8
2000
1,385
1,375
3.96
54.45
-107,524.95
57
JESSAMINE
8
2000
1,470
1,448
0.77
11.20
-20,945.75
58
JOHNSON
8
2000
1,428
1,424
1.19
16.96
-32,495.07
59
KENTON
8
2000
605
591
2.50
14.80
-29,007.89
60
KNOTT
8
2000
1,152
1,139
0.41
4.63
-8,123.07
61
KNOX
8
2000
1,481
1,464
3.21
47.04
-92,606.83
62
LARUE
8
2000
1,092
1,109
1.00
11.10
-21,091.30
63
LAUREL
8
2000
927
944
2.66
25.13
-49,311.90
64
LAWRENCE
8
2000
978
986
3.73
36.81
-72,626.00
65
LEE
8
2000
759
724
0.46
3.30
-5,882.75
66
LESLIE
8
2000
959
980
0.19
1.88
-2,784.04
67
LETCHER
8
2000
1,468
1,480
2.21
32.65
-63,829.22
68
LEWIS
8
2000
711
690
-1.10
-7.56
15,811.78
69
LINCOLN
8
2000
1,496
1,500
3.15
47.30
-93,107.12
70
LIVINGSTON
8
2000
520
524
1.59
8.32
-16,117.93
71
LOGAN
8
2000
583
585
1.48
8.65
-16,722.64
72
LYON
8
2000
547
527
0.89
4.68
-8,830.88
76
MADISON
8
2000
1,145
1,158
2.84
32.85
-64,534.95
77
MAGOFFIN
8
2000
541
526
1.88
9.91
-19,303.10
78
MARION
8
2000
991
976
1.93
18.86
-36,745.09
79
MARSHALL
8
2000
896
893
0.89
7.96
-15,023.38
80
MARTIN
8
2000
1,453
1,474
0.83
12.21
-22,953.09
81
MASON
8
2000
530
548
1.78
9.77
-18,995.26
73
MCCRACKEN
8
2000
970
983
1.77
17.34
-33,705.22
74
MCCREARY
8
2000
1,508
1,488
1.14
17.03
-32,566.06
75
MCLEAN
8
2000
554
555
1.69
9.35
-18,154.00
82
MEADE
8
2000
1,045
1,048
1.95
20.39
-39,734.05
83
MENIFEE
8
2000
449
431
0.46
2.00
-3,567.34
84
MERCER
8
2000
463
467
1.81
8.42
-16,383.01
85
METCALFE
8
2000
622
617
-0.14
-0.87
2,355.92
86
MONROE
8
2000
799
795
-0.04
-0.30
1,396.14
87
MONTGOMERY
8
2000
925
902
1.65
14.87
-28,835.31
88
MORGAN
8
2000
376
375
1.60
5.99
-11,603.94
89
MUHLENBERG
8
2000
1,872
1,869
1.14
21.29
-40,708.93
90
NELSON
8
2000
764
772
2.88
22.26
-43,742.53
91
NICHOLAS
8
2000
465
464
1.19
5.53
-10,591.92
92
OHIO
8
2000
781
786
1.15
9.08
-17,372.88
93
OLDHAM
8
2000
1,695
1,675
3.23
54.05
-106,424.30
94
OWEN
8
2000
448
454
2.03
9.22
-17,982.90
95
OWSLEY
8
2000
344
332
0.28
0.95
-1,559.99
96
PENDLETON
8
2000
946
948
2.72
25.82
-50,693.86
97
PERRY
8
2000
1,134
1,132
1.56
17.71
-34,283.38
98
PIKE
8
2000
1,944
1,936
0.86
16.70
-31,459.01
99
POWELL
8
2000
875
873
2.03
17.70
-34,531.13
100
PULASKI
8
2000
805
787
1.67
13.12
-25,451.60
101
ROBERTSON
8
2000
218
219
1.39
3.04
-5,869.66
102
ROCKCASTLE
8
2000
987
985
2.22
21.84
-42,689.47
103
ROWAN
8
2000
853
872
2.81
24.52
-48,160.57
104
RUSSELL
8
2000
1,477
1,477
1.82
26.91
-52,333.61
105
SCOTT
8
2000
1,223
1,253
3.89
48.80
-96,339.47
106
SHELBY
8
2000
742
763
2.67
20.34
-39,923.21
107
SIMPSON
8
2000
630
661
1.38
9.14
-17,613.33
108
SPENCER
8
2000
678
661
3.75
24.82
-48,985.08
109
TAYLOR
8
2000
736
746
1.97
14.72
-28,698.70
110
TODD
8
2000
592
596
2.01
11.99
-23,390.54
111
TRIGG
8
2000
604
600
1.19
7.15
-13,708.47
112
TRIMBLE
8
2000
554
527
-2.02
-10.64
21,798.65
113
UNION
8
2000
663
656
0.52
3.40
-6,149.67
114
WARREN
8
2000
1,298
1,278
2.80
35.74
-70,207.27
115
WASHINGTON
8
2000
940
954
1.23
11.72
-22,486.98
116
WAYNE
8
2000
560
565
1.35
7.64
-14,724.01
117
WEBSTER
8
2000
683
688
0.45
3.12
-5,543.08
118
WHITLEY
8
2000
1,209
1,208
1.90
22.98
-44,745.75
119
WOLFE
8
2000
468
471
0.64
3.03
-5,581.47
120
WOODFORD
8
2000
836
837
1.32
11.05
-21,263.68 | | | | | | | | | | |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------|-------------|------------------|------|-------------|--------------------|-----------------|------------------|---------------------| | | 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 | | | | | | | | | | | | | | | | | |