Statistical Evaluation of Projected Traffic Growth

Transportation Research, Education, and Technology Transfer ITQ

Contract Purchase Order No. 030219

Task 5

Final Report

Traffic Growth Forecasting System

March 14, 2005

Submitted by

Michael Baker Jr., Inc.

Submitted to

Pennsylvania Department of

Transportation
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Table of Contents

List of Tables .................................................................................................................................. 4

List of Figures................................................................................................................................. 5

1.Executive Summary..................................................................................................................... 7

2. Defining and Prioritizing Goals and Objectives......................................................................... 9

3. Reviewing State of the Art and Practice in Traffic Growth Forecasting.................................. 15

3.1. Traffic Growth Forecasting Methods—Based on the Type of Data Used......................... 15

3.1.1. Traffic Count Based Forecasting ............................................................................... 15

3.1.2. Socioeconomic Data Based Methods.......................................................................... 16

3.1.3. Travel Demand Forecasting Models........................................................................... 18

3.2. Traffic Growth Forecasting Methods—Based on Forecasting Techniques....................... 20

3.2.1. Growth Factors............................................................................................................ 20

3.2.2. Time Series Models.................................................................................................... 21

3.2.3. Econometric Modeling (Regressions)......................................................................... 21

3.3. Alternative Statistical Models............................................................................................ 23

3.4. Traffic Growth Forecasting Methods—Analytical Issues................................................. 23

3.4.1. Units of Analysis......................................................................................................... 24

3.4.2. Local Road VMT Estimation...................................................................................... 25

3.5. State of the Practice ........................................................................................................... 26

3.5.1. Kentucky.................................................................................................................... 26

3.5.2. Indiana......................................................................................................................... 28

3.5.3. Oregon......................................................................................................................... 30

3.6. Review of PENNDOT and MPO Travel Data................................................................. 33

3.6.1. Overview of PENNDOT Traffic Monitoring System................................................ 34

3.6.2. Overview of PENNDOT Trend Factors .................................................................... 35

3.6.3. Role / Limitations of PENNDOT Traffic Data for Forecasting ................................. 36

3.6.4. Role of MPO Travel Demand Model Data for Forecasting........................................ 38

3.6.5. Utilizing and Interpreting Multiple Sets of Growth Rates.......................................... 43

3.7. Review of Socioeconomic Data and Forecasts................................................................ 44

3.7.1. Socioeconomic Variables and Traffic Growth Forecasting........................................ 44

3.7.2. Data Vendor Description ............................................................................................ 47
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3.7.3. Evaluation and Selection of A Data Vendor............................................................... 52

4. Evaluation of Candidate Methods for Detailed Study.............................................................. 54

5. Growth Analysis ....................................................................................................................... 56

5.1 Traffic Growth Analysis..................................................................................................... 56

5.2 Socioeconomic Growth Analysis........................................................................................ 61

6. Statistical Modeling of Traffic Growth..................................................................................... 72

6.1. Data and Variables............................................................................................................ 72

6.2. Methodology..................................................................................................................... 75

6.3. Model Results................................................................................................................... 77

6.3.1. County-level OLS Base Models ................................................................................ 78

6.3.2. County-level OLS Difference Models....................................................................... 80

6.3.3. County-Group OLS Base Models.............................................................................. 81

6.4. Evaluation ......................................................................................................................... 82

6.5. Forecasting........................................................................................................................ 83

6.5. Discussion............................................................................................................................. 95

7. Conclusions and Recommendations....................................................................................... 98

References................................................................................................................................... 102

Appendix A. VMT and LM Growth Trend by County.............................................................. 105

Appendix B. Socioeconomic Growth Trend by County............................................................ 113

Appendix C. Model Estimation Results...................................................................................... 121

Appendix D. VMT Growth Rate Forecasts ................................................................................ 147

Appendix E. Summary Tables of Data and Studies.................................................................... 156
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List of Tables

Table 2-1. HPMS Data Used for Apportionment ........................................................................ 13

Table 3-1. Highway Facility Groups ............................................................................................ 24

Table 3-2. State Practices Used to Report VMT........................................................................... 25

Table 3-3. Pros and Cons of the General Recommendation......................................................... 32

Table 3-4. Potential Role of PENNDOT and MPO Travel Data.................................................. 33

Table 3-5. PENNDOT Traffic Data’s Use in Forecasting............................................................ 37

Table 4-1. Comparing Different Forecasting Methods against PENNDOT Needs...................... 54

Table 5-1. Long Term Historical VMT Annual Growth Rates .................................................... 57

Table 5-2. Short Term Statewide Total VMT Annual Growth Rates........................................... 57

Table 5-3. Short Term Statewide Truck VMT Annual Growth Rates.......................................... 57

Table 5-4. Long Term Annual Growth Rates............................................................................... 61

Table 5-5. Short Term Annual Growth Rates............................................................................... 62

Table 6-1. Variables Tested in the Regression Modeling............................................................. 73

Table 6-2. County Group Definition............................................................................................. 74

Table 6-3. Model Comparisons..................................................................................................... 77

Table 6-4. OLS Base Models — Model POP............................................................................... 79

Table 6-5. OLS Base Models— Model HH.................................................................................. 79

Table 6-6. OLS Base Models— Model HH JrSr.......................................................................... 79

Table 6-7. OLS Difference Models - Model HH.......................................................................... 80

Table 6-8. OLS Difference Models - Model HH JrSr .................................................................. 80

Table 6-9. County Group Models - Model HH............................................................................. 81

Table 6-10. Mean Absolute Percentage Errors for Different Models........................................... 82

Table 6-11. VMT Annual Growth Rates Forecasted by Different Models .................................. 83

Table 6-12. Growth Rates from HH Regression Model and Travel Demand Models ................. 86

Table 6-13. LM Sensitivity Tests of Model HH (VMT Annual Growth Rates)........................... 97

Table 6-14. LM Sensitivity Tests of Model CntyGrp HH (VMT Annual Growth Rates)............ 97

Table 7-1. Annual Growth Rates (Averaged for Interstates)........................................................ 99
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List of Figures

Figure 2-1. MPOs and RPOs in the Commonwealth of Pennsylvania 10

Figure 3-1. General Recommendation for Estimating Statewide VMT and Allocating to

Functional Classes 31

Figure 3-2. The PENNDOT Traffic Monitoring System 34

Figure 3-3. Pennsylvania Travel Demand Model Coverage 39

Figure 3-4. MPO Travel Demand Model Data 39

Figure 3-5. Utilize MPO Travel Demand Model Output VMT as a Cross Check 40

Figure 3-6. Utilizing MPO Socioeconomic Forecasts 41

Figure 5-1. Statewide VMT Growth Rates between 1994 and 2003 58

Figure 5-2. Statewide Lane Mile Growth Rates between 1994 and 2003 58

Figure 5-3. Statewide Interstate VMT and Lane Miles Growth Rates between 1994

and 2003 59

Figure 5-4. Statewide non-Interstate VMT and Lane Miles Growth Rates between 1994

and 2003 59

Figure 5-5. Statewide Truck VMT Growth Rates between 1994 and 2003 60

Figure 5-6. Social Data Growth Trends 1970-2000 63

Figure 5-7. Economic Growth Trends1970-2000 63

Figure 5-8. All Data Growth Trends1970-2000 63

Figure 5-9. Social Data Growth Trend Forecasting 2000-2030 64

Figure 5-10. Economic Data Growth Trend Forecasting 2000-2030 64

Figure 5-11. All Data Growth Trend Forecasting 2000-2030 64

Figure 5-12. Socioeconomic variables trends between 1970 and 2030 65

Figure 5-13. Comparison of VMT trend with Socioeconomic Variables between 1980

and 2003 65

Figure 5-14. Social variables growth rates between 1994 and 2003 66

Figure 5-15. Economic variables growth rates between 1994 and 2003 66

Figure 5-16. Population Annual Growth Rates 1970-1990 67

Figure 5-17. Population Annual Growth Rates 1990-2000 67

Figure 5-18. Population Annual Growth Rates 2000-2030 67

Figure 5-19. Households Annual Growth Rates 1970-1990 68

Figure 5-20. Households Annual Growth Rates 1990-2000 68
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Figure 5-21. Households Annual Growth Rates 2000-2030 68

Figure 5-22. Employment Annual Growth Rates 1970-1990 69

Figure 5-23. Employment Annual Growth Rates 1990-2000 69

Figure 5-24. Employment Annual Growth Rates 2000-2030 69

Figure 5-25. Per Capita Income Annual Growth Rates 1970-1990 70

Figure 5-26. Per Capita Income Annual Growth Rates 1990-2000 70

Figure 5-27. Per Capita Income Annual Growth Rates 2000-2030 70

Figure 5-28. Household Income Annual Growth Rates 1970-1990 71

Figure 5-29. Household Income Annual Growth Rates 1990-2000 71

Figure 5-30. Household Income Annual Growth Rates 2000-2030 71

Figure 6-1. County Groups 74

Figure 6-2. Models Percent Error Distribution for Rural Interstates 87

Figure 6-3. Models Percent Error Distribution for Rural Non-Interstates 87

Figure 6-4. Models Percent Error Distribution for Urban Interstates 87

Figure 6-5. Models Percent Error Distribution for Urban Non-Interstates 87

Figure 6-6. Models Interstate VMT Forecasts 88

Figure 6-7. Models Non-Interstate VMT Forecasts 88

Figure 6-8. Rural Interstate VMT 2003-2030 Annual Growth Rates 89

Figure 6-9. Rural Non-Interstate VMT 2003-2030 Annual Growth Rates 89

Figure 6-10. Urban Interstate VMT 2003-2030 Annual Growth Rates 90

Figure 6-11. Urban Non-Interstate VMT 2003-2030 Annual Growth Rates 90

Figure 6-12. Rural Interstate Truck VMT 2003-2030 Annual Growth Rates 91

Figure 6-13. Rural Non-Interstate Truck VMT 2003-2030 Annual Growth Rates 91

Figure 6-14. Urban Interstate Truck VMT 2003-2030 Annual Growth Rates 92

Figure 6-15. Urban Non-Interstate Truck VMT 2003-2030 Annual Growth Rates 92

Figure 6-16. Urban Interstate VMT 2003-2030 County Annual Growth Rates 93

Figure 6-17. Urban Non-Interstate VMT 2003-2030 County Annual Growth Rates 93

Figure 6-18. Rural Interstate VMT 2003-2030 County Annual Growth Rates 94

Figure 6-19. Rural Non-Interstate VMT 2003-2030 County Annual Growth Rates 94

Figure 7-1. Statewide VMT and Socioeconomic Growth Trend and Forecasts 99

Figure 7-2. Recommended VMT Growth Forecasting Scenarios for Interstates 100

Figure 7-3. Recommended VMT Growth Forecasting Scenarios for Non-Interstates 100

Figure 7-4. VMT Growth Forecasting System 101
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1.Executive Summary

Traffic growth forecasting plays a pivotal role in achieving a variety of PENNDOT goals and

objectives, including transportation planning by PENNDOT and its planning partners, air quality

planning and conformity analyses, calculation of performance measures, and operation and

management of the roadway transportation system for both passengers and freight.

In this study, the Michael Baker Jr., Inc. Team (Baker Team) developed a traffic growth

forecasting system that incorporates traffic data from PENNDOT’s Traffic Information System

and socioeconomic data. In particular, the Baker Team:

• Finalized and prioritized goals and objectives for improved traffic growth forecasting in

Pennsylvania and established a set of criteria for evaluating candidate forecasting

methods against PENNDOT goals and objectives.

• Synthesized the state of art research on forecasting traffic growth from a variety of

literature and surveyed the state of practice among states and transportation planning

organizations.

• Identified available data sources, including traffic information and socioeconomic data

and forecasts, and in particular, identified the linkages between traffic growth forecasts

and PENNDOT’s Traffic Information System, and recommended a consistent set of

socioeconomic forecasts.

• Evaluated the candidate methods versus the PENNDOT-approved decision criteria and

developed consensus on a short list of traffic forecasting approaches for detailed study.

• Conducted a detailed study to test several models. Specifically, we analyzed historical

traffic growth patterns and historical socioeconomic growth patterns at different levels of

geography, developed regression models based on historical traffic and socioeconomic

growth, and evaluated the predictive power, validity, and reliability of statistical models.

• Identified a preferred traffic growth forecasting method for implementation.

While the same traffic forecast can be used to serve multiple objectives and goals, each objective

or goal requires different degrees of accuracy, details and specific time horizons. In this study,

the Baker Team identified the goals that traffic growth forecasting can serve in the

Commonwealth of Pennsylvania. In conclusion, this proposed traffic growth forecasting system

should primarily serve the priorities of PENNDOT goals and objectives in the following order:

• Transportation planning

• Air quality planning

• Funding allocation

• Other purposes

To serve these goals and objectives, VMT forecasts for four functional classifications—urban

interstate, urban non-interstate, rural interstate, and rural non-interstate—were identified as the

focus of this forecasting system. With these priorities identified, the proposed forecasting

system should:
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• Incorporate a statewide county-level approach that produces consistent VMT growth

forecasts across the state.

• Utilize historical traffic growth data and socioeconomic and land use variables.

• Minimize any bias among or between highly urbanized, suburban, small urban and rural

counties/regions through including area type classification (urban vs. rural) and

socioeconomic and land use variables.

Different regression models were developed, and a set of models was used to forecast VMT for

planning horizon years 2010, 2020, and 2030. Forecasting results were evaluated, based on

• Forecasting Trend vs. Historical Trend,

• Growth Magnitude at the County Level,

• Growth Magnitude at the State Level,

• Geographic Patterns.

In summary, major conclusions and recommendations are as follows:

• Regression models best meet PENNDOT needs for a statewide VMT growth

forecasting system. In particular, regression-based forecasting models provide a

consistent statewide forecasting framework.

• Data needed for implementing a regression-based forecasting system are readily

available, updated annually, and reasonably priced. The PENNDOT database

maintained by Bureau of Research and Planning provides an excellent source for

VMT, lane miles, and other key variables. State profile database for Pennsylvania,

produced by Woods & Poole Economics, is recommended as the data source for

socioeconomics.

• It is recommended that PENNDOT forecast VMT growth as a range and averaged

middle point, as follows:

1. The upper boundary of the VMT forecasts (all facility classes) is based on the

household-based county-level regression models (MODEL HH). This model

produces the most consistent and logical forecasts among candidate models.

2. The lower boundary of the VMT forecasts (interstate classes) is based on the

household-based county-group-level regression models.

3. The “middle point estimate” of forecast VMT is created by averaging the upper

and lower boundaries of forecasts from the two models at the state level, and

then adjusting the forecasts from MODEL HH at the county level so as to

match the averaged forecasts at the state level.

• The regression models should be updated annually, based on new VMT and

socioeconomic data.

• Potential future investigation includes evaluation of air quality implications of the

recommended VMT forecasting system and incorporation of intra-county level

socioeconomic and land use variables in the forecasting system.
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2. Defining and Prioritizing Goals and Objectives

Traffic growth forecasting has been used to achieve a variety of goals and objectives in the

Commonwealth of Pennsylvania. While the same traffic forecast can be used to serve multiple

objectives and goals, each objective or goal requires different degrees of accuracy, details and

specific time horizons. This subtask will help establish the requirements for traffic growth

forecasting, specifying the criteria or thresholds for evaluating traffic growth forecast methods

and building consensus on the required accuracy of the final product.

The Baker team has identified the following goals that traffic growth forecasting can serve in the

Commonwealth of Pennsylvania:

• Transportation planning

• Air quality planning and federal compliance

• Funding allocation

• Operation and maintenance

Through meeting with the PENNDOT project team and interviewing key potential users (see the

appendices for detail), the Baker team gains a better understanding of the importance of traffic

forecasting to different PENNDOT functions and the priorities that this particular traffic growth

forecasting system should serve.

First, traffic growth forecasting is critical to the transportation planning process. Whether

highway or transit, transportation infrastructure investments are identified and prioritized using

travel demand forecasts. Efficient and equitable transportation planning – a reliable and

consistent analytic input to applying scarce PENNDOT resources to maintain and improve the

roadway system - relies on accurate traffic growth forecasts. Whether it is a Metropolitan

Planning Organization (MPO), a Rural Planning Organization (RPO) or PENNDOT, traffic

growth forecasting plays a fundamental role in the transportation planning process—

transportation improvement programs (TIPs), 12 year plan, and long-range plans (LRPs).

MPOs in Pennsylvania (see Figure 2-1) update their short-term TIPs and LRPs based on the

federal regulations. Seven of these MPOs maintain travel demand models and use these models

to produce traffic growth forecasts. As will be discussed in the following section on traffic

forecasting methods, these models use detailed socioeconomic and travel survey data and

detailed transportation analysis zone (TAZ) structure. These models are calibrated and validated

so that simulated traffic volumes from the models match ground traffic count volumes to a

reasonable degree at the regional level. While these models are developed primarily for

evaluating TIP, LRP, and air quality conformity, they are also used for corridor studies and other

purposes.

The seven MPO model sets encompass only 20 of Pennsylvania’s 67 counties. These models

and their underlying data sets vary significantly regarding the quality and age of underlying

databases, and contain limited information regarding external forces. As such, they may not be

prime candidates for incorporation into a statewide approach, but do offer a potential for crosschecks of the algorithms and data being tested in this project. For detailed discussion, see the

section on review of traffic information system and travel demand models.
10

Figure 2-1. MPOs and RPOs in the Commonwealth of Pennsylvania
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RPOs in Pennsylvania (see Figure 2-1) do not have travel demand models for their short-range

and long-range transportation planning. They rely on the data and tools that PENNDOT can

provide. Clearly, this is an important missing piece in the statewide planning. As of this writing,

PENNDOT is in the initial stage of developing a statewide travel demand model and does not

have a quantitative forecasting tool that can be used to serve counties that are not encompassed

in a travel demand model.

As can be seen, the traffic growth forecasting system that is to be developed in this project

should serve an important link in the transportation planning process in Pennsylvania. It will

provide a much-desired tool for planners at PENNDOT and RPOs to do short-range and longrange planning in rural planning areas, while complementing (not replacing) the existing travel

demand models in metropolitan planning areas.

Second, traffic growth forecasts are pivotal in air quality planning and federal compliance. The

Clean Air Act Amendments (CAAA) of 1990, the Transportation Equity Act for the 21

st Century

(TEA-21) of 1998, and the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA)

all mandate the integration of air quality into the transportation planning process. Proposed

TEA-21 reauthorization language indicates that basic language and requirements will not change

significantly. This legislation includes measurement and projection of VMT for planning, air

quality, congestion management and other purposes.

The United States Environmental Protection Agency (USEPA) requires state DOTs and MPOs to

accurately estimate, forecast and track VMT. PENNDOT undertakes the VMT forecasting for all

areas without MPO models and assists elsewhere. VMT forecasts are important components of

air pollutant emission and air quality forecasting, and are critical in the determination of air

quality conformity.

The application and use of forecasted growth factors can have a significant impact on regional

air quality analyses conducted for nonattainment and maintenance areas for which USEPA

requirements exist for conformity, state implementation plan (SIP) or planning activities.

Conformity requires a demonstration for most TIPs and LRPs that emissions in future years are

lower than or equal to either base year (2002 emissions) or a SIP emissions budget (where

applicable). Inability to make an affirmative conformity determination results in a TIP or LRP

being unacceptable and certain projects non-fundable by USDOT. Planning activities may

include analysis of potential measures to reduce emissions, such as reducing trips, VMT, and/or

cold starts or measures that raise or lower speeds to a more emissions-efficient level. As of

August 2004, 37 Pennsylvania counties are nonattainment for ozone, and 13 - 22 counties likely

to be designated nonattainment for PM2.5 in late 2004.

Additionally, EPA requires that emissions inventories be conducted on a triennial basis.

PENNDOT is the lead agency in developing the underlying data and producing emissions

outputs for on-highway vehicles (“mobile sources” for past, present and future years, and

providing this information to the Pennsylvania Department of the Environment. This data is

developed by county, and aggregated to metropolitan and nonattainment area.

Accurate mobile source emissions analysis relies, in large part, on accurate estimates of vehicle

miles of travel. The relationship is direct and indirect: Directly, it impacts the application of
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emissions factors (grams/mile) to VMT. Indirectly, VMT is used to estimate speeds by facility

class by time period: underestimation of VMT may result in speed estimates higher than

appropriate, while overestimation of VMT may result in speed estimates lower or higher than

appropriate. Both situations may influence emissions factors for criteria pollutants and their

precursors. Unrealistically high traffic growth rates may overestimate future year emissions and

require costly emission control strategies. In addition, conformity analyses conducted in these

regions must remain at or below the emission target levels set by the SIP to prevent lapses in the

TIP and LRP projects. If the growth rates used to develop SIP emission estimates significantly

underestimate travel growth as compared to actual growth as reported in RMS and HPMS,

potential conformity problems could arise.

Pennsylvania updates its traffic-related inputs triennially to satisfy EPA regulations. Future year

forecasts are updated in three ways: (1) in accordance with the TIP/LRP cycle, which parallels

the 12 Year Plan schedule, (2) as a new or revised TIP or LRP may otherwise require, and (3) as

EPA may require. The triennial update of the most recent year’s VMT data (e.g., rectified to

HPMS, etc.) forms the basis for future year projections. The biennial TIP/LRP/12 Yr Plan cycle

has historically triggered the need for updated future year projections (up through the end of the

LRP) for each nonattainment area, which involves at least 37 counties. For counties with travel

demand models, forecasts based on PENNDOT data are compared with those from travel

demand models to ensure reasonableness and consistency. For areas without travel demand

models, and PENNDOT VMT data is the sole source of VMT, speed, and link data.

As a result, in future conformity rounds, actual VMT growth that was previously forecasted in

the SIP will then be obtained from the RMS and HPMS reported totals directly. The above

discussion illustrates the importance in developing accurate growth rates and the potential

implications of overestimating or underestimating such growth factors.

Like transportation planning tools in different parts of the State, MPOs rely on travel demand

models to provide traffic growth forecasts for air quality planning. PENNDOT does not currently

have a quantitative tool that can be used to produce consistent traffic growth forecasts statewide,

particularly for rural and small urban planning areas. The forecasting system to be developed in

this project should serve fill the gap. In particular, it should generate statewide VMT forecasts

for major functional classifications. PENNDOT staff has indicated that four functional

classifications—urban interstate, urban non-interstate, rural interstate, and rural non-interstate—

are very important. The proposed forecasting system should focus on this very important

dimension.

Third, VMT estimation and traffic growth forecasts impact funding for both current and shortrange transportation improvement programs. TEA-21 allocates thirty-five percent of the

apportionment funds based on the ratio of total VMT on the principal arterial system in a state to

the total nationwide VMT on the same classes of roads. As such, traffic growth forecasting is

vital in ensuring that Pennsylvania continues to receive an equitable share of the available

funding.

Table 2-1 shows how HPMS data are used in the Federal-Aid Highway Program apportionment

formula (FHWA 2000). As can be seen from the table, VMT is an important metric used for
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federal fund allocation. In particular, VMT on the interstate highway system and VMT on the

principal arterial system receive heavy weights in the federal fund allocation formula. This is

another indication for the importance of VMT as a metric and interstate as a classification, on

which the proposed forecasting system should focus its attention. Better VMT forecasts will

allow PENNDOT to improve its funding forecasts and budgets accordingly.

Table 2-1. HPMS Data Used for Apportionment

Fund Factors Weight

Interstate System Lane Miles 33 1/3 %

Interstate Maintenance

Vehicle Miles Traveled on the Interstate System 33 1/3 %

Lane Miles of Principal Arterial Highways (excluding

Interstate System)

25 %

Vehicle Miles Traveled on Principal Arterial Highways

(excluding Interstate System)

35 %

National Highway System

(NHS)

Total Lane Miles of Principal Arterial Highways

divided by the State’s Population

10 %

Surface Transportation Lane Miles of Federal-Aid Highways 25 %

Program (STP) Vehicle Miles Traveled on Federal-Aid Highways 40 %

State Population 75 %

Highway Safety Programs

Public Road Miles 25 %

Source: FHWA (2000)

Fourth, traffic growth estimation helps PENNDOT in its day-to-day operation and management

of the Commonwealth’s transportation system. It provides useful information to a variety of

PENNDOT functions such as traffic engineering, accident analysis, winter service operations

planning, highway maintenance and construction. These PENNDOT functions require very

detailed information about highway conditions, both spatially and temporally. This is not the

scale that this project will be used to develop the forecasting system, but traffic growth forecast

will be helpful to these PENNDOT functions.

In summary, this proposed traffic growth forecasting system should primarily serve the priorities

of PENNDOT goals and objectives in the following order:

• Transportation planning

• Air quality planning

• Funding allocation

• Other purposes
14

To serve these goals and objectives, VMT forecasts for four functional classifications—urban

interstate, urban non-interstate, rural interstate, and rural non-interstate—should be the focus of

this forecasting system. Traffic volume growth forecasts, although important, have been covered

in other studies at the project, corridor, or metropolitan levels and are not a priority in this study.

With these priorities being identified, the proposed forecasting system will

• Be a statewide county-level approach that produces consistent traffic growth forecasts

across the state.

• Incorporate historical traffic growth data and socioeconomic and land use variables.

• Minimize any bias among or between highly urbanized, suburban, small urban and rural

counties/regions through incorporating area type classification (urban vs. rural) and

socioeconomic and land use variables.

Testing of multiple methods will be done, and a preferred forecasting method will be selected for

this forecasting system. To compare different candidate forecasting methods, evaluation criteria

need to be established. Saha and Fricker (1987) used eight goals of analysis for their traffic

growth forecasting study and established four criteria for variables selections. Based on their

study and the PENNDOT needs, the criteria for evaluating the statistical models may include:

1. The models should explain more than fifty percent of the variation (R

2

>50)

2. The relative predictive errors will be among the lowest.

3. Mean Absolute Percent Error or Root Mean squared error (RMSE) will be minimum.

4. The number of predictor variables should be adequate and compatible with the number of

observations.

5. Estimated coefficients should be statistically significant at an alpha level of 0.05 or 0.1.

6. There should be no discernible patterns in the residuals.

7. Data availability and reliability are good for independent variables.

8. Frequency of update meets PENNDOT needs.

9. Cost is reasonable to update and maintain the forecasting system.

10. The models can differentiate cars and light trucks from heavier vehicles, if possible.

11. The models can make forecasts by county or logical group of counties.

12. The models can provide forecasts for horizons consistent with short- and long- range plan

milestones.

This set of twelve criteria will be used to evaluate candidate forecasting models in this project.
15

3. Reviewing State of the Art and Practice in Traffic Growth

Forecasting

Traffic growth can be measured in different ways. For some people, traffic growth means

increase in traffic volumes, which can be measured in annual average daily traffic (AADT). For

others, vehicle miles of travel or vehicle miles traveled (VMT) is the common metric used to

monitor the changes in travel in the past and project the potential changes in the future travel. As

discussed below, VMT incorporates both traffic volume and travel distance, and is therefore a

more comprehensive measure in the estimation.

While the growth in traffic volumes is the basis of most forecasting efforts, VMT growth is

pivotal in understanding the impacts of increased traffic volume. VMT estimation and growth

forecasts have received much attention in federal legislation such as CAAA, ISTEA, and TEA21.

Based on the data used, traffic growth estimation and forecasting methods can be classified into

three categories: traffic-count-based methods, socioeconomic-data-based methods, and travel

demand forecasting models. Based on forecasting techniques, traffic growth forecasting can be

grouped into categories: trend/growth factors, time series, regressions, and alternative statistical

methods.

3.1. Traffic Growth Forecasting Methods—Based on the Type of Data Used

3.1.1. Traffic Count Based Forecasting

Traffic-count based methodologies are the most common approaches used to forecast traffic

growth. In particular, the USEPA recommends use of HPMS-based procedures to estimate VMT.

The HPMS-based VMT estimation procedure includes AADT and highway length estimation by

functional class. Specifically, the procedure includes calculations of the following by functional

class:

• Total traffic volume,

• Sample size,

• Average traffic volume,

• Highway miles, and

• VMT.

AADT estimation by functional class is generally based on the three types of count procedures:

(1) Continuous counts,

(2) Coverage counts, and

(3) Special needs study count.
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The continuous counts program is established to collect traffic volume and speed data on a

continuous basis, using automatic traffic recorders (ATRs) 24 hours a day and 365 days a year.

The continuous count stations are limited in number and are strategically located throughout a

state. The collected data are primarily used to develop seasonal, daily, and other adjustment

factors, which are used to expand short-term sample counts.

Short duration count programs include coverage counts and special needs counts. The HPMS

coverage count program includes short-duration sample counts that are collected through random

sampling to ensure the adequate geographic representation and statistical validity of traffic count

data in the public roads throughout a state. The FHWA recommends that each coverage roadway

segment be counted at least once every six years, and at a minimum each of the HPMS

universe/sample sections are counted once every three years. When counts are not undertaken for

a year, growth factors are usually used to estimate traffic volume for this year. These growth

factors are estimated based on historical traffic growth data and may be used for forecasting

traffic growth in the future.

Count-based methods can be biased because of sampling size, frequency, or representation, and

the extrapolation from the sampled sites to system wide (regional or statewide) total. Factors are

used to correct for temporal variations, equipment types, and counting cycle. Extrapolation of the

sample data to the entire network is usually based on expansion factors accounting for variations

among functional classes and area types. However, these expansion factors ignore factors that

could affect traffic growth in the future such as link attributes, land use and socioeconomic

characteristics of the area.

Strengths

• Traffic count data are readily available and routinely updated statewide.

• They are generally simple and easy to implement.

• They cover the whole state and provide a consistent forecasting framework.

• They have the potential of providing forecasting at detailed level such as functional types

and vehicle types.

• HPMS data are consistently reported for most data items and allow comparison among

states.

Weaknesses

• There are uncertainties associated with sampling and extrapolation.

• They generally do not account for trip-making processes and factors that drive traffic growth

such as socioeconomic variables.

• They rely heavily on the assumption that traffic growth will behave in the same or similar

manner as the past, regardless of demographic, land use and other factors

3.1.2. Socioeconomic Data Based Methods

Socioeconomic-data-based methods do not rely on the characteristics of the roadways but instead

focus on factors that affect an individual’s travel behavior in a region and use these as the basis

of estimating traffic growth. Using social economic data to predict travel changes attempts to
17

estimate traffic growth at a more fundamental level, while at the same time using variables that

can project into the future with a higher degree of confidence. Typically, socioeconomic-based

methods use data such as:

Households Employment

Household size Number of licensed drivers

Household income Odometer readings

Vehicle per household Fuel taxes

Population Fuel sales

Data sources include: Census, the National Personal Transportation Survey (NPTS), American

Travel Survey (ATS), and Residential Transportation Energy Consumption Survey (RTECS).

NPTS is a national survey of daily personal travel. It was conducted in 1969, 1977, 1983, 1990,

and 1995. The American Travel Survey (ATS) obtained information about long-distance travel

of persons living in the United States. ATS was conducted in 1995. The NPTS was combined

with the American Travel Survey (ATS) to form the National Household Travel Survey in 2001.

NPTS data include:

• Household level data – size, income, education, etc.

• Motor vehicle information – estimates of annual VMT, age, etc.

• Public transportation - use, availability, etc.

• Drivers – annual miles driven etc.

• Trips – length, travel time, etc.

• Description of geographic area characteristics for households and workplaces.

Truck travel data include the Commodity Flow Survey (CFS), the Vehicle Inventory and Use

Survey, and the International Fuel Tax Agreement (IFTA) truck mileage database. The amount

of commercial activity reported in the 1993 CFS, reported in ton-miles, was for travel within, to,

from and through each of the 50 states and the District of Columbia. Changes in the survey

format for 1997 prevented the calculation of these travel estimates.

All commercial enterprises that undertake motor carrier operations in the United States and

Canada are expected to apply for fuel tax licenses. The International Fuel Tax Agreement (IFTA)

is an agreement among 58 jurisdictions in the United States and Canada to facilitate interstate

commercial vehicle travel among the jurisdictions. The IFTA requires the carriers to report the

total annual miles traveled by all qualified vehicles for tax estimation purposes.

Vehicles qualify for licensing under the IFTA program if they meet any of the following

requirements and configurations:

• Two axles and a gross vehicle weight (GVW) exceeding 26,000 pounds,

• Two axles and a registered weight exceeding 26,000 pounds,

• Three or more axles, regardless of vehicle weight,

• Passenger vehicles that have seats for more than nine persons, and

• Combined vehicle weight exceeding 26,000 pounds (for combination vehicles).
18

However, the following vehicles are exempt from the IFTA program:

• Recreational vehicles,

Travel surveys have been used to estimate VMT based on household travel characteristics and on

licensed driver characteristics. Indiana DOT sponsored a study to develop unbiased statewide

VMT estimates for personal and commercial travel (Friker and Kumapley 2002). The study

tested three methods—licensed driver-based, household-based, and fuel-tax-based. These

methods are all based on socioeconomic data (see the Case Studies section for the detail)

To forecast state tax revenues, the ODOT Financial and Economic Analysis Section adopted a

fuel-based approach to estimating and forecasting VMT. Total VMT is divided into three

categories— Light vehicle VMT, Medium-Heavy Vehicle VMT, and Heavy Vehicle VMT. For

the first two categories, ODOT uses monthly fuel consumption data, fuel refund claims, and

national miles-per-gallon estimates to calculate VMT. For heavy vehicles, VMT is estimated

using actual reported mileage from weight-mile tax records and adjustment factors (see the Case

Studies section for the detail)

These methods also have limitations, as the data sources employed were not designed

specifically for estimating VMT. Potentially significant biases can result from some data sources

and extrapolation from sample to system wide VMT can lead to additional inconsistencies.

Furthermore, these methods do not generate VMT estimates by functional class because of the

limitation in the source data.

Strengths

• Account for major forces that drive traffic growth, including socioeconomic factors.

• Socioeconomic data are readily available and routinely updated.

• Encompass the whole state and provide a consistent forecasting framework.

Weaknesses

• Do not simulate trip-making processes.

• Provide little detail in forecasting such as detailed functional and vehicle types.

• Potentially significant biases can result from some data sources and extrapolation.

• Travel survey data may be biased because of low response rates, low response quality in trip

length, and under-reporting particularly for short trips.

3.1.3. Travel Demand Forecasting Models

Travel demand forecasting models can also be used to estimate and forecast growth in traffic

volumes and VMT. These models are generally developed and validated using household travel

surveys, socioeconomic data, land use data and traffic counts. That is, travel demand modeling

takes into account additional important variables and data sources used in the two methods

discussed above. These models are designed to simulate travel behavior of trip makers,

including:
19

• How many trips are produced and attracted at each location,

• How may trips are made from each origin to each destination,

• How trip-makers get from an origin to a destination

• Which path a trip-maker takes to get from an origin to a destination

These trip-making processes are simulated as trip generation, trip distribution, mode choice, and

traffic assignment in the traditional travel demand models. The road system is simulated as a

simplified network in the model. The models yield traffic volumes in individual links based on

socioeconomic and highway inputs. VMT by functional types or time periods of day can be

estimated, simply multiplying link volume by link length.

To the extent that travel demand models can simulate travel characteristics and behavior changes

over time in these trip-making processes, these models can be used to forecast system wide

(regional) travel. For example, socioeconomic data such as population, number and size of

households, and number and types of employment are critical input variables in these models. If

population and households are forecasted to grow (decline) in the next twenty years, these

models will forecast some degree of increase (increase) in system wide travel.

Travel demand models have been developed primarily to evaluate future transportation system

performance, not system-wide (regional or statewide) travel. Despite the recent progress in travel

demand modeling, it is still difficult to replicate traffic patterns on the ground and errors can be

introduced in various stages of these usually complex modeling processes. Differences between

traffic counts and simulation volumes are common even after validation of the process. Local

traffic is generally not accounted for in a travel demand model, and travel demand models are

not available for every county in Pennsylvania, nor does a statewide model yet exist or is

planned that would provide the level of detail desired by BPR.

Strengths

• Travel demand models usually simulate trip-making processes and account for major forces

that drive traffic growth, including socioeconomic factors.

• Readily available and routinely updated at major metropolitan areas.

• If developed for policy and planning applications, travel demand models can be used to

evaluate policy implications and the sensitivity of VMT to policy input.

• VMT by functional classes, area types, and jurisdictions can be estimated and forecasted.

Weaknesses

• Do not generally account for the local travel because most local and minor roads are not

included in the model network.

• Generally do not cover the whole state and cannot be used for statewide traffic forecasting.

• Provide less detail than can be obtained from traffic count-based methods, such as detailed

time of day, daily, seasonal, and detailed vehicle types.

• Extensive data and resources are required to calibrate and validate a model.
20

3.2. Traffic Growth Forecasting Methods—Based on Forecasting Techniques

3.2.1. Growth Factors

The growth factor method is the most popular technique used to do traffic growth forecasting. It

is simple and easy to develop and implement. Historical traffic count data and estimation of

annual traffic growth rates are used to project future volume of VMT

These traffic growth rates are either applied directly to future years or adjusted based on some

assumptions. Direct application of growth rates are based on the assumption that past traffic

growth trends will continue in the future. This assumption may hold true for a short-term

forecasting. For a long time horizon, this assumption may be problematic. Traffic growth may

exhibit varying temporal patterns for different areas, which are in different development stages

over the years. A rapidly growing, urbanizing area shows steep annual traffic growth now, but

this degree of traffic growth may or may not continue indefinitely.

For many rural areas that are far from major urban centers, economic activities are steady and so

is traffic growth. In well-developed metropolitan areas, economic growth may have already run

its course and so may traffic growth. Growth factors for these areas may be small and stable in

the future.

Polzin, Chu, and Toole-Holt (2004) analyze the trends of socioeconomic conditions that drive

VMT growth and conclude that these socioeconomic factors have reached a moderating stage.

They argue that future VMT growth would be moderate.

Strengths

• Traffic count data that are used for deriving growth factors are readily available and

routinely updated statewide.

• Generally simple and easy to implement.

• Cover the whole state and provide a consistent forecasting framework.

• Traffic growth factors can be developed to provide forecasting at detailed level such as

functional types and vehicle types.

Weaknesses

• There are uncertainties associated with traffic count sampling and extrapolation.

• Generally do not account for trip-making processes and factors that drive traffic growth.

• It is problematic to assume that for a long time horizon, traffic growth will behave in the

same or similar manner as the past.
21

3.2.2. Time Series Models

In time series models, past trend is the key to predict the future. Of course, the assumption is that

the traffic growth will behave like in the past. Past trends are modeled as linear or curvilinear

equations; Traffic is dependent variable and year is independent variable.

Benjamin (1986) presents a procedure for forecasting average daily traffic using time series

model. The procedure assumes a logistic function for modeling daily traffic volume over a

period of years. The results from the time series model were compared to those from a travel

demand model. The time series technique produces results that are close or closer to the observed

traffic volume data for small, steady growth areas.

Growth factors are a special case of time series methods. Growth factor method’s strengths and

weaknesses are also true for time series models. Time series analysis can produce a range of

answers, while econometric models provide point estimates. Time series results are sensitive to

the data quality. The historical data used to develop time series models are particularly critical to

determine if the model is applicable to forecasting the future. If the historical data include only

the rapidly growing stage of an urbanizing area, the estimated model may overestimate future

traffic growth for a long time horizon. These limitations come from the fact that time series

models do not simulate the physical processes that underlying the traffic growth.

3.2.3. Econometric Modeling (Regressions)

Regressions are generally used to describe the relationship between a dependent variable and its

explanatory variables. For forecasting traffic growth, explanatory variables may consist of

demographic and economic variables as listed above. Model’s functional forms may be linear or

nonlinear.

Using regression to forecast AADT received early attention in the literature. Neveu (1982)

developed a set of elasticity-based regression models to forecast traffic in the rural state highway

systems in New York. The dependent variable is AADT. The independent variables include

socioeconomic variables such as population, number of households, automobile ownership, and

employment. It was found that traffic growth was dependent on different factors for each of three

road classes: (1) interstates, (2) principal arterials, (3) minor arterials and major collectors.

Saha and Fricker (1987) conducted a similar study to forecast traffic volume in rural state

highways in Indiana. They investigated more independent variables such as vehicle registration,

US gasoline price, consumer price index, gross national product, and per capita disposable

personal income (nationwide). For each of four rural highway classes (interstate, principal

arterials, minor arterials, and major collectors), aggregate models were developed using stepwise

regression technique. A series of criteria was used to select independent variables and final

models. These criteria include R

2

, Cp-Criterion, mean squared error (MSE), number of

predictors, statistical significance of estimated coefficients, residual analysis, data availability,

data reliability, and data cost. They found that relative changes in state population are significant

predictor of rural interstates, relative changes in county population and state population for rural
22

principal arterials, relative changes in county households for rural minor arterials, and relative

changes in county population for rural major collectors. In the disaggregate analysis, they

developed forecasting models for each count station.

Mohamad et al. (1998) developed multiple regression models to forecast daily traffic on county

roads in Indiana. Nine independent variables were included in the model development, including

area types of county road location (urban or rural), accessibility to the state highway system,

presence of interstate highway in a county, county state highway mileage, arterial mileage, and

collector mileage. Logarithmic transformations were conducted on the dependent variable to

remedy unequal error variance. Several variable selection methods were used, and R

2

and

normality assumption were adopted as the most useful criteria to measure the goodness of a

model. They found four independent variables to be significant: county population, county

arterial mileage, area types of location, and accessibility. A series of diagnostics was run, and the

fitted model was selected for model validation using new traffic data from eight counties that

were randomly selected.

Iskander, Jaraiedi, and Thomas (1996) developed linear regression models, incorporating a trend

factor and a selected set of socioeconomic variables. The dependent variable is AADT. The

socioeconomic variables include population, total income, employment, total vehicle

registration, number of licensed drivers, and miles of roadways. These variables were also

transformed in square, square root, natural logarithms, and inverse. They were also combined

such as population divided by miles, population multiplied by miles, miles divided by

population. The selection of variables was done using stepwise regression technique. Regression

models were conducted for each road type individually for each county, and then run for each

cluster of counties. Final n-variable models were produced with the highest values of R-squared.

The authors believed that these models might not be desirable for long range forecasting,

particularly when including squares of the variables as a form of transformation. To develop long

range forecasting models, they re-ran the models without squares of the variables. Finally, they

validated the models against actual AADT data.

VMT estimation and forecasting has been a great interest in recent research, particularly in the

debate on the relationship between highway capacity expansion and traffic growth (Noland and

Lem 2002). Hansen and Huang (1997) estimated OLS and Prais-Winsten econometric models

using time series data on VMT for state highways in California, by county and metropolitan area.

Independent variables include population, personal income, population density, gasoline prices

and lane miles. These are fixed effects models using panel data, and dummy variables were

introduced to allow the intercept term to vary over cross-sectional units and time. The panel data

and dummy variables allows capturing the influence of variables unknowns or unmeasured in the

model. With the panel data, Prais-Winsten regressions were used to address the issue of

autocorrelations, removing the bias in estimation of parameters.

Two-stage least square regressions with instrumental variables have been used to address the

simultaneity and causality issues between road supply (lane miles) and demand (VMT). An

instrumental variable is a linear combination of predetermined model variables. Noland Cowart

(2000) used a two-stage least squares regression, testing several instruments including urbanized

land area. Fulton et al. (2000) used a difference (or growth) model specification and lagged

growth in lane miles as an instrument for current growth in lane miles. The dependent variable is
23

the difference or growth in VMT in logarithmic form, and independent variables include lagged

growth in lane miles, population, and income per capita. The data used are cross-sectional time

series county-level data from the Mid-Atlantic region, including North Carolina, Virginia and

Maryland.

Cervero and Hansen (2000) tested a wide range of instrumental variables reflecting political,

environmental, and demographic influences. They also used a panel data, consisting of 22 years

of observations for 34 California urban counties. Some of variables were found to be correlated

with road supply, such as carbon monoxide concentrations, the percent of population that is

white, and the political party of the governor. While these variables are useful for examining the

relationship between road supply and travel demand, it is not practical to include them in a

forecasting procedure.

Strengths

• They account for major forces that drive traffic growth, including socioeconomic factors.

• Socioeconomic data are readily available and routinely updated.

• They can cover the whole state and provide a consistent forecasting framework.

Weaknesses

• They do not simulate trip-making processes.

• They generally do not have provide forecasts as detailed as travel demand models.

3.3. Alternative Statistical Models

Alternative statistical methods have been used to address issues that are difficult to deal with

traditional regression modes. Neural Networks Modeling was tested in the Kentucky study of

traffic growth rates (Kentucky Transportation Center, 2001).

NNets are computational structures capable of learning from examples and quickly recognizing

the patterns they have learned. Learning (or training) and recognition are the two fundamental

modes in which NNets operate. For more detailed discussion, see the case study section in the

state of practice.

3.4. Traffic Growth Forecasting Methods—Analytical Issues

For the statewide traffic growth forecasting, we need to wrestle with a few analytical issues. In

the following, we will discuss two major issues: unit of analysis and VMT estimation.
24

3.4.1. Units of Analysis

Iskander, Jaraiedi, and Thomas (1996) used cluster analysis to group counties for each road type.

To define clusters, four sets of variables were tested:

a. AADT

b. Population

c. AADT, miles of roadway, population, income, employment, number of vehicle

registrations, number of licensed drivers.

d. AADT, miles of roadway, and population

The fourth set of variables produced the best results. In addition, three different clustering

methods were applied; clustering is done on the basis of Euclidean distances computed from the

defining variables:

• Fastclus Procedure—Cluster seeds are first selected and every observation is assigned to the

nearest cluster seed. Cluster seeds are then updated and replaced by the cluster means.

• Average Linkage Method— Every observation is initial a cluster itself, and then two closest

clusters are merged to form a new cluster, replacing the old ones. This process repeats until

only one cluster is left.

• Centroid Method—the same as average linkage method except that the distance is defined as

that between two clusters’ respective centroids or means.

While this clustering was done for each road type, clustering counties was also attempted for

four groups of road types

(1) Urban interstates, other urban freeways and expressways, other urban primary arterials

(2) Minor urban arterials, urban collectors

(3) Rural interstates, other rural primary arterials, minor rural arterials

(4) Major collectors, minor collectors, local.

This clustering effort was unsuccessful.

As shown in Table 3-1, the Traffic Monitoring Guide (2001) recommends the five clusters for

grouping highway facilities as a minimum.

Table 3-1. Highway Facility Groups

Clusters HPMS Functional Code

Interstate Rural 1

Other Rural 2,6,7,8

Interstate Urban 11

Other Urban 12,14,16,17

Recreational Any

The area/facility groups identified in the Goals and Objective section are basically consistent

with the Traffic Monitoring Guide.
25

3.4.2. Local Road VMT Estimation

States are required to report annually to the Federal Highway Administration (FHWA) aggregate

estimates of VMT on the rural minor collector and local functional systems in rural, small urban,

and urbanized areas. In 2001, travel on these local area systems totaled over 15 percent of

highway VMT in the United States. The current practices used by the States to prepare these

local area estimates vary significantly and often are not thoroughly documented. To gain an

understanding of the various practices in use, FHWA conducted a survey of the States in April

2002 through its field division offices; the estimation methodologies from 50 States, the District

of Columbia, and the Commonwealth of Puerto Rice are summarized Table 3-2.

Table 3-2. State Practices Used to Report VMT

State Rural

Minor

Collectors

Locals State Rural Minor

Collectors

Locals

Alabama G G; T (UZA) Montana M M

Alaska M M Nebraska R R

Arizona T T Nevada M M

Arkansas M Z; M (UZA) New

Hampshire

G M

California R R New Jersey M M

Colorado X X New Mexico G T

Connecticut M A New York R R

Delaware M A North Carolina R R

Dist. Of

Columbia

-- A North Dakota Z Z; M (UZA)

Florida M A Ohio A A

Georgia M M Oklahoma M M

Hawaii R R Oregon R R

Idaho M R Pennsylvania M M

Illinois M Z; T (UZA) Rhode island A A

Indiana M M South Carolina M A

Iowa M M South Dakota M M

Kansas M M Tennessee A A

Kentucky M A Texas M M

Louisiana M M Utah A A

Maine M M Vermont A A

Maryland M G; T (UZA) Virginia M M

Massachusetts A A;M (UZA) Washington M X

Michigan T T West Virginia M A

Minnesota C R Wisconsin R R

Mississippi M M Wyoming M M; T (UZA)

Missouri M R Puerto Rico T T

Sources: FHWA (2003). UZA - Urbanized areas
26

The estimation methodologies are classified in the following major categories:

A – Assign areawide average daily traffic based on some criteria

G – Apply the current traffic growth rate on collectors or higher systems

M – Use a limited sample of short-term traffic counts or a combination of sample counts and

estimated average daily traffic

R – Assign the residual of the statewide total VMT minus the higher systems VMT

T - Apply a statewide growth trend based on a factor(s) such as traffic, highway fuel, vehicle

registrations, population, etc.

X - Assign a fixed percentage of total area VMT

Z - Assume a zero traffic growth

In this project, local road VMT will not be dealt with separately but rather be part of urban or

rural non-interstate VMT. Changes in local road VMT estimation methodology over the years

will be examined.

3.5. State of the Practice

Previous surveys have shown that many states use traffic growth rates based on historic data and

regression analysis, while others employ socioeconomic and land use data. The University of

Kentucky surveyed 45 of the 50 states in 2002. Of the 28 responses received, 22 states used

traffic growth rates.

The experience from other states offers useful insights in developing an approach for the

Commonwealth. By examining selected DOTs in detail, advantages and disadvantages of the

various methodologies can be identified, as well as any potential issues and solutions. The

following section describes three case studies, which have three different methods.

3.5.1. Kentucky

Kentucky Transportation Cabinet sponsored an Analysis of Traffic Growth Rates (KTC

2001), with primary objectives to determine patterns of traffic flow and develop traffic growth

rates by traffic composition and highway type for Kentucky’s system of highways. The study

was carried out to

• conduct a literature search to determine if there were new procedures being used to more

accurately represent traffic growth rates,

• develop a random sampling procedure for collecting traffic count data on local roads and

streets,

• develop methods to predict vehicle miles traveled based on socioeconomic data,

• develop a procedure for explaining the relationship and magnitude of traffic volumes on

routes functionally classified as collectors and locals, and

• develop county-level growth rates based on procedures to estimate or model trends in

vehicle miles traveled and average daily traffic.
27

The study found no new approaches that could be directly applied to the predictions of growth

rates in Kentucky; the survey of states indicated that historical data and regression analysis were

most often used to predict growth rates.

With mixed results, the study developed models to predict traffic growth (VMT) for interstate

and non-interstate travel in each of the 120 counties. Socioeconomic data are independent

variables, including population, earnings, employment, per capita income, retail sales, and

number of licensed drivers. The models were evaluated on two sets of measures:

• model diagnostic tests on the inputs and outputs

• relative prediction errors should be less than 10 percent for each county.

Additionally, yearly VMT growth rates should be within general ranges based on historical

trends—1-6% for non-interstate and 2-4% for interstate VMT growth.

Two modeling methods were used: linear regression and neural networks. In the linear

regressions, two modeling approaches were adopted:

• models were developed at the county level

• models were developed at the state level and then statewide VMT were apportioned to

each county based on socioeconomic variables at the county level.

Efforts were also undertaken to minimize multicollinearity through reduced models and

transforming dependent and independent variables into logarithmic forms. A time variable was

also included in the model testing to account for possible changes in driving and travel trends

over time. K factors approach was also tested to account for missing variables.

Neural network (NNets) modeling was conducted in a few trials. NNets are computational

structures capable of learning from examples and quickly recognizing the patterns they have

learned. Learning (or training) and recognition are the two fundamental modes in which NNets

operate. Specifically, the Backpropagation Neural Network was developed through a two-level

training and testing process:

• In the first level, an NNet was developed and tested on the entire database and the

residuals were retained and treated as an independent variable in the second level.

• In the second level, another independent NNet was developed on the 1993-1997 data and

then tested on the 1998-1999 data.

The variables used in the non-interstate NNet-K model include:

• LN Urban mileage

• LN Population

• LN Employment

• LN Earnings

• LN number of Interstate Interchanges

• LN number of Parkway interchanges

• Residual (K-Factor)

Results from linear regression modeling efforts are summarized as follows:

• Development of county groups for modeling purpose was unsuccessful.
28

• County-level regression models have very high R

2

but high errors as well for many

observations (county/year combination). They were deemed inadequate to predict VMT

at the county level.

• State-based regression models are time-series-based and perform better than countybased regression models but still do not have adequate predictive errors at the county

level.

• Reduced models and models with transformed variables are acceptable at the statistical

goodness-of-fit level but unacceptable in terms of prediction accuracy criteria at the

county level.

• Corridor-based regression models do not have adequate predictive power as judged in

terms of R

2

.

The final models include:

• A non-interstate NNet Model with K factors, which produces results that meet the

predictive accuracy threshold with exception of one county, although predicted values

for a number of observations deviate from the “normal” range.

• An interstate VMT regression model with K factors, which produce much better results

than other regression models, with approximately 85% of the observations within the

predictive accuracy threshold.

Both models have generated VMT prediction values that are well beyond the normal range.

Examination of VMT data shows that

• Significant proportions of yearly VMT changes are well beyond the normal range —1-

6% for non-interstate and 2-4% for interstate VMT growth— 47% for non-interstate

VMT and 75% of interstate VMT.

• The relationship between change in interstate VMT and socioeconomic variables is weak

at the corridor level.

3.5.2. Indiana

INDOT sponsored a study to develop unbiased statewide VMT estimates for personal and

commercial travel (Friker and Kumapley 2002). The study tested three methods—licensed

driver-based, household-based, and fuel-tax-based. These methods are all based on

socioeconomic data.

The first two are survey-based cross-classification models based on licensed driver and

household travel characteristics, using data from the Nationwide Personal Transportation Survey

(NPTS). These models are intended to address the problems of sampling bias associated with

current VMT estimation procedures. Variables in these models include average annual miles

driven per licensed driver, by sex and age cohort, and average annual household VMT based on

selected demographic and socioeconomic characteristics.

The licensed driver-based method estimates and forecasts short-term and long-term statewide

VMT. Total state population estimates and projections were obtained from the Census Bureau

for years from 1990 through 2010, as well as population by sex and population eligible to drive
29

(aged 16 years and older) by sex. License driver by sex and age cohorts were extracted from the

Highway Statistics for the years 1994 through 2000. The population of licensed drivers was

projected for the years 2000 through 2010, based on the average percentage of the licensed driver

ratio to the percentage of the state population eligible to drive, over the1994-2000 period. The

statewide VMT estimates were conducted in three steps:

• Total licensed driver population was estimated for a subject year.

• The licensed driver population was distributed by sex and age groups, based on historical

distribution.

• Annual VMT for all licensed drivers by sex and age cohorts were calculated based on

average annual VMT per licensed driver, by sex and age cohorts, which was derived from

1990 and 1995 NPTS surveys.

The household-based method estimates statewide personal travel VMT based on three

socioeconomic variables—income groups, household size, and vehicle ownership by area types.

The average annual household VMT was estimated for the three sets of cross-tabulations, using

the 1995 NPTS. Area types were defined as three categories—rural, light urban, and dense

urban—an aggregation of NPTS’s original five categories. The statewide VMT was estimated in

the following three steps:

• Input the number of households in the study area for each cell of the VMT estimation

matrix – by area type and socioeconomic characteristics

• Multiply the number of households by the estimates of nationwide average annual

household VMT for each cell

• Aggregate estimates of total household miles within each cell to obtain the VMT.

Data for commercial truck travel includes IFTA and MCFT databases. The IFTA and MCFT

require the carriers to report the total annual miles traveled by all qualified vehicles for tax

estimation purposes. These annual miles represent total Indiana commercial vehicle VMT and

were adopted in this study for the estimation of commercial vehicle VMT. Data on total truck

activity by fuel type for the 58 jurisdictions were obtained from the Indiana Department of

Revenue for the period 1999 though 2001. The total miles driven by all vehicles from all

jurisdictions, for all fuel types, are taken as the interstate commercial vehicle component of

Indiana VMT. The Indiana Motor Carriers Fuel Tax License (MCFT) is required by all Indiana

based carriers whose activities are entirely within the state. All intrastate carriers in Indiana

report their quarterly tax returns to the Motor Carrier Services Division of the Indiana

Department of Revenue. Data on total truck activity by fuel type for all intrastate vehicles were

obtained from the Indiana DOR for the period 1999 though 2001. The total miles driven by all

vehicles for all fuel types are taken as the intrastate commercial vehicle component of Indiana

VMT.

As a result of the study, a licensed driver-based method was recommended for the estimation of

statewide personal travel VMT.

Some problems were identified in the travel survey based VMT methods, including:
30

• Licensed driver data from two sources—the Indiana Bureau of Motor Vehicles and the

Highway Statistics—are inconsistent and differ significantly. These discrepancies may

affect the accuracy of the VMT estimates.

• The 1995 NPTS provides odometer-recorded and self-reported vehicle mileages, which

were found to be significantly different from each other. The reliability of both data items

was questionable.

• The NPTS data does not represent commercial vehicle travel.

• None of the three methods produce VMT estimates by highway functional classes, and

the statewide VMT estimates can be used as control totals for planning purposes.

3.5.3. Oregon

In July 2000, Oregon Department of Transportation (ODOT) completed a Statewide Vehicle

Miles of Travel Study (Statewide VMT Study). The purpose of the study was to evaluate

ODOT’s existing procedures for estimating statewide VMT and to create a single unified

procedure for estimating statewide VMT. As documented in the Statewide VMT Study, three

different sections within the ODOT estimate statewide VMT for various purposes, including

strategic planning, identifying transportation system improvement needs, allocating highway

funds and determining user taxes/fees, and evaluating crash statistics.

The Transportation Data and Policy Sections of the Transportation Development Division and

the Financial and Economic Analysis Section of the Central Services Division each used a

different procedure to estimate statewide VMT. The Transportation Data Section used the

traffic-count based procedures to estimate VMT. The data include Highway Performance

Monitoring System (HPMS) and the Traffic Monitoring System databases. The HPMS is

prepared in accordance with the FHWA procedures. VMT estimation was conducted at the

system-wide level, by functional class, by vehicle class, and by jurisdictional class. Adjustment

and growth factors were used in the estimation process.

To forecast state tax revenues, the ODOT Financial and Economic Analysis Section adopted a

fuel-based approach to estimating and forecasting VMT. ODOT prepared short-range (6-year)

VMT forecasts every six months and long-range (25-year) forecasts every tow years. Long-range

forecasts were also used to support long range planning efforts including the Oregon Highway

Plan. Total VMT is divided into three categories— Light vehicle VMT, Medium-Heavy Vehicle

VMT, and Heavy Vehicle VMT. For the first two categories, ODOT uses monthly fuel

consumption data, fuel refund claims, and national miles-per-gallon estimates to calculate VMT.

For heavy vehicles, VMT is estimated using actual reported mileage from weight-mile tax

records and adjustment factors.

Three existing and two potential statewide VMT estimation procedures were evaluated based on

the following ten criteria:

Accuracy

1. Accuracy of statewide VMT estimate

2. Accuracy of SHS VMT estimate

3. Accuracy of non-SHS VMT estimate
31

4. Information by roadway class, jurisdictional class, and vehicle type

5. Not subject to year-to-year statistical or methodological fluctuations

6. Not subject to increasing error over time

Consistency

7. Consistency of methodology across roadway classes

8. Consistency in direction and magnitude with published Federal VMT estimates

9. Consistency with revenue forecasts

Required Resources and Information Availability

10. Ease of data collection and calculations

Based on the evaluation and a survey of five states, the study made a general recommendation

for purposes other than HPMS submittal and a HPMS submittal specific recommendation. The

general recommendation includes using the fuel-based method to estimate overall statewide

VMT, and then disaggregating this overall VMT into functional classes based on data from the

Integrated Transportation Information System (ITIS), HPMS and ODOT's Supplemental Count

process (see Figure 3-1). The HPMS submittal specific recommendation consists of two options:

status quo and the fuel-based+HPMS method. For the second option, the fuel-based method is

used to estimate statewide VMT. Then, the HPMS VMT estimate for the higher functional

classes is subtracted from the statewide VMT to obtain the VMT estimate for the lower

functional classes, which is later allocated to the rural minor collector, rural local and urban local

classes using percentage splits.

Statewide VM T

from Fuel-Based method

SHS VM T

from ITIS

Non-SHS VM T

remainder

Non-SHS VM T on Higher

Functional Classes

from HPMS

Non-SHS VM T on Lower

Functional Classes

remainder

Non-SHS Rural Minor

Collector VM T

% split*

Non-SHS Rural Local

VM T

% split*

Non-SHS Urban Local

VM T

% split*

* Percentage split derived by taking m oving averages of the data from ODOT’s Supplemental Count Program .

Figure 3-1. General Recommendation for Estimating Statewide VMT and Allocating to

Functional Classes

Sources: David Evans and Associates. 2001. Issues and Implications of Implementing

the General Recommendation from the July 2000 Statewide Vehicle Miles of Travel (VMT) Study. “White Paper”

Prepared for Oregon Department of Transportation.
32

While the general recommendation has its advantages, it also has some limitations (see Table 3-

3).

Table 3-3. Pros and Cons of the General Recommendation

Pros Cons

• Fuel-based method for estimating overall statewide

VMT produces the most consistent VMT forecasts

• Allows for the disaggregation of overall statewide VMT

into functional classes consistent with federal HPMS

classifications.

• Estimated VMT is consistent with forecast revenues

since it is part of revenue forecasting process

• Effective method for long-term forecasts

• Heavy vehicle VMT based on actual reported mileage

from weight-mile tax records rather than estimation

• Unifies the existing VMT estimation procedures adopted

by different ODOT divisions into one procedure

• National mpg estimate reflects the average fuel economy

of the national fleet and may not represent the average

fuel economy of the Oregon fleet. Method is very

dependent on having an accurate breakdown of the

vehicle fleet.

• Method does not adjust for the net import/export of fuel

• Weak method for estimating medium-heavy vehicle fuel

economy

• Unclear how to account for growth in the use of

alternative fuel vehicles

• Accuracy is dependent upon data collection performed by

other agencies

Source: David Evans and Associates. 2001.

Task 1: Decide traffic count locations and put data in the network
33

3.6. Review of PENNDOT and MPO Travel Data

PENNDOT’s TIS/RMS system and available MPO travel demand models can provide key data

needed for the development of a statewide travel forecasting system. This section provides

potential PENNDOT and MPO data items that can be utilized in the development of the

forecasting model framework options discussed in previous sections. Table 3-4 provides an

overview of the use of available data sources in potential forecasting components.

Table 3-4. Potential Role of PENNDOT and MPO Travel Data

Potential Forecasting

Component

PENNDOT/MPO

Data Sources

Estimating Total Trips

MPO Population & Employment

Forecasts

Estimating Trip

Lengths

MPO Population & Employment

Densities; MPO Travel Demand Model

External Travel Shares

Estimating Share of

Trips by Auto

MPO Travel Demand Model Transit

Shares

Estimating Share of

Trips by Roadway

Type

MPO Travel Model Future Year

Network Lane Miles by Roadway Type

Validation Cross

Checks

TIS Trended Annual Growth Factors;

HPMS Trended VMT;

MPO Travel Demand Model Future

Year VMT

Much of the data available from the PENNDOT TIS/RMS/HPMS systems are reasonable for

updating traffic volumes and VMT to the current year or for understanding past trends of travel

growth. However, these rates are not necessarily sufficient for estimating future forces behind

traffic growth. As a result, the PENNDOT data will most likely serve as important cross checks

to the growths produced by the completed forecasting system. These cross checks will provide

the user with important information and insights into the potential fluctuation of projected

growth rates based on the available data sources and methodologies.

MPO travel demand model input and output data may serve multiple roles in the forecasting

system. The output VMT from travel demand model can also serve as an important cross check

of growth rates. In contrast, the socioeconomic (population, households, employment) forecasts,

densities, external travel percentages, transit mode shares, and network lane miles may be direct

inputs to the forecasting model equations and analyses.

An overview of the available PENNDOT and MPO travel data, pertinent to the potential

forecasting system, is discussed in the following sections.
34

3.6.1. Overview of PENNDOT Traffic Monitoring System

PENNDOT’s Traffic Monitoring System (TMS) provides the coordination tool needed for

managing the traffic data collection activities within Pennsylvania. The system is made up of

several key components that provide valuable information needed for understanding the past and

current level of travel throughout the state. Nearly 6,500 counts are conducted annually and

uploaded to the Traffic Information System (TIS) and Roadway Management System (RMS).

Figure 3-2 summarizes the interrelationships between each of the system components.

Figure 3-2. The PENNDOT Traffic Monitoring System

Source: PENNDOT

TIS: In 2002, the BPR implemented the computer-based TIS, which consolidates existing

functions from different count programs into a modular, integrated environment. It can

exchange data with existing systems such as the RMS and the GIS. The TIS allows users to edit

continuous count data, calculate traffic adjustment factors, and produce summary data reports. A

statistical analysis module uses the entire traffic database to generate traffic volume growth and

adjustment factors (e.g. annual growth rates by functional group, seasonal adjustment factors)

that can provide a valuable resource for calculating past traffic growth trends throughout the

state. Pertinent features include:
35

• Growth Factor Estimation. The system uses a least-squares approach for determining

average traffic growth factors for various functional/geographic groups of sites.

• Seasonal Variation Factors. The system helps users maintain a system-wide set of

seasonal variation factors, based on continuous counts, for use in processing short-term

counts.

• Data Mining. The system includes other statistical functions to support ad-hoc queries

and analyses.

RMS: The Roadway Management System (RMS) is a highly evolved database for storing vast

amounts of information on Pennsylvania's large highway network. This system was implemented

in 1985 and has become substantially larger since that time. RMS provides a “snapshot” of the

current estimated traffic volumes and physical characteristics by route segment for all state

roadways in Pennsylvania (limited local coverage). This system maintains over 700 variables of

information on each segment of highway. Typically in addition to physical condition, the

maintenance planner uses highway geometry, pavement type, network designation, right-of-way,

traffic volumes, maintenance history, etc. to support the rehabilitation strategies and work

activities to be performed. In addition, RMS data serves as a valuable resource for air quality

inventory and conformity calculations, Highway Performance Monitoring System (HPMS) VMT

calculations, and other planning and design activities. The RMS database source can serve as a

primary base (providing an estimate of roadway mileage) from which to compute future growth

rates for VMT. Key traffic information contained in the database includes:

• Roadway functional class, urban/rural code, and county

• AADT traffic volume

• Roadway distance

The RMS-based HPMS VMT will play an important role in determining past trends in VMT and

for performing future year VMT trend analyses. VMT is not only a function of volume growth

but also with changing roadway mileage, travel patterns, and other travel characteristics.

3.6.2. Overview of PENNDOT Trend Factors

PENNDOT’s Bureau of Planning and Research (BPR) has been preparing annual traffic reports

since 1990, which include reported “System-wide Global Update Factors” or “Trend Factors”.

These factors are average annual growth percentages for each county, functional class

combination in the state.

Before 1998, “global update” factors were developed from permanent ATR locations

(approximately 63 locations, which have been in existence for over 20 years) and close to 1,000

raw counts representing a 4-5 year period. Raw counts were examined and processed to create a

trend factor. The process was difficult and rarely yielded clear trends. PENNDOT determined

that counts, even at the same location, were very inconsistent from year to year due to the

variability of the day, month and season in which the counts were taken (even if adjusted).

Offline “smoothing processes” were conducted by PENNDOT to account for these problems and

to produce the “global update” factors.
36

In 1998, the BPR stopped the above process and implemented a system using 250 permanent

ATR sites that collect 7-day counts 3 times a year for analysis. These counts are now used to

develop the current “global update factors” for each county, functional class grouping. Sample

size presents a challenge when data is desired by each of 67 counties and/or 4-5 functional

classes.

Recent PENNDOT research projects regarding the traffic data processes found that sufficient

sample sizes were not being collected. In 2002, the BPR implemented TIS to consolidate

existing functions from different count programs into a modular, integrated environment.

PENNDOT now uses the TIS-based annual growth factors to estimate current ADT for total

vehicles and trucks for count locations where the count is older than the current year. The growth

factors that are estimated and used are based on Traffic Pattern Group (TPG). In PENNDOT’s

Road Management System (RMS), trend factors by County/Functional Class (FCG) are used to

produce the current year traffic volume estimates, and these trend factors are derived by a linear

regression analysis of historical data.

3.6.3. Role / Limitations of PENNDOT Traffic Data for Forecasting

As indicated in Table 3-5, the historical count data and trend factors within the TIS system in

combination with the RMS traffic database and HPMS VMT are useful for identifying past

patterns and trends in VMT and traffic volume growth for the state-owned roadways. They

account for variations in traffic growth by county and by the functional class of the roadway.

While the methods used by PENNDOT are reasonable for updating traffic volumes and VMT to

the current year, these rates are not necessarily sufficient for estimating future forces behind

traffic growth. Historic trends are a significant factor to consider, but is not responsive to

changing conditions.

Although trend factors, RMS data, and HPMS VMT have limitations, they have played an

important role in estimating future VMT for air quality inventory and conformity purposes due to

the lack of other available data sources. Trend analyses have been conducted on the “global

update” factors and HPMS VMT. These relationships have been used to forecast future volume

growth for different county, functional class groupings throughout the state. The growth rates

have then been applied to the base year RMS traffic volumes to estimate future year VMT. The

VMT estimates have been used in air quality inventories and for conformity analyses in many

counties. However, the future forecasts are not used for conformity analyses in areas with travel

demand models. The travel demand models incorporate socioeconomic and land use factors into

the estimation process, which provides a theoretically more enhanced approach.

The forecasted growth rates are updated each year as new HPMS VMT and “global update”

factors are developed by the BPR. In general, the use of these growth rates has provided

conservative estimates of future VMT that actually have corresponded closer to reported HPMS

VMT growth than the growth produced by travel demand models in that same time frame. For

emission inventory development, this is an important issue since an underestimation of future

VMT and emissions may cause conformity and regional attainment problems due to unrealistic

emission budgets (targets) and the lack of necessary regional control strategies implemented to

offset such growth. The consultant’s past experience with travel demand models in Pennsylvania
37

has shown that models generally produce lower growth rates than what has been shown in

HPMS over the last 10 years. Due to air quality regulations that require consistency with HPMS,

the choice and application of future growth rates is an important issue. Likewise,

underestimation or overestimation of future growth can also have implications on regional

corridor studies and design alternatives. Such issues can result in both deficiencies and excess of

planned roadway capacity improvements to account for future travel.

Table 3-5. PENNDOT Traffic Data’s Use in Forecasting

PENNDOT

Data Item

Primary Use Limitations

Vision of Use in Travel

Forecasting

Historical

Count Data

Understanding past

volume growth trends

May produce inconsistent

results; may not directly

relate to VMT

None due to existence of Trend

Factors

Trend Factors

Understanding past

volume growth trends

Does not directly relate to

VMT; statistical

significance for individual

counties; generally do not

indicate a decline in travel

for any region in PA

Perform trend analysis to estimate

future volume growth rates; Apply

to current RMS database to

estimate future VMT; Utilize as

cross checks for forecasting system

RMS

Database

Static database used

for producing annual

HPMS VMT; Provides

source or current

roadway mileage

Does not contain

information useful for

identifying past or future

trends

Used as base roadway mileage for

computing VMT estimates

HPMS VMT

Understanding past

VMT growth trends

Local VMT estimates based

on offline estimation

process

Perform trend analysis to estimate

future volume growth rates; Apply

to current RMS database to

estimate future VMT ; Utilize as

cross checks for forecasting system

Due to the importance of producing realistic estimates of travel and VMT growth, it is

recommended that historical trend factors and HPMS VMT be examined, trended to future years,

and used as cross checks to the values produced from the completed forecasting system. The

historical rates may not have produced accurate results for individual years but have generally

produced acceptable results over a 10-year time frame. As a result, these tabulations should be

produced for informational or comparison purposes.

Additional investigations will be needed to assess what aggregation level is appropriate for

producing forecast HPMS and volume growth rates based on the historic values. At a minimum

trended growth rate equations should be developed for the following aggregations:

• Urban Interstate

• Urban Non-Interstate

• Rural Interstate

• Rural Non-Interstate
38

3.6.4. Role of MPO Travel Demand Model Data for Forecasting

The historic trends determined from the PENNDOT data are a significant factor to consider in

developing and validating future year growth rates, but they alone are not responsive to changing

conditions. Many urban MPOs have other data resources that can serve as valuable inputs and

cross checks to a statewide forecasting system. Such data includes the regional travel demand

models and socioeconomic forecasts used by the MPOs for regional and long-range

transportation planning.

Travel demand models utilize input socioeconomic information (e.g. population, households, and

employment) for predefined traffic analysis zones within the travel demand model coverage to

estimate future travel on the regional roadways. Travel demand models offer several distinct

advantages over simple trending analyses:

• Forecasts based on amount and location of population and employment

• Socioeconomic inputs produced by MPO based on the latest information on planned

developments

• Future year runs utilize highway networks with planned projects

• Travel demand model distributes trips based on location of housing and jobs

• Travel demand model estimates diversions based on increased traffic levels and roadway

enhancements

As illustrated in Figure 3-3, there are currently 20 Pennsylvania counties covered by travel

demand models with additional 5 counties to be covered within the next several years. Each

travel demand model covers the majority of state-owned roadways as well as some local and

township roads. Many of the MPOs have recently conducted, or will in the near future, updates

to the travel demand model inputs based on 2000 CENSUS information and updates to

socioeconomic forecasts. Each of the regional models is used for air quality analyses; and as a

result, future year networks and scenarios are produced based on the planned projects contained

in the region’s Transportation Improvement Program (TIP) and Long-Range Plan (LRP).

Although travel demand models are considered a robust tool for analyzing future traffic growth

and project impacts, they also have limitations. Travel demand models account for the location

of housing and jobs and the potential impact of planned project improvements on diversions;

however, the model does not accurately capture the many other changing variables that have a

significant impact on VMT. These include the status of the economy, fluctuating gas prices, and

changing travel behavior. Most of the travel demand models base trip making on the forecasted

number of households and the average number of persons per household (based on the supplied

forecasted population). These values are used to access trip production lookup tables to

determine the number and type of trips being made by each household. Based on the

consultant’s experience in utilizing travel demand models throughout the state, the travel demand

models have generally produced less travel growth than that shown in the HPMS VMT through

the 1990-2000 timeframe. The reasons for the differences are many, and some of these will be

discussed in Chapter 6.
39

Figure 3-3. Pennsylvania Travel Demand Model Coverage

MPO travel demand model input and output data may serve multiple roles in the forecasting

system. These roles range from utilizing the socioeconomic forecasts as direct inputs to the

forecasting methodology to using the output future year VMT estimates as a cross checks to the

forecasting process growth rates. As illustrated in Figure 3-4, the primary MPO travel data items

with potential use in the forecasting process are the following:

• Socioeconomic (Population, Households, Employment) Forecasts and Densities

• Future Roadway Mileage by Roadway Class (Freeway/Non-Freeway)

• Future Transit / Non-Auto Trip Shares

• Future VMT by Roadway Class and External Travel Share

Figure 3-4. MPO Travel Demand Model Data

Existing Counties Covered by Travel Model

Future Counties Covered by Travel Model

Highway

Network

Socioeconomic

Forecasts

Trip Tables

Assigned

Network

Travel

Model

Roadway Lane Miles

VMT by Roadway Class

Portion of External Travel

Population/Employment Forecasts

Future Pop/Emp Densities

Transit Mode Share

Non-Auto Mode Share
40

There are several options for incorporating travel demand model data into the forecasting

process. Three potential options are discussed below.

Option 1 – Utilize MPO Travel Demand Model Output VMT as a Cross Check. This

potential option is illustrated in Figure 3-5 and involves utilizing the travel demand model

outputs directly to obtain future VMT growth factors for county, roadway class (freeway, nonfreeway) combinations. These growth rates can be used to cross check or validate the growth

rates from the PENNDOT forecasting system. In this option, MPO data is not specifically used

in the PENNDOT forecasting process and is simply used as a comparison. Alternatively, the

MPO model growth rates can be used in combination with the PENNDOT forecast growth rates

to create a composite or average growth rate as discussed in the following section.

Figure 3-5. Utilize MPO Travel Demand Model Output VMT as a Cross Check

This methodology requires a complete travel demand model run for future year scenarios.

However, since all travel demand model areas are non-attainment areas, it can be expected that

future scenario runs with planned projects will be produced and summarized for each conformity

analysis. The growth rates can be compared to trended HPMS VMT and to growth determined

from the forecasting process, which would use population/employment forecasts from other

sources.

Utilizing the travel demand model outputs directly ensures that future VMT estimates account

for diversions and changing roadway mileage based on the planned highway projects. In

addition, the model would account for significant transit alternatives that have a significant

impact on VMT.

Historic

HPMS VMT

Historic Trend

Factors

Base Year

RMS Data

Base Year

HPMS VMT

Historic

Pop/Emp

Forecast

Pop/Emp

Socioeconomic

Forecasts

Future Highway

Model Network

Travel Demand

Model

PENNDOT Data Other Resources

(BEA, Private Source, etc.)

MPO Model Data

Trended

HPMS VMT

Growth

Compare historic HPMS VMT by FC Group

versus historic population/employment

Is there a significant relationship?

If YES

Relationship

Equation

Calculated

VMT Growth

Cross Check or Composite

Cross Check or Composite

Future Year

Volumes/VMT

VMT Growth

by

FC Grouping

If

Available
41

Option 2 – Utilize MPO Travel Demand Model Socioeconomic Forecasts. A recommended

enhancement to the PENNDOT forecasting system is the usage of available MPO socioeconomic

forecasting data as direct input to the growth rate calculation process. Figure 3-6 illustrates the

potential for integrating MPO socioeconomic forecasts into the forecasting process. For this

case, the travel demand model outputs and highway network are not utilized in the development

of growth rates. The use of MPO socioeconomic forecasts would not eliminate the need to first

develop the statistical relationships between historic population and employment growth and

VMT. Historical HPMS VMT totals by county and functional class group can be compiled and

compared to historical population and employment estimates from either the Bureau of

Economic Analysis or other state data sources (e.g. Penn State Data center or private sources) as

will be determined through future research. The comparisons will involve computing statistical

correlations to determine if there is an acceptable level of correlation between VMT and

population/employment growth. It is anticipated that comparisons will be made for counties (or

in some cases county groupings, or even statewide) and for four primary functional groups:

Urban Interstate, Rural Interstate, Urban Non-Interstate, and Rural Non-Interstate. If population,

employment, or both are correlated with HPMS VMT growth, then relationship factors (or

elasticities) must be developed. If no correlation is determined between VMT growth and

population or employment then alternative methodologies or procedures must be utilized.

`32w

Figure 3-6. Utilizing MPO Socioeconomic Forecasts

Historic

HPMS VMT

Historic Trend

Factors

Base Year

RMS Data

Base Year

HPMS VMT

Historic

Pop/Emp

Forecast

Pop/Emp

Socioeconomic

Forecasts

Future Highway

Model Network

Travel Demand

Model

PENNDOT Data Other Resources

(BEA, Private Source, etc.)

MPO Model Data

Trended

HPMS VMT

Growth

Compare historic HPMS VMT by FC Group

versus historic population/employment

Is there a significant relationship?

If YES

Relationship

Equation

Calculated

VMT Growth

Calculated

VMT Growth

Cross Check or Composite Cross Check or Composite

If

Available
42

This methodology supplements historical trend analysis with growth rates based on

socioeconomic forecasts prepared by MPOs throughout the state. The analysis does not require

travel demand model runs and simply uses the forecasted inputs to the model, which should be

readily available. There are some limitations of this methodology. If there is no statistical

significance between past population/employment and VMT growth, then it is difficult to

determine the potential impact of forecasted data on VMT. In addition, this methodology does

not directly account for future roadway mileage changes or significant diversions due to new

roadway or capacity addition projects.

Option 3 – Utilize Additional Model Input and Output Data: This potential option builds

upon Option 2 to create a greater level of integration of MPO travel demand model data. This

option is highly dependent upon the design and components utilized in the planned PENNDOT

forecasting procedure. The potential for creating a more robust statistical model to estimate

future VMT growth may create the need for additional forecasting data including the following:

• Population/Employment Densities – This data may be used to estimate potential changes

to regional average trip lengths. Such a change, even without a change in trips, could

result in changes to forecast VMT. Densities can be computed from the travel demand

model socioeconomic inputs in combination with county or zonal areas (e.g. square

miles of county).

• External Travel Percentage – External travel may impact future average trip lengths in

each region. Travel demand model forecasts of external travel may be extracted from

the assigned model network output files for use in the forecasting procedure.

• Transit and Non-Auto Shares – Large-level transit or transportation demand

management projects can have a significant impact on regional VMT. Typically these

impacts may not be accounted for in a non-model forecasting process. If statistical

models are utilizing that incorporate such data, the information can be extracted from the

travel demand models used in the state. Most of the MPO travel demand models

estimate person travel and contain a transit module. The information is contained in the

output trip tables.

• VMT by Roadway Class – The statistical forecasting models may base forecasting on

aggregate area and roadway classes. As a result, future shares of VMT by roadway type

may be a useful input in creating separate growth rates by several aggregate roadway

classes. VMT by roadway class can be determined from the output assigned model

network.

• Lane Miles by Roadway Type – Understanding the potential changes to the

transportation system may be an element to creating statistical models to estimate future

growth rates. Typically changes to lane miles will result from new construction of

roadways. Although, such changes may be limited for many counties, some counties

may experience high levels of growth due to induced land use created by new freeways

in the region. The future year input travel demand model highway networks can be

processed to obtain changes to lane miles for various roadway types.

The recommended forecasting process will most likely not involve all of the above variables;

however, the MPO travel demand model data does provide an array of data that may be useful

depending on the chosen options.
43

3.6.5. Utilizing and Interpreting Multiple Sets of Growth Rates

This section has provided an overview of the potential PENNDOT and MPO data sources that

can be integrated to the forecasting system. Several of the key data items, including trended

HPMS VMT, trended PENNDOT annual growth factors, and MPO travel demand model output

VMT, are expected to serve as cross-check validation values. Due to the inconsistencies and

differences between various forecasting methodologies, it is suggested that alternative growth

scenarios from these sources be reviewed and possibly output along with the calculated growth

rates from the developed forecasting process. The alternative growth rates in combination with

the forecasted rates may be referred to as a low, medium, and high growth option. Alternatively,

a composite growth rate may be calculated representing an average condition. By reporting all

available growth rates, the user can understand the potential fluctuation of traffic growth and

choose a value consistent with the needs of the analysis.
44

3.7. Review of Socioeconomic Data and Forecasts

This section documents findings associated with Subtask 1.5 Socioeconomic Data and Forecast

of the Statistical Evaluation of Projected Traffic Growth project. The objectives of this subtask

are to define the socioeconomic database needed to support traffic growth forecasting and

recommend procedures and data sources to obtain this information. This section starts with a

brief discussion of model design, then describes a number of potential data vendors that can

provide the data needed to construct the PENNDOT Forecasting System and drive its forecasts,

and finally evaluates these vendors and makes some preliminary recommendations.

3.7.1. Socioeconomic Variables and Traffic Growth Forecasting

The development of a traffic forecasting system requires historical data to estimate a statistical

relationship between vehicle miles traveled (VMT) and a set of variables that are expected to be

the key drivers of VMT. In turn, forecasting VMT requires projections of the variables used to

explain VMT.

The data used in the estimation of a statistical model explaining VMT depends in part on the

extent of aggregation of VMT both geographically and by road type. The primary focus of our

analysis is long-term, county level VMT projections by type of road.

VMT Forecasting: Passenger Travel

To create county-wide projections of VMT over a 20-25 year horizon for several classes of

roads, we first must identify the key elements of a model explaining VMT by county by road

type. The total private auto VMT in any county i, at time t, will depend, by definition, on the

number of trips taken, the average length of trip, the share of trips that are by private auto, and

the share of trips taken on each type of road. Different variables are likely to affect each one of

these components:

• Total Trips in county i, road type j at time t: Nijt

o Population

o Age distribution of the population

o Employment

• Average Length of Trip: Lijt

o Population Density

o Employment Density

o Extent of commuting out of the county

o Extent of shopping out of the county (Employment in Retail?)

• Share of Trips by Auto:

ijt

S

o Auto ownership

o Income

o Auto operating costs
45

o Gasoline costs

o Transit service measures

• Share of Trips by Type of Road: Rijt

o Lane miles of each road type

A multivariate model of VMT by road type by county could take the form of:

( , , , ) VMTijt ijt ijt ijt ijt ij ijt = f N L M R + e + u

for each road type j. This formulation forces relationships between drivers and VMT to be the

same across counties except for the intercept. This assumption can be completely relaxed,

however, by estimating separate models for each county.

Once satisfactory statistical models of VMT have been constructed, projections of the drivers are

needed to create the forecasts of VMT. It must be noted here that policy decisions regarding

transportation investments that add to highway capacity will directly affect VMT, as described

in the generic model above, and that they are also likely to affect VMT indirectly through their

impacts on population and employment. It therefore would be useful to construct sub-models

linking population and employment growth to lagged investment in highways. Armed with these

sub-models, PENNDOT could undertake either static simulations of traffic growth, based on

published projections of population and employment, or dynamic simulations incorporating the

feedback of highway capacity expansion on population and employment.

As outlined above, a variety of data will be needed to build and forecast the PENNDOT county

models. The historical data on VMT, lane miles by road type and other traffic data will come

from the PENNDOT Traffic Information System. It should also be possible for PENNDOT to

provide historical data on the number of licensed drivers and/or number of registered vehicles by

type by county. A quick search on the Web indicates that data on the size of each Pennsylvania

county (measured in square miles) is readily available, to compute population / employment

densities.

The economic and demographic variables needed to construct and forecast the VMT models are

available from a variety of sources, both public and private, as described in the next section. A

preliminary list of candidate variables includes the following concepts:

• Population by gender and age distribution

• Number of households, household size

• Employment, total and by industry

• Personal income, total and all components (wages, non-wage income,

transfer payments, ...)

• Gasoline usage and gasoline price index

• Vehicle fuel efficiency

• Transit use for urban counties
46

All variables should be obtained for both the 67 individual Pennsylvania counties and the state as

a whole. These time series data should be obtained annually, and for the longest historical time

period possible. If a sufficient number of observations are available, the models can be estimated

with a portion of the historical data, with the rest being used to test the simulation capabilities of

the models.

VMT Forecasting: Freight

Freight traffic is driven, in part, by a different set of variables than passenger traffic. There are

three separate components of freight VMT:

• Freight originating in PA that is either delivered locally or exported

• Freight imported into PA

• Freight originating outside of PA with final destinations outside PA

Each of these components of freight traffic is likely to be affected by different factors.

Freight that originates in PA will be affected by expected future growth in freight generating

industries as well as the growth of PA counties such as warehousing centers. At the state level,

data on Gross State Product by industrial sector are available so that aggregate projections for

industries that generate significant motor freight can be made. Unfortunately, these data are not

available generally at the county level, and if they were they would likely be of questionable

reliability. As a substitute, detailed employment data by industry sector are available at the

county level, and could be used to make projections of growth in sectors that generate significant

freight VMT.

The amount of freight traffic with a destination in PA—whether originating within state or from

out of state is likely to depend on a number of factors including:

• Growth in aggregate personal income

• Growth in industries requiring inputs shipped by truck

• Share of freight that uses highways rather than rail, water or air

Again, we could use detailed, county level employment to predict growth in industries requiring

significant shipping of inputs by truck. Additionally, we could include the future availability of

alternative shipping modes based upon expected investment (or disinvestment in the current rail

infrastructure).

VMT associated with freight passing through Pennsylvania will be affected by a number of

additional factors reflecting the economic growth in neighboring states as well as the projected

growth of ports within state and ports in neighboring states. Thus, forecasts of freight that is

passing through Pennsylvania might include data on:

• Projected tonnage of freight in the Philadelphia, Wilmington, and NY/NJ ports

• Economic growth in freight-producing sectors in neighboring states

• Identifications of counties along key cross-state routes.
47

3.7.2. Data Vendor Description

In this section we describe a number of potential data vendors that can provide the data needed to

construct the PENNDOT Forecasting System and drive its forecasts. Using the Web and followup phone calls, we have identified four private data vendors and three government data sources.

Each of these sources is described below. Contact information for all vendors is provided in an

appendix to this report.

Using the preliminary model outline presented in Section 2, we developed a series of questions

that were posed to each of the potential vendors. Those questions included:

• Name of vendor / agency providing data and forecasts, and basic information

o Where located, how long in business?

• How are the forecasts prepared; that is, what sort of models underlie the forecasts

• What variables are available (employment, income, demographics, ...)

• What is the data frequency (annual, quarterly, ...)

• How often are the historical data and forecasts updated?

• How can the data be delivered to the client (hard copy, on disk or CD, on website

with password to download)?

• What is data cost and conditions of sale

o Can the client purchase just what is needed, or must they buy a package?

o Must the client buy a subscription to gain access to the data?

The answers given by the vendors, or gleaned from their documentation are described below.

As noted above, we try to describe, in general terms, how each forecast vendor claims to prepare

their forecasts. That information is important in that it represents one way to evaluate the

potential “quality” of the forecast. To give the reader a better understanding of what those

descriptions mean, and why it is important, a brief primer on regional modeling and forecasting

is provided below.

In the regional modeling literature, models are commonly characterized as “Top-Down”,

“Bottom-Up” or some hybrid of the two. Top-down and bottom-up models take essentially

opposite approaches to modeling regional economic / demographic activity. A top-down regional

model begins with a model and forecast for the nation and links the smaller region to it. For

example, manufacturing employment in a particular state may be predicted as a function of U.S.

manufacturing employment. Few modelers would attempt to link county manufacturing

employment directly to the corresponding national concept, however. Instead, the county models

may be linked to state, Census Region or Economic Area models, which would in turn be driven

by a national model. Conversely, a bottom-up model would predict regional activity as a function

of some set of strictly regional variables. The forecasts for larger regions are obtained by adding

up the forecasts from the smaller regions.

Both approaches have pluses and minuses. The data at the national level is updated much more

frequently than the data for smaller regions, so that top-down models incorporate more current

information in their forecasts. By relying primarily on national data, however, top-down models
48

ignore the important differences between different parts of the nation, which affects the relative

rates of economic and demographic growth in those regions. Indeed, one criticism of top-down

models is that they simply share out a national forecast adding little, if any region-specific

information.

By reversing these arguments, one has the pros and cons of the bottom-up approach. There is

relatively little data that is available for the states and counties in a timely fashion. Hence,

models built from existing regional databases are at least a few years out of date. While bottomup models are rich in regional detail, they ignore important variables like interest rates that are

essentially determined in a national market.

Most regional forecasting models now employ a combination of these two basic approaches.

These hybrid models use both national and regional variables, as appropriate, to predict regional

activity. Some current models add linkages between regions, using local unemployment rates to

drive intra-regional migration and population growth. The new models represent an evolutionary

change from the top-down / bottom-up models of the past. They are in response to increased

demand from both the private and public sectors for more accurate models and forecasts for

smaller and smaller geographies.

U.S. Census Bureau, and Bureau of Economic Analysis

The Census Bureau and Bureau of Economic Analysis (BEA) are both branches of the U.S.

Department of Commerce.

The Census is the principle source of demographic information for the nation. For historical data,

the Census is unparalleled, with all of the Census 2000 data online in a variety of data formats

(Excel, PDF, CSV), that can be downloaded free of charge. The 1990 data is also available

online and in similar formats. Similarly, the BEA is the primary source for county-level

employment, wages and personal income data. The employment and wage data can be obtained

online, at various levels of industry detail, and is available in both the SIC and NAICS industry

classifications.

Some of the historical Census data may be quite useful in designing the PENNDOT Modeling

System, in particular the Census 2000 Journey To Work database. These data describe the flows

of people from their county of residence to the county in which they work. Hence, the data

provide an initial approximation of what traffic flows between counties look like. The same is

true for the BEA employment data, which could be used to identify major employment centers

by county, reinforcing the information obtained from the journey to work database.

To the extent that employment and population centers exist in peripheral parts of the state, it may

also be useful to gather some general demographic and economic data for the states surrounding

Pennsylvania to gauge traffic flows across state lines. This would entail gathering data for the

boundary counties in New York, New Jersey, Delaware, Maryland, West Virginia and Ohio.

For forecasting purposes, however, the Census and BEA can provide relatively little help for the

PENNDOT project. The BEA only provides historical data, while the Census only produces
49

demographic forecasts at the state and national level. One potential use of the Census forecast for

Pennsylvania would be as a check on any other population / household forecast used to drive the

PENNDOT model.

U.S. Department of Energy

The U.S. Department of Energy (DOE) produces an annual report, the Annual Energy Outlook,

which provides analysis and long-term forecasts of energy use by energy type and consumer

group. The current outlook extends through 2025. In particular, it includes three forecasts of oil

prices – a baseline with high and low alternatives – that can be used to project gasoline and

diesel prices for the PENNDOT VMT models. The DOE should also be able to provide historical

data on gasoline / diesel usage and on vehicle fuel efficiencies, if these data are not available

from PENNDOT.

All of these data are available for download from the DOE website.

Woods & Poole Economics Inc.

Woods & Poole is an independent firm that specializes in county-level forecasts. The company

has been in operation since 1983, and is located in the Washington, D.C. area.

The Woods & Poole county economic and demographic projections are prepared using a “TopDown” approach. They begin with detailed forecasts of U.S. economic and demographic

variables, and use them to predict the same or similar concepts for 172 “Economic Areas” (EA),

as defined by the Bureau of Economic Analysis (BEA). The EA’s are aggregates of contiguous

counties that attempt to measure cohesive economic regions in the U.S. The EA forecasts are

then used to drive their individual county forecasts. In the marketing literature on the Woods &

Poole website, the company claims that the average absolute percent error for their 10 year

county population projections is 10.2%.

Woods & Poole offers a variety of data / forecast packages. For the present purposes, the

appropriate choice would be their “State Profile”. The package includes historical data and

forecasts by year from 1969 through 2025 for every county and MSA for one state. The State and

U.S. totals are also included. Both history and forecast data have annual frequency. Variables for

which historical and forecast data are provided include:

• Population by age (single year cohorts), gender and race

• Number of households, household size and households by income level for 11

income ranges

• Employment and earnings for 13 sectors (using SIC industry designations)

• Personal Income by component

• Retail Sales for 10 sectors

• Educational Attainment (history only)

• Labor force and unemployment (history only)

• Private Non-farm Establishments by size and industry (history only)
50

The current forecast horizon is 2025. Forecasts are updated annually. The current price of the

State Profile package is $395 for a print volume and a CD-ROM containing all of the data tables

in spreadsheet files. No ongoing subscription fee is required to purchase these data.

Global Insight

Global Insight was created by combining two of the largest economic and financial forecasting

companies in the world - DRI (formerly Data Resources Inc.) and WEFA (formerly Wharton

Econometric Forecasting Associates). The merged company originally operated as DRI•WEFA.

It changed its name to Global Insight in October, 2002. In its different incarnations, Global

Insight has been in operation for over 25 years. The forecasting center is based in Eddystone,

Pennsylvania.

County-level forecasts are produced by the company’s U.S. Regional Service. Like Woods &

Poole, the county forecasts are generated from a “Top-Down” modeling system. Global Insight

starts with their U.S. macroeconomic forecast, and uses it to drive long-term state and MSA

forecasts. Those, in turn, are used to generate the county forecasts. Both history and forecast data

have annual frequency. The concepts forecast include:

• Employment for 9 industry sectors (using NAICS industry designations)

• Wage and salary disbursements and nonwage income, in both real and nominal

terms, and average annual wage for nonfarm employment.

• Personal income, per capita and per household income, in both real and nominal

terms

• Population, total and by age cohort for eight cohorts

• Number of households, total and by age cohort for six cohorts

The current long-term county forecasts extend to 2029. The forecasts are updated twice a year.

The data are stored in spreadsheets, can be obtained via the Web or burned onto CD. Based on

discussions with U.S. Regional staff, the price quoted for this package is $6,000. No ongoing

subscription fee is required to purchase the data.

Economy.com

Economy.com, Inc. is an independent provider of economic, financial, country, and industry

research. Economy.com was founded in 1990, and is based in West Chester, Pennsylvania.

The company’s forecasting methodology differs from other vendors in that their model of the

U.S. economy incorporates both top-down and bottom-up approaches. In that model, those

variables that are national in nature are modeled nationally, while those that are regional in

nature are modeled regionally, subject to data availability. In particular, the regional modeling

system links each state's economy to other states through migration flows and unemployment

rates. Those linkages permit them to take worker mobility into account. The county forecasts are

linked into this regional system.
51

The Economy.com’s county forecast “core” database includes approximately 100 variables,

including:

• Employment for 9 industry sectors (using NAICS industry designations)

• total and wage & salary income

• population and households

• labor force and unemployment rate

• bankruptcies and retail sales

• residential permit issuance, single-family housing stock, existing sales, sales

price, affordability index and mortgage originations

The data series extends to 20 years of forecast at an annual frequency. History and forecasts

updated quarterly. The data are available via the Web, and can be downloaded into a variety of

common computer programs. The cost for this service for one year is $5,700.

NPA Data Services, Inc.

NPA Data Services, Inc. was established in 1985, and is located in the Washington, DC area.

The company specializes in developing county, metropolitan statistical area, state, economic area

(EA), region and U.S. databases. The company represents an outgrowth of work done at the

National Planning Association.

Regional projections are generated by using linked regional economic and demographic growth

models. The regional economic model utilizes the latest detailed data specific to each area to

project employment, earnings, personal income, and total population series for the area. The

demographic projection model estimates the details of total population by age, sex and race using

cohort components analysis, estimates of local fertility and mortality trends, and a set of

relationships which characterize the domestic migration patterns of the given area. The sum of

the regional economic and demographic projections is constrained by the company’s national

projections, which act as control total.

The NPA regional databases include historical data and projections for each year from 1967 to

2030 for 56 economic and household data series and 153 population series. The concepts

forecasted include:

• Population by age (16 cohorts), race and gender

• Number of households, household size and income per household

• Employment and earnings for 10 sectors (using SIC industry designations)

• Personal Income by component

All databases are updated twice a year. The company distributes this information as hardcopy or

on diskettes or CD-ROM. The cost for a single state delivery is $400.
52

3.7.3. Evaluation and Selection of A Data Vendor

The evaluation of a vendor must be based on a number of selection criteria. First, one must

consider the appropriateness of the methodology used in generating the forecast. That is, can the

methodology employed to generate county level projections distinguish differential patterns of

growth from national and state control totals. It is unlikely, in our opinion, that the Global Insight

model could provide this important feature. Instead, we believe that the hybrid models used by

Economy.com, NPA or Woods & Poole are more likely to produce distinct, useful county-level

forecasts.

Second, it is convenient to utilize a vendor that can provide enough detail to drive the planned

county-level models. Simply put, it is much more efficient to obtain all necessary data, both

history and forecast, from one source rather than try to construct a coherent database from

several different sources.

All of the vendors appear to offer sufficient employment and income data for the purposes of the

PENNDOT project. Further, all appear to be able to provide detailed demographic information,

which is key to predicting VMT. Some of the vendors also offer other data that may be useful for

this project. For example, Woods & Poole provide detailed projections of county level retail

sales, which may be helpful in predicting traffic flows. Economy.com provides significant

information about residential construction activity at the county level, which may be used to

forecast changing traffic patterns over time.

A third important criterion is price. As can be seen from the comparison above there is a wide

range of prices offered. Given the differentials, it would appear that Woods & Poole or NPA

provide the most economical offerings and they are sufficient to satisfy the needs of the models

that will be constructed.

We do not include forecast accuracy as one of our selection criteria for two reasons. First,

forecast accuracy can be measured in myriad different ways, depending upon the forecast user’s

objectives, and can be very sensitive to the time period selected for the test. Our second reason is

more fundamental, and less technical in nature. It is our strong belief that the principle value of a

long-term forecasting model is that it provides a fixed, coherent structure with which to study

infrastructure issues over a long time horizon. All long-term forecasts will miss the mark; a

forecasting model that is carefully constructed and used will get the user into the ballpark.

Given our criteria, it is our opinion, at this point, that PENNDOT should select either Woods &

Poole or NPA as the data/forecast vendor for this project. Indeed, one idea that we have

discussed is that PENNDOT purchase both the Woods & Poole and NPA forecasts, given their

relatively low costs. The companies use somewhat different modeling techniques, and so should

produce somewhat different forecasts. The two forecasts could then be used as checks on each

other, and could be combined into a single, weighted average forecast. There is empirical

research that suggests that “averaged” forecasts of this kind are the most accurate. By combining

two or more forecasts from different modeling systems, the errors inherent in each particular

system tend to cancel one another out and produce the best single forecast.
53

Subsequently, we evaluated the sample forecasts from both sources. Based on this evaluation and

other factors, we recommend that Woods & Poole data be used for this VMT growth forecasting

study.
54

4. Evaluation of Candidate Methods for Detailed Study

In Chapter 3, we reviewed the state of the art and the state of practice in traffic growth

forecasting. It was concluded that there was no single, perfect approach for estimating and

forecasting traffic growth. However, the review identified approaches and techniques that show

the greatest potential for serving the goals and objectives of the Commonwealth.

Table 4-1. Comparing Different Forecasting Methods against PENNDOT Needs

PENNDOT Needs for a Traffic Growth Forecasting System

Forecasting Level of Detail Forecasting Variables and Data

Candidate

Methods

Four Area/

Functional

Groups

1

CountyLevel (all

counties)

Passenger

vs. Truck

Travel

Socioeconomic

Variables

Traffic

Information

Variables

Land

Use

Methods—Based on the Type of Data Used

Traffic Count

Based

Forecasting

X X X X

Socioeconomic

Data Based

Methods

2

X X X

Travel Demand

Forecasting

Models

X X X X X

Methods—Based on Forecasting Techniques

Growth Factors X X X X

Time Series

Models

X X X

Econometric

Modeling

(Regressions)

X X X X X X

Note:

1. urban interstate, urban non-interstate, rural interstate, and rural non-interstate

2. based on surveys such as NPTS and/or energy consumption such as fuel

Table 4-1 compares what each forecasting method offers with what PENNDOT needs. Some of

the forecasting methods reviewed do not serve the PENNDOT needs. For example,

socioeconomic-data-based methods such as travel survey-based methods and fuel-consumptionbased methods do not produce forecasts by functional class and area groups. They can be used to

forecast statewide VMT as a control total for cross-checking. However, both methods have

serious limitations. The travel survey data currently used are secondary data sources from
55

national surveys like NPTS. Sampling can introduce a major bias unless a state has an add-on,

because these surveys are designed to represent the national population. Fuel-based VMT

forecasting has also major limitations in terms of data bias. The estimation and forecasting of

fuel economy present the most difficult problem for this method, and fuel consumption across

the state borders is another challenging issue for VMT forecasting. Based on the overall

evaluation, we do not recommend these two methods in the development of the forecasting

system for Pennsylvania. Fuel-based VMT methods may be used for validation purposes.

Travel demand forecasting models from MPOs do not serve all PENNDOT needs because of

their limited geographic coverage. However, they can provide useful data for the statewide

forecasting system. MPO travel demand models can be used in different ways:

• Use travel demand model output VMT as a cross check

• Use travel demand model input data for socioeconomic forecasting

• Use additional input and output data from travel demand models

MPO model data and results will be examined in Section 6.5 to evaluate candidate statistical

models.

The growth factors method is the most popular technique used to do traffic growth forecasting in

practice. However, its major limitation is that growth factors do not respond to changing

socioeconomic conditions in the future. This method does offer useful information for the

forecasting system to be developed in this project. Historical growth factors will be examined,

and future growth factors will be estimated from the forecasting system and evaluated against

historical trends.

Considering the state of the art and practice in traffic growth forecasting, we recommend testing

a range of regression methods (econometric models. Different model specifications should be

tested in Ordinary Least Square (OLS) regressions, which are popular in the traffic volume

forecasting literature. Additional regression methods may include two-stage least square method,

generalized least square method, and non-parametric regression. These methods have been

employed in previous research, but have not been used for VMT forecasting purposes.

Independent variables will include demographic, economic, and land use variables.
56

5. Growth Analysis

The objective of this analysis was to review current and historical traffic and socioeconomic

growth patterns within the Commonwealth and identify future socioeconomic growth trends.

Historical traffic and socioeconomic growth patterns are crucial to understand the underlying

causes of traffic growth and the relationships between traffic growth and socioeconomic

variables. Socioeconomic growth trends will play a critical role in shaping traffic growth in the

future. This descriptive analysis will lay a solid foundation for developing quantitative, statistical

models in the next section. Description of the data used in this growth analysis and modeling can

be found in Section 6.1.

To achieve this objective, historical traffic growth patterns were analyzed by stratifying the data

into several different subcategories, including: county, county groups, functional class,

functional class groups, county/functional class groups. We concentrated on four stratifications

based on functional classes and area types—urban interstate, urban non-interstate, rural

interstate, and rural non-interstate. Total VMT growth is the focus of this analysis, but we also

touch on truck traffic growth patterns. Our geographic units of analysis are county and county

groups.

Similarly, historical socioeconomic growth patterns by county and county groups were analyzed.

Major socioeconomic variables include

• Number of households

• Population/population density

• Employment/employment density

• Per capita income/Household income

• Population by Age, and

• Retail Sales

The socioeconomic forecast data recommended in Chapter 3 were used to analyze future

socioeconomic growth patterns by county and county groups. GIS is used for display and

descriptive analyses. Historical and future growth patterns were compared, with particular

attention to how growth patterns differ between the past and the future.

5.1 Traffic Growth Analysis

As shown in Table 5-1, long-term growth trends between 1980 and 2003 include:

• Statewide total VMT grew by almost 50 percent, representing a compound annual growth

rate of 1.7 percent.

• Pennsylvania’s historical VMT growth is lower than the national average, which has

averaged over 3 percent since 1970.

• Overall VMT growth appears to be moderating; 1.8 percent for the 1980-1989 period

versus 1.5 percent for the 1994-2003 period.
57

• Interstate VMT increased at a higher rate (3.9 percent annually) than non-interstate VMT

(1.2 percent annually).

• Urban interstate VMT grew more rapidly than rural interstate VMT, with annual growth

rates of 5.2 and 2.6 percent, respectively.

• Urban interstate VMT growth appeared to be moderating, but rural interstate VMT grew

at a faster pace over the recent past decade than in the 1980s.

Table 5-1. Long Term Historical VMT Annual Growth Rates

As shown in Tables 5-2 and 5-3, short-term growth trends between 1994 and 2003 include:

• Statewide VMT increased at a lower rate in 2000 and after, with a rate of 1 percent

compared with 2 percent for the years before 2000.

• Interstate VMT grew more rapidly than non-interstate VMT, averaging 4.8 and 0.8

percent annually for interstates and non-interstates, respectively.

• Reclassification in 2003 significantly affects lane miles and VMT distribution among the

four categories—a significant shift from rural to urban categories (Figures 5-1, 5-2 and 5-

5).

• VMT growth rates, for most years, move in tandem with lane mile growth rates; this is

particularly evident for the 2003/2002 and 1996/1995 growth rates (Figure 5-3 and5-4).

• Truck VMT growth shows similar patterns as total VMT (see Table 5-3).

Table 5-2. Short Term Statewide Total VMT Annual Growth Rates

Table 5-3. Short Term Statewide Truck VMT Annual Growth Rates

RURAL URBAN RURAL NON- URBAN NON- TOTAL TOTAL

INTERSTATE INTERSTATE INTERSTATE INTERSTATE STATEWIDE US

1980-1989 2.60% 5.70% 1.80% 3.60%

1994-2003 4.10% 4.80% -0.80% 1.90% 1.50% 2.40%

Total (1980-2003) 2.60% 5.20% 1.70% 2.80%

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03

Rural Interstate 5.3 20.3 3.3 4.6 4.2 1.2 1.3 5.4 -6.9

Rural Non-Interstate 2.1 -0.9 2.8 1.9 3.7 0.0 0.1 0.1 -16.0

Urban Interstate 6.1 2.7 3.8 4.3 2.9 1.0 3.9 2.7 9.8

Urban Non-Interstate 0.6 0.3 0.4 0.6 -0.1 -0.9 0.8 0.1 14.7

Total 2.4 2.0 2.0 2.1 2.1 -0.1 1.1 1.1 1.2

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03

Rural Interstate 6.6 11.8 6.1 4.4 3.7 3.1 1.4 3.5 -5.0

Rural Non-Interstate 2.3 1.1 3.0 2.2 1.9 0.3 -0.6 -0.7 -18.2

Urban Interstate 3.5 0.7 2.9 5.8 2.7 1.6 6.1 3.2 11.6

Urban Non-Interstate 0.1 0.0 0.4 0.2 -0.3 -1.1 0.8 -0.8 29.7

Total 3.1 3.5 3.3 3.1 2.1 1.1 1.6 1.3 0.8
58

Figure 5-1. Statewide VMT Growth Rates between 1994 and 2003

Figure 5-2. Statewide Lane Mile Growth Rates between 1994 and 2003

-20

-15

-10

-5

0

5

10

15

20

25

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03

A

n

n

u

al Gro

wth

R

ate (P

erc

e

nt)

Rural Interstate

Rural Non-Interstate

Urban Interstate

Urban Non-Interstate

-10

-5

0

5

10

15

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03

A

n

n

u

al Gro

wth

R

ate (P

erc

e

nt)

Rural Interstate

Rural Non-Interstate

Urban Interstate

Urban Non-Interstate
59

Figure 5-3. Statewide Interstate VMT and Lane Mile Growth Rates between 1994 and 2003

Figure 5-4. Statewide Non-Interstate VMT and Lane Mile Growth Rates between 1994 and 2003

-10

-5

0

5

10

15

20

25

A

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03

n

n

u

al Gro

wth

R

ate (P

erc

e

nt)

Rural Interstate VMT

Rural Interstate LM

Urban Interstate VMT

Urban Interstate LM

-20

-15

-10

-5

0

5

10

15

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03

A

n

n

u

al Gro

wth

R

ate (P

erc

e

nt)

Rural Non-Interstate VMT

Rural Non-Interstate LM

Urban Non-Interstate VMT

Urban Non-Interstate LM
60

Figure 5-5. Statewide Truck VMT Growth Rates between 1994 and 2003

-20

-15

-10

-5

0

5

10

15

20

25

30

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03

Annual Gro

wth Rate (Percent)

Rural Interstate

Rural Non-Interstate

Urban Interstate

Urban Non-Interstate
61

5.2 Socioeconomic Growth Analysis

As shown in Table 5-4 and Figures 5-6 through 5-29, long-term growth trends between 1970 and

2000 include:

• Statewide population growth has been slow in the last three decades, with less than a 0.5

percent annual growth rate.

• Households grew at a faster rate than population.

• Employment increased at a rate of around 1 percent.

• Income growth rates were higher than population, households and employment (Figure 5-

8)

• The state has been losing the young population and gaining the elderly population

(Figures 5-6 ,5-12 and 5-13).

• The state has been aging, despite the elderly population increasing at a much lower rate in

the 1990s than in the previous two decades.

• Significant regional differences are shown in geographic patterns of growth— strong

growth in the eastern and south-central regions and decline and stagnancy in the western

and northern regions (Figures 5-16, 5-17, 5-19, 5-20, 5-22, 5-23, 5-25, 5-26, 5-28 and 5-

29).

• Older urban areas have declined in population, while population has spread out to urban

fringes and exurban areas (Figures 5-16 and 5-17).

Table 5-4 and Figures 5-9, 5-10 and 5-11, show the following forecasted trends between 2000

and 2030:

• Population growth rates will increase, catch up and even surpass the declining household

growth rates in the next twenty years.

• Employment will grow at a slightly lower rate than the past three decades.

• Income growth will be moderating but still much higher than population and household

growth.

• The state will continue to age, more rapidly after 2010 as baby-boomers enter the senior

ranks.

• Regional divide and urban sprawl are forecast to continue in the next 25 years unless

strong government policies reverse the trends (Figures 5-18, 5-21, 5-24, 5-27, and 5-30).

Table 5-4. Long Term Annual Growth Rates

1970-80 1980-90 1990-00 2000-10 2010-20 2020-30

Population 0.05% 0.03% 0.32% 0.27% 0.36% 0.45%

Households 1.25% 0.63% 0.60% 0.53% 0.42% 0.23%

Employment 0.76% 1.19% 0.98% 0.75% 0.89% 0.97%

Income PerCapita 2.50% 1.92% 1.73% 1.20% 1.25% 1.27%

Mean HH Income 1.34% 1.24% 1.30% 0.90% 1.16% 1.41%

Pop <17 yrs -2.16% -1.08% 0.04% -0.79% 0.09% 0.09%

Pop 65+ yrs 1.87% 1.71% 0.13% -0.26% 1.82% 1.87%

Retail Sales 1.34% 1.46% 2.63% 1.38% 1.38% 1.50%
62

As shown in Table 5-5, short-term growth trends between 1994 and 2003 include:

• Population growth was low, while household growth was higher than population growth.

• Employment and income growth peaked in the late 1990s and has become weaker since

2000.

• The state lost young population, while the elderly’s share of total population remained

stagnant or declined slightly during the period.

Table 5-5. Short Term Annual Growth Rates

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03

Population 0.27% 0.18% 0.06% 0.15% 0.15% 0.18% 0.14% 0.26% 0.33%

Households 0.87% 0.98% 0.37% 0.35% 0.26% 0.64% 0.37% 0.53% 0.64%

Employment 1.61% 0.90% 1.74% 1.39% 1.51% 2.09% 0.16% 0.80% 0.83%

Income PerCapita 0.65% 2.10% 2.60% 3.74% 1.53% 3.67% 0.84% 1.36% 1.19%

Mean HH Income 0.00% 1.09% 2.10% 3.35% 1.20% 3.14% 0.53% 1.04% 0.82%

Pop <17 yrs 0.00% -0.41% 0.00% -0.42% -0.42% -0.84% -0.85% -0.85% -0.43%

Pop 65+ yrs 0.64% 0.00% 0.00% 0.00% -0.63% -0.64% 0.00% -0.64% -0.65%

Retail Sales 1.81% 3.02% 1.33% 2.97% 6.22% 4.63% -0.14% 3.68% 1.31%
63

Figure 5-6.

Social Data

Growth Trends

1970-2000

Figure 5-7.

Economic

Growth Trends

1970-2000

Figure 5-8.

All Data

Growth Trends

1970-2000

70

90

110

130

150

170

190

1970 1975 1980 1985 1990 1995 2000

Population

Households

Employment

Pop <17 yrs

Pop 65+ yrs

70

90

110

130

150

170

190

1970 1975 1980 1985 1990 1995 2000

Income PerCapita

Mean HH Income

Retail Sales

70

90

110

130

150

170

190

1970 1975 1980 1985 1990 1995 2000

Population

Households

Employment

Income PerCapita

Mean HH Income

Pop <17 yrs

Pop 65+ yrs

Retail Sales

2000 Index: (2000=100)

1970 Index: (1970=100)

1970 Index: (1970=100)

1970 Index: (1970=100)
64

Figure 5-9.

Social Data

Growth Trend

Forecasting

2000-2030

Figure 5-10.

Economic Data

Growth Trend

Forecasting

2000-2030

Figure 5-11.

All Data

Growth Trend

Forecasting

2000-2030

70

90

110

130

150

170

190

2000 2005 2010 2015 2020 2025 2030

Population

Households

Employment

Pop <17 yrs

Pop 65+ yrs

70

90

110

130

150

170

190

2000 2005 2010 2015 2020 2025 2030

Income PerCapita

Mean HH Income

Retail Sales

70

90

110

130

150

170

190

2000 2005 2010 2015 2020 2025 2030

Population

Households

Employment

Income PerCapita

Mean HH Income

Pop <17 yrs

Pop 65+ yrs

Retail Sales

2000 Index: (2000=100)

2000 Index: (2000=100)
65

Figure 5-12. Socioeconomic variables trends between 1970 and 2030

Figure 5-13. Comparison of VMT trend with Socioeconomic Variables between 1980 and 2003

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

Population

Households

Employment

Income PerCapita

Mean HH Income

Pop <17 yrs

Pop 65+ yrs

Retail Sales

90

100

110

120

130

140

150

1980 1985 1990 1995 2000 2005

State VMT

Population

Households

Employment

PC Income

HH Income

1970 Index: (1970 = 100)

1980 Index: (1980 = 100)
66

Figure 5-14. Social variables growth rates between 1994 and 2003

Figure 5-15. Economic variables growth rates between 1994 and 2003

-1.000%

0.000%

1.000%

2.000%

3.000%

4.000%

5.000%

6.000%

7.000%

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03

Income PerCapita

Mean HH Income

Retail Sales

-1.000%

-0.500%

0.000%

0.500%

1.000%

1.500%

2.000%

2.500%

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03

Population

Households

Employment

Pop <17 yrs

Pop 65+ yrs
67

Figure 5-16.

Population

Annual Growth

Rates 1970-1990

Figure 5-17.

Population

Annual Growth

Rates 1990-2000

Figure 5-18.

Population

Annual Growth

Rates 2000-2030
68

Figure 5-19.

Households

Annual Growth

Rates 1970-1990

Figure 5-20.

Households

Annual Growth

Rates 1990-2000

Figure 5-21.

Households

Annual Growth

Rates 2000-2030
69

Figure 5-22.

Employment

Annual Growth

Rates 1970-1990

Figure 5-23.

Employment

Annual Growth

Rates 1990-2000

Figure 5-24.

Employment

Annual Growth

Rates 2000-2030
70

Figure 5-25.

Per Capita

Income

Annual Growth

Rates 1970-1990

Figure 5-26.

Per Capita

Income

Annual Growth

Rates 1990-2000

Figure 5-27.

Per Capita

Income

Annual Growth

Rates 2000-2030
71

Figure 5-28.

Household

Income

Annual Growth

Rates 1970-1990

Figure 5-29.

Household

Income

Annual Growth

Rates 1990-2000

Figure 5-30.

Household

Income

Annual Growth

Rates 2000-2030
72

6. Statistical Modeling of Traffic Growth

The objective of this modeling effort is to develop several statistical models based on historical

traffic and socioeconomic data to forecast traffic growth in the Commonwealth and to evaluate

the predictive power and reliability of the best statistical models.

6.1. Data and Variables

A number of data sources were used to develop the models in this study. The Bureau of

Planning and Research at the PENNDOT provided an extensive traffic database. This database

includes the VMT, linear mile, and lane mile by FHWA functional classification, by county, by

year, for all public roads for the years between 1994 and 2003. These data are also available for

roadways with federal aid only, for the years between 1994 and 2003. Truck VMT data are also

available, but only for PENNDOT maintained roads for the years between 1993 and 2003, while

no corresponding lane miles were provided.

The 2004 State Profile, developed by Woods & Poole Economics, is the major source for

socioeconomic data in this study. In Task1, Econsult, a subcontractor to Baker, reviewed

socioeconomic data and forecasting methods and recommended two data vendors—Woods &

Poole Economics and NPA data services. Data samples were requested from both vendors and

after an extensive evaluation, Woods & Poole data were chosen for implementation in this

project. The 2004 State Profile includes historical data and forecasts by year from 1969 through

2030 for every county and MSA in Pennsylvania. The State and U.S. totals are also included.

Both historical and forecast data have annual frequency. Variables in the 2004 State Profile

database include:

• Population by age (single year cohorts), gender and race

• Number of households, household size and households by income level for 11 income

ranges

• Employment and earnings for 13 sectors (using SIC industry designations)

• Personal Income by component

• Retail Sales for 10 sectors

• Educational Attainment (history only)

• Labor force and unemployment (history only)

• Private Non-farm Establishments by size and industry (history only)

As can be seen in Table 6-1, a variety of variables, different forms of these variables, and indices

have been tested in the statistical modeling process. These include the natural logarithmic forms

and the difference (growth) forms of these variables, as well as density and mix indices. These

variables were used at the county and county grouping levels. The grouping of counties is based

on a few factors, including census defined metropolitan areas, metropolitan planning

organization areas, rural planning organization areas, CTPP worker flow data, and major

interstate corridors. The idea is to identify groups of counties that have strong social and

economic interrelationships, which have significant effects on the group’s highway travel but no
73

significant effects outside the group. As a result, thirteen groups of counties were identified (see

Table 6-2 and Figure 6-1).

Table 6-1. Variables Tested in the Regression Modeling

VMT VMT

LN_VMT Ln(VMT)

Diff_VMT VMT94-95 = VMTn – VMTn-1 (e.g. VMT1995 – VMT1994)

LNVMTPC Ln(VMT / County Population)

logdiff_VMT Ln(VMTn) – LN(VMTn-1)

D

e

p

e

n

d

e

nt

V

a

ria

bles logdiff_LM Ln(LMn)– LN(LMn-1)

YEAR Year

EMPL County Employment

HH County Households

INCHH County Mean HH Income

INCPC County Income per Capita

POP County Population

LM County Lanes Miles by Category

POPDEN Population Density (County Population / County Area)

EMPDEN Employment Density (County Employment / County Area)

COMDEN Combined Density

AREA

EMP

StateEmployment

StatePopulation

POP + ( )

B

A

S

E

V

A

RIA

B

L

E

S

LUMIX Land Use Mix Index

EMP

StateHH

StateEmployment

HH

EMP

StateHH

StateEmployment

HH

[ *( )] +

[ * ( )]*

LN_(Base

Variables)

Natural Logarithm of Base Variables

e.g. LN_HH = LN(HH)

LNLM*PC LN (Lane Miles for each functional Class / County Population)

LNVMT*PC LN (VMT for each functional Class / County Population)

Diff_(Base

Variables)

Difference of each variables between two consecutive years

e.g. EMPL94-95 = EMPLn – EMPLn-1 (e.g. EMPL1995 – EMPL1994)

logdiffPop Ln(POPn) – LN(POPn-1)

logdiffPCI Ln(INCPCn) – Ln(INCPCn-1)

logdiffHH Ln(HHn) – Ln(HHn-1)

logdiffHHI Ln(INCHHn)– Ln(INCHHn-1)

logdiffLM Ln(LMn) – Ln(LMn-1)

logdiffLM2 Ln(LMn) – Ln(LMn-2)

Dummy Variable 0 or 1 for each Year & 0 or 1 for each County or County Group

INC_Low Percent households in the lowest quartile of household income distribution

INC_VHigh Percent households in the highest quartile of household income distribution

Pop17 Percent population aged under 17 years old

Pop65+ Percent population aged over 65 years old

RETS County Retail Sales (in 1996 dollars)

In

d

e

p

e

n

d

e

nt V

a

ria

bles LN_RETS Ln(RETS)
74

Table 6-2. County Group Definition

IDENTIFIER County Group

Name

Counties

ALTGR Altoona Group Bedford, Blair, Cambria, Fulton, Huntingdon, Somerset

CNTRGR Centre Group Centre, Clearfield, Clinton, Lycoming

EPAGR East PA Group Berks, Carbon, Lehigh, Northampton

HARRGR Harrisonburg Group Adams, Cumberland, Dauphin, Franklin, Lancaster,

Lebanon, Perry, York

I81GR I-81 Group Lackawanna, Luzerne, Schuylkill, Susquehanna,

Wyoming

NCNTGR North Centre Group Cameron, Elk, Forest, McKean, Potter, Warren

NEPAGR NE PA Group Monroe, Pike, Wayne

NTIERGR Northern Tier Group Bradford, Sullivan, Tioga

PHILY Philadelphia Group Bucks, Chester, Delaware, Montgomery, Philadelphia

SEGA-COG SEGA-COG Columbia, Juniata, Mifflin, Montour, Northumberland,

Snyder, Union

SHVGR Shenango Valley

Group

Crawford, Erie, Mercer

SWPAC SW PA Commission Allegheny, Armstrong, Beaver, Butler, Fayette, Greene,

Indiana, Lawrence, Washington, Westmoreland

WPAGR West PA Group Clarion, Jefferson, Venango

Figure 6-1. County Groups
75

6.2. Methodology

The earlier evaluation of candidate methods identified econometric modeling (regressions) as the

preferred modeling approach for this study. In the following study, different types of regressions

and model specifications were tested, including:

• Ordinary Least Squares (OLS)

• Cross-sectional Time Series OLS

• Two-stage Least Squares

The Ordinary Least Squares estimator is the optimal estimator, or the so-called Best Linear

Unbiased Estimator (BLUE) of a classical linear regression model (CLR) if the CLR’s

assumptions are met. A CLR model for VMT is formulated as

= + +

k

k k VMT c λ (X ) ε

Where VMT = the annual vehicle miles of travel;

c = a constant term;

λ

k

= the coefficient of the kth explanatory variable;

X

k

= the value of explanatory variable k;

εit = random error term.

A regular regression model generally regresses dependent variables on independently variables,

forcing units of analysis (in this case, county) to have the same constant. The cross-sectional

time-series fixed effect models use cross-sectional and/or time intercepts for each unit of

observation. These so-called dummy variables capture factors that are not measured or unknown

in the models. The factors that contribute to VMT growth may include labor force participation,

vehicle availability, and spatial patterns of development. The cross-sectional time-series fixed

effect models can also reduce the bias associated with the correlations between independent

variables and error terms.

The general form of a cross-sectional time-series OLS regression is:

= + + + +

k

it

k

it

k VMTit

c α i β t λ (X ) ε

Where VMTit = the annual vehicle miles of travel for county i in year t;

αi = the fixed effect for county i, estimated in the analysis;

βt = the fixed effect for year t, estimated in the analysis;

c = a constant term;

X

k

it = the value of explanatory variable k for county i and year t;

λ

k

= the coefficient of the kth explanatory variable;

εit = random error term for county i in year t, assumed to be normally distributed with mean zero.

The logarithmic specification of a cross-sectional time-series OLS regression is:
76

( ) = + + + +

k

it

k

it

k

Ln VMTitc α i β t λ (LnX ) ε

Where Ln (VMTit) = the logarithmic form of the annual vehicle miles of travel for county i in

year t;

LnX

k

it = the logarithmic form of the value of explanatory variable k for county i and year t.

In this formulation, the estimated coefficient λ

k

can be interpreted as elasticity of VMT with

respect to independent variables.

The difference or growth model was also tested, and it is formulated as:

( ) − ( − ) = + + + − − +

k

it

k

i t

k

it

k

Ln VMTit Ln VMTi tc α i β t λ ((LnX ) (LnX )) ε ( 1) ( 1)

This formulation is used to explain the annual growth of VMT using annual growth in the

independent variables.

In the two-stage least square estimation, an instrument is selected for a regressor variable that is

correlated with the error term. An instrumental variable for lane mile growth in the current year

could be lane mile growth over the lagged years. In this case, a two-year lag was tested. Similar

to the VMT growth model above, the lane mile growth model is specified as:

( ) − ( − ) = + + + − − +

k

it

k

i t

k

it

k

Ln LMit Ln LM i tc α i β t λ ((LnX ) (LnX )) ε ( 1) ( 1)

Similar to the log-linear VMT model, the lane mile model is formulated as:

( ) = + + +  +

k

it

k

it

k

Ln LM itc α i β t λ (LnX ) ε

As determined from Chapter 2, the major unit of analysis is at the county level. County grouping

was also tested and modeled, and results were compared with those of county-level models at the

aggregate state level. In addition, the focus of the forecasting system should be four functional

classifications:

• urban interstate,

• urban non-interstate,

• rural interstate, and

• rural non-interstate.

During the modeling process, statistical diagnostic tests were conducted to identify violations of

OLS assumptions such as nonlinearity, heteroskedasticity, nonnormality, autocorrelation, and

multicollinearity. These statistical models were also evaluated against criteria established in

Chapter 2 as well as against standard regression goodness-of-fit and error statistics. These

measures included adjusted R-squared, root mean squared error (RMSE), and mean absolute

percentage error (MAPE).
77

Another checking is to compare results from this modeling effort with those from MPO travel

demand forecasting models. Five counties were selected to represent three area types: (1) small

rural county with a population less than 50,000, (2) medium urbanized county with a population

between 50,000 and 200,000, and (3) large urbanized county with a population over 200,000.

These stratifications were set up to be consistent with the HPMS area type definitions and be

representative of the range of areas in Pennsylvania. A variety of factors were considered in the

selection process, including availability and quality of a travel demand forecast model,

geographic balance, existence of different roadway types representing the various roadway

functional classifications, historical socioeconomic conditions and growth potential. For each of

the five selected counties, traffic growth was projected for the planning horizon year 2030. The

best statistical models developed were used to conduct traffic growth forecasts. Results from the

travel demand forecasting models were reviewed for future years, and growth rates were

summarized by functional classes. The traffic volume and VMT growth from statistical models

were compared with results from the models. See section 6.5 for the detail.

6.3. Model Results

This section presents a brief summary of the major modeling results from a selected group of

models. A variety of statistical modeling tests have been conducted, and for the brevity purposes,

many tests and their results are not reported here. Detailed results are available in Appendix C.

Table 6-3 shows a summary of independent variables used in different models. It should be noted

that correlations among independent variables exist in these models and the size and sign of the

estimated coefficients represent the interactions among independent variables. This should be

kept when reading the following discussion about model results.

Table 6-3. Model Comparisons

Population

Per capita

Income Households

Household

Income

%

Junior

%

Senior

Lane Miles

per capita

POP x X x

HH x x x

HH JrSr x x x x x

CntyGrp HH X X X

Truck HH x x x

Variables

Model

Name
78

6.3.1. County-level OLS Base Models

County level OLS base models are log-linear models with fixed effects and are estimated using

ordinary least squares. Dependent variables are natural logs of VMT by four categories.

Independent variables consist of demographic and economic variables in natural logarithmic

forms, as well as lane miles per capita. Three separate forms of the OLS based model were

developed and evaluated. Model POP has population and per capita income as independent

variables, while Model HH has households and mean household income as independent

variables. In Model HH JrSr, percentages of population 17 years old and under and of population

65 years old and over were added to those independent variables that were already in Model HH.

As shown in Tables 6-4, 6-5 and 6-6, all models have very goodness-of-fit, as indicated by very

high R squares. Population, households, and lane miles are all significant and positive, as

expected. Coefficients for population and households are consistent among interstates and rural

non-interstates across models. Urban non-interstates have the largest coefficients for population

and households, among four facility categories and across all three forms of the models. Lane

miles have consistent coefficients in the rural categories across the various forms of the models,

and lower coefficients for urban interstates and lowest coefficients for urban non-interstates.

The results for income and age variables are mixed. Coefficients for income are more varied and

less significant than those of population, households and lane miles. Rural interstate and urban

non-interstate models have negative signs for income, contrary to expectation that VMT tends to

increase with income. Similarly, coefficients for age variables are more varied and less

significant across four categories of facilities. Contrary to our expectation, percent elderly

population was positively related with VMT for rural non-interstates and insignificantly for rural

interstate. Also unexpectedly, percent young population shows negative signs for all but rural

interstates, although insignificant.

Two other income variables were also tested—percentage of households in the lowest quartile of

household income distribution and percentage of households in the highest quartile of household

income distribution (see Appendix C for detail). Both variables are insignificant, except for the

rural non-interstate categories.

These mixed results with income and age variables may reflect a diversity of relationships

between travel behaviors and these variables across the state. Clearly, rural areas in the state are

dramatically different from urban areas in many ways, including the lack of public transit and

longer trip lengths. These results indicate that rural VMT may have a different relationship with

income and age variables than urban VMT.
79

Table 6-4. OLS Base Models — Model POP

* County dummy coefficients are omitted for brevity. See Appendix C for details.

Table 6-5. OLS Base Models— Model HH

* County dummy coefficients are omitted for brevity. See Appendix C for details.

Table 6-6. OLS Base Models— Model HH JrSr

* County dummy coefficients are omitted for brevity. See Appendix C for details.

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -62.062 -11.11 4.309 0.63 -45.659 -5.74 -35.761 -5.32

LN (Population) 1.231 8.88 1.2606 6.44 1.23078 27.5 1.90763 48.99

LN (Per Capita Income) -0.1121 -0.7 0.3606 1.94 0.2062 0.98 -0.6681 -3.6

LN (Lane Miles Per Capita) 0.99697 42.7 1.3765 14.41 0.53263 11.4 0.15485 6.58

Year 0.03766 10.51 0.001285 0.31 0.02603 5.4 0.019822 4.85

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates

99.1 97.6 99.7 99.6

434 660 421 544

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -52.459 -12.93 9.838 2.07 -37.766 -6.28 -12.264 -2.56

LN (Households) 1.422 9.88 1.5046 7.44 1.2391 27.33 1.92057 49.25

LN (Mean Household Income) -0.2743 -1.75 0.2075 1.16 0.1663 0.79 -0.6862 -3.82

LN (Lane Miles Per Capita) 0.99463 43.81 1.38995 14.9 0.53123 11.33 0.12889 5.45

Year 0.033239 11.39 -0.001631 -0.48 0.022734 5.84 0.009236 2.96

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates

99.1 97.6 99.7 99.6

434 660 421 544

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -63.74 -9.89 26.425 3.69 -28.875 -3.5 -11.332 -1.61

LN (Households) 1.3415 8.91 1.2152 5.93 1.5244 6.52 1.7685 8.41

Pop17 (% of Pop<17 yrs) 2.108 2.26 -1.3825 -1.49 -2.006 -1.79 -0.559 -0.55

Pop65+ (% of Pop>65+ yrs) 0.599 0.46 9.117 6.09 -4 -2.2 -2.995 -2.02

LN (Mean Household Income) -0.3587 -2.23 0.4663 2.61 0.1956 0.89 -0.6987 -3.77

LN(Lane Miles Per Capita) 0.9923 43.83 1.30628 14.4 0.51138 10.15 0.10233 4.04

Year 0.039523 9.8 -0.010259 -2.3 0.016859 3.21 0.00994 2.26

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates

99.1 97.8 99.7 99.7

434 660 421 544
80

6.3.2. County-level OLS Difference Models

As described in the methodology section, county-level OLS difference models are difference-innatural log-linear models with fixed effects and estimated using ordinary least squares.

Dependent variables are differences in natural logs of VMT by four categories between year t

and year t-1. Independent variables consist of demographic and economic variables, as well as

lane miles per capita, all in the form of differences in natural logs between year t and year t-1.

These differences are essentially growth in the logarithmic form. Similar to county-level OLS

base models, the county-level difference models tested the same combination of independent

variables. Tables 6-7 and 6-8 show summaries of estimation results for the household (HH)

model and the HH JrSr model based on households, junior and senior population proportions.

As can be seen from the Tables 6-7 and 6-8, the models do not have as high goodness-of-fit

measures as the county-level models. The R

2

values are much lower, which is not surprising, as

similar results have been reported in the previous studies using difference models. There are

some similarities and differences in the coefficient estimation results. The coefficients for

difference in lane miles per capita are consistent with those for lane miles per capita in the base

models and highly significant. However, households and household income difference variables

are mostly insignificant or in the wrong sign. The results for percent young and percent elderly

population are also varied, like the base models.

Table 6-7. OLS Difference Models - Model HH

* County dummy coefficients are omitted for brevity. See the appendix section for details.

Table 6-8. OLS Difference Models - Model HH JrSr

* County dummy coefficients are omitted for brevity. See the appendix section for details.

Dependent Var=Diff LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant 0.0191 0.71 -0.03736 -1.14 0.02839 0.86 0.03152 0.96

Diff LN (Households) 0.2307 0.25 0.2261 0.27 0.843 0.68 1.007 0.93

Diff LN (Mean Household Income) -0.2157 -0.95 0.5792 2.48 0.3599 1.16 -0.8725 -3.22

Diff LN (Lane Miles Per Capita) 0.99638 36.44 1.81149 19.03 0.57852 11.6 0.17826 6.32

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates

78.9 40.3 25 6.3

390 594 378 486

Dependent Var=Diff LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -0.5042 -1.87 -1.0917 -4.06 -0.0585 -0.19 1.2415 4.43

Pop17 (% of Pop<17 yrs) 1.7419 2.92 2.705 4.93 0.6563 0.87 -3.2193 -5.11

Pop65+ (% of Pop>65+ yrs) 0.828 0.55 3.102 2.12 -0.503 -0.27 -3.329 -2.11

Diff LN (Households) -0.758 -0.77 -1.355 -1.46 0.423 0.32 2.585 2.3

Diff LN (Mean Household Income) -0.3188 -1.4 0.3693 1.59 0.3254 1.04 -0.625 -2.33

Diff LN (Lane Miles Per Capita) 0.98419 35.83 1.75303 18.56 0.58859 11.2 0.16173 5.87

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates

79.3 42.8 24.7 11.6

390 594 378 486
81

6.3.3. County-Group OLS Base Models

Like county-level models, county group OLS base models are log-linear models with fixed

effects and estimated using ordinary least squares. The major difference lies in the unit of

analysis, which is aggregated at the county group level as defined previously (Table 6-2).

Dependent variables are natural logs of VMT by four categories. Independent variables consist

of demographic and economic variables, as well as lane miles per capita, in natural logarithmic

forms.

Like county-level models, all county-group models have excellent goodness-of-fit, as indicated

by very high R squares in Table 6-9. Households and lane miles are all significant with positive

sign, as expected. Unlike county-level models, coefficients for households are not consistent

among interstates and rural non-interstates across models. Rural non-interstates have the largest

coefficients for households, among the four facility categories and across models. Lane miles

have a wide range of coefficients across the models, rural non-interstates have the highest

coefficients, and urban non-interstates the lowest coefficients.

Again, income and age variables have varied results. Rural and urban non-interstate models

have negative signs for income, contrary to our expectation that VMT tends to increase with

income. Similarly, coefficients for age variables are more varied and less significant across four

categories of facilities (not shown here). Contrary to our expectation, percent elderly population

was positively related with VMT for rural interstate and non-interstate facilities. Also

unexpectedly, percent young population shows negative signs for urban non-interstates and

although insignificant, for rural interstates.

Table 6-9. County Group Models - Model HH

* County group dummy coefficients are omitted for brevity. See the appendix section for details.

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -9.203 -4.59 -10.57 -4.01 -2.037 -0.64 0.318 0.15

LN (Households) 1.7318 7.46 4.0981 12.26 1.6203 5.15 2.2667 8.51

LN (Mean Household Income) 1.5384 10.55 -0.2818 -2.01 0.7692 3.82 -0.3778 -2.32

LN (Lane Miles Per Capita) 1.00868 23.28 3.5944 14.8 0.90262 9.65 0.30991 5.64

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates

98.9 98.7 99.8 99.9

110 130 120 130
82

6.4. Evaluation

As shown above, all county-level base models and county-group models have superior goodnessof-fit values, as demonstrated by near perfect adjusted R

2

values. Additional measures to

evaluate these models, including regression diagnostics, were also used.

In addition, Mean Absolute Percentage Error (MAPE) was used to evaluate the model errors.

The MAPE measures how the fitted values are deviated from the observations. Table 6-10

summarized the results from different models. Overall, these models have very low errors.

Table 6-10. Mean Absolute Percentage Errors for Different Models

Rural

Interstates

Rural NonInterstatesUrban

Interstates

Urban NonInterstates

Model POP 4.72 6.31 5.57 5.41

Model HH 4.70 6.20 5.55 5.26

Model HH JrSr 4.62 6.00 5.43 4.80

County Group

Model HH

4.14 4.39 4.45 3.81

Truck Model

HH

5.55 6.20 5.87 4.74

Another way of evaluating the models is to examine the error distributions of the fitted versus

observed values. Figures 6-2 to 6-5 show the cumulative percent error distributions by facility

categories. As can be seen from these figures, more than ninety percent of the observations fall

within the error range of –10% to +10%. This is a clear indication for a good fit. Three countylevel models—Model POP, HH, and HH JrSr—almost have identical error distributions. County

group models show slightly better results than county-level models, because the steeper slopes of

their error distribution curves indicate lower errors.

It should be noted that all model results reflect the quality and nature of the data. Chapter 5

describes some major characteristics of VMT and socioeconomic data. In particular, the 2003

VMT re-classifications have dramatically shifted VMT among different categories. These

dramatic VMT changes reflect primarily reclassification of lane miles and associated traffic

volumes, while changes in travel behavior and socioeconomic variables were incremental.

Although lane miles were introduced in the models to account for VMT changes, lane miles

cannot fully account for the dramatic shifts in VMT. This is because lane mile reclassification is

not random and high-volume facilities tended to be affected much more than low-volume

facilities in the re-classification process. In many cases, when roads were re-classified from a

rural to urban category, they tended to be in or close to urban areas initially and carry much

traffic. This bias toward high-volume facilities presents a difficulty for lane miles to account for

VMT shifts. This is the primary reason why the fitted models still have errors, some even outside

the error range of –10% to +10%.
83

The research next examined forecasting results from these models and if the forecasting results

meet expectations.

6.5. Forecasting

Selected models were used to forecast VMT for 2010, 2020, and 2030. VMT growth rates were

calculated for periods of 2003-2010, 2010-2020, and 2020-2030. Table 6-11 summarizes annual

growth rates by four different models, in comparison with 1994-2003 growth rates. See

Appendix C for detailed results at the county level. Figures 6-7 and 6-8 show both 1994-2003

VMT growth and forecasted 2003-2030 VMT growth.

Table 6-11. VMT Annual Growth Rates Forecasted by Different Models

Model Facility Category 1994-03 2003-10 2010-20 2020-30

Rural Interstates 4.09% 3.80% 3.82% 3.83%

Rural Non-Interstates -0.87% 0.52% 0.52% 0.52%

Urban Interstates 4.12% 3.13% 3.19% 3.25%

Urban Non-Interstates 1.73% 1.76% 1.92% 2.07%

Total 1.54% 1.93% 2.11% 2.31%

Rural Interstates 4.09% 3.72% 3.40% 2.95%

Rural Non-Interstates -0.87% 0.48% 0.24% -0.13%

Urban Interstates 4.12% 3.03% 2.88% 2.62%

Urban Non-Interstates 1.73% 1.41% 1.07% 0.57%

Total 1.54% 1.74% 1.58% 1.31%

Rural Interstates 4.09% 3.75% 4.15% 3.74%

Rural Non-Interstates -0.87% 0.66% 2.99% 3.47%

Urban Interstates 4.12% 3.11% 1.21% 0.69%

Urban Non-Interstates 1.73% 1.54% 0.12% -0.22%

Total 1.54% 1.86% 1.66% 1.76%

Rural Interstates 4.09% 2.11% 2.15% 2.10%

Rural Non-Interstates -0.87% 0.91% 0.09% -1.09%

Urban Interstates 4.12% 1.39% 1.31% 1.12%

Urban Non-Interstates 1.73% 0.76% 0.42% -0.13%

Total 1.54% 1.07% 0.72% 0.23%

Model: Pop

Model HH

Model HH JrSr

Model CntyGrp

HH
84

Forecast annual growth rates vary considerably with different models, although they are all in

normal historical ranges as shown in Tables 5-1 and 5-2. These growth rate differences show

relatively small impact on VMT growth forecasts in the short term (2003-2010), but lead to

dramatically different long range VMT forecasts due to compounding.

The variation in the forecasted values has to do with the growth forecasting of independent

variables in different models. For example, per capita income has the highest growth rates among

independent variables (as shown in Figure 5-12), and is a major cause of driving up the VMT

growth rates.

General consensus exists that overall VMT growth will be moderating over the next three

decades because many driving forces for VMT growth have shown the signs of stabilization.

Household growth rates are decreasing, and the rate of household size decrease is slowing. The

population is aging, and the elderly share will pick up rapidly in 2010 and after. The elderly

cohorts tend to travel less than the general population, as shown in previous Nationwide Personal

Transportation Surveys. Labor force participation rates and vehicle availability have reached a

stabilization range. While real income has increased significantly and will likely continue to do

so, the household travel spending has remained relatively constant over time. It can be argued

that the importance of real income growth in travel will be smaller in the next three decades than

in previous decades (Polzin et al. 2003).

Although VMT growth has been over 3 percent for the nation as a whole, nationwide VMT

growth has been forecasted to be lower. A 2002 US DOT report cited an annual growth rate of

2.08 percent through 2020. Since Pennsylvania has historically grown slower in travel than the

national average, it would be reasonable to expect a lower overall growth rate in the future.

Considering VMT moderation in the future and growth magnitude, we can conclude that Model

Pop is not a good candidate for our VMT forecasting system. Model Pop generates growth rates

that are increasing over time. The magnitude of growth rates is also higher than expected.

Model HH appears to produce forecasts that are consistent with expectations. Forecast growth

rates are moderating across all facilities over time, and the overall growth rates seem to be in the

expected range. One may argue that forecasted annual growth rates for interstate categories

appear to be too high. This is certainly a manifestation of historical VMT data inputs to the

model estimation. As shown in Table 5-2, annual growth rates are over 3 percent for rural

interstate VMT in 6 out of 9 years and for urban interstate VMT in 5 years. Overall annual

growth rates averaged 4.1 percent for rural interstates and 4.8 percent for urban interstates during

the period 1994-2003.

Model HH JrSr has some irregularities in the VMT growth forecasting results, which are related

to irregularities in model coefficients. As described in the model results section, for example,

percent elderly population was positively related with VMT for rural non-interstates. Although

overall VMT growth rates are in the normal range, the forecasting patterns and trend

irregularities render this model less reliable.

County group models produce significantly lower VMT growth rates than the other three model

sets. Although all county group models show declining growth rates in the future, there are some
85

troubling items. The decline of rural non-interstate VMT is too steep, and the negative growth

for the 2020-2030 period is particularly too steep beyond expectations and the literature.

County groups were developed to reflect interactions among jurisdictions and at the corridor

level. It was hypothesized that these interactions are particularly common among

interstate/freeway categories and useful for forecasting interstate/freeway VMT. It would be less

useful for non-interstate categories to have county group formulation. Therefore, county-group

non-interstate models may be less reliable. Comparing the interstate growth rates from Model

HH and Model County Groups, we see the differences that are not trial, particularly in the short

term. Because the statewide interstate VMT growth rates over the most recent decade are over 4

percent (see Table 5-2), the forecasted growth rates of 1.4% and 2.1% for the 2003-2010 period

seem particularly low.

At the statewide level, the household models appear to produce the best VMT growth forecasting

among the four models, while county-group models do offer a means of cross-checking.

Figures 6-8 through 6-15 show the geographic distribution of forecasted annual VMT growth

rates by facility category, based on the county group-level model and Figures 6-16 through 6-19

based on the county-level HH model. All maps illustrate the pattern of high VMT growth in the

east and south-central regions and low growth in the west and north regions. This pattern is

consistent with the regional divide of socioeconomic growth as described in Chapter 3.

We also compare the modeling results with VMT growth rates from metropolitan travel demand

models and Table 6-12 summarizes these results. Overall, projected VMT growth rates from the

household model are higher than those from travel demand models. In some categories, the

forecasted growth rates are close. The growth rate patterns are similar in that interstate categories

have higher growth rates than non-interstate categories.

Many factors contribute to the difference between the household model developed in this study

and travel demand models. The VMT models developed in this project are dynamic in the sense

that they incorporate temporal changes as a driving force behind VMT growth over the years.

Trends embedded in these panel data were captured in the models and carried over the future

forecasting horizons. Most traditional travel demand models are static and validated against a

single base year data, and no longitudinal changes are incorporated in the model to affect the

future forecasting. In other words, travel behavior in 2030 is assumed to be the same as that in

2000 in most travel demand models. For example, past or current survey data are used to develop

trip length distribution, which is then carried over to the planning horizon year. To the extent that

trip length has increased over the years, the fixed trip length assumption makes the VMT growth

smaller than it should be.

Traditional travel demand models have some other deficiencies that may lead to underestimation

in VMT growth. One major deficiency, for example, is the models’ failure to account for the

feedback from transportation system performance to land development patterns. In traditional

travel demand models, the relationship between land use and transportation system is one-way.

Land use and spatial patterns of land development are simply input to the models, affecting

travel demand and transportation system performance. In realty and theory, land use and
86

transportation system is a two-way relationship. Transportation system performance shapes the

land development patterns in the long run.

Table 6-12. Growth Rates from HH Regression Model and Travel Demand Models

Growth Rates 2000/2002---2030

Base Year Pop HH Emp VMT Urb Int

VMT Rur

Int

VMT Urb

Non-Int

VMT Rur

Non-Int

Growth Rates from TDM

Rural < 50,000 (Growing) Perry 2002 0.79% 0.79% 1.20% 1.43% 1.20%

Rural < 50,000 (Declining) N/A

Small Urban 50,000 - 200,000 (Growing) Centre 2000 1.59% 1.71% 1.77% 2.97% 3.63% 0.29% 0.54%

Small Urban 50,000 - 200,000 (Declining) Beaver 2002 0.11% 0.58% 0.04% 0.94% 0.20% 0.46%

Urban > 200,000 (Growing) Lancaster 2002 0.82% 0.83% 1.70% 1.58% 1.77% 1.50% 0.46%

Urban > 200,000 (Declining) Allegheny 2002 0.03% 0.46% 0.16% 0.87% -0.03% 0.23%

Growth Rates from Regressions County

Rural < 50,000 (Growing) Perry 2002 1.10% 1.15% 1.03% 2.52% 0.18%

Rural < 50,000 (Declining) N/A

Small Urban 50,000 - 200,000 (Growing) Centre 2000 0.80% 0.88% 1.38% 3.21% 3.48% 1.63% 0.32%

Small Urban 50,000 - 200,000 (Declining) Beaver 2002 0.17% 0.16% 0.95% 2.64% 0.30% 0.12%

Urban > 200,000 (Growing) Lancaster 2002 0.89% 1.03% 0.92% 3.28% 3.70% 2.13% 0.34%

Urban > 200,000 (Declining) Allegheny 2002 -0.15% -0.17% 0.75% 2.42% -0.44% 0.10%

Region Type County
87

Rural Interstates

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-50 -40 -30 -20 -10 0 10 20 30 40 50

% Error

Cumulative Frequency (% Less than)

Model Pop

Model HH

Model HH JrSr

Model CntyGrp

Rural Non-Interstates

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-50 -40 -30 -20 -10 0 10 20 30 40 50

% Error

Cumulative Frequency (% Less than)

Model Pop

Model HH

Model HH JrSr

Model CntyGrp

Urban Interstates

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-50 -40 -30 -20 -10 0 10 20 30 40 50

% Error

Cumulative Frequency (% Less than)

Model Pop

Model HH

Model HH JrSr

Model CntyGrp

Urban Non-Interstates

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-50 -40 -30 -20 -10 0 10 20 30 40 50

% Error

Cumulative Frequency (% Less than)

Model Pop

Model HH

Model HH JrSr

Model CntyGrp

Figure 6-2.

Models Percent Error

Distribution for

Rural Interstates

Figure 6-4.

Models Percent Error

Distribution for

Urban Interstates

Figure 6-3.

Models Percent Error

Distribution for

Rural Non-Interstates

Figure 6-5.

Models Percent Error

Distribution for

Urban Non-Interstates
88

Figure 6-6. Models Interstate VMT Forecasts

Figure 6-7. Models Non-Interstate VMT Forecasts

5

10

15

20

25

30

35

40

45

50

1990 1995 2000 2005 2010 2015 2020 2025 2030

Billio

n

s

Interstate

V

M

T

Model Pop, Rural

Model Pop, Urban

Model HH, Rural

Model HH, Urban

Model HH JrSr, Rural

Model HH JrSr, Urban

Model CntyGrp, Rural

Model CntyGrp, Urban

Urban

Rural

20

30

40

50

60

70

80

1990 1995 2000 2005 2010 2015 2020 2025 2030

Billions N

o

n-Interstate

V

M

T

Model Pop, Rural

Model Pop, Urban

Model HH, Rural

Model HH, Urban

Model HH JrSr, Rural

Model HH JrSr, Urban

Model CntyGrp, Rural

Model CntyGrp, Urban

Urban

Rural
89

Figure 6-8. Rural Interstate VMT 2003-2030 Annual Growth Rates

Figure 6-9. Rural Non-Interstate VMT 2003-2030 Annual Growth Rates
90

Figure 6-10. Urban Interstate VMT 2003-2030 Annual Growth Rates

Figure 6-11. Urban Non-Interstate VMT 2003-2030 Annual Growth Rates
91

Figure 6-12. Rural Interstate Truck VMT 2003-2030 Annual Growth Rates

Figure 6-13. Rural Non-Interstate Truck VMT 2003-2030 Annual Growth Rates
92

Figure 6-14. Urban Interstate Truck VMT 2003-2030 Annual Growth Rates

Figure 6-15. Urban Non-Interstate Truck VMT 2003-2030 Annual Growth Rates
93

Figure 6-16. Urban Interstate VMT 2003-2030 County Annual Growth Rates

Figure 6-17. Urban Non-Interstate VMT 2003-2030 County Annual Growth Rates
94

Figure 6-18. Rural Interstate VMT 2003-2030 County Annual Growth Rates

Figure 6-19. Rural Non-Interstate VMT 2003-2030 County Annual Growth Rates
95

6.5. Discussion

As noted in the previous section, the 2003 VMT re-classifications have dramatically shifted

VMT among different categories. These VMT re-classifications were undertaken to account for

changes in area types over the years, which happens during the urbanization process. It should be

noted that the urbanization is a gradual process, and as a result, VMT shifts among urban and

rural categories should also be a gradual process. However, VMT re-classification is undertaken

only once every few years, in this case, ten years. Clearly, there is a mismatch between VMT

data and socioeconomic variables, which change gradually.

One may wonder or even be concerned about the impacts of VMT reclassification on the

regression estimations. It can be argued that these large shifts lead to a spurious correlation

between VMT and lane miles. By definition, however, VMT is a function of lane miles and

traffic volumes. Therefore, the relationship between VMT and lane mile is real. In economic

sense, VMT reveals demand for travel, and lane miles represents supply. The degree to which the

demand is elastic to supply is a subject of debate on induced travel. In the induced travel

literature, the issue of reclassification impacts has received no attention. Re-classification does

not affect the demand-supply relationship for the aggregate VMT, but may shift the relationships

in individual facility categories. Therefore, the elasciticities, represented by the coefficients of

lane mile per capita in this study, represent the combined effects of new supply and

reclassifications, and cannot be interpreted as induced travel.

Various test runs were conducted to examine the impacts of re-classifications. First, the 2003

data were dropped, and models were re-estimated. For the household-based county-level

regression models (MODEL HH), re-estimated coefficients for interstate categories are similar to

the original estimates with the 2003 data. The differences in re-estimated coefficients reveal

considerable impacts of re-classifications on the non-interstate categories. This does not mean

that the 2003 data skew the model results and should be dropped. In fact, the models estimated

without the 2003 data depict a relationship between VMT and independent variables under the

condition of no classification, which is biased for the non-interstate categories. The 2003 data are

a correction to this bias; the estimated models inclusive of the 2003 data represent a corrected

relationship under the condition of reclassification. This corrected relationship is likely to hold in

the future because the urbanization will continue and re-classification will be conducted in the

future. It can be expected that the next re-classification will happen sometimes after the 2010

Census. It is possible that re-classification will take place in a shorter interval as the American

Community Survey will replace the long form in 2010.

The second group of tests examines the explanatory powers of various independent variables

including lane miles. As noted earlier, VMT is a function of lane miles and traffic volumes, and

it is no surprise that VMT and lane miles are strongly correlated. Indeed, correlation coefficients

are higher than 0.9 for all but the rural non-interstate category, which has a correlation

coefficient of 0.81. When regressing VMT on lane miles alone, lane miles can explain more than

85 percent of the variance for facility categories, except for 66 percent for the rural non-interstate

category. Lane mile per capita is used in the models that are presented in the earlier sections.

The correlations between VMT and lane miles per capita are much weaker than those between
96

VMT and lane miles. When regressing VMT on lane miles per capita alone, lane miles per

capita can only explain from 1 to 53 percent of the variance. This is a clear indication that lane

miles per capita are not a variable that dominates the estimated models. Indirectly, this shows

that reclassification does not overwhelm the models, although having some impacts on the model

results. Adding socioeconomic variables in the regressions increases the explanatory power to

higher than 90 percent, except for 84 percent for the rural non-interstate category. Clearly,

socioeconomic variables and lane mile per capita together have substantial explanatory power for

the VMT variations. It is also an indication that the dummy variables added to the regressions do

not dominate the estimated models.

A series of sensitivity tests was also conducted to examine the effects of lane mile growth

assumptions on the future VMT growth. Lane miles were assumed to grow the same rates as

those in the 1994-2003 period and alternatively, half those rates. Table 6-13 shows forecasted

VMT annual growth rates from the county-level household-based models, based on different

assumptions about lane mile changes in the future. Table 6-14 shows the same type of results

from the county-group-level household-based models. As can be seen in these two tables, the

models show some degree of sensitivity to the lane mile assumption in the future. It should be

noted that lane miles will be not likely, if not impossible, to change in the 2003-2030 period with

the same growth rates as those in the 1994-2003. Therefore, the last sensitivity testing results in

these two tables are only shown for the illustration purpose.
97

Table 6-13. LM Sensitivity Tests of Model HH (VMT Annual Growth Rates)

Table 6-14. LM Sensitivity Tests of Model CntyGrp HH (VMT Annual Growth Rates)

Model (Forecasting

Assumption for LM) Facility 1994-03 2003-10 2010-20 2020-30

Rural Interstates 4.09% 3.72% 3.40% 2.95%

Rural Non-Interstates -0.87% 0.48% 0.24% -0.13%

Urban Interstates 4.12% 3.03% 2.88% 2.62%

Urban Non-Interstates 1.73% 1.41% 1.07% 0.57%

Total 1.54% 1.74% 1.58% 1.31%

Rural Interstates 4.09% 3.90% 3.58% 3.12%

Rural Non-Interstates -0.87% 0.23% -0.02% -0.39%

Urban Interstates 4.12% 3.54% 3.39% 3.14%

Urban Non-Interstates 1.73% 1.50% 1.16% 0.65%

Total 1.54% 1.84% 1.72% 1.52%

Rural Interstates 4.09% 4.07% 3.75% 3.30%

Rural Non-Interstates -0.87% -0.03% -0.28% -0.64%

Urban Interstates 4.12% 4.06% 3.90% 3.65%

Urban Non-Interstates 1.73% 1.58% 1.25% 0.74%

Total 1.54% 1.95% 1.89% 1.75%

Model HH (Asumming

Constant LM for the

Future)

Model HH (Asumming

LM Grows as 1994-

2003)

Model HH (Asumming

LM Grows as Half Rates

of 1994-2003)

Model (Forecasting

Assumption for LM) Facility 1994-03 2003-10 2010-20 2020-30

Rural Interstates 4.09% 2.11% 2.15% 2.10%

Rural Non-Interstates -0.87% 0.91% 0.09% -1.09%

Urban Interstates 4.12% 1.39% 1.31% 1.12%

Urban Non-Interstates 1.73% 0.76% 0.42% -0.13%

Total 1.54% 1.07% 0.72% 0.23%

Rural Interstates 4.09% 2.28% 2.33% 2.28%

Rural Non-Interstates -0.87% 0.24% -0.57% -1.74%

Urban Interstates 4.12% 2.25% 2.16% 1.97%

Urban Non-Interstates 1.73% 0.97% 0.62% 0.08%

Total 1.54% 1.17% 0.88% 0.50%

Rural Interstates 4.09% 2.45% 2.50% 2.46%

Rural Non-Interstates -0.87% -0.43% -1.23% -2.40%

Urban Interstates 4.12% 3.11% 3.02% 2.83%

Urban Non-Interstates 1.73% 1.17% 0.83% 0.29%

Total 1.54% 1.29% 1.10% 0.87%

Model CntyGrp HH

(Asumming LM Grows

as Half Rates of 1994-

2003)

Model CntyGrp HH

(Asumming LM Grows

as 1994-2003)

Model CntyGrp HH

(Asumming Constant

LM for the Future)
98

7. Conclusions and Recommendations

The ultimate objective of this study is to identify a preferred traffic growth forecasting method

for implementation. To achieve this objective, the results of all above tasks and subtasks were

synthesized and finalist methods were evaluated in terms of PENNDOT needs. We also briefed

the PENNDOT project team, explaining the study and evaluation undertaken, findings, results

and preliminary conclusions. As a result, consensus was developed on the preferred traffic

forecasting method for implementation.

In summary, major conclusions and recommendations are as follows:

• Regression models best meet PENNDOT needs for a statewide VMT growth

forecasting system. In particular, regression-based forecasting models provide a

consistent statewide forecasting framework.

• Data needed for implementing a regression-based forecasting system are readily

available, updated annually, and reasonably priced. The PENNDOT database

maintained by Bureau of Research and Planning provides an excellent source for

VMT, lane miles, and other key variables. State profile database for Pennsylvania,

produced by Woods & Poole Economics, is recommended as the data source for

socioeconomics.

• It is recommended that PENNDOT forecast VMT growth as a range and averaged

middle point (see Figures 7-1, 2 and 3 and Table 7-1), as follows:

a. The upper boundary of the VMT forecasts (all facility classes) is based on the

household-based county-level regression models (MODEL HH). This model

produces the most consistent and logical forecasts among candidate models.

b. The lower boundary of the VMT forecast (for interstate facility classes) is based

on the household-based county-group-level regression models.

c. The “middle point estimate” of forecast VMT is created by averaging the upper

and lower boundaries of forecasts from the two models at the state level, and then

adjusting the forecasts from MODEL HH at the county level so as to match the

averaged forecasts at the state level.

• The regression models should be updated annually, based on new VMT and

socioeconomic data. The VMT forecasting system is summarized in Figure 7-4. A

spreadsheet file is created as a prototype for the forecasting system implementation

(see Appendix E for a brief description and the spreadsheet for the details).

• Potential future investigation includes evaluation of air quality implications of the

recommended VMT forecasting system, improved lane mile data for future years, and
99

incorporation of intra-county level socioeconomic and land use variables in the

forecasting system. Socioeconomic and land use data at the sub-county level such as

township will provide more fine-grained picture than those at the county level and

may improve the VMT growth forecasting.

Table 7-1. Annual Growth Rates (Averaged for Interstates)

Figure 7-1. Statewide VMT and Socioeconomic Growth Trend and Forecasts

Model Facility Category 1994-03 2003-10 2010-20 2020-30

Rural Interstates 4.09% 2.93% 2.83% 2.58%

Rural Non-Interstates -0.87% 0.48% 0.24% -0.13%

Urban Interstates 4.12% 2.23% 2.16% 2.00%

Urban Non-Interstates 1.73% 1.41% 1.07% 0.57%

Total 1.54% 1.49% 1.31% 1.03%

Averaged Rates

for Interstates

(HH+CntyGrp

HH)
100

Figure 7-2. Recommended VMT Growth Forecasting Scenarios for Interstates

Figure 7-3. Recommended VMT Growth Forecasting Scenarios for Non-Interstates

0

5

10

15

20

25

30

35

40

45

50

1990 1995 2000 2005 2010 2015 2020 2025 2030

Year

Interstate V

M

T

Model HH, Rural

Model CntyGroup, Rural

Model Combo Avg, Rural

Model HH, Urban

Model CntyGroup, Urban

Model Combo Avg, Urban

Billions

Urban

Rural

20

30

40

50

60

70

80

1990 1995 2000 2005 2010 2015 2020 2025 2030

Billions

N

o

n-Interstate

V

M

T

Model HH, Rural

Model HH, Urban

Model CntyGrp, Rural

Model CntyGrp, Urban

Urban

Rural
101

Figure 7-4. VMT Growth Forecasting System
102

References

Bhat, C.R., Nair, H.S., (2000). VMT mix modeling for mobile source emissions forecasting:

formulation and empirical application. 79

th Annual Meeting, Transportation Research Board,

Washington DC

Barr, L.C. (2000). Testing for the significance of induced highway travel demand in metropolitan

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Benekohal, R.F., Girianna, M., (2003). Methodologies for calculating truck vehicle miles

traveled. 82

nd Annual Meeting, Transportation Research Board, Washington DC

Benekohal, R.F., Girianna, M., (2003). Methodologies used for truck classification and

methodologies for estimating truck VMT. 82

nd Annual Meeting, Transportation Research Board,

Washington DC

Benjamin, J. 1986. A Time-Series Forecast of Average Daily Traffic Volume. Transportation

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Bowling, S.T., Aultman-Hall, L., (2002). The development of a random sampling procedure for

local road traffic count locations. 81

st Annual Meeting, Transportation Research Board,

Washington DC

Cervero, R., Hansen, M., (2001) Road supply-demand relationships: sorting out causal linkages.

80

th Annual Meeting, Transportation Research Board, Washington DC

Chin, S., Greece, D.L., Hopson, J., Hwang, H., Thompson, B. (1999). Towards national

indicators of VMT and congestion based on real-time traffic data. 78

th Annual Meeting,

Transportation Research Board, Washington DC

Crouch, J.A., Seaver, W.L., Chatterjee, A. (2001). Estimation of traffic volumes on rural roads

in Tennessee. 80

th Annual Meeting, Transportation Research Board, Washington DC

David Evans and Associates. 2001. Issues and Implications of Implementing the General

Recommendation from the July 2000 Statewide Vehicle Miles of Travel (VMT) Study. “White

Paper” Prepared for Oregon Department of Transportation.

Federal Highway Administration. (2000). Highway Performance Monitoring System. US

Department of Transportation

Federal Highway Administration. (2003). State Practices Used to Report Local Area Travel. US

Department of Transportation

Fricker, J., Kumapley, R. (2002). Updating Procedure to Estimate and Forecast Vehicle Miles

Traveled. Purdue University, West Lafayette IN
103

Fulton, L. M., Noland, R.B., Mezler, D.J., Thomas, J.V. (2000). A statistical analysis of induced

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Hansen, M., Huang, Y., (1997). Road supply and traffic in California urban areas.

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th Annual Meeting, Transportation Research Board,

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th Annual Meeting, Transportation Research Board,

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st Annual Meeting, Transportation

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th Annual Meeting, Transportation Research Board, Washington DC

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rd Annual Meeting, Transportation Research Board, Washington DC
104

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rd Annual Meeting, Transportation Research Board, Washington

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rd Annual Meeting, Transportation Research

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th Annual

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rd Annual Meeting, Transportation Research Board, Washington DC

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rd Annual Meeting,

Transportation Research Board, Washington DC

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th Annual

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Soot, S., Metaxatos, P., Thakuriah, P., Canham J., (2000). Factors contributing to urban VMT

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th Annual Meeting, Transportation Research Board, Washington, DC.
105

Appendix A. VMT and LM Growth Trend by County

1994-2002

Notations:

VMT A: Rural Interstates Vehicle Miles Traveled

LM A: Rural Interstates Lane Miles

VMT B: Rural Non-Interstates Vehicle Miles Traveled

LM B: Rural Non-Interstates Lane Miles

VMT C: Urban Interstates Vehicle Miles Traveled

LM C: Urban Interstates Lane Miles

VMT D: Urban Non-Interstates Vehicle Miles Traveled

LM D: Rural Non-Interstates Lane Miles
106

ADAMS

-60

-50

-40

-30

-20

-10

0

10

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

ALLEGHENY

-25

-20

-15

-10

-5

0

5

10

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

BEAVER

-6

-4

-2

0

2

4

6

8

10

12

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

BEDFORD

-15

-10

-5

0

5

10

15

20

25

30

35

40

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

BERKS

-10

-5

0

5

10

15

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

BLAIR

-30

-25

-20

-15

-10

-5

0

5

10

15

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

BRADFORD

-50

-40

-30

-20

-10

0

10

20

30

40

50

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

BUCKS

-8

-6

-4

-2

0

2

4

6

8

10

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

BUTLER

-30

-25

-20

-15

-10

-5

0

5

10

15

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

ARMSTRONG

-50

-40

-30

-20

-10

0

10

20

30

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02
107

CAMERON

-15

-10

-5

0

5

10

15

20

25

30

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

CARBON

-100

-50

0

50

100

150

200

250

300

350

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

CENTRE

-30

-20

-10

0

10

20

30

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

CHESTER

-10

-5

0

5

10

15

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

CLARION

-100

-50

0

50

100

150

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

CLEARFIELD

-20

-10

0

10

20

30

40

50

60

70

80

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

CLINTON

-30

-20

-10

0

10

20

30

40

50

60

70

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

COLUMBIA

-20

-10

0

10

20

30

40

50

60

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

CRAWFORD

-20

-15

-10

-5

0

5

10

15

20

25

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

CAMBRIA

-15

-10

-5

0

5

10

15

20

25

30

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02
108

CUMBERLAND

-40

-30

-20

-10

0

10

20

30

40

50

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

DAUPHIN

-10

-5

0

5

10

15

20

25

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

DELAWARE

-20

-15

-10

-5

0

5

10

15

20

25

30

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

ELK

-5

0

5

10

15

20

25

30

35

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

ERIE

-5

0

5

10

15

20

25

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

FAYETTE

-15

-10

-5

0

5

10

15

20

25

30

35

40

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

FOREST

-10

-5

0

5

10

15

20

25

30

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

FRANKLIN

-10

-5

0

5

10

15

20

25

30

35

40

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

FULTON

-6

-4

-2

0

2

4

6

8

10

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

GREENE

-40

-30

-20

-10

0

10

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02
109

HUNTINGDON

-10

-5

0

5

10

15

20

25

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

INDIANA

-10

0

10

20

30

40

50

60

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

JEFFERSON

-10

-5

0

5

10

15

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

JUNIATA

-6

-4

-2

0

2

4

6

8

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

LACKAWANNA

-10

-5

0

5

10

15

20

25

30

35

40

45

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

LANCASTER

-20

-15

-10

-5

0

5

10

15

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

LAWRENCE

-40

-30

-20

-10

0

10

20

30

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

LEBANON

-20

-10

0

10

20

30

40

50

60

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

LEHIGH

-50

0

50

100

150

200

250

300

350

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

LUZERNE

-20

-10

0

10

20

30

40

50

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02
110

LYCOMING

-15

-10

-5

0

5

10

15

20

25

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

MCKEAN

-10

-5

0

5

10

15

20

25

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

MERCER

-10

-5

0

5

10

15

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

MIFFLIN

-20

-10

0

10

20

30

40

50

60

70

80

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

MONROE

-40

-30

-20

-10

0

10

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

MONTGOMERY

-60

-50

-40

-30

-20

-10

0

10

20

30

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

MONTOUR

-10

-5

0

5

10

15

20

25

30

35

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

NORTHAMPTON

-10

-5

0

5

10

15

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

NORTHUMBERLAND

-10

-5

0

5

10

15

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

PERRY

-8

-6

-4

-2

0

2

4

6

8

10

12

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02
111

POTTER

-10

-5

0

5

10

15

20

25

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

SCHUYLKILL

-10

-5

0

5

10

15

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

SNYDER

-10

0

10

20

30

40

50

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

SOMERSET

-50

-40

-30

-20

-10

0

10

20

30

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

SULLIVAN

-8

-6

-4

-2

0

2

4

6

8

10

12

14

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

SUSQUEHANNA

-4

-2

0

2

4

6

8

10

12

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

TIOGA

-4

-2

0

2

4

6

8

10

12

14

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

UNION

-40

-20

0

20

40

60

80

100

120

140

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

PHILADELPHIA

-3

-2

-1

0

1

2

3

4

5

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

PIKE

-10

-5

0

5

10

15

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02
112

WASHINGTON

-10

-5

0

5

10

15

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

WAYNE

-2

0

2

4

6

8

10

12

14

16

18

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

WESTMORELAND

-10

-8

-6

-4

-2

0

2

4

6

8

10

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

WYOMING

-4

-3

-2

-1

0

1

2

3

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

YORK

-10

-5

0

5

10

15

20

25

30

35

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

VENANGO

-25

-20

-15

-10

-5

0

5

10

15

20

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02

WARREN

-10

-5

0

5

10

15

20

25

30

35

40

1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02
113

Appendix B. Socioeconomic Growth Trend by County
114

ALLEGHENY

0

50

100

150

200

250

300

350

400

1970 1980 1990 2000 2010 2020 2030

ARMSTRONG

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

BEAVER

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

BEDFORD

0

50

100

150

200

250

300

350

1970 1980 1990 2000 2010 2020 2030

BERKS

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

BLAIR

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

BRADFORD

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

BUCKS

0

50

100

150

200

250

300

350

400

1970 1980 1990 2000 2010 2020 2030

BUTLER

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

ADAMS

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030
115

CAMBRIA

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

CAMERON

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

CARBON

0

50

100

150

200

250

300

350

1970 1980 1990 2000 2010 2020 2030

CENTRE

0

50

100

150

200

250

300

350

400

1970 1980 1990 2000 2010 2020 2030

CHESTER

0

50

100

150

200

250

300

350

400

450

1970 1980 1990 2000 2010 2020 2030

CLARION

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

CLEARFIELD

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

CLINTON

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

CRAWFORD

0

50

100

150

200

250

1970 1980 1990 2000 2010 2020 2030

COLUMBIA

0

50

100

150

200

250

1970 1980 1990 2000 2010 2020 2030
116

CUMBERLAND

0

50

100

150

200

250

300

350

1970 1980 1990 2000 2010 2020 2030

DAUPHIN

0

50

100

150

200

250

300

350

1970 1980 1990 2000 2010 2020 2030

DELAWARE

0

50

100

150

200

250

300

350

1970 1980 1990 2000 2010 2020 2030

ELK

0

50

100

150

200

250

300

350

1970 1980 1990 2000 2010 2020 2030

ERIE

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

.

FAYETTE

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

FOREST

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

FRANKLIN

0

50

100

150

200

250

1970 1980 1990 2000 2010 2020 2030

FULTON

0

50

100

150

200

250

300

350

1970 1980 1990 2000 2010 2020 2030

GREENE

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030
117

HUNTINGDON

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

INDIANA

0

50

100

150

200

250

300

350

1970 1980 1990 2000 2010 2020 2030

JEFFERSON

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

JUNIATA

0

50

100

150

200

250

1970 1980 1990 2000 2010 2020 2030

LACKAWANNA

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

LANCASTER

0

50

100

150

200

250

1970 1980 1990 2000 2010 2020 2030

LAWRENCE

0

50

100

150

200

250

1970 1980 1990 2000 2010 2020 2030

LEBANON

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

LEHIGH

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

LUZERNE

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030
118

LYCOMING

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

MCKEAN

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

MERCER

0

50

100

150

200

250

1970 1980 1990 2000 2010 2020 2030

MIFFLIN

0

50

100

150

200

250

1970 1980 1990 2000 2010 2020 2030

MONROE

0

100

200

300

400

500

600

700

1970 1980 1990 2000 2010 2020 2030

MONTGOMERY

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

MONTOUR

0

50

100

150

200

250

300

350

400

450

1970 1980 1990 2000 2010 2020 2030

NORTHAMPTON

0

50

100

150

200

250

1970 1980 1990 2000 2010 2020 2030

NORTHUMBERLAND

0

50

100

150

200

250

300

1970 1980 1990 2000 2010 2020 2030

PERRY

0

50

100

150

200

250

300

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119

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PIKE

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POTTER

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SNYDER

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1970 1980 1990 2000 2010 2020 2030

SULLIVAN

0

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1970 1980 1990 2000 2010 2020 2030

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VENANGO

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1970 1980 1990 2000 2010 2020 2030

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121

Appendix C. Model Estimation Results
122

Table C-1. Estimation Results of County-Level OLS Base Model POP

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -62.062 -11.11 4.309 0.63 -45.659 -5.74 -35.761 -5.32

LN (Population) 1.231 8.88 1.2606 6.44 1.23078 27.5 1.90763 48.99

LN (Per Capita Income) -0.1121 -0.7 0.3606 1.94 0.2062 0.98 -0.6681 -3.6

LN (Lane Miles Per Capita) 0.99697 42.7 1.3765 14.41 0.53263 11.4 0.15485 6.58

Year 0.03766 10.51 0.001285 0.31 0.02603 5.4 0.019822 4.85

County Dummy

ADAMS - - 0.3576 1.36 - - 0.07477 1.73

ALLEGHENY - - 1.0395 3.22 -0.1121 -1.46 -0.47504 -7.6

ARMSTRONG - - -0.3965 -1.33 -0.79277 -10.12 1.00783 18.84

BEAVER -0.3821 -3.58 0.3783 2.14 -0.09108 -1.69 0.92615 18.36

BEDFORD 0.0001 0 -0.2862 -0.78 - - - -

BERKS -0.37838 -9.91 0.14665 3 -0.26035 -5.99 0.07778 1.94

BLAIR -0.8182 -5.39 0.6593 2.95 -0.23662 -4.51 0.97466 19.17

BRADFORD - - -0.9239 -2.85 -0.85161 -10.95 0.20555 4.01

BUCKS -0.02969 -0.33 0.7053 5.49 0.0164 0.24 0.19327 3.17

BUTLER -0.2694 -2.41 0.0977 0.66 -0.30961 -6.69 0.52619 12.74

CAMBRIA - - 0.4446 2.52 -0.55727 -9.72 0.47954 10.26

CAMERON - - -0.3024 -0.4 - - - -

CARBON -0.2283 -0.89 0.6504 1.84 -0.44646 -5.49 0.87689 14.95

CENTRE -0.3943 -2.74 0.3211 1.65 -0.69962 -13.53 0.8366 19.47

CHESTER 0.00528 0.06 0.2487 2.22 0.1011 0.87 0.5516 5.38

CLARION 0.0396 0.13 -0.5346 -1.35 - - 0.92638 16.32

CLEARFIELD -0.1933 -0.9 -0.281 -1.01 -0.9998 -9.53 0.65109 12.19

CLINTON -0.1982 -0.62 -0.0862 -0.21 -0.4076 -4.29 1.38265 19.56

COLUMBIA 0.0648 0.26 -0.4789 -1.48 -0.11576 -1.46 1.48577 23.13

CRAWFORD -0.535 -2.64 -0.6215 -2.38 -1.3606 -12.52 0.59962 11.91

CUMBERLAND -0.06771 -0.78 0.3683 2.57 0.4978 7.05 0.84711 14.87

DAUPHIN 0.11566 1.65 0.7319 5.42 0.63557 9.65 0.95437 17.96

DELAWARE - - 2.2717 5.69 0.30163 4.64 0.19818 3.37

ELK - - 0.1575 0.36 - - 1.36136 15.28

ERIE -0.54703 -9.36 0.12955 1.59 -0.50098 -7.68 0.32284 8.02

FAYETTE - - 0.0572 0.32 -0.69366 -11.65 0.2385 4.75

FOREST - - -0.4589 -0.59 - - - -

FRANKLIN -0.0089 -0.06 0.2102 1.04 -0.16195 -2.55 0.61746 14.77

FULTON -0.0289 -0.06 -0.3284 -0.56 - - - -

GREENE -0.1502 -0.48 -0.7622 -1.89 - - - -

HUNTINGDON -0.1768 -0.58 -0.1837 -0.48 - - 0.60715 9.61

INDIANA - - -0.1854 -0.71 -1.37224 -19.66 0.42349 9.53

JEFFERSON -0.087 -0.3 -0.4285 -1.13 - - 0.66308 11.28

JUNIATA - - 0.0676 0.13 - - - -

LACKAWANNA -0.52277 -6.26 0.6015 3.63 0.20056 3.54 0.57895 13.39

LANCASTER -0.41388 -9.41 0.29998 5.01 -0.10164 -2.26 -0.17803 -4.23

LAWRENCE -0.3857 -1.99 0.4199 1.57 -0.53642 -8.5 0.80008 13.67

LEBANON 0.0011 0.01 0.5748 2.52 - - 0.70923 14.8

LEHIGH -0.1492 -3.29 0.3928 3.62 0.34383 6.77 0.64943 13.71

LUZERNE -0.51955 -10.86 0.2421 3.06 0.05569 1.4 0.30095 7.34

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates

99.1 97.6 99.7 99.6

434 660 421 544
123

LYCOMING -0.6091 -3.71 -0.2367 -1.12 0.18908 3.2 0.86042 16.6

MCKEAN - - -0.0616 -0.16 -0.76842 -8.86 0.5294 8.64

MERCER -0.4332 -2.67 -0.1708 -0.8 -0.6275 -10.64 0.7786 14.91

MIFFLIN - - 0.4553 1.16 -0.09974 -1.12 1.38057 20.34

MONROE -0.1841 -1.24 0.5178 2.61 0.06767 0.95 -0.12756 -3.32

MONTGOMERY -0.024 -0.18 0.9742 4.64 0.2855 2.61 0.27375 2.9

MONTOUR 0.3362 0.82 -0.0077 -0.01 - - 2.8497 21.6

NORTHAMPTON - - 0.6477 5.54 0.21349 5.24 0.38764 9.33

NORTHUMBERLAND -0.4984 -2.57 0.0798 0.31 -0.90343 -10.72 0.89541 16.56

PERRY - - 0.2196 0.56 - - 0.52548 9.84

PHILADELPHIA - - - - -0.11273 -1.19 -1.34893 -16.04

PIKE -0.3231 -1.08 0.6322 1.56 - - - -

POTTER - - -0.9062 -1.66 - - - -

SCHUYLKILL -0.3159 -2.38 0.3189 1.83 - - -0.18439 -4.51

SNYDER - - -0.0097 -0.02 -0.5829 -5.51 2.14174 24.07

SOMERSET 0.0243 0.11 -0.4 -1.42 -0.35753 -4.33 0.51564 10.03

SULLIVAN - - -0.863 -1.18 - - - -

SUSQUEHANNA -0.061 -0.2 -0.9044 -2.3 - - - -

TIOGA - - -0.5261 -1.32 - - - -

UNION -0.1749 -0.58 0.4327 1.05 - - 1.57424 23.59

VENANGO -0.1347 -0.52 -0.3224 -0.95 - - 1.60274 22.08

WARREN - - -0.3354 -0.87 - - 1.52184 21.4

WASHINGTON -0.218 -2.44 -0.1042 -0.87 0.24642 4.44 0.61151 12.68

WAYNE -0.3842 -1.32 -0.4162 -1.11 - - - -

WESTMORELAND -0.2402 -7.56 0.40551 7.24 -0.02488 -0.49 0.23946 5.7

WYOMING - - 0.0916 0.19 - - - -

YORK - - - - - - - -
124

Table C-2. Estimation Results of County-Level OLS Base Model HH

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -52.459 -12.93 9.838 2.07 -37.766 -6.28 -12.264 -2.56

LN (Households) 1.422 9.88 1.5046 7.44 1.2391 27.33 1.92057 49.25

LN (Mean Household Income) -0.2743 -1.75 0.2075 1.16 0.1663 0.79 -0.6862 -3.82

LN (Lane Miles Per Capita) 0.99463 43.81 1.38995 14.9 0.53123 11.33 0.12889 5.45

Year 0.033239 11.39 -0.001631 -0.48 0.022734 5.84 0.009236 2.96

County Dummy

ADAMS - - 0.7563 2.65 - - 0.18302 3.98

ALLEGHENY - - 0.7311 2.21 -0.18756 -2.51 -0.66649 -10.98

ARMSTRONG - - -0.07 -0.23 -0.81509 -10.4 0.94825 18.08

BEAVER -0.3002 -2.84 0.5175 2.97 -0.1135 -2.1 0.88474 17.65

BEDFORD 0.3267 1.14 0.1227 0.32 - - - -

BERKS -0.3335 -8.81 0.18834 3.81 -0.23569 -5.37 0.1212 3.02

BLAIR -0.6716 -4.4 0.8748 3.9 -0.25585 -4.79 0.92758 18.29

BRADFORD - - -0.5401 -1.6 -0.84257 -10.82 0.21107 4.17

BUCKS 0.03132 0.36 0.7338 5.79 0.09014 1.15 0.36515 5.31

BUTLER -0.0756 -0.63 0.32 1.99 -0.27092 -5.63 0.59582 14.06

CAMBRIA - - 0.6203 3.46 -0.56811 -9.3 0.43355 8.91

CAMERON - - 0.5906 0.76 - - - -

CARBON 0.0586 0.23 1.0371 2.89 -0.47148 -5.86 0.8032 14.2

CENTRE -0.1012 -0.64 0.6678 3.08 -0.60073 -11.11 0.98486 22.29

CHESTER 0.17057 1.76 0.3904 3.41 0.1919 1.5 0.7229 6.62

CLARION 0.4567 1.45 -0.0188 -0.05 - - 0.94594 16.71

CLEARFIELD 0.0416 0.19 0.0253 0.09 -1.0087 -9.47 0.61932 11.52

CLINTON 0.2012 0.61 0.4275 0.98 -0.392 -4.08 1.37675 19.53

COLUMBIA 0.378 1.49 -0.0745 -0.22 -0.09696 -1.2 1.5013 23.36

CRAWFORD -0.2725 -1.31 -0.2999 -1.09 -1.3422 -12.26 0.62764 12.53

CUMBERLAND 0.06681 0.73 0.549 3.78 0.52032 7.7 0.8526 16

DAUPHIN 0.13201 2.04 0.8004 6.38 0.59482 9.89 0.84456 17.96

DELAWARE - - 2.3275 5.95 0.34996 5.15 0.28725 4.75

ELK - - 0.6778 1.52 - - 1.33115 15.28

ERIE -0.46161 -7.68 0.23007 2.68 -0.47104 -7.21 0.3819 9.62

FAYETTE - - 0.1983 1.11 -0.73132 -11.54 0.15595 2.98

FOREST - - 0.4817 0.6 - - - -

FRANKLIN 0.1747 1.13 0.4451 2.14 -0.1643 -2.58 0.59771 14.5

FULTON 0.552 1.2 0.3922 0.64 - - - -

GREENE 0.2982 0.92 -0.2174 -0.51 - - - -

HUNTINGDON 0.2515 0.79 0.3488 0.85 - - 0.70892 11.14

INDIANA - - 0.1667 0.6 -1.32921 -18.76 0.47564 10.7

JEFFERSON 0.2502 0.85 0.0061 0.02 - - 0.60322 10.48

JUNIATA - - 0.7375 1.37 - - - -

LACKAWANNA -0.47763 -5.82 0.7045 4.4 0.17179 3.05 0.49834 11.68

LANCASTER -0.35085 -8.84 0.33416 6.02 -0.03799 -0.83 -0.03537 -0.83

LAWRENCE -0.1601 -0.81 0.7202 2.63 -0.53539 -8.39 0.80385 13.79

LEBANON 0.2265 1.36 0.8602 3.64 - - 0.72687 15.26

LEHIGH -0.10672 -2.38 0.4623 4.41 0.34415 7.06 0.62973 13.98

LUZERNE -0.57815 -12.03 0.22096 2.91 0.00816 0.18 0.17896 4.01

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates

99.1 97.6 99.7 99.6

434 660 421 544
125

LYCOMING -0.4284 -2.56 0.0057 0.03 0.19192 3.18 0.85404 16.41

MCKEAN - - 0.4169 1.05 -0.75344 -8.67 0.50659 8.47

MERCER -0.2411 -1.45 0.0813 0.37 -0.61844 -10.19 0.78347 14.9

MIFFLIN - - 0.8924 2.21 -0.11142 -1.25 1.35369 20.15

MONROE 0.1241 0.77 0.8458 3.86 0.15305 2.14 0.04836 1.25

MONTGOMERY -0.0493 -0.39 0.9359 4.57 0.3173 2.88 0.31502 3.39

MONTOUR 0.9538 2.2 0.7597 1.29 - - 2.8904 22.35

NORTHAMPTON - - 0.7805 6.48 0.24624 5.88 0.45056 10.69

NORTHUMBERLAND -0.3508 -1.83 0.3184 1.23 -0.95214 -10.88 0.76968 13.89

PERRY - - 0.7371 1.79 - - 0.56729 10.33

PHILADELPHIA - - - - -0.12683 -1.31 -1.38761 -16.28

PIKE 0.0956 0.31 1.1382 2.7 - - - -

POTTER - - -0.2106 -0.37 - - - -

SCHUYLKILL -0.224 -1.7 0.4734 2.7 - - -0.28745 -6.48

SNYDER - - 0.6421 1.41 -0.4798 -4.21 2.33256 24.44

SOMERSET 0.283 1.27 -0.0712 -0.24 -0.3531 -4.21 0.50318 9.76

SULLIVAN - - 0.0201 0.03 - - - -

SUSQUEHANNA 0.3369 1.08 -0.4236 -1.03 - - - -

TIOGA - - -0.0294 -0.07 - - - -

UNION 0.5398 1.57 1.2468 2.66 - - 2.02686 26.96

VENANGO 0.2003 0.76 0.1022 0.29 - - 1.5931 22.43

WARREN - - 0.1138 0.29 - - 1.45617 21.29

WASHINGTON -0.13656 -1.53 0.0144 0.12 0.22769 4.25 0.56276 12.2

WAYNE 0.0036 0.01 0.063 0.16 - - - -

WESTMORELAND -0.30356 -9.3 0.36255 6.57 -0.06374 -1.22 0.15575 3.63

WYOMING - - 0.7123 1.44 - - - -

YORK - - - - - - - -
126

Table C-3. Estimation Results of County-Level OLS Base Model HH JrSr

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -63.74 -9.89 26.425 3.69 -28.875 -3.5 -11.332 -1.61

LN (Households) 1.3415 8.91 1.2152 5.93 1.5244 6.52 1.7685 8.41

Pop17 (% of Pop<17 yrs) 2.108 2.26 -1.3825 -1.49 -2.006 -1.79 -0.559 -0.55

Pop65+ (% of Pop>65+ yrs) 0.599 0.46 9.117 6.09 -4 -2.2 -2.995 -2.02

LN (Mean Household Income) -0.3587 -2.23 0.4663 2.61 0.1956 0.89 -0.6987 -3.77

LN(Lane Miles Per Capita) 0.9923 43.83 1.30628 14.4 0.51138 10.15 0.10233 4.04

Year 0.039523 9.8 -0.010259 -2.3 0.016859 3.21 0.00994 2.26

County Dummy

ADAMS - - 0.3714 1.27 - - -0.0578 -0.18

ALLEGHENY - - 0.3345 1.01 -0.4294 -1.38 -0.3276 -1.22

ARMSTRONG - - -0.8282 -2.51 -0.2093 -0.51 0.8227 2.26

BEAVER -0.3608 -2.66 -0.1673 -0.83 0.2646 1.32 0.9384 5.32

BEDFORD 0.1443 0.46 -0.5309 -1.34 - - - -

BERKS -0.33982 -7.63 -0.00325 -0.06 -0.16425 -2.96 0.17079 3.4

BLAIR -0.7625 -4.34 0.2217 0.9 0.1813 0.68 0.8919 3.72

BRADFORD - - -1.0256 -2.89 -0.2195 -0.5 -0.0171 -0.04

BUCKS 0.07283 0.8 0.7729 6.14 -0.0529 -0.44 0.4146 3.97

BUTLER -0.1435 -1.13 0.049 0.3 -0.0036 -0.02 0.4965 2.75

CAMBRIA - - -0.1698 -0.78 -0.1292 -0.51 0.4764 2.14

CAMERON - - -0.9609 -1.17 - - - -

CARBON -0.079 -0.27 0.0842 0.22 0.2025 0.45 0.6557 1.6

CENTRE -0.0415 -0.26 0.6121 2.91 -0.5437 -2.16 0.6832 3.11

CHESTER 0.20511 2.08 0.3976 3.53 0.1235 0.94 0.7058 6.5

CLARION 0.3047 0.92 -0.6342 -1.49 - - 0.6057 1.28

CLEARFIELD -0.089 -0.37 -0.5846 -1.92 -0.4891 -1.25 0.48 1.44

CLINTON 0.0216 0.06 -0.3953 -0.87 0.373 0.68 1.1067 2.23

COLUMBIA 0.2744 1.02 -0.7257 -2.09 0.4404 1.04 1.2934 3.44

CRAWFORD -0.4256 -1.87 -0.7404 -2.57 -0.8654 -2.28 0.4642 1.46

CUMBERLAND 0.07797 0.8 0.1663 1.1 0.7009 4.73 0.8062 5.99

DAUPHIN 0.10909 1.58 0.5369 4.21 0.7402 7.21 0.83173 9.21

DELAWARE - - 1.7794 4.62 0.329 3.18 0.4229 4.67

ELK - - -0.2164 -0.46 - - 1.0908 2.13

ERIE -0.51592 -7.63 0.06826 0.74 -0.3409 -2.99 0.37681 4.48

FAYETTE - - -0.41 -1.98 -0.3059 -1.25 0.1543 0.7

FOREST - - -1.0966 -1.29 - - - -

FRANKLIN 0.0815 0.48 -0.0237 -0.11 0.2125 0.78 0.4962 2.06

FULTON 0.2653 0.54 -0.4131 -0.66 - - - -

GREENE 0.1181 0.34 -0.8564 -1.95 - - -0.6249 -1.22

HUNTINGDON 0.0853 0.26 -0.1945 -0.47 - - 0.3706 0.8

INDIANA - - -0.2707 -0.96 -0.9097 -2.61 0.2603 0.83

JEFFERSON 0.0527 0.16 -0.8212 -1.97 - - 0.3931 0.85

JUNIATA - - -0.0454 -0.08 - - - -

LACKAWANNA -0.5078 -4.11 -0.0877 -0.44 0.5365 2.9 0.6007 3.78

LANCASTER -0.38176 -9.1 0.32805 5.94 -0.02569 -0.48 0.01927 0.39

LAWRENCE -0.2954 -1.26 -0.1619 -0.52 0.0778 0.22 0.7769 2.42

LEBANON 0.1291 0.7 0.2776 1.1 - - 0.6321 2.42

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates

99.1 97.8 99.7 99.7

434 660 421 544
127

LEHIGH -0.11081 -1.88 0.0807 0.7 0.4839 5.63 0.68127 8.9

LUZERNE -0.56315 -5.92 -0.4435 -3.61 0.2343 1.88 0.3462 3.37

LYCOMING -0.5432 -2.95 -0.4318 -1.86 0.6202 2.24 0.7593 3.03

MCKEAN - - -0.3496 -0.84 -0.0357 -0.07 0.2623 0.57

MERCER -0.3488 -1.8 -0.5816 -2.34 -0.132 -0.44 0.7505 2.84

MIFFLIN - - 0.1152 0.27 0.6324 1.26 1.1331 2.5

MONROE -0.0001 - 0.7027 3.17 0.4472 1.59 -0.1708 -0.7

MONTGOMERY 0.0451 0.34 0.6459 3.12 0.174 0.91 0.4877 3.03

MONTOUR 0.7095 1.53 -0.3841 -0.63 - - 2.5352 3.82

NORTHAMPTON - - 0.3952 3.03 0.4217 3.84 0.466 4.66

NORTHUMBERLAND -0.4594 -2.05 -0.5083 -1.75 -0.4092 -1.15 0.7284 2.37

PERRY - - 0.3842 0.92 - - 0.1528 0.33

PHILADELPHIA - - - - -0.4812 -1.51 -1.1498 -3.95

PIKE -0.1383 -0.41 0.5107 1.16 - - - -

POTTER - - -1.1011 -1.85 - - - -

SCHUYLKILL -0.2757 -1.62 -0.3349 -1.56 - - -0.2544 -1.11

SNYDER - - 0.0081 0.02 0.203 0.36 1.9814 3.92

SOMERSET 0.1497 0.6 -0.7885 -2.48 0.2297 0.58 0.3848 1.1

SULLIVAN - - -1.6113 -2.01 - - - -

SUSQUEHANNA 0.111 0.33 -1.0198 -2.38 0.7404 1.39 -0.0124 -0.03

TIOGA - - -0.6519 -1.51 - - - -

UNION 0.4282 1.22 0.6083 1.3 - - 1.6174 3.23

VENANGO 0.0269 0.09 -0.6057 -1.62 - - 1.4086 3.44

WARREN - - -0.6258 -1.5 - - 1.2208 2.64

WASHINGTON -0.1589 -1.36 -0.5775 -3.87 0.5446 3.1 0.6148 3.95

WAYNE -0.1934 -0.59 -0.7294 -1.74 - - 0.0349 0.07

WESTMORELAND -0.28082 -4.06 -0.11969 -1.36 0.0879 0.83 0.31053 4.11

WYOMING - - 0.1409 0.28 - - -0.7454 -1.3

YORK - - - - - - - -
128

Table C-4. Estimation Results of County Group Level OLS Base Model HH

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -9.203 -4.59 -10.57 -4.01 -2.037 -0.64 0.318 0.15

LN (Households) 1.7318 7.46 4.0981 12.26 1.6203 5.15 2.2667 8.51

LN (Mean Household Income) 1.5384 10.55 -0.2818 -2.01 0.7692 3.82 -0.3778 -2.32

LN (Lane Miles Per Capita) 1.00868 23.28 3.5944 14.8 0.90262 9.65 0.30991 5.64

County Group Dummy

ALTGR -0.8023 -2.89 -2.1681 -6.55 ALTGR 0.9106 -1.4089 -4.25

CNTRGR -0.7058 -3.32 -1.1861 -5 CNTRGR 1.0716 -0.8638 -3.35

EPAGR -1.6003 -3.83 -0.7787 -1.83 EPAGR 0.8926 -1.7606 -3.51

HARRGR -1.7356 -3.23 -3.3265 -5.51 HARRGR 0.6105 -2.506 -3.92

I81GR -1.325 -3.47 -1.8243 -4.51 I81GR 0.8868 -1.7826 -3.88

NCNTGR - - -0.77196 -9.45 NCNTGR 0.7229 -0.46727 -14.43

NEPAGR -0.32236 -3.73 0.94645 9.51 NEPAGR 2.0861 -1.10514 -11.74

NTIERGR - - -0.6606 -7.56 NTIERGR 1.122 -0.7242 -6.68

PHILY -2.3944 -3.42 -0.4939 -0.69 PHILY 0.2204 -3.21 -3.85

SEDA-COG -0.5315 -2.97 -0.8908 -4.49 SEDA-COG 0.9863 -0.7034 -3.22

SHVGR -1.0437 -3.84 -1.2023 -4.24 SHVGR 0.957 -1.0862 -3.29

SWPAC -2.3904 -3.66 -4.5742 -6.21 SWPAC - -3.0514 -3.88

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates

99 99 100 100

110 130 120 130
129

Table C-5. Estimation Results of County Group Level OLS Base Model HH JrSr

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -7.987 -2.69 -11.706 -3.75 -1.905 -0.43 5.903 1.87

Pop17 (% of Pop<17 yrs) -1.835 -1.27 1.52 1.2 0.293 0.15 -3.095 -2.04

Pop65+ (% of Pop>65+ yrs) 6.373 2.14 13.547 5.18 -6.089 -1.53 -11.342 -4.01

LN (Households) 1.6844 7.51 3.6237 11.68 1.6515 5.24 2.4337 9.16

LN (Mean Household Income) 1.4228 6.52 -0.03 -0.16 0.7838 2.94 -0.805 -3.8

LN (Lane Miles Per Capita) 1.01674 23.86 3.2502 14.38 0.8651 8.33 0.33997 5.51

County Group Dummy

ALTGR -0.8574 -3.2 -1.9107 -6.39 0.9382 1.61 -1.5264 -4.73

CNTRGR -0.5543 -2.37 -0.5742 -2.36 0.9099 1.35 -1.3634 -4.9

EPAGR -1.4273 -3.54 -0.4105 -1.06 0.7882 2.22 -2.0842 -4.06

HARRGR -1.428 -2.71 -2.3925 -4.23 0.3983 1.81 -3.0667 -4.63

I81GR -1.4552 -3.95 -1.7869 -4.95 1.0185 2.58 -1.8092 -4.03

NCNTGR - - -0.73322 -9.71 0.7016 0.72 -0.39864 -10.87

NEPAGR -0.1003 -0.89 1.2392 12.01 1.8564 2.03 -1.3505 -12.3

NTIERGR - - -0.54088 -6.68 1.071 1.03 -0.7098 -6.22

PHILY -1.9824 -2.9 0.1184 0.18 -0.0391 -0.26 -3.8222 -4.43

SEDA-COG -0.5059 -2.9 -0.6508 -3.56 0.9356 1.29 -0.8886 -4.09

SHVGR -0.9015 -3.4 -0.8323 -3.18 0.8258 1.35 -1.3637 -3.98

SWPAC -2.3162 -3.7 -3.9167 -5.86 - - -3.3927 -4.31

110 130 120 130

100 99 100 100

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates
130

Table C-6. Estimation Results of County-Level OLS Difference Model HH

Dependent Var=Diff LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant 0.0191 0.71 -0.03736 -1.14 0.02839 0.86 0.03152 0.96

Diff LN (Households) 0.2307 0.25 0.2261 0.27 0.843 0.68 1.007 0.93

Diff LN (Mean Household Income) -0.2157 -0.95 0.5792 2.48 0.3599 1.16 -0.8725 -3.22

Diff LN (Lane Miles Per Capita) 0.99638 36.44 1.81149 19.03 0.57852 11.6 0.17826 6.32

County Dummy

ADAMS - - 0.02996 0.68 - - 0.0108 0.26

ALLEGHENY - - 0.0741 1.65 -0.0154 -0.35 -0.0119 -0.27

ARMSTRONG - - 0.02743 0.62 -0.00247 -0.06 0.00538 0.12

BEAVER 0.01456 0.41 0.02519 0.56 -0.01433 -0.33 -0.00352 -0.08

BEDFORD -0.00327 -0.1 0.03362 0.77 - - - -

BERKS 0.01472 0.43 0.00543 0.13 -0.00263 -0.06 0.00676 0.16

BLAIR -0.03991 -1.05 0.0175 0.39 -0.00798 -0.18 -0.01282 -0.29

BRADFORD - - 0.04084 0.94 -0.06126 -1.49 -0.02113 -0.5

BUCKS 0.02928 0.81 -0.04544 -1.04 -0.02594 -0.63 -0.00072 -0.02

BUTLER 0.02325 0.68 0.02322 0.53 0.0102 0.25 0.00474 0.11

CAMBRIA - - 0.03994 0.88 -0.00165 -0.04 -0.02063 -0.46

CAMERON - - 0.05324 1.19 - - - -

CARBON 0.0393 1.13 -0.01264 -0.29 0.02583 0.63 0.04495 1.07

CENTRE 0.0279 0.82 0.05333 1.23 0.01268 0.31 -0.00726 -0.17

CHESTER 0.03189 0.91 -0.0031 -0.07 -0.025 -0.59 0.01439 0.34

CLARION 0.0426 1.23 0.03136 0.72 - - -0.03312 -0.78

CLEARFIELD 0.02538 0.74 0.04854 1.11 0.02338 0.57 -0.03801 -0.91

CLINTON 0.02691 0.78 0.07183 1.64 -0.00834 -0.2 -0.03995 -0.94

COLUMBIA 0.04226 1.23 0.0436 1 0.02208 0.53 -0.02908 -0.69

CRAWFORD 0.02547 0.74 0.02926 0.67 0.01057 0.25 -0.0245 -0.58

CUMBERLAND 0.02655 0.78 0.02994 0.69 -0.00537 -0.13 -0.01339 -0.32

DAUPHIN 0.02525 0.73 0.03797 0.87 -0.01303 -0.32 -0.01472 -0.35

DELAWARE - - -0.00969 -0.22 0.01107 0.26 -0.02435 -0.57

ELK - - 0.07726 1.75 - - 0.01747 0.41

ERIE -0.00089 -0.03 0.0366 0.83 0.02932 0.69 -0.00946 -0.22

FAYETTE - - 0.01234 0.28 -0.0388 -0.92 0.01143 0.27

FOREST - - 0.05884 1.34 - - - -

FRANKLIN 0.02124 0.63 0.04411 1.02 -0.011 -0.27 -0.02674 -0.64

FULTON 0.01461 0.43 0.03892 0.9 - - - -

GREENE -0.00203 -0.06 0.0341 0.78 - - - -

HUNTINGDON 0.02335 0.68 0.04665 1.07 - - -0.02598 -0.62

INDIANA - - 0.03511 0.8 0.00185 0.04 -0.05611 -1.33

JEFFERSON 0.03788 1.09 0.04737 1.08 - - -0.02524 -0.59

JUNIATA - - 0.04149 0.95 - - - -

LACKAWANNA 0.01808 0.51 0.019 0.43 -0.01243 -0.29 -0.00137 -0.03

LANCASTER 0.03005 0.89 0.00473 0.11 -0.00976 -0.24 0.01952 0.47

LAWRENCE 0.02381 0.68 0.01292 0.29 0.01083 0.25 -0.02417 -0.56

LEBANON 0.03345 0.98 0.05122 1.18 - - 0.01139 0.27

LEHIGH -0.01321 -0.39 -0.01277 -0.29 -0.00423 -0.1 -0.01742 -0.42

LUZERNE 0.00716 0.2 -0.01634 -0.37 -0.01705 -0.39 -0.01506 -0.34

LYCOMING 0.02333 0.66 0.01178 0.27 0.01121 0.26 0.02488 0.58

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates

78.9 40.3 25 6.3

390 594 378 486
131

MCKEAN - - 0.05795 1.29 -0.0277 -0.63 -0.01917 -0.44

MERCER 0.03348 0.95 0.0385 0.87 0.00685 0.16 -0.01001 -0.23

MIFFLIN - - 0.06068 1.38 -0.02183 -0.52 -0.02446 -0.57

MONROE 0.02006 0.51 0.03218 0.69 -0.02579 -0.53 0.0266 0.56

MONTGOMERY 0.01671 0.46 -0.11667 -2.69 -0.01041 -0.26 -0.00879 -0.21

MONTOUR 0.04069 1.18 0.0487 1.11 - - -0.04033 -0.96

NORTHAMPTON - - 0.0055 0.13 0.00846 0.21 0.00604 0.15

NORTHUMBERLAND 0.03174 0.88 0.04863 1.09 -0.08931 -2.05 -0.03347 -0.76

PERRY - - 0.03833 0.88 - - 0.06937 1.67

PHILADELPHIA - - - - -0.01045 -0.23 -0.01914 -0.43

PIKE 0.01429 0.34 0.05388 1.1 - - - -

POTTER - - 0.04231 0.97 - - - -

SCHUYLKILL 0.03662 1.02 0.03525 0.79 - - -0.00847 -0.19

SNYDER - - 0.05118 1.17 0.0172 0.42 -0.01563 -0.37

SOMERSET 0.00826 0.24 0.03793 0.86 -0.00208 -0.05 0.00407 0.1

SULLIVAN - - 0.04921 1.13 - - - -

SUSQUEHANNA 0.02726 0.8 0.02726 0.63 - - - -

TIOGA - - 0.05724 1.31 - - - -

UNION 0.0146 0.43 0.06142 1.42 - - -0.04064 -0.98

VENANGO 0.03359 0.94 0.04268 0.96 - - -0.02516 -0.58

WARREN - - 0.06451 1.45 - - -0.04329 -1

WASHINGTON 0.0067 0.19 0.01956 0.44 -0.00204 -0.05 -0.0027 -0.06

WAYNE 0.01725 0.5 0.03459 0.79 - - - -

WESTMORELAND 0.00474 0.14 0.00585 0.13 -0.00474 -0.11 0.00265 0.06

WYOMING - - 0.0313 0.71 - - - -

YORK - - - - - - - -
132

Table C-7. Estimation Results of County-Level OLS Difference Model HH JrSr

Dependent Var=Diff LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -0.5042 -1.87 -1.0917 -4.06 -0.0585 -0.19 1.2415 4.43

Pop17 (% of Pop<17 yrs) 1.7419 2.92 2.705 4.93 0.6563 0.87 -3.2193 -5.11

Pop65+ (% of Pop>65+ yrs) 0.828 0.55 3.102 2.12 -0.503 -0.27 -3.329 -2.11

Diff LN (Households) -0.758 -0.77 -1.355 -1.46 0.423 0.32 2.585 2.3

Diff LN (Mean Household Income) -0.3188 -1.4 0.3693 1.59 0.3254 1.04 -0.625 -2.33

Diff LN (Lane Miles Per Capita) 0.98419 35.83 1.75303 18.56 0.58859 11.2 0.16173 5.87

County Dummy

ADAMS - - 0.01757 0.41 - - 0.02552 0.61

ALLEGHENY - - -0.01599 -0.21 0.0184 0.2 0.07185 0.92

ARMSTRONG - - -0.08634 -1.13 0.02677 0.29 0.12047 1.5

BEAVER -0.00708 -0.09 -0.09908 -1.23 0.01663 0.17 0.12191 1.44

BEDFORD -0.01518 -0.27 -0.04167 -0.69 - - - -

BERKS 0.00028 0.01 -0.04865 -1 0.00499 0.1 0.06434 1.34

BLAIR -0.05578 -0.83 -0.08795 -1.26 0.01551 0.19 0.09377 1.29

BRADFORD - - -0.06144 -1.15 -0.06016 -1.07 0.09217 1.74

BUCKS 0.02539 0.66 -0.03221 -0.71 -0.03596 -0.78 -0.01245 -0.28

BUTLER 0.02396 0.66 0.00963 0.22 0.01698 0.39 0.01933 0.45

CAMBRIA - - -0.09616 -1 0.0426 0.35 0.114 1.1

CAMERON - - -0.1628 -1.6 - - - -

CARBON 0.03724 0.46 -0.11178 -1.36 0.0643 0.63 0.14258 1.63

CENTRE 0.16764 2.23 0.32759 4.15 0.03878 0.44 -0.32177 -4

CHESTER 0.0332 0.79 0.02874 0.59 -0.03832 -0.72 -0.01627 -0.33

CLARION 0.06595 1.64 0.04036 0.86 - - -0.0528 -1.12

CLEARFIELD 0.01937 0.32 -0.02614 -0.4 0.04935 0.64 0.03676 0.55

CLINTON 0.03054 0.51 0.01387 0.22 0.02039 0.27 0.01484 0.22

COLUMBIA 0.07392 1.46 0.04562 0.84 0.05386 0.83 -0.0425 -0.75

CRAWFORD -0.00395 -0.09 -0.05573 -1.06 0.01611 0.29 0.06672 1.27

CUMBERLAND 0.06017 1.48 0.05708 1.21 0.01795 0.35 -0.05037 -1.07

DAUPHIN 0.01872 0.52 0.01181 0.27 -0.00946 -0.22 0.01204 0.28

DELAWARE - - -0.09355 -1.74 0.0189 0.33 0.06126 1.15

ELK - - -0.0489 -0.72 - - 0.15094 2.15

ERIE -0.03234 -0.84 -0.03171 -0.69 0.02388 0.51 0.06528 1.45

FAYETTE - - -0.10542 -1.33 -0.00804 -0.08 0.13123 1.56

FOREST - - -0.1065 -1 - - - -

FRANKLIN 0.01031 0.21 -0.01835 -0.34 0.00442 0.07 0.0387 0.7

FULTON 0.00123 0.03 -0.001 -0.02 - - - -

GREENE 0.00906 0.21 0.01692 0.35 - - - -

HUNTINGDON 0.05104 1.31 0.06647 1.45 - - -0.05537 -1.22

INDIANA - - 0.05275 1.13 0.02333 0.46 -0.08365 -1.78

JEFFERSON 0.00392 0.05 -0.08614 -1.14 - - 0.11524 1.47

JUNIATA - - -0.02262 -0.46 - - - -

LACKAWANNA 0.00278 0.03 -0.11637 -1.24 0.0299 0.25 0.1336 1.32

LANCASTER -0.01121 -0.3 -0.06905 -1.53 -0.02144 -0.48 0.10552 2.41

LAWRENCE -0.01246 -0.13 -0.15123 -1.62 0.0447 0.38 0.14698 1.48

LEBANON 0.01908 0.36 -0.02509 -0.43 - - 0.09039 1.52

LEHIGH -0.02194 -0.45 -0.0749 -1.37 0.01147 0.19 0.04489 0.81

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates

79.3 42.8 24.7 11.6

390 594 378 486
133

LUZERNE 0.00235 0.02 -0.14063 -1.45 0.0315 0.25 0.106 1.01

LYCOMING 0.00118 0.02 -0.0664 -1.22 0.02197 0.36 0.10576 1.92

MCKEAN - - -0.04273 -0.68 -0.0119 -0.16 0.0848 1.32

MERCER 0.00591 0.08 -0.08906 -1.15 0.03294 0.35 0.1221 1.5

MIFFLIN - - -0.06121 -0.94 -0.00878 -0.12 0.1059 1.58

MONROE 0.02027 0.5 0.05415 1.14 -0.03444 -0.66 0.01088 0.23

MONTGOMERY 0.01195 0.28 -0.14966 -3.06 0.00315 0.06 0.02329 0.48

MONTOUR 0.01026 0.17 -0.06288 -0.96 - - 0.07824 1.16

NORTHAMPTON - - -0.03393 -0.65 0.02678 0.46 0.04366 0.83

NORTHUMBERLAND 0.01703 0.19 -0.07934 -0.89 -0.0503 -0.44 0.09426 0.98

PERRY - - 0.04838 1.04 - - 0.06252 1.36

PHILADELPHIA - - - - -0.02068 -0.43 0.06594 1.42

PIKE -0.00102 -0.02 0.00355 0.06 - - - -

POTTER - - -0.0945 -1.5 - - - -

SCHUYLKILL 0.0326 0.32 -0.09101 -0.92 - - 0.1141 1.06

SNYDER - - 0.03303 0.77 0.0168 0.41 0.00303 0.07

SOMERSET -0.00489 -0.07 -0.06794 -0.88 0.02992 0.32 0.11003 1.35

SULLIVAN - - -0.1136 -0.94 - - - -

SUSQUEHANNA -0.01154 -0.25 -0.07033 -1.33 - - - -

TIOGA - - -0.01246 -0.23 - - - -

UNION 0.09002 2.12 0.17949 3.69 - - -0.18022 -3.72

VENANGO -0.00347 -0.06 -0.07313 -1.18 - - 0.09784 1.54

WARREN - - -0.04495 -0.72 - - 0.07176 1.12

WASHINGTON 0.00412 0.06 -0.06759 -0.9 0.03308 0.35 0.08175 1.02

WAYNE 0.00325 0.05 -0.06016 -0.84 - - - -

WESTMORELAND 0.00247 0.03 -0.08695 -1.1 0.03343 0.34 0.09232 1.09

WYOMING - - 0.00152 0.03 - - - -

YORK - - - - - - - -
134

Table C-8. Estimation Results of County-Level OLS Truck Model HH

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -8.503 -5.9 7.712 4.39 -7.034 -3.35 2.016 1.12

LN (Households) 1.923 12.15 1.4747 7.67 1.624 7.17 1.7676 9.11

LN (Mean Household Income) 1.2242 12.56 0.13562 1.38 1.141 10.01 -0.15223 -1.55

LN (Lane Miles Per Capita) 0.99677 38.03 1.38644 14.92 0.61115 12.47 0.1099 4.51

County Dummy

ADAMS - - 0.711 2.65 - - -0.0374 -0.13

ALLEGHENY - - 0.7689 2.39 -0.8664 -2.98 -0.5341 -2.15

ARMSTRONG - - -0.1271 -0.45 0.0448 0.12 0.7952 2.59

BEAVER 0.2142 1.94 0.4869 3 0.2094 1.38 0.8561 6.44

BEDFORD 1.7804 5.98 0.0466 0.14 - - - -

BERKS -0.39204 -9.05 0.19045 3.87 -0.23768 -4.87 0.09174 2.28

BLAIR 0.0931 0.59 0.8314 4.05 0.3036 1.33 0.8652 4.42

BRADFORD - - -0.6043 -1.94 0.0882 0.22 0.0455 0.13

BUCKS -0.68054 -9.71 0.7655 7.08 -0.39489 -4.91 0.25572 3.61

BUTLER 0.3489 2.67 0.2964 1.94 0.0462 0.25 0.4797 2.94

CAMBRIA - - 0.5795 3.67 0.02 0.1 0.4208 2.6

CAMERON - - 0.4636 0.64 - - - -

CARBON 1.1952 4.33 0.9731 2.92 0.42 1.02 0.6012 1.72

CENTRE 0.5782 3.41 0.6299 3.13 -0.0741 -0.3 0.8644 4.06

CHESTER -0.71122 -10.63 0.43159 5.68 -0.41257 -6.11 0.43653 7.07

CLARION 1.8985 5.7 -0.0958 -0.25 - - 0.7069 1.67

CLEARFIELD 1.1929 5.37 -0.0348 -0.14 -0.0353 -0.1 0.5296 1.89

CLINTON 1.766 5.15 0.3434 0.86 0.7469 1.49 1.1698 2.7

COLUMBIA 1.5895 5.99 -0.1397 -0.45 0.8426 2.13 1.3531 4.06

CRAWFORD 0.7824 3.64 -0.3547 -1.42 -0.4214 -1.22 0.5223 1.92

CUMBERLAND 0.188 1.79 0.5382 3.76 0.5708 4.05 0.7179 5.77

DAUPHIN 0.19651 2.65 0.7927 6.38 0.59408 6.22 0.76201 9.13

DELAWARE - - 2.342 6.01 -0.02856 -0.39 0.20421 3.1

ELK - - 0.606 1.44 - - 1.0232 2.26

ERIE -0.18907 -2.97 0.21446 2.7 -0.1843 -1.79 0.38299 5.58

FAYETTE - - 0.1536 1 -0.105 -0.55 0.1675 1.02

FOREST - - 0.3341 0.45 - - - -

FRANKLIN 0.8477 5.16 0.4081 2.11 0.4023 1.61 0.4822 2.33

FULTON 2.5166 5.1 0.2853 0.5 - - - -

GREENE 1.8973 5.63 -0.3017 -0.78 - - -0.4743 -1.05

HUNTINGDON 1.9166 5.91 0.2614 0.71 - - 0.5652 1.39

INDIANA - - 0.1126 0.45 -0.528 -1.62 0.3471 1.25

JEFFERSON 1.6151 5.18 -0.0667 -0.19 - - 0.3907 0.99

JUNIATA - - 0.6423 1.28 - - - -

LACKAWANNA -0.09062 -1.05 0.6799 4.48 0.3911 3.27 0.4736 4.53

LANCASTER -0.54173 -13.04 0.3435 6.62 -0.13904 -2.7 -0.04903 -1.08

LAWRENCE 0.8304 4.05 0.6655 2.67 0.1851 0.62 0.7137 2.79

LEBANON 0.8712 4.84 0.8226 3.69 - - 0.5769 2.56

LEHIGH -0.17257 -3.36 0.4617 4.41 0.29056 4.54 0.55237 9.51

LUZERNE -0.33192 -6.7 0.20679 2.96 0.174 3.87 0.24164 5.71

LYCOMING 0.4242 2.46 -0.0391 -0.2 0.7802 3.2 0.7904 3.72

434 660 421 544

98.8 97.6 99.7 99.6

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates
135

MCKEAN - - 0.3454 0.94 0.237 0.51 0.2724 0.68

MERCER 0.6204 3.63 0.0353 0.18 0.0428 0.17 0.7236 3.41

MIFFLIN - - 0.8116 2.21 0.9534 2.13 1.1923 3.07

MONROE 0.8017 4.64 0.8081 3.95 0.744 2.79 -0.0961 -0.43

MONTGOMERY -1.15526 -12.04 0.9854 5.56 -0.4549 -3.55 0.1547 1.4

MONTOUR 2.2644 4.7 0.6825 1.21 - - 2.3562 3.92

NORTHAMPTON - - 0.7696 6.51 0.34954 3.66 0.38508 4.6

NORTHUMBERLAND 0.6863 3.51 0.2628 1.14 -0.1019 -0.34 0.7063 2.88

PERRY - - 0.6679 1.73 - - 0.2645 0.62

PHILADELPHIA - - - - -0.513 -1.61 -1.1271 -4.05

PIKE 1.5006 4.6 1.0602 2.72 - - - -

POTTER - - -0.3044 -0.57 - - - -

SCHUYLKILL 0.5423 4.15 0.4328 2.82 - - -0.3088 -1.88

SNYDER - - 0.5792 1.33 0.4075 0.74 1.9398 4.15

SOMERSET 1.4223 6.2 -0.1304 -0.49 0.5573 1.6 0.3923 1.35

SULLIVAN -0.1165 -0.17 - - - -

SUSQUEHANNA 1.7133 5.17 -0.4962 -1.3 1.0612 2.16 0.0358 0.08

TIOGA - - -0.11 -0.29 - - - -

UNION 1.9547 5.29 1.1673 2.67 - - 1.7253 3.71

VENANGO 1.2221 4.24 0.046 0.14 - - 1.3479 3.75

WARREN - - 0.0454 0.12 - - 1.2082 2.99

WASHINGTON 0.158 1.6 -0.0021 -0.02 0.4032 2.98 0.4884 4.1

WAYNE 1.3953 4.4 -0.011 -0.03 - - 0.0712 0.18

WESTMORELAND -0.22319 -6.07 0.35775 6.59 -0.06669 -1.19 0.20891 4.92

WYOMING - - 0.6268 1.35 - - -0.5932 -1.15

YORK - - - - - - - -
136

Table C-9. Estimation Results of County-Level OLS Truck Model RETAIL

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant 1.4964 1.67 16.546 16.35 0.739 0.58 12.889 12.31

LN (Retail Sales) 1.19394 29.67 0.43155 8.68 1.08228 20.77 0.37588 8.18

LN (Lane Miles Per Capita) 0.98354 38.96 1.22066 13.61 0.57064 12.21 0.07185 2.9

County Dummy

ADAMS - - -0.546 -5.23 - - -1.99877 -20.98

ALLEGHENY - - 1.5481 5.2 -0.02854 -0.36 1.25531 17.22

ARMSTRONG - - -1.4918 -12.01 -0.6957 -6.42 -1.31795 -13.56

BEAVER 0.00324 0.06 -0.18758 -1.95 0.12641 1.58 0.06558 0.97

BEDFORD 0.0208 0.19 -1.8127 -12.06 - - - -

BERKS -0.4124 -9.81 0.11283 2.35 -0.27659 -6.22 0.0014 0.04

BLAIR -0.96643 -16.09 -0.35317 -4.83 -0.55169 -8.66 -0.58949 -9.77

BRADFORD - - -2.1866 -14.25 -1.0483 -9.96 -2.42342 -25.7

BUCKS -0.43437 -7.01 0.8567 7.83 -0.19894 -3.55 0.61418 11.71

BUTLER -0.25015 -4.76 -0.48434 -7.46 -0.40673 -6.81 -0.66292 -12.24

CAMBRIA - - -0.26812 -4.03 -0.56272 -8.4 -0.70954 -11.48

CAMERON - - -3.2473 -13.64 - - - -

CARBON 0.3247 2.92 -0.6643 -5.64 -0.2034 -1.62 -1.741 -15.43

CENTRE -0.36423 -5.6 -0.48782 -7.06 -0.82649 -12.29 -0.68178 -10.97

CHESTER -0.5612 -12.45 0.28768 4.8 -0.24179 -4.86 0.25182 5.4

CLARION 0.1663 1.45 -2.1414 -14.31 - - -2.3589 -20.99

CLEARFIELD -0.21179 -2.45 -1.4516 -12.71 -1.2303 -11.73 -1.54781 -19.7

CLINTON -0.0796 -0.68 -1.8269 -12.94 -0.6805 -5.03 -1.9682 -16.89

COLUMBIA 0.12068 1.38 -1.8335 -16.68 -0.3245 -3.19 -1.05942 -11.33

CRAWFORD -0.25995 -3.09 -1.5908 -13.01 -1.2944 -11.27 -1.39538 -16.38

CUMBERLAND -0.52361 -10.06 -0.27779 -4.44 0.01298 0.22 -0.23099 -5.48

DAUPHIN -0.08578 -2.25 0.26941 3.19 0.42107 7.15 0.22968 5.26

DELAWARE - - 1.9798 5.06 0.3919 8.11 0.62676 14.29

ELK - - -1.487 -10.29 - - -2.0755 -15.59

ERIE -0.55416 -11.09 -0.17468 -3.34 -0.54069 -8.39 -0.06271 -1.49

FAYETTE - - -0.6476 -8.72 -0.69078 -9.96 -0.97397 -15.21

FOREST - - -3.5511 -12.94 - - - -

FRANKLIN 0.10131 1.45 -0.59559 -8.01 -0.20138 -2.66 -0.96774 -14.64

FULTON 0.6005 3.32 -2.5782 -12.36 - - - -

GREENE 0.2624 2.18 -2.3079 -14.23 - - -3.639 -23.43

HUNTINGDON 0.3799 3.43 -1.6277 -10.59 - - -2.2864 -18.43

INDIANA - - -1.2123 -10.74 -1.47113 -16.53 -1.66098 -20.54

JEFFERSON 0.2878 2.5 -1.8761 -12.85 - - -2.3708 -20.34

JUNIATA - - -1.9004 -10.69 - - - -

LACKAWANNA -0.55309 -11.52 -0.01298 -0.13 0.05345 0.81 -0.23662 -4.87

LANCASTER -0.54721 -14.51 0.40278 7.94 -0.17745 -3.63 0.07804 1.82

LAWRENCE -0.05448 -0.7 -0.62506 -6.91 -0.46774 -4.83 -1.04583 -11.89

LEBANON -0.12401 -1.9 -0.42245 -6.08 - - -1.06957 -17.18

LEHIGH -0.47971 -13.69 0.04324 0.53 0.04862 1.15 0.18298 4.57

LUZERNE -0.48285 -10.1 0.02341 0.36 0.03307 0.8 0.13652 3.29

LYCOMING -0.69665 -12.47 -1.14617 -13.59 -0.08963 -1.18 -0.77754 -11.99

MCKEAN - - -1.5106 -11.14 -0.6957 -5.18 -2.5071 -21.19

434 660 421 544

98.9 97.5 99.7 99.6

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates
137

MERCER -0.59688 -8.23 -1.14228 -15.17 -0.9707 -14.92 -0.88243 -14.3

MIFFLIN - - -1.2221 -10.74 -0.3301 -2.62 -1.6085 -14.81

MONROE -0.22652 -3.42 -0.35228 -5.15 -0.11813 -1.59 -1.73008 -27.36

MONTGOMERY -0.53229 -7.52 1.2289 6.86 0.06839 1.02 0.86094 13.87

MONTOUR 0.4748 3.3 -2.2142 -13.94 - - -1.827 -11.94

NORTHAMPTON - - 0.37296 4.37 0.41965 8.13 -0.05514 -1.12

NORTHUMBERLAND -0.18281 -2.34 -0.92171 -10.07 -0.8003 -8 -0.98064 -11.42

PERRY - - -1.1422 -7.57 - - -2.5956 -19.29

PHILADELPHIA - - - - 0.59127 7.5 0.9821 16.02

PIKE 0.5088 3.8 -0.831 -5.95 - - - -

POTTER - - -2.8779 -12.88 - - - -

SCHUYLKILL 0.00302 0.04 -0.31612 -4.28 - - -1.41157 -21.35

SNYDER - - -1.7783 -14.16 -0.9998 -8.34 -1.444 -13.31

SOMERSET 0.35489 3.87 -1.4377 -11.13 -0.2934 -2.82 -1.64469 -17.93

SULLIVAN -3.5929 -13.37 - - - -

SUSQUEHANNA 0.5555 4.37 -2.2768 -12.99 0.2468 1.49 -2.8607 -18.83

TIOGA - - -2.0544 -11.79 - - - -

UNION 0.2694 2.28 -1.1679 -8.97 - - -1.5748 -12.68

VENANGO 0.08747 0.9 -1.6245 -13.32 - - -1.1465 -10.98

WARREN - - -2.1803 -16.64 - - -1.9345 -23.86

WASHINGTON -0.01467 -0.27 -0.47646 -7.81 0.32924 4.72 -0.23693 -4.19

WAYNE -0.38778 -4.15 -1.9874 -14.08 - - -2.8189 -21.36

WESTMORELAND -0.19209 -5.49 0.35469 6.43 -0.01626 -0.31 0.29936 7.06

WYOMING - - -1.8387 -12.09 - - -4.2776 -27.17

YORK - - - - - - - -
138

Table C-10. Estimation Results of County Group Level OLS Base Model Pop wo 2003 data

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -67.322 -10.56 -14.188 -3.17 -46.378 -5.74 -5.678 -1.25

LN (Per Capita Income) -0.1777 -1.03 -0.0402 -0.35 0.1622 0.8 -0.3934 -3.42

LN (Population) 1.1651 7.51 0.4645 3.65 0.9702 4.29 0.5152 4.1

LN (Lane Miles Per Capita) 0.94768 34.8 0.03929 0.57 0.38197 3.96 -0.00022 -0.01

Year 0.040849 10.14 0.014849 5.48 0.027683 5.71 0.011902 4.48

County Dummy

ADAMS - - 0.1583 0.94 - - -2.0969 -11.12

ALLEGHENY - - -2.5598 -11.52 0.2947 0.98 1.2879 8.1

ARMSTRONG - - -0.1937 -1.02 -1.3331 -3.27 -1.2917 -6.21

BEAVER -0.4654 -3.93 -0.9753 -8.33 -0.1535 -1.05 0.07611 0.85

BEDFORD -0.0261 -0.08 0.1127 0.48 - - - -

BERKS -0.34967 -8.7 0.01354 0.47 -0.3142 -5.74 0.02621 1.1

BLAIR -0.8271 -4.92 -0.5214 -3.59 -0.5026 -2.08 -0.4079 -3.07

BRADFORD - - -0.2034 -0.98 -1.3221 -3.2 -2.3638 -10.3

BUCKS -0.0873 -0.89 -0.38735 -4.6 0.1824 1.5 0.80642 12.16

BUTLER -0.2898 -2.33 0.02278 0.24 -0.4981 -2.83 -0.5875 -5.74

CAMBRIA - - -0.11 -0.98 -0.8584 -3.71 -0.667 -5.94

CAMERON - - -1.0048 -2.08 - - - -

CARBON -0.2782 -0.98 -0.3787 -1.66 -1.0464 -2.34 -1.7905 -7.6

CENTRE -0.4137 -2.57 -0.078 -0.62 -0.9997 -4.1 -0.5856 -4.46

CHESTER -0.01857 -0.19 0.02875 0.43 0.228 1.94 0.60291 9.52

CLARION -0.0232 -0.07 -0.362 -1.43 - - -2.234 -7.93

CLEARFIELD -0.2227 -0.93 -0.0482 -0.27 -1.6401 -3.73 -1.4178 -7.36

CLINTON -0.2615 -0.74 -0.3053 -1.14 -0.9592 -1.89 -1.7918 -6.18

COLUMBIA -0.0139 -0.05 -0.7024 -3.4 -0.6476 -1.52 -0.8777 -3.97

CRAWFORD -0.5847 -2.6 -0.1875 -1.12 -2.0065 -4.62 -1.3691 -7.52

CUMBERLAND -0.03736 -0.38 -0.60075 -6.35 0.4914 3.96 0.08851 1.14

DAUPHIN 0.05095 0.66 -0.54278 -5.96 0.67988 6.9 0.45177 7.8

DELAWARE - - -3.1347 -11.02 0.38918 4 0.72977 12.41

ELK - - -0.2575 -0.93 - - -1.8883 -6.36

ERIE -0.51398 -8.16 -0.3604 -6.94 -0.7669 -4.88 -0.0188 -0.44

FAYETTE - - -0.0801 -0.7 -0.9526 -4.25 -1.0232 -8.55

FOREST - - -0.3804 -0.76 - - - -

FRANKLIN -0.0228 -0.14 -0.0955 -0.74 -0.5406 -1.89 -0.8958 -6.48

FULTON -0.0831 -0.17 -0.4773 -1.26 - - - -

GREENE -0.1961 -0.57 -0.4852 -1.89 - - - -

HUNTINGDON -0.3676 -1.09 -0.0781 -0.32 - - -2.3771 -8.79

INDIANA - - 0.0887 0.53 -1.8162 -5.2 -1.6131 -8.76

JEFFERSON -0.155 -0.48 -0.3259 -1.35 - - -2.3167 -8.66

JUNIATA - - -0.2317 -0.71 - - - -

LACKAWANNA -0.53078 -5.79 -0.9381 -8.42 0.1888 1.61 -0.12177 -1.69

LANCASTER -0.41581 -8.87 0.21677 5.93 -0.08665 -1.54 0.08782 2.54

LAWRENCE -0.4611 -2.14 -0.4566 -2.66 -0.8773 -2.86 -0.984 -5.74

LEBANON -0.0328 -0.18 -0.3011 -2.05 - - -0.9135 -6.23

LEHIGH -0.15625 -3.23 -0.71046 -9.78 0.35018 5.95 0.38744 10.38

LUZERNE -0.48659 -9.6 -0.51718 -9.82 0.03645 0.74 0.17046 5.68

390 594 378 486

99.10% 99.20% 99.80% 99.90%

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates
139

LYCOMING -0.7028 -3.85 -0.1913 -1.43 -0.0502 -0.21 -0.6649 -4.65

MCKEAN - - -0.2946 -1.21 -1.3061 -2.75 -2.4442 -9.17

MERCER -0.4424 -2.45 -0.3646 -2.69 -1.0023 -3.61 -0.6968 -4.89

MIFFLIN - - -0.5154 -2.03 -0.5894 -1.28 -1.4718 -5.59

MONROE -0.2082 -1.25 0.1387 1.08 -0.3408 -1.15 -1.7375 -12.44

MONTGOMERY -0.0512 -0.35 -0.9677 -6.93 0.5327 2.82 1.1593 11.48

MONTOUR 0.2389 0.52 -1.0076 -2.79 - - -1.3883 -3.61

NORTHAMPTON - - -0.41265 -5.3 0.14883 1.8 -0.06801 -1.38

NORTHUMBERLAND -0.5759 -2.68 -0.3829 -2.32 -1.3715 -3.58 -0.9285 -5.38

PERRY - - 0.1136 0.45 - - -2.8102 -9.98

PHILADELPHIA - - - - 0.147 0.5 0.591 3.32

PIKE -0.3639 -1.09 -0.3546 -1.36 - - - -

POTTER - - -0.3172 -0.91 - - - -

SCHUYLKILL -0.3264 -2.23 0.0802 0.72 - - -1.4643 -12.41

SNYDER - - -0.4229 -1.57 -1.234 -2.29 -1.0672 -3.66

SOMERSET -0.0209 -0.09 0.0261 0.15 -0.9 -2.25 -1.6387 -8.32

SULLIVAN - - -0.7708 -1.65 - - - -

SUSQUEHANNA -0.1217 -0.36 -0.3085 -1.22 - - - -

TIOGA - - 0.085 0.33 - - - -

UNION -0.2535 -0.75 -0.4117 -1.56 - - -1.5022 -5.38

VENANGO -0.2131 -0.74 -0.4145 -1.91 - - -0.9508 -4.04

WARREN - - -0.2137 -0.87 - - -1.4514 -5.37

WASHINGTON -0.2056 -2.08 -0.19136 -2.52 0.2001 1.59 -0.15803 -2.01

WAYNE -0.5082 -1.56 -0.2499 -1.04 - - - -

WESTMORELAND -0.25605 -7.79 -0.03601 -1.01 0.08757 1.14 0.33242 13.36

WYOMING - - -0.2811 -0.93 - - - -

YORK - - - - - - - -
140

Table C-11. Estimation Results of County Group Level OLS Base Model HH wo 2003 data

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -57.073 -12.2 -9.788 -3.13 -40.418 -6.79 2.099 0.68

LN (Households) 1.3635 8.4 0.5913 4.44 1.007 4.11 0.6899 5.22

LN (Mean Household Income) -0.3274 -1.97 -0.0837 -0.77 0.1162 0.58 -0.4529 -4.15

LN (Lane Miles Per Capita) 0.94707 35.53 0.05259 0.77 0.37407 3.9 -0.00233 -0.16

Year 0.036 10.94 0.0124 5.59 0.025089 6.29 0.007721 3.7

County Dummy

ADAMS - - 0.3611 1.95 - - -1.8001 -8.85

ALLEGHENY - - -2.7091 -11.92 0.2005 0.6 1.0015 5.74

ARMSTRONG - - -0.0257 -0.13 -1.3089 -3.06 -1.0435 -4.88

BEAVER -0.3783 -3.2 -0.8977 -7.65 -0.1469 -0.97 0.16876 1.86

BEDFORD 0.3142 0.97 0.3272 1.34 - - - -

BERKS -0.30866 -7.72 0.03192 1.07 -0.29576 -5.14 0.05021 2.06

BLAIR -0.6748 -3.96 -0.4058 -2.74 -0.4886 -1.93 -0.2591 -1.9

BRADFORD - - -0.0055 -0.03 -1.264 -2.85 -2.0597 -8.61

BUCKS -0.03031 -0.32 -0.37386 -4.47 0.2352 2.03 0.81869 13.1

BUTLER -0.0924 -0.69 0.1342 1.3 -0.4419 -2.24 -0.4245 -3.83

CAMBRIA - - -0.0195 -0.17 -0.847 -3.53 -0.5503 -4.78

CAMERON - - -0.5491 -1.09 - - - -

CARBON 0.0202 0.07 -0.175 -0.75 -1.0166 -2.16 -1.5093 -6.25

CENTRE -0.1182 -0.66 0.0887 0.63 -0.8899 -3.19 -0.3597 -2.45

CHESTER 0.14 1.38 0.08433 1.22 0.3092 2.55 0.68215 10.5

CLARION 0.407 1.15 -0.1017 -0.38 - - -1.8562 -6.27

CLEARFIELD 0.0217 0.09 0.1099 0.6 -1.6163 -3.53 -1.185 -5.96

CLINTON 0.152 0.41 -0.0436 -0.15 -0.8789 -1.61 -1.4176 -4.68

COLUMBIA 0.3108 1.09 -0.4973 -2.28 -0.585 -1.29 -0.5864 -2.53

CRAWFORD -0.3139 -1.35 -0.0241 -0.14 -1.9636 -4.29 -1.1241 -5.88

CUMBERLAND 0.0962 0.94 -0.51554 -5.29 0.5348 3.96 0.18816 2.3

DAUPHIN 0.06801 0.95 -0.50875 -5.89 0.66538 6.93 0.47468 8.9

DELAWARE - - -3.0909 -10.98 0.41925 4.5 0.71288 12.78

ELK - - 0.009 0.03 - - -1.5028 -4.87

ERIE -0.42996 -6.57 -0.309 -5.56 -0.7426 -4.51 0.0478 1.03

FAYETTE - - -0.0018 -0.02 -0.9601 -4.2 -0.9124 -7.62

FOREST - - 0.0928 0.18 - - - -

FRANKLIN 0.1679 0.97 0.0267 0.2 -0.5157 -1.71 -0.7219 -5.04

FULTON 0.5205 1 -0.1041 -0.26 - - - -

GREENE 0.2636 0.72 -0.211 -0.77 - - - -

HUNTINGDON 0.0768 0.22 0.1896 0.71 - - -1.9934 -6.91

INDIANA - - 0.2659 1.49 -1.7429 -4.59 -1.3537 -6.92

JEFFERSON 0.1967 0.59 -0.1019 -0.4 - - -1.9885 -7.21

JUNIATA - - 0.1149 0.33 - - - -

LACKAWANNA -0.48404 -5.35 -0.8812 -8.03 0.1871 1.57 -0.06855 -0.96

LANCASTER -0.35541 -8.46 0.23337 6.94 -0.04189 -0.79 0.11689 3.88

LAWRENCE -0.2254 -1.02 -0.2971 -1.66 -0.8366 -2.56 -0.7629 -4.27

LEBANON 0.1979 1.06 -0.1543 -1 - - -0.7132 -4.62

LEHIGH -0.11542 -2.41 -0.67639 -9.47 0.35947 5.98 0.41594 11.34

LUZERNE -0.54666 -10.75 -0.52631 -10.34 0.00128 0.03 0.13556 4.67

390 594 378 486

99.20% 99.20% 99.80% 99.90%

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates
141

LYCOMING -0.5136 -2.75 -0.0683 -0.49 -0.0131 -0.05 -0.4878 -3.28

MCKEAN - - -0.0541 -0.21 -1.2333 -2.42 -2.1039 -7.58

MERCER -0.2456 -1.32 -0.2366 -1.66 -0.966 -3.27 -0.5167 -3.47

MIFFLIN - - -0.2817 -1.07 -0.538 -1.1 -1.1406 -4.19

MONROE 0.1061 0.58 0.3112 2.17 -0.2475 -0.75 -1.4809 -9.56

MONTGOMERY -0.0867 -0.62 -0.9952 -7.25 0.5466 2.91 1.08458 10.96

MONTOUR 0.8713 1.79 -0.6266 -1.64 - - -0.8528 -2.11

NORTHAMPTON - - -0.34355 -4.22 0.18795 1.97 0.01697 0.31

NORTHUMBERLAND -0.4186 -1.95 -0.2588 -1.55 -1.3815 -3.54 -0.7623 -4.4

PERRY - - 0.3821 1.43 - - -2.4131 -8.14

PHILADELPHIA - - - - 0.0871 0.27 0.3355 1.79

PIKE 0.071 0.2 -0.0839 -0.3 - - - -

POTTER - - 0.0356 0.1 - - - -

SCHUYLKILL -0.2303 -1.57 0.1593 1.42 - - -1.362 -11.52

SNYDER - - -0.098 -0.33 -1.0815 -1.8 -0.5954 -1.88

SOMERSET 0.2476 0.99 0.1933 1.02 -0.8591 -2.03 -1.3904 -6.79

SULLIVAN - - -0.325 -0.66 - - - -

SUSQUEHANNA 0.2919 0.83 -0.0611 -0.23 - - - -

TIOGA - - 0.338 1.25 - - - -

UNION 0.4679 1.21 -0.0185 -0.06 - - -0.9592 -2.99

VENANGO 0.1326 0.45 -0.2012 -0.88 - - -0.6421 -2.62

WARREN - - 0.0139 0.05 - - -1.1197 -4.02

WASHINGTON -0.12322 -1.24 -0.13396 -1.75 0.2079 1.59 -0.07906 -1

WAYNE -0.1028 -0.3 -0.0039 -0.02 - - - -

WESTMORELAND -0.31988 -9.37 -0.05638 -1.6 0.06052 0.74 0.29154 11.15

WYOMING - - 0.0394 0.12 - - - -

YORK - - - - - - - -
142

Table C-12. Estimation Results of County Group Level OLS Base Model HH JrSr wo 2003 data

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -71.66 -10.07 -3.282 -0.71 -33.103 -4.14 -7.333 -1.69

LN (Households) 1.2526 7.28 0.4871 3.57 1.1867 4.65 0.3623 2.6

Pop17 (% of Pop<17 yrs) 2.674 2.59 -0.3548 -0.58 -1.798 -1.66 2.5967 4.1

Pop65+ (% of Pop>65+ yrs) -0.6 -0.4 4.5037 4.54 -4.126 -2.24 5.0622 5.13

LN (Mean Household Income) -0.4389 -2.59 0.0284 0.26 0.1402 0.68 -0.441 -3.96

LN (Lane Miles Per Capita) 0.94019 35.41 0.05787 0.86 0.34394 3.55 -0.0201 -1.34

Year 0.04426 9.88 0.008897 3.05 0.020603 4.03 0.013622 4.99

County Dummy

ADAMS - - 0.1916 0.99 - - -2.3464 -10.81

ALLEGHENY - - -2.7852 -12.16 0.1137 0.32 1.2749 7.14

ARMSTRONG - - -0.3918 -1.8 -0.8713 -1.9 -1.7743 -7.33

BEAVER -0.3974 -2.6 -1.1795 -8.9 0.1738 0.87 -0.2499 -2.16

BEDFORD 0.1143 0.32 0.0072 0.03 - - - -

BERKS -0.28895 -5.88 -0.05887 -1.68 -0.22824 -3.55 -0.05466 -1.73

BLAIR -0.7427 -3.74 -0.6777 -4.19 -0.1545 -0.54 -0.7644 -4.84

BRADFORD - - -0.2593 -1.1 -0.8348 -1.75 -2.7981 -10.64

BUCKS 0.00519 0.05 -0.30959 -3.63 0.1318 1.07 0.97923 14.79

BUTLER -0.1725 -1.19 0.011 0.1 -0.2574 -1.23 -0.7365 -6.16

CAMBRIA - - -0.3803 -2.7 -0.4924 -1.77 -1.0773 -7.33

CAMERON - - -1.2546 -2.33 - - - -

CARBON -0.0956 -0.29 -0.5909 -2.32 -0.5272 -1.04 -2.3208 -8.51

CENTRE -0.0922 -0.52 0.108 0.78 -0.945 -3.39 -0.3941 -2.77

CHESTER 0.159 1.54 0.11101 1.6 0.2483 2.01 0.75124 11.73

CLARION 0.2191 0.58 -0.3837 -1.37 - - -2.6152 -8.34

CLEARFIELD -0.1051 -0.39 -0.1864 -0.93 -1.2659 -2.65 -1.8273 -8.26

CLINTON -0.0428 -0.11 -0.4197 -1.41 -0.3466 -0.6 -2.2963 -6.99

COLUMBIA 0.2001 0.66 -0.7893 -3.46 -0.2286 -0.48 -1.2107 -4.87

CRAWFORD -0.4883 -1.88 -0.2498 -1.31 -1.657 -3.49 -1.7218 -8.17

CUMBERLAND 0.1282 1.18 -0.65266 -6.55 0.6699 4.54 0.00288 0.03

DAUPHIN 0.04659 0.61 -0.59057 -6.8 0.7833 7.32 0.32895 5.71

DELAWARE - - -3.1686 -11.44 0.439 4.26 0.71113 12.32

ELK - - -0.3945 -1.28 - - -2.451 -7.23

ERIE -0.48465 -6.43 -0.37484 -6.25 -0.6591 -3.89 -0.12917 -2.38

FAYETTE - - -0.2932 -2.17 -0.6228 -2.36 -1.4153 -9.74

FOREST - - -0.6433 -1.15 - - - -

FRANKLIN 0.0777 0.4 -0.1832 -1.26 -0.2578 -0.81 -1.1831 -7.42

FULTON 0.1528 0.27 -0.4673 -1.13 - - - -

GREENE 0.0456 0.12 -0.5163 -1.79 - - - -

HUNTINGDON -0.1417 -0.38 -0.0594 -0.22 - - -2.7196 -8.93

INDIANA - - 0.0589 0.32 -1.4739 -3.75 -1.858 -8.95

JEFFERSON -0.0073 -0.02 -0.4979 -1.81 - - -2.8928 -9.4

JUNIATA - - -0.236 -0.64 - - - -

LACKAWANNA -0.4403 -3.14 -1.2112 -9.36 0.5197 2.77 -0.4829 -4.59

LANCASTER -0.38636 -8.79 0.22754 6.67 -0.02164 -0.4 0.08061 2.68

LAWRENCE -0.3257 -1.22 -0.6921 -3.4 -0.3572 -0.95 -1.4753 -6.94

LEBANON 0.1069 0.51 -0.3984 -2.4 - - -1.2225 -7.07

390 594 378 486

99.20% 99.30% 99.80% 99.90%

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates
143

LEHIGH -0.08677 -1.34 -0.8197 -10.76 0.48768 5.78 0.25015 5.08

LUZERNE -0.4375 -4.01 -0.82383 -10.39 0.2328 1.85 -0.13494 -2

LYCOMING -0.6402 -3.06 -0.2766 -1.82 0.3024 1.06 -0.9682 -5.86

MCKEAN - - -0.4065 -1.48 -0.7321 -1.34 -2.9504 -9.69

MERCER -0.3227 -1.47 -0.5491 -3.39 -0.5969 -1.82 -1.0897 -6.23

MIFFLIN - - -0.6176 -2.2 -0.0113 -0.02 -1.9931 -6.64

MONROE -0.0743 -0.38 0.2545 1.71 -0.0811 -0.24 -1.8664 -11.4

MONTGOMERY 0.0595 0.4 -1.06 -7.6 0.4833 2.38 1.2311 11.92

MONTOUR 0.5901 1.12 -1.1181 -2.78 - - -2.0211 -4.6

NORTHAMPTON - - -0.48913 -5.68 0.3332 2.97 -0.1902 -2.9

NORTHUMBERLAND -0.4887 -1.92 -0.6394 -3.37 -0.9801 -2.34 -1.4226 -6.98

PERRY - - 0.2291 0.83 - - -3.1246 -10.06

PHILADELPHIA - - - - -0.1274 -0.38 0.714 3.72

PIKE -0.2211 -0.58 -0.3503 -1.2 - - - -

POTTER - - -0.4017 -1.02 - - - -

SCHUYLKILL -0.2091 -1.09 -0.2218 -1.59 - - -1.9032 -12.5

SNYDER - - -0.3663 -1.2 -0.6535 -1.05 -1.3945 -4.17

SOMERSET 0.1317 0.46 -0.1608 -0.77 -0.4415 -0.98 -2.0951 -9.02

SULLIVAN - - -1.0856 -2.06 - - - -

SUSQUEHANNA 0.0166 0.04 -0.3597 -1.27 - - - -

TIOGA - - 0.0274 0.1 - - - -

UNION 0.3071 0.77 -0.2624 -0.85 - - -1.6396 -4.96

VENANGO -0.0591 -0.18 -0.5284 -2.16 - - -1.4086 -5.21

WARREN - - -0.3359 -1.22 - - -1.9684 -6.44

WASHINGTON -0.0895 -0.68 -0.41314 -4.26 0.482 2.74 -0.4355 -4.25

WAYNE -0.3183 -0.84 -0.3809 -1.38 - - - -

WESTMORELAND -0.22539 -2.89 -0.27376 -4.86 0.2334 1.84 0.13148 2.72

WYOMING - - -0.2079 -0.62 - - - -

YORK - - - - - - - -
144

Table C-13. Estimation Results of County Group Level OLS Base Model HH wo 2003 data

Dependent Var=LN(VMT)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -8.481 -3.65 5.416 2.39 0.096 0.03 13.544 11.86

LN (Households) 1.7306 6.53 0.9829 2.85 0.9961 2.41 0.5246 3.45

LN (Mean Household Income) 1.4781 8.84 0.503 4.51 1.1029 4.77 0.01153 0.14

LN (Lane Miles Per Capita) 1.01902 22.04 0.3205 1.03 0.5246 2.6 0.02811 0.98

County Group Dummy

ALTGR -0.7897 -2.48 0.0966 0.33 -0.482 -0.58 0.7485 3.95

CNTRGR -0.691 -2.84 0.055 0.28 -0.2953 -0.34 0.8136 5.53

EPAGR -1.5561 -3.26 -0.4572 -1.52 0.0875 0.18 1.6103 5.6

HARRGR -1.6961 -2.76 -0.2265 -0.46 0.1676 0.64 1.7624 4.81

I81GR -1.305 -2.98 -0.3829 -1.24 0.0434 0.08 1.2709 4.82

NCNTGR - - 0.22787 2.28 -1.635 -1.17 -0.37804 -23.67

NEPAGR -0.30627 -3.23 0.24566 2.85 -0.187 -0.14 -0.878 -19.47

NTIERGR - - 0.2161 2.37 -1.212 -0.84 -1.51292 -23.95

PHILY -2.3131 -2.87 -1.3242 -2.65 0.13327 1.37 2.4131 5.04

SEDA-COG -0.5169 -2.52 0.0476 0.3 -0.708 -0.69 0.7359 5.9

SHVGR -1.0282 -3.31 -0.3475 -1.64 -0.6132 -0.68 1.1193 5.9

SWPAC -2.3276 -3.11 -0.7299 -1.2 - - 2.2381 4.95

WPAGR - - - - - - - -

99 117 108 117

98.90% 99.50% 99.90% 100%

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates
145

Table C-14. Estimation Results of County-Level OLS Base Model HH for VMT per LM

Dependent Var=LN(VMTPLM)

Adjusted R

2

N

Coefficient T-stat Coefficient T-stat Coefficient T-stat Coefficient T-stat

Constant -61.879 -15.1 -0.286 -0.06 -30.277 -4.73 15.687 1.78

LN (Households) 0.3889 2.72 0.1525 0.8 0.9037 3.79 1.9625 5.26

LN (Mean Household Income) -0.2795 -1.77 0.0988 0.54 0.2644 1.13 -0.5474 -1.61

Year 0.037728 12.8 0.004824 1.39 0.015866 3.64 -0.009898 -1.63

County Dummy

ADAMS - - 0.4718 1.64 - - 2.8278 5.06

ALLEGHENY - - -0.0828 -0.33 -1.1557 -3.72 -2.3994 -4.94

ARMSTRONG - - -0.1631 -0.52 0.6529 1.7 3.0778 5.12

BEAVER -0.2988 -2.85 0.0741 0.52 0.0721 0.42 1.0667 4.01

BEDFORD 0.2555 0.88 0.0284 0.07 - - - -

BERKS -0.36 -10.95 0.11875 2.42 -0.04731 -1.02 0.21523 2.78

BLAIR -0.6914 -4.49 0.438 2.14 0.4779 1.91 1.8293 4.69

BRADFORD - - -0.537 -1.55 0.4629 1.09 2.9603 4.45

BUCKS -0.00685 -0.08 0.3796 3.65 -0.3047 -2.54 -0.4054 -2.22

BUTLER -0.137 -1.14 0.1749 1.07 0.19 0.95 1.5961 5.1

CAMBRIA - - 0.3797 2.16 0.2972 1.41 1.3458 4.08

CAMERON - - -0.0247 -0.03 - - - -

CARBON 0.0171 0.07 0.5583 1.59 1.0272 2.37 3.2438 4.79

CENTRE -0.2222 -1.39 0.341 1.59 0.1636 0.62 2.2978 5.56

CHESTER 0.11544 1.21 0.2441 2.14 -0.1245 -0.89 0.3454 1.68

CLARION 0.3483 1.09 -0.2462 -0.58 - - 4.4347 5.4

CLEARFIELD -0.0113 -0.05 -0.0628 -0.21 0.8041 2.25 2.5952 4.65

CLINTON 0.1085 0.33 0.102 0.23 1.0536 1.93 4.2367 4.98

COLUMBIA 0.3028 1.18 -0.3505 -1.03 1.3551 3.22 3.3366 5.08

CRAWFORD -0.3452 -1.64 -0.352 -1.25 0.4859 1.41 2.535 4.72

CUMBERLAND 0.03511 0.39 0.1954 1.59 0.5061 3.36 1.1462 4.83

DAUPHIN 0.17015 2.74 0.45216 5.28 0.4988 4.96 0.8893 5.56

DELAWARE - - 0.7214 7.79 0.1768 1.68 -0.335 -2.07

ELK - - 0.3185 0.71 - - 3.7367 4.27

ERIE -0.5069 -9.05 0.02394 0.31 0.23854 2.74 0.5815 4.2

FAYETTE - - 0.0911 0.5 0.0016 0.01 1.2966 3.81

FOREST - - 0.0336 0.04 - - - -

FRANKLIN 0.1365 0.88 0.2482 1.19 0.9305 3.64 2.1369 5.33

FULTON 0.4331 0.93 -0.0131 -0.02 - - - -

GREENE 0.1612 0.49 -0.4529 -1.04 - - 4.9785 5.78

HUNTINGDON 0.1159 0.37 0.0585 0.14 - - 3.9684 4.95

INDIANA - - 0.0446 0.16 -0.1185 -0.34 2.7944 5.14

JEFFERSON 0.1901 0.64 -0.1747 -0.44 - - 3.6407 4.73

JUNIATA - - 0.2896 0.53 - - - -

LACKAWANNA -0.46861 -5.7 0.231 2.06 0.1794 1.36 0.8112 3.89

LANCASTER -0.40586 -10.32 0.26222 4.63 -0.02244 -0.39 -0.17182 -1.85

LAWRENCE -0.2082 -1.04 0.3183 1.2 0.3613 1.11 2.0382 4

LEBANON 0.1723 1.03 0.4628 2.05 - - 2.1152 4.86

LEHIGH -0.10535 -2.33 0.1167 1.84 0.33396 4.77 0.7128 6.35

LUZERNE -0.54426 -13.59 0.03919 0.69 0.1212 2.12 0.123 1.35

LYCOMING -0.4644 -2.79 -0.1105 -0.5 0.7694 2.83 1.9104 4.5

434 660 421 544

94.10% 93.10% 96.60% 67.90%

Rural Interstates Rural Non-Interstates Urban Interstates Urban Non-Interstates
146

MCKEAN - - 0.1104 0.27 0.7032 1.41 3.451 4.44

MERCER -0.294 -1.76 -0.1036 -0.46 0.3328 1.22 1.8281 4.3

MIFFLIN - - 0.3956 0.99 1.194 2.43 3.8741 5.05

MONROE 0.0001 0 0.5258 2.41 1.3041 4.86 2.1665 5.15

MONTGOMERY -0.0315 -0.25 0.4014 2.53 -0.2838 -1.51 -0.7943 -2.79

MONTOUR 0.8311 1.9 0.1022 0.17 - - 6.143 5.36

NORTHAMPTON - - 0.38999 4.5 0.4187 4.14 0.627 3.9

NORTHUMBERLAND -0.3524 -1.82 0.0929 0.36 0.5626 1.78 2.2414 4.55

PERRY - - 0.4291 1.02 - - 4.5585 5.61

PHILADELPHIA - - - - -0.6798 -2 -2.4855 -4.66

PIKE -0.0154 -0.05 0.581 1.4 - - - -

POTTER - - -0.3966 -0.68 - - - -

SCHUYLKILL -0.2317 -1.75 0.3442 1.95 - - 1.1522 3.47

SNYDER - - 0.1664 0.36 1.1961 2.1 4.9216 5.5

SOMERSET 0.2206 0.98 -0.1194 -0.4 1.1399 3.1 2.6248 4.57

SULLIVAN - - -0.3902 -0.51 - - - -

SUSQUEHANNA 0.2481 0.79 -0.5032 -1.2 1.5381 2.92 4.3319 5.24

TIOGA - - -0.1214 -0.28 - - - -

UNION 0.2328 0.67 0.4657 1.01 - - 4.8746 5.42

VENANGO 0.1399 0.52 -0.1252 -0.35 - - 3.5264 5.07

WARREN - - -0.058 -0.14 - - 4.037 5.14

WASHINGTON -0.13801 -1.54 -0.0456 -0.37 0.332 2.26 0.8157 3.52

WAYNE -0.0893 -0.3 -0.1495 -0.37 - - 4.2094 5.44

WESTMORELAND -0.26705 -8.13 0.2812 5.75 -0.35681 -7.72 -0.30446 -3.96

WYOMING - - 0.2718 0.54 - - 4.9149 4.98

YORK - - - - - - - -
147

Appendix D. VMT Growth Rate Forecasts
148

Table D-1. Forecasted Growth Rates Based on County-Level OLS Base Model HH

HH Model

COUNTY CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D

ADAMS 0 0.52 0 2.66 0 0.26 0 2.19 0 -0.1 0 1.51

ALLEGHENY 0 0.39 2.6 0.05 0 0.16 2.46 -0.34 0 -0.19 2.22 -0.94

ARMSTRONG 0 0.42 2.8 0.83 0 0.19 2.67 0.41 0 -0.1 2.47 -0.07

BEAVER 3.45 0.41 2.82 0.76 3.13 0.18 2.68 0.39 2.69 -0.16 2.45 -0.18

BEDFORD 3.44 0.59 0 0 3.17 0.3 0 0 2.85 -0.04 0 0

BERKS 3.72 0.4 3.17 1.87 3.38 0.16 3 1.42 2.93 -0.19 2.73 0.8

BLAIR 3.22 0.22 2.45 0.1 2.89 -0.04 2.3 -0.28 2.39 -0.46 2 -0.91

BRADFORD 0 0.59 2.99 1.15 0 0.35 2.84 0.82 0 0.04 2.64 0.37

BUCKS 4.23 0.72 3.64 3.02 3.89 0.46 3.42 2.51 3.49 0.14 3.16 1.93

BUTLER 3.99 0.57 3.56 2.71 3.68 0.31 3.34 2.27 3.25 -0.06 3.03 1.67

CAMBRIA 0 0.15 2.16 -0.52 0 -0.12 1.98 -0.96 0 -0.71 1.53 -1.85

CAMERON 0 0.16 0 0 0 -0.08 0 0 0 -0.46 0 0

CARBON 3.56 0.45 3.07 1.32 3.21 0.22 2.92 0.85 2.82 -0.07 2.72 0.31

CENTRE 3.88 0.62 3.44 2.19 3.57 0.39 3.25 1.74 3 -0.07 2.88 0.94

CHESTER 4.24 0.74 3.84 3.33 3.87 0.47 3.59 2.71 3.41 0.12 3.26 2

CLARION 3.71 0.66 0 1.15 3.41 0.39 0 0.84 3 0.04 0 0.35

CLEARFIELD 3.53 0.51 3.07 1.12 3.24 0.27 2.91 0.79 2.8 -0.12 2.63 0.24

CLINTON 3.51 0.3 2.7 0.92 3.12 0.13 2.62 0.36 2.64 -0.23 2.37 -0.28

COLUMBIA 3.72 0.42 3.06 1.66 3.39 0.21 2.92 1.22 2.9 -0.16 2.65 0.57

CRAWFORD 3.32 0.32 2.72 0.54 3.04 0.06 2.56 0.24 2.56 -0.36 2.27 -0.32

CUMBERLAND 4.33 0.6 3.8 3.73 3.93 0.36 3.56 3 3.34 -0.04 3.18 2.03

DAUPHIN 3.52 0.39 3.03 1.28 3.21 0.14 2.85 0.88 2.74 -0.23 2.58 0.25

DELAWARE 0 0.46 2.9 1.1 0 0.24 2.76 0.72 0 -0.12 2.51 0.11

ELK 0 0.58 0 0.24 0 0.3 0 -0.04 0 -0.02 0 -0.42

ERIE 3.39 0.35 2.79 0.72 3.09 0.11 2.64 0.38 2.59 -0.32 2.34 -0.25

FAYETTE 0 0.43 2.79 0.68 0 0.18 2.64 0.28 0 -0.21 2.37 -0.34

FOREST 0 0.38 0 0 0 0.22 0 0 0 -0.12 0 0

FRANKLIN 4.14 0.72 3.37 2.4 3.82 0.46 3.2 2.01 3.41 0.13 2.97 1.47

FULTON 3.94 0.72 0 0 3.64 0.4 0 0 3.3 0.06 0 0

GREENE 3.47 0.46 0 0.78 3.19 0.2 0 0.5 2.63 -0.28 0 -0.2

HUNTINGDON 3.4 0.46 0 0.77 3.16 0.21 0 0.55 2.55 -0.35 0 -0.21

INDIANA 0 0.57 2.99 0.97 0 0.34 2.86 0.64 0 -0.03 2.61 0.08

JEFFERSON 3.42 0.4 0 0.57 3.11 0.15 0 0.23 2.72 -0.17 0 -0.24

JUNIATA 0 0.39 0 0 0 0.15 0 0 0 -0.15 0 0

LACKAWANNA 3.31 0.31 2.54 0.23 3 0.08 2.4 -0.12 2.57 -0.27 2.16 -0.66

LANCASTER 4.1 0.63 3.51 2.71 3.75 0.39 3.31 2.2 3.31 0.06 3.05 1.56

LAWRENCE 3.51 0.37 2.76 0.85 3.17 0.17 2.64 0.41 2.72 -0.13 2.45 -0.19

LEBANON 3.75 0.44 0 1.74 3.44 0.2 0 1.34 2.97 -0.15 0 0.7

LEHIGH 3.68 0.4 3.17 1.81 3.33 0.17 3.01 1.31 2.86 -0.17 2.74 0.64

LUZERNE 3.3 0.28 2.56 0.31 2.99 0.04 2.42 -0.05 2.52 -0.34 2.16 -0.63

LYCOMING 3.4 0.4 2.78 0.62 3.1 0.14 2.63 0.3 2.67 -0.22 2.38 -0.23

MCKEAN 0 0.47 2.82 0.61 0 0.21 2.66 0.37 0 -0.22 2.35 -0.18

MERCER 3.44 0.35 2.69 0.67 3.14 0.12 2.56 0.31 2.65 -0.27 2.28 -0.31

MIFFLIN 0 0.32 2.69 0.51 0 0.08 2.54 0.17 0 -0.24 2.34 -0.3

MONROE 4.44 0.58 4.1 4.46 3.97 0.3 3.74 3.51 3.48 -0.03 3.36 2.59

MONTGOMERY 3.79 0.49 3.19 1.87 3.48 0.25 3.01 1.46 3.05 -0.09 2.76 0.9

MONTOUR 3.52 0.68 0 0.98 3.26 0.38 0 0.73 2.83 0 0 0.23

NORTHAMPTON 0 0.39 3.26 2.36 0 0.17 3.08 1.83 0 -0.17 2.82 1.12

NORTHUMBERLAND 3.29 0.34 2.58 0.2 2.99 0.1 2.44 -0.14 2.55 -0.24 2.21 -0.69

PERRY 0 0.47 0 3.16 0 0.21 0 2.56 0 -0.09 0 1.91

PHILADELPHIA 0 0 2.45 -0.08 0 0 2.31 -0.54 0 0 2.04 -1.22

PIKE 4.67 0.59 0 0 4.07 0.33 0 0 3.5 0.04 0 0

POTTER 0 0.55 0 0 0 0.27 0 0 0 -0.02 0 0

SCHUYLKILL 3.57 0.51 0 0.66 3.26 0.28 0 0.28 2.76 -0.12 0 -0.35

SNYDER 0 0.45 3.1 1.54 0 0.21 2.94 1.22 0 -0.23 2.62 0.66

SOMERSET 3.46 0.43 2.79 0.69 3.18 0.18 2.64 0.4 2.72 -0.24 2.34 -0.15

SULLIVAN 0 0.4 0 0 0 0.19 0 0 0 -0.21 0 0

SUSQUEHANNA 3.84 0.58 3.22 1.84 3.53 0.31 3.04 1.48 3.18 0 2.83 1.07

TIOGA 0 0.67 0 0 0 0.38 0 0 0 0 0 0

UNION 4.21 0.7 0 3.03 3.84 0.45 0 2.47 2.97 -0.26 0 1.2

VENANGO 3.37 0.45 0 0.29 3.09 0.2 0 0.01 2.69 -0.15 0 -0.47

WARREN 0 0.47 0 0.54 0 0.22 0 0.2 0 -0.09 0 -0.24

WASHINGTON 3.49 0.48 2.9 0.83 3.18 0.26 2.76 0.45 2.77 -0.05 2.56 -0.06

WAYNE 4.04 0.5 0 2.92 3.66 0.27 0 2.3 3.17 -0.08 0 1.54

WESTMORELAND 3.43 0.47 2.85 0.67 3.14 0.23 2.71 0.32 2.72 -0.1 2.48 -0.18

WYOMING 0 0.34 0 1.13 0 0.11 0 0.65 0 -0.18 0 0.1

YORK 3.78 0.43 3.23 2.04 3.44 0.18 3.05 1.56 2.98 -0.17 2.78 0.93

Rates between 2003 - 2010 Rates between 2010 - 2020 Rates between 2020 - 2030
149

Table D-2. Forecasted Growth Rates Based on County-Level OLS Base Model POP

POP MODEL

COUNTY CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D

ADAMS 0 0.4 0 3.01 0 0.38 0 3.05 0 0.38 0 3.06

ALLEGHENY 0 0.69 2.77 0.56 0 0.71 2.85 0.66 0 0.73 2.93 0.76

ARMSTRONG 0 0.56 2.9 1.22 0 0.58 2.99 1.33 0 0.59 3.07 1.45

BEAVER 3.7 0.6 2.97 1.22 3.72 0.61 3.04 1.36 3.73 0.62 3.13 1.48

BEDFORD 3.67 0.76 0 0 3.71 0.69 0 0 3.75 0.61 0 0

BERKS 3.86 0.45 3.32 2.37 3.87 0.46 3.37 2.45 3.88 0.46 3.41 2.52

BLAIR 3.64 0.57 2.69 0.76 3.66 0.57 2.77 0.91 3.68 0.57 2.84 1.07

BRADFORD 0 0.59 2.98 1.28 0 0.58 3.04 1.45 0 0.56 3.11 1.62

BUCKS 3.94 0.41 3.55 2.96 3.95 0.4 3.55 2.99 3.95 0.39 3.55 3.02

BUTLER 3.95 0.44 3.62 3.02 3.96 0.41 3.61 3.09 3.97 0.38 3.6 3.17

CAMBRIA 0 0.61 2.42 0.13 0 0.61 2.48 0.24 0 0.61 2.55 0.35

CAMERON 0 0.7 0 0 0 0.69 0 0 0 0.66 0 0

CARBON 3.78 0.57 3.22 1.82 3.79 0.6 3.3 1.87 3.79 0.62 3.36 1.94

CENTRE 3.87 0.51 3.46 2.47 3.88 0.51 3.48 2.51 3.88 0.51 3.5 2.55

CHESTER 3.99 0.43 3.79 3.38 3.98 0.44 3.77 3.3 3.98 0.44 3.73 3.25

CLARION 3.7 0.62 0 1.23 3.73 0.58 0 1.46 3.76 0.56 0 1.64

CLEARFIELD 3.74 0.66 3.21 1.53 3.76 0.63 3.25 1.69 3.78 0.61 3.3 1.85

CLINTON 3.74 0.45 2.85 1.43 3.73 0.56 2.99 1.4 3.73 0.59 3.07 1.46

COLUMBIA 3.83 0.44 3.16 2.09 3.83 0.47 3.23 2.15 3.84 0.49 3.3 2.23

CRAWFORD 3.69 0.61 2.95 1.15 3.72 0.57 3 1.37 3.75 0.55 3.06 1.57

CUMBERLAND 4.07 0.26 3.79 3.9 4.04 0.31 3.77 3.7 4.02 0.37 3.76 3.51

DAUPHIN 3.78 0.56 3.22 1.84 3.8 0.55 3.26 1.97 3.81 0.56 3.32 2.05

DELAWARE 0 0.55 2.98 1.41 0 0.55 3.05 1.53 0 0.57 3.13 1.64

ELK 0 0.74 0 0.43 0 0.69 0 0.7 0 0.65 0 0.9

ERIE 3.71 0.59 2.99 1.28 3.73 0.59 3.05 1.44 3.75 0.58 3.12 1.61

FAYETTE 0 0.62 2.92 1.07 0 0.63 3 1.2 0 0.64 3.08 1.34

FOREST 0 0.64 0 0 0 0.64 0 0 0 0.6 0 0

FRANKLIN 3.84 0.44 3.23 2.23 3.87 0.42 3.28 2.39 3.88 0.43 3.34 2.5

FULTON 3.85 0.58 0 0 3.88 0.51 0 0 3.92 0.43 0 0

GREENE 3.69 0.64 0 1.16 3.72 0.6 0 1.4 3.75 0.59 0 1.56

HUNTINGDON 3.7 0.7 0 1.25 3.74 0.63 0 1.52 3.76 0.61 0 1.7

INDIANA 0 0.64 3.04 1.26 0 0.63 3.1 1.41 0 0.63 3.18 1.57

JEFFERSON 3.67 0.6 0 1.01 3.7 0.58 0 1.19 3.72 0.58 0 1.36

JUNIATA 0 0.32 0 0 0 0.36 0 0 0 0.39 0 0

LACKAWANNA 3.64 0.58 2.7 0.77 3.66 0.57 2.78 0.92 3.68 0.58 2.85 1.07

LANCASTER 3.92 0.42 3.48 2.81 3.92 0.42 3.5 2.83 3.93 0.43 3.53 2.85

LAWRENCE 3.72 0.52 2.88 1.3 3.72 0.56 2.98 1.35 3.72 0.61 3.08 1.41

LEBANON 3.83 0.45 0 2.11 3.84 0.45 0 2.22 3.85 0.47 0 2.27

LEHIGH 3.86 0.47 3.34 2.35 3.86 0.51 3.4 2.36 3.86 0.53 3.45 2.39

LUZERNE 3.66 0.57 2.76 0.91 3.68 0.57 2.83 1.06 3.7 0.57 2.91 1.21

LYCOMING 3.68 0.61 2.93 1.12 3.72 0.58 3 1.34 3.74 0.58 3.07 1.49

MCKEAN 0 0.66 2.94 0.98 0 0.6 2.98 1.24 0 0.56 3.03 1.45

MERCER 3.69 0.54 2.84 1.15 3.71 0.55 2.91 1.28 3.73 0.55 2.98 1.41

MIFFLIN 0 0.59 2.89 1.11 0 0.57 2.96 1.29 0 0.57 3.03 1.44

MONROE 4.19 0.21 4.15 4.79 4.14 0.25 4.02 4.41 4.1 0.27 3.92 4.12

MONTGOMERY 3.84 0.47 3.26 2.2 3.85 0.46 3.3 2.31 3.87 0.46 3.35 2.41

MONTOUR 3.7 0.77 0 1.29 3.74 0.69 0 1.57 3.77 0.65 0 1.77

NORTHAMPTON 0 0.34 3.37 2.83 0 0.37 3.42 2.81 0 0.41 3.47 2.8

NORTHUMBERLAND 3.64 0.62 2.75 0.74 3.66 0.61 2.82 0.91 3.67 0.62 2.9 1.03

PERRY 0 0.27 0 3.49 0 0.28 0 3.43 0 0.29 0 3.39

PHILADELPHIA 0 0 2.56 0.28 0 0 2.64 0.34 0 0 2.72 0.4

PIKE 4.34 0.11 0 0 4.22 0.25 0 0 4.13 0.35 0 0

POTTER 0 0.57 0 0 0 0.53 0 0 0 0.52 0 0

SCHUYLKILL 3.64 0.6 0 0.8 3.66 0.6 0 0.92 3.68 0.61 0 1.05

SNYDER 0 0.51 3.22 2 0 0.48 3.25 2.15 0 0.43 3.29 2.39

SOMERSET 3.68 0.61 2.91 1.07 3.71 0.58 2.97 1.28 3.74 0.55 3.03 1.49

SULLIVAN 0 0.45 0 0 0 0.46 0 0 0 0.47 0 0

SUSQUEHANNA 3.81 0.51 3.23 2.03 3.84 0.48 3.27 2.21 3.87 0.44 3.32 2.41

TIOGA 0 0.66 0 0 0 0.58 0 0 0 0.5 0 0

UNION 3.96 0.37 0 3.11 3.96 0.38 0 3.11 3.95 0.41 0 3.05

VENANGO 3.62 0.66 0 0.66 3.65 0.62 0 0.88 3.68 0.6 0 1.06

WARREN 0 0.58 0 0.81 0 0.57 0 1 0 0.55 0 1.18

WASHINGTON 3.7 0.62 3 1.22 3.71 0.63 3.07 1.34 3.73 0.65 3.16 1.46

WAYNE 3.99 0.35 0 3.3 3.97 0.39 0 3.2 3.96 0.41 0 3.14

WESTMORELAND 3.68 0.66 2.98 1.07 3.7 0.65 3.04 1.23 3.72 0.64 3.11 1.39

WYOMING 0 0.39 0 1.53 0 0.44 0 1.6 0 0.48 0 1.69

YORK 3.88 0.45 3.37 2.49 3.88 0.45 3.4 2.54 3.89 0.46 3.44 2.59

Rates between 2003 - 2010 Rates between 2010 - 2020 Rates between 2020 - 2030
150

Table D-3. Forecasted Growth Rates Based on County-Level OLS Base Model HH JrSr

HH,Jr,Sr Model

COUNTY CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D

ADAMS 0 1.15 0 2.42 0 2.86 0 1 0 3.14 0 0.06

ALLEGHENY 0 -1.51 2.78 0.61 0 2 0.67 -1.09 0 2.48 0.15 -0.69

ARMSTRONG 0 -0.51 2.98 1.14 0 3.27 0.65 -0.7 0 5.04 -0.47 -0.67

BEAVER 3.54 -0.52 2.96 1.05 3.85 2.85 0.78 -0.63 3.58 4.79 -0.45 -0.69

BEDFORD 3.68 2.63 0 0 3.92 2.87 0 0 3.56 3.99 0 0

BERKS 3.79 -0.96 3.56 2.17 4.09 0.83 1.88 0.87 3.67 1.17 1.32 0.04

BLAIR 3.63 -0.96 2.25 0.42 3.84 1.99 0.31 -1.1 3.15 1.91 0.09 -0.63

BRADFORD 0 2.54 2.23 0.59 0 4.03 0.61 -0.53 0 4.37 0.12 -0.42

BUCKS 4.19 1.55 3.42 2.57 4.48 3.54 1.63 1.17 4.29 3.69 0.95 0.16

BUTLER 4.13 0.95 3.3 2.35 4.34 3.3 1.5 0.95 4.05 3.94 0.66 0.03

CAMBRIA 0 -1.23 1.95 -0.07 0 3.12 -0.57 -2.07 0 3.38 -1.17 -1.13

CAMERON 0 -0.22 0 0 0 1.68 0 0 0 0.14 0 0

CARBON 3.31 -0.34 3.6 1.65 3.91 2.37 1.27 -0.07 3.62 3.99 0.27 -0.42

CENTRE 4.07 0.78 3.16 1.93 4.56 2 1.5 0.73 3.68 1.42 1.52 0.08

CHESTER 4.25 1.51 3.62 2.83 4.5 3.43 1.82 1.35 4.19 3.11 1.3 0.24

CLARION 3.8 0.76 0 1.08 4.2 2.84 0 -0.2 3.79 2.59 0 -0.24

CLEARFIELD 3.43 1.31 2.83 0.92 3.88 3.54 0.93 -0.42 3.58 4.85 -0.12 -0.54

CLINTON 3.8 -0.28 2.46 1.02 4.05 1.59 0.9 -0.38 3.47 2.31 0.36 -0.45

COLUMBIA 3.75 -0.09 3.16 1.76 4.37 2.27 0.98 0.17 3.69 1.78 0.97 -0.11

CRAWFORD 3.52 1 2.13 0.32 3.89 3.34 0.27 -0.99 3.29 2.34 0.33 -0.47

CUMBERLAND 4.47 0.79 3.65 3.32 4.77 2.91 1.7 1.64 4.2 2.1 1.42 0.33

DAUPHIN 3.66 -0.49 3.14 1.47 3.98 2.51 1.03 -0.13 3.49 1.8 0.89 -0.22

DELAWARE 0 -1.59 3.51 1.72 0 1.35 1.37 0.1 0 2.04 0.63 -0.29

ELK 0 1.13 0 0.25 0 4.44 0 -1.41 0 7.37 0 -1.02

ERIE 3.58 -0.1 2.62 0.83 3.88 2.74 0.68 -0.65 3.3 2.48 0.38 -0.45

FAYETTE 0 -0.29 2.71 0.87 0 3.32 0.49 -0.88 0 3.53 -0.04 -0.6

FOREST 0 5.01 0 0 0 1.3 0 0 0 0.11 0 0

FRANKLIN 4.56 0.51 2.94 2.15 4.68 1.57 1.75 1.21 4.11 1.65 1.55 0.25

FULTON 3.73 2.51 0 0 4.05 2.84 0 0 4.02 5.23 0 0

GREENE 3.41 -0.11 0 1.02 4.04 4.54 0 -1.06 3.38 2.97 0 -0.49

HUNTINGDON 3.62 1.52 0 0.39 4.1 2.72 0 -0.54 3.29 2.83 0 -0.49

INDIANA 0 1.23 2.53 0.74 0 3.96 0.45 -0.75 0 3.18 0.45 -0.4

JEFFERSON 3.57 0.54 0 0.54 3.91 2.78 0 -0.78 3.56 4.31 0 -0.65

JUNIATA 0 0.48 0 0 0 2 0 0 0 2.8 0 0

LACKAWANNA 3.44 -1.12 2.77 0.73 3.9 2.11 0.47 -0.95 3.4 2.89 -0.1 -0.64

LANCASTER 4.27 0.28 3.45 2.57 4.43 1.71 2.04 1.38 3.94 1.93 1.61 0.25

LAWRENCE 3.7 -0.93 2.9 1.22 4.03 3.12 0.46 -0.74 3.55 3.47 0.08 -0.54

LEBANON 3.98 0.88 0 1.46 4.17 2.59 0 0.29 3.68 2.88 0 -0.16

LEHIGH 3.77 -0.69 3.44 2.03 4.02 1.37 1.74 0.63 3.56 1.93 1.12 -0.09

LUZERNE 3.45 -0.77 2.63 0.68 3.8 2.22 0.55 -0.89 3.36 3.01 -0.18 -0.65

LYCOMING 3.45 -0.07 2.78 0.8 3.91 2.38 0.77 -0.61 3.43 3.03 0.2 -0.5

MCKEAN 0 0.29 3 0.81 0 2.64 0.76 -0.6 0 4.12 -0.34 -0.62

MERCER 3.62 -0.65 2.73 0.97 4.01 1.91 0.78 -0.49 3.52 2.71 0.07 -0.51

MIFFLIN 0 1.52 1.85 0.13 0 2.43 0.64 -0.76 0 3.52 -0.02 -0.58

MONROE 3.65 1.68 4.77 4.1 4.43 3.53 2.14 2.07 4.33 3.69 1.08 0.35

MONTGOMERY 3.94 -0.39 3.3 2 4.17 1.49 1.71 0.76 3.84 1.88 1.07 0

MONTOUR 3.15 0.46 0 1.2 3.94 3.06 0 -0.33 3.86 4.93 0 -0.58

NORTHAMPTON 0 0.38 3.33 2.28 0 3.17 1.14 0.55 0 3.61 0.47 -0.13

NORTHUMBERLAND 3.27 -0.17 2.65 0.49 3.74 2.33 0.61 -0.97 3.31 3.77 -0.26 -0.73

PERRY 0 1.77 0 2.64 0 4.64 0 0.76 0 3.53 0 0.14

PHILADELPHIA 0 0 2.75 0.63 0 0 1.07 -0.78 0 0 0.57 -0.62

PIKE 3.27 2.21 0 0 4.43 3.46 0 0 4.62 4.88 0 0

POTTER 0 1.48 0 0 0 3.76 0 0 0 4.49 0 0

SCHUYLKILL 3.63 -0.55 0 1.03 4.02 3.4 0 -0.85 3.54 5.3 0 -0.77

SNYDER 0 2.13 2.83 1.15 0 3.21 0.92 0 0 3.81 0.15 -0.3

SOMERSET 3.57 0.58 2.5 0.66 3.96 3.98 0.25 -0.96 3.56 4.61 -0.49 -0.65

SULLIVAN 0 -0.52 0 0 0 -1.18 0 0 0 0.3 0 0

SUSQUEHANNA 3.48 2.77 2.8 1.27 4.22 3.99 0.87 0.06 4.07 4.72 0.06 -0.25

TIOGA 0 2.18 0 0 0 2.91 0 0 0 3.2 0 0

UNION 4.02 1.9 0 2.54 4.56 3.27 0 1.15 3.73 3.65 0 -0.11

VENANGO 3.44 0.83 0 0.26 3.95 3.51 0 -1.2 3.51 3.88 0 -0.67

WARREN 0 1.12 0 0.45 0 3.5 0 -1.01 0 3.9 0 -0.61

WASHINGTON 3.7 0.01 2.73 0.91 4.02 3.84 0.41 -0.88 3.63 3.93 0.05 -0.55

WAYNE 3.77 2.06 0 2.37 4.36 3.83 0 0.8 4.11 4.26 0 -0.07

WESTMORELAND 3.57 0.35 2.64 0.7 3.89 3.98 0.38 -1.01 3.59 4.27 -0.2 -0.63

WYOMING 0 2.75 0 0.54 0 5.2 0 -1.04 0 4.82 0 -0.59

YORK 3.79 0.98 3 1.77 4.11 2.69 1.32 0.47 3.79 2.58 0.8 -0.08

Rates between 2003 - 2010 Rates between 2010 - 2020 Rates between 2020 - 2030
151

Table D-4. Forecasted Growth Rates Based on County-Group Level OLS Model HH

GR Model

GROUP CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D

ALTGR 1.73 0.38 0.93 -0.36 1.74 -0.45 0.81 -0.69 1.56 -1.84 0.51 -1.35

CNTRGR 2.43 0.9 1.53 0.86 2.47 0.11 1.43 0.51 2.37 -1.16 1.18 -0.14

EPAGR 2.15 0.95 1.47 1.45 2.27 0.1 1.39 0.99 2.3 -1.08 1.21 0.35

HARRGR 2.28 1.38 1.64 1.92 2.3 0.53 1.51 1.45 2.28 -0.64 1.3 0.78

I81GR 1.67 0.62 0.95 -0.12 1.75 -0.19 0.88 -0.46 1.72 -1.39 0.68 -1.02

NCNTGR 0 0.82 1.26 0.08 0 0.02 1.11 -0.22 0 -1.04 0.88 -0.69

NEPAGR 2.72 1.83 2.21 4 2.69 0.81 1.98 3.04 2.61 -0.34 1.69 2.08

NTIERGR 0 1.28 1.46 0.69 0 0.49 1.3 0.38 0 -0.55 1.06 -0.08

PHILY 2.28 1.07 1.45 0.78 2.33 0.26 1.36 0.43 2.29 -0.86 1.17 -0.11

SEDA-COG 2.11 0.75 1.32 0.67 2.15 -0.04 1.22 0.33 2.08 -1.31 0.98 -0.3

SHVGR 1.97 0.51 1.14 0.14 2.01 -0.28 1.05 -0.19 1.89 -1.6 0.78 -0.82

SWPAC 2.14 0.61 1.23 0.05 2.24 -0.2 1.17 -0.3 2.27 -1.35 1.01 -0.84

WPAGR 2.09 0.89 0 0.12 2.05 0.08 0 -0.19 1.98 -0.99 0 -0.67

Rates between 2003 - 2010 Rates between 2010 - 2020 Rates between 2020 - 2030
152

Table D-5. Forecasted Growth Rates Based on County-Level OLS Base Truck Model HH

Truck HH Model

COUNTY CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D

ADAMS 0 0.59 0 1.98 0 0.33 0 1.61 0 -0.04 0 1.10

ALLEGHENY 0 0.47 1.50 -0.22 0 0.22 1.58 -0.46 0 -0.15 1.58 -0.85

ARMSTRONG 0 0.51 1.49 0.35 0 0.26 1.61 0.11 0 -0.04 1.63 -0.20

BEAVER 1.82 0.49 1.67 0.36 1.82 0.24 1.71 0.13 1.75 -0.11 1.71 -0.24

BEDFORD 2.57 0.63 0 0 2.29 0.34 0 0 1.96 0.01 0 0

BERKS 2.07 0.48 1.99 1.28 2.02 0.23 1.98 0.98 1.86 -0.14 1.89 0.54

BLAIR 1.17 0.32 1.02 -0.32 1.14 0.05 1.05 -0.56 0.94 -0.38 0.93 -1.00

BRADFORD 0 0.67 1.77 0.66 0 0.42 1.75 0.44 0 0.10 1.69 0.13

BUCKS 2.52 0.80 2.42 2.23 2.37 0.53 2.32 1.86 2.16 0.21 2.16 1.43

BUTLER 2.64 0.64 2.56 2.08 2.41 0.37 2.39 1.73 2.07 -0.01 2.12 1.25

CAMBRIA 0 0.26 0.55 -0.93 0 -0.02 0.53 -1.22 0 -0.62 0.24 -1.90

CAMERON 0 0.24 0 0 0 -0.01 0 0 0 -0.39 0 0

CARBON 2.20 0.51 2.07 0.90 2.26 0.27 2.18 0.62 2.22 -0.04 2.19 0.27

CENTRE 2.63 0.68 2.51 1.67 2.55 0.44 2.47 1.35 2.23 -0.03 2.23 0.75

CHESTER 2.99 0.79 2.91 2.63 2.83 0.52 2.79 2.16 2.52 0.16 2.54 1.61

CLARION 2.16 0.74 0 0.71 2.03 0.46 0 0.49 1.83 0.10 0 0.13

CLEARFIELD 2.38 0.57 2.22 0.79 2.25 0.32 2.15 0.56 2.00 -0.07 1.98 0.15

CLINTON 1.27 0.41 1.15 0.32 1.63 0.20 1.54 0.06 1.57 -0.17 1.54 -0.37

COLUMBIA 1.83 0.51 1.73 1.04 1.90 0.28 1.83 0.78 1.78 -0.11 1.78 0.33

CRAWFORD 1.73 0.40 1.59 0.18 1.59 0.13 1.51 -0.03 1.30 -0.30 1.31 -0.46

CUMBERLAND 2.54 0.68 2.52 2.81 2.48 0.43 2.49 2.29 2.28 0.01 2.34 1.59

DAUPHIN 2.12 0.46 2.01 0.86 2.01 0.20 1.95 0.58 1.87 -0.18 1.88 0.14

DELAWARE 0 0.55 1.60 0.58 0 0.31 1.65 0.34 0 -0.06 1.63 -0.06

ELK 0 0.65 0 -0.03 0 0.36 0 -0.24 0 0.04 0 -0.53

ERIE 1.77 0.43 1.63 0.33 1.73 0.18 1.64 0.11 1.48 -0.26 1.48 -0.35

FAYETTE 0 0.51 1.61 0.28 0 0.25 1.64 0.02 0 -0.16 1.58 -0.40

FOREST 0 0.46 0 0 0 0.29 0 0 0 -0.06 0 0

FRANKLIN 2.08 0.82 1.94 1.62 1.99 0.54 1.90 1.35 1.88 0.21 1.85 0.98

FULTON 2.86 0.77 0 0 2.50 0.46 0 0 2.12 0.12 0 0

GREENE 1.95 0.53 0 0.40 1.83 0.27 0 0.21 1.49 -0.22 0 -0.32

HUNTINGDON 2.27 0.51 0 0.51 2.06 0.26 0 0.33 1.61 -0.30 0 -0.27

INDIANA 0 0.63 1.99 0.60 0 0.40 2.00 0.39 0 0.01 1.93 0.00

JEFFERSON 1.63 0.49 0 0.15 1.58 0.23 0 -0.06 1.47 -0.11 0 -0.38

JUNIATA 0 0.49 0 0 0 0.23 0 0 0 -0.08 0 0

LACKAWANNA 1.31 0.41 1.15 -0.19 1.30 0.16 1.19 -0.41 1.18 -0.20 1.14 -0.78

LANCASTER 2.39 0.71 2.30 1.98 2.31 0.45 2.26 1.63 2.16 0.11 2.16 1.17

LAWRENCE 1.49 0.47 1.35 0.33 1.65 0.25 1.54 0.09 1.71 -0.08 1.66 -0.27

LEBANON 1.86 0.53 0 1.11 1.80 0.28 0 0.84 1.69 -0.08 0 0.40

LEHIGH 2.14 0.47 2.05 1.26 2.17 0.23 2.13 0.95 2.07 -0.13 2.08 0.49

LUZERNE 1.34 0.37 1.19 -0.11 1.33 0.13 1.23 -0.34 1.17 -0.27 1.15 -0.74

LYCOMING 1.82 0.48 1.66 0.25 1.72 0.21 1.62 0.04 1.55 -0.16 1.52 -0.34

MCKEAN 0 0.55 1.75 0.26 0 0.28 1.61 0.08 0 -0.15 1.36 -0.35

MERCER 1.45 0.44 1.30 0.18 1.45 0.21 1.35 -0.04 1.27 -0.20 1.25 -0.48

MIFFLIN 0 0.41 1.48 0.12 0 0.15 1.48 -0.10 0 -0.18 1.44 -0.41

MONROE 2.99 0.65 3.02 3.52 2.72 0.36 2.78 2.78 2.40 0.02 2.49 2.05

MONTGOMERY 2.07 0.57 1.96 1.26 1.98 0.32 1.92 0.99 1.82 -0.03 1.83 0.58

MONTOUR 2.91 0.71 0 0.83 2.59 0.42 0 0.61 2.33 0.03 0 0.22

NORTHAMPTON 0 0.48 1.86 1.61 0 0.24 1.91 1.27 0 -0.11 1.90 0.78

NORTHUMBERLAND 1.50 0.43 1.33 -0.16 1.46 0.18 1.34 -0.37 1.36 -0.18 1.31 -0.75

PERRY 0 0.56 0 2.27 0 0.29 0 1.84 0 -0.02 0.00 1.36

PHILADELPHIA 0 0 1.05 -0.47 0 0 1.15 -0.74 0 0 1.13 -1.20

PIKE 3.35 0.65 0 0 3.21 0.37 0 0 3.04 0.07 0 0

POTTER 0 0.61 0 0 0 0.32 0 0 0 0.03 0 0

SCHUYLKILL 1.49 0.61 0 0.15 1.50 0.37 0 -0.08 1.30 -0.05 0 -0.53

SNYDER 0 0.52 1.98 1.03 0 0.28 1.88 0.80 0 -0.16 1.62 0.36

SOMERSET 1.75 0.52 1.58 0.28 1.64 0.26 1.53 0.07 1.33 -0.17 1.31 -0.34

SULLIVAN 0 0.49 0 0 0 0.27 0 0 0 -0.15 0 0

SUSQUEHANNA 2.16 0.66 2.02 1.25 2.02 0.38 1.94 1.00 1.83 0.06 1.81 0.70

TIOGA 0 0.74 0 0 0 0.45 0 0 0 0.07 0 0

UNION 2.58 0.77 0 2.27 2.53 0.51 0 1.89 1.93 -0.21 0 0.89

VENANGO 1.68 0.53 0 -0.06 1.54 0.27 0 -0.26 1.36 -0.08 0 -0.60

WARREN 0 0.57 0 0.06 0 0.31 0 -0.16 0 -0.02 0 -0.46

WASHINGTON 1.99 0.55 1.83 0.45 2.01 0.32 1.89 0.22 1.99 -0.01 1.92 -0.11

WAYNE 2.43 0.58 0 2.18 2.38 0.33 0 1.75 2.23 -0.03 0 1.20

WESTMORELAND 2.00 0.54 1.82 0.33 1.95 0.29 1.82 0.11 1.83 -0.06 1.77 -0.23

WYOMING 0 0.46 0 0.41 0 0.21 0 0.16 0 -0.10 0 -0.17

YORK 2.13 0.51 2.05 1.43 2.06 0.25 2.03 1.10 1.89 -0.11 1.93 0.65

Rates between 2003 - 2010 Rates between 2010 - 2020 Rates between 2020 - 2030
153

Table D-6. Forecasted Growth Rates Based on County-Level OLS Base Model HH – Tot LM

Growth Rates

HH w Tot LM growth

COUNTY CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D

ADAMS 0 0 0 2.84 0 -0.25 0 2.36 0 -0.61 0 1.68

ALLEGHENY 0 -0.13 3.62 0.22 0 -0.35 3.48 -0.17 0 -0.7 3.24 -0.77

ARMSTRONG 0 -0.09 3.82 1 0 -0.32 3.69 0.59 0 -0.61 3.49 0.1

BEAVER 3.8 -0.1 3.85 0.93 3.48 -0.34 3.7 0.56 3.04 -0.68 3.47 -0.01

BEDFORD 3.79 0.07 0 0 3.52 -0.22 0 0 3.2 -0.55 0 0

BERKS 4.07 -0.11 4.2 2.04 3.73 -0.35 4.02 1.59 3.28 -0.7 3.75 0.97

BLAIR 3.57 -0.3 3.47 0.27 3.24 -0.55 3.31 -0.11 2.73 -0.97 3.02 -0.74

BRADFORD 0 0.07 4.01 1.32 0 -0.17 3.86 0.99 0 -0.48 3.66 0.54

BUCKS 4.59 0.2 4.67 3.19 4.24 -0.06 4.45 2.68 3.84 -0.37 4.19 2.11

BUTLER 4.35 0.06 4.59 2.89 4.03 -0.21 4.37 2.44 3.6 -0.58 4.06 1.84

CAMBRIA 0 -0.37 3.18 -0.35 0 -0.64 2.99 -0.79 0 -1.22 2.54 -1.68

CAMERON 0 -0.35 0 0 0 -0.6 0 0 0 -0.97 0 0

CARBON 3.91 -0.07 4.09 1.49 3.56 -0.3 3.94 1.02 3.17 -0.58 3.74 0.48

CENTRE 4.24 0.1 4.46 2.37 3.92 -0.13 4.28 1.91 3.35 -0.59 3.9 1.12

CHESTER 4.6 0.22 4.87 3.51 4.22 -0.04 4.62 2.88 3.76 -0.4 4.29 2.17

CLARION 4.06 0.15 0 1.32 3.76 -0.12 0 1.01 3.35 -0.47 0 0.52

CLEARFIELD 3.88 0 4.1 1.29 3.59 -0.25 3.93 0.96 3.15 -0.63 3.65 0.41

CLINTON 3.86 -0.22 3.73 1.09 3.47 -0.39 3.64 0.53 2.99 -0.74 3.39 -0.11

COLUMBIA 4.07 -0.1 4.08 1.84 3.74 -0.31 3.94 1.39 3.25 -0.68 3.67 0.74

CRAWFORD 3.67 -0.2 3.75 0.71 3.38 -0.45 3.58 0.41 2.91 -0.87 3.28 -0.15

CUMBERLAND 4.69 0.08 4.83 3.91 4.28 -0.16 4.59 3.17 3.69 -0.55 4.21 2.2

DAUPHIN 3.87 -0.12 4.05 1.45 3.56 -0.38 3.87 1.06 3.08 -0.74 3.6 0.42

DELAWARE 0 -0.05 3.93 1.27 0 -0.28 3.78 0.89 0 -0.64 3.53 0.28

ELK 0 0.06 0 0.41 0 -0.22 0 0.13 0 -0.54 0 -0.25

ERIE 3.74 -0.17 3.81 0.9 3.44 -0.4 3.67 0.55 2.94 -0.83 3.36 -0.08

FAYETTE 0 -0.08 3.82 0.85 0 -0.33 3.66 0.45 0 -0.72 3.39 -0.17

FOREST 0 -0.13 0 0 0 -0.3 0 0 0 -0.64 0 0

FRANKLIN 4.5 0.2 4.4 2.57 4.17 -0.06 4.22 2.19 3.75 -0.38 3.99 1.64

FULTON 4.29 0.2 0 0 3.99 -0.11 0 0 3.65 -0.45 0 0

GREENE 3.82 -0.06 0 0.95 3.54 -0.31 0 0.67 2.98 -0.8 0 -0.03

HUNTINGDON 3.75 -0.06 0 0.95 3.51 -0.31 0 0.72 2.89 -0.86 0 -0.04

INDIANA 0 0.05 4.02 1.15 0 -0.18 3.88 0.81 0 -0.54 3.63 0.25

JEFFERSON 3.77 -0.11 0 0.74 3.46 -0.36 0 0.4 3.07 -0.68 0 -0.07

JUNIATA 0 -0.13 0 0 0 -0.36 0 0 0 -0.66 0 0

LACKAWANNA 3.66 -0.2 3.56 0.4 3.35 -0.44 3.42 0.05 2.92 -0.78 3.18 -0.49

LANCASTER 4.45 0.12 4.54 2.89 4.1 -0.13 4.34 2.38 3.66 -0.46 4.07 1.74

LAWRENCE 3.86 -0.14 3.78 1.03 3.52 -0.34 3.66 0.58 3.07 -0.64 3.46 -0.02

LEBANON 4.1 -0.08 0 1.92 3.79 -0.32 0 1.51 3.32 -0.66 0 0.87

LEHIGH 4.03 -0.12 4.2 1.98 3.68 -0.34 4.03 1.48 3.2 -0.69 3.77 0.81

LUZERNE 3.65 -0.24 3.58 0.48 3.34 -0.47 3.44 0.12 2.87 -0.85 3.17 -0.46

LYCOMING 3.75 -0.11 3.8 0.79 3.45 -0.37 3.65 0.48 3.01 -0.73 3.4 -0.06

MCKEAN 0 -0.04 3.84 0.78 0 -0.3 3.68 0.54 0 -0.73 3.37 -0.01

MERCER 3.79 -0.17 3.72 0.85 3.49 -0.39 3.58 0.49 3 -0.79 3.29 -0.14

MIFFLIN 0 -0.19 3.71 0.68 0 -0.44 3.56 0.34 0 -0.75 3.35 -0.13

MONROE 4.79 0.06 5.13 4.64 4.33 -0.22 4.77 3.69 3.83 -0.55 4.38 2.77

MONTGOMERY 4.14 -0.03 4.21 2.04 3.83 -0.27 4.04 1.64 3.4 -0.61 3.79 1.07

MONTOUR 3.87 0.16 0 1.15 3.61 -0.13 0 0.91 3.18 -0.51 0 0.4

NORTHAMPTON 0 -0.12 4.28 2.54 0 -0.35 4.11 2.01 0 -0.69 3.84 1.29

NORTHUMBERLAND 3.64 -0.17 3.6 0.37 3.33 -0.41 3.46 0.03 2.89 -0.75 3.22 -0.52

PERRY 0 -0.05 0 3.33 0 -0.3 0 2.74 0 -0.6 0 2.08

PHILADELPHIA 0 0 3.47 0.09 0 0 3.33 -0.37 0 0 3.05 -1.05

PIKE 5.02 0.07 0 0 4.43 -0.18 0 0 3.85 -0.47 0 0

POTTER 0 0.03 0 0 0 -0.25 0 0 0 -0.53 0 0

SCHUYLKILL 3.92 -0.01 0 0.83 3.61 -0.23 0 0.45 3.11 -0.64 0 -0.18

SNYDER 0 -0.07 4.13 1.71 0 -0.3 3.96 1.39 0 -0.74 3.64 0.84

SOMERSET 3.81 -0.08 3.81 0.86 3.53 -0.33 3.66 0.57 3.07 -0.75 3.36 0.02

SULLIVAN 0 -0.12 0 0 0 -0.32 0 0 0 -0.72 0 0

SUSQUEHANNA 4.19 0.06 4.24 2.01 3.88 -0.21 4.07 1.66 3.53 -0.52 3.85 1.25

TIOGA 0 0.15 0 0 0 -0.13 0 0 0 -0.52 0 0

UNION 4.56 0.18 0 3.21 4.19 -0.07 0 2.65 3.32 -0.78 0 1.37

VENANGO 3.72 -0.06 0 0.46 3.44 -0.32 0 0.18 3.04 -0.66 0 -0.3

WARREN 0 -0.04 0 0.71 0 -0.3 0 0.37 0 -0.61 0 -0.07

WASHINGTON 3.84 -0.03 3.92 1 3.53 -0.26 3.78 0.62 3.12 -0.56 3.58 0.11

WAYNE 4.39 -0.01 0 3.09 4.01 -0.25 0 2.47 3.52 -0.59 0 1.71

WESTMORELAND 3.78 -0.05 3.88 0.84 3.48 -0.28 3.73 0.49 3.07 -0.62 3.5 -0.01

WYOMING 0 -0.18 0 1.31 0 -0.4 0 0.83 0 -0.69 0 0.27

YORK 4.13 -0.09 4.26 2.21 3.79 -0.33 4.07 1.73 3.33 -0.68 3.8 1.1

Rates between 2003 - 2010 Rates between 2010 - 2020 Rates between 2020 - 2030
154

Table D-7. Forecasted Growth Rates Based on County-Level OLS Base Model HH – Half LM

Growth Rates

HH w 1/2 LM growth

COUNTY CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D

ADAMS 0 0.26 0 2.75 0 0 0 2.28 0 -0.36 0 1.59

ALLEGHENY 0 0.13 3.11 0.14 0 -0.1 2.97 -0.26 0 -0.44 2.73 -0.86

ARMSTRONG 0 0.16 3.31 0.91 0 -0.06 3.18 0.5 0 -0.36 2.99 0.01

BEAVER 3.62 0.15 3.34 0.85 3.31 -0.08 3.19 0.47 2.86 -0.42 2.96 -0.09

BEDFORD 3.62 0.33 0 0 3.35 0.04 0 0 3.03 -0.29 0 0

BERKS 3.89 0.15 3.68 1.95 3.56 -0.1 3.51 1.5 3.11 -0.45 3.24 0.88

BLAIR 3.39 -0.04 2.96 0.19 3.07 -0.29 2.81 -0.19 2.56 -0.71 2.51 -0.82

BRADFORD 0 0.33 3.5 1.24 0 0.09 3.35 0.9 0 -0.22 3.15 0.46

BUCKS 4.41 0.46 4.16 3.11 4.06 0.2 3.94 2.6 3.67 -0.11 3.68 2.02

BUTLER 4.17 0.32 4.08 2.8 3.86 0.05 3.86 2.35 3.43 -0.32 3.55 1.76

CAMBRIA 0 -0.11 2.67 -0.43 0 -0.38 2.49 -0.87 0 -0.96 2.04 -1.76

CAMERON 0 -0.09 0 0 0 -0.34 0 0 0 -0.71 0 0

CARBON 3.74 0.19 3.58 1.4 3.39 -0.04 3.43 0.93 2.99 -0.33 3.23 0.4

CENTRE 4.06 0.36 3.95 2.28 3.74 0.13 3.77 1.83 3.18 -0.33 3.39 1.03

CHESTER 4.42 0.48 4.36 3.42 4.04 0.21 4.1 2.79 3.59 -0.14 3.78 2.08

CLARION 3.88 0.41 0 1.24 3.59 0.13 0 0.93 3.18 -0.22 0 0.43

CLEARFIELD 3.7 0.25 3.59 1.2 3.42 0.01 3.42 0.88 2.98 -0.37 3.15 0.33

CLINTON 3.69 0.04 3.22 1 3.3 -0.13 3.13 0.45 2.81 -0.49 2.88 -0.19

COLUMBIA 3.9 0.16 3.57 1.75 3.56 -0.05 3.43 1.3 3.08 -0.42 3.17 0.65

CRAWFORD 3.49 0.06 3.24 0.62 3.21 -0.2 3.07 0.32 2.74 -0.62 2.78 -0.24

CUMBERLAND 4.51 0.34 4.32 3.82 4.1 0.1 4.07 3.09 3.51 -0.3 3.7 2.12

DAUPHIN 3.7 0.14 3.54 1.36 3.38 -0.12 3.36 0.97 2.91 -0.48 3.09 0.34

DELAWARE 0 0.21 3.41 1.19 0 -0.02 3.27 0.8 0 -0.38 3.02 0.2

ELK 0 0.32 0 0.32 0 0.04 0 0.04 0 -0.28 0 -0.34

ERIE 3.57 0.09 3.3 0.81 3.27 -0.14 3.16 0.46 2.76 -0.58 2.85 -0.16

FAYETTE 0 0.17 3.31 0.77 0 -0.08 3.15 0.36 0 -0.46 2.88 -0.25

FOREST 0 0.13 0 0 0 -0.04 0 0 0 -0.38 0 0

FRANKLIN 4.32 0.46 3.88 2.49 4 0.2 3.71 2.1 3.58 -0.12 3.48 1.56

FULTON 4.12 0.46 0 0 3.81 0.14 0 0 3.48 -0.2 0 0

GREENE 3.64 0.2 0 0.86 3.36 -0.05 0 0.58 2.81 -0.54 0 -0.11

HUNTINGDON 3.57 0.2 0 0.86 3.33 -0.05 0 0.64 2.72 -0.6 0 -0.13

INDIANA 0 0.31 3.51 1.06 0 0.08 3.37 0.72 0 -0.29 3.12 0.17

JEFFERSON 3.6 0.14 0 0.66 3.29 -0.11 0 0.32 2.89 -0.43 0 -0.15

JUNIATA 0 0.13 0 0 0 -0.1 0 0 0 -0.4 0 0

LACKAWANNA 3.48 0.05 3.05 0.32 3.18 -0.18 2.91 -0.04 2.74 -0.53 2.67 -0.57

LANCASTER 4.27 0.38 4.03 2.8 3.93 0.13 3.83 2.29 3.49 -0.2 3.56 1.65

LAWRENCE 3.68 0.12 3.27 0.94 3.34 -0.09 3.15 0.5 2.89 -0.39 2.96 -0.11

LEBANON 3.93 0.18 0 1.83 3.61 -0.06 0 1.42 3.15 -0.4 0 0.79

LEHIGH 3.86 0.14 3.68 1.9 3.51 -0.08 3.52 1.4 3.03 -0.43 3.26 0.73

LUZERNE 3.47 0.02 3.07 0.4 3.16 -0.21 2.93 0.04 2.69 -0.6 2.67 -0.54

LYCOMING 3.57 0.15 3.29 0.7 3.27 -0.12 3.14 0.39 2.84 -0.48 2.89 -0.14

MCKEAN 0 0.21 3.33 0.69 0 -0.05 3.17 0.45 0 -0.47 2.86 -0.09

MERCER 3.62 0.09 3.21 0.76 3.32 -0.13 3.07 0.4 2.83 -0.53 2.79 -0.22

MIFFLIN 0 0.07 3.2 0.6 0 -0.18 3.05 0.25 0 -0.49 2.85 -0.22

MONROE 4.61 0.32 4.62 4.55 4.15 0.04 4.25 3.6 3.66 -0.29 3.87 2.68

MONTGOMERY 3.97 0.23 3.7 1.96 3.65 -0.01 3.53 1.55 3.23 -0.35 3.28 0.98

MONTOUR 3.69 0.42 0 1.07 3.43 0.13 0 0.82 3.01 -0.26 0 0.32

NORTHAMPTON 0 0.13 3.77 2.45 0 -0.09 3.6 1.92 0 -0.43 3.33 1.21

NORTHUMBERLAND 3.46 0.08 3.09 0.29 3.16 -0.16 2.95 -0.05 2.72 -0.5 2.72 -0.61

PERRY 0 0.21 0 3.25 0 -0.05 0 2.65 0 -0.35 0 2

PHILADELPHIA 0 0 2.96 0 0 0 2.82 -0.45 0 0 2.54 -1.13

PIKE 4.85 0.33 0 0 4.25 0.08 0 0 3.67 -0.21 0 0

POTTER 0 0.29 0 0 0 0.01 0 0 0 -0.28 0 0

SCHUYLKILL 3.75 0.25 0 0.75 3.44 0.03 0 0.37 2.94 -0.38 0 -0.26

SNYDER 0 0.19 3.61 1.63 0 -0.04 3.45 1.31 0 -0.48 3.13 0.75

SOMERSET 3.64 0.18 3.3 0.78 3.35 -0.08 3.15 0.48 2.89 -0.5 2.85 -0.06

SULLIVAN 0 0.14 0 0 0 -0.07 0 0 0 -0.47 0 0

SUSQUEHANNA 4.01 0.32 3.73 1.92 3.71 0.05 3.55 1.57 3.36 -0.26 3.34 1.16

TIOGA 0 0.41 0 0 0 0.12 0 0 0 -0.26 0 0

UNION 4.38 0.44 0 3.12 4.02 0.19 0 2.56 3.15 -0.52 0 1.28

VENANGO 3.55 0.19 0 0.38 3.27 -0.06 0 0.09 2.87 -0.41 0 -0.39

WARREN 0 0.21 0 0.63 0 -0.04 0 0.28 0 -0.35 0 -0.15

WASHINGTON 3.67 0.22 3.41 0.91 3.36 0 3.27 0.54 2.95 -0.3 3.07 0.02

WAYNE 4.21 0.25 0 3.01 3.84 0.01 0 2.38 3.35 -0.33 0 1.63

WESTMORELAND 3.61 0.21 3.36 0.75 3.31 -0.03 3.22 0.41 2.9 -0.36 2.99 -0.09

WYOMING 0 0.08 0 1.22 0 -0.15 0 0.74 0 -0.43 0 0.18

YORK 3.95 0.17 3.75 2.13 3.61 -0.08 3.56 1.64 3.16 -0.42 3.29 1.01

Rates between 2003 - 2010 Rates between 2010 - 2020 Rates between 2020 - 2030
155

Table D-8. Forecasted Growth Rates Based on County-Group Level OLS Model HH – Tot LM

Growth Rates

Table D-9. Forecasted Growth Rates Based on County-Group Level OLS Model HH – Half LM

Growth Rates

Table D-10. Averaged Annual Growth Rates (for Interstate Categories)

GR w Tot LM growth

GROUP CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D

ALTGR 2.08 -0.95 2.64 0.05 2.08 -1.76 2.52 -0.29 1.91 -3.14 2.21 -0.95

CNTRGR 2.78 -0.43 3.25 1.28 2.82 -1.22 3.15 0.92 2.72 -2.47 2.89 0.27

EPAGR 2.5 -0.39 3.19 1.87 2.62 -1.23 3.11 1.41 2.65 -2.39 2.93 0.76

HARRGR 2.63 0.04 3.36 2.34 2.65 -0.8 3.23 1.86 2.63 -1.96 3.02 1.2

I81GR 2.01 -0.71 2.66 0.29 2.1 -1.51 2.59 -0.05 2.07 -2.69 2.39 -0.61

NCNTGR 0 -0.51 2.98 0.49 0 -1.31 2.83 0.19 0 -2.35 2.59 -0.28

NEPAGR 3.07 0.48 3.94 4.43 3.05 -0.53 3.7 3.47 2.96 -1.66 3.42 2.5

NTIERGR 0 -0.06 3.18 1.1 0 -0.84 3.02 0.79 0 -1.87 2.78 0.33

PHILY 2.63 -0.27 3.17 1.2 2.68 -1.07 3.08 0.84 2.64 -2.17 2.88 0.3

SEDA-COG 2.46 -0.58 3.03 1.08 2.5 -1.36 2.94 0.74 2.43 -2.62 2.69 0.11

SHVGR 2.32 -0.82 2.86 0.55 2.36 -1.6 2.76 0.22 2.24 -2.91 2.49 -0.41

SWPAC 2.49 -0.72 2.95 0.46 2.59 -1.52 2.88 0.11 2.63 -2.65 2.72 -0.43

WPAGR 2.44 -0.45 0 0.53 2.4 -1.25 0 0.22 2.33 -2.3 0 -0.27

Rates between 2003 - 2010 Rates between 2010 - 2020 Rates between 2020 - 2030

GR w Tot LM growth

GROUP CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D CAT A CAT B CAT C CAT D

ALTGR 1.91 -0.28 1.79 -0.16 1.91 -1.11 1.67 -0.49 1.74 -2.49 1.36 -1.15

CNTRGR 2.6 0.23 2.39 1.07 2.65 -0.56 2.29 0.71 2.54 -1.82 2.03 0.07

EPAGR 2.33 0.28 2.33 1.66 2.44 -0.57 2.25 1.2 2.47 -1.73 2.07 0.56

HARRGR 2.46 0.71 2.5 2.13 2.47 -0.14 2.37 1.65 2.45 -1.3 2.16 0.99

I81GR 1.84 -0.05 1.8 0.08 1.93 -0.86 1.73 -0.25 1.9 -2.04 1.53 -0.81

NCNTGR 0 0.15 2.12 0.28 0 -0.65 1.97 -0.02 0 -1.7 1.74 -0.49

NEPAGR 2.89 1.16 3.07 4.21 2.87 0.14 2.84 3.25 2.78 -1.01 2.56 2.29

NTIERGR 0 0.6 2.32 0.9 0 -0.18 2.16 0.59 0 -1.21 1.92 0.13

PHILY 2.45 0.4 2.31 0.99 2.51 -0.41 2.22 0.63 2.47 -1.52 2.02 0.1

SEDA-COG 2.29 0.08 2.18 0.87 2.33 -0.7 2.08 0.53 2.25 -1.96 1.84 -0.1

SHVGR 2.15 -0.15 2 0.35 2.18 -0.94 1.91 0.01 2.06 -2.26 1.63 -0.62

SWPAC 2.31 -0.05 2.09 0.25 2.41 -0.86 2.02 -0.09 2.45 -2 1.87 -0.63

WPAGR 2.26 0.22 0 0.32 2.22 -0.59 0 0.01 2.15 -1.65 0 -0.47

Rates between 2003 - 2010 Rates between 2010 - 2020 Rates between 2020 - 2030

Model Facility 1994-03 2003-10 2010-20 2020-30

Rural Interstates 4.09% 2.93% 2.83% 2.58%

Rural Non-Interstates -0.87% 0.48% 0.24% -0.13%

Urban Interstates 4.12% 2.23% 2.16% 2.00%

Urban Non-Interstates 1.73% 1.41% 1.07% 0.57%

Total 1.54% 1.49% 1.31% 1.03%

Rural Interstates 4.09% 3.28% 3.18% 2.93%

Rural Non-Interstates -0.87% -0.03% -0.28% -0.64%

Urban Interstates 4.12% 3.59% 3.48% 3.28%

Urban Non-Interstates 1.73% 1.58% 1.25% 0.74%

Total 1.54% 1.75% 1.67% 1.52%

Rural Interstates 4.09% 3.11% 3.00% 2.76%

Rural Non-Interstates -0.87% 0.23% -0.02% -0.39%

Urban Interstates 4.12% 2.91% 2.82% 2.63%

Urban Non-Interstates 1.73% 1.50% 1.16% 0.65%

Total 1.54% 1.62% 1.48% 1.25%

Averaged Rates

for Interstates

(HH+CntyGrp) -

Same LM

Averaged Rates

for Interstates

(HH+CntyGrp) -

Tot LM Growth

Averaged Rates

for Interstates

(HH+CntyGrp) -

Half LM Growth
156

Appendix E. Summary Tables of Data and Studies
157

Table E-1. HMPS Database aggregation to Functional Categories

Functional VMT Forecasting System

Classification Description Functional Categories

01 Interstate Category A

02 Other Principal Arterial Category B

06 Minor Arterial Category B

07 Major Collector Category B

08 Minor Collector Category B

09 Local Category B

11 Interstate Category C

12 Other Freeway/Expressway Category C

14 Other Principal Arterials Category D

16 Minor Arterial Category D

17 Collector Category D

19 Local Category D

Urban

FEDERAL HIGHWAY CLASSIFICATION

Rural
158

Table E-2. Descriptive Statistics by Functional Group

Rural Interstates

Rural Non-Interstates

Urban Interstates

Urban Non-Interstates

Variable Obs Mean Std. Dev Min Max

Year 434 1998.555 2.855719 1994 2003

INC_Low 434 0.2516567 0.0593653 0.106 0.371

INC_VHigh 434 0.1507373 0.0742201 0.067 0.439

Pop17 434 0.2331313 0.0185871 0.174 0.271

Pop65+ 434 0.1597949 0.0225673 0.096 0.204

Ln_HH 434 10.68282 0.9267186 8.586346 12.60115

Ln_INCHH 434 10.94149 0.2009428 10.59768 11.70263

Ln_INCPC 434 10.01877 0.1965541 9.650078 10.73285

Ln_Pop 434 11.63982 0.9216653 9.560152 13.5556

Ln_VMTA 434 19.05257 0.7422932 16.55452 20.38007

Ln_LMA 434 4.47632 0.7167639 2.282382 5.595974

LNLMAPC 434 -7.163496 1.154841 -11.05416 -4.477458

Variable Obs Mean Std. Dev Min Max

Year 660 1998.5 2.87446 1994 2003

INC_Low 660 0.2581667 0.0597614 0.106 0.399

INC_VHigh 660 0.141603 0.0704938 0.047 0.439

Pop17 660 0.2349697 0.0182304 0.174 0.277

Pop65+ 660 0.1615561 0.0231499 0.096 0.225

Ln_EMP 660 10.68953 1.221152 7.609862 13.70912

Ln_HH 660 10.44137 1.140835 7.552762 13.20491

Ln_INCHH 660 10.92603 0.1937604 10.54276 11.70263

Ln_INCPC 660 10.00405 0.1891247 9.650078 10.73285

Ln_Pop 660 11.39535 1.141005 8.491876 14.10202

Ln_VMTB 660 19.83448 0.673214 16.42299 21.24498

Ln_LMB 660 7.700081 0.6403377 4.151984 8.619741

LNLMBPC 660 -3.695269 1.232235 -9.07359 -1.455964

Variable Obs Mean Std. Dev Min Max

Year 421 1998.511 2.880642 1994 2003

Ln_EMP 421 11.38946 0.9940644 9.6435 13.7091

Ln_HH 421 11.09865 0.9036711 9.4817 13.3042

Ln_INCHH 421 10.97561 0.2070614 10.6773 11.7026

Ln_INCPC 421 10.05504 0.1987715 9.7415 10.7328

Ln_Pop 421 12.04917 0.9031036 10.5295 14.2657

Ln_VMTC 421 18.7076 1.628664 15.7073 21.7575

Ln_LMC 421 3.978961 1.236713 1.67896 6.54435

LNVMTCPC 421 6.65842 0.8929743 4.31613 8.28033

LNLMCPC 421 -8.070216 0.642233 -9.73278 -6.85247

Ln_POPDEN 421 5.572744 1.118894 3.73404 9.3054

Ln_EMPDEN 421 4.91303 1.189631 2.84312 8.61248

Ln_COMBDEN 421 6.236654 1.151051 4.3053 9.97182

Ln_LUMIX 421 10.72468 0.9462911 9.0651 12.9472

Variable Obs Mean Std. Dev Min Max

Year 544 1998.533 2.890068 1994 2003

Ln_EMP 544 11.10114 1.050332 9.3426 13.7091

Ln_HH 544 10.82495 0.9790381 8.8303 13.3042

Ln_INCHH 544 10.9592 0.1945581 10.5977 11.7026

Ln_INCPC 544 10.03762 0.1888516 9.6501 10.7328

Ln_Pop 544 11.77986 0.9728314 9.8015 14.2657

Ln_VMTD 544 19.42156 1.441886 15.3064 22.6153

Ln_LMD 544 6.293646 1.387897 2.18155 9.32112

LNVMTCPD 544 7.641694 0.6093985 5.07312 8.63603

LNLMCPD 544 -5.486218 0.602522 -8.42914 -4.52322

Ln_POPDEN 544 5.344677 1.108069 3.72065 9.3054

Ln_EMPDEN 544 4.665955 1.183734 2.84312 8.61248

Ln_COMBDEN 544 6.00001 1.141315 4.3053 9.97182

Ln_LUMIX 544 10.44283 1.012275 8.6162 12.9472
159

Figure E-1. Flowchart for A Prototype VMT Forecasting System File

The VMT Forecasting System file is a prototype Vehicle Mile of Travel (VMT) growth

forecasting system, which produces VMT forecasts and VMT growth rates at the county, county

group and state levels. This VMT growth forecasting system is based on the County-level

Household model (Model HH) and County Group Household model (Model CntyGrp HH) as

developed and recommended in this final report. Both models use variables such as population,

households and mean household income as well as road lane miles (LM). Socioeconomic data

source is the state profile created by Woods & Poole Economics Inc., and traffic data are derived

from PENNDOT TIS and RMS databases.
160

The purpose of this file is to offer a user-friendly platform for PENNDOT to forecast VMT and

VMT growth rates for different years based on its needs. These forecasts can be calculated

between a Base Year and a future year (Horizon 1) or between two future years (Horizon 1 to

Horizon 2), all of which are user-defined. In the main text of this technical report as well as in

the appendices the county, county group or statewide growth rates presented use the same base

year (in this study is 2003) LM for all horizon years assuming no growth. The file, however,

offers the option to increase/decrease the state lane miles to account for possible changes, which

occur due to roadway reclassification, road construction/closure and change in area types

between urban and rural areas. The results are being automatically calculated (with the help of

some macros) and the results are in two worksheets.

The “Model Rates” worksheet contains the model rates for each county and county group based

on the HH Model, County Group HH Model and an average between the two models. Because

the average rate is generated by comparing total statewide VMT between the two models, two

different methods have been adopted to disaggregate the statewide average to the county-level

detail. The “Summary” worksheet contains the actual VMT forecasts based on the rates

described in the “Model Rates” worksheet. The VMT forecast results, combined with the

historical trends, can be illustrated into a series of VMT charts, using macros.

Currently, the file’s predictive capabilities allow it to give forecasts until 2030 based on 2003

data and models. In the future as more data become available PENNDOT will have three options

for future predictions:

1. Do nothing alternative: Use the file as it is (only the left leg of the Figure E-1) and

change the two horizon years.

2. Simple update alternative (recommended only for small year-to-year changes):

Incorporate the new VMT and LM into the “1994-2003 All Public Roads”

worksheet and follow the first six steps of the right flowchart leg to update the

traffic data.

3. Full update alternative (recommended for big changes and longer time intervals):

Follow the whole Figure E-1 flowchart starting from the right leg going all the

way down and back to the left leg.
161

Table E-3. Summary of Variables Used in Different Studies

Source Benjamin 1986

Crouch &

Seaver &

Chatterjee

2001

Iskander

& Jaraiedi

& Thomas

1996

Harmatuc

k 1997

Indiana

Report

2002

Kentucky

Report

2000

Mohamad& Sinha &

Kuczek &

Scholer

1998

Morey &

Niemeier

&

Limanond

Model 1

2002

Morey &

Niemeier

&

Limanond

Model 2

2002

Noland

Model

1999

Noland

&

Cowart

2000

Oregon

Report

2000

Parthasarathi

& Levinson &Karamalaputi

2004

Dependent Variable

VMT X X X X X X X X (change) AADT X

X (for non

bridge) X X X 

Independent Variables

Demographic

Age XCounty households County population X X X X HHSize X State households State population X Year X X X X 

Economic

Consumer price index County driving licenses X XCounty employment X X County vehicle registrations X Gross National Product Income X X Per capita disposable personal

income X X 

State employment State vehicle registrations US gasoline prices X X X Vehicle ownership / HH X 

Land Use

Area type - Location (urban, rural) X X ( %)Land-use mix X Population density X X

Highway and Accessibility

Accessibility index X Number of jobs within a 5-km radius ADT X (mean) X Density of paved roads XDistance to CBD Intersection per road kilometer Miles of roadway X X X X Number of lanes XRoad Type (paved, unpaved) X XRural grid road type VMT X
162

Table E-4. Summary of Model and Method Types

Model Type Barr 2000 Benjamin 1986

Cervero

&

Hansen

2001

Crouch &

Seaver &

Chatterjee

2001

Fulton

et al

2000

Iskander

&

Jaraiedi

&

Thomas

1996

Hansen

&

Huang

1997

Harmatuck

1997

Indiana

2002

Kentucky

2000

Mohamad& Sinha &

Kuczek &

Scholer

1998

Morey &

Niemeier

&

Limanond

2002

Noland

1999

Noland

&

Cowart

2000

Oregon

2000

Parth

i & Le

Karam

i 2

Regression X X X X X Stepwise Regression Seemingly UnrelatedRegression xTwo-Stage Least Square

Regression X X X

Exponential Regression Neural Networks XTime Series X X X XCross sectional X XGrowth rates X X