Investigating Interchange Traffic and Commercial Development at Rural Interstate Highway Exits

A thesis presented to

the faculty of

the Russ College of Engineering and Technology of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Shah Mahmood

August 2016

This thesis titled

Investigating Interchange Traffic and Commercial Development at Rural Interstate Highway Exits

SHAH MAHMOOD

has been approved for

the Department of Civil Engineering

and the Russ College of Engineering and Technology by

Benjamin R. Sperry

Assistant Professor of Civil Engineering

Dennis Irwin

Dean, Russ College of Engineering and Technology 3

ABSTRACT

MAHMOOD, SHAH., M.S., August 2016, Civil Engineering

Investigating Interchange Traffic and Commercial Development at Rural Interstate Highway

Exits

Director of Thesis: Benjamin R. Sperry

This thesis investigates interchange traffic and commercial development at 69 rural interstate highway exits in Ohio. According to the literature, the following factors influence commercial development growth at rural and small-town Interstate exits: motels, hotels, restaurants, gas stations and convenience stores, truck stops or truck parking lots, geography, access to firmer markets, traffic volume of the intersecting highway, intersecting highway types, site competition, and other developments. This study examined those factors which influence traffic volume at the interchange exit such as gas stations and convenience stores, fast food restaurants, hotels and motels, distance to the nearest rural city and town, distance to the nearest and furthest interchange, and intersecting highway. The geographic information system (GIS) is used to identify 69 Interstate exits and local trade area characteristics along Interstate 70 and

Interstate 75 in Ohio.

Statistical analysis software SPSS was used to analyzed the data. Two models,

Interchange AADT model and daily truck percentage (T24) model, were developed using the

regression stepwise backward technique separately to quantify and estimate commercial

development at each exit of Interstate 70 and 75 in Ohio. Results showed a moderate correlation

between most development units. The final output of these two models may be good source of

information for commercial development planning at rural and small town Interstate exits in

Interstate 70 and Interstate 75 in Ohio.

DEDICATION

Complex work requires both strong effort and the guidance of elders, namely those close to our

heart.

I dedicate my accomplishments to my sweet and loving

Father and Mother,

Whose affection, endless love, encouragement and prayer offered me success and shaped my

character. I appreciate your sacrifices and know that I wouldn’t be at this state without you.

ACKNOWLEDGMENTS

Foremost, I am highly thankful to Allah for His blessing that continue to flow into my life, and because of You, I made this through against all odds.

For the ancestors who concreted the path before me upon whose shoulders I stand. This is

also dedicated to my family and the many friends who supported me on this journey. Thank you.

I cannot find words to express my gratitude to my supervisor Benjamin R. Sperry for his

unwavering support, collegiality, and mentorship throughout this project. I would also like to thank

Dr. Deborah McAvoy, Dr. Bhaven Naik, and Dr. Gaurav Sinha for serving on my committee.

Lastly, I’d like to acknowledge my associates in the Facility for their support and motivation. 6

TABLE OF CONTENTS

Page

Abstract ...... 3 Dedication ...... 4 Acknowledgments ...... 5 List of Tables ...... 9 List of Figures ...... 12 Chapter 1: Introduction ...... 14 1. 1. Background ...... 14 1. 2. Gap Statement ...... 21 1. 3. Research Goal ...... 23 1. 4. Research Objectives ...... 23 1. 5. Justification of the Research Study ...... 24 Chapter 2: Related Literature Review ...... 26 2. 1. Introduction ...... 26 2. 2. Exit Survey ...... 27 2. 3. Exit Form ...... 29 2. 4. Interchange Scale ...... 32 2. 5. Mix and Regional Incidence ...... 33 2. 6. Interchange Structure ...... 34 2. 7. Related Methodology ...... 40 2. 8. Summary of Literature ...... 48 Chapter 3: Data Analysis ...... 49 3. 1. Introduction to Interstate 70 and Interstate 75 ...... 49 3. 2. General Form of Regression Models ...... 52 3. 3. Data Base Development ...... 54 3. 3. 1. Sample of Interstate Exit Selections ...... 54 3. 3. 2. Traffic Count Location Maps ...... 55 3. 3. 3. Interchange Annual Average Daily Traffic (AADT) ...... 56 3. 3. 4. Daily Truck Percentage (T24) Data Collection ...... 57 3. 3. 5. Intersecting Highway Annual Average Daily Traffic (ADT) ...... 57 3. 3. 6. Population of the Nearest Cities and Towns ...... 57 7

3. 3. 6. Population of the Nearest Cities and Towns ...... 57 3. 3. 7. Population of County ...... 58 3. 3. 8. Distance to the Nearest City and Town Centers ...... 58 3. 3. 9. Distance to the Nearest and Furthest Neighboring Interchange ...... 60 3. 3. 10. Exit Development ...... 60 3. 3. 11. Developed Axes ...... 64 3. 4. Preliminary Data Analysis ...... 64 Chapter 4: Analyis of the Regression Models ...... 72 4. 1: Preliminary Analysis ...... 72 4. 1. 1. Descriptive Statistics ...... 73 4. 1. 2. Correlation Analysis – Interchange AADT Model ...... 74

4. 1. 3. Correlation Analysis – Daily Truck Percentage (T24) Model ...... 80 4. 1. 4. Regression Analysis Method ...... 83 4. 2. Interchange AADT Model ...... 85 4. 2. 1. Model Summary ...... 86 4. 2. 2. Interchange AADT Model - ANOVA ...... 87 4. 2. 3. Equation and its Parameters of Interchange AADT Model ...... 89 4. 2. 4. Excluded Variables of Interchange AADT Model ...... 96 4 .2. 5. Assessing the Assumption of Non-Multicollinearity ...... 97 4. 2. 6. Casewise Diagnostics ...... 100 4. 2. 7. Cross-validity of the Model ...... 100 4. 2. 8. Assumptions of the Multiple Regression (MR) ...... 101 4. 2. 9. Result of the Interchange AADT Model ...... 109

4. 3. Daily Truck Percentage (T24) Model ...... 111

4. 3. 1. T24 Model Summary ...... 111

4. 3. 2. T24 Model ANOVA ...... 113

4. 3. 3. T24 Model Equation and its Coefficients ...... 114

4. 3. 4. T24 Model Excluded Variables ...... 119

4. 3. 5. Assessing the Assumption of no Multicollinearity for T24 Model ...... 120

4. 3. 6. Casewise Diagnostics of T24 model ...... 122

4. 3. 7. Cross-Validity of T24 Model ...... 123 4. 3. 8. Case Summary ...... 124

4. 3. 9. Checking the Assumptions of Multiple Regression (MR) of T24 Model ...... 126 8

4. 3. 10. Result of the T24 Model ...... 133 Chapter 5: Discussions and Conclusions ...... 135 5. 1. Summary of the Research Study ...... 135 5. 2. Review of the Methodology ...... 136 5. 3. Discussion ...... 137 5. 4. Result and Conclusions ...... 141 5. 4. 1. Interchange AADT Model: ...... 141

5. 4. 2. Daily Truck Percentage (T24) Model: ...... 143 5. 5. Limitation of the Study ...... 145 5. 6. Recommendation for Future Research ...... 146 References ...... 148 Appendix A: Development Units Breakdown ...... 151

Appendix B: Correlation Matrix of Interchange AADT and T24 Model ...... 153 Appendix C: Interchange AADT Model ...... 159

Appendix D: Daily Truck Percentage (T24) Model ...... 168

LIST OF TABLES

Page

Table 1: Non-Metropolitan Exits, Interstate 75 ...... 29

Table 2: Maximum Distance of Services from Interstate 75 Exits ...... 32

Table 3. Establishments by Type and Regional Incidence, Interstate 75 ...... 33

Table 4. States in Interchange Development ...... 47

Table 5. Interchange’s Developed Axes on Interstate 70 and Interstate 75 ...... 64

Table 6. Variables Classification Lists and Their Short Form ...... 65

Table 7. Development Units by Type in Ohio ...... 66

Table 8. Distribution of Development Types ...... 67

Table 9. Descriptive Statistics ...... 74

Table 10. Correlation Analysis of Interchange AADT Model ...... 76

Table 11. Correlation Analysis of Daily Truck Percentage (T24) Model ...... 82

Table 12. Correlation Matrix, Multicollinearity ...... 83

Table 13. Interchange AADT Model Summary ...... 86

Table 14. Interchange AADT Model ANOVA ...... 88

Table 15. Interchange AADT Model Coefficients ...... 90

Table 16. Interchange AADT Model Excluded Variables ...... 97

Table 17. Tolerance and Variance Inflation Factor (VIF) ...... 98

Table 18. Correlation Matrix of Multi-Collineraiaty ...... 99

Table 19. Casewise Diagnostics ...... 100

Table 20. Interchange AADT Model Interpretation ...... 110

Table 21. T24 Model Summary ...... 112

Table 22. T24 Model ANOVA ...... 114

Table 23. T24 Model Coefficients ...... 115 10

Table 24. T24 Model Excluded Variables ...... 120

Table 25. Tolerance and Variance Inflation Factor (VIF) – T24 Model ...... 121

Table 26. Casewise Diagnostics ...... 122

Table 27. Case Summary ...... 124

Table 28. Interpretation of T24 Multiple regression model ...... 134

Table A 29. Interstate 70 Development Units ...... 151

Table A 30. Interstate 75 Development Units ...... 152

Table B 31. Correlation Matrix – Part A ...... 153

Table B 32. Correlation Matrix Part B ...... 155

Table B 33. Correlation Matrix Part C ...... 157

Table C 34. Interchange AADT Model ANOVA Table ...... 159

Table C 35. Interchange AADT Coefficients for Model 1 ...... 160

Table C 36. Interchange AADT Coefficients for Model 2 ...... 161

Table C 37. Interchange AADT Coefficients for Model 3 ...... 162

Table C 38. Interchange AADT Coefficients for Model 4 ...... 163

Table C 39. Interchange AADT Coefficients for Model 5 and 6 ...... 164

Table C 40. Interchange AADT Coefficients for Model 7 and 8 ...... 165

Table C 41. Interchange AADT Coefficients for Model 9, 10 and 11 ...... 166

Table C 42. Collinearity Diagnostics ...... 167

Table D 43. T24 Model ANOVA ...... 168

Table D 44. T24 Model Coefficients for Step 1 ...... 169

Table D 45. T24 Model Coefficients for Step 2 ...... 170

Table D 46. T24 Model Coefficients for Step 3 ...... 171

Table D 47. T24 Model Coefficients for Step 4 ...... 172

Table D 48. T24 Model Coefficients for Step 5 ...... 173 11

Table D 49. T24 Model Coefficients for Step 6 ...... 174

Table D 50. T24 Model Coefficients for Step 7 and Step 8 ...... 175

Table D 51. T24 Model Coefficients for Step 9 and Step 10 ...... 176

Table D 52. T24 Model Coefficients for Step 11 and Step 12 ...... 177

Table D 53. Collinearity Diagnostics (T24) ...... 178

LIST OF FIGURES

Page

Figure 1: U.S. Official Route Map Aug 14 1957...... 15

Figure 2: Primary Highways in Ohio ...... 16

Figure 3. Interstate 70 Ohio Eastbound Exit 141...... 18

Figure 4. Interstate 75 Ohio Exit 18...... 18

Figure 5: Selected Rural and Small Town Exits on Interstate 70 and Interstate 75 in Ohio ...... 22

Figure 6. Interstate 75 Exit Directory...... 28

Figure 7. Basic and Hybrid Forms of Interstate Highway Development...... 31

Figure 8: Special Distribution of Commercial Development at Rural Interchange...... 36

Figure 9. The Marketing Regions in a System of Central Places...... 37

Figure 10. London Circuit and State Circle, Hexagon Business Center, Australia...... 37

Figure 11. Degree of Development and Average Daily Traffic on the Cross Route...... 42

Figure 12. Degree of Development and Distance from Nearest Area...... 43

Figure 13. United States Interstate 70 Map ...... 49

Figure 14. Ohio’s Interstate 70 Map...... 50

Figure 15: Interstate 75 Map...... 51

Figure 16: Ohio Interstate 70 Map ...... 52

Figure 17: Selected Rural and Small Town Exits on Interstate 70 and Interstate 75 in Ohio ...... 55

Figure 18. Distance from Closest Interchange Ramp and Center of the City...... 59

Figure 19. Distance Between Interchanges...... 60

Figure 20. Lodging Establishment Measurement Top and Front Views. American Best Value Inn...... 62

Figure 21. Lodging Establishment Measurement Top and Front Views. Interchange Exit 49, Holiday Inn Express...... 63

Figure 22. Development Units Percentage at Interstate 75 Exits in Ohio ...... 69 13

Figure 23. Development Units Percentage at Interstate 70 Exits in Ohio ...... 70

Figure 24: Development Units Percentage at Interstate 70 Exits in Ohio ...... 71

Figure 25: Log10 (Interchange AADT) Vs Log10 (Intersecting Highway ADT) ...... 103

Figure 26: Log10 (Interchange AADT) Vs Developed Axes ...... 103

Figure 27: Log10 (Interchange AADT) Vs Federal Highway ...... 104

Figure 28: Log10 (Interchange AADT) Vs Distance to Nearest City and Town ...... 104

Figure 29:Log10 (Interchange AADT) Vs Local Highway ...... 105

Figure 30: Log10 (Interchange AADT) Vs Distance to Nearest Interchange...... 105

Figure 31: Log10 (Interchange AADT) Vs Acres of Truck Parking Lots ...... 106

Figure 32: Interchange AADT Model Plot ZRESID Against ZPRED ...... 107

Figure 33: Histogram Normal Distribution (Interchange AADT Model) ...... 108

Figure 34: Normal P-P Plot of Regression Standardized Residual (Interchange AADT Model) 109

Figure 35. T24 Vs Number of Gas and Convenience Store ...... 128

Figure 36. T24 and Square Footage of Restaurants ...... 128

Figure 37. T24 Vs Acres of Truck Parking Lots ...... 129

Figure 38. T24 Vs Intersecting State Highway ...... 129

Figure 39. T24 Vs Square Footage of Gas and Convenience Stores ...... 130

Figure 40. T24 Vs Intersecting Federal Highway ...... 130

Figure 41. Plot ZRESID against ZPRED ...... 131

Figure 42. Histogram of Normal Distribution (T24 Model) ...... 132

Figure 43. Normal P-P Plot of Regression Standardized Residual (T24 Model) ...... 133

CHAPTER 1: INTRODUCTION

1. 1. Background

It has always been a part of man’s nature to travel and communicate with others. According to Preston (1973), these necessities brought forth to the fore the need to develop the infrastructure for transportation and communication systems. The highway system remains the most common form of communication for technologically advanced nations. For instance, in the United States, the modern highway system has made a significant impact on the socio-economic life of everyone.

This has virtually transformed the rural areas to a modern economy.

The awareness of the positive impact of modern highways on growth led to improvement in transportation facilities. As a result, the National System of Interstate and Defense Highways sent a report to the United States Congress in 1939 addressing the limited number of Interstate superhighway system (Moon, 1988). Consequently, the U.S. congress proposed the construction of

26,694.1 miles of transportation network in the United States (Moon, 1988). In 1944, another report addressed the design and size of new proposed Highways (Moon, 1988). As a result, the Congress approved the construction of 39,992 miles of the Interstate Highway System to connect the cities, metropolitan areas, business centers and industrial centers (Moon, 1988). Congress later authorized the National System of Interstate and Defense Highways by passing the Federal Aid Highway Act of 1956. This act was signed by President Dwight D. Eisenhower that authorize the construction of a 41,000-mile network of Interstate highways that would span the nation. By this act, the initial construction of the Interstate Highway began on June 30, 1956. The National System of Interstate and Defense Highways is later referred to as the “Interstate System” (Federal Highway Act of 1956,

1956, p.23).

Five years after President Eisenhower had approved the Federal Aid Highway Act of 1956,

President John F. Kennedy approved the Federal Aid Act of 1961 (Weingroff, 2006). The Federal

Aid Act of 1961 authorized approved over 40 billion dollars for the construction of 41,000 mile of 15

Interstate highway system (Preston, 1973). This multi-lane divided Interstate highway system was completed in 1975 and included 14,000 interchanges throughout the entire Interstate system

(Twark, 1967). The travel route that linked together most of the cities are made and presented in

Figure 1.

Figure 1: U.S. Official Route Map Aug 14 1957. Source (Preston, 1973)

The Ohio Interstate highways were also part of the initial plan which were constructed among other U.S. federal highways. There are 21 Interstate Highways in Ohio including both primary and auxiliary routes. These 21 Interstate Highways are 1,572.35 miles (Federal Highway

Administration (FHWA), 2016). The numbers of primary highways are shown in Figure 2. All the highway locations and highway lines lie within the State of Ohio Interstate and have interchange systems which include many commercial development services on each exit. 16

Figure 2: Primary Highways in Ohio

When the Interstate highway system was being constructed, the effects on economic growth for the rural and small towns were a concern. Equal employment right, quality of life, education, and other services along the Interstate highway were also of serious concern for the residents living close to the Interstate highway system (Hartgen, 1991).

According to Sauerlender et al. (1966), there were many important items incorporated in the Highway Act of 1956 meant to control roadside development units that caused problems in the past. For example, one of these controls was the provision of removing many development units 17 along the roadway which could potentially cause traffic congestion. So, the highway Act prohibited roadside development units such as gasoline stations, fast food restaurants, and motels. However, due to the absence of these service facilities, drivers and travelers have to leave the highway and drive for few minutes to get their required services. In expectation of this occurrence, the highway designers designed interchanges at locations that better facilitate the inflow and outflow of traffic to help move rapidly without traffic congestion.

When the highways were constructed, there was little to no development growth to the narrow band along the cross streets (Sauerlender et al., 1966). Therefore, Hartgen et al. (1992) assessed Interstate 40 soon after construction to find the development elements and forecast for the future development pressure. According to Hartgen et al. (1992), changing travel patterns in metropolitan areas have greatly affected the Interstate highway system. Every large city and town has their own transportation system plans and forecasts. Therefore, the effects of Interstate system on suburban economic growth and commercial development are very well understood, but those factors which measure the amount of development at each interchange exit are very poorly understood. In Ohio especially, some of Interstate highways do not have commercial development and economic growth. For instance, exit number 141 on Interstate 70 are shown in Figure 3 where there are no development type close to the interchange exit. Indeed, the interstate highway system increases job opportunities between rural and suburban residents and also increases the shipment of farm produce to the market. In addition, some Interstate exits have big commercial development growth such as exit number 181 on Interstate 75 (Figure 4).

Figure 3. Interstate 70 Ohio Eastbound Exit 141.Source (Google Maps Aerial Image)

Figure 4. Interstate 75 Ohio Exit 18. Source (Google Maps Aerial Image)

In the earlier studies, Twark (1967) said that the interchange area brings community development opportunities. He further explained that the commercial development units and recreational facilities at interchange exits stimulate business activities, create new jobs for the residents, increase local income, and expand the tax base of the community.

In an earlier study, Preston (1973) found out that these interchange developments have generated some questions such as:

1. What are the conditions that promote or inhibit the growth of this development?

2. What is the nature and range of these growth and distribution of highway-related

development at interchange locations?

To resolve the interchange development problems, the Bureau of Public Roads recommended the following steps (Preston, 1973):

1. Determine the types of land use for different types of rural and urban interchanges

2. Determine the types of land use controlling devices which can be accepted to support

the most satisfactory functioning of the interchanges.

3. Determine the types of economic activities that are required, and

4. Use the correct data to develop the predictive model of the interchange development.

However, twenty five years later Hartgen and Kim (1998) observed that rural and small town commercial developments at exits have been growing so rapidly they have been affecting the

U.S. Interstate highway system. This growth not only changes the land use and value but it also develops the economy of local markets such as access to fast food restaurants, gas stations, motels, parking lots, supermarkets and stores. The most common factors of these developments are traffic volumes, local market buying power, competition between exits, local market sizes, population of county, population of the nearest city and town, geography of the exit relative to major cities and towns, demographic of the exit relative to major cities and towns, interchange types, site visibility, 20 utilities, business attitudes, regional economy and neighborhood condition (Hartgen and

Kim,1998).

Hartgen and Kim (1998) further explained that this development growth at each interchange has different characteristics which ranges from gasoline stations to fast food restaurants, lodging , and gas convenience and store units. The Interstate exits in rural areas which have low traffic volume and is distant from neighboring cities and towns have showed development growth and pressure. Simmilary, Hartgen and Kim (1998) observed that this growth has both positive and negative impacts on interchange exits. For instance, it has positive impact for the communities by increasing tourism and its tax but also has negative impacts by increasing transient crime and traffic volume. Furthermore, interchanges at exits are different in terms of the amount of development; some interchange facilities generate development within a short period of time, while others show little or no growth after many years.

The United States Interstate 70 and 75 national highway were constructed with other

Interstate highways. The U.S. Transportation Secretary Anthony Foxx said that “The work being done along I-75 not only creates jobs, but also lays the foundation for long-term economic growth for entire regions" (Gaffney, 2014). Essentially, Interstate 70 and 75 tie the Interstate highway region together and have facilitated easy accessibility of goods and services. Indeed, the present economic growth on interchanges depicts a very good picture of the modern United States.

The Ohio’s Interstate 70 and 75 system serve not only in state of Ohio but also the entire nation because these two highways in Ohio give accessibility of goods and services to other states.

The interstate highway network provides accessibility to private, government, and public investments in rural and small town interchange communities. Therefore, the most recent development of the Interstate highways and their interchanges are indeed the developmental growth. For example, these interchanges remain the most developed centers in future, just like rail road stations and river junctions were in the 1980s and 1990s (Sauerlender et al., 1966). 21

1. 2. Gap Statement

Development factors at the Interstate exits are studied by other researchers’ nationwide but this research studied investigating interchange traffic and commercial development at rural and interstate highway exits in Ohio only. The map in Figure 5 is the initial focus of this study which include commercial development on each exit. Each interchange has its own number which is named “Milepost” in the Ohio Department of Transportation (ODOT) website. The Geographic

Information System (GIS) was used to investigate interchange traffic and commercial development at rural and small town interstate exits in Ohio. Ohio’s Interstate 70 and interstate 75 were chosen for the research study which contain 69 interchanges in the rural and small town interstate exits

(Figure 5).

Figure 5: Selected Rural and Small Town Exits on Interstate 70 and Interstate 75 in Ohio

1. 3. Research Goal

The goal of this research study is to investigate the relationships between interchange

traffic and commercial development at rural and small towns in selected interstate 70 and interstate

75 exits in Ohio. Therefore, two models will develop: first, Interstate AADT model and the second,

a daily truck percentage (T24) model. The stepwise regression backward model will estimate each development type and their percentage on each exit along Interstate 70 and Interstate 75 highways in Ohio. For each development type, the data is collected from different sources for the regression analyses process. The SPSS Version 22 of stepwise regression techniques is able to analyse all dependent and independent variables.

1. 4. Research Objectives

The specific objectives of this study are as follow:

1. Quantify commercial development at rural and small town of Interstate 70 and Interstate

75 exits in Ohio.

2. Find those factors that influence interchange traffic at the Interstate 70 and Interstate 75

exits in Ohio.

3. Explore the relationships between interchange traffic and exit characteristics.

4. Develop the regression models to estimate Interchange traffic at Interstate 70 and Interstate

75 exits in Ohio.

5. Develop the regression model to estimate daily truck percentage (T24) at Interstate 70 and

Interstate 75 exits in Ohio.

To this end, this study will find and evaluate those factors which influence commercial growth at rural and small town of Interstate 70 and Interstate 75 exits in Ohio. Those factors are studied on considering how the highway satisfies certain demands such as gas stations and convenience stores, truck parking lots, fast food restaurants, lodging (hotels and motels) etc.

However, this study did not include a detail evaluation of those non-local variables which influence 24

local and regional growth. The objective of this study also considered the size of the service station,

the capacity of the gas station and convenience stores, the number of pumps at each gas station,

truck parking lots by acres, lodging and any fast food restaurant along the Interstate 70 and

Interstate 75 exits at rural and small town in Ohio. The current study is exploratory in nature.

1. 5. Justification of the Research Study

Hartgen and Kim (1998) paper was nationwide even though they studied only few variables

such as gas stations, convenience stores, fast food restaurants, sit-down restaurants, and lodging.

This research study developed two models: the first model studying development growth at the

Interstate 70 and Interstate 75 exits in Ohio and the second, studying Daily Truck Percentage (T24)

percentage at the Interstate exits on Interstate 70 and Interstate 75 in Ohio. In addition, these two

models contain 19 variables for this research study.

Furthermore, traffic access to all commercial development units such as fast food

restaurants, hotels and motels, gas stations, parking lots, shopping centers, and movie theaters

generate traffic congestion at the Interstate cross route. This congestion also affect the traffic flow

at the Interstate exit ramps and especially in the main Interstate Highway. This knowledge of the

interchange growth potentials is very essential to be used to alert communities to plan their needs.

However, before resolving land use and traffic congestion problems, it is important to find those

factors which cause development pressure at the interchange exit. The factors which influence the

growth at the Interstate exits are already determined. Therefore, modeling systems which will be

able to predict future development growth at the rural and small town Interstate exits in Ohio are

required.

Additionally, what is needed to develop the interchange AADT model and daily truck

percentage (T24) model at the Interstate exits with high utilitarian value that can provide a maximum explanation with a minimum number of independent variables? These kinds of models are developed in the following chapters. They are composed of a system of regression equations where 25 in the first model, the dependent variable is Interchange Annual Average Daily Traffic (AADT) and in the second model, the dependent variable is the daily truck percentage (T24). In both models, the independent variables are the developmental factors that cause the development pressure at rural and small town Interstate exits.

CHAPTER 2: RELATED LITERATURE REVIEW

2. 1. Introduction

This chapter presents a related literature review on those factors which influence

commercial development growths at the Interstate exits in general. There are only few studies

conducted in this field such as:

 Sauerlender et al. (1966) who studied 36 Interchanges in Pennsylvania;

 Twark (1967) who studied 105 non-urban interchange sites on Pennsylvania;

 Preston (1973) who studied 126 rural interchanges in the state of Oregon;

 Norris (1987) who studied 354 interchanges on Interstate 75;

 Moon (1988) who studied 65 interchanges in many different counties in the state

of Kentucky;

 Hartgen et al. (1992) who studied 22 interchanges on Interstate 40 from Raleigh to

Wilmington in North Carolina, and

 Hartgen and Kim (1998) studied 63 interchanges along the entire U.S. Interstate

highways.

This chapter will discuss their finding which will give an extensive understand of those factors which influence development growth at the interchange exits. Localized Commercial developments at rural and small town exits have been growing so rapidly that they have affected the U.S. Interstate highway system. This growth not only changes the land use and value but it also develops the economy of local markets such as access to fast food restaurants, gas stations, motels, supermarkets, and stores (Hartgen & Kim, 1998). Interchange development also provided recreation opportunities and job opportunities for neighboring towns and cities. However, there are only few studies conducted on factors that influence economic development at rural and small town interchange exits (Hartgen & Kim, 1998). Hartgen et al. (1992) studied the growth at rural 27 interchanges on Interstate 40 in rural North Carolina that “many interchange developments are strip-like patterns between the community and the exit (p.3).”

In his research, where Norris (1987) observed that automobile services close to Interstate highways are important factors of the commercial strip’s economic development. Norrish (1987) conducted survey of 1524 miles route on Interstate 75 which connects Florida, Georgia, Tennessee,

Kentucky, Ohio and Michigan. He noted that there are 354 nonmetropolitan exits along Interstate

75 between Florida and Michigan, and found that close to 90 percent of exit establishments are located within 0.5 mile from the exit ramp. His study gives us a wide and fundamental description of commercial development on Interstate exits, where he provides a good description of exit survey, exit form, interchange scale, mix incidence, and interchange structure on Interstate 75.

This literature review covers his survey method and also briefly describes interchange exit development on Interstate 75 as well as in Ohio. While Norrish’s (1987) survey is a fundamental study to this research study, commercial development on Interstate exit studies are described below.

2. 2. Exit Survey

In this research study, there is no exit survey conducted to measure commercial development at rural and small town Interstate exits in Ohio. However, some other researchers conducted commercial development exit surveys at the Interstate exits. This research study focuses on the Hartgen and Kim (1998) research study, which did not survey all the 63 sites, they found these exits using GIS software. Based on the Hartgen and Kim (1998) study method which found all 63 exits sites by GIS, this study follows the same procedure to quantify exit development types on rural and small town of Interstate 70 and Interstate 75 exits in Ohio. Indeed it was discovered that many elements of the American cultural landscape exhibit structure are the same for rural and small town interchange exit.

Gasoline station, motel, outlet mall, movie theater, fast food restaurants and many more demonstrate the remarkable adaptability and rapid change of the American Interstate highway on 28 interchange exit (Norris, 1987). A good example is Norris’s (1987) survey of 1524 route miles of

Interstate 75 which connect Florida, Georgia, Tennessee, Kentucky, Ohio and Michigan. He found that Interstate 75 caries heavy commercial traffic, serving major nodes like Cincinnati, Atlanta, and

Detroit. He reported 354 exits are excluded from Interstate 75 which serve major metropolitan areas.

As a result, those exit commercial clusters are only isolated from major sources of revenue rather than Interstate traffic. However, maps are available which include data of 2,598 establishments at

354 exits along Interstate 75 where all development types are gasoline stations, motels, eating establishments and some other commercial cluster, as shown in Figure 6.

Figure 6. Interstate 75 Exit Directory. Source (Norris, 1987) 29

This map, made in 1987, describes the American roadside interchange exit development

types. The map is not made to scale and it only describes interchange exit development types

(Norris, 1987). The researcher traveled 210 miles in Ohio along Interstate 75 where he observed

that 11 interchange exits were undeveloped, 52 interchange exits were developed, and the numbers

of commercial development establishments were 467 on these interchanges, which are shown in

Table 1.

Table 1: Non-Metropolitan Exits, Interstate 75 States Florida Georgia Tennessee Kentucky Ohio Michigan Interstate 75 Mileage 211 355 162 192 210 394 Undeveloped Exits 3 14 2 4 11 18 Developed Exits 39 86 33 29 52 63 No. of Establishment 274 720 229 361 467 567 Establishments per Exit 7 8.4 6.9 12.4 9 9 Source (Norris, 1987)

2. 3. Exit Form

Interstate exit forms range from isolated intersections lacking of services, to fully commercially developed intersections including all types of establishments. According to Norris

(1987) research, an exit should fulfill driver and passenger’s immediate needs and quality of satisfaction that they are able to get in terms of convenient access and easy Interstate re-entry. He further added that cluster of exit services are subjected to two main principles. First, commercial establishments should be located close to the exit ramp and second, such establishments should be close to the complementary service, as shown in Figure 7.

The basic form of Interstate exits should be characterized by how many of its four sideway axes are developed to provide all services to motorist (Norris, 1987). Thus, arrangements of services at Interstate exits depend on development chronology, for the initial arrivals naturally favor

locations close to the ramps (Norris, 1987). This arrangement is also related to driver or passenger

desires; for instance, if a driver or passenger needs a quick service then it should be arranged in 30 prime location interchange exit. According to Norris (1987), such arrangement depends on the businesses’ ability to attract top-dollar reward for premium sites, and if businesses will sacrifice availability for agglomeration and settle for motel strips, fast food restaurants and gasoline paths.

Norris (1987) further observed that, in general, agglomeration infrequently exceeds modest quantities at rural Interstate exit collections because the total number of businesses supported is infrequently very large. In his survey on Interstate 75, he observed that there are two kinds of exit forms as shown in Figure 7: basic forms and hybrid forms. Hybrid forms contain additional exit cluster factors such as commuter traffic, or pre-expressway radial highway commerce, parallel strips, and major mall. While these forms are very common in metropolitan areas and tourist areas

(Norris, 1987), the parallel strips are connected from two or more exits which tend to primarily serve visitors and local residents on a highway. 31

Figure 7. Basic and Hybrid Forms of Interstate Highway Development. Source (Norris, 1987) 32

2. 4. Interchange Scale

Hartgen and Kim (1998) measured the center of business or closest major cities within a

distance of 1 to 3 miles from the interchange. The distance from the center of city and the

interchange exit ranged from 0.3 mile to 10 miles. However, Norris (1987) found that when he

analyzed 354 sites along Interstate 75, “the reach of commercial roadside development sustained

by Interstate 75 does not commonly exceed one mile on either side of its exits.” Based on Norris

(1987) study, Table 2 shows the maximum distance of services from Interstate 75 exits below.

Table 2: Maximum Distance of Services from Interstate 75 Exits Distance in Miles to Farthest Establishment No. Developed Axes Less Than ½ ½ - 1 Greater than 1 (percent of exits, row sum) 1 87.0 13.0 - 2 69.7 21.1 9.2 3 56.1 25.6 18.3 4 37.7 48.0 14.3 Source (Norris, 1987)

In Table 2, the first column indicates the number of developed axes, where 1, 2, 3, and 4 are the number of axes on each interchange along Interstate 75. When four-leg interchange or axes were developed in 1987 on Interstate 75, the majority of establishments were located within a half of a mile from the exit ramp (Norris, 1987). According to (Norris, 1987), weak exit clusters generally imply some external source of income, for instance a nearby town. It should be useful to measure exit commercial stretch and directional bias relative to cities and small towns near the

Interstate. The range or scale of exit of commercial clusters to rural and small town Interstate exits are usually very compact, as they should be, while the real driving time from the highway to closest establishments ranges from a few seconds to a few minutes (Norris, 1987). However, driving time 33

is less important than the number of services that are available in each exit to fulfill passenger or

driver needs.

2. 5. Mix and Regional Incidence

The mix of services along the Interstate 75 highway provided three kinds of American

roadside commerce: food, fuel, and lodging. As shown in Table 3, gas stations cover 37 percent of

exit developments on Interstate 75. The major gasoline outlets operations were linking the

industrial Midwest to Florida’s resort communities while the minor gasoline outlets and truck

parking lot exits were characterized in Florida and Tennessee (Norris, 1987). Various types of

eating establishment units cover 24 percent of Interstate 75 exits (Table 3). This percentage mainly

covers Ohio exits, which are caused from high traffic volume and trade from nearby town centers.

Independent diner and truck stops are the main elements of exit morphology in Ohio and Kentucky

segments of Interstate 75 exits (Norris, 1987).

Table 3. Establishments by Type and Regional Incidence, Interstate 75 Type of Roadside Business Number of Locational Indices by State Establishment FL GA TN KY OH MI (index 100 for Overall I-75 Incidence) Gasoline Stations Major Oil Companies 730 90 111 131 119 80 79 All other gasoline stations 231 120 100 118 91 103 83 Motels and Motor Hotels Major Chains 179 149 119 95 96 74 74 All other Motels 187 97 149 73 92 74 73 Eating Establishments Fast Food Chains 203 75 80 123 53 121 138 Restaurant Chains 167 74 114 170 60 127 66 All other Eating 263 65 91 9 134 146103 Establishments Other Services Retail outlets, plazas, and 357 179 94 73 78 128 68 malls All other roadside services 281 50 49 89 118 73 201 Source (Norris, 1987)

Hotels and motels cover 14 percent of the establishment units in Interstate 75 exits (Norris,

1987). These establishment units are divided between chain and independent operations as shown

in Table 3. The number of motels are not adequate in the mix of exit establishments in Michigan

and Ohio because motorist are unlikely to stay overnight and away from the Interstate highway

(Motels are located in far distance from the Interstate exit in Interstate 75 as notified in Table 1 )

(Norris, 1987).

2. 6. Interchange Structure

According to Norris (1987), the structure of a commercial cluster exit arises from the reality

that the majority of its customers share the same point of arrival and departure. This means that the

driver makes one or two fixed cycles for its axes. The structure of interchange is sequential and not

made difficult by many roadway approaches. Also, it is not made by key traffic generating anchors

(Norris, 1987). Therefore, the structure of interchange exits becomes a little dotted by major retail

magnets with main streets, pedestrian malls, and suburban strips. Consequently, it is natural to

expect that drivers will bear a lengthy deviation from the main highway driving overnight, but will

expect a very short drive to get gasoline or fast food. This supports the theory that, “the typical

sequence of services at an exit should balance whatever time is wasted with whatever time is productively spent. The consecutive sequence of exit establishments should therefore be gasoline stations first, followed by eating establishments, stores, and finally lodging facilities” (Norris,

1987).

The gas station remains one of the key factors of interchange exit development. According to Norris (1987) survey, gas stations are the most common type of developments located close to exits. He was able to capture 408 out of 836 spots development along Interstate 75. In reality, the gasoline station is not a champion as far as the interchange exit game is concerned. Although gas stations and motel chains are very different, it is clear that these are prime commercial properties close to exit locations, where drivers first stop en route then destinations. In contrast, chain eating 35

development units on Interstate 75 rarely command prime exit properties because they are not

situated in optimum locations. Norris’s (1987) survey on Interstate 75 reveals that fast food and

restaurant chain outlets are in at least four distinct locations from the exit for two main reasons.

First, the chain helps the prime exit cluster which makes a familiar series of restaurants advertising

roadside cuisine. Second, major fast food and restaurant chains were located at the end of

interchange exits where the motorist had enough space to form a waiting line.

In the same vein, Hartgen and Kim (1998) found that, if more gas stations are available at

an exit, more travelers will stop and further needed development, such as restaurants and motels,

will start growing. He defined highway related development, such as service station, restaurants,

motels and others, whereas rural interchange is defined as that located outside the urbanized area

and where the “interchange areas” prior to highway construction are in rural land use.

There are a number of theories for commercial development at interchange exits. One of

them is called Classical Land Use Theory of Smith, Taaffe, and King (1968) which stated that there

are two ways in which a company can gain the benefits of an agglomeration. One is to increase the

concentration of products by enlarging its factory and the second is to select a location close to the

interchange exit. Preston (1973) further stated that “social” agglomeration is earnings profits from

sharing equipment and services, greater division of labor, purchasing, and marketing. This “social”

agglomeration is evident in interchange development. For example, if a motel is located at the

interchange exit then many other service stations and stores start up and the development starts

growing. The second land use interchange development theory is of the (Alonso, 1964). Alonso’s theory explains that, “Isochrones are shown schematically for a city or interchange having two high speed highways, XX and YY, crossing at right angles at the center of the city. The rest of the area has a grid system of streets on which travelers doing their business” (as cited in Preston, 1973), this theory is better explained in (Figure 8) where x and y directions are shown as passing from the middle of the business center. 36

Figure 8: Special Distribution of Commercial Development at Rural Interchange. Source (Alonso, 1964)

There is another theory of commercial development at interchanges which is called the

Central Place Theory. Christaller (1966) stated that, “A town is the center of a regional community and the mediator of the community’s commerce; thus, functioning as the central place of the community” (Figure 9). Following the Central Place Theory, an interchange is constructed in

London which is shaped like Hexagon (Figure 10). This Figure shows that a hexagon is the most economic shape for business areas of central places, and an interchange of the hexagon is to be constructed in central places in Australia.

Figure 9. The Marketing Regions in a System of Central Places. Source (Christaller, 1966)

Figure 10. London Circuit and State Circle, Hexagon Business Center, Australia. Source (Tests,

2016).

There are certain critical variables to find economic growth at the Interstate exit. Eagle and Stephanedes (1987) suggested four ways that affect economic growth at the Interstate exit: residential location, work place location, enterprise location resulting from change in labor supply, and enterprise location resulting from decreased transportation costs. They further found that there are some counties along Interstate 40 which had advantages over other counties with respect to employment and population growth within 25 miles from the metropolitan area. These growths were related to service stations, motels, and fast food restaurants which were associated with serving highway users but not associated with a manufacturing operation (Eagle & Stephanedes,

1987).

Many other researchers also studied the correlation between the economic units and highway Interstate where they suggested:

1. Wilson (1986) suggested that it was very hard to connect the relationship between

economic development growth with the highway at the beginning of the 1960s. He

determined that the economic development growth is a very complicated process where

the role of transportation is not enough for fundamental relationships to be recognized.

While Hartgen et al. (1992) suggested that economic development growths have a

significant relationship to highway investments if some others criteria is met.

2. The University of Iowa dictated a report, if critical factors are not presented then investing

in better highways will not foster economic growth (Forkenbrock, et al., 1990).

3. Huddleston and Pangotra (1990) described that we can only gain the net amount from the

highway investments if we can employ all those human and other resources which are not

previously used.

4. Bohm and Patterson (1971) studied all counties population growth changes in the United

States between 1960s to 1970s, they found that population growth has a significant

correlation with the Interstate highway. 39

5. Stephanedes (1985) stated highway development investment affect community patterns,

location of firms and how the resources can be develop. However, two other researchers

argued and stated that the relationship between employment and highway expenditure is

associated with two main factors such as:

a. Higher employment levels attract higher levels of highway expenditure (Eagle &

Stephanedes, 1987).

b. During the year of construction, employment levels increase (Eagle &

Stephanedes, 1987).

Some other researchers studied interchange exit growth and as stated below:

6. Stein found that a large portion of development units close to mostly rural interchanges of

highway-adjusted business. For example: gas station, truck parking lots, fast food

restaurants and motels. These development units can brought a rapid growth in shopping

centers, industrial parks, apartments, churches, and schools near mostly suburban

interchanges (Hartgen, 1992).

7. Another scholar Moon Jr (1987) discovered that there are four variables which are very

important for the interchange development study: “the amount of development in place

before the interchange was built, distance to the nearest neighboring interchange, traffic

volume, and distance to the nearest city”. However, Hartgen et al. (1992) strongly supports

that distance to regional centers and traffic volumes are key factors that influence the

commercial development at the interchange locations.

Highway development will have an impact on the regions through which they travel.

Moreover, there is a big discrepancy over the kind and the strength of the impact they make. In addition, Moon Jr (1987) and Epps and Stafford (1974) have strongly suggested that there are six variables which have an impact on the interchange developments, “(1), Average daily traffic (ADT) on interstate highway; (2), ADT on crossroads; (3), location and population of communities within 40

10 mi of the interchanges; (4), distances to the nearest major urban center; (5), amount of

development before the interchange construction; and (6), distance to the next interchange.”

Soon after the initial construction of the Interstate highway system, there was a lack of

attention to the non-urban Interstate exits. However, the social scientists, planners and other

researchers’ major points of contention was bypassed in order to reshape the non-urban Interstate

highway system in the United States (Moon, 1988). Most of the research study focused on land

use change, Interchange area development, the decentralization of retail and industrial activity,

commercial land use succession, redevelopment in the central city, and aggregate land use change

(Wilder, 1985). Only a few studies focused on development growth at the interchange exits.

To summarize, many researchers agreed that relationship among factors which influence

commercial development growth at the interstate exits is very complex. Usually, the mount of

business activity observed at the interstate exit is directly related to traffic factors, more

specifically, cross route traffic, and traffic volume and truck mix on the interstate exit (Hartgen,

1992). Therefore, this research study main focus is to model AADT at the Interstate exit and daily

truck percentage (T24) model which are associated with traffic factors. Location factors also influence commercial development growth at the interstate exit: “the distance from the interchange to major cities, the distances to the next interchange in each direction, the proximity to rest areas, and competition from other interchanges” (Hartgen, 1992). He further recommended that site factors also influence commercial development growth; for instance, “sewer and water service, zoning, visibility, ease of access and egress, slope, and advertising” (Hartgen, 1992).

2. 7. Related Methodology

Sauerlander et al. (1967) studied 36 non-urban interchanges in Pennsylvania. Data was collected through a field survey, the Pennsylvania Department of Transportation and others. The following variables were considered for the study:

1- Type of interchange 41

2- Average daily traffic (ADT) on the interstate and cross-route

3- Distance to the nearest urban area

4- Age of the interchange

5- Topography within the interchange community

6- Population characteristics

7- Market value characteristics

8- Service stations

9- Restaurants

10- Motel

11- Industries

In earlier studies, the data was analyzed using a sample graph where “average units of the development per interchange” was on the Y axes and “average daily traffic distance in miles” was on X axes, which are shown in Figure 11 and Figure 12 below. In addition, a simple correlation analysis was run. The proportion of variation explained by each predictor variable was also calculated (Sauerlander et al., (1967).

Figure 11. Degree of Development and Average Daily Traffic on the Cross Route. Source (Sauerlender, 1967) 43

Figure 12. Degree of Development and Distance from Nearest Area.Source (Sauerlender et al. 1967)

Preston (1973) has developed three predictive models. The three models which also serve as the dependent variables in his research study are presented below:

Model 1: Area of commercial development 44

Model 2: Compactness of commercial development

Model 3: Shape-Pattern of commercial development

There were 11 predictor variables which were used in each predicted model separately.

1. Population per mile ratio of the nearest urban center on the intersecting route

2. Distance to the nearest developed interchange

3. Average daily traffic on the interstate highway (ADT)

4. Average daily traffic on the intersecting route (ADT)

5. Distance to the nearest urban center on the interstate highway (miles)

6. Distance to the nearest urban center on the intersecting route (miles)

7. Number of interchange quadrants with frontage roads

8. Number of interchange quadrants available for development

9. Environmental suitability of an interchange for teritorial activities (topography)

10. Interchange design

11. Interchange exposure (seconds of visibility at 70 mph)

The preliminary analysis of the three models including dependent and independent variables was developed. The statistical measures of mean, standard deviation, and range of the data were calculated in order to find that variability exists among the highway and interchange characteristics. A further step in the analysis of the data was to develop simple correlations for each of the three models separately. Then, the three models were developed using the multiple linear regression technique. The final equations of the three models were also developed. The structure of the linear multiple regression model of the three models is shown below:

Y = a + b1X1 + b2* X2 + b3* X3 …………………………bn* Xn

Y = Measure of commercial development models

a = Constant (intercept). 45

b1, b2, b3, ….. = The regression coefficients, and

X1, X2, X3…… = Independent variables to influence the spatial distribution of commercial development

In a research study by Moon (1988), the regression analysis program was used for modeling land use change around non-urban interstate highway interchanges. Moon (1988) observed that various forms of the regression methods existed which can be used for different kinds of application and purposes, but the stepwise regression technique can especially be used for data analysis. The stepwise multiple regression technique is an acceptable and widely used for handling large number of variables. According to Moon (1988), “stepwise multiple regression analysis is a search procedure, for the technique first determines the contribution of each variable to total variance then enters variables into the regression equation in order of contribution, with the most influential variable entered first.” The stepwise multiple regression technique is designed to search in the variable list for the combination of variables which can explain the most variation in the outcome variables and finally it can be used to build the regression equation for the introduction and excluded variables (Moon, 1998). Moon studied 65 interchanges and collected 31 variables.

Finally, he developed two models namely; the non-metro model and the rural model. The non- metro model which accounted for 53.07 percentage the total variance in the outcome variable and had four variable statistically significant outcome 31 variables, while the rural model accounted for

68.9 percent of the total variance in the outcome variable and had seven variable statistically significant in the outcome variables.

In this study, the Hartgen and Kim (1998) was adopted. It includes the classification and linear regression models used together to explore relationships in the data set. The classification system Knowledge Seeker (Angoss Software International Ltd.) was used to find those predictors which most effectively explain the number of development units. According to Hartgen and Kim 46

(1998) that for subgroups within each classification, linear regression models were used to sharpen or improve overall predictive ability. Maps were prepared showing the predicted development by location to determine the geographic bias in the models.

In an earlier research, Hartgen et al. (1992) studied 22 interchanges on six North Carolina interstates and on two South Carolina interstates. A simple field sheet was used to record information on each interchange exit. The survey report of the interchange exits included 36 predictor variables and the data were entered into an excel spreadsheet. The stepwise multiple regression technique was used to develop commercial development growth equations separately for residential development, fast food and sit down restaurants, gasoline stations, total interchange, and motels. In addition, the correlation matrix of all the predictor variables were also developed.

More specifically, in the Hartgen et al. (1992) study, interchange development was done in seven stages with respect to interchange AADT volume. The typical sequence of the interchange development is shown in Table 4. Hartgen et al. (1992) have designed interchange development in seven stage in respect to interchange AADT. For example, if the interchange exit has 4000 AADT on the cross street and is within 10 miles of a small town and an interstate rest area, the interchange is likely to support one gas station and one small motel (State 2A) (Hartgen et al., 1992) (Table 4).

In addition, if interchange exits are closer than two miles to the community which has the traffic volume greater than 12,000 AADT, “economic integration” (Stage 2C) can occur (Hartgen et al.,

1992) (Table 4). 47

Table 4. States in Interchange Development

Source: Hartgen et al., 1992) 48

2. 8. Summary of Literature

After a review of literature, it is concluded that there are some factors such as gas station and convenience stores, lodging and fast food restaurants which influence commercial development growth at the interstate exits. In addition, they recommended that there are some other factors which influence commercial development growths. Therefore, this research study will study all those factors which were studied by other researchers. This research study will collect and combine all other researchers’ factors in the data analysis section and also will focus on those factors which were recommended for future research study.

Most of the scholars studied and focused on finding factors that influence commercial development growth at the interchange exits. The correlation analysis was the main method of their research study. The goal was to find the relationship between the interchange traffic and the development units while they evaluated each predictor at a 0.01 and 0.05 significant level. The linear regression equation were used to make the general commercial development growth models at the interchange exits. To date, the most sophisticated and successful attempt at developing a predictive model of commercial development growth at interchange areas is a study prepared by

Moon (1988). Moon’s (1988) primary investigation was to develop a model for predicting the commercial development that is likely to occur at rural and small town areas on the interstate highways in Kentucky.

The following chapters will describe the methodology for the data collection and statistical analysis as well as the results and conclusions of the analysis.

CHAPTER 3: DATA ANALYSIS

3. 1. Introduction to Interstate 70 and Interstate 75

The setting for this study is rural and small-town interchanges on Interstate 70 and

Interstate 75 in Ohio. Interstate 70 is an Interstate highway that runs between Cove Fort, Utah and

Baltimore, Maryland Interstate and runs through many major cities such as Denver, Kansas city,

St. Louis, Indianapolis, Dayton, Columbus, Cambridge, Hagerstown, and Baltimore (Figure 13).

The total length of Interstate 70 is 2,153.13 miles making it the fifth longest Interstate highway in the United States (FHWA, 2016). Interstate 70 connects Ohio and West Virginia as it enters Ohio from the east side of the interchange with US 40 at Richmond, Indiana and enters West Virginia at

Wheeling. Interstate 70 passes through 10 counties in Ohio Preble, Montgomery, Clark, Madison,

Franklin, Fairfield, Licking, Muskingum, Guernsey, and Belmont (Figure 14). The length of the

Interstate 70 segment in Ohio is 225.60 miles (AARoads, 2015).

Figure 13. United States Interstate 70 Map 50

Figure 14. Ohio’s Interstate 70 Map.

On the other hand, Interstate 75 is another major Interstate highway that runs from southern

Florida to the northern Michigan. It is the seventh longest Interstate highway and the second longest north south Interstate highway of the United States. Interstate 75 passes through six states: Florida,

Georgia, Tennessee, Kentucky, Ohio, and Michigan (Figure 15). It is 1,786.47 miles long connecting Kentucky and Michigan through Ohio (FHWA, 2016). It enters Ohio from Kentucky of Brent Spence Bridge to Cincinnati and enters into Michigan through the Great Black Swamp.

Interstate 75 runs through Ohio between Cincinnati and Toledo and passes through 11 counties:

Hamilton, Butler, Warren, Montgomery, Miami, Shelby, Auglaize, Allen, Hancock, Wood, and

Lucas (Figure 16). Interstate 75 is 211.55 mile long (ODOT). 51

Figure 15: Interstate 75 Map. 52

Figure 16: Ohio Interstate 70 Map

3. 2. General Form of Regression Models

An examination of related literature and the result of previous studies of commercial development at rural and small town Interstate exits for 63 sites, studied by Hartgen and Kim

(1998), and modeling land use change around non-urban Interstate highway interchange, studied by Moon (1988), suggest that commercial development at rural and small town Interstate exits is growing rapidly. This growth brings land use change in the interchanges as well as commercial development at the interchange exits. Therefore, a mathematical model can be used to investigate 53

the relationships between commercial development and AADT at the Interstate exits and truck

percentage at the Interstate exits in Interstate 70 and Interstate 75 in Ohio.

According to Preston (1973) an interchange can be described by four types of characteristics: geographical, demographical, physical and site, and by the area and spatial distribution of its local land use. Therefore, these interrelated interchange characteristics should be presented by statistical equation model. In the first model, the Annual Average Daily Traffic

(AADT) at an interchange exit (dependent variable) and development types (independent variables) at each interchange exit can be used to make this model. In the second model, the daily truck percentage (T24) at the interchange exit is the dependent variable and the development types are

independent variables which can be used to make a regression model. The prediction of interchange

development growth at the interchange exits will allow government, public, and private sectors to

plan for future development at interchange exit.

In construction of the equations of the Interchange AADT model and daily truck percentage

(T24) model, the problem of finding that form the model equations which best describe the

relationship between dependent and independents variables should be considered. In this study, the

linear regression analysis is the primary estimation method. For instance, the equation form will

be:

Y = F (X1 + X2 + X3 + X4 + X5, ……., Xn)

Where Y represents the dependent variables and X represent the independent variables of

the equation. In this equation, the value of Xs predict the value of Y. There are some variables

needed to form the regression models in order to quantify the development growth at the

interchange exits in Interstate 70 and Interstate 75 in Ohio. Thus, two models are needed. The first

model forecasts Interchange AADT and the second model will predict daily truck percentage (T24); 54 specifically, how large trucks can affect development growth at the interchange exits. This section of the study contains two sections: database development and preliminary analysis of the data set.

3. 3. Data Base Development

This section contains the description of proposed analysis and the procedures of how and from where the data was obtained. Each variable source and the analysis process for obtaining the variable are explained below.

3. 3. 1. Sample of Interstate Exit Selections

The goal of the study was to quantify commercial development at rural and small town

Interstate exits. Therefore, it was clear that only those interchanges should be identified which were located at rural and small town Interstate 70 and Interstate 75 exits in Ohio. To find these Interstate exits at the interchanges locations, the Geographic Information System, GIS, 10.3 version was used to determine these interchanges. The Ohio Department of Transportation, ODOT, has

Environmental System Research Institute (ESRI) shapefile which contain all Interstate routes in

Ohio. These shapefiles such as Interstate routes, counties, and census are analyzed together to determine rural and small town Interstate exits in Interstate 70 and Interstate 75 in Ohio. Finally,

69 rural and small town Interstate exits were identified through GIS software (Figure 17). This map, fundamental map of the research study, contains milepost number representing rural and small town Interstate exits only, while the urban interchanges are excluded from the map. The map was made by GIS and each Interstate number or milepost were identified. Next, the milepost or

Interchange location was determined. All interchanges on Interstate 70 and Interstate 75 outside urban area boundaries were considered rural interchanges and were included in the database.

Figure 17: Selected Rural and Small Town Exits on Interstate 70 and Interstate 75 in Ohio

3. 3. 2. Traffic Count Location Maps

The Ohio Department of Transportation has traffic count locations maps which are available on the ODOT website. These maps contains all exit information such as federal highways, state highways, mile markers and intersecting highways. These maps also contain traffic count ID 56 numbers for 24 hour and 48 hour, which are the reference numbers of AADT. These were very helpful in tracking actual AADT of the Interstate exits.

The third step was to determine the Average Annual Daily Traffic (AADT). The Ohio

Department of Transportation has all traffic count data in their website. However, there are two types of AADT data: traffic counts (AADT) on the Interstate highway for the eastbound and westbound side of exits, and AADT on the exit cross street to and from town or intersecting highway AADT. . Also, each traffic count has its own ID number which are available in the traffic count location maps, as described in the above section. These AADTs are further described below.

3. 3. 3. Interchange Annual Average Daily Traffic (AADT)

The next step was to determine the Average Annual Daily Traffic (AADT). The Ohio

Department of Transportation has all traffic count data in their website. However, there are two types of AADT data: traffic counts (AADT) on the Interstate highway for the eastbound and westbound side of exits, and AADT on the exit cross street to and from town or intersecting highway AADT. Also, each traffic count has its own ID number which are available in the traffic count location maps, as described in the above section. These AADTs are further described below.

The annual average daily traffic is defined as the total volume of vehicle traffic of a highway or road for a year divided by 365 days. According to Kansky (1963), the importance of traffic volumes in affecting growth at interchange areas is evident due to the fact that traffic is the source of potential interchange service users; the greater the traffic flow, the greater the potential number of service users. In this study, AADT counts on the Interstate highway on all ramps of the exits were obtained from Ohio Department of Transportation (2014). The total interchange AADT was taken as the sum of all the individual ramp AADT values. The traffic data, or AADT, was available from 2010 to 2014, while most of the traffic data was from 2013. 57

3. 3. 4. Daily Truck Percentage (T24) Data Collection

ODOT (2007) defined that, “T24 represents the percentage of ADT that is comprised of

heavy and commercial trucks (B&C commercial classes).” T24 is the daily truck percentage of

truck traffic in the total stream for 24 hours. Heavy and commercial trucks (B&C commercial

classes) data was obtained from Ohio DOT traffic survey report. To date, B&C commercial classes

traffic data was available from 2012 to 2014. AADT was found and discussed in previous section.

So, T24 data was calculated using equation 1.

& Daily Truck Percentage = (1)

3. 3. 5. Intersecting Highway Annual Average Daily Traffic (ADT)

The Intersecting Highway AADT id defined as the AADT of the AADT of the highway intersecting the interstate at the interchange location. According to Preston (1973), the higher the traffic volumes, the greater the land use intensity at interchange areas. Preston (1973) further described that field observation shows that the most highly developed interchanges are nearly always associated with high traffic volumes on the intersection route. This kind of traffic count, or

AADT, on the intersecting road to and from town were obtained from the Ohio Department of

Transportation traffic survey report (2015). Most traffic data was for 2013, with a few 2010 counts.

3. 3. 6. Population of the Nearest Cities and Towns

Populations of the nearest cities and towns were collected from the Ohio Census Bureau

Research Office which has each town, city, and town census data (Ohio Research Office, 2015)

(Population-Estimate, 2015). In addition, some small towns such as Abledell have not been included in the past census counts, so there was no census information for the community.

Therefore, the closest county or city populations that included the Abledell boundary census data 58 was used for the analysis. Some interchanges do not have any towns or villages near to the interchange exit, closer village or town Census data is used for the analysis. However, there was some problem while adding the closer town or village census data to the closer interchange: some towns and villages such as Interchange 193 and 198 on Interstate 70 cross the county boundary.

Interchange 193 is not close to any village or town, but Fairview census data is added even though both interchanges are located in different counties.

3. 3. 7. Population of County

County population data was collected from the Ohio Department Services Agency.

Population projects for individual counties are available in the Development Services Agency website by clicking on the map (Ohio Department Services Agency, 2015). To date, the data of

2013 was available.

3. 3. 8. Distance to the Nearest City and Town Centers

The official website Ohio State offers detailed information for each city, township and county. Each city, township and county has standard map where the center of the city is showed.

The center of the city is could also be found from the map available in the ODOT website. The

Google Map tool was also used to find the distance between the city and closest interchange ramp on Interstate 70. For example, Interchange 10 on Interstate 70 is located close to Eaton city in

Preble County. The official Ohio State website, https://ohio.gov/government/localities/ which contains county maps was used. The county maps has a list of all counties in the State of Ohio. By clicking the county map displays the interstate number, interchange exit number and center of the city are displayed. Once the center of the city was identified, the Google Map was used to find

Eaton city center and the closest Interchange 10 exit ramp. If direction is chosen followed by a click on the exit ramp and the center of the city, the distance will appear between the exit ramp and the center of the city (Figure 18).

Figure 18. Distance from Closest Interchange Ramp and Center of the City. Source (Google)

3. 3. 9. Distance to the Nearest and Furthest Neighboring Interchange

The distances to the nearest neighboring interchange was measured using the Google Map tool. By clicking on the Direction command in Google Map tool, and click the starting point of the exit ramp and then clicking on the other interchange exit ramp entrance point (Figure 19). The distance between the two interchanges appears in the screen which was recorded in the database excel sheets.

Figure 19. Distance Between Interchanges. Source (Google)

3. 3. 10. Exit Development

The exit development was defined by many researchers. For example, Hartgen and Kim

(1998) defined development units within 1 mile distance from the interchange exit. Twark found that the establishment units are constructed at Interstate highway interchanges within a half mile of the intersection, some development units are mixed with the service stations such as gas stations and convenience stores (as cited in Moon, 1988). The total establishment, or development units, to the closest interchange exits are obtained in both directions in order to estimate the competition share between the two sides of the interchange exit. The data of the exit development units such as 61

gas station, lodging, and fast food restaurants were collected from a book called “The Next Exit

2015: The Most Complete Interstate Highway Guide” (Watson & Next Exit, 2015). The exit

developments are written below:

1. Number of gas station and convenience stores

2. Number of gas pumps

3. Number of lodging establishments (hotel and motel)

4. Number of hotel rooms

5. Number of restaurants

6. Truck stop and truck Parking lots

The square footage for each development units are also determined using the aerial images of google maps and the distance measurement tool to measure the size of the building.

7. Square footage of gas station and convenience stores

8. Square footage of lodging (hotels and motels)

9. Square footage of restaurants

By right clicking on the aerial image of google map, the measure distance measurement bar appears.

By clicking on all four sides of the building, the distance measurement bar appears which includes the total square footage of the building. For example, two different loading establishments which have different shapes or dimensions are shown in Figure 20 and Figure 21. By right clicking on the aerial image on google map, the irregular shapes of the loading establishment are measured by few clicks and finally the total square footage measurement bar appears (Figure 20 and Figure 21). The same techniques are used to measure gas stations and convenience stores, and restaurants along

Interstate 70 and Interstate 75 exits in Ohio. 62

Figure 20. Lodging Establishment Measurement Top and Front Views. American Best Value Inn. Source (Google Maps Aerial Image)

Figure 21. Lodging Establishment Measurement Top and Front Views. Interchange Exit 49, Holiday Inn Express. Source (Google Maps Aerial Image)

When all data were collected on the interchange exits then the data were compiled into excel file in order to use it in the statistical analysis software called SPSS.

3. 3. 11. Developed Axes

ODOT interchange maps were used to identify the number of developed axes. Table 5

present the number of interchange axes along Interstate 70 and Interstate 75. For instance, 21

interchanges that have no developed axes and 16 interchanges have one developed axis.

Table 5. Interchange’s Developed Axes on Interstate 70 and Interstate 75 Number of Interchanges 21 16 13 11 8 Number of Developed Axes 0 1 2 3 4

3. 4. Preliminary Data Analysis

There are few factors that influence interchange development: which are traffic volume and distance to nearest city or town. The goal of this study is to quantify commercial development and develop models to estimate development at rural and small town Interstate exits in Ohio.

Therefore, the study data was collected for each interchange which serve as independent variables and dependent variables of the analysis.

The following data was collected for each study interchange, where Interchange AADT and T24 serve as the dependent variables and all other variables serve as independent variables in

this study analysis (Table 6).

Table 6. Variables Classification Lists and Their Short Form Variable short Names Descriptions Units Traffic data counts TotalAADT Total Annual Average Daily Traffic vehicle/day IntersectHWYADT Intersecting Highway Average Daily vehicle/day Traffic T24 Daily Truck Factor(s) B&C/(vehicle/day) Economic Development Units DevelopAxes Developed Axes Number Nrestaurants Number of restaurants Number Sftrestaurants Square Footage of restaurants Square feet Ngconvenience Number of gas convenience Number Sftgconvenience Square Footage of gas convenience Square feet Nhotels Number of Hotels Number Sfthotels Square Footage of hotels Square feet Ngaspump Number of Gas Pump Number TParkingLot Truck Parking Lots Acres NHotelRooms The Number of Hotel Rooms Number Geographic Characteristics DisNearInterchange Distance to Nearest Neighbor Interchange Miles DisFurthNInterchange Distance to Furthest Neighbor Interchange Miles DisNCityTown Distance to the nearest city and town Miles Demographic Characteristics Popcounty Population density of the county Person Poptowncity Population of the nearest rural town and Person city Cross street highway types FedHWY Federal Highway Number StateHWY State Highway Number LocalHWY Local Highway Number

The number of exit development units were determined through GIS and ODOT maps. The next step was to find the development units at Interstate exits. A book called “The Next Exit 2015:

The Most Accurate Interstate Highway Service Guide Ever Printed” was used to determine the development units containing gas stations and convenience stores, lodging, and food centers of

United States at each exits of Interstate systems (Watson & Next Exit, 2015). As a result, 401 development units were collected from 69 interchange locations with the average development 5.8 units per interchange of Interstate 70 and Interstate 75 of Ohio. The data file of these studies can 66

be found in Appendix A. Actual development unit quantified to 69 hotels & motels, 130 gas station

and convenience stores, and 202 restaurants. In addition to these three types of development units,

the number of hotel rooms and square footage of hotels, the number of gas pumps, square footage

of gas and convenience stores, and square footage of restaurants was also quantified which all serve

as independent variables in the analysis.

Truck stop or truck parking lots were also quantified using ODOT maps and the Google

Map tool. There are 14 truck parking lots on Interstate 70 and 11 truck parking lots on Interstate

75. Truck parking lot areas are estimated in square feet and then converted to acres which also

serve as development units. The number of development units on Interstate 70 and Interstate75 can

be summarized in Table 7.

Table 7. Development Units by Type in Ohio Type of Roadside Business Number of development units Interstate Highways Interstate 70 Interstate 75 Gasoline Stations and convenience stores (All Types) 63 62 Gasoline Pumps (All Types) 610 677 Eating Establishments (All kind of fast food chain and other eating restaurants) 79 124 Loading (Hotels, Inns and Motels) 35 32 Trucking Parking Lots 14 11 Source: Extracted from “The Next Exit 2015 : The Most Accurate Interstate Highway Service Guide Ever Printed,” and Plus Google Maps (Watson & Next Exit, 2015).

The data was further analyzed using the excel spreadsheet to determine the distribution of each development type at rural and small town Interstate 70 and Interstate 75 exits. The distribution of the development units such as the number of restaurants, numbers of hotels and motels, number of gas pumps, and the number gas and convenience stores at each interchange exit are calculated

(Table 8).

Table 8. Distribution of Development Types Number Number of gas station and Number Number of Lodging of Exits convenience stores of Exits 0 65.22% 45 0 33.33% 23 1 14.49% 10 1 15.94% 11 2 8.70% 6 2 18.84% 13 3 2.90% 2 3 13.04% 9 4 1.45% 1 4 5.80% 4 5 2.90% 2 5 8.70% 6 7 1.45% 1 6 2.90% 2 8 1.45% 1 8 1.45% 1 12 1.45% 1

Number Number Number of restaurants Number of gas pumps of Exits of exits 0 33.33% 22 0 57.97% 40 4 2.90% 2 1 11.59% 8 8 7.25% 5 2 7.25% 5 10 4.35% 3 3 4.35% 3 12 1.45% 1 4 4.35% 3 14 4.35% 3 5 1.45% 1 16 4.35% 3 7 1.45% 1 18 1.45% 1 15 1.45% 1 20 2.90% 2 16 2.90% 2 22 2.90% 2 22 2.90% 2 24 4.35% 3 28 1.45% 1 26 2.90% 2 29 1.45% 1 28 1.45% 1 32 1.45% 1 33 1.45% 1 34 1.45% 1 36 4.35% 3 38 1.45% 1 42 1.45% 1 46 1.45% 1 48 4.35% 3 52 1.45% 1 60 1.45% 1 70 1.45% 1 74 2.90% 2 76 1.45% 1 The data is analyzed completely. The same data is used for the regression model in Chapter 4 below. 68

In addition, a detailed breakdown of all development units is presented in Table A 29 and

Table A 30 in Appendix A. The number of restaurants, gas station and convenience stores, and lodging (hotels and motels) along the Interstate 70 and Interstate 75 at each exit was determined.

These three development units are found to be the main development units at the Interstate exits, whereas the number of restaurants are more than the other two development types. There are 80 restaurants at Interstate 70 and 202 restaurants at Interstate 75 rural and small town exits. Gas station and convenience stores are the second highest numbers at the interstate exits. The data collected does not distinguish between services and development units; therefore, gas stations and convenience stores are integrated. The number of gas station and convenience stores are 68 at

Interstate 70 exits and 130 at Interstate 75 exits (Table A 29). Finally, lodging establishments are the third highest numbers at the Interstate exits. There are 35 hotel and motels at Interstate 70 exits, and 69 hotels and motels at Interstate 75 exits in Ohio.

These three kinds of exit development at nearby exits in both directions of the Interstate 70 and Interstate 75 was also obtained to measure the competition share between exits. The GIS maps were made to show the competition share between each exit, as shown in Figure 22, 23, and 24.

These figures illustrate the total quantity of the number of restaurants, gas stations and convenience stores, and loading at each exit on Interstate 70 and Interstate 75. The number of these development units at each exit are presented in Table A 29 and Table A 30 in Appendix A. As earlier illustrated, some of the Interstate highways do not have commercial development and economic growth at exits; therefore, the following figures do not showed pie chart at the Interstate exits. After data collection and data analysis, Chapter 4 presents the two regression models, Interchange AADT model and daily truck percentage (T24) model, to explore the relationship in the data. 69

Figure 22. Development Units Percentage at Interstate 75 Exits in Ohio 70

at Interstate Ohio 70 Exits in . Development Units Percentage . Development igure 23 F

: Development Units Percentage at Interstate Ohio 70 Exits in : Development Figure 24

CHAPTER 4: ANALYIS OF THE REGRESSION MODELS

4. 1: Preliminary Analysis

Data was collected for each study interchange, as shown in Table 6, whereas two variables, such as Total AADT and daily truck percentage (T24), serve as dependent variables and all other predictor variables serve as independent variables in the analysis.

In this analysis, there are two kinds of variables: numeric and non-numeric. The independent variables, federal highway, state highway, and local highways were changed to dummy variables. For instance, if an interchange connects to a state or county route, the variable would take the value of 1 which introduce “Yes”; if the interchange doesn’t connect to a state or county route, the variable would take the value of 0 which introduce “No”. So, all dummy variables get a value of one or zero in the analysis. These dummy variables, or indicator variables, are artificial variables created to represent an attribute with two or more distinct categories. Only three variables were changed to dummy variables where all the other independent variables are numeric and the dependent variables are numeric. In addition, the dependent variable, the Annual Average

Daily Traffic (AADT) on the Interstate highway, transformed to log10. One independent variable the intersecting highway was also transformed to log10.

Two variables in the data set were not normal in distribution. Thus it was necessary to transform the data. The aim of data transformation is to correct some distributional abnormality such as skew and Kurtosis (Field, 2005). Simply put, transformation is replacement that changes the shape of a distribution or relationship. Non-normally distributed data may give misleading results while, data transformation and their uses are different, e.g. log transformation, square root transformation, and reciprocal transformation. Choosing the transformation method is just a trial and error: “try one out and see if it helps and if it doesn’t then try a different one” (Field, 2005).

However, if any of the three method is chosen for the analysis, then the same transformation method must apply to all other variables. In this study, all three methods of data transformation was used. 73

As a result, the log10 transformation method led us to the idea that taking the log10 of the data can

improve the skew and distribution. After the data was transformed, the transformed variables and

untransformed variables were checked. As a result, the log10 transformation of the two variables

(AADT served as dependent variable and Intersecting highway AADT served as independent

variable) gave a good skew and distribution.

There are two models in the analysis: Interchange AADT Model at the Interstate exits and

daily truck percentage (T24) at the Interstate exits in Ohio. The regression stepwise backward multiple regression technique is used for both models which is able to handle a large number of variables. This technique is a search procedure which finds the contribution of each predictor variable to the total variance. It searches the list of all predictors to combine those variables which contribute the most variation to the outcome variable. At the end, this technique builds an equation with those predictor variables which contribute significantly to the outcome variable and build another equation with excluded variables.

4. 1. 1. Descriptive Statistics

The descriptive statistics for both modes are presented in Table 9 which illustrates the mean, median, standard deviation, and range of the data set. As it is a quantitative data, two general checks are very important: first, to check the data graphically in order to find what the general trends in the data are and second, to check the fit of the statistical model of the data. So the graph of the data will be discussed at the end of the analysis section as well as the center of a frequency distribution that lies. The center of a frequency distribution contain three measures: the mean, the mode, and the median. In addition, the standard deviation column of the table is the most important part of the table because it can help interpret the standardized coefficient (Beta) values for each development predictor. Therefore, the standard deviation will also interpreted later in the model.

Table 9. Descriptive Statistics Variables Mean Median Std. Minimum Maximum Deviation Total INTERCHANGE AADT 8667.04 8263 4993.61 621.00 22,144.00 Log10 (Total 3.85 3.92 0.31 2.79 4.35 INTERCHANGE AADT) Intersecting Highway ADT 6,989.06 5,740.00 5,875.88 559.00 30,570.00 Log10 (Intersecting Highway 3.71 3.76 0.36 2.75 4.49 ADT) T24 - Daily Truck Factor(s) 0.139 0.10 0.086 0.02 0.40 Developed Axes 1.55 1.00 1.38 0.00 4.00 Number of Restaurants 2.93 0.00 6.56 0.00 29.00 Square footage of Restaurants 12,129.52 0.00 27,477.21 0.00 137,035.00 Number of Gas and 1.83 2.00 1.85 0.00 8.00 Convenience Stores Square footage of Gas and 6,601.94 4,115.00 7,801.97 0.00 29,465.00 Convenience Stores Number of Hotels 1.00 0.00 2.12 0.00 12.00 Square footage of hotels 17,272.74 0.00 39,915.15 0.00 189,466.00 Number of Gas Pumps 18.65 14.00 20.81 0.00 76.00 Truck Parking Lots (Acres) 1.22 0.00 2.76 0.00 12.12 Distance to the Nearest 1.90 1.60 1.28 0.30 5.50 Interchange (Miles) Distance to the Nearest City 1.73 1.40 1.29 0.30 6.40 and Town (Mile) Distance to Furthest 4.49 4.30 2.34 0.70 11.00 Interchange (Miles) Population County 85,266.81 75,130.00 39,577.94 39,480.00 173520.00

Population of nearest city and 10,347.78 4,028.00 14,615.08 82.00 62,486.00 town Federal Highway 20% State Highway 58% Local Highway 22% T24 - Daily Truck Factor(s) Note: Highway types (federal, state and local) are coded 1 = yes, 0=No.

4. 1. 2. Correlation Analysis – Interchange AADT Model

SPSS output Table 10 provides a matrix of the correlation coefficients for the 19 variables.

The correlation matrix provides the relationship between the predictor variable and the outcome

variable. It is very important to check for multicollinearity in the correlation matrix.

Multicollinearity refers to predictor that is correlated with the other predictor variable in the data 75 set and it occurs when the regression model contains multiple factors that are not only correlated with a response variable but also to other predictors in the model. According to Field (2009), there will not be an issue with multicollinearity if there is not a substantial correlation (r > 0.9) between the predictors in the data set meaning that all predictor Pearson’s correlations should be 0.8 or less.

The correlation matrix table contain three things. First, the table illustrates the value of Pearson’s correlation coefficient between two predictor variables. For example, the developed axes has a moderate positive correlation with Log10.total AADT, (r = 0.54). In addition, each predictor variable correlation will be described later at the end of this section. Second, the one tailed significance of each correlation is presented in the Table. For example, the correlation between developed axes and Log10.Total AADT is significant, p < 0.05. Finally, the sample size (N=69) for each predictor in the correlation matrix. The correlation matrix for the Interchange AADT model is presented in three parts in Table B 31, Table B 32 and Table B 33 of Appendix B.

In the correlation matrix table, there are values of 1 in a diagonal order which show the correlation coefficient for each predictor, this value (1) represents the correlation of each predictor variable with itself.

The correlation matrix is obtained from the regression model and contains the dependent variable, or the Annual Average Daily Traffic (AADT), and the set of all independent variables which are presented with their short and full name in Table 6. The result of the correlation matrices are presented in Table B 31, Table B 32 and Table B 33 in Appendix B. The proportion of variation for each predictor variable is also calculated and displayed in Table 10. The strongest relationship between the dependent variables and an independent variables is the ADT of the intersecting highway (r = 0.80). The developed axes showed positively strong correlation (r = 0.54). Some other variables which showed moderate positive correlation with the dependent variables are the number of gas and convenience stores (r = 0.49), the number of gas pumps (r = 0.46), the square footage of gas and convenience stores (r =0.46), the number of restaurants (r = 0.42), the square footage of 76

restaurants (r = 0.41), the number of hotels (r = 0.39), the square footage of hotels (r = 0.39), the

population of the nearest city and town (r = 0.33), and all other predictors showed very low

correlation.

Table 10. Correlation Analysis of Interchange AADT Model Variables Correlation Proportion of Variation Coefficient Explained (%) Log10 (Total AADT) 1.00 1.00 Log10 (Intersecting highway ADT) 0.80 0.65 Developed Axes 0.54 0.29 Number of Restaurants 0.42 0.18 Square footage of Restaurants 0.41 0.17 Number of Gas and Convenience stores 0.49 0.24 Square footage of gas and convenience Store 0.46 0.21 Number of Hotels 0.39 0.15 Square footage of Hotels 0.39 0.15 Number of Gas Pump 0.46 0.21 Acres of Truck Parking lots 0.19 0.004 Population of nearest city and town 0.33 0.11 Population of county 0.07 0.005 Distance to nearest interchange type (Miles) -0.21 0.04 Distance to furthest interchange (Miles) -0.07 0.004 Distance to nearest city and town -0.004 0.00002 Intersecting highway = Federal -0.001 0.000001 Intersecting highway = STATE 0.11 0.01 Intersecting highway = Local -0.13 0.02

From the correlation matrix, the more significant determents to commercial development growth are: distance to the nearest interchange, local highway, distance to furthest interchange, distance to nearest city and town, and federal highway. All these predictor showed negative correlation with the dependent variable. For example, the distance to the nearest neighboring interchange in any Interchange exit provides the strongest negative correlation in the correlation matrix (r = -0.21). As the development mix increases the traffic volume also increases, as a result, the distance to the other interchanges nearby decreases. Similarly, If the traffic volume at the 77

Interchange exits increases, the distance to further neighbor interchanges decreases (r = - 0.07)

(Table B 32).

Total Interchange AADT is positively related to developed axes with a Pearson correlation

coefficient of r = 0.54, p (one-tailed) <0.05. As suggested by Field (2009), we cannot make a direct

conclusion of the commercial growth at the Interstate exit from a correlation matrix alone but it is

better to take one step further by squaring the correlation coefficient. The correlation coefficient

squared, which is also known as the coefficient of determination (R2), is a measure of variability in

one variable that is shared by the other. For example, total AADT (dependent variable) and

developed axes (independent variable) have a correlation of 0.54, so the R2 value will be (0.54)2 =

0.292. This value tells us how much of the variability AADT is shared by developed axes in total.

If the value 0.292 is multiplied by 100, then it indicates that developed axes shares 29.2 percent of

the variability in annual average daily traffic (AADT). In order to put this value into a perspective,

this value leaves 70.8 percent of the variability still to be accounted for by the other variables. All

other predictor variables of the correlation matrix will be interpreted in the same method below

(Table 10).

AADT is positively related to ADT of intersecting highway, with a coefficient of r = 0.80,

p (one-tailed) <0.05, so the R2 value will be (0.80)2 = 0.64. It is concluded that the ADT of the intersecting highway accounts for 64 percent of the variability in AADT and the remaining 36 percent of the variability still to be accounted for by other variables (Table B 31). In addition,

AADT is positively related to the number of restaurants, with a coefficient of r = 0.42, p (one- tailed) <0.05, so the R2 value will be (0.42)2 = 0.176. It is concluded that the number of restaurants accounts for 17.6 percent of the variability in AADT, so the remaining 82.4 percent of the variability will to be accounted for by other variables.

AADT is positively related to the square footage of restaurants, with a coefficient of r =

0.41, p (one-tailed) <0.05, so the R2 value will be (0.41)2 = 0.168. It is concluded that square footage 78

of restaurants accounts for 16.8% of the variability in AADT. Therefore, remaining 83.2 percent

of the variability will to be accounted for by other variables (Table B 31). In addition, AADT is

positively related to the number of gas and convenience stores, with a coefficient of r = 0.49, p =

0.00 (one-tailed) < 0.05, so the R2 will be (0.49)2 = 0.24. It is concluded that the number of gas

and convenience stores accounts for 24 percent of the variability in AADT and the remaining 76

percent of the variability is still to be accounted for by other variables (Table B 33).

AADT is positively related to the square footage of gas and convenience stores, with a

coefficient of r = 0.46, p (one-tailed) <0.05, so the R2 value will be (0.46)2 = 0.212. It is concluded

that the square footage of gas and convenience stores accounts for 21.2 percent of the variability in

AADT. The remaining 78.8 percent of the variability is still to be accounted for by other variables

(Table B 32). In addition, AADT is positively related to the number of hotels, with a coefficient of

r = 0.39, p (one-tailed) <0.05, so the R2 value will be (0.39)2 = 0.152. It is concluded that the number of hotels accounts for 15.2 percent of the variability in AADT. The remaining 84.8 percent of the variability is still to be accounted for by other variables.

AADT is positively related to the square footage of hotels, with a coefficient of r = 0.39, p

(one-tailed) <0.05; therefore, the R2 will be (0.39)2 = 0.152 and it is concluded that the square footage of hotels accounts for 15.2 percent of the variability in AADT. The remaining 84.8 percent of the variability is still to be accounted for by other variables. In addition, AADT is positively related to number of gas pump, with a coefficient of r = 0.46, p = 0.00 (one-tailed) < 0.05, so the

R2 will be (0.46)2 = 0.212. It is concluded that the number of gas pump accounts for 21.2 percent

of the variability in AADT. The remaining 78.8 percent of the variability is still to be accounted

for by other variables (Table B 33).

AADT is not related to acres of truck parking lots, with a coefficient of r = 0.19, p = 0.06

(one-tailed) > 0.05, so the R2 will be (0.19)2 = 0.04. This concludes that the acres of truck parking lots accounts for 4 percent of the variability in AADT and the remaining 96 percent of the 79

variability is still to be accounted for by other variables (Table B 33). In addition, AADT is

positively related to the population of the nearest city and town, with a coefficient of r = 0.33, p

(one-tailed) <0.05, so the R2 value will be (0.33)2 = 0.109. It is concluded that the population of the nearest city and town accounts for 10.9 percent of the variability in AADT and the remaining 98.1 percent of the variability is still to be accounted for by other variables. Note that the population is counted per person.

AADT is not related to county population, with a coefficient of r = 0.07, p = 0.29 (one- tailed) > 0.05. Therefore, the R2 will be (0.07)2 = 0.0049. It is concluded that the county population

accounts for 0.49 percent of the variability in AADT. The remaining 99.51 percent of the variability

is still to be accounted for by other variables. Note, the population is counted by person. In addition,

AADT is negatively related to the distance from the nearest neighbor interchange, with a coefficient

of r = -0.21, p = 0.04 (one-tailed) < 0.05. Therefore, the R2 will be (-0.21)2 = 0.044 and it concludes that the distance to nearest neighbor interchange accounts for 4.41 percent of the variability in

AADT. The remaining 95.59 percent of the variability is still to be accounted for by other variables.

AADT is not related to the distance to furthest neighbor interchange, with a coefficient of r = -0.07, p = 0.30 (one-tailed) > 0.05. Therefore, the R2 will be (-0.07)2 = 0.0049. It is concluded

that the distance from the furthest neighbor interchange accounts for 0.49 percent of the variability

in AADT. The remaining 99.51 percent of the variability is still to be accounted for by other

variables. In addition, AADT is not related to distance from the nearest city and town, with a

coefficient of r = -0.004, p = 0.49 (one-tailed) > 0.05, so the R2 will be (-0.004)2 = 0.000016. This

concludes that the distance to the nearest city accounts for 0.0016 percent of the variability in

AADT. The remaining 99.999984 percent of the variability is still to be accounted for by other

variables.

AADT is not related to federal highway, with a coefficient of r = - 0.001, p = 0.49 (one-

tailed) > 0.05, so the R2 will be (-0.001)2 = 0.000001. This concludes that the federal highway 80

accounts for 0.0001 percent of the variability in AADT and the remaining 99.9999 percent of the

variability is still to be accounted for by other variables.

AADT is not related to state highway, with a coefficient of r = 0.11, P (one-tail) = 0.19, so

the R2 will be (0.11)2 = 0.012. This concludes that the state highway accounts for 1.21 percent of

the variability in AADT and the remaining 98.79 percent of the variability is still to be accounted

for by other variables. In addition, AADT is not related to the local highway, with a coefficient of

r = -0.13, P = 0.15 (one-tailed) > 0.05. So, the R2 will be (-0.13)2 = 0.0196. This concludes that the local highway accounts for 1.69 percent of the variability in AADT and the remaining 98.31 percent of the variability still to be accounted for by other variables (Table B 32).

Furthermore, the Pearson’s correlation of the 18 predictor variables which influence commercial development growth at the Interstate exits are checked with the interchange traffic (in this case Total AADT). As a result, there was no multicollinearity issue between the dependent variable (AADT) and all the predictors. However, the correlation matrix exhibits that some predictors are highly inter-correlated, which indicates multicollinearity issues in the correlation matrix. These include: developed axes being highly inter-correlated with the number of gas and convenience stores, the number of gas and convenience stores being inter-correlated with the square footage of gas and convenience stores, the number of gas and convenience stores being inter- correlated with the number of gas pumps, the square footage of gas and convenience stores being inter-correlated with the number of gas pumps, the number of restaurants being inter-correlated with square footage of restaurants, the number of restaurants is inter-correlated with the number of hotels, and the number of restaurant variable being inter-correlated with the square footage of hotels.

4. 1. 3. Correlation Analysis – Daily Truck Percentage (T24) Model

The correlation matrix, containing the dependent variable which is the Daily Truck

Percentage (T24) and the set of all independent variables which are presented with their short and 81

full name in Table 6, is obtained from the regression model. The result of the correlation matrix is

presented in Table B 31, Table B 32, and Table B 33 in Appendix B. The strongest relationship in

the data set between the dependent variables and the independent variable is the truck parking lots

(r = 0.70). Some variables showed positively low correlation such as the square footage of gas and

convenience stores (r = 0.45) and the number of gas pump (r = 0.41). Some variables also showed

positively very low correlation such as the number of gas and convenience stores (r = 0.29), federal

highways (r = 0.20), developed axes (r = 0.10), state highways (r = 0.06), distance to the furthest

neighbor interchange (r = 0.03), and distance to the nearest cities and towns (r = 0.02).

The local highway provides the strongest negative relationship in the data set, r = - 0.27,

as daily truck percentage decreases local highway increases. However, all those independent

variables which have negative correlation with the dependent variable (daily track percentage)

exhibit very low correlation such as the number of restaurants (r = -0.11), the square footage of

restaurants (r = -0.10), the square footage of hotels ( r = -0.11), the population of the nearest city

and town (r = -0.10), the number of hotels ( r = -0.09), the number of hotel rooms ( r = -0.08), the

distance to the nearest neighbor interchange ( r = -0.06), the county population (r = - 0.04), and

ADT at the intersecting highway (r = -0.03). As daily truck percentage decreases result in increasing

all these variables (Table B 31, Table B 32, and Table B 33). So we can conclude that, from the

correlation matrix, only that truck parking lot variable is a strong factor in daily truck percentage

(T24) model. All the other variables in the correlation matrix are not satisfactorily strong factors in

daily truck percentage (T24) model. Also, the collinearity is highly exhibited in certain areas of the correlation matrix (Refer to Table 12 for more detail). These two characteristics of the correlation matrix can be resolved by using the stepwise method, as it searches for multivariate influence and reduces collinearity (Moon, 1988). The proportion of variation for the each dependent variable is measured for daily truck percentage (T24) model and presented in Table 11.

Table 11. Correlation Analysis of Daily Truck Percentage (T24) Model Variables Correlation Proportion of Variation Coefficient Explained (%) T24 1 1 Intersecting highway ADT -0.03 0.001 Developed Axes 0.10 0.01 Number of Restaurants -0.11 0.01 Square footage of Restaurants -0.10 0.010 Number of Gas and Convenience stores 0.29 0.09 Square footage of gas and convenience Store 0.45 0.20 Number of Hotels -0.09 0.01 Square footage of Hotels -0.11 0.01 Number of Gas Pump 0.41 0.17 Acres of Truck Parking lots 0.70 0.48 Population of nearest town and city -0.10 0.01 Population of county -0.04 0.002 Distance to nearest interchange type (Miles) -0.06 0.004 Distance to furthest interchange (Miles) 0.03 0.001 Distance to nearest city and town 0.02 0.001 Intersecting highway = Federal 0.20 0.04 Intersecting highway = STATE 0.06 0.004 Intersecting highway = Local -0.27 0.07 The number of hotel rooms -0.08 0.01

Overall, multi-collinearity is highly exhibited in many areas of the correlation matrix of

Table 12. The multi-collinearity problem occurs when two or more of the explanatory variables are highly correlated with each one another. When multi-collinearity exits in the correlation matrix, it is hard to disentangle which of these two correlated variables best explains any shared variance with the outcome variable. It also suggested that two correlated variables represent the same factor.

This problem of the correlation matrix can be resolved by using the stepwise regression technique which is designed to search for multivariate influences and reduce collinearity (Moon, 1988). 83

Table 12. Correlation Matrix, Multicollinearity

umber of Gas and convenience stores umber of Gas Pumps umber of Restaurants umber of Hotels Square footage of Gas and Convenience Square footage Stores Square footage of Restaurants of Hotels Square footage Develop Axes N N N N Developed Axes 0.82 Number of Gas and Convenience 0.82 0.92 0.94 stores Square footage of Gas and 0.92 0.95 Convenience Stores Number of Gas Pump 0.94 0.95 Number of Restaurants 0.98 0.89 0.93 Square Footage of Restaurants 0.98 0.87 0.90 Number of Hotels 0.89 0.87 0.95 Square Footage of Hotels 0.93 0.90 0.95 Variables presented in Table 12 are Extracted from the Correlation Matrix of Table B 31, Table B 32 and Table B 33 of Appendix B, Which had the R2 Value Higher Than 0.8.

4. 1. 4. Regression Analysis Method

The stepwise multiple regression technique combines backward and forward methods to

make a model. Moon (1988) described the advantage of the stepwise procedure that “a significant

advantage that this more advanced stepwise procedure holds over prior techniques is its ability to

emulate a backward-entry procedure by removing once-entered variables from the equation to

achieve the optimal equation. This ideal end product is a combination of variables that fits together

in a way that maximizes explanatory power and minimizes collinearity within the model” (p.399).

Furthermore, this technique greatly decreases the tendency whereas some other regression

procedures can explain a small portion of the total variation in an outcome variable by addressing 84 the collinearity problem in the model. The stepwise technique chooses predictors to enter them into the regression equation based on their ability. Lastly, these predictors describe a different portion of the overall variation of the model.

SPSS methods for performing regression analysis such as forward selection, backward elimination, and stepwise regression are all under the common title of stepwise methods (Field,

2009). These three methods are described as: Stepwise regression is the same as the forward selection method except that variables are removed from the model, as other predictors are added, if they become non-significant (Field, 2009). Also, the stepwise regression is a combination of backward and forward methods which check for entering the variables and then remove until no variables are added or removed. The backward method, the opposite of the forward method, starts with all of the predictors in the model. It then calculates the contribution of each one by looking at the significance value of each predictor. As a result, the variable which has the largest P value is removed from the model which is then refitted for the remaining predictors (Field, 2009).

Dattalo (2013) describes the advantages of the backward elimination methods. It begins with all independent variables and removes one independent variable at a time, continuing until one makes a significant difference in the R-Squared (R2) value. He further recommended backward elimination method over forward selection and stepwise regression method, since it is possible for a set of predictor variables to have considerable predictive ability while any subset of these predictor variables does not. However, stepwise regression and forward selection method do not have the capability to identify them. In stepwise regression and forward selection methods, the independent variables do not predict separately. These predictors do not even enter into the regression model to have their behavior noticed. Conversely, backwards elimination technique has the capability to start with all independent variables in the regression model. Finally, the joint predictive capability will be measured. 85

Additionally, Donnan (2008) said that the backwards elimination technique has the

capability to eliminate the least significant factor from the full model and has some advantages over

the forward method. First, in the backward method, factors that are correlated can stay in the model,

whereas these factors may not be entered into the forward method. Second, the criteria for removal

tends to be more lenient in backward, resulting in more parameters.

According to Field (2009), if it is decided to use the stepwise method, the backward method

is better than the forward method because of the suppressor effects. Suppressor effects occur when one predictor shows a significant affect but the other variable is held constant. Therefore, the forward selection method is more prospective than the backward elimination method which excludes predictors involved in suppressor effects. In this case, the forward method creates a higher risk of making a type 2 error. In this research analysis, all methods are used to find the best fit of the model which is explained the following section.

In this model, 18 predictor variables were introduced into the backward regression method.

The dependent variable, the Annual Average Daily Traffic (AADT) on the Interchange exits, is transformed into a logarithm. Also, one independent variable such as the intersecting highway is also transformed to logarithmic. Each step of the regression model and each variable is explained below:

4. 2. Interchange AADT Model

In this model, the dependent variable is the Annual Average Daily Traffic (AADT) and the independent variables are those factors which influence development at the interchange exits such as traffic volume, intersecting routes, geography, and topography which are presented in Table 6.

From the list of variables, the dependent variable, Interchange AADT, and one independent variable, intersecting highway ADT, are transformed to log10. Eighteen (18) independent variables are introduced into the backward regression model at one at a time, meaning that the predictors are introduced into the regression model based on their explanatory power (r). Those variables which 86

have the higher explanatory power (r) values in the correlation matrix are entered first into the

model. Each step of the regression model is presented below:

4. 2. 1. Model Summary

Table 13 displays the Model Summary table which contains the R squared and adjusted R

squared values for each step, as well as the amount of R squared change. In the first step, 17

variables were entered into the model by SPSS. The R Square value in the first step was 0.780 and

it decreased to 0.779 in step 8 in a step wise manner. Finally, the R square value in step 9 decreased

to 0.773, which is the chosen model of the research study based on having 6 significant variables.

The selected model contains 9 variables which can be seen from the footnote beneath the Model

summary table.

Table 13. Interchange AADT Model Summary Step of the R R- Square Adjusted R Std. Error of Durbin- Model Square the Estimate Watson 1 0.883 0.780 0.706 0.169 2 0.883 0.780 0.712 0.167 3 0.883 0.779 0.717 0.165 4 0.883 0.779 0.722 0.164 5 0.883 0.779 0.727 0.163 1.7 6 0.882 0.779 0.731 0.161 7 0.882 0.777 0.734 0.160 8 0.881 0.776 0.738 0.159 9 0.879 0.773 0.739 0.159 10 0.875 0.766 0.735 0.160 11 0.874 0.764 0.737 0.159 Note, the following variables are in this model (Step 9): Log10 (Intersecting Highway ADT, Developed axes, Number of Restaurants, Square Feet of Hotels, Truck Parking lots (Acres), Distance to the nearest interchange, Distance to nearest city and town, Intersecting Highway = Federal, Intersecting Highway = Local.

R = √ (2)

R = 0.773 0.879

The goal is to choose model with the best fit that provides the most statistically significant variables. Therefore, Step 9 is chosen because most of the predictor variables were statistically significant. In this table, the R = 0.879 value is the correlation between the predicted and observed values of the dependent variable, Interchange AADT, which can be obtained from equation 2. Table

13 shows the percent variability in the dependent variable (in this case Annual Average Daily

Traffic) which accounts for 77.3 percent of all the predictors of the model. The change in R squared evaluates the percentage of predictive power which was added to the model by adding all other predictors, in step 9, of the model. The R-squared value in step 1 was 0.780 and it reduced to 0.773 by step 9. So the variation in the model, from step 1 to step 9 only, decreased 0.007 percent (0.780

– 0.773 = 0.007). Therefore, we only have 0.007 percent of R square change from step1 to step 9 in the model, which is the model of the study.

Finally, the Durbin-Watson statistic will be found in the last column of Table 13. The

Durbin-Watson can show whether the assumption of independent errors is acceptable. The statistic value should be between 1 and 3 and, for this model, the value is 1.7 which is in range. Therefore, we can say that the assumption was satisfied.

4. 2. 2. Interchange AADT Model - ANOVA

The next output of this model contains an Analysis Of Variance (ANOVA), which tests whether the model is significantly better at predicting the outcome than using the mean as a ‘best guess’ (Field, 2009). The ANOVA table contains the breakdown of the variance in the outcome variable, these categories are: regression, residual and total. The sum of squares are associated with three kind of variances. The variance which is explained by the independent variable is called regression, the variance which is not explained by the independent variable is called residual, and the total variance (6.576) is the partitioned of the regression (5.085) and the residual (1.491) variances Table 14. 88

The degree of freedom (df) is associated with the sources of variance. The total variance

has N-1 degree of freedom. The degrees of freedom are simply the sample size minus 1 (df = N –

1 = 69). The Residual degree of freedom is the degree of freedom total value minus the degree of

freedom of the model, so we can say that 68 – 9 = 59.

Table 14. Interchange AADT Model ANOVA Regression Residual Total Sum of squares 5.085 1.491 6.576 df 9 59 68 Means Square 0.565 0.025 F 22.351 Sig 0.00 This Section of the Table is Extracted From Table C 34 of Appendix C. Note, the following variables are in this model (Step 9): Log10 (Intersecting Highway ADT, Developed axes, Number of Restaurants, Square feet of Hotels, Truck Parking lots (Acres), Distance to the nearest interchange, Distance to nearest city and town, Intersecting Highway = Federal, Intersecting Highway = Local.

The mean square can be obtained from the sum of the square divided by their respective degree of freedom, 5.085 / 9 = 0.565 and 1.491 / 59 = 0.025. The F-ratio is called the F – Statistic where the p-value is associated with it. The F- ratio is the mean square of the regression divided by the mean square of the residual of the model. For example: 0.565/0.025 = 22.351 (step 9 of the model). The p-value 0 is compared to an alpha level of p < .05 to test the null hypothesis that all of the model coefficients are zero. According to Field (2009), the value of F is greater than 1 if the improvement is caused by fitting the regression model is much greater than the inaccuracy within the model. In this case, the SPSS will calculate the particular probability of obtaining the value of

F by chance.

For the first model the F-ratio is 10.609, indicating that the relationship is unlikely caused

by chance (p = 0 < .05). Refer to Table C 34 for more detail. The F –statistics for all models were

increasing step-wisely. The value of F for the model, in the 9th step is even higher where F (9, 59) 89

= 22.351. We can interpret these findings to suggest that the first model significantly improved our

ability to predict the outcome variable, the total traffic volume at the Interchange exits, but that the

new models, with the extra predictors were even better. The ANOVA SPSS output for all steps of

the regression model is presented in Table C 34.

4. 2. 3. Equation and its Parameters of Interchange AADT Model

The general equation of the model is Y1 = b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + b6X6 + b7X7

+ b8X8 + b9X9 + U1. This equation contains the development units and all other predictor variables that contribute to the commercial development at rural and small town Interstate exits of Interstate

70 and Interstate 75 exits in Ohio. Y is the Total AADT, X’s are all those predictors which contribute to the commercial development, and U is the error term. The error term, U, can occur because of the imperfect fit of the equation to the observed prediction of commercial development at Interstate 70 and Interstate 75 in Ohio. The Y and X values are defined in Table 1, and the parameters of the equation can be obtained by a multiple stepwise regression of backward method from Table 15 below. These estimations were obtained while using the data associated with 69 rural and small town Interstate exits in Ohio. The final model equation can be used to predict AADT at the interchange exits in Ohio.

The backward regression method starts with the full model and deletes variables with large p-values sequentially. The backward method provided many models in the analysis. However, we are interested in the model which includes predictors that make a significant contribution to the outcome variable, the Annual Average Daily Traffic (AADT) at the Interstate exit. Therefore, model 9 is the model with the best fit that includes predictors which significantly contributed to the model. In addition, the multiple regression model takes the form of an equation which includes the coefficient estimates, B, for each predictor. The coefficient, B, values indicate the individual contribution for each predictor to the final model. 90

Another coefficient is the standardized coefficient, β. Explained in this section, the β values and their significance are very important statistics to check in multiple regression. The standardized coefficients, β, are not dependent on the units of measurement of the variables; therefore, they are much easier to interpret (Field, 2009). The standardized beta values are presented as labelled Beta

(β) in Table 15. According to Field (2009), that the beta values indicate the number of standard deviations that will be changed as a result of one standard deviation change in the predictor. The standardized beta, β, values can be estimated in standard deviation units which are directly comparable. Thus, the Beta value provides a good understanding of the importance of a predictor variable in a model.

Table 15. Interchange AADT Model Coefficients Variables Unstandardized Standardized Std. T - P – Coefficients - Coefficients Deviation Statistic Value Beta (β) (Constant) 1.472 6.400 0.000 Log10(Intersecting 0.627 0.726 0.36 10.206 0.000 Highway ADT Developed axes 0.049 0.215 1.38 2.406 0.019 Number of Restaurants 0.012 0.246 6.56 1.348 0.183 Square feet of Hotels -0.000002 -0.259 39,915.15 -1.507 0.137 Truck Parking lots 0.014 0.126 2.76 1.897 0.063 (Acres) Distance to the nearest -0.034 -0.142 1.28 -2.0620.044 interchange (Miles) Distance to nearest city 0.040 0.166 1.29 2.549 0.013 and town Intersecting Highway = -0.111 -0.144 0.41 -2.1090.039 Federal Intersecting Highway = -0.105 -0.140 0.42 -2.0620.047 Local This Table Contain the Standard Deviation Column Which is Extracted from Table 9.

Table 15 above also includes two dummy variables: federal highway and local highway.

As, the β value tells us the change in the outcome is due to a unit change in the predictor. In this 91

case, a unit change in the predictor is the change from 0 to 1 (1 = Yes, 0 = No). To interpret

standardized beta values, we need to know the standard deviations of all of the predictor variables

and these values can be found in Table 9.

In this model, 18 predictor variables were introduced into the regression stepwise backward

method. As a result, the final model displayed six variables as statistically significant with p-value

<0.05 and three predictor variables displayed as non-significant with as p-values >0.05 (see Table

15). Nine variables are excluded by the stepwise regression technique (Table 16). The Y intercept of the Log10.Total AADT is labeled as the constant and has a value here of 1.472.

Log10 (Interchange AADT) = 1.472 + 0.627 * X1 + 0.049 * X2 + 0.012 * X3 - 0.000002 * X4 +

0.014 * X5 – 0.034 * X6 + 0.040 * X7 – 0.111 * X8+ – 0.105 * X9 (3)

Where,

X1 = Log10 (Intersecting Highway ADT)

X2 = Developed axes

X3 = Number of Restaurants

X4 = Square feet of Hotels

X5 = Truck Parking lots (Acres)

X6 = Distance to the nearest interchange (Miles)

X7 = Distance to nearest city and town

X8 = Intersecting Highway = Federal

X9 = Intersecting Highway = Local

b1, b2, b3… b7 = Coefficient estimates (Beta). 92

The Interchange AADT model equation is presented above. This equation 3 include the

unstandardized coefficients, B, value for each predictor variable. Unstandardized coefficients, B,

and standardized Coefficients, β, values of the model are interpreted below:

ADT of the intersecting highway (B = 0.627) value is positive which indicates a positive

relationship with the outcome variable. This means that when the AADT of the intersecting

highway increases by one, total AADT on the Interstate highway increase by (B = 0.627). Both

predictors describe Average Daily Traffic (ADT) which can be express by vpd (vehicle per day).

In addition, the standardized coefficient value, β = 0.726, indicates that as ADT on the intersecting

highway increases by one standard deviation, 0.36, AADT on the Interchange highway increase by

0.726 standard deviation. The standard deviation for the total AADT is 0.31 and so this constitutes

a change of 0.225 (0.31 x 0.726). For every increase of 0.36 of ADT on the intersecting highway,

AADT is increased by 0.225 units on the Interstate exits. This interpretation is true only if the

effects of all other 8 variables are held constant.

The number of developed axes, B = 0.049, value is positive which indicates a positive

relationship with the outcome variable. The coefficient for the number of developed axes is 0.049.

This means, when the number of developed axes along at the Interstate 70 and Interstate 75 exits

increases by one unit, AADT, express by vpd (vehicles per day), increased by 0.049 units.

Additionally, the standardized coefficient, β = 0.215, indicates that as developed axes increases by one standard deviation,1.38, AADT on the Interstate highway increases by 0.215 standard deviation. The standard deviation for the total AADT is 0.31 and so this constitutes a change of

0.07 (0.31 x 0.215). Therefore, for every increase of 1.38 of developed axes, this results in an increase of 0.07 units in AADT at the Interstate exits. This interpretation is true only if the effects of all other 8 variables are held constant.

The number of restaurants, B = 0.012, is positive indicating a positive relationship with the outcome variable. The coefficient for the number of restaurants is 0.012. It means, when the number 93 of restaurants increases by one unit the AADT volume increases by 0.012 units. In addition, the standardized coefficient, β = 0.246, value indicates that as the number of restaurants increases by one standard deviation, 6.56, AADT increases by 0.246 standard deviation. The standard deviation for the total AADT is 0.311 and so this constitutes a change of 0.076 (0.311x0.246). Therefore, for every 6.56 increase in units of restaurants, an additional 0.076 units in AADT are increased. This interpretation is true only if the effects of all other 8 variables are held constant.

The square footage of hotels coefficient, B = - 0.000002, is negative which indicates a negative relationship with the outcome variable. Meaning, when the square footage of hotels increase by one unit, there is a 0.000002 decrease in total AADT. In addition, the standardized coefficient, β = -0.259, indicates that as the square footage of hotels increases by one standard deviation, 39915.15, AADT decreases by 0.259 standard deviations. The standard deviation for the total AADT is 0.311 constituting a change of 0.08 (0.311x0.259). For every increase of 39,915.15 square footage of hotels, there is a decrease of 0.0.08 units of AADT at the interstate exits. This interpretation is true only if the effects of all other 8 variables are held constant (Table 15).

The acres of truck parking lots coefficient, B = 0.014, is positive which indicates a positive relationship with the outcome variable. The coefficient for the truck parking lots is 0.014 which that, when the number of truck parking lots at the interchange exit increase by one unit, AADT increase by 0.014 units. In addition, the standardized coefficient, β = 0.126, value indicates that as the number of truck parking lots increase by one standard deviation 2.76, AADT increase by 0.126 standard deviation. The standard deviation for the total AADT is 0.311 so this constitutes a change of 0.039 (0.311x0.126). Therefore, every 2.76 increase in units of truck parking lots results in an increase of 0.039 units of AADT are increased. This interpretation is true only if the effects of all other 8 variables are held constant (Table 15).

Distance to the nearest neighbor interchange coefficient, B = -0.034, value is negative which indicates a negative relationship with the outcome variable. The coefficient for the distance 94

to the nearest neighbor interchange is negative 0.034. This means that when the distance to the

nearest neighbor interchange increases by one unit, AADT decreases by 0.034 units. The distance

measures by mile while total AADT is the annual average daily traffic is expressed in vpd (vehicles

per day). In addition, the standardized coefficient, β = -0.142, indicates that as the distance to the nearest neighbor interchange increases by one standard deviation, 1.28, AADT decreases by 0.142 standard deviation. The standard deviation for the total AADT is 0.311 and so this constitutes a change of 0.044 (0.311x0.142). Therefore, every increase of 0.142 unit of federal highway results in a decrease of 0.044 units in AADT. This interpretation is true only if the effects of all other 8 variables are held constant.

Distance to the nearest city and town coefficient, B = 0.040, value is positive which indicates a positive relationship with the outcome variable. The coefficient for the distance to the nearest city and town is 0.040. This means that when the distance to the nearest city and town increases by one unit, AADT increases by 0.040 units. In addition, the standardized coefficient, β

= 0.166, indicates that as the distance to the nearest city and town increases by one standard

deviation, 1.29, AADT increases by 0.166 standard deviation. The standard deviation for the total

AADT is 0.311, so this constitutes a change of 0.052 (0.311x0.166). Therefore, every increase of

1.29 units in distance to the nearest city and town results in an increase of 0.052 units in AADT.

The distance measures by mile while total AADT is the annual average daily traffic which can be

express by vpd (vehicle per day). This interpretation is only true if the effects of all other 8 variables

are held constant.

Federal highway coded as a dummy variable. In this case, a unit change in the predictor is

the change from 0 to 1 where 1 = Yes, 0 = No. The Federal highway type coefficient, B = -0.111,

value is negative which indicates a negative relationship with the outcome variable. The coefficient

for the federal highway type is negative 0.111. It means, when the number of federal highway

increases by one, the interchange AADT decreases by 0.111 units. This interpretation is only true 95 if the effects of all other 8 variables are held constant. Additionally, the standardized coefficient, β

= - 0.144, value indicates that as the federal highway increases by one standard deviation, 0.41,

AADT decreases by 0.144 standard deviation. The standard deviation for the total AADT is 0.311 which constitutes a change of 0.045 (0.311x0.144). For every increase of 0.41 unit of federal highway, the result is a decrease of 0.045 unit in AADT. This interpretation is true only if the effects of all other 8 variables are held constant (Table 15).

Local highway coded as a dummy variable. Like the case above, a unit change in the predictor is the change from 0 to 1 where 1 = Yes, 0 = No. The local highway type coefficient, B

= -0.105, value is negative which indicates a negative relationship with the outcome variable. The coefficient for the local highway type is negative 0.105 meaning that when the number of local highway increases by one, AADT decreases by 0.105 units. In addition, the standardized coefficient, β = - 0.140, value indicates that as the local highway increases by one standard deviation, 0.42, AADT decrease by 0.140 standard deviation. The standard deviation for the total

AADT is 0.311 and so this constitutes a change of 0.043 (0.311x0.140). Therefore, every increase of 0.42 unit of local highway results in a decrease of 0.043 unit in ADT. This interpretation is true only if the effects of all other 8 variables are held constant.

According to Field (2009) each coefficients (b) values are accompanying with the standard error which indicates to what extent the (b) value can differ across different samples. The standard error is used to find whether or not the coefficient (b) value differs significantly from zero. If the t- test associated with a coefficient (b) value is significant, p<0.05, then that predictor variable makes a significant contribution to the model. Furthermore, (Field, 2009) stated that the smaller the value of significant b-values, when the t value is larger, the greater the contribution of that predictor.

Also, the t-statistic is associated with p-value to test whether a given coefficient is significantly different from zero. The smaller the value of significant, the greater the contribution of the predictor. In this model, the t-statistic and their p-values are interpreted below: 96

o ADT at the Intersecting Highway t (59) = 10.206, p <0.05),

o Developed Axes t (59) = 2.406, p <0.05,

o Federal Highway t (59) = - 2.109, p <0.05,

o Distance to the Nearest City and Town t (59) = 2.549, p < 0.05,

o Local Highway t (59) = -2.028, p < 0.05)

o Distance to the Nearest Neighboring Interchange t (59) = -2.062, p < 0.05) All these variables are significant predictors of the Interchange AADT model (Table 15).

From the magnitude of the t-statistics we can see that the ADT at the intersecting highway had the

highest impact, whereas develop axes, federal highway, distance to the nearest city and town, local

highway and distance to the nearest neighboring interchange had less impact.

4. 2. 4. Excluded Variables of Interchange AADT Model

Table 16 below presents a summary table of the excluded variables of this research study.

This summary table contains an estimation of the beta value which was entered into the equation.

As a result, it provides an estimation of a t-test for the value which was entered into the equation in the first step. The stepwise method (the backward method is used in this analysis) will continue entering predictors of the t-statistics until it finishes entering variables that have less than 0.05

significant value. The following variables were excluded due to their non-significant contribution

to the outcome.

Table 16. Interchange AADT Model Excluded Variables Variables Beta In t-statistics p –value. State highway 0 Number of Gas Pump 0.011 0.096 0.924 Population of the Nearest city and town 0.004 0.055 0.956 Square footage of Restaurants 0.030 0.083 0.934 County Population -0.014 -0.214 0.832 Distance to the furthest neighboring 0.027 0.385 0.702 interchanges Square footage of gas and convenience -0.005 -0.041 0.968 stores Number of gas convenience 0.043 0.348 0.729 Number of hotels 0.179 0.916 0.363

Table 16 displayed those variables which are excluded by the regression model, indicating that these predictor variables have no significant impact on the model ability to predict total AADT at the interchange exits.

4 .2. 5. Assessing the Assumption of Non-Multicollinearity

The SPSS output contains the Collinearity Diagnostics Table which is obtaining collinearity statistics such as the Variance Inflation Factor (VIF), tolerances, eigenvalues and variance proportions. Each of these measures are checked in this section. The SPSS output Table

17 contains two columns called Variance Inflation Factor (VIF) and Tolerances in order to check collinearity statistics in the data set. To check the assumption of multi-collinearity, Field (2009) has recommended the following four points to be considered: 1) if the largest VIF is greater than

10 then there is cause for concern. 2) If the average VIF is substantially greater than 1 then the regression may be biased. 3) Tolerance below 0.1 indicates a serious problem. 4) Tolerance below

0.2 indicates a potential problem.

Table 17. Tolerance and Variance Inflation Factor (VIF) Variables Tolerance VIF

Log10 (Intersecting Highway ADT .761 1.315 Developed axes .480 2.082 Number of Restaurants .116 8.635 Square footage of Hotels .130 7.682 Truck Parking lots (Acres) .872 1.147 Intersecting Highway = Federal .822 1.216 Distance to nearest city and town .906 1.104 Distance to Furthest Interchange (Miles) .803 1.245 Intersecting Highway = Local .815 1.227 Tolerance and VIF values are Extracted from Table C 41.

For our current model, the VIF values for all predictors are well below 10 and the tolerance statistics are well above 0.1 which do not indicate a serious problem. However, the tolerance statistics values are 0.116 for the number of restaurants and 0.130 for the square footage hotels which are below 0.2 and indicate a potential problem in the dataset (see Table 17). Therefore, we can conclude that there is a collinearity issue with these two predictor variables in the dataset based on their tolerance statistics. Equation 4 is used to calculate the average VIF, simply add the VIF values for each predictor and divide by the number of predictors (k):

∑ VIF = (4)

VIF = Variance Inflation Factor

K = Number of Predictor

. . . . . . . . . . VIF 2.850

As the average VIF is very well above 1, multicollinearity might be an issue. However, we

can further check the model by checking two predictors in the correlation matrix. The square

footage of hotels predictor is highly correlated with the number of restaurants, square footage of

restaurants, and number of hotels in the same dimension. The number of restaurant commercial

development predictors is highly correlated in the same dimension as the square footage of

restaurants, square footage of hotels and the number of hotels. This means that these predictors are

inter-correlated and their correlations coefficient squared, R2 are above 0.8 (Table 18).

Consequently, the multi-collinearity is existed with only two predictors: the number of restaurants and the square footage of hotels.

Table 18. Correlation Matrix of Multi-Collineraiaty Variables Number of Square footage of Number Square footage of restaurants restaurants of hotels hotels Number of Restaurants 1 0.983 0.890 0.928 Square feet of hotels 0.928 0.897 0.945 1 Extracted from the Correlation Matrix of Table B 31, Table B 32 and Table B 33

We can investigate this issue further by examining the collinearity diagnostics. In the multicollinearity diagnostics Table C 42, we need to check eigenvalues and variance proportions. Each predictor should have the most of variance loaded onto a different dimension and also, all the variance proportion should range between zero and one. In order check the collinearity of the model, we should look for large variance proportion with small eigenvalue in the table.

For this model, each predictor variable has most of its variance loaded onto a different dimension except two variables: the number of restaurants and the square footage of hotels. The variance proportions of the number of restaurants, 0.96 and the square footage of hotels, 0.88, are loaded in dimension 9. After checking several measures of the regression model, it is concluded 100 that the number of restaurants and the square footage of hotels are the only two predictors which have multicollinearity issue.

4. 2. 6. Casewise Diagnostics

SPSS produces a summary table of the residual statistics to check for extreme cases. There are 69 data points in this dataset. Reasonable data points are said to have a standardized residual less than -2 or greater than +2, so this should represent 95% of the data. The remaining 5 % (0.05*69

= 3.45 cases) of the data points should lie outside of the limit to still have an acceptable dataset, or, there should be at least 3.45 extreme variables in the dataset (Table 19).

o Expected = 0.05 x 69 = 3.45 cases (5%)

o Obtained = 3 cases/69 = 0.043 (4.35%)

o 4.35 % - 5% = -0.65% less cases obtained. In reviewing the standardized residuals in the Casewise Diagnostics, there is no single case to have a standardized residual greater than 3. Based on the above calculation, we can say that we got 0.65% less cases. Consequently, it is confirmed that there is no extreme cases for concern.

Table 19. Casewise Diagnostics Case Number Std.Residual Log10.Total.AADT Predicted Value Residual 28 -2.471 2.79 3.1868 -0.39372 33 2.375 4.35 3.9668 0.37846 46 2.397 4.10 3.7188 0.38203

4. 2. 7. Cross-validity of the Model

The adjusted R-Square can give an idea of how well the model generalizes and we want this value the same or very close to R-Square. The difference in the model is that the R-square of

0.773 minus the Adjusted R-square of 0.739 is equal 0.031 (0.773 – 0.739 = 0.031) Table 13. We can say that there is 3.1 % shrinkage in the dataset which means that if the model is derived from the population rather than a sample it would account for approximately 3.1% less variance in the 101 outcome (Field, 2009). Furthermore, we can apply equation 5 to determine an idea of its likely value in different samples.

Adjusted R2 = 1 – [( 1 . (5)

n = Sample size (69 interchanges) k = Number of predictor

Adjusted R2 = 1 – [( 1 0.773

Adjusted R2 = 1 – [(1.1525)*(1.1552)*(1.0145)] (0.227)

Adjusted R2 = 1 – [1.350] (0.227)

Adjusted R2 = 0.694 (69.4%)

This value is very close to the observed value of R-Square (0.773) which indicates that the cross validity of this model is satisfactory.

4. 2. 8. Assumptions of the Multiple Regression (MR)

There are five steps to checking the model assumption in the multiple regression model which are: linearity, independence of errors, homoscedasticity, normality, and collinearity. These assumption checks are interpreted below:

4. 2. 8. 1. Linearity

Linearity check is one of the important assumption which is directly related to the result of the whole multiple regression analysis (Keith, 2014) . According to (Darlington, 1968) that linearity define the outcome variable as a linear function of the independent variables. Also,

Osborne and Waters (2002) suggested that when the relationship between the outcome variable and independent variables is linear in nature, the multiple regression model can estimate this relationship more accurately. Osborne and Waters (2002) further explained that if this relationship is not linear, then the results of the multiple regression analysis will estimate under or over the true 102 relationship and also increases the risk of the Type 1 and Type 2 errors. The regression technique provides residual plots and scatter plots to check the linear versus curvilinear relationship.

In this research study, the regression scatter plots are provided for each variable clearly shows the positive and negative relationships. As the traffic increases at the Interchange exits, the development growth pressure also increases, indicating a positive relationship. On the contrary, as the traffic decreases at the interchange exits, the development growth pressure decreases which indicates a negative relationship. From the list of scatterplots, Figure 25, 26, 28 and 31 illustrate positive relationships between the outcome variable and the predictors. However, Figure 27, 29, and 30 illustrate negative relationship between the outcome variable and the predictor variables.

The scatterplots also show the line of the best fit for these data. All scatterplots are clearly show linear relationship between the outcome and the predictor variables. In the following graphs, there is not any sort of curve to break the assumption of linearity.

The relationship between intersecting highway ADT and total AADT is strong and positive. There are no outliers in the data and the data points seem to cluster closely to the regression line, therefore indicating that homoscedasticity was not violated.

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Figure 25: Log10 (Interchange AADT) Vs Log10 (Intersecting Highway ADT)

Figure 26: Log10 (Interchange AADT) Vs Developed Axes 104

Figure 27: Log10 (Interchange AADT) Vs Federal Highway

Figure 28: Log10 (Interchange AADT) Vs Distance to Nearest City and Town 105

Figure 29:Log10 (Interchange AADT) Vs Local Highway

Figure 30: Log10 (Interchange AADT) Vs Distance to Nearest Interchange 106

Figure 31: Log10 (Interchange AADT) Vs Acres of Truck Parking Lots

4. 2. 8. 2. Independence of Errors

The Durbin–Watson statistic tests the assumption of “independence of errors:, meaning that for any pair of cases, the error term should be independent or uncorrelated (Field, 2009). In this research study, the Durbin–Watson statistic is 1.7 which falls within the recommended boundaries of 1 and 3, suggesting that errors are reasonably independent (Table 13).

4. 2. 8. 3. Homoscedasticity

The scatterplot of the regression model can help to assess both homoscedasticity and independent of errors. For each value of the predictors, the variance of the error term should be constant. This means that the errors are spread out consistently between the variables. Figure 32 demonstrates a good representation of homoscedasticity where the residuals are randomly scattered around zero. This means that if we draw the line from zero to zero between the quadrants, we can 107 see that all data is nicely distributed around zero. Therefore, it is concluded that the homoscedasticity is not a concern and assumption of the homoscedasticity is met.

Figure 32: Interchange AADT Model Plot ZRESID Against ZPRED

4. 2. 8. 4. Normally-distributed Errors

The final stage of the analysis is to check the assumption of the model. Therefore, it is important to test the normality of residuals by checking the histogram and normal probability plot.

The SPSS for the histogram and normal probability plot are presented in Figure 33 and Figure 34 respectively. The histogram in Figure 33 is in a bell-shape, showing the normal distribution which presents no deviation from normality. The normal probability did not show any deviation from normal distribution of residual either, as shown in Figure 33. There are two main things in the normal probability plot. First, the straight line represent a normal distribution and second, the points on and around the straight line represent the observed residuals. Most of the points are lying on the 108 line which represents a normally distributed data set, but only some points at the edges are deviated from normality (Figure 34). Finally, we can say that the histogram displays a skewed distribution, indicating that the normality of the errors assumption has been met. The normal P–P plot also verifies normal distribution. We can say that the normally distributed assumption of the model has been met.

Figure 33: Histogram Normal Distribution (Interchange AADT Model)

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Figure 34: Normal P-P Plot of Regression Standardized Residual (Interchange AADT Model)

4. 2. 9. Result of the Interchange AADT Model

Those factors which influence commercial development at the interchange exits, such gas stations, lodging, fast food restaurants, and others, were used in a multiple regression model to predict total AADT at the Interstate 70 and Interstate 75 exits in Ohio (see the list of variables in

Table 1). From the list of variables, two variables are transformed to log10, total AADT which is the dependent variable and intersecting highway ADT which is independent variable. The multiple regression backward method was used to analyze 18 commercial development predictor variables.

From the SPSS output, model 9 was chosen as the best fitted model which included 9 variables statistically significant and 9 variables were excluded from the model.

A multiple linear regression was calculated to predict Annual Average Daily Traffic based on commercial development factors at the interchange exits on Interstate 70 and Interstate 75 in 110

Ohio. The list of all predictors are presented in Table 1. A significant regression equation was found

(F (9, 59) = 22.351, p <.000.

AADT increased with predictor variables such as intersecting highway ADT, developed axes, number of restaurants, truck parking lots, distance to the nearest rural city, and town interchange. However, AADT decreased with the square footage of hotels, distance to the nearest interchange, federal highway, and local highway. In addition, ADT on the intersecting highway, developed axes, distance to the nearest interchange, distance to the nearest city and town, federal highway and local highway were statistically significant since their p-values are less than 0.05. The number of restaurants, square footage of hotels, and truck parking lots were not statistically significant as their p-values were greater than 0.05.

Table 20. Interchange AADT Model Interpretation Model ( Step 9) Standardized Unstandardized Coefficients Coefficients Variables B Std. Error Beta (β) Constant 1.472 .230 Log10 (Intersecting Highway ADT 0.627 .061 .726 Developed axes 0.049 .020 .215 Number of Restaurants 0.012 .009 .246 Square feet of Hotels -0.000002 .000 -.259 Truck Parking lots (Acres) 0.014 .007 .126 Distance to the nearest interchange (Miles) -0.111 .052 -.144 Distance to nearest city and town 0.040 .016 .166 Intersecting Highway = Federal -0.105 .052 -.140 Intersecting Highway = Local -0.034 .017 -.142 Note: R2 = 0.780 for Step 1: ΔR2 = -0.002 for Step 11. R2 = 0.776 for Step 8: ΔR2 = -0.003 for Step 9 (P <0.05*). Final Model R2 = 0.773

To report the multiple regression result, the model parameters, standard error and the beta

(standardized coefficients) values are presented in Table 20. The prediction model, which is to

predict the commercial development growth at the interchange exit on Interstate 70 and Interstate

75 in Ohio, accounts for 77.3% of the variance of total AADT (R2 = 77.3%). The R2 change for 111 step one was 0.780 and for step 9, denoted as ΔR2, is -0.003. As we have chosen step 9 for this research study, the R2 change is -0.003, R-Square change between step 1 and step 9 = -0.003.

We could summarize that this model appears to be a good model because more of the assumptions have been met and it can be assumed that this model would generalize to any commercial development growth at the interchange exits in Interstate 70 and Interstate 75 in Ohio.

4. 3. Daily Truck Percentage (T24) Model

(T24) is the daily truck percentage of truck traffic in the total traffic stream for 24 hours.

T24 is the proportion of B&C commercial vehicles in the design hour. Ohio Department of

Transportation in the Ohio Certified Traffic Manual (2007) defined daily truck factor ,T24, by saying, “T24 represents the percentage of ADT that is comprised of heavy and commercial trucks

(B&C commercial classes), and is another important factor in highway design and transportation planning” (p. 12).

The multiple regression stepwise backward method, already discussed above, is used to find the daily truck percentage at the Interchange exits. In this model 19 predictor variables were introduced into the regression backward method. The dependent variable, which is the Daily Truck

Percentage (T24) and all other 19 predictors are defined in Table 6. Each step of the T24 regression model is discussed below:

4. 3. 1. T24 Model Summary

The SPSS output, as labeled the Model Summary, provides an overview of the results

(Table 21). In the very first step, 18 predictors were entered into the model. The R- Square with these predictors in the model was 0.671. This value is the square of the correlation between T24 and

all predictor variables that are entered (R2 = (0.8192)2 = 0.671). This value is also the R Square

Change (0.671). The R Square value stayed constant until step 4 and continually decreased step wisely. By the time we arrive at step 12, the R square value has reached 0.650 which was chosen the best fit of the model for this research study based on having 6 variables significant (Table 21). 112

In step ten (12th), as can be seen from the footnote beneath the Model Summary Table 22, seven predictor variables such as square footage of restaurants, the number of gas and convenience stores, truck parking lots , distance to the nearest neighbor interchange, federal highways, and state highways were entered into the model. The R Square value from step 1 to step 4 was 0.671. Thus, it decreased 0.001 in the value of R Square (0.671 – 0.670 = .001) and this is reflected in the R

Square Change from step 4 to step 5. By the time we arrive at the end of the thirteen the step, the

R Square value has reached 0.638.

Table 21. T24 Model Summary Model R R- Square Adjusted R Std. Error of R Square Durbin- Square the Estimate Change Watson 1 0.819 .671 .553 .058 .671 2 0.819 .671 .561 .057 .000 3 0.819 .671 .570 .056 .000 4 0.819 .671 .578 .056 .000 5 0.818 .670 .584 .055 -.001 6 0.818 .669 .591 .055 -.001 7 0.817 .668 .596 .055 -.001 1.7 8 0.816 .666 .601 .054 -.002 9 0.814 .663 .605 .054 -.003 10 0.813 .660 .608 .054 -.002 11 0.810 .657 .611 .054 -.003 12 0.806 .650 .610 .054 -.007 13 0.799 .638 .603 .054 -.012 Model (step 12) is the Research Study Model. Predictors: (Constant), Square Footage of Gas and Convenience Stores, State Highway, Distance to the Nearest Neighbor Interchange, Truck Parking Lots, Federal highway, Square Footage of Restaurants and Number of Gas and Convenience Stores.

The goal is to choose the best fit model of the research study that provides the most statistically significant variables. Thus, step 12 is chosen because six of the predictor variables were statistically significant, where the R value which represents a higher correlation as R = 0.806. Table

21 shows the percent of variability in the dependent variable which accounted for 65 percent by all the predictors in model 12. The change in the R square is to evaluate the percentage of predictive 113 power which was added to the model by adding all others predictors in step 12. In step 1 the R-

Square is 0.671 and in step 12 the R-square decreases to 0.638. Therefore, the variation in model

12 decreased from 67.1 percent to 65.0 percent (67.1 – 65.0 = 2.1). So we have only 2.1 percent of

R square change in model 12, which is the model of the study. The final model accounted for 65.0 percent of the total variance.

Finally, the Durbin-Watson statistic will be found in the last column of Table 21. This

Durbin-Watson can tell whether or not the assumption of independent errors is acceptable. The statistic value should be between 1 and 3. For this model the value is 1.74 which is in the range and we can say that the assumption was satisfied.

4. 3. 2. T24 Model ANOVA

The next output of this model contains An Analysis Of Variance (ANOVA). The ANOVA table contains the breakdown of the variance in the outcome variable which are regression, residual, and total. The sum of squares is associated with three kind of variances. The variance which is explained by the independent variable is called regression. The variance which is not explained by the independent variable is called residual. Finally, the total variance (0.503) is the partitioned of the regression (0.327) and the residual (0.176) variances (see Table 22).

The degree of freedom, df, is associated with the sources of variance. The total variance has N-1 degree of freedom which corresponds to the number of coefficients calculated minus 1 (df

= N – 1). For example, there are 8 coefficients, so the model has 7 (8 - 1 = 7) degree of freedom.

The Residual degree of freedom is the degree of freedom total value minus the degree of freedom of the model, so we can say that 68 – 7 = 61. The mean square can be obtained from the sum of square divided by their respective degree of freedom, 0.337 / 7 = 0.047 and 0.176 / 61 = 0.003. The

F-ratio is called the F – Statistic where the p-value is associated with it. The F- ration is the mean square of the regression divided by the mean square of the residual of the model. For example,

0.047/0.003 = 16.173 (step 12 of the model). 114

Table 22. T24 Model ANOVA Regression Residual Total Sum of squares 0.327 0.176 0.503 df 7 61 68 Means Square 0.047 0.003 F 16.173 Sig 0.00 Refer to Table D 43 for all steps of daily truck percentage (T24) model

In the first step of the model, F-ratio is 5.666 which indicates that the relationship is unlikely due to chance (p = 0 < .05) (Table D 43). The F –statistics for all models were increasing step wisely, the value of F for the model (12th step) is even higher F (7, 61) = 16.173, p <0.05. We

can interpret these findings to suggest that the first model significantly improved our ability to

predict the outcome variable which is the total traffic truck percentage at the Interstate exits, but

that the new models with the extra predictors were even better.

4. 3. 3. T24 Model Equation and its Coefficients

Equation 6, presented below, contains the development units and all other predictor variables that contribute to the daily truck percentage (T24) at rural and small town Interstate exits

of Interstate 70 and Interstate 75 in Ohio. The Y and X values are defined below. The coefficients

of the equation can be obtained by a multiple stepwise regression of backward method from Table

23. These estimations were obtained while using the data associated with 69 Interstate exits at rural

and small towns in Ohio. The final equation of the model can be used to predict the daily truck

percentage at these Interstate exits. The stepwise backward method was used for the multiple

regression model and the backward method provided many models in the analysis. However, we

are interested in the model which includes predictors that make a significant contribution to the

outcome variable; in this case T24 at the Interstate exits. Therefore, model 12 is the best fit model that includes predictors which significantly contributed to the model.

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Table 23. T24 Model Coefficients Variables B Standardized Std. T - P – (Beta) Deviation Statistic Value Coefficients (Constant) 0.103 6.020 0.000 Number of Gas and -0.031 -0.670 1.847 -3.182 0.02 Convenience Store Square Footage of Gas and 0.00001 0.950 7,801.975 4.050 0.000 Convenience stores Square Footage of -0.000001 -0.297 27,477.214 -3.024 0.004 Restaurants Truck Parking Lots 0.014 0.461 2.764 4.813 0.000 Distance to the Nearest -0.008 -0.121 1.279 -1.459 0.150 Neighbor Interchange Intersecting Highway = 0.048 0.227 0.405 2.297 0.022 Federal Intersecting Highway = State 0.040 0.231 0.497 2.427 0.025 This table contain the Standard Deviation Column which is Extracted from Table 9.

Y2 (T24) = 0.103 – 0.031×X1 + 0.00001×X2 – 0.000001×X3 + 0.014×X4 – 0.008×X5 + 0.048×X6

+0.040×X7 (6)

Where,

Y2 = Daily Truck Percentage

X1 = Number of Gas and Convenience Store

X2 = Square Footage of Gas and Convenience Stores

X3 = Square Footage of Restaurants

X4 = Truck Parking Lots

X5 = Distance to the Nearest Neighbor Interchange

X6 = Intersecting Federal Highway

X7 = Intersecting State Highway

Error term = 0.103

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Table 23 contains beta values which indicate that the relationship between daily truck percentage (T24) and each predictor in the model. If the value is positive it indicates that the relationship between the variable and the outcome is positive, whereas a negative coefficient indicates a negative relationship. For this model, three predictors have negative B values indicating

a negative relationship with the outcome variable, and four predictors have positive B values

indicating a positive relationship with the outcome variables. So this model contains 7 predictor

variables where six predictor variables are statistically significant, as p-value is less than 0.05, and

one predictor variables displayed non-significant results as p-value >0.05 (see Table 23). Finally,

the model equation number 5 is formulated above. The Y intercept of the T24 percentage is labeled as constant and has a value of 0.103.

Equation number 5 contains the unstandardized coefficients, B, values which are the partial regression coefficients because their values take into account the other predictor variable in the model. The partial regression coefficient values signify the predicted change in the outcome variable for every unit increase in that predictor. The unstandardized coefficients, B, and standardized coefficients, β, terms are interpreted below:

The square footage of restaurants is associated with a partial regression coefficient of B =

– 0.000001 and signifies that for every additional point on the square footage of restaurants measure, we would predict a reduction of 0.000001 points on the daily truck percentage (T24) measure. In addition, the standardized coefficient β = -0.297 value indicates that as the square footage of restaurants increases by one standard deviation, 27,477.214, daily truck percentage decreases by 0.297 standard deviations. The standard deviation for the total daily truck percentage

(T24) is 0.086 which constitutes a change of 0.025 (0.086 x 0.297). Therefore, for every increase of

27,477.214 square footage of restaurants result in decrease of 0.025 units in daily truck percentage.

This interpretation is true only if the effects of all other 6 variables are held constant (Table 23). 117

The number of gas and convenience stores is associated with partial regression coefficients

of B = -0.031 and signifies that for every additional point of the number of gas and convenience

stores measure, we would predict a reduction of 0.031 points on the daily truck percentage (T24)

measure. In addition, the standardized coefficient β = -0.670 value indicates that as the number of

gas and convenience stores increases by one standard deviation 1.847, daily truck percentage

decreases by 0.670 standard deviations. The standard deviation for the total daily truck percentage

0.086 constituting a change of 0.057 (0.086 x 0.670). Therefore, for every increase of 1.847 number

of gas and convenience store result in decrease of 0.057 units in daily truck percentage (T24). This interpretation is true only if the effects of all other 6 variables are held constant.

Square footage of gas and convenience store has positive affect which is associated with a partial regression coefficient of B = 0.00001 and signifies that for every additional point on the positive affect measure, we would predict a gain of 0.00001 points on the daily truck percentage measure. In addition, the standardized coefficient β = 0.950, value indicates that as the square footage of gas and convenience stores increases by one standard deviation, 7,801.975, daily truck percentage increases by 0.920 standard deviations. The standard deviation for the total daily truck percentage is 0.086 which constitutes a change of 0.082 (0.086 x 0.950). Therefore, every increase of 7,801.975 square footage of gas and convenience stores results in an increase of 0.082 units in daily truck percentage (T24). This interpretation is true only if the effects of all other 6 variables are held constant (Table 23).

Truck parking lots has a positive affect is associated with a partial regression coefficient of

B = 0.014 and signifies that for every additional point on the positive affect measure, we would predict a gain of 0.014 points on the T24 measure. In addition, the standardized coefficient β = 0.461 value indicates that as the number of truck parking lots increases by one standard deviation 2.764, daily truck percentage increase by 0.461 standard deviation. The standard deviation for the T24 is

0.086 and so this constituting a change of 0.039 (0.086 x 0.461) units. Therefore, for every 2.764 118 increase in units of truck parking lots, an additional 0.039 units in daily truck percentage (T24) are increased. This interpretation is true only if the effects of all other 6 variables are held constant.

Distance to the nearest neighbor interchange is associated with partial regression coefficients of B = -0.008 and signifies that for every additional point of distance to the nearest neighbor interchange measure, is predicted a reduction of 0.008 points on the daily truck percentage

(T24) measure. In addition, the standardized coefficient β = -0.121, value indicates that as the

distance to the nearest neighbor interchange increases by one standard deviation 1.279, T24

decreases by 0.121 standard deviation. The standard deviation for the daily truck percentage (T24)

is 0.086 which constitutes a change of 0.010 (0.086 x 0.121) units. Therefore, for every increase of

1.279 units results in decrease of 0.010 units in daily truck percentage (T24). This interpretation is

true only if the effects of all other 6 variables are held constant (Table 23).

Federal highway coded as a dummy variable, in this case, a unit change in the predictor is

the change from 0 to 1 where 1 = Yes and 0 = No. A positive affect is associated with a partial

regression coefficient of B = 0.048 and signifies that for every additional point on the positive effect

measure, we would predict a gain of 0.048 points on the daily truck percentage (T24) measure. In addition, the standardized coefficient (β = 0.227) value indicates that as the federal highway increases by one standard deviation 0.405, daily truck percentage increases by 0.227 standard deviation. The standard deviation for the daily truck percentage (T24) is 0.086 constituting a change

of 0.019 (0.086 x 0.227) units. Therefore, every 0.405 increase in units of federal highway, results

in an additional 0.019 units in daily truck percentage (T24) being increased. This interpretation is true only if the effects of all other 6 variables are held constant (Table 23).

State highway is coded as a dummy variable, in this case, a unit change in the predictor is the change from 0 to 1 where1 = Yes and 0 = No. A positive affect is associated with a partial regression coefficient of B = 0.040 and signifies that for every additional point on the positive effect measure, we would predict a gain of 0.040 points on the daily truck percentage (T24) measure. In 119 addition, the standardized coefficient β = 0.231, value indicates that as the state highway increases by one standard deviation, 0.497, daily truck percentage increases by 0.231 standard deviation. The standard deviation for the daily truck percentage (T24) is 0.086 which constitutes a change of 0.020

(0.086 x 0.231) units. Therefore, for every 0.497 increase in units of state highway, an additional

0.020 units in daily truck percentage (T24) are increased. This interpretation is true only if the effects

of all other 6 variables are held constant.

The significance levels of the model parameters or the predictor coefficient (B) and the

standardized (Beta) coefficients can be determined by t-tests. Also, the t-statistic is associated with

p-value to test whether a given coefficient is significantly different from zero and formulated below

(Table 23):

o Square Footage of Restaurants t (61) = -3.024, p < 0.05

o Number of Gas and Convenience Stores t (61) = -3.182, p < 0.05,

o Square Footage of Gas and Convenience Stores t (61) = 4.050, p < 0.05,

o Truck Parking Lots t (61) = 4.813, p < 0.05,

o Distance to the Nearest Neighbor Interchange t (61) = -1.459, p > 0.05,

o Federal Highways t (61) = 2.344, p < 0.05

o State Highways t (61) = 2.297, p < 0.05 From the magnitude of the t-statistics we can see that the truck parking lots, square footage

of gas and convenience stores, federal highway, and state highway had the highest impact, whereas

the other predictor variables had less impact.

4. 3. 4. T24 Model Excluded Variables

In a regression analysis, SPSS provides a summary table of the excluded variables of the model. Table 24 present a summary table of the excluded variables of the Daily Truck Percentage

(T24) model. This summary table contains an estimation of the beta value, which was entered into

the equation, and as a result, it provides an estimation of a t-test for the value which was entered 120 into the equation in the first step. The backward stepwise method will continue entering predictors of the t-statistics until it finishes entering variables which have less than 0.05 significant value.

Excluded variables by the regression model tell us that these predictor variables have no significant impact on the models ability to predict daily truck percentage (T24) at the Interchange exits in Ohio

for Interchange 70 and Interchange 75. Therefore, the following variables were excluded due to

their non-significant contribution to the outcome.

Table 24. T24 Model Excluded Variables Variables Beta In t-statistics p –value. Intersecting Highway ADT 0.001 0.013 .990 Developed Axes -0.172 -1.101 .275 Number of Restaurants 0.205 0.469 .641 Number of Hotels 0.105 0.668 .507 Square Footage of Hotels 0.069 0.388 .699 Number of Gas Pumps 0.146 0.488 .627 Number of Hotel Rooms 0.133 0.803 .425 Distance to Furthest Interchange (Miles) 0.005 0.058 .954 Distance to the Nearest City and Town (Mile) 0.028 0.351 .727 County Population -0.061 -0.769 .445 Population of Nearest City and Town 0.024 0.291 .772 Local Highway - - -

4. 3. 5. Assessing the Assumption of no Multicollinearity for T24 Model

The SPSS output contains the collinearity diagnostics table which includes collinearity

statistics such as the Variance Inflation Factor (VIF), tolerances, eigenvalues, and variance

proportions (Table 25). Each of these measures are checked in this section.

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Table 25. Tolerance and Variance Inflation Factor (VIF) – T24 Model Variables Tolerance VIF

Square footage of restaurants 0.594 1.683 Number of gas and convenience store 0.130 7.716 Square footage of gas and convenience stores 0.104 9.574 Truck parking lots 0.627 1.595 Distance to the nearest neighbor interchange 0.831 1.204 Intersecting highway = Federal 0.614 1.628 Intersecting highway = State 0.568 1.759 Tolerance and VIF values are extracted from Table D 52.

For our current model, the VIF values for all predictors are well below 10 and the tolerance statistics are well above 0.1, which do not indicate a serious problem. However, the tolerance statistics values 0.130 for the number of gas and convenience stores and 0.104 for the square footage of gas and convenience stores are below 0.2 which indicate a potential problem in the dataset (Table 25). Therefore, we can conclude that there is a collinearity issue with these two

predictor variables in the dataset based on their tolerance statistics. To calculate the average VIF

we simply add the VIF values for each predictor and divide by the number of predictors (k). There

are 7 predictors in the model.

∑ VIF =

....... . VIF 3.594

As the average VIP is very well above 1 (3.594 > 1), the regression model may be biased.

However, we can further check the model, especially by checking these two predictors in the correlation matrix. The number and square footage of gas and convenience stores are highly correlated with each other meaning that these two predictor correlations coefficient squared (R2) is 122 above 0.8 (Table B 31, Table B 32 and Table B 33). Consequently, the multi-collinearity exists with only two predictors which are the number and square footage of gas and convenience stores.

We can investigate this issue further by examining the collinearity diagnostics. All variance proportions are ranging from 0 to 1 and each predictor variable should be loaded into different dimensions. For this model, each predictor variable has most of its variance loaded onto a different dimension except two variables: the number and square footage of gas and convenience stores. The variance proportions of the number of gas and convenience stores (0.93) and the square footage of gas and convenience stores (0.94) are loaded in the same dimension 8 (Table D 53). After checking several measures of the regression model, it is concluded that the number and square footage of gas and convenience stores.

4. 3. 6. Casewise Diagnostics of T24 model

SPSS produces a summary table of the residual statistics to check for extreme cases (Table

26). There are 69 data points in this dataset. Reasonable data points are 95% cases to have a standardized residual less than -2 or greater than +2. We are expecting to get 3.45 cases (0.05*69

= 3.45) to have standardized residuals outside of the range and still have an acceptable dataset or there should be at least 3.45 extreme variables in the dataset (see Table 26). While we obtained 4 cases (5.79%) that lie outside of the limits, it is clear that this sample has 0.79% extreme variables.

The remaining 99.21% data points should lie within -2 and 2 and 0.79% data points outside of the limits, in the other hand, there is no single case that has a standardized residual greater than 3 (Table

26). Therefore, we can say that it is an accurate model based on this information.

Table 26. Casewise Diagnostics Case Number Std.Residual T24 Predicted Value Residual 11 -2.690 .04 .1859 -.14592 24 2.356 .40 .2722 .12776 25 2.422 .30 .1686 .13137 31 -2.012 .02 .1291 -.10910 123

Expected = 0.05×96 = 3.45 Cases (5%)

Obtained = Casewise Diagnostics = 4/69=0.0579 (5.79%)

Expected 5%

5.79% - 5% = 0.79% additional cases.

4. 3. 7. Cross-Validity of T24 Model

The difference in the model as the R-square of 0.650 minus the adjusted R-square of 0.610 is equal 0.04 (0.650 – 0.610 = 0.04) (Table 21). We can say that there is 4 percent shrinkage in the dataset which means that if the model is derived from the population rather than a sample it would account for approximately 4 percent less variance in the outcome (Field, 2009). Furthermore, we can apply equation (4) to determine an idea of its likely value in different samples.

Adjusted R2 = 1 – [( 1

n = Sample size (69 interchanges)

k = Number of predictor

Adjusted R2 = 1 – [( 1 0.650

Adjusted R2 = 1 – [(1.115) × (1.117) × (1.0145)] × (0.35)

Adjusted R2 = 1 – [1.264] × (0.35)

Adjusted R2 = 0.5576 (55.76%)

This value is very close to the observed value of R-Square, 65%, which indicates that the

cross validity of this model is satisfactory. 124

4. 3. 8. Case Summary

Based upon the influential cases, this section describes the Cook’s distance, Leverage values, covariance ratio and central leverage value of the model (Table 27).

Table 27. Case Summary Mahalanobis Cook’s Distance Centered Leverage Value Covratio Distance 10.899 0.265 0.160 0.467 11.014 0.206 0.162 0.608 4.182 0.075 0.061 0.569 4.990 0.061 0.073 0.731

4.3.8.1. Cook’s Distance

Cook’s distance is a measure of the overall influence of a case on the model. According to

Field (2009) and Cook and Weisberg (1982) that the values greater than one may be cause for concern. In this model, the absolute value is not greater than 1; therefore, there is no cause for concern based upon Cook’s Distance, (NUI > 1) (Table 27).

4. 3. 8. 2. Leverage Values

Next, it measures the influence of the observed value of the outcome variable over the predicted values. Leverage values lie between 0 and 1. According to Field (2009), if no cases apply undue influence over the model then the leverage values are expected to be close to the average value which can be found by equation 7. Although, Hoaglin and Welsch (1978) recommended that these cases values should be greater than twice the average value which can be found by equation

8 but another scholar Steven (2002) recommended that the investigating cases with values should be greater than three times the average value which can be found by equation 9. So, in terms of influential cases, it is better to find any cases that are a concern based upon the leverage values using 3 standard deviations as the basis. The average leverage value can be found from equation number 7 where K is the number of predictor and n is the number of data points. This model 125 contains 7 predictor variables and number of data points are 69. The Average Leverage value is calculated below:

Average Leverage Value = (7)

Average Leverage Value = 2 × ( ) (8)

Average Leverage Value = 3 × ( ) (9)

Average Leverage Value = 0.116

Average Leverage Value = 2 × ( 0.232

Average Leverage Value = 3 × ( 0.348

Now we are looking for values either twice as large as this (0.232) or three times as large

(0.348). As a result, all cases are within the boundary of three times the average leverage value

(Table 27).

4. 3. 8. 3. Mahalanobis Distance

Following that, it measures the distance of cases from the means of the predictor variables.

In terms of the influential cases, we need to review cases that are a concern based upon the mahalanobis distance. According to Bamnett and Lewis (1994), that in small sample size (N =

100) and with 3 predictors value greater than 15 should be a concern. This research study data sample size is 69 and has 7 predictors in the model. While reviewing the mahalanobis value in

Table 27, none of the values are greater than 15. We can say that there is no undue influence in the data set. 126

4. 3. 8. 4. Covariance Ratio

The final measure of the influential cases is the covariance ratio (CR) which is to measure of whether a case influences the variance of the regression parameters. It is better to find the upper limit and lower limit of the covariance ratio which can help to find the influential cases of the covariance ratio. The upper and lower limit of the covariance ratio can be found in equation 10 and equation 11 respectively. Where, K is the number of predictor and N is the number of data points.

Again, step 12 of the model has 7 predictors.

3∗k1 CVRi 1 10 n

3∗k1 CVRi 1 11 n

3∗71 3∗71 CVRi 1 1 1.34 n 69

3∗K1 3∗71 CVRi 1 1 0.65 n 69

Now, the boundaries of upper and lower limit are found. Cases that have a covariance ratio

that fall outside of these limit may be a concern. While reviewing the covariance ratio column in

Table 27, three cases covariance ratios’ are blow the bottom limit. Consequently, four measures

are checked and there is only concern based upon the covariance ratio.

4. 3. 9. Checking the Assumptions of Multiple Regression (MR) of T24 Model

4. 3. 8. 1. Linearity

In this section of the analysis, the linearity of the model for each predictor variable is discussed by providing the following figures:

The number of gas and convenience stores linearity graph is presented in Figure 35 which clearly illustrates that the graph funnels out and there is heteroscedasticity in the data. In addition, 127 there is no curve in the graph to break the assumption of linearity which is good. Also, the number of gas and convenience stores plot shows a negative relationship to daily truck percentage (T24).

Square footage of restaurants linearity graph is represented in Figure 36 where most of the dots are dispersed around zero and the graph funnels out. There is not any sort of curve in the graph showing that data breaks the assumption of linearity. Also, square footage of restaurants plot shows a negative relationship to daily T24 truck percentage. The acres of truck parking lots linearity graph is presented in Figure 37 and all the dots are dispersed around zero with no curve in the graph. As it is the most important predictor of the model and luckily there is not any issue with the data to violate the assumption of homoscedasticity. In addition, the acres of truck parking lots plot shows a positive relationship to daily truck percentage (T24).

The intersecting state highway linearity graph is presented in Figure 38 where there is a sort of curve in the graph and there is a chance that the data of the intersecting state highway

variable might violate the assumption of linearity. The intersecting state highway plot shows a

positive relationship to daily truck percentage (T24). A square footage of gas and convenience store linearity graph is presented in Figure 39, which clearly illustrates that the assumption is met and there is a positive relationship between square footage of gas and convenience stores and daily truck percentage (T24) model. The intersecting federal highway linearity graph is presented Figure

40 which illustrates that the graph does not funnel and there is also not any sort of curve. Therefore,

the linearity assumption has been met. In addition, there is a positive relationship between

intersecting federal highway and daily truck percentage (T24). Consequently, most predictors’

linearity assumptions have been met and there are only two predictors which have some linearity

issues. Consequently, it is advised to collect some more data in the future research study for these

predictors in order to improve and verify the current model.

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Figure 35. T24 Vs Number of Gas and Convenience Store

Figure 36. T24 and Square Footage of Restaurants 129

Figure 37. T24 Vs Acres of Truck Parking Lots

Figure 38. T24 Vs Intersecting State Highway 130

Figure 39. T24 Vs Square Footage of Gas and Convenience Stores

Figure 40. T24 Vs Intersecting Federal Highway 131

4. 3. 8. 2. Independence of Errors

The Durbin–Watson statistic tests the assumption of ‘independence of errors’, meaning that for any pair of cases, the error term should be independent or uncorrelated (Field, 2007). In

the truck percentage model, the Durbin–Watson statistic is 1.7 which falls within the recommended

boundaries of 1–3, suggesting that errors are reasonably independent (Table 21).

4. 3. 8. 3. Homoscedasticity

The scatterplot of the regression model can help to assess both homoscedasticity and

independence of errors. For each value of the predictors the variance of the error term should be

constant, meaning that the errors are spread out consistently between the variables. If we draw the

line from zero to zero between the quadrants then we can see that all data are distributed around

zero (Figure 41). As a result, the assumption of homoscedasticity has not been met because the

dots are not distributed around zero in all four quadrants.

Figure 41. Plot ZRESID against ZPRED 132

4. 3. 8. 4. Normally-Distributed Errors

It is important to test the normality of residuals by checking the histogram and normal probability plot. The SPSS output for the histogram and normal probability plot are presented in

Figure 42 and Figure 43. The histogram in Figure 42 looks like a bell-shaped curve is a little bit leptokurtic but kurtosis not the main issue. The main issue is with skew but this curve displays a normal distribution which presents no deviation from normality. The normal P-P plot verifies that the dots are deviated from the straight line only in some parts which indicates normal distribution errors (Figure 43). However, based on the histogram, we can say that it is really good data.

Figure 42. Histogram of Normal Distribution (T24 Model)

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Figure 43. Normal P-P Plot of Regression Standardized Residual (T24 Model)

4. 3. 10. Result of the T24 Model

Commercial development factors such as gas station, lodging, and fast food restaurants; geographical factors such as distance to furthest interchange, nearest interchange exits, and distance to the nearest city and town; demographical factors such as county population, and population to the nearest city and town; and interchange type such as federal, state and local highways were used in a stepwise backward multiple regression analysis to predict daily traffic percentage (T24) at the interchange exits in Ohio. The daily truck percentage (T24) prediction model contained 13 steps.

Step 12 was chosen the best fit model of the research study which contained 7 of 19 predictors

whereas 12 predictors were removed. The model was statistically significant, F (7, 61) = 16.173, p

< 0.05, and accounted for approximately 65% of the variance of daily truck percentage (T24) at the

Interstate exits in Ohio (R2 = 0.650, Adjusted R2 = 0.610). The R2 change for step 12 (denoted as 134

ΔR2) is -0.007 and the model parameters, standard error and the beta (Standardized Coefficients) values are presented in Table 28.

Table 28. Interpretation of T24 Multiple regression model Model ( Step 9) Standardized Unstandardized Coefficients Coefficients Variables B Std. Error Beta (β) (Constant) 0.103 0.017 Number of gas and convenience store -0.031 0.010 -0.670 Square footage of gas and convenience stores 0.00001 0.000003 0.950 Square footage of restaurants -0.000001 0.0000003 -0.297 Truck parking lots 0.014 0.003 0.461 Distance to the nearest neighbor interchange -0.008 0.006 -0.121 Intersecting highway = Federal 0.048 0.021 0.227 Intersecting highway = State 0.040 0.017 0.231 Note: For step 13, R2 = 0.650 and ΔR2 = -0.007 (Final Model)

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CHAPTER 5: DISCUSSIONS AND CONCLUSIONS

5. 1. Summary of the Research Study

The purpose of this study was to develop two models for predicting highway interchange traffic at rural and small town on Interstate 70 and Interstate 75 exits in Ohio. The first model was to investigate Interchange traffic and commercial development at rural Interchange highway exits, and the second model was to investigate daily truck percentage (T24) at the Interstate exits. The

study examined two research questions. The literature review was presented in Chapter 2.

Some of the difficulties encountered in the development of the models involve the rapid

growth of commercial development at the Interstate exit. As found in an earlier study by Hartgen

and Kim (1998) the rapid growth impacts on the geographical reorientation of the commercial

development thus making it necessary to change the land use and values. Another type of problem

studied is the quantification of certain factors that determine the amount of commercial

development growth at rural and small town of Interstate 70 and Interstate 75 exits in Ohio.

A research study of 69 rural and small town interchanges was conducted at Interstate 70

and Interstate 75 exits in Ohio. The general form of data analysis is presented in Chapter 3 and the

multiple regression analysis of the two models is presented in Chapter 4. The two models contained

two types of variables. The dependent variables in the analysis were interchange annual average

daily traffic (AADT), and daily truck percentage (T24). The independent variables were intersecting highway AADT, development units (such as gas stations and convenience stores, square footage of gas and convenience stores, gas pumps, hotels and motels, hotel and motel rooms, square footage of hotel rooms, restaurants, square footage of restaurants, developed axes, and truck parking lots), geographic characteristics, demographic characteristics, and intersecting highway types (federal, state, and local).

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5. 2. Review of the Methodology

The procedures used in conducting the study were presented in Chapter 4. The sample used in the construction of the two models was defined as the rural and small town interchanges on the contiguous Interstate 70 and Interstate 75 highway system in Ohio. The selection of Interstate 70 and Interstate 75 among other Interstate highways in Ohio was based on high traffic volume. The completion of this study has offered a unique opportunity for studying the growth at rural and small town Interstate exits on Interstate 70 and Interstate 75 in Ohio. Primarily 69 rural and small town

Interchanges of two Interstate highway systems (Interstate 70 and Interstate 75) were studied in

this research study. ODOT GIS shapefiles were used to make a map for the research study which

contained Interstate 70 and Interstate 75 highway interchanges exit locations and numbers.

This research study contained a large number of predictor variables. Therefore, different

sources are used for collecting the data for the analysis. The development factors data were obtained

from a book called The Next Exit 2015: The Most Accurate Interstate Highway Service Guide Ever

Printed (Watson & Next Exit, 2015). For the accuracy of the actual number of development units

at each interchange exit, a website called “i75exitguide”, which contain each development along

Interstate 70 and Interstate 75 in Ohio, was checked. Traffic volume such as interchange AADT

and intersecting highway ADT were obtained from ODOT, where all AADT data is available in

ODOT website. The geographical data such as distance to the nearest city and town, distance to

the nearest interchange and distances to the furthest interchange, were collected using the google

map tool. Demographic data was collected from the Ohio Census Bureau Research Office.

In Chapter 4, a preliminary analysis was started in order to estimate the development

growth at the rural and small town Interstate exits in Interstate 70 and Interstate 75. The analysis in

Chapter 4 employed (1) the mean, standard deviation, and range of the dependent variables and

independent predictor were estimated in order to determine what variability existed among the

Interstate highway and interchange characteristics; (2) a simple correlation of all the influential 137 predictors and their growth and development distribution at the interchange exits were analyzed for both predicted models; (3) Utilizing the data discussed in chapter 3, two predicted models were prepared in chapter 4; and (4) histograms and frequency distribution curves of the two residuals are presented in the models.

The development factors documented in this research study are 19 predictor variables.

Prior studies attempted to study and predict interchange developments with a maximum of five predictor variables, whereas the two models presented in this research study used 19 predictor variables.

The stepwise multiple regression analysis function of SPSS was used in this study. It is widely used, especially in handling large number of predictor variables. In this analysis, the most influential variable was entered first to the regression model. The stepwise regression analysis is capable of building equations with those predictor variables which contributes significantly to the outcome variable, and builds another equation with excluded variables.

5. 3. Discussion

The rate of development differs according to rural and small town Interstate exit characteristics, cross route traffic volume, distance to the nearest neighboring interchange, distances to the nearest city and town, and competition from neighboring exits. These factors play an important role in development growth at rural and small town Interstate exits. There are some exit development factors which influence development at rural and small town Interstate exits but the amount of these developmental factors are different based on the characteristics of the exits, traffic volume from the cross routes, distances to neighboring interchanges, and distances to nearest cities and towns.

Hartgen (1997) suggests that the importance of key factors in exit development have a wide range of variations. These factors are written below in order.

1. Prior development mix at the exit 138

2. The local population within 1 mile of the exit, relative to the population of the nearby

town

3. Cross-street traffic toward city and town

4. Distance to the nearest city and town

5. Competitive share of development to neighboring exits (p. 45).

As per the earlier research studies, the following findings compared to this study:

First, all these key factors are considered in the statistical analyses to find the variation of exit developments. Prior development mix at the interchange exits is a combination of several predictors. Thus, the development mix such as the number of restaurants, number of hotels and motels, number of hotel rooms, number of gas pumps, number of gas and convenience stores, and the square footage of all these predictors were also used as individual predictors. In both regression models, interchange AADT model and daily truck percentage (T24) model, the development mix

did not display a significant result with the outcome variable. This means that the p-value was

greater than the significant level (0.05). As a result, the development mix factors did not influence

development growth at rural and small town Interstate 70 and Interstate 75 exits in Ohio.

Second, the demographic characteristics of the Interstate exits, such as county population

and population of the nearest city and town, were also added to the regression analysis. In both

regression models, the demographic factors did not display a significant result with the outcome

variable.

Third, the cross street traffic, intersecting highway AADT, toward and away from the exits

was one of the most important factors of the analysis which had the highest correlation with the

outcome variable (intersecting highway AADT) among all other independent variables. This means

that the cross street traffic influences development growth at the Interstate exits. However, it did

not display a significant result in the daily truck percentage (T24) model because this variable was excluded by the regression stepwise technique. This means that cross street traffic predictor did not 139 display a significant impact in the truck percentage model or that it did not influence development growth at rural and small town of Interstate 70 and Interstate 75 exits in Ohio.

Fourth, distance to the nearest city and town predictor was added to the analysis. As a result, distance from the nearest city and town to the interchange exits displayed significant impact on development at the Interchange AADT model, while it was removed by the regression stepwise technique in the daily truck percentage model. This means that distance to the nearest city and town influence development at rural and small town Interstate exits displayed significant result with the outcome variable (p-value <0.05). We can therefore say that the closer the Interstate exit is to the nearest city and town, the greater the development growth contribution at the Interstate exits in

Ohio. In contrast, heavy truck traffic did not display a good result and this predictor was removed by the regression stepwise technique. We can then say that the distance from the nearest city and town to the interchange exits were not relevant to the development.

Fifth, competition share of developments to the nearest and furthest interchange exits are not included in this analysis.

The distance to the nearest neighboring interchange exits and distance to furthest neighboring interchange exits are used as two separate predictors in the analysis. As a result, distance to nearest neighboring interchange exits displayed a significant result with the outcome variable in both models as p-value is less than 0.05 significant level. In contrast, distance to furthest neighboring interchange exits predictor was removed in both models from the regression technique.

This means decreasing the distance between two interchange exits influence development growth.

On the other hand, increasing the distance between two interchange exits does not influence development growth as distance to furthest interchange predictors were removed by the regression stepwise technique in the analysis.

In addition to Hartgen’s (1997) finding wide variations of the development factors, this research study included some other predictors in the analysis such as developed axes and type of 140 intersecting highways regardless of their traffic volume consideration. These intersecting highways are federal highway, state highway and local highway. The number of the developed axes on

Interstate 70 and Interstate 75 was a separate predictor used in the analysis. As a result, it displayed a significant result with the dependent variable, interchange total AADT, as p-value is less than

0.05. This means that interchange developed axes influence development growth at the interchange exits. There are 69 developed axes along Interstate 70 and Interstate 75 in Ohio. In contrast, the developed axes predictor variable was excluded by the regression technique in the daily truck percentage model meaning daily truck percentage (T24) is not relevant to developed axes to

influence development growth at the interchange exits. Furthermore, three types of intersecting

highways were included in the analysis. As a result, intersecting federal highway and intersecting

local highway displayed a significant result with the outcome variable in the Interchange total

AADT model, as p-value is less than 0.05 while intersecting state highway was excluded by

regression stepwise technique. In contrast, intersecting federal highway and intersecting state

highway displayed significant results with the outcome variable in the Daily Truck Percentage

model, as p-value is less than 0.05. However, intersecting local highway was excluded by

regression stepwise technique. This means trucks are not stopping at local interchange exits and

therefore are not relevant to the outcome variable.

As Hartgen (1997) suggests that services such as fast food restaurants, gas pumps and gas

stations, and convenience stores are important factor of local market which can be supported by the

Interstate traffic alone, particularly if Interstate traffic volume is substantive. Following the

suggestion of Hartgen (1997), gas stations and fast food restaurants are used as individual predictor

in this analysis.

On the other hand, geographical distances such as the nearest neighboring interchange,

distance to furthest neighboring interchange, and distance to the nearest city and town were

important factors for gas stations and convenience stores, fast food restaurants and hotel and motel 141 developments at the Interstate exit. In the current study, these cluster development factors displayed a negative relationship with geographical distances. This means that the exits do not fit in with neighboring exits and their distances to the nearest city and town. For example, the distance to the nearest city and town has negative relationship to the number of hotels (r = - 0.184). We can say that when distance increases, the number of hotels decreases at the exits. This makes sense, because the farther away from a city or town an exit is, the less development (including number of hotels) we expect to have.

Intersecting highway AADT is an important factor of the development at the Interstate exits and especially of this research study. Gas station and convenience stores, lodging (hotels and motels), and restaurants are dependent on high volume of the intersecting highway AADT. In contrast, the Interstate highway AADT is not important because the traffic is not entering the city and town but passing from the Interstate exits. Therefore, Interstate traffic volume does not influence development growth just by passing the Interstate.

5. 4. Result and Conclusions

As a first step in the preliminary analysis, the descriptive statistics of the data was made.

In addition, a simple correlation analysis for the two models was developed. Some of the important findings of the Interchange AADT model and daily truck percentage (T24) model included the

following:

5. 4. 1. Interchange AADT Model:

First, AADT at interchange exits range from 621 to 22,144 vehicles per day, averaged

11,382.5 traffic volume per interchange, and had a standard deviation of 4,993.61 vehicle per day.

Second, the regression correlation matrix revealed that those factors that influence

development growth at the interchange exits are as follow: (a) Average Daily Traffic at the

intersecting highway, developed axes, number of restaurants, square footage of restaurants, number

of gas and convenience stores, square footage of gas and convenience stores, number of hotels, 142 square footage of hotels, population to the nearest city and town, and the number of gas pumps were directly related (significant at the 5 percent level) to the outcome variable (Interchange

AADT). (b) The distance to the nearest neighboring interchange were directly related (significant at the 1 percent level) to the outcome variable. (c) Federal highway, state highway, local highway, distance to furthest neighboring interchange, county population, and truck parking lots were not related (as P >0.05 significant level) to the outcome variable.

Third, utilizing the data from Chapter 3, Interchange AADT particular structure of the model was developed in Chapter 4. Substitution of the regression coefficients in the regression model of the commercial development growth at rural and small town of Interstate 70 and Interstate

75 exits in Ohio results in the following equation 12:

Log10 (INTERCHANGE AADT) = 1.472*

+ 0.627 *Log10 (Intersecting Highway ADT)

+ 0.049*Developed axes

+ 0.012 *Number of Restaurants

– 0.000002*Square feet of Hotels

+ 0.014*Truck Parking lots (Acres)

– 0.034*Distance to the nearest interchange (Miles)

+ 0.040*Distance to nearest city and town

– 0.111*Intersecting Highway = Federal

– 0.105*Intersecting Highway = Local

* Error term (1.472) (12)

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5. 4. 2. Daily Truck Percentage (T24) Model:

T24 mode is to find the daily truck percentage at the Interstate 70 and Interstate 75 exits in

Ohio.

First, the daily truck percentage at the interchange exits ranged from 1.28 to 20.81, average

2.34 and had a standard deviation of 0.42 (Table 9).

Second, the regression correlation matrix revealed that those factors that influence

development growth at the interchange exits are as follow: (a) Truck parking lots or truck parking

stop, the number of gas and convenience stores, square footage of gas and convenience stores and

number of gas pumps were directly related (significant at the 5 percent level) to the outcome

variable, T24. (b) Federal, and local highway were significantly related (significant at the 1 percent

level) to the outcome variable, T24. (c) Other variables such as developed axes, county population,

and distance to the nearest neighboring interchange were not related as p-value was greater than

0.05.

Third, utilizing the data from chapter 3, daily truck percentage (T24) model was developed in chapter 4. Substitution of the regression coefficients in the regression model for predicting the daily truck percentage (T24) at the interchange exits results in the following equation 13:

T24 = 0.103*

– 0.031*Number of gas and convenience store

+ 0.00001*Square footage of gas and convenience stores

– 0.000001*Square footage of restaurants

+ 0.014*Truck parking lots

– 0.008*Distance to the nearest neighbor interchange

+ 0.048*Intersecting Federal highway

+ 0.040*Intersecting State highway 144

*Error term = 0.103 (13)

Estimates of the parameters of the equations of the two models were obtained by the regression stepwise backward technique. Such estimates were obtained by using the data associated with the 69 rural and small town interchanges on Interstate 70 and Interstate 75 in Ohio. The final equations were then used to predict the influential growth of commercial development at each of the 69 rural and small town Interchange locations in Ohio. The final finding of the two models are outlined below.

In summarizing the Interchange AADT model, the model accounted for approximately

77.3 percent of the variation in the interchange AADT of the commercial development at the

Interstate 70 and Interstate 75 exits in Ohio. Eighteen predictor variables were used in the analysis, six of the multiple regression coefficients out of nine predictor variables were significant. These were the Log10.Intersecting highway ADT, developed axes, distance to the nearest neighboring interchange, distance to the nearest city and town, federal highway, and local highway. While the remaining three predictor variables coefficients were not significant as their p-value was greater than 0.05 significance level. These three non-significant predictor variables are the number of restaurants, square footage of hotels, and truck parking lots. The detail of the variables is shown in

Table 16. In summarizing the interchange AADT model, the model was predicted best using the above six predictor variables.

In summarizing the daily truck percentage (T24) model, the T24 model accounted for about

65 percent of the variation in the truck percentage of the commercial development at the Interstate

70 and Interstate 75 exits in Ohio. Six of the multiple regression coefficients out of nine predictor variables were significant. These predictor variables are the square footage of restaurants, number of gas and convenience stores, square footage of gas and convenience stores, truck parking lots, distance to the nearest neighbor interchange, federal highway, and state highway. The remaining 145 one predictor variable coefficient was not significant as the p-value was greater than 0.05 significance level. This predictor variable was distance to the nearest neighbor interchange. The detail of the variables is shown in Table 23. In summarizing the daily truck percentage model, the model was predicted best using the above six predictor variables.

The finding of the two interchange developmental models predicts commercial developmental growth at the Interstate exits, if interchange AADT variables (intersecting highway

ADT, developed axes, number of Restaurants, square feet of hotels, truck parking lots, distance to the nearest interchange, distance to nearest city and town, intersecting federal highway, and intersecting local highway) and interchange daily truck percentage variables (square footage of restaurants, number of gas and convenience stores, square footage of gas and convenience stores, truck parking lots, distance to the nearest neighbor interchange, intersecting federal highway, and intersecting state highway) are known.

5. 5. Limitation of the Study

The main limitation in the current study was that precise measurement data was not available. For example, the exact acreage data for the truck parking lots was not available and was hard to calculate due to their irregular dimension sizes. Also, the square footage of some development factors, such gas station and convenience stores, hotels and motels, and restaurants exact dimensions were also not available. Therefore, special measurement consideration was taken to measure these development factors using the Google Map tool. The distances between two interchanges were easy to assess. The distance from the interchange exits to the nearest city and town was not so easy to ascertain because there are so many routes to get to the center of the city.

Therefore, the shortest distances were recorded. In addition, due to limited resources, site factors such as sewer and water and zoning were not studied in this research study.

It should be noted that Hartgen and Kim (1998) did not do a site visit in their research study. Therefore, the current results may be informative. However, it would not be recommended 146 to use these models for investment purposes without a site visit. Results should be interpreted with caution, as these results still have policy implications which are supported by the earlier study by

Hartgen and Kim (1988). The following are the models and recommendation based on the findings of this research study.

1- Assessing the development potential for urban exits.

2- Evaluating exit overdevelopment or underdevelopment, particularly whether additional

development has a market.

3- Estimating the future development at exits along a new Interstate corridor or at new

interchanges of an existing interstate highway.

4- Community planning for service to exits, such as utilities or road widening.

5- Estimating the value of vacant land near exits.

5. 6. Recommendation for Future Research

The impacts of modern access highways on the growth of commercial development and their influence at the rural and small town Interstate exits are far reaching. It is very important to alert the local communities, who are living close to the interchange exits, about the influence of commercial growth at the interchange exits for proper future planning. Therefore, it is required to have more information concerning the rapid growth of commercial development at the Interstate exits in order to control the formation concern of uncontrolled commercial spread and environmental problems. Based on these finding, the following recommendation should be considered for the future research study:

First, the two models, interchange AADT model and daily truck percentage model, presented in this research study are the primary attempts at investigating interchange traffic and commercial development growth at the Interstate 70 and Interstate 75 in Ohio. It is recommended that more refined models should be developed that utilize more sophisticated tools and measure the variables more accurately. The new modeling systems should be strengthened by adding a variety 147 of factors which contribute to developments such as shopping centers, local zoning, utilities, business attitude, land values, automobile and equipment sale agencies, and exit geometry (such as exit design, number of lanes etc.). In addition to these factors, some local factors also should be added to the new models such as public land, public parks and environmental factors. These variables were not added to this research study because of time limitations.

Second, strengthen the new models by adding interchange type and age because these two factors may influence development complexity.

Third, a large number of data set should be collected from other Interstate exits in Ohio.

The present research study only analyzed data of Interstate 70 and Interstate 75 exits.

Fourth, site factors which influence commercial development growth should be considered for future research study. The site factors include: sewer and water service, zoning, visibility, ease of access and egress, slope and advertising.

Fifth, this research study only studied commercial development at rural and small town

Interstate exits of the Ohio section of Interstate 70 and Interstate 75. Additional research should focus on urban and rural Interstate exits as well as the environmental impacts of such developments.

148

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APPENDIX A: DEVELOPMENT UNITS BREAKDOWN

Table A 29. Interstate 70 Development Units COUNTY COUNTY Routes MILEPOST Hotels/Motels Hotel Rooms of Square Footage Hotels and Station Gas Stores Convenience Gas Pumps of Square Footage and Gas Stores Convenience Restaurants Square footage of Restaurants Total Development Preble IR70 1 0 0 0 0 0 0 0 0 0 Preble IR70 10 0 0 0 2 36 10850 2 3,900 4 Preble IR70 14 1 17 10,376 2 14 4900 1 1000 4 Clark IR70 59 0 0 0 1 8 2195 0 0 1 Clark IR70 62 1 12 4976 1 10 3999 0 0 2 Clark IR70 66 0 0 0 2 16 7580 0 0 2 Madison IR70 72 0 0 0 1 8 2475 1 3,600 2 Madison IR70 79 2 147 48,516 4 38 23377 5 17,986 11 Madison IR70 80 0 0 0 0 0 0 0 0 0 Madison IR70 85 0 0 0 0 0 0 0 0 0 Licking IR70 118 0 0 0 5 60 21910 3 12,780 8 Licking IR70 122 1 24 8,426 2 34 9142 1 8,640 4 Licking IR70 126 2 78 27,335 8 76 25329 4 12,560 14 Licking IR70 132 0 0 0 3 24 5448 0 0 3 Licking IR70 141 0 0 0 0 0 0 0 0 0 Licking IR70 142 0 0 0 0 0 0 0 0 0 Muskingum IR70 152 1 62 8,250 2 16 4115 2 4,820 5 Muskingum IR70 157 0 0 0 3 16 4231 0 0 3 Muskingum IR70 160 3 175 0 6 48 7917 4 12,110 13 Muskingum IR70 164 1 53 3,6879 1 4 1590 0 0 2 Muskingum IR70 169 0 0 0 0 0 0 0 0 0 Guernsey IR70 176 1 18 6996 1 10 2650 0 0 2 Guernsey IR70 178 12 1,015 189,466 4 48 20440 22 84,423 38 Guernsey IR70 186 0 0 0 3 42 17838 0 0 3 Guernsey IR70 193 0 0 0 3 24 12891 0 0 3 Belmont IR70 198 0 0 0 0 0 0 0 0 0 Belmont IR70 202 0 0 0 1 8 990 0 0 1 Belmont IR70 204 0 0 0 0 0 0 0 0 0 Belmont IR70 208 1 76 12,348 3 32 11788 3 12,012 7 Belmont IR70 213 0 0 0 4 22 5766 1 3,720 5 Belmont IR70 215 0 0 0 0 0 0 3 9,085 3 Belmont IR70 216 0 0 0 1 8 1380 0 0 1 Belmont IR70 218 7 602 128,980 2 20 4896 28 120,132 37 Belmont IR70 219 0 0 0 0 0 0 0 0 0 Belmont IR70 220 2 191 35,392 3 18 10605 0 0 5 Total Development Units 35 2470 517940 68 640 224302 80 306768 183

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Table A 30. Interstate 75 Development Units

COUNTY Routes MILEPOST Hotels/Motels Hotel Rooms of Square Footage Hotels and Station Gas Stores Convenience Pumps Gas of Square Footage and Gas Stores Convenience Restaurants Square footage of Restaurants Total Development Miami IR75 78 0 0 0 0 0 0 0 0 0 Miami IR75 82 3 254 53,829 3 36 15607 16 74,700 22 Miami IR75 83 1 50 11,904 1 10 3450 1 6,075 3 Shelby IR75 90 1 94 20,730 2 26 7215 0 0 3 Shelby IR75 92 4 304 133,178 5 48 15978 22 68,177 31 Shelby IR75 93 0 0 0 0 0 0 0 0 0 Shelby IR75 94 0 0 0 2 20 5344 0 0 2 Shelby IR75 99 0 0 0 5 70 19596 3 12,600 8 Shelby IR75 102 0 0 0 0 0 0 0 0 0 Shelby IR75 104 2 50 18,222 2 14 4800 2 13,850 6 Auglaize IR75 110 0 0 0 0 0 0 0 0 0 Auglaize IR75 111 5 286 71,544 5 46 16372 16 55,123 26 Auglaize IR75 113 0 0 0 0 0 0 0 0 0 Auglaize IR75 118 0 0 0 3 22 6392 2 5,076 5 Allen IR75 120 0 0 0 1 12 1904 0 0 1 Allen IR75 122 0 0 0 2 26 11776 1 1,300 3 Allen IR75 124 0 0 0 0 0 0 0 0 0 Allen IR75 130 0 0 0 0 0 0 0 0 0 Allen IR75 134 0 0 0 0 0 0 0 0 0 Allen IR75 135 0 0 0 6 74 29465 4 32,280 10 Allen IR75 140 0 0 0 0 0 0 0 0 0 Hancock IR75 142 2 96 50,216 2 14 4644 7 24,677 11 Hancock IR75 145 0 0 0 0 0 0 0 0 0 Hancock IR75 156 0 0 0 0 0 0 0 0 0 Hancock IR75 157 2 50 12,350 1 8 2850 2 10,782 5 Hancock IR75 159 8 707 155,484 5 52 24309 29 137,035 42 Hancock IR75 161 1 61 18,530 3 28 13370 1 5,292 5 Hancock IR75 164 0 0 0 2 24 5670 0 0 2 Wood IR75 167 0 0 0 5 74 18895 0 0 5 Wood IR75 168 0 0 0 2 33 6674 1 18,614 3 Wood IR75 171 0 0 0 1 4 576 0 0 1 Wood IR75 179 0 0 0 0 0 0 0 0 0 Wood IR75 181 5 433 127,892 4 36 16345 15 64,588 24 Wood IR75 187 0 0 0 0 0 0 0 0 0 Total Development Units 34 2,385 673,879 62 677 231,232 122 530,169 218 Total I-70 and I-75 Development Units 69 4,855 1,191,819 130 1,317 455,534 202 836,937 401

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APPENDIX B: Correlation Matrix of Interchange AADT and T24 Model

Table B 31. Correlation Matrix – Part A

24 Variables Log10 (Total INTERCHANGE AADT T Log10 (Intersecting Highway ADT) ADT) Highway (Intersecting Log10 Axes Developed of Restaurants Number Restaurants of Footage Square Square FootageGas of and ConvenienceStores Log10 (Total Interchange AADT) 1 0 .804** .541** .421** .410** .463** Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 ** T24 0 1 0 0 0 0 .434 Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Log10 (Intersecting Highway ADT) .804** 0 1 .411** .411** .392** .339** Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Developed Axes .541** 0 .411** 1 .629** .616** .766** Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Number of Restaurants .421** 0 .411** .629** 1 .983** .501** Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Square Footage of Restaurants .410** 0 .392** .616** .983** 1 .510** Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Square Footage of Gas and .463** .434** .339** .766** .501** .510** 1 Convenience Stores Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Number of Hotels .388** 0 .414** .569** .890** .869** .464** Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Population of Nearest City and Town .327** 0 .429** 0 0 .203* 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 154

Table B 31 Continued Intersecting Highway = Federal 0 .202* 0 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Intersecting Highway = State 0 0 0 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Distance to Furthest Interchange 0 0 0 -.209* -.255* -.251* 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Distance to Nearest City and Town 0 0 0 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Intersecting Highway = Local 0 -.277* 0 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Distance to Nearest Interchange -.211* 0 0 0 -.245* -.266* 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Number of Gas Pumps .455** .396** .295** .760** .439** .445** .948** Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Number of Gas and Convenience .486** .287** .337** .824** .476** .466** .925** Stores Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 County Population 0 0 0 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Acres of Truck Parking Lots 0 .671** 0 .230* 0 0 .497** Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Square Footage of Hotels .387** 0 .431** .566** .928** .897** .474** Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 **. Correlation is significant at the 0.01 level (1-tailed). *. Correlation is significant at the 0.05 level (1-tailed).

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Table B 32. Correlation Matrix Part B Variables of Hotels Number Population of Nearest Town and City IntersectingFederal = Highway State = Intersecting Highway Interchange Furthest to Distance Town City and Nearest Distance to IntersectingLocal = Highway Log10 (Total INTERCHANGE .327* .388** 0 0 0 0 0 AADT * Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 * * T24 0 0 .202 0 0 0 -.277 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Log10 (Intersecting Highway ADT) .429* .414** 0 0 0 0 0 * Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Developed Axes .569** 0 0 0 -.209* 0 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Number of Restaurants .890** 0 0 0 -.255* 0 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Square Footage of Restaurants .869** .203* 0 0 -.251* 0 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Square Footage of Gas and .464** 0 0 0 0 0 0 Convenience Stores Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Number of Hotels 1 .202* 0 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Population of Nearest City and Town .202* 1 0 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Intersecting Highway = Federal 0 0 1 -.593** 0 0 -.266* Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 156

Table B 32 Continued Intersecting Highway = State - 0 0 1 0 0 -.619** .593** Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Distance to Furthest Interchange 0 0 0 0 1 0 -.250* Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Distance to Nearest City and Town 0 0 0 0 0 1 0 Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Intersecting Highway = Local 0 0 -.266* -.619** -.250* 0 1 Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Distance to Nearest Interchange 0 0 0 .318** .269* 0 -.251* Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Number of Gas Pumps .376** 0 0 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Number of Gas and Convenience .428** 0 0 0 0 -.211* 0 Stores Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 County Population 0 0 -.215* 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Acres of Truck Parking Lots - 0 0 0 0 0 0 .234* Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 Square Footage of Hotels .945** .212* 0 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 0 N 69 69 69 69 69 69 69 **. Correlation is significant at the 0.01 level (1-tailed). *. Correlation is significant at the 0.05 level (1-tailed).

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Table B 33. Correlation Matrix Part C Variables Interchange Nearest Distance to Pumps Gas of Number Stores Convenience and Gas of Number County Population Acres of Truck Parking Lots Hotels of Footage Square Log10 (Total INTERCHANGE AADT -.211* .455** .486** 0 0 .387** Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 ** ** ** T24 0 .396 .287 0 .671 0 Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 Log10 (Intersecting Highway ADT) 0 .295** .337** 0 0 .431** Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 Developed Axes 0 .760** .824** 0 .230* .566** Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 Number of Restaurants -.245* .439** .476** 0 0 .928** Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 Square Footage of Restaurants -.266* .445** .466** 0 0 .897** Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 Square Footage of Gas and Convenience 0 .948** .925** 0 .497** .474** Stores Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 Number of Hotels 0 .376** .428** 0 0 .945** Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 Population of Nearest City and Town 0 0 0 0 -.234* .212* Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 Intersecting Highway = Federal 0 0 0 -.215* 0 0 Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 Intersecting Highway = State .318** 0 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 158

Table B 33 Continued Distance to Furthest Interchange .269* 0 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 Distance to Nearest City and Town 0 0 -.211* 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 Intersecting Highway = Local -.251* 0 0 0 0 0 Sig. (1-tailed) 0 0 0 0 0 0 N 69 69 69 69 69 69 Distance to Nearest Interchange 1 0 0 0 0 -.225* Sig. (1-tailed) 0 0 0 0 0 N 69 69 69 69 69 69 Number of Gas Pumps 0 1 .944** 0 .498** .385** Sig. (1-tailed) 0 0 0 0 0 N 69 69 69 69 69 69 Number of Gas and Convenience Stores 0 .944** 1 0 .403** .445** Sig. (1-tailed) 0 0 0 0 0 N 69 69 69 69 69 69 County Population 0 0 0 1 0 0 Sig. (1-tailed) 0 0 0 0 0 N 69 69 69 69 69 69 Acres of Truck Parking Lots 0 .498** .403** 0 1 0 Sig. (1-tailed) 0 0 0 0 0 N 69 69 69 69 69 69 Square Footage of Hotels -.225* .385** .445** 0 0 1 Sig. (1-tailed) 0 0 0 0 0 N 69 69 69 69 69 69 **. Correlation is significant at the 0.01 level (1-tailed). *. Correlation is significant at the 0.05 level (1-tailed).

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APPENDIX C: INTERCHANGE AADT MODEL

Table C 34. Interchange AADT Model ANOVA Table

Model Sum of Mean Square Squares df F Sig. 1 Regression 5.126 17 .302 10.609 .000 Residual 1.450 51 .028 Total 6.576 68 2 Regression 5.126 16 .320 11.493 .000 Residual 1.450 52 .028 Total 6.576 68 3 Regression 5.126 15 .342 12.485 .000 Residual 1.451 53 .027 Total 6.576 68 4 Regression 5.124 14 .366 13.616 .000 Residual 1.452 54 .027 Total 6.576 68 5 Regression 5.123 13 .394 14.916 .000 Residual 1.453 55 .026 Total 6.576 68 6 Regression 5.120 12 .427 16.406 .000 Residual 1.456 56 .026 Total 6.576 68 7 Regression 5.111 11 .465 18.077 .000 Residual 1.465 57 .026 Total 6.576 68 8 Regression 5.106 10 .511 20.145 .000 Residual 1.470 58 .025 Total 6.576 68 9 Regression 5.085 9 .565 22.351 .000 Residual 1.491 59 .025 Total 6.576 68 10 Regression 5.039 8 .630 24.583 .000 Residual 1.537 60 .026 Total 6.576 68 11 Regression 5.027 7 .718 28.285 .000 Residual 1.549 61 .025 Total 6.576 68

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Table C 35. Interchange AADT Coefficients for Model 1

Variables Coefficients Understandardized Coefficients Standardized Collinearity Statistics Model Model B Std. Error Beta Tolerance VIF T - StatisticT - P – Value (Constant) 1.47 .26 5.60 0.00 Log10 (Intersecting Highway 0.00 0.62 .07 .72 8.65 0.6 1.6 AADT) Developed Axes 0.036 .03 .16 1.13 0.3 0.2 4.6 Number of Gas and Convenience 0.029 .04 .17 0.67 0.5 0.1 15.7 Stores Square Footage of Gas and -0.0000049 .00 -.12 -0.46 0.6 0.1 16.5 Convenience Stores

Number of Gas Pumps -0.00019 .00 -.01 -0.05 1.0 0.1 17.7

Number of Restaurants 1 0.0088 .02 .19 0.38 0.7 0.0 56.3 Square Footage of Restaurants 0.0000009 .00 .08 0.18 0.9 0.0 40.0 Number of Hotels 0.027 .03 .19 0.89 0.4 0.1 10.3 Square Footage Hotels -0.0000034 .00 -.43 -1.54 0.1 0.1 18.0 Population of Nearest City and 0.00000029 .00 .01 0.17 0.9 0.7 1.5 Town Acres of Truck Parking Lots 0.016 .01 .14 1.59 0.1 0.6 1.8 County Population - .00 -.02 -0.27 0.8 0.8 1.3 0.00000016 Intersecting Highway = Federal -0.11 .06 -.14 -1.77 0.1 0.7 1.5 Distance to Nearest City and Town 0.041 .02 .17 2.37 0.0 0.8 1.2 Distance to Furthest Interchange 0.0035 .01 .03 0.35 0.7 0.8 1.3 Intersecting Highway = Local -0.11 .06 -.14 -1.88 0.1 0.8 1.3 Distance to Nearest Interchange -0.035 .02 -.15 -1.87 0.1 0.7 1.4

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Table C 36. Interchange AADT Coefficients for Model 2

Variables Collinearity Statistics Standardized Coefficients Coefficients Standardized P – Value P – Value T - StatisticT - Understandardized Coefficients Coefficients Understandardized B VIF VIF Beta Std. Error Tolerance Tolerance Model Model (Constant) 1.47 .26 5.66 0.00 Log10 (Intersecting Highway AADT) 0.63 .07 .72 8.74 0.00 .62 1.61 Developed Axes 0.04 .03 .16 1.15 .26 .22 4.62 Number of Gas and Convenience 0.03 .04 .17 .75 .46 .08 12.06 Stores

Square Footage of Gas and -0.000005 .00 -.13 -.60 .55 .09 11.15 Convenience Stores

Number of Restaurants 0.01 .02 .18 .38 .71 .02 55.64

2 Square Footage of Restaurants 0.000001 .00 .08 .18 .86 .02 40.03 Number of Hotels 0.03 .03 .19 .90 .37 .10 10.26 Square Footage Hotels -0.000003 .00 -.43 -1.58 .12 .06 17.16 Population of Nearest City and Town 0.0000003 .00 .01 .18 .86 .69 1.45 Acres of Truck Parking Lots 0.02 .01 .14 1.61 .11 .56 1.79 County Population - .00 -.02 -.28 .78 .80 1.25 0.0000002 Intersecting Highway = Federal -0.11 .06 -.14 -1.81 .08 .70 1.43 Distance to Nearest City and Town 0.04 .02 .17 2.41 0.02 .86 1.17 Distance to Furthest Interchange 0.004 .01 .03 .35 .73 .75 1.33 Intersecting Highway = Local -0.11 .06 -.14 -1.90 .06 .76 1.31 Distance to Nearest Interchange -0.04 .02 -.14 -1.89 .07 .72 1.39

162

Table C 37. Interchange AADT Coefficients for Model 3

Variables Understandardized Coefficients Coefficients Understandardized Coefficients Standardized Collinearity Statistics Model Model B Std. Error Beta Tolerance VIF T - StatisticT - P – Value (Constant) 1.45 .24 6.00 0.00 Log10 (Intersecting Highway 0.63 .07 .73 9.61 0.00 .72 1.38 AADT) Developed Axes 0.04 .03 .16 1.18 0.24 .22 4.59 Number of Gas and Convenience 0.03 .04 .16 0.73 0.47 .09 11.65 Stores Square Footage of Gas and - -0.00001 .00 -.13 0.56 .09 10.91 Convenience Stores 0.59 Number of Restaurants 0.01 .02 .18 0.37 0.71 .02 55.40 Square Footage of Restaurants 0.000001 .000005 .08 0.20 0.84 .03 39.84

Number of Hotels 0.03 .03 .19 0.91 0.37 .10 10.26

- Square Footage Hotels -0.000003 .00 -.43 0.12 .06 17.16 1.60 Acres of Truck Parking Lots 0.02 .01 .14 1.63 0.11 .59 1.69 - - County Population .000001 -.02 0.79 .81 1.23 3 0.0000002 0.27 - Intersecting Highway = Federal -0.11 .06 -.14 0.07 .70 1.43 1.84 Distance to Nearest City and Town 0.04 .02 .17 2.51 0.02 .89 1.13 Distance to Furthest Interchange 0.004 .01 .03 0.36 0.72 .75 1.33 - Intersecting Highway = Local -0.11 .06 -.14 0.06 .76 1.31 1.93 - Distance to Nearest Interchange -0.04 .02 -.15 0.06 .73 1.38 1.93

163

Table C 38. Interchange AADT Coefficients for Model 4

Variables Coefficients Understandardized Coefficients Standardized Collinearity Statistics T - StatisticT - P – Value Model Model B Std. Error Beta Tolerance VIF (Constant) 1.46 .24 6.10 0.00 Log10 (Intersecting Highway AADT) 0.63 .06 .73 9.74 0.00 .73 1.36 Developed Axes 0.04 .03 .16 1.19 0.24 .22 4.59

Number of Gas and Convenience 0.02 .04 .15 0.71 0.48 .09 10.66 Stores

Square Footage of Gas and -0.000004 .00 -.11 -0.56 0.58 .10 9.65 Convenience Stores Number of Restaurants 0.01 .01 .26 1.36 0.18 .11 9.29 4 Number of Hotels 0.03 .03 .19 0.95 0.35 .10 10.10 Square Footage Hotels -0.000003 .00 -.44 -1.72 0.09 .06 16.00 Acres of Truck Parking Lots 0.02 .01 .14 1.63 0.11 .60 1.68 County Population - .00 -.02 -0.23 0.82 .86 1.16 0.0000001 Intersecting Highway = Federal -0.11 .06 -.14 -1.87 0.07 .70 1.42 Distance to Nearest City and Town 0.04 .02 .17 2.53 0.01 .89 1.13 Distance to Furthest Interchange 0.004 .01 .03 0.38 0.71 .76 1.32 Intersecting Highway = Local -0.11 .05 -.14 -1.94 0.06 .77 1.31 Distance to Nearest Interchange -0.04 .02 -.15 -2.03 0.05 .76 1.32

164

Table C 39. Interchange AADT Coefficients for Model 5 and 6

Variables Understandardized Coefficients Standardized Coefficients Collinearity Statistics T - StatisticT - P – Value Model Model B Std. Error Beta Toleranc e VIF (Constant) 1.46 .24 6.15 0.00 Log10 (Intersecting Highway 0.00 0.63 .06 .72 9.87 0.75 1.34 AADT) Developed Axes 0.04 .03 .16 1.20 0.24 0.22 4.59 Number of Gas and Convenience 0.02 .03 .15 0.71 0.48 0.09 10.65 Stores Square Footage of Gas and - .00001 -.11 -0.57 0.57 0.10 9.64 Convenience Stores 0.000004

Number of Restaurants 0.01 .01 .26 1.35 0.18 0.11 9.23 5 Number of Hotels 0.03 .03 .19 0.97 0.34 0.10 10.07 Square Footage Hotels - .000002 -.44 -1.72 0.09 0.06 15.90 0.000003 Acres of Truck Parking Lots 0.02 .01 .14 1.65 0.10 0.60 1.68 Intersecting Highway = Federal -0.11 .06 -.14 -1.88 0.07 0.73 1.36 Distance to Nearest City and Town 0.04 .02 .17 2.56 0.01 0.89 1.13 Distance to Furthest Interchange 0.003 .01 .03 0.35 .073 0.77 1.29 Intersecting Highway = Local -0.11 .05 -.14 -1.95 0.06 0.77 1.31 Distance to Nearest Interchange -0.04 .02 -.15 -2.07 0.04 0.76 1.32 (Constant) 1.46 .23 6.23 0.00 Log10 (Intersecting Highway 0.63 .06 .73 10.06 0.00 .76 1.32 AADT) Developed Axes 0.04 .03 .16 1.18 .24 .22 4.53 Number of Gas and Convenience 0.03 .03 .15 0.73 .47 .09 10.65 Stores Square Footage of Gas and - .00001 -.11 -0.58 .56 .10 9.64 Convenience Stores 0.000005

Number of Restaurants 0.01 .01 .25 1.32 .19 .11 8.76 6 Number of Hotels 0.03 .03 .20 0.98 .33 .10 10.07 Square Footage Hotels - .000002 -.42 -1.71 .09 .06 15.66 0.000003 Acres of Truck Parking Lots 0.02 .01 .13 1.65 .10 .60 1.68 Intersecting Highway = Federal -0.10 .06 -.14 -1.86 .07 .75 1.33 Distance to Nearest City and Town 0.04 .02 .17 2.58 0.01 .89 1.13 Intersecting Highway = Local -0.11 .05 -.15 -2.07 0.04 .79 1.26 Distance to Nearest Interchange -0.04 .02 -.15 -2.05 0.04 .79 1.26 165

Table C 40. Interchange AADT Coefficients for Model 7 and 8

Variables Coefficients Understandardized Coefficients Standardized Collinearity Statistics T - StatisticT - P – Value Model Model B Std. Error Beta Tolerance VIF (Constant) 1.47 .23 6.35 0.00 Log10 (Intersecting Highway 0.00 0.63 .06 .72 10.11 .76 1.32 AADT) Developed Axes 0.04 .03 .16 1.24 .22 .22 4.50 Number of Gas and Convenience 0.01 .02 .05 0.44 .66 .25 3.95 Stores Number of Restaurants 0.01 .01 .24 1.32 .19 .11 8.76

Number of Hotels 0.03 .03 .19 0.95 .35 .10 10.02

Square Footage Hotels - 7 .000002 -.43 -1.75 .09 .06 15.62 0.000003 Acres of Truck Parking Lots 0.01 .01 .11 1.56 .12 .73 1.36 Intersecting Highway = Federal -0.11 .05 -.15 -2.07 0.04 .80 1.25 Distance to Nearest City and Town 0.04 .02 .17 2.63 0.01 .89 1.12 Intersecting Highway = Local -0.11 .05 -.15 -2.09 0.04 .79 1.26 Distance to Nearest Interchange -0.04 .02 -.15 -2.11 0.04 .79 1.26 (Constant) 1.48 .23 6.41 0.00 Log10 (Intersecting Highway 0.63 .06 .73 10.19 0.00 .76 1.31 AADT) Developed Axes 0.05 .02 .21 2.31 0.02 .48 2.10 Number of Restaurants 0.01 .01 .24 1.29 0.20 .12 8.66

Number of Hotels 0.03 .03 .18 0.92 0.36 .10 9.92 8 Square Footage Hotels - .00 -.42 -1.71 0.09 .07 15.26 0.000003 Acres of Truck Parking Lots 0.01 .01 .13 1.91 0.06 .87 1.15 Intersecting Highway = Federal -0.12 .05 -.15 -2.18 0.03 .82 1.23 Distance to Nearest City and Town 0.04 .02 .17 2.62 0.01 .90 1.11 Intersecting Highway = Local -0.11 .05 -.15 -2.11 0.04 .79 1.26 Distance to Nearest Interchange -0.04 .02 -.15 -2.17 0.03 .80 1.25

166

Table C 41. Interchange AADT Coefficients for Model 9, 10 and 11

Understandardized Coefficients Coefficients Standardized Collinearity Statistics Variables T - StatisticT - P – Value Model Model B Std. Error Beta Tolerance VIF (Constant) 1.47 .23 6.40 0.00 Log10 (Intersecting Highway 0.00 0.63 .06 .73 10.21 .76 1.31 AADT) Developed Axes 0.05 .02 .22 2.41 0.02 .48 2.08 Number of Restaurants 0.01 .01 .25 1.35 0.18 .12 8.64

Square Footage Hotels - .00000 9 -.26 -1.51 0.14 .13 7.68 0.000002 1 Acres of Truck Parking Lots 0.01 .01 .13 1.90 0.06 .87 1.15 Intersecting Highway = Federal -0.11 .05 -.14 -2.11 0.04 .82 1.22 Distance to Nearest City and Town 0.04 .02 .17 2.55 0.01 .91 1.10 Intersecting Highway = Local -0.11 .05 -.14 -2.03 0.05 .80 1.25 Distance to Nearest Interchange -0.03 .02 -.14 -2.06 0.04 .81 1.23 (Constant) 1.48 .23 6.41 0.00 Log10 (Intersecting Highway 0.62 .06 .72 10.08 0.00 .76 1.31 AADT) Developed Axes 0.06 .02 .26 3.02 0.00 .54 1.84 Square Footage Hotels - .00000 10 0.000000 -.06 -0.67 0.50 .57 1.75 1 4 Acres of Truck Parking Lots 0.01 .01 .12 1.84 0.07 .87 1.15 Intersecting Highway = Federal -0.10 .05 -.13 -1.88 0.07 .85 1.17 Distance to Nearest City and Town 0.04 .02 .16 2.47 0.02 .91 1.10 Intersecting Highway = Local -0.10 .05 -.13 -1.91 0.06 .81 1.24 Distance to Nearest Interchange -0.04 .02 -.15 -2.15 0.04 .82 1.22 (Constant) 1.51 .23 6.71 0.00 Log10 (Intersecting Highway 0.61 .06 .71 10.22 0.00 .80 1.25 AADT) Developed Axes 0.05 .02 .23 3.13 0.00 .73 1.37 Acres of Truck Parking Lots 0.01 .01 .13 2.06 0.04 .92 1.08 11 Intersecting Highway = Federal -0.10 .05 -.13 -1.91 0.06 .85 1.17 Distance to Nearest City and Town 0.04 .02 .16 2.51 0.01 .91 1.10 Intersecting Highway = Local -0.10 .05 -.14 -1.97 0.05 .81 1.23 Distance to Nearest Interchange -0.03 .02 -.14 -2.08 0.04 .84 1.19 167

Table C 42. Collinearity Diagnostics

Model Variance Proportions

Eigenvalue Condition Index (Constant) ADT) Highway (Intersecting Log10 Axes Developed of Restaurants Number Hotels of Footage Square Acres of Truck Parking Lots Federal HWY Town City and Nearest Distance to Local HWY Interchange Nearest Distance to 1 5.24 1.00 .00 .00 .01 .00 .00 .01 .01 .01 .00 .01 2 1.63 1.79 .00 .00 .00 .02 .02 .01 .00 .02 .00 .01 3 1.05 2.24 .00 .00 .00 .00 .00 .10 .19 .00 .31 .00 4 0.80 2.56 .00 .00 .01 .00 .00 .64 .15 .02 .02 .01 5 0.62 2.91 .00 .00 .01 .00 .00 .03 .41 .00 .34 .09 6 0.28 4.30 .00 .00 .10 .00 .02 .05 .09 .73 .05 .03 7 0.19 5.22 .00 .00 .46 .01 .04 .15 .06 .12 .02 .45 8 0.13 6.45 .01 .01 .28 .00 .02 .00 .05 .11 .24 .37 9 0.05 9.88 .00 .00 .09 .96 .88 .00 .05 .00 .02 .00 10 0.00 38.57 .98 .98 .04 .00 .02 .01 .00 .00 .00 .03

168

APPENDIX D: DAILY TRUCK PERCENTAGE (T24) MODEL

Table D 43. T24 Model ANOVA

Model Sum of Mean Squares df Square F Sig. 1 Regression .338 18 .019 5.666 .000 Residual .166 50 .003 Total .503 68 2 Regression .338 17 .020 6.119 .000 Residual .166 51 .003 Total .503 68 3 Regression .338 16 .021 6.628 .000 Residual .166 52 .003 Total .503 68 4 Regression .338 15 .023 7.203 .000 Residual .166 53 .003 Total .503 68 5 Regression .337 14 .024 7.819 .000 Residual .166 54 .003 Total .503 68 6 Regression .337 13 .026 8.551 .000 Residual .167 55 .003 Total .503 68 7 Regression .336 12 .028 9.376 .000 Residual .167 56 .003 Total .503 68 8 Regression .335 11 .030 10.313 .000 Residual .168 57 .003 Total .503 68 9 Regression .333 10 .033 11.394 .000 Residual .170 58 .003 Total .503 68 10 Regression .332 9 .037 12.741 .000 Residual .171 59 .003 Total .503 68 11 Regression .331 8 .041 14.352 .000 Residual .173 60 .003 Total .503 68 12 Regression .327 7 .047 16.173 Residual .176 61 .003 Total .503 68 13 Regression .321 6 .053 18.183 Residual .182 62 .003 Total .503 68 169

Table D 44. T24 Model Coefficients for Step 1

Variables Understandardized Coefficients Coefficients Standardized Collinearity Statistics Model Model B Std. Error Beta Tolerance VIF T - StatisticT - P – Value (Constant) 0.11 .03 4.05 0.00 Developed Axes -0.01 .01 -.19 -1.09 0.28 .21 4.69 Intersecting Highway AADT -0.000001 .000002 -.05 -0.44 0.66 .45 2.23 Number of Gas and -0.03 .02 -.55 -1.68 0.10 .06 16.26 Convenience Stores

Number of Gas Pumps 0.001 .001 .18 0.52 0.61 .06 17.82

Number of Restaurants 0.004 .01 .28 0.44 0.66 .02 59.85 Square Footage of Restaurants -0.000002 .000002 -.57 -1.04 0.30 .02 45.12 Number of Hotels -0.002 .02 -.05 -0.10 0.92 .03 37.56 1 Square Footage of Hotels -0.0000004 .000001 -.19 -0.50 0.62 .05 21.09 Number of Hotel Rooms 0.0002 .0003 .31 0.55 0.59 .02 49.33 Population of Nearest City and 0.0000004 .0000 .07 0.72 0.47 .63 1.58 Town Acres of Truck Parking Lots 0.01 .003 .46 4.19 0.00 .54 1.85 Intersecting Highway = State 0.04 .02 .26 2.32 0.02 .53 1.89 County Population .000000 -0.0000001 -.06 -0.63 0.53 .79 1.26 2 Intersecting Highway = Federal 0.05 .02 .25 2.14 0.04 .50 2.01 Distance to Nearest City and 0.0005 .01 .01 0.08 0.94 .82 1.21 Town Distance to Furthest - Interchange -0.00004 .003 .00 -0.01 0.99 .73 1.37 1 Distance to Nearest -0.01 .01 -.12 -1.21 0.23 .72 1.40 Interchange Square Footage of Gas and 0.00001 .000004 .80 2.41 0.02 .06 16.90 Convenience Store Dependent Variable: T24 (Daily Truck Percentage)

170

Table D 45. T24 Model Coefficients for Step 2

Variables Understandardized Coefficients Coefficients Standardized Collinearity Statistics Model Model B Std. Error Beta Tolerance VIF T - StatisticT - P – Value (Constant) .11 .03 4.27 0.00 Developed Axes -.01 .01 -.19 -1.10 .28 .21 4.67 Intersecting Highway AADT -.000001 .000002 -.05 -.45 .65 .46 2.19 Number of Gas and -.03 .02 -.55 -1.70 .10 .06 16.20 Convenience Stores

Number of Gas Pumps .001 .001 .18 .52 .60 .06 17.82

Number of Restaurants .004 .01 .28 .45 .66 .02 58.90 Square Footage of Restaurants -.000002 .000002 -.57 -1.06 .30 .02 45.06 Number of Hotels -.002 .02 -.05 -.10 .92 .03 37.15 2 Square Footage of Hotels -.0000004 .000001 -.19 -.51 .62 .05 21.00 Number of Hotel Rooms .0002 .0003 .31 .56 .58 .02 48.56 Population of Nearest City and .0000004 .000001 .07 .73 .47 .63 1.58 Town Acres of Truck Parking Lots .01 .003 .46 4.24 0.00 .54 1.85 Intersecting Highway = State .04 .02 .26 2.38 0.02 .55 1.83 County Population -.0000001 .0000002 -.06 -.64 0.53 .80 1.25 Intersecting Highway = Federal .05 .02 .25 2.26 0.03 .55 1.83 Distance to Nearest City and .0005 .01 .01 .08 0.94 .82 1.21 Town Distance to Nearest Interchange -.01 .01 -.12 -1.24 0.22 .75 1.34 Square Footage of Gas and .00001 .000004 .80 2.44 0.02 .06 16.83 Convenience Store Dependent Variable: T24 (Daily Truck Percentage)

171

Table D 46. T24 Model Coefficients for Step 3

Variables Coefficients Understandardized Coefficients Standardized Collinearity Statistics Model Model B Std. Error Beta Tolerance VIF T - StatisticT - P – Value (Constant) .11 .02 4.73 0.00 Developed Axes -.01 .01 -.19 -1.12 .27 .21 4.66 Intersecting Highway AADT -.000001 .000002 -.05 -.47 .64 .46 2.15 Number of Gas and -.03 .01 -.55 -1.72 .09 .06 16.17 Convenience Stores

Number of Gas Pumps .001 .001 .18 .55 .59 .06 17.45

Number of Restaurants .004 .01 .28 .46 .65 .02 58.84 Square Footage of Restaurants -.000002 .000002 -.57 -1.08 .29 .02 44.81 Number of Hotels -.002 .02 -.05 -.11 .91 .03 36.68 3 Square Footage of Hotels -.0000004 .000001 -.18 -.51 .62 .05 20.82 Number of Hotel Rooms .0002 .0003 .32 .57 .57 .02 48.28 Population of Nearest City and .0000004 .000001 .08 .77 .44 .66 1.51 Town Acres of Truck Parking Lots .01 .003 .46 4.30 0.00 .54 1.84 Intersecting Highway = State .04 .02 .26 2.40 0.02 .55 1.82 County Population -.0000001 .0000002 -.06 -.65 0.52 .80 1.24 Intersecting Highway = Federal .05 .02 .25 2.31 0.02 .55 1.80 Distance to Nearest Interchange -.01 .01 -.11 -1.26 0.21 .77 1.30 Square Footage of Gas and .00001 .000004 .80 2.48 0.02 .06 16.52 Convenience Store Dependent Variable: T24 (Daily Truck Percentage)

172

Table D 47. T24 Model Coefficients for Step 4

Variables Understandardized Coefficients Coefficients Standardized Collinearity Statistics Model Model B Std. Error Beta Tolerance VIF T - Statistic T - P – Value (Constant) .11 .02 4.77 0.00 Developed Axes -.01 .01 -.20 -1.17 .25 .22 4.47 Intersecting Highway AADT -.000001 .000002 -.05 -.46 .65 .48 2.10 Number of Gas and Convenience -.03 .01 -.55 -1.76 .08 .06 15.91 Stores

Number of Gas Pumps .001 .001 .18 .56 .58 .06 17.31

Number of Restaurants .003 .01 .27 .45 .66 .02 57.06 Square Footage of Restaurants -.000002 .000002 -.56 -1.09 .28 .02 42.83 Square Footage of Hotels -.0000004 .000001 -.18 -.51 .61 .05 20.82 4 Number of Hotel Rooms .0001 .0001 .26 .91 .36 .07 13.45 Population of Nearest City and .0000004 .000001 .07 .77 .44 .69 1.45 Town Acres of Truck Parking Lots .01 .003 .46 4.35 0.00 .55 1.83 Intersecting Highway = State .04 .02 .26 2.42 0.02 .55 1.82 County Population -.0000001 .0000002 -.06 -.65 0.52 .80 1.24 Intersecting Highway = Federal .05 .02 .25 2.35 0.02 .56 1.80 Distance to Nearest Interchange -.01 .01 -.11 -1.27 0.21 .77 1.30 Square Footage of Gas and .00001 .000004 .80 2.51 0.02 .06 16.46 Convenience Store Dependent Variable: T24 (Daily Truck Percentage)

173

Table D 48. T24 Model Coefficients for Step 5

Variables Understandardized Coefficients Coefficients Standardized Collinearity Statistics Model Model B Std. Error Beta Tolerance VIF T - StatisticT - P – Value (Constant) .11 .02 4.80 0.00 Developed Axes -.01 .01 -.19 -1.13 .26 .23 4.38 Intersecting Highway AADT -.000001 .000002 -.05 -.42 .68 .48 2.07 Number of Gas and -.03 .01 -.54 -1.74 .09 .06 15.71 Convenience Stores

Number of Gas Pumps .0008 .001 .20 .62 .53 .06 17.07

Square Footage of Restaurants -.000001 .000001 -.35 -1.68 .10 .14 7.11 Square Footage of Hotels -.0000002 .000001 -.10 -.33 .75 .07 15.16 Number of Hotel Rooms .0001 .0001 .23 .84 .40 .08 12.75 5 Population of Nearest City and .0000004 .000001 .07 .74 .46 .70 1.44 Town Acres of Truck Parking Lots .01 .003 .47 4.41 0.00 .55 1.82 Intersecting Highway = State .04 .02 .26 2.43 0.02 .55 1.82 County Population -.0000001 .0000002 -.07 -.78 0.44 .85 1.18 Intersecting Highway = Federal .05 .02 .25 2.43 0.02 .56 1.77 Distance to Nearest Interchange -.01 .01 -.11 -1.23 0.22 .79 1.27 Square Footage of Gas and .000008 .000003 .76 2.51 0.02 .07 15.06 Convenience Store Dependent Variable: T24 (Daily Truck Percentage)

174

Table D 49. T24 Model Coefficients for Step 6

Variables Coefficients Understandardized Coefficients Standardized Collinearity Statistics Model Model B Std. Error Beta Tolerance VIF T - StatisticT - P – Value (Constant) .11 .02 4.83 0.00 Developed Axes -.01 .01 -.18 -1.13 0.26 0.23 4.38 Intersecting Highway AADT -.000001 .000002 -.05 -0.47 0.64 0.49 2.04 Number of Gas and -.03 .01 -.56 -1.89 0.06 0.07 14.78 Convenience Stores

Number of Gas Pumps .0009 .001 .22 0.71 0.48 0.06 16.39

Square Footage of Restaurants -.000001 .000001 -.38 -1.98 0.05 0.17 5.99 Number of Hotel Rooms .00008 .00009 .17 0.88 0.38 0.17 6.04 Population of Nearest City and .0000004 .0000005 .07 0.73 0.47 0.70 1.43 6 Town Acres of Truck Parking Lots .01 .003 .47 4.45 0.00 0.55 1.82 Intersecting Highway = State .04 .02 .25 2.43 0.02 0.55 1.81 County Population - .0000002 -.06 -0.75 0.46 0.86 1.17 .0000001 Intersecting Highway = Federal .05 .02 .26 2.47 0.02 0.57 1.77 Distance to Nearest Interchange -.01 .01 -.11 -1.22 0.23 0.79 1.27 Square Footage of Gas and .000008 .000003 .76 2.53 0.01 0.07 15.06 Convenience Store Dependent Variable: T24 (Daily Truck Percentage)

175

Table D 50. T24 Model Coefficients for Step 7 and Step 8

Variables Understandardi zed Coefficients Standardized Coefficients Collinearity Statistics T - StatisticT - P – Value Model Model B Std. Error Beta Tolerance VIF (Constant) .11 .02 4.88 0.00 Developed Axes -.01 .01 -.18 -1.11 0.27 .23 4.35 Number of Gas and Convenience -.03 .01 -.57 -1.94 0.06 .07 14.71 Stores Number of Gas Pumps .0009 .001 .23 .73 0.47 .06 16.37

Square Footage of Restaurants -.000001 .0000006 -.36 -1.95 0.06 .17 5.83

Number of Hotel Rooms .00007 .00009 .13 .77 0.45 .19 5.14 Population of Nearest City and .0000003 .0000005 .05 .60 0.55 .83 1.20 Town Acres of Truck Parking Lots .01 .003 .47 4.55 0.00 .55 1.80 7 Intersecting Highway = State .04 .02 .25 2.42 0.02 .56 1.79 County Population -.0000001 .0000002 -.07 -.80 0.43 .86 1.16 Intersecting Highway = Federal .05 .02 .25 2.49 0.02 .57 1.77 Distance to Nearest Interchange -.01 .01 -.10 -1.20 0.42 .79 1.26 Square Footage of Gas and .000008 .000003 .75 2.51 0.01 .07 14.93 Convenience Store (Constant) .11 .02 5.09 0.00 Developed Axes -.01 .01 -.17 -1.05 0.30 .23 4.28 Number of Gas and Convenience -.03 .01 -.60 -2.07 0.40 .07 14.36 Stores Number of Gas Pumps .0009 .001 .22 .70 0.48 .06 16.32

Square Footage of Restaurants -.000001 .0000006 -.36 -1.94 0.06 .17 5.82

8 Number of Hotel Rooms .00006 .00008 .13 .77 0.45 .19 5.14 Acres of Truck Parking Lots .01 .003 .46 4.56 0.00 .59 1.70 Intersecting Highway = State .04 .02 .25 2.45 0.02 .56 1.79 County Population -.0000001 .0000002 -.06 -.71 0.48 .89 1.13 Intersecting Highway = Federal .05 .02 .25 2.49 0.02 .57 1.77 Distance to Nearest Interchange -.01 .01 -.11 -1.29 0.20 .81 1.24 Square Footage of Gas and .000009 .000003 .78 2.68 0.01 .07 14.42 Convenience Store 176

Table D 51. T24 Model Coefficients for Step 9 and Step 10

Variables Understandardized Coefficients Coefficients Standardized Collinearity Statistics T - StatisticT - P – Value Model Model B Std. Error Beta Tolerance VIF (Constant) .11 .02 5.09 0.00 Developed Axes -.01 .01 -.17 -1.08 .28 .23 4.28 Number of Gas and Convenience -.02 .01 -.50 -1.99 .05 .09 10.91 Stores Square Footage of Restaurants -.000001 .0000006 -.34 -1.87 .07 .17 5.72

Number of Hotel Rooms .00005 .00008 .11 .64 .52 .20 4.94

Acres of Truck Parking Lots .01 .003 .47 4.77 0.00 .61 1.65 Intersecting Highway = State .04 .02 .25 2.44 0.02 .56 1.78 County Population -.0000001 .0000002 -.05 -.64 0.52 .90 1.12 9 Intersecting Highway = Federal .05 .02 .24 2.43 0.02 .58 1.74 Distance to Nearest Interchange -.01 .01 -.11 -1.29 0.20 .81 1.24 Square Footage of Gas and .00001 .000003 .89 3.72 0.00 .10 9.96 Convenience Store (Constant) .11 .02 5.26 0.00 Developed Axes -.01 .01 -.17 -1.10 0.27 .23 4.27 Number of Gas and Convenience -.02 .01 -.51 -2.06 0.04 .09 10.81 Stores Square Footage of Restaurants -.0000008 .0000003 -.25 -2.24 0.03 .47 2.13

Acres of Truck Parking Lots .01 .003 .46 4.75 0.00 .63 1.60 10 Intersecting Highway = State .04 .02 .25 2.43 0.02 .56 1.78 County Population -.0000001 .0000002 -.06 -.78 0.44 .93 1.08 Intersecting Highway = Federal .05 .02 .24 2.39 0.02 .58 1.72 Distance to Nearest Interchange -.01 .006 -.10 -1.25 0.22 .81 1.23 Square Footage of Gas and .00001 .000003 .92 3.89 0.00 .10 9.70 Convenience Store Dependent Variable: T24 (Daily Truck Percentage)

177

Table D 52. T24 Model Coefficients for Step 11 and Step 12

Variables Understandardized Coefficients Standardized Coefficients Collinearity Statistics T - StatisticT - P – Value Model Model B Std. Error Beta Tolerance VIF (Constant) .10 .02 6.08 0.00 Developed Axes -.01 .01 -.17 -1.10 0.28 .23 4.27 Number of Gas and -.02 .01 -.52 -2.11 0.04 .09 10.79 Convenience Stores Square Footage of Restaurants -.0000008 .0000003 -.24 -2.20 0.03 .47 2.12

Acres of Truck Parking Lots .01 .003 .46 4.76 0.00 .63 1.60

Intersecting Highway = State .04 .02 .24 2.41 0.02 .56 1.78 Intersecting Highway = Federal .05 .02 .25 2.52 0.01 .59 1.69 Distance to Nearest Interchange -.01 .01 -.11 -1.31 0.19 .82 1.22 Square Footage of Gas and 11 .00001 .000003 .92 3.90 0.00 .10 9.70 Convenience Store (Constant) .10 .02 6.02 0.00 Number of Gas and -.03 .01 -.67 -3.18 0.00 .13 7.72 Convenience Stores Square Footage of Restaurants .00001 .000003 .95 4.05 0.00 .10 9.57 Acres of Truck Parking Lots -.0000009 .0000003 -.30 -3.02 0.00 .59 1.68

Intersecting Highway = State .01 .003 .46 4.81 0.00 .63 1.60 12 Intersecting Highway = Federal .04 .02 .23 2.30 0.03 .57 1.76 Distance to Nearest Interchange .05 .02 .23 2.34 0.02 .61 1.63 Square Footage of Gas and Convenience Store -.01 .01 -.12 -1.46 0.15 .83 1.20

(Constant) .09 .02 6.04 0.00 Number of Gas and -.03 .01 -.65 -3.05 0.00 .13 7.67 Convenience Stores Square Footage of Restaurants -.000001 .0000003 -.27 -2.76 0.01 .62 1.61 Acres of Truck Parking Lots .01 .003 .47 4.89 0.00 .63 1.59

Intersecting Highway = State .03 .02 .19 1.93 0.06 .62 1.60 13 Intersecting Highway = Federal .05 .02 .21 2.21 0.03 .62 1.62 Square Footage of Gas and Convenience Store .00001 .000003 .93 3.92 0.00 .10 9.53

Dependent Variable: T24 (Daily Truck Percentage) 178

Table D 53. Collinearity Diagnostics (T24)

Model Variance Proportions Step 12 Eigenvalue Condition Index (Constant) Stores Convenience and Gas of Number Restaurants of Footage Square Acres of Truck Parking Lots State Highway Highway Federal Interchange Nearest Distance to Square FootageGas of and ConvenienceStore 1 4.46 1.00 .01 .00 .01 .01 .01 .00 .01 .00 2 1.20 1.92 .01 .00 .09 .01 .05 .06 .04 .00 3 0.91 2.21 .01 .00 .02 .02 .01 .34 .02 .00 4 0.83 2.32 .00 .00 .20 .39 .00 .01 .00 .00 5 0.27 4.06 .00 .06 .58 .45 .01 .01 .01 .04 6 0.18 5.01 .02 .00 .02 .00 .49 .19 .75 .00 7 0.11 6.49 .86 .00 .02 .00 .43 .34 .16 .01 8 0.03 11.75 .09 .93 .06 .12 .00 .04 .02 .94

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