Investigating Real-Time Employer-Based Ridesharing Preferences Based on Stated Preference Survey Data

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Nathan Michael Shay

Graduate Program in Civil Engineering

The Ohio State University

2016

Master's Examination Committee:

Dr. Mark McCord, Co-Advisor

Dr. Rabi Mishalani, Co-Advisor

Dr. Gulsah Akar

Copyrighted by

Nathan Michael Shay

2016

Abstract

Expanding travel choices by providing ridesharing can improve mobility and accessibility and reduce congestion and the negative externalities associated with single occupancy automobile use. To realize these benefits, sufficient demand must be generated by matching drivers and passengers with similar origins and destinations and who are willing to travel with potential strangers. Technological developments have facilitated the provision of real-time ridesharing programs, where travelers are matched to share a ride shortly before they travel. Real-time ridesharing offers additional flexibility and the possibility of occasional use that may be desirable in an increasingly complex society with varying schedules. While initial real-time travel options have been perceived as unattractive due to reliability and personal safety concerns, the growing success of real-time ride-sourcing services suggests that perceptions may be shifting. Furthermore, large employer-based ridesharing offers additional promise due to a network of co- workers with similar work locations facilitating good matches, increased familiarity with fellow travelers, and the ability to incentivize participation.

A stated preference survey of The Ohio State University community was used to analyze willingness to participate in an idealized real-time employer-based ridesharing program. Individual characteristics and travel behaviors associated with unwillingness to

ii participate in an ideal program are analyzed. Also, the characteristics and behaviors associated with interest in a passenger or driver role in such a program are identified.

Many findings support results presented elsewhere and a priori expectations, for example an increased willingness of younger travelers to participate in ridesharing, an increased willingness of females to participate as passengers, and an increased willingness of those with experience driving to participate as drivers. In addition three findings provide important insights previously unidentified about traveler preferences toward ridesharing. Firstly, it seems that those who travel in automobiles, whether shared or alone, are more likely than those who do not travel in automobiles to participate in ridesharing. Also, those who walk or use transit seem to be less willing to participate in a ridesharing program than those who do not use these alternative modes. These findings are encouraging in light of the desire to attract single occupancy vehicle users, rather than transit users or walkers, to ridesharing to realize its social benefits. Secondly, the notion that providing ridesharing expands mobility and accessibility seems to be supported by the fact that those who do not have a car available to them tend to be more interested in being rideshare passengers than those who have a car available to them. Lastly, while those living with younger dependent children are more likely to reject ridesharing due to the constraints associated with this mode, among those who are interested in ridesharing, individuals living with children—whether younger dependent ones or otherwise—are more willing to drive in a ridesharing program than those who do not live with children, possibly due to having experience traveling in vehicles with passengers.

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Dedication

This document is dedicated to Ronald Francis Shay.

Acknowledgments

Firstly, I would like to express my gratitude to my co-advisors Dr. Mark R.

McCord and Dr. Rabi G. Mishalani. They provided the vital input necessary to complete this thesis as well as guiding my work as a graduate research associate in the Campus

Transit Lab under the auspices of the Department of Civil, Environmental, and Geodetic

Engineering at The Ohio State University. Our spirited discussions were not only the driving force behind a thesis I can be proud of, but also some of the most valuable educational experiences of my academic career.

The analysis presented in this study would not be possible without the valuable work of Nicole Sell in designing the stated preference survey and Chris Holloman of the

Ohio State University’s Statistical Consulting Services in implementing and administering the survey as well as compiling the resulting data. This research is financially supported by Region 5 University Transportation Center funded by US

Department of Transportation (DOT), Office of the Assistant Secretary for Research and

Technology (OST-R) (Grant no. DTRT12-UTC05) with additional financial support provided by The Ohio State University. The views, opinions, findings, and conclusions presented in this thesis are the responsibility of the author and co-advisors and do not represent the official policy or position of any of these entities or any others.

I would also like to acknowledge the entities that provided funding for my graduate education. I would like to thank the Graduate School at The Ohio State

University for the generous award of a University Fellowship. I would also like to acknowledge the gracious support of Tau Beta Pi, the engineering honor society, for the award of a Tau Beta Pi-Fife Fellowship. Finally, I would like to offer my gratitude to the

Ohio Chapter of the American Public Works Association for the award of a graduate scholarship. All of this support allowed me to focus on my studies and make the most of my graduate education.

Vita

2014 ...... B.S. Civil Engineering, Ohio Northern

University

2015 to Present ...... Graduate Research Associate, Department

of Civil Engineering, The Ohio State

University

2016 to present ...... Associate Engineer/Planner/Modeler, Mid-

Ohio Regional Planning Commission

Fields of Study

Major Field: Civil Engineering

vii

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments ...... v

Vita ...... vii

Table of Contents ...... viii

List of Tables...... xi

List of Figures ...... xii

Chapter 1: Introduction ...... 1

1.1 Motivation...... 1

1.2 Scope ...... 7

1.3 Organization...... 8

Chapter 2: Survey Data ...... 10

2.1 Overview ...... 10

2.2 Survey ...... 10

2.3 Sample and Response Rates ...... 15

viii

Chapter 3: Methodology ...... 19

3.1 Overview ...... 19

3.2 Logit Model Estimation Methodology ...... 19

3.3 Dependent and Explanatory Variable Processing, Definition, and Motivations ..... 22

3.3.1 Data Processing ...... 22

3.3.2 Variable Definitions and Motivations ...... 24

3.4 Modeling Structures ...... 34

Chapter 4: Modeling Unwillingness to Participate in Ridesharing ...... 51

4.1 Overview ...... 51

4.2 Model Specification ...... 52

4.3 Model Results ...... 58

4.4 Model Interpretation ...... 65

Chapter 5: Modeling Preferences for Passenger and Driver Ridesharing Roles ...... 75

5.1 Overview ...... 75

5.2 Passenger Model Specification ...... 78

5.3 Passenger Model Results ...... 84

5.4 Passenger Model Interpretation ...... 91

5.5 Driver Model Specification ...... 98

5.6 Driver Model Results ...... 103

5.7 Driver Model Interpretation ...... 109

Chapter 6: Conclusions ...... 117

6.1 Summary of Results and Conclusions ...... 117

6.2 Future Research...... 125

References ...... 130

Appendix A: Survey Questionnaire...... 133

Appendix B: Variable Definitions ...... 158

List of Tables

Table 1: Survey Branching Structure and Respondent Branch Assignment Probability .. 15

Table 2: Summary of Survey Sample Sizes and Response Rates ...... 16

Table 3: Number and Proportion of Remaining Respondents for Driver and Passenger

Investigations ...... 18

Table 4: Summary of Estimation Results of Model of Likelihood of Rejecting an Ideal

Ridesharing Program ...... 60

Table 5: Summary of Estimation Results of Model of Likelihood of being a Passenger 86

Table 6: Summary of Estimation Results of Model of Likelihood of being a Driver .... 105

Table 7: Variable Definitions ...... 159

List of Figures

Figure 1: Modeling Stuructures Estimated in Empirical Study ...... 35

Figure 2: Model of Likelihood of Rejecting an Ideal Ridesharing Program Sample and

Response Statistics ...... 52

Figure 3: Models of Likelihood of being a Passenger and Likelihood of being a Driver

Sample and Response Statistics ...... 77

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Chapter 1: Introduction

1.1 Motivation

Ridesharing has the potential to provide many benefits. The provision of a ridesharing program increases travelers’ choices, which can improve individual mobility and accessibility throughout a community (Miller et al., 2016). In addition, ridesharing has the potential to reduce congestion, travel costs, energy consumption, emissions, and the capital and operating costs of providing parking (Rodier et al., 2016). Some have shown that shared modes in general (e.g. ridesharing, car-sharing, ride-sourcing, bike sharing, etc.) are used in a supplemental (non-competitive) way to transit and that these modes are associated with lower overall transportation costs, reduced car ownership, and healthier lifestyles (Feigon and Murphy, 2016). Large employer-based ridesharing programs offer additional promise due to the large network of co-workers with a similar destination and increased familiarity with fellow travelers. Employers also have the ability to incentivize participation (Amey et al., 2011). Furthermore, developments in information technology are facilitating the provision of real-time ridesharing programs, where travelers are matched to share a ride in real-time shortly before the intention to travel (Amey et al., 2011). Real-time ridesharing offers users additional flexibility and the possibility of occasional participation. These attributes may be desirable in a society with increasingly complex and varying work and social schedules (Amey et al., 2011). 1

Initially real-time ridesharing programs were perceived as unattractive due to reliability and personal safety concerns (Amey et al., 2011). While real-time ride- sourcing services (e.g., Uber, Lyft, Didi Chuxing) are distinct from real-time ridesharing programs, which are the focus of this study, the passenger experience of both types of travel are similar under certain circumstances. The popularity and growing ubiquity of real-time ride-sourcing may suggest that perceptions of real-time travel options are shifting away from the noted initial concerns of reliability and safety as the public grows more familiar with the use of these options. Specifically, some studies have shown that ride-sourcing services have shorter wait times than other options, like taxi services, which may indicate improved reliability (Rayle et al., 2014).

Many studies have investigated various aspects of ridesharing programs, such as modeling the travel times of ridesharing travelers (Li et al., 2016), investigating pricing structures (Zhang et al., 2016), investigating how technological advances can improve ridesharing services (McCoy et al., 2016), studying how implementing ridesharing can improve citizen mobility (Miller et al., 2016), and assessing the positive congestion- mitigating effects of ridesharing (Rodier et al., 2016). In contrast, this study focuses on how individual characteristics impact willingness to participate in ridesharing and, furthermore, assesses how these characteristics impact preferences toward the ridesharing roles of passenger or driver participation.

Previous studies have used varied methods of investigating preferences toward ridesharing programs. These methods include revealed preference surveys of ridesharing travelers, stated preference surveys, and focus group discussions. These studies have

reported various findings worth summarizing. Cartmell and Carter (2015) used a stated preference survey to identify the characteristics of hypothetical individuals to illustrate what “types” of travelers may benefit from the use of real-time information in general.

“Career driven commuters” are identified as unlikely to change their mode choice and unconcerned about environmental impacts of travel, but possibly willing to change trip scheduling for the purposes of saving time on their commute. “Bohemian boomers” are identified as individuals in a later stage of their life who do not use technology unnecessarily, but may be willing to use technology in order to more conveniently meet their daily needs. “Cosmopolitan youth” are identified as those who love using new technologies, do not own cars or prefer not to drive, and may more readily change travel behavior. For the purposes of this study focused on the use of real-time information for the use of ridesharing, it seems that the “bohemian boomers” and “cosmopolitan youth” may be willing to participate in the type of program examined here.

Nielsen et al. (2015) used a focus group of Danish citizens to create a similar typology of individuals with varying perceptions of ridesharing programs on the basis of stated preferences. Unlike Cartmell and Carter, however, Nielsen et al.’s typology focuses on previous pattern of mode choice, rather than a mix of demographic characteristics, to form their typologies. The following types were identified:

“unconscious commuters” (regular auto travelers), “mass transiters”, and “steadfast bikers” as individuals dedicated to their chosen mode and unlikely to participate in ridesharing. Those who might utilize ridesharing programs are identified as “calculating deliberators”, who carefully calculate the cost of each alternative mode of travel available

to him or her, and the “convenience cycler”, who bikes when it is convenient, but uses other modes when it better suits him or her.

Bruns and Farrokhikhiavi (2011) used a revealed preference survey focused on users of a private ridesharing app in Germany. This study reveals two distinct groups of users for ridesharing programs. The first group is one that they deem “carpoolers,” who regularly share cars as a means of daily commuting. These individuals, when compared to the general population, are usually younger, more frequently married with children, and are highly educated with a higher income. These “carpoolers” often live in areas with poor connections to transit and high car availability and travel relatively longer distances to get to work. The other group identified by Bruns and Farrokhikhiavi (2011) are those who simply “need a lift” or those who are willing to “offer a lift” every once in a while.

These individuals occasionally use ridesharing to complete irregular trips. Those needing or offering a lift, when compared to the population, tended to be younger, were from smaller households, were unmarried, and did not have children. These individuals more frequently use transit or bike, drive less than the general public, or do not have a car at all

(for those who need a lift).

Mundler et al. (2016) used a similar methodology to profile users of a private ridesharing app in France. This study also found that users tended to be younger, but noted that in general those who used ridesharing had a similar distribution of income as the population. Mundler et al. (2016) made the further distinction that among the users of this private ridesharing app those with lower incomes were more likely to be passengers, while higher income participants were likely drivers. This trend may be further

understood by the analysis of Amey et al. (2011) relating to the economic and social dynamics of ridesharing choices, in which they noted that passengers gain more from not using their personal car, but drivers are able to maintain more control.

Feigon and Murphy (2016) used a revealed preference survey to assess the preferences of users of multiple types of shared mobility options such as ridesharing, ride-sourcing, carpooling, bike sharing, and transit. Their study showed that users of shared modes are often using these modes as a supplement to regular use of transit. What form of shared mobility the survey respondents used tended to be related to trip purpose and time of day, with most commuting trips utilizing transit and a variety of other shared modes utilized for recreational trips.

Other studies also used revealed preference surveys or a combination of stated and revealed preference surveys to assess real-time ridesharing preferences on university campuses specifically. Tezcan (2016) used a stated and revealed preference survey to investigate the preference of undergraduate students at a university in Istanbul, Turkey toward the role of being a passenger in a hypothetical ridesharing program. Part of the purpose of the study was to identify acceptable fees and walking times, but also to identify demographic characteristics of likely participants. The results identified females and those who frequently travel to and from campus (as opposed to those who travel to and from campus infrequently) as likely users of a ridesharing program as passengers.

DeFrancisco et al. (2014) and Tahmasseby et al. (2014) both used revealed preference surveys to investigate users of newly offered ridesharing programs on campuses in central Florida, USA and Calgary, Canada respectively. DeFrancisco et al.’s overarching

finding was that preferences toward ridesharing are influenced by current travel behavior, and that current travel behavior was influenced by demographic characteristics, which is further evidence supporting the results of Feigon and Murphy (2016) discussed above.

Tahmasseby et al. studied a program with an associated fee and found both demographic and behavioral attributes associated with user preferences. They noted that usually behavioral and individual characteristics were influential on the preference of individuals desiring to be passengers, but the actual characteristics of the program, such as drive times, were more important factors influencing preferences toward participating as drivers. They also noted students were more likely passengers and faculty and staff members were more likely drivers, which can be interpreted similarly to the results of

Mundler et al. (2016) and Amey et al. (2011) regarding the dynamics of ridesharing discussed above.

The study reported in this thesis may support or provide further clarity on these findings. In addition, it offers the opportunity to provide new insights. The stated preference survey used in this study uses an idealized hypothetical scenario as a base for gauging respondent likelihood of participation in an employer-based real-time ridesharing program. Investigating this stated preference allows for the assessment of how individual characteristics may affect one’s likelihood of participating in ridesharing in general, before considering the intricate details of cost, delays, or geographical disparities. This may allow one to better understand which groups of individuals may be interested in real-time ridesharing programs as conditions improve due to technological advances or the relative costs and times associated with using other modes change.

Moreover, this study may provide additional insights that could better describe the characteristic makeup of the users described by the typologies of other studies.

Furthermore, this study could provide insights into how urbanities in a Midwestern

American city are similar and differ from those living in cities in Canada, Turkey,

Denmark, Germany, or France.

1.2 Scope

The flagship main campus of The Ohio State University (OSU) is a large, urban, public university campus located on over 1,500 acres in Columbus, Ohio. The community is made up of more than 58,500 students and over 31,000 non-student employees including both faculty and staff members employed by the university and its co-located medical center (OSU Statistical Summary, 2015). This research uses a stated- preference survey of this community to identify what individual characteristics may be associated with willingness to participate in an employer-based real-time ridesharing program. Due to the size, location, number of affiliates, and diversity of affiliation within the OSU community, it is reasonable to believe that the identified preferences may be generalizable to other large urban employer-based real-time ridesharing programs, especially when exclusively considering the community members traveling from off- campus locations to their on-campus destinations.

The employer-based real-time ridesharing program investigated in this study differs from other types of ridesharing programs. In non-employer-based programs, participants are matched with others based solely on willingness to participate and

proximity of origin and destination of trips. In contrast, an employer-based program requires a shared destination and may imply an added level of familiarity with fellow participants because of a shared affiliation with the same organization.

In addition, the real-time program investigated here is distinct from a pre- scheduled program. A real-time program offers added flexibility and dynamic matching of willing rideshare passengers and drivers near the desired time of travel. On the other hand, in a pre-scheduled program a previously agreed upon arrangement is carried out on an agreed upon schedule of previously established frequency and time of day. This study specifically aims to identify what individual characteristics may impact one’s likelihood to participate in an employer-based real-time ridesharing program and one’s likelihood to accept a specific role in the program either as a driver or as a passenger.

1.3 Organization

The remainder of this investigation is organized as follows: Chapter 2 summarizes the stated-preference survey used as the basis of this investigation. Chapter 3 discusses the methodology and approach used in formulating the models developed to investigate the preferences of respondents toward an employer-based real-time ridesharing program.

Chapter 4 discusses the specification, results, and interpretations of the model used to investigate the preferences of those who are likely to reject a ridesharing program under ideal conditions with incentives. Chapter 5 discusses the specification, results, and interpretations of two models used to investigate the willingness of interested individuals to be ridesharing passengers and the willingness of interested individuals to be

ridesharing drivers, respectively. Chapter 6 summarizes and concludes the findings of this study and suggests directions for potential future research.

Chapter 2: Survey Data

2.1 Overview

This chapter summarizes the survey from which responses were used for the empirical investigation of preferences toward a real-time employer-based ridesharing program. This survey was administered in an effort by the Campus Transit Lab (CTL,

2016) at The Ohio State University during the Spring Semester of 2014. The data collected through this survey were used in the preparation of the models that form the basis of this study.

Section 2.2 describes the survey’s questionnaire and administration. Section 2.3 describes the response sample and response rates of this survey.

2.2 Survey

A stated preference survey was designed by the CTL and administered by the

Ohio State University Statistical Consulting Service (SCS) in an effort to investigate the attitudes and preferences of members of The Ohio State University community toward real-time tide-sharing. The survey questionnaire is provided in Appendix A. The design and administration off the questionnaire was conducted during the Spring Semester of

2014. Four types of university affiliates were sampled: faculty, staff, undergraduate students, and graduate students. A random sample was drawn from each of these groups. 10

The selected individuals were contacted via email and asked to complete an online questionnaire.

The questionnaire included questions relating to the individual’s demographic characteristics, prior travel behavior to and from campus, and stated preferences in regard to hypothetical scenarios proposed to evaluate interest in a real-time ridesharing program.

The contents of the survey questionnaire are summarized in more detail in what follows:

 Ten potential questions requested demographic information, such as age, gender,

affiliation (faculty, staff, student, and other), and living situation. Some of these

questions were only asked based on the response to a previous question. For example,

only staff members were asked a follow-up question to identify their job designation

as administrative professional, civil service, or other.

 Forty questions were potentially asked about the individuals’ generic travel behavior

during the semester in which they were responding in addition to specific information

about a recent trip to campus on a pre-specified day. Many of these questions were

asked only as follow-up questions to certain responses. For example, if the respondent

indicated they drove a single occupancy vehicle to campus, they were asked where

they parked.

 Two questions gauged likelihood of participation in a hypothetical real-time

ridesharing program as either a passenger or driver. In this hypothetical program there

were no appreciable delays, changes of schedule relative to current travel behavior, or

incentives to participate in the program.

 Between one and eight additional hypothetical real-time ridesharing scenario

questions were asked. The additional hypothetical scenarios investigated either

incentives to drive in a ridesharing program, incentives to be a passenger in a

ridesharing program, realistic scheduling changes that make driving in the program

less appealing, or realistic scheduling changes that make being a passenger in the

program less appealing. These additional hypothetical scenarios varied for each

respondent according to a branching scheme described further below.

The third set of questions—the two questions gauging likelihood—elicited responses on a five-level Likert scale with potential responses “Very unlikely”,

“Somewhat unlikely”, “Unsure”, “Somewhat likely”, and “Very likely”. A branching scheme was designed where the branch to be followed for subsequent questions depended on the responses indicating how likely the respondent was to participate in the ridesharing program with no significant delays, no changes of schedule relative to current travel behavior, and no incentives to participate in the program.

Four branches are considered. Branch 1 investigated hypothetical scenarios in which respondents were offered incentives to participate in the initial, ideal program as a passenger. These hypothetical questions were only asked of individuals who were not initially “Very likely” or “Somewhat likely” to participate in the ideal program as a passenger without incentives. Branch 2 investigated hypothetical rideshare passenger scenarios that were more realistic than those offered in the initial, ideal program. For example, scenarios were presented in which respondents were asked to modify their departure time to participate in the program. These hypothetical questions were only

asked of individuals who were initially “Very likely” or “Somewhat Likely” to participate in the ideal program as a passenger. Branch 3 investigated hypothetical scenarios in which respondents were offered incentives to participate in the ideal program as a driver. These hypothetical questions were only asked of individuals who were not initially “Very likely” or “Somewhat Likely” to participate in the ideal program as a driver without incentives and indicated that they had driven to campus sometime during the current semester (alone or in a shared car). Branch 4 investigated hypothetical rideshare driver scenarios that were more realistic than those offered in the initial, ideal program. For example, scenarios were presented in which respondents were asked to modify their departure time to participate in the program. These hypothetical questions were only asked of individuals who were initially “Very likely” or “Somewhat Likely” to participate in the ideal program as a driver.

The branching scheme is presented in Table 1. The rows in this table represent possible responses to hypothetical ridesharing driver scenarios. The columns in the table represent possible responses to the hypothetical ridesharing passenger scenarios.

Respondents who responded as “Very likely” or “Somewhat likely” to participate as drivers were considered to be likely participants as drivers and are represented by a “Yes” in the first row of Table 1. Conversely, respondents answering “Very unlikely”,

“Somewhat unlikely” or “Unsure” toward participating as a driver were considered unlikely participants as drivers represented by a “No” in the second and third row of

Table 1. The “No” category associated with participation as a driver is further broken down into two categories. Any respondent who drove to campus, either alone or in a

shared car, is indicated as “No (Yes drove)” in the second row of Table 1 otherwise he or she is indicated as “No (Did not drive)” in the third row of Table 1. Similarly, respondents who responded as “Very likely” or “Somewhat likely” to participate as passengers were considered to be likely participants as passengers and are represented by a “Yes” in the first column of Table 1. Conversely, respondents answering “Very unlikely”, “Somewhat unlikely” or “Unsure” toward participating as a passenger were considered unlikely participants as passengers represented by a “No” in the second column of Table 1.

Depending on which combination of driver and passenger categories a respondent falls in, certain branches are considered for the next set of questions. These branches are indicated in the corresponding cell in the table along with the probabilities that a respondent whose responses correspond to this cell is randomly selected to proceed with answering the questions of each branch. For example, someone who responded

“Somewhat likely” or “Very likely” to the prompt “Please rate how likely you are to participate in the above program at least once a week as a driver” (i.e., “Yes” in row one of Table 1) and responded “Unsure”, “Somewhat unlikely”, or “Very Unlikely” to the prompt “Please rate how likely you are to participate in the above program at least once a week as a passenger” (i.e., “No” in column two of Table 1) was randomly assigned questions from Branch 1 or Branch 4. Those who did not drive to campus were eligible for one or two branches based on their response to the driving scenario. If the respondent who did not drive to campus responded “Somewhat likely” or “Very likely” to the prompt “Please rate how likely you are to participate in the above program at least once a

week as a driver” (i.e., “Yes” in row one of Table 1), he or she was eligible for two branches and was assigned a branch in the same way as those who drove. If the respondent responded “Unsure”, “Somewhat unlikely”, or “Very unlikely” to the prompt

“Please rate how likely you are to participate in the above program at least once a week as a driver” (i.e., “No (Did not drive)” in row three of Table 1), he or she was deterministically assigned questions from Branch 1 or Branch 2 based on his or her indicated likelihood of participating as a passenger in the ridesharing scenario.

Table 1: Survey Branching Structure and Respondent Branch Assignment Probability

Passenger Yes No Branch 2- 50% Branch 1- 50% Yes Branch 4- 50% Branch 4- 50% Driver Branch 2- 50% Branch 1- 50% No (Yes drove) Branch 3- 50% Branch 3- 50% No (Did not Drive) Branch 2- 100% Branch 1- 100%

2.3 Sample and Response Rates

Responses to the online survey were received between March 24th and April 28th,

2014. There were a total of 4,633 respondents to the 21,900 survey invitations (i.e., an overall response rate of 21.15%). Surveys were randomly sent to individuals sampled from four campus subgroups: faculty, staff, graduate students and undergraduate students. Prior CTL surveys exhibited lower response rates from the undergraduate student subgroup (McCord et al., 2009; Mishalani et al., 2011). Therefore, undergraduate 15

students were sampled at a higher rate, in an attempt to elicit a representative sample.

Table 2 presents the sizes of the population groups sampled, number of responses (i.e., sample size in each subgroup), and the response rates for each of the four subgroups. As expected, the undergraduate response rate was lower than that of the other respondent categories. Specifically, the undergraduate student response rate was only 9.27% compared to response rates ranging from 19.67% to 35.77% in the other respondent categories. For comparison, the last surveys administered by the CTL achieved an overall response rate of about 24% and had a undergraduate response rate of around 14%

(McCord et al., 2009; Mishalani et al., 2011). This survey had a slightly lower overall response rate and a lower response rate among undergraduate students, but the rates are generally comparable.

Table 2: Summary of Survey Sample Sizes and Response Rates

Sub Group Population Size Responses (Sample Response Rate Sampled Size) (%) Faculty 4,800 1,255 26.15 Staff 4,800 1,718 35.77 Graduate Student 4,800 944 19.67 Undergraduate Student 7,500 695 9.27 Not Specified* - 10 - Total 21,900 4,633 21.15 *Represents respondents that did not answer the affiliation category question.

Some responses were removed from consideration in this study due to missing or potentially flawed data. Respondents marked as incomplete due to a failure to reach the end of the survey were removed from analysis. In addition, within completed surveys, respondents could elect not to answer questions. Moreover, respondents electing not to provide responses for all of the ridesharing scenarios of interest were also removed from the study (i.e., did not provide answer for the ideal passenger or driver scenario).

Furthermore, errant entries, in which respondents were mistakenly assigned the wrong branch, were removed because they did not satisfy the conditions for that branch as intended by the survey designers. Many of these errors were due to a coding error that was detected and corrected early in the survey administration process.

In order to adequately represent the scope of investigating a real-time ridesharing program provided by a large employer, respondents who were not currently enrolled or employed at the main Columbus campus location of The Ohio State University during the semester of the survey administration were excluded from analysis. The Ohio State

University has a few smaller branch campuses elsewhere and some of the members of the population sampled belonged to those branch campuses. The records corresponding to students who lived in dorms were also not considered because those students’ indicated unique living situation renders their responses as outside the scope of this study, namely the preferences of travelers who could feasibly be potentially interested in employer- based ridesharing.

Those who did not have a car available during the current semester were excluded from any analyses that investigated preferences for driving in a ridesharing program

because they were assumed to be unable to perform that role. After the removal of missing or potentially errant entries and records corresponding to respondents whose situations, as indicated by their responses, render their responses as outside the desired scope of the study, the sample size was reduced to 3,125 for models investigating ridesharing driving preferences and 3,327 for models investigating ridesharing passenger preferences. All 3,125 respondents considered in the investigation of driver preferences are common to both of these two groups. Table 3 reports the number and proportion of respondents from each sub group of the population remaining in each of the samples used for investigations into driver and passenger preferences respectively.

Table 3: Number and Proportion of Remaining Respondents for Driver and Passenger Investigations

Sub Group Driver Responses Percent of Passenger Percent of Remaining Driver Responses Passenger Respondents Remaining Respondents (%) (%) Faculty 1,007 32 1040 31 Staff 1,234 39 1258 38 Graduate Student 610 20 703 21 Undergrad. Student 274 9 325 10 Total 3,125 100 3326 100

Chapter 3: Methodology

3.1 Overview

This chapter describes the methodology and approach used to investigate preferences toward real-time employer-based ridesharing through the development and interpretations of the models and estimation results discussed in detail in Chapters 4 and

5. Section 3.2 discusses the discrete choice modeling methodology used throughout the investigation. Section 3.3 describes the types of variables that were formed from survey responses and the motivation and specification of explanatory variables used in this investigation. Section 3.4 discusses the preliminary investigation which led to the model structures adopted in the models presented in Chapter 4 and 5.

3.2 Logit Model Estimation Methodology

To investigate which individual characteristics (explanatory variables) are associated with preferences (dependent variables), a discrete-choice Logit model is adopted. During this investigation, both multinomial and binary choice models were considered, however, the binary models were found to provide clear explanatory value and were easily interpreted. The dependent variables used in this study, discussed in detail in Sections 3.3 and 3.4, are derived from five-level Likert scale responses, which may make an ordinal Logit model seem appropriate. However, in the case of binary 19

comparisons, the ordinal Logit model is equivalent to the binary logit model (Amemiya,

1981).

A standard binary Logit formulation was used to determine the probability of an individual (n), with characteristics Xn1,..,Xnk, making a choice i . Since the binary methodology is used, choice i can be represented by a binary dependent variable Y, which takes a value of 1 for the choice being considered (e.g., respondent choses to indicate

“Very likely” or “Somewhat likely” to participate in a ridesharing program as a passenger), and 0 otherwise. The resulting probability calculation is given by (Ben-Akiva and Lerman, 1985):

푃 (푌 = 1) = 1 (1) 푛 푛 1+푒−(푉푛) where, 푃푛(푌푛 = 1) is the probability of individual n making choice i (as indicated by the dependent choice variable 푌푛 taking the value of 1) and 푉푛 is specified to be a linear in the parameters function of explanatory variables:

푉푛 = 훽1푋푛1 + 훽2푋푛2 + ⋯ + 훽푘푋푛푘 + ⋯ + 훽퐾푋푛퐾 (2) where, 푋푛푘 is the kth independent variable (individual characteristic) for individual n, 훽푘 is the coefficient associated with independent variable 푋푘, and K is the number of independent variables.

In each of the models used in this investigation, the first independent variable,

푋푛1, is set equal to one for every individual, resulting in 훽1푋푛1 being a constant for all individuals. This results in the 푋1 = 1 variable representing an individual with characteristics to be those resulting when other explanatory variables are set to zero.

Other than the constant, 푋1 = 1, the specified independent variables used in the different models vary from model to model as described in Chapters 4 and 5.

Each binary Logit model was estimated with the mlogit package (Croissant, 2013) in the statistical program R (R Core Team, 2015). This program determines maximum likelihood estimates for the coefficients for the explanatory variables being investigated, as well as the corresponding statistics for each explanatory variable and the model as a whole. The t-statistic and p-value produced by the estimation correspond to the traditional hypothesis test values obtained under the null hypothesis of the coefficient being equal to zero. When a high p-value is estimated for the coefficient of a variable, representing a failure to reject the null hypothesis, the corresponding explanatory variable is considered insignificant to the investigation and removed from the specification or replaced with an alternative explanatory variable. The Logit model was then re-estimated and the specification process repeated until there were few or no variables with p-values indicating high probabilities of insignificance.

During this iterative specification and estimation process, overall goodness of fit measures, such as the Chi-Squared Log-Likelihood Ratio Test (and corresponding p- value), calculated by the software were also assessed to determine how different specifications affected the overall explanatory power of each model. The Chi-Squared

Log-Likelihood Ratio Test is a hypothesis test with a null hypothesis that the improvement in log-likelihood of the specified model compared to the log-likelihood of the model with only an alternative specific constant is equal to zero (Ben-Akiva and

Lerman, 1985). This hypothesis test effectively corresponds to a comparison of the user

specified model to a model based on only the proportion of all respondents selecting the choices of interest. Therefore, if a high p-value is reported, the user specified model fails to reject the null hypothesis, suggesting that the user specified model is not providing significant additional explanatory value.

3.3 Dependent and Explanatory Variable Processing, Definition, and Motivations

3.3.1 Data Processing

To investigate the impact of various factors, survey responses stored in a csv text file were exported into an Excel file. The responses were then sorted and manipulated to form variables for use as dependent and explanatory variables in several discrete choice models. Once the variables were determined, the Excel file was exported as a tab delimited text file for use in the statistical software package R (R Core Team, 2015).

The majority of variables investigated were specified as discrete indicator variables. For example, a variable representing gender is given a value of 1 if the respondent indicated he or she was a female and 0 if he or she indicated that he or she was a male. Similarly, each university affiliation type is manipulated to form a discrete indicator variable. For example, the undergraduate variable is given a value of 1 if the individual indicated that he or she was an undergraduate student and 0 if he or she indicated any other affiliation. Some survey questions requested responses on a five-level

Likert scale ranging from “Very unlikely” to “Very likely”. These responses were mapped into a binary variable that is consistent with the branching scheme used in the survey as discussed in Section 2.2. For example, “Very likely” or “Somewhat likely”

responses to the prompt “Please rate how likely you are to participate in the above program at least once a week as a driver” were given a value of 1 for a binary variable to indicate that the corresponding individuals are likely to participate as a driver, and

“Unsure”, “Somewhat unlikely”, and “Very unlikely” responses were given a value of 0 to indicate that these individuals were not likely to participate in the program as a driver.

At times, it was desirable to combine characteristics to form a potential dependent or explanatory variable. In these cases, more complex binary variables were coded using Boolean operators to consider the responses to multiple survey questions or to consider multiple categories in one variable (e.g., the variable given a value of 1 if the response indicated that he or she was a graduate student OR an undergraduate student, and a value of 0 otherwise).

Some survey questions elicited a categorical response indicating a range of potential values for a continuous characteristic. For example, a respondent was asked to indicate age as either less than twenty-five years old, between twenty-five and forty years old, between forty-one and fifty-five years old, or over fifty-five years old. Variables based on such questions were formed in two ways, as binary indicators for each category, where the variable was equal to one for exactly one of the categories, and as a single variable with a numeric value taken as the midpoint of the range the respondent indicated

(e.g., the variable assigned a value of 48 if he or she indicated he or she was between 41 and 55).

It is possible that the respondent elected not to answer some question(s) that were needed to determine a variable value. When no response was recorded for an individual

that variable was given a value of “NA” for the individual. An “NA” response prevented the consideration of that individual in models in which that variable was investigated.

3.3.2 Variable Definitions and Motivations

Nearly every question in the survey was used as a potential dependent or explanatory variable in models designed to investigate what factors could impact certain preferences. Table 7 in Appendix B summarizes all of the variables discussed in this section. Throughout the preliminary investigations used to determine model structures discussed in Section 3.4, certain variables were determined to be important due to statistical significance observed in multiple estimated models, potential generalizability, and interpretability. Some other variables were repeatedly considered even when significance was not immediately observable due to their common use and importance in discrete choice models. Based on these considerations, the following categories of variables were determined to be of interest for the purposes of this study: gender, living situation, affiliation, years on campus, age, previous mode choice, frequency/regularity of travel to and from campus, recently experienced travel time, and recently experienced tour planning (where multiple destinations are combined over the course of a day). The corresponding variables are discussed and motivated subsequently.

Gender is an often investigated factor in travel behavior models and was considered an important factor to continually consider in the discrete choice specifications of this study. There are also potentially interesting intersections of gender and living situation discussed further below.

Living situation was determined to be an interesting variable throughout the preliminary investigation. The living situation variables reflected whether the participant lived alone, with parents or grandparents, with roommates, with a spouse, or with children. Since these variables were based on a “select all that apply” question, the binary variables representing them were not mutually exclusive. Therefore, an individual could have binary indicators for multiple of the listed living situations. Throughout the investigation the most consistently significant factor associated with preferences seemed to be the presence of children in the household. Since the presence of children in the household likely represented additional responsibilities at home, other factors were also considered that may impact the level of responsibility that an individual may have.

Living situation might have different effects depending on one’s gender due to societal gender roles, which remain common in many communities. If this is indeed the case, the expectation would be that females with children may have more childcare responsibilities in the home than females without children or males with or without children. To investigate this notion, a variable was defined to take a value of 1 if the respondent was female AND lived with children, and had a value of 0 otherwise.

It was also considered that single parents may have more childcare responsibilities than those who have a spouse with whom to share the responsibilities. To investigate this possibility the single parent variable is defined to take a value of 1 if the respondent lived with children AND did not live with a spouse, and 0 otherwise.

Similarly, it was considered that if someone lived in a household with children and no other adults in the home (i.e., did not live with a spouse, roommates, parents, or

grandparents) he or she may be more constrained with childcare responsibilities.

Therefore, another lived with children variable is defined to take a value of 1 if the respondent lived with children AND did not live with anyone else, and 0 otherwise.

Additional variables are defined to investigate the intersection of age, living situation, affiliation, and years on campus. These variables are discussed further below.

Affiliation is used to represent how an individual is employed by or enrolled at

OSU. This “role” could be a potentially interesting factor influencing preferences. All of the affiliation categories elicited within the survey were considered. These categories consisted of undergraduate student, graduate student, faculty, administrative and professional staff, civil service staff, and other staff. Throughout the preliminary investigations the most influential affiliations seemed to be undergraduate students, graduate students, faculty, and civil service staff members. In some cases it was observed that the estimated impacts of both student affiliations (undergraduate and graduate) were similar to one another (i.e., the estimated coefficients associated with each affiliation had the same sign and similar magnitude). Therefore, a generalized student variable is also defined to take a value of 1 if undergraduate student OR graduate student, and 0 otherwise. Similarly, in some instances faculty members and civil service staff members were observed to have similar preferences, so a variable representing the union of these affiliations is defined to take a value of 1 if civil service staff member OR faculty member, and 0 otherwise. It was also considered that one’s affiliation may be related to one’s age or years on campus and that such combinations may influence preferences

accordingly. The manner in which these relationships were addressed is discussed further below.

Age could be associated with preferences toward ridesharing for many reasons.

One might expect age to represent differences in generational perceptions, differences in affiliation and responsibilities on campus, differences in living situation and at home responsibilities, or differences in earning potential. To avoid assumptions of monotonic trends, these variables were first tested as binary variables for each category. For example, a respondent was given a value of 1 if he or she indicated that he or she was between 41 and 55, and 0 if he or she indicated another age category. Similar variables were specified for each of the other age categories. If the trends in these variables are found to be seemingly monotonic, an alternative continuous-like variable is considered.

This variable is formed from the categorical responses combined by taking the midpoint of the range indicated as the value for the respondent. For example, the variable was given a midpoint value of 48 if the respondent indicated that he or she was between 41 and 55. This method requires the assumption of realistic lower and upper bounds for the lowest age “less than 25” and highest age “greater than 55” categories. In the case of

“less than 25”, a realistic lower bound was taken to be 17 years old, since the vast majority of individuals associated with the university would be at least 17 years old.

Therefore, the midpoint for the “less than 25” category is 21. In the case of “greater than

55”, a realistic upper bound was taken as 75 to encompass the assumed majority of those that would have received the survey, and therefore the midpoint for the “over 55” variable takes the value of 65.

Number of years on campus was also determined throughout the investigation to be a potential explanatory variable of interest. This variable could be a proxy for multiple factors, such as duration of current routine, experience, or financial stability. Survey respondents indicated their number of years on campus as either less than one, between one and two, between two and five, between five and 15, or over 15 years on campus.

This characteristic was considered in the same way as the age variable, first with individual binary indicators for each range, then, if monotonic trends were revealed, a continuous specification was considered. However, the trends were not monotonic.

Since a respondent’s age and affiliation are likely related and are both thought to be potentially important in categorizing preferences, specifications were considered to control for these interactions. Students, both undergraduate and graduate, were expected to have different preferences than those who were employed by the university because of a likely difference in schedule and level and type of responsibility on campus as previously discussed in this section. There are also expected differences in preference due to generational difference and establishment level, which are likely reflected by one’s age. Since most students are younger individuals the effects of one’s affiliation may be difficult to separate from one’s generation. Therefore, a variable was defined to test the effects of age on individuals who are not students in the same manner as the general age, first in a categorical manner and then in a continuous manner if warranted. The categorical variable takes a value of 1 if the respondent is under twenty-five AND not a student, and 0 otherwise. This allows one to compare generational differences between

individuals with similar affiliations without considering the fact that many in the younger generations are affiliated with the university in a substantially different way.

In the same way, a respondent’s affiliation could be expected to be related to the years he or she has spent on campus. It is much more likely for faculty or staff members to have more years on campus than students. A variable indicating years on campus conditional on not being a student is defined such that it takes a value of 1 if the respondent has been on campus between two and five years AND is not a student, and 0 otherwise to attempt to separate the effects of experience from the effects of affiliation.

There are similar concerns that one’s generational (age) characteristics may be related to one’s living situation (having children at home). For instance, living with children may be more likely as one enters the middle age categories, i.e., an individual in the 25-40 age group is more likely than someone in the <25 age group to have children living in his or her home. Similarly, as one reaches the oldest age groups (41-55 and >55) the likelihood of living with children may decrease because children born when the respondent was younger have aged and may move out of the home. Therefore, a variable is defined to control for the presence of children in the household that takes a value of 1 if the respondent is between 25 and 40 AND lived with children, and 0 otherwise. This variable, when used with an age variable, would indicate if individuals within an age category with children are significantly different than those in that category without children. For example, if both an age variable (e.g., 1 if between 25 and 40, and 0 otherwise) and a combined age and living with children status variable (e.g., 1 if between

25 and 40 AND lived with children, and 0 otherwise) are specified, a respondent in this

age category with children at home will have a binary indicator of one for both variables, whereas an individual with no children at home would only have a binary indicator of one for the age variable. If both of these variables prove significant in a model estimation it would indicate that having a child at home while being in this age range makes one of significantly different preference than someone in this age range with no children.

It is also conceivable to consider that three of these factors—age, affiliation, and living situation; or years on campus, affiliation, and living situation—may all interact with one another. For instance, a young student without children may be expected to be significantly different than a young staff member with children at home. To test this possibility variables are defined to reflect the three characteristics jointly (e.g., 1 if between 41 and 55 AND not a student AND lived with children, and 0 otherwise).

In addition there is concern that a likely relationship between years on campus and age could cloud model estimation results. To address this matter, categorical binary variables are defined to capture possible combinations of these two factors (e.g., 1 if less than 40 AND less than 5 years on campus, and 0 otherwise). Such variables allow distinctions, for example, between young people who have been on campus for a long time to young people who have been on campus for a short time.

Because past behavior is often a good predictor of future behavior, an individual’s previous travel behavior was expected to be an important factor associated with ridesharing preferences. The survey elicited responses about what modes respondents had used in the current semester and details about a recent trip to campus on a specific day.

When asked about travel in the past semester, a respondent was asked to “select all that

apply” from a list of modes including drove a car alone, shared car as a driver, shared car as a passenger, rode a motorbike alone, shared a motorbike as a driver, shared a motorbike as a passenger, Central Ohio Transit Authority bus (COTA) (city bus transit),

Campus Area Bus Service (CABS) (campus bus transit), bicycle, walk, or other. Each of these modes was investigated as a non-mutually exclusive individual binary indicator

(e.g., 1 if drove a car alone this semester, and 0 otherwise). Tests indicated that the estimated coefficients associated with the COTA and CABS modes had the same sign and similar magnitudes. Therefore, a transit variable was defined to take a value of 1 if an individual used COTA or CABS during the semester, and 0 otherwise.

Interest in how an individual’s travel regularity and habits may affect his or her preferences led to the investigation of additional variables. One indicator of highly habitual behavior is the repetitive use of only one mode. To investigate the association of this behavior with preferences toward ridesharing, variables indicating exclusive use of a mode during the current semester are defined for each mode (e.g., 1 if drove alone this semester AND did not indicate the use of any other mode, 0 if otherwise). The use of these variables provides the opportunity to identify when someone who exclusively uses a mode is different from one who uses multiple modes. For instance, this specification can be used to compare someone who only drives an automobile alone to and from campus, to someone who drives sometimes, but uses other modes at other times. An anticipated difference in the resulting preferences could be associated with multiple factors that lead to exclusive mode use. Firstly, the difference could be the result of a strong preference for one mode, irrespective of what it may be. Secondly, one could

exclusively use one mode because circumstances restrict his or her choice set for travel. It was found that most of the exclusive users were individuals who only drove alone to campus and that the other modes had relatively few exclusive users. To investigate whether it is the regular, exclusive use of any mode or the particular exclusive use of a single-occupancy vehicle that was most significantly associated with the resulting preference, another variable is defined to indicate exclusive use of any mode that takes a value of 1 if the respondent only selected one mode when asked how one traveled to and from campus, and 0 otherwise.

Frequency and regularity of travel schedule is also deemed potentially interesting.

How many days usually spent on campus, the times of day that travel to and from campus occurred, and the first day of the week that the individual traveled to campus were all tested to investigate how the pattern, regularity, and frequency of current travel are associated with preferences for real-time ridesharing. After investigating many variations and combinations of these variables, the variable that appeared to have an impact in various models was a continuous variable specification of days per week usually spent on campus (0.5 for the response of zero or one day on campus, 2.5 for the response of two or three days on campus, 4.5 for the response of four or five days on campus, and 6.5 for the response of six or seven days on campus). A categorical specification was considered first. The results implied monotonic trends and the continuous specification proved to be more statistically significant. As a result, the monotonic specification is used to represent how one who travels frequently with a likely regular schedule differs from one who makes infrequent trips following an irregular schedule.

It is also worthwhile to assess the relationship between one’s preferences and his or her choice set for mode of travel to and from campus. A key indicator of the availability of choices elicited in the survey is whether a car was available to the respondent during the current semester. To investigate this influence, a binary variable was defined to take a value of 1 if he or she did not have a car available during the current semester, and 0 otherwise.

The time one spends traveling to and from campus is thought to be a potentially important variable as well. The survey questionnaire asked for information based on a recent trip on a specific day to and from campus. If an individual stopped on either of his or her trips to and from campus, survey questions requested a response on the actual time spent traveling including stops and a second response on the estimated time that would have been spent traveling if no stops were made. In order to have consistent values across individuals, for those who stopped the estimated value for travel without stopping was used. Stopping was accounted for in a separate variable discussed further below. As with the other continuous variables previously discussed, no a priori assumptions of monotonic trends were imposed. Therefore variables based on each solicited range of travel times were considered first, and only after monotonic trends appeared, continuous specifications were investigated.

The nature of the mode of travel was also considered to have an effect on duration of travel time. Specifically, given the physical effort involved, walking or bicycling may result in different travel time effects on preferences towards ridesharing. Therefore, a

motorized travel time set of variables was considered (e.g., 1 if time to campus was between ten and nineteen minutes AND did not walk or bicycle, and 0 otherwise).

How one plans a travel tour, specifically how one chooses to chain (or not chain) trips of various purposes, was also considered a potentially influential factor in this study.

Respondents were asked whether and why they stopped on a trip to or from campus. The various reasons for stopping included socializing, stopping for food or beverage, shopping, exercising, dropping off or picking up a passenger, or other. These reasons were considered individually (e.g., 1 if stopped to socialize, and 0 otherwise) and collectively (i.e., 1 if stopped for any reason, and 0 if did not stop).

3.4 Modeling Structures

This section describes the specifics of the ideal hypothetical ridesharing scenario investigated in this study. Once this scenario is explained, various structures are discussed that were used to identify what characteristics may impact an individual’s likelihood of participating in the ideal hypothetical ridesharing scenario. In this context, the word “structure” is used to describe the process of simultaneously specifying the dependent variable and the portion of the sample that was used to estimate each discrete choice model considered. Figure 1 depicts the various structures considered. The final model structure discussed (Structure F in Figure 1) is the one selected for detailed specification, estimation, and interpretation presented in Chapters 4 and 5. This structure was selected due to its superior fit and interpretability.

Structure A: Model 1 (Binary) Model 2 (Binary)

Dependent Variable: 1 if Likely Dependent Variable: 1 if Likely Passenger, 0 Otherwise Driver, 0 Otherwise

Sample Portion: Entire Sample Sample Portion: Those with Car Available Structure B:

Model 1 (Multinomial) Model 1 (Binary)

Dependent Variable: “Likely Driver AND Likely Passenger”,Dependent “Likely Variable: Driver 1AND if Likely Not Likely Passenger”, “Not Likely Driver AND Likely Passenger”, or “Not Likely Driver AND Not Likely Passenger” Driver, 0 Otherwise

Sample Portion: Those with CarSample Available Portion: Entire Sample

Structure C:

Model 1 (Binary) Model 2 (Binary)

Dependent Variable: 1 if Likely Driver Dependent Variable: 1 if Not Likely AND Likely Passenger, 0 Otherwise Driver AND Likely Passenger, 0 Otherwise Sample Portion: Those with Car Available Sample Portion: Those with Car Available

Model 4 (Binary) Model 3 (Binary)

Dependent Variable: 1 if Not Likely Dependent Variable: 1 if Likely Driver Driver AND Not Likely Passenger, 0 AND Not Likely Passenger, 0 Otherwise Otherwise

Sample Portion: Those with Car Sample Portion: Those with Car Available Available continued Figure 1: Modeling Stuructures Estimated in Empirical Study

Figure 1 continued Structure D: Model 1 (Binary)

Dependent Variable: 1 if Not Likely Driver AND Not Likely Passenger, 0 Otherwise

Sample Portion: Those with Car Available

Model 2 (Binary)

Dependent Variable: 1 if Likely Driver AND Likely Passenger, 0 Otherwise

Sample Portion: Those who did not respond “Not Likely Driver AND Not Likely Passenger” and had a car available

Model 3 (Binary)

Dependent Variable: 1 if Likely Driver AND Not Likely Passenger, 0 if Not Likely Driver AND Likely Passenger

Sample Portion: Those who did not respond “Not Likely Driver AND Not Likely Passenger” or “Likely Driver AND Likely Passenger” and had a car available continued

Figure 1 continued Structure E: Model 1 (Binary)

Dependent Variable: 1 if Not Likely Driver AND Not Likely Passenger, 0 Otherwise

Sample Portion: Those with Car Available

Model 3 (Binary) Model 2 (Binary)

Dependent Variable: 1 if Likely Dependent Variable: 1 if Likely Driver, 0 Otherwise Passenger, 0 Otherwise

Sample Portion: Those who did not Sample Portion: Those who did not respond “Not Likely Driver AND Not respond “Not Likely Driver AND Not Likely Passenger” Likely Passenger” and had a car available

Structure F: Model 1 (Binary)

Dependent Variable: 1 if Not Likely Driver AND Not Likely Passenger AND None of the Above Incentives, 0 Otherwise

Sample Portion: Those with Car Available

Model 3 (Binary) Model 2 (Binary)

Dependent Variable: 1 if Likely Dependent Variable: 1 if Likely Driver, 0 Otherwise Passenger, 0 Otherwise

Sample Portion: Those who did not Sample Portion: Those who did not respond “Not Likely Driver AND Not respond “Not Likely Driver AND Not Likely Passenger AND None of the Likely Passenger AND None of the Above Incentives” and had a car Above Incentives” available

All the models in Chapters 4 and 5 are developed to investigate the preferences of respondents when faced with the first ideal hypothetical rideshare program presented in the survey questionnaire, namely an ideal real-time employer-based ridesharing program with minimal changes in schedule and delays. Each respondent was asked to indicate the likelihood of participating as a passenger or a driver at least once a week during the current semester, specifically to “Please rate how likely you are to participate in the above program at least once a week…”, with a five-level Likert scale of response choices of “Very unlikely”, “Somewhat unlikely”, “Unsure”, “Somewhat Likely”, and “Very

Likely”. The program was described as follows: “A free program becomes available that gives OSU affiliated individuals the chance to share rides to and from OSU. You enter limited information into a database where your privacy is protected. The program matches drivers and passengers.” (see survey questionnaire in Appendix A). The ideal passenger experience was described as follows: “As a passenger, you would be picked up from your home by an OSU affiliated individual whenever you choose. Also, you would not have to walk any farther than you usually do to get to your campus destination. In addition, you are guaranteed a ride home at a time of your choosing.” The ideal driver experience was described as follows: “If you were a driver, rather than a passenger, in this ridesharing program, you would pick up another OSU affiliated individual at the same time you usually depart home. The individual lives less than 5 minutes away. You would drop the individual off wherever you park on campus.” The preference responses to these two roles in the hypothetical cases were the base for all of the dependent variables used in the various structures described subsequently.

The initial models estimated were based on the simplest modeling structure. The simplest structure involves testing the preferences in two separate models: one that identifies likelihood of participating as a passenger and one that identifies likelihood of participating as a driver (Structure A, Figure 1). For the model investigating preferences toward participating as a passenger the entire sample was used. That is, all respondents that were not eliminated because of errors, inconsistencies, or other reasons discussed in

Section 2.3, were used to estimate a binary logit model with the dependent variable Y set to 1 if the respondent indicated he or she was “Very likely” or “Somewhat likely” to be a passenger in a ridesharing program, and 0 otherwise. The entire sample less those who do not have a car available was used to estimate a similar model with a dependent variable defined to investigate the likelihood of participating in the ideal rideshare program as a driver. Recall from the discussion in Section 2.3, those with no car available are assumed to be unable to participate in a ridesharing program as a driver and are thus not considered in the estimation of any models investigating driver preferences. The estimated models showed some reasonable trends, but many explanatory variables that one may expect to have an impact on preferences had estimated little to no coefficients of statistical significance.

After examining the results of the simplest models, a second set of structures was considered in which the individual’s joint response to both driver and passenger roles was considered. Structures B and C of Figure 1 were conceived as alternatives to implement this idea. These structures represent binary and multinomial choice approaches to modeling the respondents’ responses to the ideal ridesharing program as a passenger and

as a driver as a single choice. Specifically, in Structure B, which represents the multinomial structure, each potential pair of responses was one possible choice (“Likely

Driver AND Likely Passenger”, “Likely Driver AND Not Likely Passenger”, “Not

Likely Driver AND Likely Passenger”, and “Not Likely Driver AND Not Likely

Passenger”). The dependent variables in the binary models of Structure C were binary indicators of each possible pair of responses in four separate models. Specifically, one model was specified for the choice of “Driver = 1 AND Passenger = 1”, where the response variable Y was assigned a value of 1 if the respondent indicated that he or she was “Very likely” or “Somewhat likely” to participate in the program in each role (i.e.,

Driver = 1 and Passenger = 1), and 0 otherwise. Similar models were specified for the choices “Driver = 0 AND Passenger = 1”, “Driver = 1 AND Passenger = 0”, and

“Driver = 0 AND Passenger = 0” where the indicator of 0 for the role variable indicates

“Very unlikely”, “Somewhat unlikely”, or “Unsure” to participate in the program in that role. Models from Structure B and Structure C were estimated with all respondents that were not eliminated because of errors, inconsistencies, or other reasons discussed in

Section 2.3 including not having a car available to them. Recall from Section 2.3 that because the models of these structures investigate the response to the driving role that those with no car available are assumed to be unable to be drivers and are not considered.

After specifying and estimating all of the models in Structure B and Structure C, the multinomial results could be compared to the binary results. It was determined that the results of the models in both structures had similar implications. Therefore, the simpler

binary models of Structure C were chosen for further investigation because of ease of interpretability.

Analyzing the results of the estimated models of the binary Structure C led to interesting interpretations, especially in the extreme negative case of the binary choice between “Not Likely Driver AND Not Likely Passenger” versus any other preference.

For example, respondents indicating they had exclusively driven alone to campus in the last semester were estimated to be more likely to reject either role in the program than respondents who drove alone and took other modes of transportation. However, the estimated models of joint responses indicating a preference for one role over the other

(i.e., “Driver = 1 AND Passenger = 0” and “Driver = 0 AND Passenger = 1”) were not found to have many coefficients of explanatory variables that have p-values indicating statistical significance. This result led to considerations that those who provided an

“extreme” response of “Driver = 0 AND Passenger = 0” or “Driver = 1 AND

Passenger = 1” may have fundamentally different perceptions of a ridesharing program than other members of the sampled community.

If a respondent has one constraint or characteristic that restricts participation in a ridesharing program, such as a the responsibility for taking several children to school before coming to campus, one may believe that this respondent would likely rarely if not never participate in a ridesharing program regardless of how ideal it may be. On the other hand, if a respondent has one characteristic or constraint that makes him or her a highly likely participant in a ridesharing program regardless of role, such as an extreme aversion to the externalities of single-occupancy vehicle use, one may believe that this individual

would utilize even an inconvenient ridesharing program to avoid single-occupancy vehicle use. Those who express a preference for one role and not the other (i.e.,

“Driver = 1 AND Passenger = 0” and “Driver = 0 AND Passenger = 1”) are likely not characteristically unlikely or likely to participate in a ridesharing program in general and are making a more pragmatic choice based on a personal assessment of the “costs” and

“benefits” of each ridesharing role. If one particular characteristic or combination of characteristics can cause an “extreme” preference for or against ridesharing, one may expect that the “extreme” respondents could worsen the results of a model of a more pragmatic choice between roles.

To illustrate such an effect, consider an extreme hypothetical scenario where every individual who has children will not participate in ridesharing regardless of other characteristics or circumstance (i.e., Prob. (“Driver = 0 AND Passenger = 0”| Children) =

1.00). Also imagine that every individual who is a student will participate in a ridesharing program as a passenger, but not as a driver, unless they have children, in which case based on the previous probability statement they will not participate in either role (i.e.,

Prob. (“Driver = 0 AND Passenger = 1”|Student AND No Children) = 1.00). Now, assume one was to estimate a logit model of the “Driver = 0 AND Passenger = 1” preference choice using the binary variable indicating students as the only explanatory variable. The significance, sign, and magnitude of the coefficient for the binary variable indicating students in the estimated model would depend on how many students have children at home, even though, in the absence of children, the student affiliation has a

deterministic effect on this choice. Even if an additional “lives with children” variable were included, the model would not reproduce the underlying deterministic choice.

The suspicion of the presence of characteristics that are associated with respondents providing “extreme” responses led to the testing of another model structure,

Structure D in Figure 1. Structure D represents a set of three binary choice models. The first model aims to identify what may impact one’s preference to completely reject the ideal ridesharing program. Specifically, the binary logit model had a dependent variable

Y given a value of 1 if the respondent indicated the joint choice “Driver = 0 AND

Passenger = 0”, and 0 otherwise. Recall that, as before, a 0 is given for a particular role if the respondent answered that they were “Unsure”, “Somewhat unlikely”, or “Very unlikely” to participate in that role, otherwise a value of 1 is given for this role. This first model is estimated with all respondents that were not eliminated because of errors, inconsistencies, or other reasons discussed in Section 2.3 including not having a car available to them, again due to their assumed inability to drive in the program. Note that this first model is identical to the binary Model 4 indicating the joint choice of

“Driver = 0 AND Passenger = 0” described in Structure C. The second model of

Structure D in Figure 1 investigates what may make one likely to have an interest in both roles in the ideal ridesharing program. Specifically, the binary logit model had a dependent variable Y given a value of 1 if the respondent indicated the joint choice of

“Driver = 1 AND Passenger = 1”, and 0 if they made another joint choice. The second model was estimated only considering those who expressed some interest in the program.

Specifically, the second model’s estimation using the same subset of the sample as the

first model, but excluded those who gave the joint response “Driver = 0 AND

Passenger = 0”.The third and final model of Structure D in Figure 1, was used to investigate what characteristics influence the preference for a specific role. Specifically, the dependent variable took a value of 1 if the respondent indicated the joint choice of

“Driver = 1 AND Passenger = 0”, and a value of 0 if the respondent indicated the joint choice of “Driver = 0 AND Passenger = 1”. The third model was estimated based only on those who had expressed a preference for one role in the program and not the other.

That is, this model used the same subset of the sample as the first model, but excluded individuals who had indicated the joint response of “Driver = 0 AND Passenger = 0” and

“Driver = 1 AND Passenger = 1”.

The first two models of Structure D in Figure 1 were considered as a way to identify those who were either extremely disinterested or extremely interested in the program. One may believe that these “extreme” respondents may be described by a characteristic or combination of characteristics which makes them very likely to oppose or support ridesharing and thus may not have been as pragmatic in their consideration.

Those who identified a preference for one role over the other were considered to make a more pragmatic decision because this would be necessary to perceive the benefits of one role as more or less than that of another role.

Naturally, the results of the estimation of those not likely to participate in either role (“Driver = 0 AND Passenger = 0”) (Model 1 of Structure D) are the same as the

(interesting) estimation results of binary Model 4 of Structure C. The other two models showed some interpretable results as well, but they do not allow for the possibility that

those willing to participate in both roles may not be significantly different than those with a specific role preference. This possibility is supported logically because the presented program has few “costs”, so one could easily pragmatically decide that both roles are attractive. If this is the case, and there is little to no negative effect on the estimation of models considering preference for the role of driver or passenger from individuals who have an “extreme” positive response, then eliminating those who provided the “extreme” positive joint response of “Driver = 1 AND Passenger = 1” from models considering preference for passenger or driver may worsen the statistical fit due to reduction of sample size. To investigate this possibility Structure E was specified, and the resulting estimations were compared to the estimations of Structure D.

Structure E includes the same model of those who are not likely participants as drivers or passenger (Model 1) as structure D (Model 1) and structure C (Model 4), but eliminates the model specifically investigating those who are interested in both roles

(Model 1 in Structure C and Model 2 in Structure D). That is, the first model in Structure

E has a dependent variable Y with a value of 1 for a joint response of “Driver = 0 AND

Passenger = 0”, and 0 otherwise. Recall that, as before, a 0 is given for a particular role if the respondent answered that he or she was “Unsure”, “Somewhat unlikely”, or “Very unlikely” to participate in that role. Again, Model 1 of Structure E is estimated with all respondents that were not eliminated because of errors, inconsistencies, or other reasons discussed in Section 2.3 including not having a car available to them.

Structure E includes two additional models: one investigating what impacts one’s stated preference for participating in the ideal program as a passenger (Model 2) and the

other investigating what impacts one’s stated preference for participating in the ideal program as a driver (Model 3). Specifically, the binary logit model of passenger preference has a dependent variable Y set to 1 if the respondent indicated he or she was

“Very likely” or “Somewhat likely” to be a passenger in a ridesharing program, and 0 otherwise. This model of preference toward being a rideshare passenger is estimated using only those who did not provide the “extreme” negative joint response of

“Driver = 0 AND Passenger = 0”. However, in this case those who did not have a car available were considered because the model focuses solely on interest in the passenger role. A binary logit model was specified for driver preference based on a dependent variable Y taking a value of 1 if the respondent indicated he or she was “Very likely” or

“Somewhat likely” to be a driver, and 0 otherwise. The driver model was similarly estimated excluding the portion of the sample providing the “extreme” negative joint response of “Driver = 0 AND Passenger = 0”. However, in this case those who did not have a car available were not considered because they were assumed to be unable to perform the role of driver in the presented ridesharing program as discussed in Section

2.3.

Once Structure E was specified and estimated, the results for Model 2 and Model

3 (Model 1 of Structure D and Structure E is the same) demonstrated lower p-values for many explanatory variables, as well as improvement in the overall explanatory power of the models when compared to Model 3 of Structure D. It is also important to note that the trends apparent in the model of those interested in both roles in Structure D (Model 2:

“Driver = 1 AND Passenger = 1”) were still apparent in Model 2 and Model 3 of

Structure E. Those who had previously been identified as likely to be interested in both roles showed an increased likelihood of participation in the two separate interest in driver and passenger roles models of this structure. For example, students indicated an increased likelihood of providing the response “Driver = 1 AND Passenger = 1” in Model 2 of

Structure D and students also showed an increased likelihood of participating as a passenger in Model 2 of Structure E and an increased likelihood of participating as a driver in Model 3 of Structure E. These findings support using Structure E instead of using Structure D. Specifically, this supports the suspicion that those who provide an

“extreme” positive joint response (“Driver = 1 AND Passenger = 1”) are not characteristically different than those who prefer one role and not the other. While the models in this structure provided fairly satisfactory and interpretable results, more thorough consideration of the basis of the structure led to a new specification to more precisely identify those who were least likely to participate in a ridesharing program.

Structure F is based on the same rationale as Structures E, that is, that respondents providing “extreme” negative responses may have specific characteristics that lead to this response that differentiate them from the rest of the community. This structure more precisely defines what makes one “extreme”. In this model, these “extreme” respondents are defined not only by not likely responses to both driver and passenger roles of the program, but also refusing to accept incentives to participate in the program. Recall from

Section 2.2, that any respondent who responded negatively to both the driver and passenger roles in the program was subsequently assigned questions from Branch 1 or

Branch 3, in which he or she was offered hypothetical incentives to participate as a

passenger or driver, respectively. Each of these branch’s first question listed multiple possible incentives and asked which incentive might change the respondent’s original response of “Very unlikely”. “Somewhat Unlikely”, or “Unsure” to a “Somewhat likely” or “Very likely” response. It was determined that an individual who was initially unlikely to participate in an ideal ridesharing program as a driver or passenger and subsequently selected that no incentive would make him or her a likely participant in his or her assigned branch was truly a respondent exhibiting the “most extreme” negative response.

To investigate these “most extreme” respondents, Structure F’s first model

(Model 1) seeks to analyze what characteristics are associated with preferring to reject both roles in the ridesharing program, even when offered incentives. Specifically, the binary logit model has a dependent variable Y given a value of 1 if the respondent indicated the joint choice of “Driver = 0 AND Passenger = 0 AND Incentives = 0”, and 0 otherwise. As before, a 0 is given for a particular role if the respondent answered that they were “Unsure”, “Somewhat unlikely”, or “Very unlikely” to participate in that role.

Additionally, a value of 0 is given to the incentives indicator if the respondent indicates that “None of the above” incentives would make him or her likely to participate. Model 1 is estimated with all respondents that were not eliminated because of errors, inconsistencies, or other reasons discussed in Section 2.3 including not having a car available to them, since the response to the driver role is consider in the dependent variable. The subsequent two models of Structure F (Model 2 and Model 3) separately investigate what may affect one’s likelihood to participate as a passenger or as a driver.

That is, a binary logit model with the dependent variable Y set to 1 if the respondent

indicated he or she was “Very likely” or “Somewhat likely” to be a passenger in a ridesharing program, and 0 otherwise, was used to investigate passenger preferences.

Model 2 was estimated using only respondents who did not provide the “most extreme” negative response of rejecting both roles in the program even when offered incentives.

Specifically, all respondents that were not eliminated because of errors, inconsistencies, or other reasons discussed in Section 2.3 and who did not respond “Driver = 0 AND

Passenger = 0 AND Incentives = 0” were used to estimate the binary logit model.

However, in this case those who did not have a car available were considered because the model focuses solely on interest in the passenger role. Model 3 of Structure F is a similar model with the dependent variable Y set to 1 if the respondent indicated he or she was

“Very likely” or “Somewhat likely” to be a driver, and 0 otherwise. Similarly, the estimation of this model of driver preference excluded those who provide the “most extreme” negative response “Driver = 0 AND Passenger = 0 AND Incentives = 0”.

However, in this case those who did not have a car available were not considered due to their assumed inability to perform the role of driver in the presented ridesharing program as discussed in Section 2.3.

The estimated results of the models of Structure F have improved fit and explanatory variable coefficients with lower p-values compared to the estimation results of the models of Structure E. This structure also provided the most interpretable results.

This structure is the one that was determined to be the most appropriate to the investigation and, therefore, the models of this structure are discussed in detail in

Chapters 4 and 5. In Chapter 4 the results of Model 1 are presented and interpreted in

detail. In Chapter 5 the results of Model 2 and Model 3 are presented and discussed in detail.

Chapter 4: Modeling Unwillingness to Participate in Ridesharing

4.1 Overview

This chapter discusses the specification, results, and interpretations of the model used to investigate the preferences of those who are likely to reject a ridesharing program under ideal conditions with incentives. The model discussed in this chapter is the first model of Structure F defined in Section 3.4.

Recognizing characteristics associated with unlikely participants in a program can have useful planning and marketing implications when developing or creating a ridesharing program. Recognizing what characteristics may make one unlikely to participate can also have implications on the estimation of benefits and feasibility of such programs before organizing a program, by helping to educate estimations of possible participation.

Recall that the choice investigated in this model is the preference of respondents to reject participation in either role in the program as well as rejecting all incentives offered to them and that this model is estimated using all respondents that were not eliminated because of errors, inconsistencies, or other reasons discussed in Section 2.3.

Figure 2 depicts this model, the sample size, and number of respondents stating each preference.

Model 1 (Binary)

Dependent Variable: 1 if Not Likely Driver AND Not Likely Passenger AND None of the Above Incentives, 0 Otherwise

Sample Portion: Those with car available (3,015)

1, No to Passenger AND No to Driver 0, Otherwise AND No to (2,357) Incentives (658)

Figure 2: Model of Likelihood of Rejecting an Ideal Ridesharing Program Sample and Response Statistics

Section 4.2 of this chapter explains the specification of the model including expectations of the impact of the explanatory variables. Section 4.3 briefly outlines the estimated results of the model. Section 4.4 details the possible interpretations and implications of the estimation results.

4.2 Model Specification

All of the explanatory variables discussed in Section 3.3.2 were tested in the specification of this model to investigate the preference of individuals to reject both the passenger and driver roles in the ideal hypothetical real-time employer-based ridesharing program as well as all incentives offered to them. The only variable not tested in this

model specification process was the “No Car Available” variable. As discussed in

Section 2.3, individuals who did not have a car were assumed to be unavailable as drivers. Recall that the choice variable in this model includes consideration of the preference toward the role of driving (i.e., choice variable Y defined to take a value of 1 when “Driver = 0 AND Passenger = 0 AND Incentives = 0” as discussed in Section 3.4).

Therefore, because they were assumed to be unable to provide a valid response for the preference toward the role of driver, respondents who did not have a car were not considered in the estimation of this model. The a priori expectations for the impact of explanatory variables on the preferences toward rejecting both roles in the program and incentives (i.e., before models were estimated) are discussed in this section.

The impact of having children at home is expected to increase one’s likelihood of rejecting participation in a ridesharing program. The added responsibility of having children at home may be restricting on a parent’s travel schedule. Parents are often responsible for preparing children for a day at school or with a daytime childcare provider. This may restrict when an individual leaves the home or require him or her to chain trips to drop off children at their destination, both of which are not conducive to participating in a rideshare program. Similar constraints could exist when scheduling and planning travel from campus. Having children may also require a higher degree of flexibility in travel, with many parents desiring the ability to get home or to the children’s daytime location quickly in case of emergency. All of these factors may make those with children more reluctant to participate in a program in which they would be dependent or responsible for other individuals. These impacts may be expected to be even larger on

those who share a larger portion of these childcare responsibilities due to either

“traditional” gender roles or being the exclusive parent or adult in the household.

Similarly, those who choose to chain trips might be unlikely participants in a ridesharing program. Someone who plans tours in a way to accomplish multiple tasks likely would not want to depend on someone else for their travel or to have an added responsibility within that tour. This expectation is amplified when the purpose of the trip chaining is a nondiscretionary stop. For instance, someone who drops off a passenger

(e.g., spouse, older child) at a “nondiscretionary activity,” such as school or work, would be expected to be even less likely to participate in a ridesharing program than someone who stops for food or drink. A stop purpose, such as shopping or exercise may be assumed to be more constraining than those who stop for food or socializing, but more discretionary in scheduling than stopping to pick up or drop off a passenger.

The type of affiliation with the university one has may be expected to impact the decision to not participate in ridesharing in various ways. Affiliation may represent something about the routine nature or level of responsibility one has on campus. For instance, due to class schedules and extracurricular activities, students may have more irregular and erratic schedules than staff members or faculty members, who have more traditional and regular work schedules. Those with an erratic schedule may find the dynamic nature of real-time ridesharing especially appealing because travel can be scheduled at the time it is needed rather than agreeing to consistent participation as is the case in a traditional pre-arranged ridesharing service. Students may also be expected to be more flexible in their travel scheduling because their role in the campus community is

more “voluntary” in nature than that of a staff or faculty member who is reliant on the university for employment and thus his or her livelihood. Students may also be more financially constrained than other affiliations because many students, especially undergraduate students, may not have a consistent or substantial source of income. All of these factors may support the expectation that students would be more likely participants in a ridesharing program due to the potential cost saving benefits, their potential ability to be more flexible in travel scheduling, and a likely erratic schedule leading to an expanded appeal of a real-time program.

Days on campus might reflect the regularity of travel. Those who travel regularly may have more responsibilities on campus and have a more routine travel schedule.

Those who have more responsibilities on campus may be less willing than those with fewer responsibilities to rely on others for their travel or to take on the additional responsibility for another’s travel. In addition, one who follows a routine may exhibit

“inertia” toward changing his or her pattern.

Generational differences may also be expected to affect one’s initial level of interest in a real-time ridesharing program. Younger people are generally more comfortable with the use of technology, which is necessary to participate in a real-time program, such as the one described in the survey. Younger travelers have also been observed to have lower rates of car ownership than those of older travelers (Dutzik et al.,

2014). This could be explained by an aversion to single occupancy vehicle travel or by a lack of sufficient funds to support the purchase of a private automobile. These considerations could support the expectation that, on the one hand, younger individuals

are more likely real-time ridesharing participants. Older travelers, on the other hand, are more likely to be more financially stable, which may make them less incentivized by a program that may save them money.

Similarly, years on campus may also imply financial stability. Increased years on campus might imply that the individual has been traveling to campus longer and is thus potentially more habitual in his or her travel behavior. One who has a well-established routine may experience more “inertia” when considering alternative travel options.

Therefore, people with the most experience traveling to campus may have an increased likelihood of rejecting the presented ridesharing program, regardless of how attractive the program may be.

Previous mode choice is expected to be an influential factor on the consideration of a ridesharing program. Travelers who drive to campus alone participate in a very flexible mode of travel in a society and community designed primarily for travel of this nature. Therefore, these travelers may be content in their travel and thus unlikely participants in a ridesharing program. Those who already share a car as a mode of travel have had a similar experience to that of the ridesharing program investigated in the survey, so they may be expected to be unlikely to reject such a program outright.

Bicyclists and walkers may be expected to reject a ridesharing program for multiple reasons. Firstly, the revealed behavior associated with having already chosen a non- motorized mode may be associated with underlying preferences away from non- motorized modes. Also, if an individual lives close enough to campus to walk or bike, they likely do not have a long commute, so they may be more content with their current

travel choices. Also, those living close enough to campus are much more likely to have attractive access to other alternative modes of transportation to campus, such as campus or city transit services. Already having multiple attractive travel options could reduce the potential attractiveness of a new ridesharing option. Transit users are potentially interesting because, on the one hand, ridesharing may provide a way to travel to campus that is less onerous than automobile travel if they do not like the costs or externalities associated with single occupancy vehicles. Transit users are also used to operating on a travel schedule set by someone else, so they may have a higher likelihood of accepting a program that involves relying on others. On the other hand, transit users may still be opposed to traveling in a private automobile due to its negative externalities if they deem it unnecessary or if they are already well-served by a transit service.

Exclusivity of travel mode may imply an added level of contentedness with the current mode of travel. For example, exclusive use of a single occupancy vehicle may show an exaggerated sense of the benefits of driving alone, as detailed above. Therefore, one who travels in an exclusive way may be more likely to reject the presented ridesharing program, even in the incentivized ideal case.

Travel time already incurred may be directly related to the time one is willing to spend traveling to or from campus. Those who travel for longer periods of time may have more to gain from a new travel option that could reduce travel costs than those who already have short travel times. Moreover, those with longer trips will likely incur proportionally lower additional costs (in time or otherwise) by engaging in ridesharing than those with shorter trips. Therefore, those with a shorter travel time may be expected

to reject such a program because the potential individual cost and time savings are negligible due to their already short trips.

The expected impact of gender is unclear because of conflicting possibilities regarding gender impacts on transportation decisions in general and how gender may impact perceptions of ridesharing. There is an existing “conventional wisdom” within the transportation community supported by empirical evidence through numerous mode choice estimation results that females are more likely to use public transportation modes.

Although ridesharing is not a form of public transportation, due to its increased occupancy it can be associated with some of the same societal benefits as public transportation, such as congestion mitigation, reduced energy consumption, and reduced emissions. However, one may expect that females would be disinclined to be ridesharing participants due to a heightened sense of the negative perception of “stranger danger” that has been associated with real-time ridesharing as discussed in Section 1.1 (Amey et al.,

2011). Both of these conflicting effects may apply here, leaving the expected net impact of gender unclear.

4.3 Model Results

Several specifications were examined to investigate the association of explanatory variables discussed previously with one’s preference toward rejecting a ridesharing program outright. After examining the results, variables whose coefficients were of particularly low statistical significance (i.e., had very high p-values) were removed, and new specifications were estimated. This process was repeated until a final estimation was

selected based on a combination of reasonable statistical significance and interpretability.

The final estimation results of the model used to describe preferences toward rejecting a ridesharing program outright is presented in Table 4. The resulting model has a very good fit, having a p-value less than 2.22e-16 (the lowest possible value the mlogit program in R reports). This value indicates that it is highly unlikely that a model of this specification has no more predictive power than one based solely on the response proportions. All the remaining explanatory variables have coefficients that are significant to a confidence level of at least 82% (i.e., p-value ≤ 0.18), and most variables’ coefficients are statistically significant at levels exceeding 90% (i.e., p-value ≤ 0.10).

Any variable not presented in the table had substantially lower statistical significance in previous iterations of specification estimations. As evidenced by their absence from the table, the gender and travel time variables were determined not to be significant factors associated with unwillingness to participate in the ideal ridesharing program.

Recall that the dependent variable in this model is a binary indicator taking a value of 1 for those who responded that they were not likely to participate in an ideal ridesharing program as either a passenger or a driver even when incentives were offered to them and a value of 0 for those who were likely participants in a ridesharing program as a driver and/or a passenger under the original ideal program with or without the offered incentives. Therefore, positive coefficient estimates indicate an increased likelihood of providing this extremely disinterested response, and a negative value indicates a reduced likelihood of holding this preference. The first eight explanatory

variables within the estimated model (after the constant) represent characteristics of the respondents’ current travel behavior. This supports the notion that certain aspects of one’s past behavior are strongly associated with the individual’s stated willingness to participate in the presented real-time employer-based ridesharing program.

Table 4: Summary of Estimation Results of Model of Likelihood of Rejecting an Ideal Ridesharing Program

Model Statistics Statistic Value Sample Size 3015 Log-Likelihood -1505.3 Likelihood Ratio Test (χ2) 153.22 P-value < 2.22e-16 Coefficient Estimation and Statistics Explanatory Variable (Xi) Coefficient (βi) P-value Constant -1.3245587 2.42E-05 Days on Campus (continuous) 0.0644772 0.088782 Drove Alone -1.241196 8.88E-16 Exclusively Drove Alone 0.1891946 0.179582 Shared Car as Driver -0.3039363 0.046243 Shared Car as Passenger -0.1924972 0.178915 Transit User 0.305388 0.095071 Walk 0.2587375 0.080765 Picked-Up or Dropped-Off a Passenger 0.3287139 0.042245 Faculty or Civil Service Staff 0.2574188 0.011729 Years on Campus ≥ 15 0.2755686 0.034798 Age (continuous) 0.013133 0.003269 Age 25-40 and Lives with Children 0.3144439 0.03349

Specifying the “Days on Campus” variable as a set of individual binary variables

(i.e., Xi = 1 if days on campus ≤ 1, 0 otherwise; Xi +1 = 1 if 1 < days on campus ≤ 3, 0

otherwise; Xi +2 = 1 if 3 < days on campus ≤ 5, 0 otherwise; Xi +3 = 1 if 5 < days on campus ≤ 7, 0 otherwise) revealed a seemingly monotonic trend with successively increasing coefficient estimates, so the continuous variable specification was selected where the numerical value of a category was set to the midpoint of the range defining that category (see Section 3.3.2). The coefficient corresponding to the “Days on Campus” variable is positive and highly significant (p-value = 0.09). This result indicates that the more time one spends on campus, the more likely he or she is to completely reject even the ideal ridesharing program.

The next several travel related variables indicate something about the respondent’s choice of mode for travel to and from campus. Recall that these variables are specified on a “select all that apply” basis and are thus not mutually exclusive.

Therefore, it is not necessary to leave a particular mode out of the specification in order to have a reference to compare the other modes to. Instead, each variable can be interpreted to represent the effect that using that mode has on an individual’s willingness to participate in the program compared to those who never use that mode, all other things being equal.

Likely because of such a small number of respondents selecting the motorbike mode among modes they use, estimated coefficients of the motorbike related variables

(i.e., use a motorbike, share a motorbike as a driver, and share a motorbike as a passenger), have little statistical significance when included as explanatory variables in the considered models. Also, those who bicycle, whether among using other modes or

exclusively, do not prove to be statistically significantly different than those who do not bicycle based on the high p-value of the estimated coefficient of this mode.

Contrary to initial expectation, the “Drove Alone” variable, representing whether an individual had driven a single occupancy vehicle to campus at any time during the semester in which he or she responded to the survey, is negative and significantly different than zero with a confidence level of over 99% (p-value = 8.88e-16). Therefore, all other things equal, if a respondent drives alone to campus they are less likely to state that they are likely to reject the ideal program than someone who never drives alone.

Those who exclusively take this mode, represented by the “Exclusively Drove

Alone” variable, are more likely to reject the ideal program than those who drove alone at some point in the semester considered, but also used another mode during the semester.

The positive coefficient, even though having one of the highest p-values (0.18), may indicate that those who use this mode exclusively have different preferences toward ridesharing than those who use the mode along with other modes.

Travelers who share a car as a driver (p-value = 0.05) or a passenger

(p-value = 0.18) are both estimated to have a lower probability of being unlikely participants, as indicated by the negative coefficients shown in Table 4. Note that the statistical significance of the coefficient of the shared car passenger mode is weak, but due to the expected and interpretable effect on ridesharing preferences, this variable is included in the final model specification. The “Transit User” variable’s estimated positive coefficient implies that one who uses transit is more likely to reject the program than others (p-value = 0.095). Those who walk are also estimated to be more likely to be in

the “extreme negative” response group as indicated by the positive coefficient

(p-value = 0.08).

Considering the remaining travel behavior variables, the result of the estimation shows a positive sign for the coefficient associated with those with a stop to pick up or drop off a passenger, indicating that these travelers are more likely to reject an ideal ridesharing program, even when offered incentives, with a confidence level of 96%

(p-value = 0.04). The existence of any stop was tested (i.e., stopping for any reason while traveling to or from campus), as well as the other stopping purposes (i.e., socializing, food or beverage, shopping, exercising, dropping off or picking up a passenger, or other), but the model only considering stops for the purpose of picking-up or dropping-off a passenger is the most meaningful, and the estimated coefficient’s corresponding p-value is very low.

The last four variables in Table 4 relate to individual characteristic. As mentioned in the explanation of the definition of variables in Section 3.3.2, it was observed that being affiliated as a faculty member or a civil service staff member often resulted in similar estimated coefficients. Therefore, the combined variable is used. The corresponding positive coefficient, significant at the 99% level (p-value = 0.01), indicates that individuals with these affiliations are more likely to reject the ideal ridesharing program presented in the survey. Although these affiliations seem to have a similar impact on preference, the reasons behind this result do not have to be the same for the two combined affiliation categories as discussed in the interpretation section subsequently.

Those who had been on campus for the longest period of time (over 15 years) prove to be the only statistically significant group when considering years on campus.

Respondents in this category are more likely to reject the ideal ridesharing program as is indicated by a statistically significant (p-value = 0.03) positive coefficient.

The last two variables in Table 4 indicate something about the age and living situation of the respondent. As explained in Section 3.3.2 while defining variables, age was first tested in a categorical manner. The estimated coefficients were observed to be successively increasing in magnitude from youngest to oldest. As a result, a continuous variable using midpoints of the age categories was specified. This variable proved to have an estimated coefficient that is statistically significant at a confidence level of over 99 %

(p-value = 0.003). The positive coefficient indicates that increasing age results in an increasing likelihood of reacting negatively to the ridesharing program.

Throughout the iterative process used to arrive at the presented model estimation results, the age variable specified for those who were between 25 and 40 who have children proves to be the most meaningful manner to capture the effects of having children in the home. The statistical significance of over 95% confidence level

(p-value = 0.03) of the coefficient associated with this variable indicates that respondents within this age group who live with children are significantly different than those in this age group who do not live with children. The positive estimated coefficient associated with having children at home for this age group implies that having this combined characteristic leads to an even more likely rejection of the program than those of the same age cohort but who do not live with children.

4.4 Model Interpretation

What follows are the interpretations of the estimation results presented in the previous section. The variable representing how many days one spent on campus is used as a proxy for a regular schedule, with those with more frequent travel to and from campus assumed to have a more regular schedule than those who travel to and from campus infrequently. A priori expectations are supported by the results. That is, those with a regular schedule are more likely to reject the ridesharing program than those with irregular schedules. This result suggests that frequent, regular, and likely habitual travel leads one to be unlikely to seriously consider alternative means of travel. Those who make frequent trips to campus also have more responsibilities while on campus and thus may be unwilling to rely or wait on others to travel.

One’s current mode or modes of choice for travel to and from campus seems to be a very influential factor on his or her considerations of the new ridesharing travel option being offered. Some of the results support expected behavior. Others seem to imply that factors previously not considered may be important. For instance, it was expected that individuals who drove alone would prefer not to participate in a ridesharing program due to the reduced flexibility associated with the program. On the contrary, the results imply that those who drove alone are less likely to reject the program than users of other modes.

It appears that anyone who drives a car to campus (alone or with a passenger) is less likely to reject the program compared to those who never drive. Also, even though the statistical significance of the corresponding coefficient is relatively weak, it seems that those who arrived to campus in a car as a passenger are also unlikely to reject the

program. It is also interesting that those using alternative modes such as transit or walking tend to be more likely than those who do not use these modes to reject the program. All of these trends taken together may indicate that ridesharing might not be as large of a change for someone who already travels to campus in a car than for those who are taking alternative transportation. In addition, due to the urban, campus setting and premium paid for parking, the fact that those traveling in cars are unlikely to reject a potentially cost saving alternative travel option may be evidence of some dissatisfaction with the experience of driving and parking in this environment. The urban campus environment is one in which auto travelers experience appreciable congestion during peak periods. The parking facilities on campus are operated such that, if one does not possess the most exclusive parking pass, one often must drive around multiple nearly full parking lots to find parking close to their ultimate destination during peak periods, and one pays a premium to park in these lots. Since it appears people who take a car are less likely than others to reject a new ridesharing alternative, this result could be evidence that traveling in an environment in which one has a relatively high cost of traveling by automobile can influence one’s preferences when it comes to considering alternative travel options.

Walking seems to have the expected effect on ridesharing preferences. As mentioned previously, those who walk to campus are more likely to be in the group who provided an extremely negative response to ridesharing. This is expected if one assumes that the use of a non-motorized mode indicates that one is motivated by health or environmental considerations when it comes to travel choice. Walking is likely related to

one’s proximity to campus, with walkers tending to live closer to campus than non- walkers. Proximity to campus may imply a fairly easy commute no matter the mode. In addition, bus service to and from campus tends to be more frequent and reliable closer to campus than farther from campus. Therefore, living close to campus would tend to be associated with a larger choice set for modes of travel to campus. If one already has multiple relatively attractive ways to travel to campus available, he or she may be less likely than others to consider new alternatives. The estimation results indicate that transit users seem to be similarly affected by an attractive existing choice set. Those who are well served by transit for their trip to campus are also likely to live relatively closer to campus, and thus have multiple convenient means to travel. For example, for one living on the East Residential campus bus line, traveling by transit is exceptionally convenient because one can be picked up very close to his or her origin and dropped off near his or her destination by a free transit service.

From a societal perspective, the results indicating that transit users and those who walk are more likely to reject the presented ridesharing program and those who already take a car are less likely to reject the program are encouraging. It is desirable for ridesharing to reduce auto traffic and not simply take market share from non-auto modes in order to have an impact on reducing congestion and mitigate the negative externalities of motorized travel.

Those who exclusively drive alone exhibit an increased likelihood to reject ridesharing compared to those who drive alone and use other modes, even though the coefficients statistical significance is weak. This may capture the effects of what is often

referred to as “modal inertia”. Even though the costs of driving may be perceived as high in this environment, those who make the choice to exclusively use a single occupancy vehicle may have a stronger force of habit than those who vary their travel modes. In this particular model, this trend was pronounced more within the group of those who exclusively drive to campus than those who use any other single mode exclusively. This distinction may be indicative of the notion discussed in the specification that even in an urban, campus environment where auto travel costs are perceived as relatively high, the infrastructure is still mostly designed with this mode in mind and, therefore, it is still an attractive alternative.

Whether an individual stops to pick-up or drop-off a passenger proves to have an impact on preferences in an expected manner. Those who pick-up of drop-off passengers tend to be more likely to reject the program than others who do not stop or who stop for other reasons. Since a stop to pick-up or drop-off a passenger is likely nondiscretionary, a respondent who needs to pick-up or drop-off a passenger would tend to be more likely than those who do not stop or make discretionary stops to reject a ridesharing program, even when incentives are offered. Upon further consideration, this might appear contradictory to the trend of those who share a car as a driver being willing to participate, but there is a seemingly reasonable interpretation. One who has a normal routine of picking up or dropping someone off, especially a family member, such as a child at school or a spouse at a nearby workplace, may not be willing to participate in a ridesharing program because he or she may expect the program to interfere with his or her ability to carry out this role. Contrast that with someone who occasionally gives a

friend a ride who normally walks, but, for example, needs to carry a large package or does not wish to walk in the rain. The latter individual with the more flexible schedule may be more willing to participate in ridesharing because it does not interfere with a normal responsibility. Moreover, of the two individuals described, the former is the one who is more likely to have picked-up or dropped-off someone on the specific day about which the survey questionnaire asked specific travel choice questions, and thus have the binary indication of a stop (recall the questions about the modes used related to use of that mode anytime during the semester which the survey was administered).

Those affiliated as faculty or civil service staff also prove more likely to be in the group of individuals who expressed clear disinterest in the presented ridesharing program. Although these two affiliations seem to have a similar impact and were grouped into a single variable, the interpretation of this occurrence is partly common to the two groups and partly differs for each group. Both of these groups rely on the university for employment and thus are under more pressure to arrive on campus to perform their assigned duties. They are also both likely to have more regular schedules than those of students, which may negatively affect the appeal of a real-time ridesharing program.

These differences may explain why these types of affiliates differ from students, but neither of these explanations give an indication as to why these individuals would be more likely than other staff members to reject the program. This is where different interpretations for each group become helpful. On the one hand, faculty members, by nature of the parking policies on campus, have the most convenient parking locations.

This benefit may lead them to be more content with driving than students and staff

members who have a harder time finding parking on campus and pay almost as much for their parking passes. If faculty members are more content with their travel experience to and from campus than other staff or students, as the situation suggests, they are expected to exhibit an increased likelihood to decline participation in a ridesharing program. Civil service staff members are not in a similar situation regarding the level of contentedness with their travel experience, as they compete for the same parking with other staff members (a much larger group than faculty members) in parking lots further from their ultimate destinations. Regarding the possible effect of their specific situation, civil service staff members on campus are likely to have some of the most highly regimented schedules. Therefore their arrival time and departure time are likely more inflexible and thus they may be less willing to rely or wait for others for their travel to meet this schedule.

The only indication of how long one has been on campus that proves to have a statistically significant coefficient is that of the more than 15 years category. The corresponding coefficient implies an increased probability of rejecting the ideal ridesharing program. This variable represents the largest amount of experience with traveling to campus of anyone to take the survey. One who has been traveling to campus for such a long time could be expected to have the most habitual behavior among travelers to campus, and thus may be disinterested in ridesharing because they are disinterested in changing their travel behavior in general. The years of experience may also indicate a certain level of compensation earned from the institution, and thus these individuals may be less fiscally constrained than others. This situation may lead them to

view the marginal cost savings of ridesharing or the offered incentives as not sufficiently appreciable to cause them to change their travel mode. This trend may also indicate something about generational differences explored further in the interpretation of the age variable next.

The age of the respondent impacted preferences in the expected manner described previously in the specification section. The older one indicated that he or she was, the more likely he or she is to reject the ideal ridesharing program, even when offered incentives to participate. This could be due to the more prevalent acceptance of younger people of on-demand transportation services or an increased comfort with the technology likely involved. Older individuals may also have more responsibilities to attend to and thus not want to deal with the potential added inconvenience caused by ridesharing. Older individuals might also be less willing to change than younger travelers as a simple generational difference. This trend seems to be refined further by the subsequently discussed models regarding interest in the passenger and driver roles discussed in

Chapter 5.

In this model specification individuals between the ages of 25 and 40 who had children are shown to be significantly different than those who fall in the same age category without children, or those with children, but who fall in a different age category.

Those between 25 and 40 with children are more likely to reject the presented ridesharing program. Upon consideration, there is a convincing explanation for this phenomenon.

There is some correlation between an individual’s age and the age of his or her children.

Individuals in the 25 to 40 age group are likely to have younger children than those who

are over 40 and have children. Also, those who are less than 25 years old are less likely to have children. Considering the constraints that having children may impose on one’s responsibilities at home or the need to be able to respond to a child’s emergency quickly, these responsibilities are much larger and more constraining when the children are young and therefore dependent than when they are older and can potentially take care of themselves more independently. As a result, it is expected that those with young children are more likely than those with older children, or those with no children at all, to reject the extra responsibility of taking someone else to campus or the added constraint of needing to rely on someone else for their travel that is associated with real-time ridesharing.

As discussed previously, there may be two potentially conflicting factors impacting how females perceive a real-time employer-based ridesharing program. The fact that the gender variable does not prove to exhibit a significant coefficient could be interpreted as both of these opposite effects being in place and having roughly equal impact on the attractiveness of ridesharing. If some females prefer ridesharing because of its public transport like qualities, but others fear it due to “stranger danger”, despite the fact that the program is an employer-based one, the conflicting trends could make a net prevailing impact absent.

Travel time also does not prove to have the expected trend. Two specifications were tested to investigate the possible effects of travel time on the preference to reject both roles (passenger and driver) in an ideal ridesharing program even when incentives were offered. One specification included a set of categorical travel time variables (i.e.,

Xi = 1 if travel time ≤ 5 minutes, 0 otherwise; Xi+1 = 1 if 5 minutes < travel time ≤ 10 minutes, 0 otherwise; etc.) and another consisted of a set of categorical travel time variables conditional on using motorized transportation (i.e., Xi = 1 if travel time < 10 minutes AND did not walk or bicycle, 0 otherwise; Xi+1 = 1 if 10 minutes ≤ travel time ≤

19 minutes AND did not walk or bicycle, 0 otherwise ; etc.). The corresponding coefficients for variables in each of these specifications do not prove significant in any of the estimated models. This result may imply that the added benefit derived from participating in a ridesharing program for those with longer trips, compared to the lower benefits for travelers with shorter trips, may not be an overwhelming factor in determining who may reject a ridesharing program. This interpretation does not necessarily suggest that the added benefit of ridesharing is not perceived. Rather, some travelers with long trips may have other constraints that prevent them from participating, or some travelers with shorter trips may see enough benefit in the ideal program that they would not reject it.

After exploring all of the trends discussed above, the key notable finding is that the provision of ridesharing attracts those who already use cars to ridesharing rather than those who use alternative modes, such as walking or taking transit, who are more likely to reject the program. These results are promising for ridesharing because to maximize the potential congestion mitigation benefits of ridesharing, it is desirable to reduce the amount of travel in single occupancy vehicles or other private autos with substantially underutilized passenger capacity.

It is also noteworthy that several of the findings discussed previously relate to one another and point to broader trends. Many individuals who have a highly constrained schedule or larger responsibility burdens, such as those with younger children at home, tended to be unwilling participants in the presented ridesharing program. This trend was also present among those who travel to campus frequently, those who were responsible for picking up or dropping off a passenger, and those with civil service jobs that likely have a strict hourly schedule. These individuals were found to be more likely than others to reject the ideal ridesharing program. Individuals who likely partake in habitual behavior or are content in their travel choices, such as those who have been traveling to campus for many years, also tend to be unlikely to change their behavior for a program such as this one. This trend is consistent with trends of individuals who travel to campus frequently, those who exclusively drive alone, faculty members who are likely content with their parking situation, and those who walk and are likely have a robust choice set of travel options. These individuals tend to be more likely to reject the ideal ridesharing program.

Chapter 5: Modeling Preferences for Passenger and Driver Ridesharing Roles

5.1 Overview

This chapter discusses the specification, results, and interpretations of two models used to investigate the preferences of interested individuals toward being a ridesharing passenger and the preferences of interested individuals toward being a ridesharing driver, respectively. The models discussed in this chapter are the second and third models of

Structure F defined in Section 3.4.

Identifying the characteristics of those who would be willing to be drivers and those who would be willing to be passengers could be useful in assessing the feasibility and planning for implementation of a ridesharing program. A large imbalance in preference between the driver and passenger roles could indicate potential feasibility issues. For instance, if too many respondents want to be passengers and too few want to drive, there could be excess passenger demand and limited driver capacity. The models presented in this section aim to help understand the characteristics of people who prefer each role. In addition to helping assess driver and passenger imbalances, these models could possibly be used to target marketing for a ridesharing program to individuals who are likely to participate in a certain role if an existing or planned program is lacking or has excess capacity.

Two separate models were used to investigate respondent preferences for role in a ridesharing program. One model was used to investigate the preference of individuals toward the driver role. The other model was used to investigate the preference of individuals toward the passenger role. The structure of both models (established in

Structure F of Section 3.4) includes a dependent binary variable indicating the likelihood of participation in the role in the ideal program without incentives, and each model is estimated from the set of responses corresponding to individuals who responded that they were “Very likely” or “Somewhat likely” to be a passenger, driver, or both, either when confronted with the original ideal program or when presented with incentives. Figure 3 depicts the structure of the two models summarizing sample sizes and response proportions. Recall that those who did not have a car available to them during the semester of the survey are included in the passenger model, but not in the driver model.

This is why the passenger model has a larger sample size than the driver model.

Model 2 (Binary)

Dependent Variable: 1 if Likely Passenger, 0 Otherwise

Sample Portion: Those who did not respond “Not Likely Driver AND Not Likely Passenger AND None of the Above Incentives” (2,471)

1, Likely Passenger 0, Otherwise (932) (1,539)

Model 3 (Binary)

Dependent Variable: 1 if Likely Driver, 0 Otherwise

Sample Portion: Those who did not respond “Not Likely Driver AND Not Likely Passenger AND None of the Above Incentives” and had a car available (2,349)

1, Likely Driver 0, Otherwise (797) (1,552)

Figure 3: Models of Likelihood of being a Passenger and Likelihood of being a Driver Sample and Response Statistics

Section 5.2 explains the specification of the passenger model including expectations of the impact of the explanatory variables. Section 5.3 briefly outlines the

estimated results of the passenger model. Section 5.4 details the possible interpretations and implications of the estimation results of the passenger model. Section 5.5 explains the specification of the driver model including expectations of the impact of the explanatory variables. Section 5.6 briefly outlines the estimated results of the driver model. Section 5.7 details the possible interpretations and implications of the estimation results of the driver model.

5.2 Passenger Model Specification

All of the explanatory variables discussed in Section 3.3.2 were investigated to identify factors associated with the preferences of those who responded “Very likely” or

“Somewhat likely” to being a passenger in the ridesharing program as originally presented. Recall from the discussion of structure F in Section 3.4, those who expressed that they were unlikely to participate and rejected all incentives in their assigned branch were considered “extreme” in their preference and, therefore, they are not included in the sample used to estimate the preference to participate as a passenger model. That is, this model seeks to identify preference for the passenger role among those who are in the potential pool of those interested in either passenger or driver roles of the ridesharing program.

It is important to note that the dependent variable in this model (and the model investigating driver preferences presented in Sections 5.5 to 5.7) is based on the respondents’ likelihoods of participating in the program without incentives. That is, the dependent binary variable is given a value of 1 if the individual indicated that he or she

was “Very likely” or “Somewhat likely” to participate in the program as a passenger in the ideal ridesharing program without any incentives and 0 if not. Therefore, there are some individuals in the sample used for estimating the passenger role preference model

(and similarly in the driver model) who could have a 0 value in each of the dependent binary variables of role, indicating that this individual is neither a likely passenger nor a likely driver. These individuals are included in the estimation of the passenger (and driver) model because when offered incentives they indicated that they would be likely participants in one of the two roles, and thus do not have a characteristic or constraint that renders a ridesharing program extremely unattractive.

All of the expectations of general interest in a ridesharing program stated in

Section 4.2 could apply here. That is, characteristics associated with the preference of rejecting both roles in the program even when incentivized could possibly, but not necessarily, prove to have a negative preference toward the passenger (and the driver) role in the passenger (and driver) model. This section describes some expectations for the impact of variables specifically as they apply to the preference for the role of passenger based on a priori knowledge and based on the results of the model of those who reject the ridesharing program presented and discussed in Chapter 4. As previously stated, all of the a priori expectations for general interest in Section 4.2 may still apply and some of these expectations thought to be pertinent to the passenger model are presented in this section.

Similarly to the expectations expressed when discussing the model of those who were likely to respond negatively to participating in the ridesharing program, even after being offered incentives, the expectations for gender’s impact on the preference for being

a rideshare passenger are mixed. If interested females are more likely than males to be public transport users and supporters, they may be more likely to be a passenger in the ridesharing program, not only due to the benefits of higher occupancy that ridesharing offers, but also because they are used to relying on others for transportation as public transport users. If, however, the employer-based program does not mitigate the perception of “stranger danger”, females may be less likely to be ridesharing passengers in a stranger’s vehicle. As a result of the opposing effects, net expectations are again unclear regarding how gender will impact preferences toward the passenger role in a ridesharing program. Gender did not reveal an association with the preference toward the overall program discussed in presenting the results of that model in Chapter 4. A similar absence of association may materialize in the case of preference for the passenger role.

Individuals with young children were identified as unlikely participants in the program model presented in Chapter 4. When considering only those who were considered likely to participate, the presence of children in the home is still expected to have an impact on preference of role. If responsibilities at home were not enough to completely deter interest in the program for those living with children, it may at least deter their interest in being a passenger. Passengers in a ridesharing program are more dependent on others than are drivers, who still have most of the flexibility associated with driving alone. A parent living with children may see the more dependent passenger role as unappealing if he or she is responsible for dropping-off, picking-up, or responding to unexpected daytime needs of his or her children. Therefore, one might expect those who live with children to be unlikely passengers.

Affiliation is again expected to capture something about regularity and flexibility in schedule, as well as a different level of responsibility. Students with an expected higher level of flexibility in trip scheduling, less responsibility on campus, and potentially a more limiting financial situation, may be expected to be more likely to tolerate the dependence of being a passenger in the program for the benefits it offers.

Age could show the effects of generational and professional differences between the old and young. If many older individuals were uncomfortable with some aspect of the program and were thus more likely than younger individuals to be in the group of those

“highly unlikely” to participate in either role, there might be few older individuals remaining in the sample of potential participants used to estimate this model. If this is the case, age may prove to have an undiscernible effect on preference toward being a passenger in the hypothetical ridesharing program. However, if age indicates something about an individual’s stage in life, level of responsibility on campus, and financial stability, one might expect younger individuals to be more likely passengers than older individuals, who might be deterred by the required dependence.

Similar to age, those who have been on campus for longer time and, thus, have more experience are expected to be more advanced in their career and less likely to take a dependent role in their travel. Therefore, it is expected that the passenger role would be less attractive to these potentially habitual travelers because the potential additional cost savings offered to passengers may not be enough to make up for the onerous perception of relying on someone else for travel.

One’s choice of mode or modes of travel to and from campus is expected to reveal a willingness, or lack thereof to be a dependent traveler. Those who take the most control of their travel—those who drove alone, shared a car as a driver, used a bicycle, or walked—may be less likely to give up that control than those who already take a dependent role. Therefore, those who take a dependent role in current travel, namely, transit users or passengers in a shared car, may be more likely passengers in the program.

Those who use a mode exclusively have a strong habit, strong preference, or restrictive mode choice set. Any of these factors would likely make these individuals less likely passengers. This may be even more pronounced among those who have exclusively driven alone. These individuals are used to having control of their travel, and the modal inertia might be exceptionally hard to overcome to get an exclusive driver to play the role of passenger.

The duration of trip did not appear to be a factor in the model describing those who were highly unlikely to participate in the ridesharing program. However, one might still expect those with longer trips to have more to benefit from joining a ridesharing program than those with shorter trips. Their longer travel time implies larger travel costs, and a ridesharing program would allow for the reduction of some of those costs. They also are already used to long periods of time spent traveling, so any additional delay incurred due to dependency on others would have a proportionally lower relative effect on their travel time. This may lead those with long travel times to be more likely passengers (as well as drivers) in the program than those who have relatively short trips.

One who stops during a trip to accomplish another task could be expected to be an unlikely rideshare passenger. As a passenger, a rideshare participant would lose control of the trip itinerary and therefore would likely have a much harder time scheduling other activities in a to and from campus tour. This effect might be expected to be even more impactful on someone who makes a non-discretionary stop than on someone who makes stops for discretionary purposes, such as food or socializing. Those who stop are expected to be less likely to respond positively to being passengers, and this impact might be intensified for those who stop for less discretionary activities.

Those who spend many days on campus, reflecting frequent travel, may be expected to have a regular schedule. Those with a regular and frequent travel schedule could be expected to be more habitual and could be expected to have a stricter schedule that they wish to maintain. If one is a habitual traveler or has a strict schedule to maintain, one may expect the required dependency of the role of passenger in a ridesharing program to be particularly unappealing because relying on others may be perceived as unreliable, and unreliable travel could disrupt the comfort of the routine and ability to fulfill responsibilities in a timely manner.

One who does not have a car available to him or her may be one of the most likely to be a passenger in a ridesharing program. Not having a car available eliminates the drive alone option from the choice set. One without a car must either rely on others for transportation or utilize non-motorized transportation. In an auto-centric environment, this reduction of choice-set can severely restrict mobility. A ridesharing program may

give these individuals an appealing alternative way of traveling to and from campus.

These individuals are expected to be likely passengers.

5.3 Passenger Model Results

Several specifications were examined to investigate how the explanatory variables discussed in the previous section are associated with one’s preference toward being a ridesharing passenger. After examining the results, variables with coefficients of particularly low statistical significance were removed, and new specifications were estimated. This process was repeated until a final estimation was selected based on a combination of reasonable statistical significance and interpretability. The estimation results of the final specification of the model that describes preferences for being a passenger in the presented ridesharing program are summarized in Table 5. Variables not present in the table are considered to not have an identifiable significant impact on the preferences of respondents.

Recall that the dependent variable in this model is a binary indicator representing whether one is a likely participant in the ideal ridesharing program as a passenger without incentives and that this model is only estimated using responses of those who did not reject being both a passenger and a driver, even when incentives were offered. The dependent variable is specified so that positive coefficients are associated with an increased likelihood to accept being a passenger, and, conversely, negative coefficients represent a lower likelihood of participation as a passenger.

The final model specification estimation results exhibit strong overall fit, as indicated by the p-value associated with the likelihood ratio test. Specifically, the value is less than the minimum value reported in the software used (p-value < 2.2x10-16). The variables presented in the table are all significantly different from zero at a level of confidence of at least nearly 65% (p-value = 0.35). Most variables are significant at a confidence level of over 85% (p-value < 0.15). Note that some variables with coefficients of higher p-values are included because the signs are sensible and interpretable, and inclusion of those variables is useful for comparison to the other estimated models.

Table 5: Summary of Estimation Results of Model of Likelihood of being a Passenger

Model Statistics Statistic Value Sample Size 2471 Log-Likelihood -1573.7 Likelihood Ratio Test (χ2) 127.55 P-value < 2.22e-16 Coefficient Estimation and Statistics Explanatory Variable (Xi) Coefficient (βi) P-value Constant -0.3804514 0.209403 Female 0.1380267 0.12241 Days on Campus (continuous) -0.0525357 0.128916 No Car Available 0.5472785 0.021442 Drove Alone -0.4778967 0.004663 Shared Car as a Driver -0.213116 0.096766 Shared Car as a Passenger 0.1470794 0.2479 Transit User 0.438354 0.007722 Picked-Up or Dropped-Off a Passenger -0.2725617 0.113203 Travel Time to Campus (continuous) 0.0121685 4.47E-05 Exclusive Mode User -0.3399849 0.003362 Faculty or Civil Service Staff -0.0937472 0.351052 Years on Campus ≥ 15 -0.4111689 0.002979 Age (continuous) 0.0100105 0.014811 Age 25-40 and Lives with Children -0.2232619 0.122502

The first variable shown in the Table 5 is the gender variable. The p-value of 0.12 for its estimated coefficients is among the higher values, and the positive sign associated with the coefficient indicates that females are more likely than males to be willing to participate as passengers in the ridesharing program.

As in the previous model describing overall interest in the program, a large number of variables capturing current travel behavior prove to have statistically significant coefficients, indicating a clear association with the preferences of survey respondents. The number of days normally spent on campus is estimated to have a 86

negative impact on respondent preference, with a p-value for its coefficient of 0.13.

While having one of the higher p-values, the trend seems to indicate those who travel to and from campus more frequently are less likely to be passengers than those who travel less frequently.

The “No Car” variable’s corresponding coefficient has a positive sign with a low p-value (0.02). This result indicates that an individual with no car available is more likely to be a passenger than an individual with a car available.

The next several variables indicate current mode choices. Recall that these variables are not mutually exclusive and thus do not require an intentionally unspecified reference mode. This means that each variable can be interpreted to represent the effect that using that mode has on an individual’s willingness to participate in the program as a passenger compared to those who never use that mode, all other things being equal.

Similar to the results of the model of those rejecting the ideal ridesharing program even when offered incentives presented in Chapter 4, the motorbike related mode choice variables do not prove to be significant factors. Those who walk and those who bicycle also do not prove to reflect statistically significant differences in their preferences compared to the preferences of those who do not travel via these modes.

Driving alone proves to be a highly significant factor, with a p-value of 0.004.

Those who drive alone are estimated to be more likely to reject the role of passenger in an ideal ridesharing program than those who do not drive alone, as indicated by a negative coefficient associated with this variable.

A traveler who shares a car as a driver is estimated to be less willing to be a passenger than one who does not share a car as a driver. This trend is identified by the negative sign of the coefficient corresponding to this characteristic’s variable. This trend is fairly significant with a p-value of just under 0.10.

In contrast, individuals who share a car as a passenger are estimated to be more willing to be passengers than those who do not share a car as a passenger, as indicated by the positive coefficient. While this trend is associated with a high p-value of nearly 0.25, the trend is as expected as discussed subsequently in the next section and, therefore, it is deemed acceptable to include this variable in the final model specification.

The coefficient corresponding to the “Transit User” variable is estimated to be positive, which indicates that an individual who uses a COTA or CABS bus when traveling to or from campus, is more willing to be a passenger than someone who does not use these services. This trend is significant with the coefficient’s level of confidence of over 99% (p-value = 0.008).

Picking-up or dropping-off a passenger proves to be more significant when specified alone than when specified in conjunction with any other stop purpose or when simply specifying a single variable indicating any stop, regardless of purpose. The resulting negative coefficient (p-value = 0.11) indicates that those making this kind of stop are less likely to be passengers than those who made stops for another purpose or those who did not stop at all.

Travel times to and from campus were considered in separate specifications and in the same specification. After examining the results, it seemed the trend regarding

travel time to campus is similar to the trend regarding travel time from campus when each of the two variables are included in the specification without the other. When these two variables are included in the same specification, the trends were no longer clear, and statistical significance was largely reduced. This is likely caused by a high degree of correlation between the two travel time variables. Therefore, only travel times for trips to campus were considered for the final specification.

When investigating travel time to campus, two categorical travel time specifications were tested independently. These specifications included a categorical specification of travel time (i.e., Xi = 1 if travel time ≤ 5 minutes, 0 otherwise; Xi +1 = 1 if

5 minutes < travel time ≤ 10 minutes, 0 otherwise; etc.) and categorical travel time conditional on using motorized transportation (i.e., Xi = 1 if travel time < 10 minutes

AND did not walk or bicycle, 0 otherwise; Xi +1 = 1 if 10 minutes ≤ travel time ≤ 19 minutes AND did not walk or bicycle, 0 otherwise; etc.). Both of these specifications led to monotonically increasing magnitudes of estimated coefficients for categories representing monotonically increasing travel time and were thus tested in their continuous form as well (see Section 3.3.2). Both specifications—travel time on its own and travel time using motorized modes—revealed the same trend (i.e., the corresponding coefficients had the same sign) and the p-value associated with the estimated coefficient of the non-conditional specification is lower than the p-value of the estimated coefficient of the conditional specification. Therefore, the specification of a continuous, non- conditional time variable is selected for the final model. The positive sign of the estimated coefficient indicates that a traveler with a longer trip to campus is more likely

to be a passenger in a ridesharing program than if he or she had a shorter trip

(p-value = 4 x 10-5).

Both the exclusive drove alone and exclusive use of any mode variables were specified in separate estimations. Both specifications led to estimated coefficients of similar signs and magnitudes, and the specification including all exclusive users of any mode variable led to a lower p-value. Therefore, the variable indicating those who exclusively use any mode of travel is included in the final model. The negative coefficient (p-value = 0.003) indicates individuals who exclusively use one mode in their trips to and from campus are estimated to be less willing to be passengers than individuals who use more than one mode.

Multiple affiliation variables were tested. The faculty and civil service staff independent variables again proved to have the most significant coefficients of the same sign and similar magnitudes. Therefore, they are combined into a single variable for a more parsimonious specification. Although this variable has the highest p-value in the specification (0.35), the negative coefficient value is sensible and, therefore, the variable is deemed useful to include for comparative purposes. The negative coefficient implies that faculty and civil service staff members are less likely than those with other affiliations to be a ridesharing passenger.

The longest amount of time spent on campus (i.e., over 15 years) again proves to be the only significant variable capturing the impact of years on campus. The negative sign of the coefficient (p-value = 0.002) implies that those who spent more than 15 years

on campus are less willing to be passengers than those who have been on campus for a shorter period of time.

Specifications using various age variables as discussed in Section 3.3.2 were estimated. The simplest specification of a continuous age variable, considered independently of any other factor, proves to be the most significant. Increasing age is found to be associated with increased willingness to participate as a passenger, as indicated by the positive coefficient (p-value = 0.01). .

The specification capturing the effects of living situation that results in the lowest p-value was the combined specification indicating whether an individual is between 25 and 40 years old and lived with children or not. The negative coefficient, while associated with a higher p-value (p-value = 0.12), implies that individuals in this category are less willing to be passengers than those who are not in this category (i.e., those who are either not between 25 and 40 or between 25 and 40 but with no children).

5.4 Passenger Model Interpretation

This section seeks to provide interpretation and explanation of the trends revealed through the model of individuals’ willingness to be passengers in an ideal real-time employer-based ridesharing program. Some of these trends support a priori expectations expressed in the model specification and others reveal other potential interpretations.

One of the most noteworthy trends observed in the estimated model is that individuals with no car available are more willing to be passengers than those with a car available. Those with no car have fewer mode choices than those with a car and therefore

could tend to find the expansion of their choice set offered by the ridesharing program more appealing. This may indicate that the provision of a ridesharing program can actually expand mobility and accessibility for those with a limited choice set. This trend is likely more pronounced in an auto-centric environment, such as Columbus, because one with no car is likely not well served by existing infrastructure and services.

Another expectation that seems supported by these results is the effect of current mode choice on one’s preferences toward the role of passenger. Those who already take control of their travel in an automobile (i.e., drivers in a single occupancy vehicle or shared car), are less likely to give up this control for the dependent role of passenger. In contrast, those who already rely on others for travel in a bus are more likely to be willing participants as a passenger. Those who shared a car as a passenger, although associated with a higher p-value, seem to indicate a similar trend toward willingness to be a ridesharing passenger. This may suggest that providing this type of program may draw prospective passengers from transit for the role of passengers, rather than attracting those who are driving.

At first glance, this result may be slightly discouraging given that it is desirable for ridesharing to reduce auto traffic and not simply take market share from non-auto modes in order to reduce congestion and the negative externalities of motorized travel.

However, because those who do not travel in cars are already more likely to reject the program outright (see Chapter 4), and because those who rejected the program in this manner are not considered in the estimation of the model of interested passenger participants, the trend that transit users and shared car passengers are more likely

passengers may not be as discouraging. Due to the fact that these modes were selected on a select all that apply basis, those who use transit or share a car as a passenger are not necessarily exclusive users of these modes. This may mean that those who already use multiple modes would be offered an appealing alternative, with some congestion and negative externalities-limiting benefits, to use when it best fits their needs in comparison to the other modes in their choice sets.

The proposition that those who use more than one mode are more willing to be passengers is supported by the variable capturing the effects of exclusive mode use. The fact that in this model an exclusive user of any mode behaves similarly to the exclusive single occupancy vehicle drivers implies that even those who are exclusive users of alternative modes to the single occupancy auto are not likely passengers in a ridesharing program. This result implies that those who are daily users of transit or exclusive shared car passengers are not necessarily being attracted to the program. Rather, individuals who use modes like transit as alternatives among the multiple modes they use may find another mode of transportation attractive. From the provider’s perspective, this result suggests that this newly offered ridesharing program provides an alternative means of transportation that has fewer negative externalities than driving alone to users who already use multiple modes of transportation. This trend supports the a priori expectation that those who repetitively select the same mode either because of habit, contentedness, or both are unlikely to modify their behavior.

Those who pick-up or drop-off others on their trips to and from campus are found to be less willing to be passengers than those who stop for any other reason or who do not

stop at all. This result supports the assumption that this type of stop is one that occurs frequently and one that is likely fulfilling a nondiscretionary purpose. If one already has responsibilities built in to his or her to and from campus tour, it is reasonable that he or she would be less likely to start relying on others as a passenger for his or her travel.

Becoming a passenger in such a program would likely mean that this individual could no longer ensure that whomever he or she was picking up or dropping off still arrives at his or her destination in a timely manner.

The result that faculty or civil service staff members are less likely to be passengers is not surprising. As discussed in Chapter 4, there are sensible behavioral reasons why these individuals were likely to reject the idealized program even when incentivized. Similarly, these users are also less likely to take this dependent passenger role. The reasons discussed for the overall interest in ridesharing model include both affiliations having added responsibility on campus compared to students, civil service staff members likely having strict scheduling constraints, and faculty members likely being content with and financially insensitive to mode choice. These same reasons can be expected to make one unwilling to rely on others as a passenger in a ridesharing program.

As expected, those who have the most experience, and by extension the most potential for long held travel habits, are less likely to be passengers than those who had been on campus for a shorter period of time. As is the case in the results presented and discussed in Chapter 4, it appears that highly habitual behavior may lead one to be content and unwilling to change his or her current behavior.

Unlike the variables discussed thus far, the effect of age on preference is unexpected. In the model of those who are likely to reject the program even when incentivized, likelihood of rejecting the program increases with age. However, in the passenger model (and in the driver model explained in the subsequent sections) age has a positive effect on willingness to be a passenger. While this result may seem paradoxical, recall that those who rejected the overall ridesharing program even when incentivized are excluded from the estimation of this model, and, therefore, only individuals who are interested in one or both roles of the program, at least when offered incentives, are included in this model estimation. Consequently, the interpretation of the age related result is distinctly different from the interpretation relating to the model results discussed in Chapter 4.

Specifically, the trend observed for the passenger model might be explained as follows. Since older individuals are likely more established in their careers and have more responsibilities, they may have a firmer preferences toward ridesharing than younger individuals. This may mean that an older individual is either completely disinterested, as shown in the first model (Chapter 4), or firmly in favor of participating, with fewer ambivalent responses in between these extremes. This interpretation may be supported by the notion that older individuals that are interested in the program are likely more financially stable and thus less drawn to the program for monetary reasons, but more attracted by the altruistic environmental benefits of limiting single occupancy vehicle travel. Also note that this trend does not mean younger individuals are all unwilling to be passengers in a ridesharing program, but other factors, such as affiliation,

which may be associated with age, may have combined associations leading to some younger travelers exhibiting higher willingness to participate as passengers in some cases.

As expected, and consistent with findings of Chapter 4, those who are between the ages of 25 and 40 and have children are less likely to take the dependent role of passengers in the program than those in a different age and with children or those with no children. The fact that those between 25 and 40 with children are more likely to reject the role of passengers supports the idea that younger parents are likely parents of younger dependent children, which makes them the most severely constrained by the responsibilities of parenthood.

In this model, females appear more willing to travel as passengers than males.

This may support the conventional wisdom of women being more likely to take a mode, such as public transit, where they are dependent on others for their travel. When taken along with the result that gender did not have a significant effect in the model investigating willingness to be either a driver or passenger (see Chapter 4), it may be possible to interpret the results in a more refined way. Some females may still have

“stranger danger” concerns even though the presented program pairs one with fellow university affiliates. This aversion may be why there is an undiscernible trend associated with gender in the first model. These concerned individuals are likely in the group of

“unreachable” participants who rejected the program even when offered incentives.

However, when these individuals are no longer considered in the estimation of the passenger model, the willingness of females to be dependent travelers prevails.

If frequency and regularity of travel is indicative of responsibility and habit, which is likely, then the negative sign of the “days on campus” variable is expected. If a traveler has more responsibility on campus and travels frequently, he or she may be hesitant to rely on others for his or her travel. This trend may also imply that the habitual nature of coming to campus on a daily basis during the work week makes one hesitant to change travel behavior.

In conclusion, the most notable finding from this model seems to be the fact that the result of the “no car” variable supports the assertion that providing ridesharing expands the mobility and accessibility of those with limited choice sets. This result is promising for ridesharing because it supports one of the major benefits often thought to be derived from ridesharing.

Other trends in this estimation support a priori expectation and the findings of studies reported in the literature. Those who are likely to participate in habitual travel— for example, those repeatedly choosing the same mode, those who have spent many years on campus, and those who spend many days a week on campus—tended to be less likely to participate in a ridesharing program as a passenger than their counterparts. Another overarching trend is that those who likely have restrictive schedules or a higher level of responsibility at home or on campus tend to be less willing to be passengers than those with comparatively less restrictive schedules and less responsibility. Individuals who likely have young children at home, individuals responsible for picking-up or dropping- off others, and faculty and civil staff members all appeared less willing to be passengers than their counterparts. In addition, those with experience as dependent travelers, such as

those who use transit or those who share a car as a passenger, tend to be more willing to be passengers than those without these experiences.

5.5 Driver Model Specification

All the variables discussed in Section 3.3.2 were investigated to identify factors associated with the willingness to participate in a ridesharing program as a driver. Recall that only individuals who were considered potential participants were included in this estimation. That is, individuals were considered only if they answered that they were

“Somewhat likely” or “Very likely” to participate in either the ideal program as originally presented or after they were offered incentives in at least one of the ridesharing roles.

Additionally, individuals with no car available were not considered in the estimation of this model because they were considered unable to drive in a ridesharing program. The dependent variable is based on the response to the originally presented program with the choice variable taking a value of 1 for individuals who responded that they were

“Somewhat likely” or “Very likely” to participate in the program as a driver without incentives, and 0 if otherwise. Again, this means that it is possible to have a negative preference toward the driver role as well as a negative preference toward the passenger role. Therefore, expectations of generally negative perceptions discussed in Chapter 4 may apply. This section describes some a priori expectations for the impact of variables as they apply to the role of driver based on prior knowledge and based on the results of the model of those who reject the ridesharing program even when offered incentives as discussed in Chapter 4.

Again, the conflicting perceptions of females being potentially more averse than males to “stranger danger” and having a greater proclivity for taking public transit, when compared to males, makes the impact of gender hard to predict. If females are more averse to sharing a vehicle with a “stranger” than males, even if the stranger is a fellow campus affiliate, they may be more inclined than males to maintain control of the situation and reflect a preference to participate as a driver. However, if a female has a preference for public transportation because of the reduced stress of not having to drive, she may not want to take responsibility for others’ travel as a driver. In this case, females are expected to be less likely than males to be ridesharing drivers. The conflicting effects again leave the net impact of gender unclear.

Having children at home could have multiple impacts on the preference of one offered the opportunity to drive in a ridesharing program. Those who live with children are likely used to traveling with children in the vehicle. If one is used to traveling with passengers in the car, he or she may find traveling with passengers less onerous than someone who is used to traveling alone. Also, the investigated ideal ridesharing program does not require a driver who offered someone a ride to campus to give that passenger a ride home, leaving parents who wish to be able to get home quickly, if necessary, less deterred to participate as drivers. It is also reasonable to believe that one could still drop off a child at school or a daytime childcare provider, before or after picking up another passenger on a trip to campus. Therefore, with few obstacles to participation and a likely familiarity with traveling with others, the cost savings and societal benefits of ridesharing may make individuals living with children more likely rideshare drivers than those living

without children. However, if one takes responsibility for his or her children’s travel or morning preparation, they may be hesitant to take on additional responsibility. A parent responsible for his or her children’s travel may also be hesitant to have their children around an unfamiliar adult, due to “stranger danger”. If this is the case, respondents living with children at home, especially those with younger children, may be unwilling rideshare drivers compared to individuals who do not have to take responsibility for children at home. In light of the conflicting effects, the net expected effect of having children on preference toward participating as a driver is unclear.

Affiliation may have an effect on one’s preferences toward being a rideshare driver. The same characteristics that make faculty and civil service staff members more likely to reject the program, even when offered incentives, may make them unwilling rideshare drivers. If these respondents perceive ridesharing as an unnecessary extra responsibility that only adds to the extra responsibilities they are already assumed to have, they will likely be unwilling to participate as a driver when compared to other affiliates. Also, if students are unwilling to incur the extra costs usually perceived of the driving role, due to their financial constraints, they may prove to be less likely drivers than other affiliates.

Individuals who are older may reveal a preference for driving compared to those who are younger. As discussed in the specification of the model of those likely to reject the program in Section 4.2, members of the younger generation are less likely to own cars potentially due to financial constraints or aversion to auto use. If these younger individuals do not like driving due to cost or preference, they may prove to be less likely

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to drive. However, if they are averse to the high costs associated with driving, but often drive anyway, they may wish to reduce some of that cost by participating in ridesharing.

Therefore, given the conflicting effects the net impact of age remains unclear.

If respondents who have spent more years on campus are more habitual travelers, they may be unwilling to change their routine to accommodate others as a rideshare driver. These individuals are likely financially stable and the costs savings of ridesharing may not sway them to be a rideshare driver, even though this may be a less onerous role than that of passenger. Therefore, it is expected that individuals who have been on campus for a longer period of time would be less willing rideshare drivers than other individuals.

Contrary to the expectations discussed regarding the specification of the passenger model, it is expected that an individual who chooses a mode that allows him or her to take control of his or her travel would be more willing to be a driver than someone who does not choose such a mode. Therefore, travelers who drove alone, shared a car as a driver, bicycled, or walked might be expected to be more willing to drive than an individual who shared a car as a passenger or took transit.

As discussed previously, one who chooses to use a mode exclusively is expected to be a more habitual traveler and to experience more modal inertia than one who uses more than one mode. A negative preference toward driving in a ridesharing program might be expected for these exclusive users. This effect may not be as strong among respondents who drove alone exclusively compared to those who used other modes

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exclusively because driving in the ridesharing program would be a smaller change from current behavior for these individuals, especially under the ideal program.

Similarly to the expectations of the preceding specifications, one’s willingness to participate in ridesharing in general may increase as one’s travel time increases. If an individual is spending a long time traveling, he or she is likely to have larger transportation related costs. Therefore, the ability to share those costs with a passenger may be especially appealing. Also, the additional delays due to ridesharing are likely to be proportionally lower than those associated with shorter trips. This preference is expected to carry over to the driver model.

As previously stated, the driver role in the ridesharing program presented in the survey is flexible enough that if one wishes to carry out other tasks during their trip he or she could likely do so. Therefore, if one who makes a stop sees value in the cost savings of ridesharing he or she may be a likely rideshare driver, but no more so than anyone else who was presented with the program. Conversely, if one who stops does not like the added responsibility in addition to whatever task they are accomplishing during his or her trip, they may be unwilling to be a rideshare driver. This preference may be especially pronounced among those who stop to pick-up or drop-off passengers, since this stop is likely non-discretionary and may require more precise timing than a stop for a discretionary purpose such as socializing or shopping. Therefore, a net a priori expectation is unclear.

Those who spend many days a week on campus, representing frequent and potentially regular travel, may be deterred from being a rideshare driver compared to

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those who spend fewer days on campus because of all of the responsibilities and habits they may already have, as discussed in the previous two specifications. Therefore a negative a priori association with the preference toward ridesharing as a driver is expected.

5.6 Driver Model Results

Several specifications were examined to investigate how the explanatory variables discussed in the previous section are associated with one’s preference toward being a ridesharing driver. After examining the results, variables with coefficients of particularly low statistical significance were removed, and new specifications were estimated. This process was repeated until a final estimation was selected based on a combination of reasonable statistical significance and interpretability. The estimation result of the final specification of the model that describes preferences for being a driver in the presented ideal ridesharing program is summarized in Table 6. Variables not present in the table are considered to not have an identifiable significant impact on the preferences of respondents.

Recall that the structure of this model includes a binary indicator representing willingness to participate as a driver in the presented ideal ridesharing program without incentives. The model is specified in such a way that characteristics associated with increased likelihood of being a rideshare driver are represented by positive coefficients and characteristics associated with decreased likelihood are represented by negative coefficients. In addition, recall that the responses considered when estimating this model

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were only those of individuals who indicated that they were “Somewhat likely” or “Very likely” to participate in at least one role in the program in the presented program or did so when offered incentives.

After the final specification of the model was selected, the estimation results exhibit overall strong statistical significance of fit, with a p-value related to the log- likelihood ratio test of approximately 1.2 x 10-5. While fewer variables prove significant in this model than in the passenger model or the model identifying those unlikely to participate in the overall program even with incentives, the estimated coefficients of variables that are included all have nearly an 80% confidence level of significance

(p-value = 0.20), and most can be deemed significant with over 90% confidence

(p-value < 0.10). Note that some variables with coefficients of higher p-values are included because the signs are sensible and interpretable and inclusion of those variables is useful for comparison to the other estimated models.

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Table 6: Summary of Estimation Results of Model of Likelihood of being a Driver

Model Statistics Statistic Value Sample Size 2349 Log-Likelihood -1485.2 Likelihood Ratio Test (χ2) 38.878 P-value 1.2119e-05 Coefficient Estimation and Statistics Explanatory Variable (Xi) Coefficient (βi) P-value Constant -2.0944392 4.59E-10 Days on Campus (continuous) 0.0485954 0.1762372 Drove Alone 0.2339835 0.2019404 Shared Car as Driver 0.1914695 0.0658535 Bike 0.3716066 0.0055554 Travel Time to Campus (continuous) 0.0079706 0.0097779 Student 0.2002397 0.0939782 Years on Campus ≥ 15 -0.4042899 0.0034967 Age (continuous) 0.0159434 0.0001775 Lives with Children 0.1984257 0.0490183

As evidenced by their absence from Table 6, the gender, exclusivity of mode choice, and trip chaining variables are not statistically significant factors associated with preferences for driving in the presented ideal ridesharing program.

Increased frequency of an individual’s trips to campus is estimated to have a positive association with the willingness to be a driver in the ridesharing program, although the corresponding estimated coefficient has a higher p-value of 0.18.

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that using that mode has on an individual’s willingness to participate in the program as a driver, compared to those who never use that mode, all other things being equal.

As in the model describing preferences toward rejecting a ridesharing program even when offered incentives and the model describing passenger preferences, the motorbike variables are not significant factors. Coefficients of variables representing transit, walking, and sharing a car as a passenger also do not reflect significant differences in these travelers’ preferences compared to the preferences of those who do not use these modes.

While having the highest p-value of any coefficient for a variable in the model, with a value of 0.21, the positive coefficient associated with the “Drove Alone” variable indicates that of those who are willing to participate in the program, those who drive alone to campus are more willing to be a driver than are those who do not drive alone.

The variable representing those who share a car as a driver also has a positive coefficient.

This result implies that, when compared to those who do not share a car as a driver, shared car drivers are more willing to be a driver in the presented ridesharing program.

The coefficient of this variable seems to be significantly different from zero as evidenced by a p-value of 0.07. The coefficient associated with the variable for those who bicycle is estimated to be positive implying that travelers who bicycle are more likely to drive in the presented ridesharing program than those who do not bicycle. The positive coefficient corresponding to this bicycling variable is highly statistically significant, with a p-value of 0.005.

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As discussed in the passenger model specification, in the driver model specifications investigated trends were observed that suggested a potentially high degree of correlation between the travel times to and from campus variables. Therefore, the variables for travel times to campus were selected for further investigation.

5 minutes < travel time ≤ 10 minutes, 0 otherwise; etc.) and categorical travel time conditional on using motorized transportation (i.e., Xi = 1 if travel time < 10 minutes

AND did not walk or bicycle, 0 otherwise; Xi +1 = 1 if 10 minutes ≤ travel time ≤ 19 minutes AND did not walk or bicycle, 0 otherwise; etc.). Both of these specifications led to monotonically increasing magnitudes of estimated coefficients for categories representing monotonically increasing travel time and were thus tested in their continuous form as well (see Section 3.3.2). Both specifications—travel time on its own and travel time on motorized modes—revealed the same trend (i.e., corresponding coefficients had the same sign), and the and the p-value associated with the estimated coefficient of the non-conditional specification is lower than the p-value of the estimated coefficient of the conditional specification. Therefore, the specification of a continuous, non-conditional time variable is selected for the final model estimation. The positive sign of the estimated coefficient indicates that a traveler with a longer trip to campus is more likely to be a driver in a ridesharing program than if he or she has a shorter trip

(p-value = 0.01).

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All of the affiliation variables outlined in Section 3.3.2 were tested. Only the two student (undergraduate and graduate) affiliation variables proved to be significant factors.

Both student variables had coefficients of the same sign and similar magnitudes.

Therefore, a combined “student” variable is used taking a value of 1 if the individual is an undergraduate or graduate student and 0 otherwise. The coefficient of this variable is significant at a confidence level of 91% (p-value = 0.09) and is estimated to be positive.

This result implies that students are more likely to express a positive preference toward driving in the presented ridesharing program than those with other affiliations.

As in the model describing preferences toward rejecting a ridesharing program even when offered incentives and the model describing passenger preferences, of all the years on campus variables tested categorically, only the longest period of time proved significant in the iterative process of specification. The variable representing individuals on campus for over 15 years has a negative coefficient. This result implies that the individuals who have spent the most years on campus are less likely to accept the role of rideshare drivers than those who have spent fewer years on campus. The p-value corresponding to this coefficient is 0.003.

Similar to the passenger model results discussed previously, specifications using various age variables as presented in Section 3.3.2 were estimated for the model of driver preferences. The simplest specification of a continuous age variable, considered independently of any other factors, proved to be the most significant. The continuous age variable is estimated to have a positive coefficient associated with the lowest p-value of

0.0001. This result suggests that the willingness to be a driver is estimated to increase

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with one’s age. So older individuals are estimated to be more likely ridesharing drivers than younger individuals.

Various models were estimated to investigate the multiple living situation variables described in Section 3.3.2. Recall that in the model investigating those who reject the program even when incentivized and the model of preference toward the role of passenger, the most significant specification was a variable indicating that an individual was between 25 and 40 years old and lived with children. In contrast, in the estimation of the driver model, the simplest specification using the binary indicator taking a value of 1 if the respondent lived with children, and 0 otherwise is the most significant specification. The coefficient corresponding to this variable is positive, indicating that those who live with children are more willing to be rideshare drivers than those who do not live with children. This variable is significant with a level of confidence of over 95%

(p-value < 0.05).

5.7 Driver Model Interpretation

This section seeks to interpret the trends revealed in the model investigating preferences toward the role of driving in a real-time employer-based ridesharing program.

Revealed trends are also compared to a priori expectations to identify what is supported and what may have been the result of unanticipated factors.

After having mixed expectations about the impact of children in the home on travelers’ driving preferences, an interesting trend is revealed in the driving model.

Unlike the previous models where individuals between 25 and 40 with children proved

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different than others with children, in this model individuals with children in general, irrespective of their age category, seem to be a more monolithic group. This group has an increased likelihood of participating as a driver. This result might be explained by the fact that, especially in the idealized ridesharing program, driving for a ridesharing program is not perceived as being very restrictive. In the case of those living with young children, travelers who take responsibility for their children in the morning may anticipate still being able to do so, even if they pick up another passenger. Similarly, if such travelers need to leave campus in a hurry to fulfill an at home responsibility, nothing in the stated driving role would prevent them from doing so at any time of their choice.

More generally, in the case of those living with children of any age, the positive sign of the coefficient corresponding to the variable indicating the presence of children in the home is particularly interesting. The increased willingness of parents living with children to be drivers might indicate that these respondents are already used to traveling in vehicles with other passengers and that this activity is thus less onerous to them than to those without this experience. This may mean that, especially in such a flexible program, the benefits of cost savings offered by ridesharing outweigh any inconveniences, especially for those who have experience traveling with passengers.

The effects of the modes used were mostly as expected and reveal a sensible symmetry with the preferences of ridesharing passengers. In the passenger model, users of dependent travel modes (transit users and shares car passengers) tend to be more willing to be passengers, and users of other modes that allow one to directly control his or her own travel (drives alone, shares car as a driver, bicycles) are less willing to be

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passengers. In the model of driver preferences, those users who take control of their travel, as drivers of single occupancy or shared vehicles and bicyclists, are likely to take the role of driver in a ridesharing program. The only unexpected trend is that those who walk are no more interested in driving than those who did not walk, despite the control these individuals take over their travel. This result can potentially be understood by considering the proximity to campus required to walk. If one lives close enough to campus to walk, driving in a ridesharing program may be more inconvenient than walking due to the required distance to drive to pick up a passenger. For instance, one with a ten minute walk to campus may actually slightly extend their travel time to campus if they are required to pick up a passenger five minutes from their home in order to take him or her to campus.

This interpretation is supported by the travel time trend revealed in the model estimation results. Increasing travel time is associated with increasing willingness to participate as a driver. Not only do those who have a longer travel time have more to gain in ridesharing savings, but the potential of a slight increase in travel time has a diminishing relative effect as travel time increases. For example, one who travels thirty minutes to campus who picks someone up five minutes from his or her home experiences a much smaller relative change than one who only travels for five minutes when they do not pick-up a passenger. This trend matches a priori expectations stated in the specification section.

One interesting deviation from prior expectations is the lack of significant impact of exclusive mode use. Neither exclusive drivers nor exclusive users of any mode prove

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to be significantly more or less willing to be drivers in a ridesharing program. Although exclusive drivers are more likely to reject the program even when incentivized, and all exclusive users tend to be unwilling rideshare passengers, neither exclusive drivers nor exclusive users of any mode prove to be significantly more or less willing to be drivers in the presented ridesharing program. This result may suggest that driving is not as onerous as being a passenger in a ridesharing program to these exclusive users who likely have established habits or strong mode preference. This interpretation is consistent with the more general notion that a role as a ridesharing driver is less of a perceived change for individuals who habitually travel to campus, than a role as a passenger and thus is a potentially more feasible change to induce.

Another somewhat unexpected trend is the fact that students are more likely to be drivers than those of other affiliations. While it was expected that the additional control the driver role offers might attract faculty and civil service staff members who are disinterested in the passenger role, it seems that at least when compared to students, these other affiliations are not likely to be interested in either role without incentives. This trend further supports the notion that the more flexible travel schedules and likely greater financial constraints that are associated with students render students as more promising targets for a ridesharing program than others, regardless of role. (Recall, in the passenger model faculty and civil service staff are less likely participants as passengers and, therefore, students among the other categories are more likely participants as passengers.)

As expected, the longest affiliation is related to a disinterest in both roles of the program with or without incentives. This trend of disinterest is indicated in all three

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models discussed thus far. This may be a strong indicator that those who are the most established in their careers and travel habits are the least likely to accept a change in travel behavior.

Age, as mentioned in the passenger model interpretation, has an unexpected effect on preference toward the driver (and passenger) role. The likelihood of rejecting the program even when incentivized increases with age, but the willingness to be a driver in the program also increases with age. Again, recall that this is not a paradoxical result because those who rejected the overall ridesharing program even when incentivized are excluded from the estimation of this model, and, therefore, only individuals who are interested in one or both roles of the program, at least when offered incentives, are included in this model estimation. As stated in the passenger model interpretation, the interpretation of this phenomenon is distinctly different from the one presented in

Chapter 4. The posited interpretation of the driver (and passenger) model estimation results is that older individuals are more likely than younger individuals to have a strong opinion on ridesharing. Many are against the program, indicated by the increased likelihood of being in the group likely to reject the ideal ridesharing program overall.

However, those who are not likely to reject the program overall are likely to participate in the driver role.

In the model investigating those who are likely to reject the program even when offered incentives, the days on campus variable appears to indicate that the more days one spent on campus the more willing he or she was to reject the program. After observing this trend and observing that in the model investigating preferences toward the

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role of passenger these same frequent travelers are less willing to be passengers than infrequent travelers, one may expect that these frequent travelers were unlikely to participate in either role. Unexpectedly, frequent travelers tend to be more likely than infrequent travelers to be ridesharing drivers. This result may reveal that the biggest aversion to ridesharing for frequent travelers is actually the dependency of the passenger mode. If these individuals are offered a role in which they still ultimately control their travel and reap the benefits of ridesharing, those who did not entirely dismiss the program were seemingly interested. This may support the idea that the role of passenger is a larger change for those who may have established travel habits due to frequent to and from campus travel.

The fact that gender proves insignificant in this model may show that the conventional wisdom of females being more likely users of public transportation may be tied to preferences surrounding travel modes where the traveler is dependent on a service provided by others. In the two models investigating preferences of role, females prove to be more likely than males to be passengers, but no more or less likely than males to be drivers. This result may indicate that the reason women tend to be more likely to take public transportation than males is tied to the willingness to be a passenger in a shared mode, even though they are no less willing than males to participate in a mode where they are in control of their travel.

The likely nondiscretionary stop purpose of picking-up or dropping-off others prove to have a negative impact on one’s willingness to participate in ridesharing in general and willingness to be a passenger, as indicated in the previous two models. Those

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who did not reject ridesharing with incentives, as identified in Chapter 4, do not have a discernable preference for or against driving in a ridesharing program. This result may imply that some individuals who already pick-up or drop-off passengers anticipate being able to do so even if they participate in the ridesharing program as drivers. Another interesting note is that none of the other stop purposes are associated with a preference for or against participating as a driver either. While not making one a more likely driver, it seems that some individuals who stop do not see the driver role as restrictive of their trip chaining behavior.

The most interesting and noteworthy finding in this model may be the fact that interested individuals with children are actually more willing to be rideshare drivers than those who do not live with children. Again, this result might suggest that the likely shared experience of traveling with children as passengers, irrespective of their age, makes the act of taking responsibility for others less onerous to these individuals.

Other observed trends supported a priori expectations or findings of previous studies. Those who previously appeared to be disinterested in ridesharing in general or in the role of passenger, due to restrictive schedules or additional responsibilities—such as those with younger children—tend to be more likely to participate in a ridesharing program as drivers. This finding may indicate that the ability to take control of travel reduces the perceived restrictiveness of a ridesharing program. Also, those who likely partake in habitual travel behaviors—such as those who exclusively utilize one mode of transportation or those who travel to campus frequently—interestingly do not tend to have a negative or positive preference for the role of driver even though they were likely

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to reject the program in general and were likely to reject the role of passenger. This finding may indicate that the role of driver that maintains one’s flexibility as well as providing cost saving opportunities is less onerous than the role of passenger, especially for habitual travelers.

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Chapter 6: Conclusions

6.1 Summary of Results and Conclusions

Ridesharing has many potential benefits for a community, including increased traveler choice, reduced congestion, reduced transportation costs, reduced emissions, and reduced energy consumption. These benefits can only be realized if there is sufficient demand to make a ridesharing system viable. Generating this demand requires matching drivers and passengers with near or common origins and destinations. Ample demand also relies on participants who are willing to travel with potential strangers. Employer- based ridesharing programs, where employees share a ride to and from a shared work destination, have the advantage of a network of co-workers with a common work location and who are likely more familiar with one another than total strangers, thus mitigating the negative “stranger danger” perception of ridesharing programs. Technology has allowed for the provision of real-time ridesharing, in which passengers and drivers are matched shortly before the intention to travel. Real-time ridesharing options have the potential advantage of allowing for occasional use rather than a long term commitment in a pre- scheduled program and more flexibility to meet travelers’ increasingly complex and varying schedules. Although such real-time matching has initially been perceived negatively by some due to reliability and personal safety concerns, the growing ubiquity

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of real-time ride-sourcing programs may indicate a shift in public perception of the attractiveness of real-time on-demand travel services.

This study used responses to a stated preference survey of the OSU community in the Spring of 2014 to investigate preferences toward a hypothetical real-time employer- based ridesharing program. This program is considered “ideal” because it is assumed to require minimal travel scheduling changes or delays relative to current travel behavior.

The survey elicited demographic characteristics, current travel behavior, willingness to participate in the idealized program, and stated willingness to participate in a similar program with changes to either include incentives or realistic scheduling changes.

The responses from this survey were used in various specifications of discrete choice models to investigate willingness to participate as a function of various explanatory variables. Explanatory variables were defined to investigate the effects of respondent gender, living situation, affiliation, years on campus, age, mode choice, travel frequency, travel time, and tour planning characteristics on preferences toward the ridesharing program presented in the survey. After investigating many possibilities, three dependent variables were selected for use in the binary logit models: the preference for an individual to reject both the role of passenger and driver even when offered incentives, the preference of those who are interested toward the role of being a passenger in the idealized ridesharing program without incentives, and the preference of those who are interested toward being a driver in the idealized ridesharing program without incentives.

It was determined that individuals who rejected both roles in the program even when offered incentives may be characteristically different than other respondents due to

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either a strongly held opinion or a highly restrictive constraint that prevents them from considering participating in a ridesharing program. Therefore, the two models investigating preference toward the role of passenger and preference toward the role of driver were estimated using only individuals who stated that they were likely to participate in at least one of the roles of the ideal program with or without incentives.

Each of the three models was initially specified to include all possible variables.

The specification was then revised by iteratively removing explanatory variables that did not prove to be statistically significant (loosely defined) or to have a reasonable interpretation. Once final specifications were estimated for each model, statistically significant trends at fair confidence levels were observed identifying clear associations between travel behavior, living situation, and demographic characteristics with respondent preferences toward the presented ridesharing program and the two roles of passenger and driver.

In addition to identifying trends consistent with a priori expectations and supporting findings of various previous studies, each of the three models revealed notable conclusions previously not identified. The model investigating those who are unwilling to participate even when incentivized revealed the promising notions that those who already travel in cars are more likely to participate in the program than those who do not travel in cars and that those who use alternative modes like walking or transit are less likely to participate in the program than those who do not use these alternative modes. These findings are promising because, to mitigate congestion and the negative externalities of single occupancy vehicle travel, it is desirable to attract more individuals traveling by car

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to a ridesharing program than individuals who are already utilizing alternative modes.

The model investigating willingness to be a passenger among those who are interested in the program revealed that those who may have limited mobility and accessibility, due to a smaller travel choice set (as indicated by not having a car available), were likely to participate in the program as passengers. This finding seems to support the existence of one of the major societal benefits often associated with ridesharing programs, namely expanding mobility and accessibility to those with limited travel options. Finally, the model investigating interest in the driver role among those who are willing to participate revealed that those who live with children were more likely to participate as drivers than those who do not live with children. This result may indicate that experience taking responsibility for passengers in travel may reduce the perceived “cost” of traveling with passengers.

Several additional, overarching implications are identified when considering the sets of results from the three models. Individuals who are likely established in their travel habits—such as those who exclusively use one mode of transportation or those who have been traveling to the same destination for many years—tended to have negative preferences toward a ridesharing program. Respondents with potentially restrictive schedules or added responsibilities—such as those who live with younger children or those who travel to campus frequently—were more likely to reject the program even when offered incentives. However, when interested in the program, those living with younger children were likely drivers and unlikely passengers. In contrast, those with experience traveling in a shared vehicle as a passenger, either in a car or transit vehicle,

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tended to have a preference towards the role of passenger. Individuals with a restrictive choice set, indicated by the lack of access to a car, were likely to utilize the program as a passenger, which, as already discussed, supports the importance of ridesharing in providing increased mobility to a community. Older individuals appeared to have a strong preference for or against ridesharing. This finding is indicated by the fact that in the model investigating those rejecting the program, older individuals had an increased probability of holding this negative preference. However, older individuals who were interested tended to be more willing to participate in both passenger and driver roles.

It is valuable, both for having confidence in the empirical results obtained in this study and for increasing confidence in knowledge of general attitudes and preferences toward ridesharing (broadly defined) to compare the conclusions drawn from this study with those of others who have investigated similar traveler preferences. The trend relating to age in this study may provide some insight into what portions of the community may fall into Cartmell and Carter’s (2015) typologies of “cosmopolitan youth” or “bohemian boomers”. The “cosmopolitan youth”, who Cartmell and Carter describe as younger individuals who like using technology and may be willing to change travel behavior, may be represented in this study by the fact that younger individuals were less likely to reject the real-time ridesharing program than older individuals. In contrast, the fact that interested older individuals tended to be more likely than younger individuals to participate in passenger and driver roles may indicate that, while not a large part of the older population at OSU, Cartmell and Carter’s “bohemian boomers”, who are older

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individuals willing to utilize convenient technologies, may have strong preferences for ridesharing.

Nielsen et al. (2015) noted that not all travelers were “calculating deliberators” who compare the costs and benefits of each mode before selecting the mode for that trip.

This finding may be supported by the fact that including individuals who rejected both roles in the program even with incentives, seemed to worsen the results of driver and passenger preference models in terms of statistical significance and interpretability.

Respondents providing this negative response to ridesharing in general were likely not in a position to make calculated trade-offs. Also, the likely fiscally constrained students in this study may be more likely to be the “calculating deliberators” identified by Nielsen et al. because they tended to be more willing than other affiliates to participate in the program. This increased willingness to participate may indicate that students perceive the potential benefits of reduced cost that ridesharing offers. In contrast, those who have the most years of experience traveling to campus (i.e., indicating having spent 15 years or more employed or enrolled at OSU) and likely have higher salaries were unlikely to participate in the program, which may indicate that the potential cost saving benefits mean less to them. It is interesting to note that the results of Nielsen et al.’s study of

Danish travelers seem to agree with the results of this study in the Midwestern United

States.

Bruns and Farrokhikhiavi (2011) identified a difference in travelers who use ridesharing for daily commuting (“carpoolers”) and those who use ridesharing for irregular trips (“needing/offering a lift”). In this study, it seemed that most of the travelers

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who Bruns and Farrokhikhiavi identified as more likely “carpoolers”, such as individuals with children, those who travel to campus frequently, and those who already drive, were more likely to participate in the program as drivers. This result may reflect a perception of unreliability related to real-time ridesharing as identified by Amey et al. (2011), because those who are likely to use the program regularly would rather take control of their own travel in order to ensure their timely arrival. In contrast, those who Bruns and

Farrokhikhiavi identified as more likely to simply “need/offer a lift” for irregular trips, such as those without children and transit users, seem to be more willing passengers in this study. Bruns and Farrokhikhiavi’s finding that both of these groups of users tended to be younger is supported by the fact that in this study younger individuals are less likely to reject both roles in the program when offered incentives.

The finding of more willing younger participants also supports similar findings of younger individuals using ridesharing in Mundler et al.’s study (2016). However, this study found that students are more likely to participate in the program in each role, whereas Mundler et al. reported that drivers were usually higher income individuals, which is an unlikely characteristic of the students in this study.

Feigon and Murphy (2016) found that most users of shared transportation modes prefer to use transit for commuting trips. Due to the employer-based nature of the ridesharing program investigated in this study and the questionnaire’s focus on soliciting preferences regarding participation at least once a week, it is reasonable to assume that most respondents view the travel choices they responded to questions about and their stated preferences toward the presented ridesharing program as related to commuting.

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Therefore, the result of this study that transit users tend to be more likely to reject both roles in the program even when offered incentives, is consistent with those of Feigon and

Murphy noted above.

Tezcan (2016) reported findings that at a university in Istanbul, Turkey females and those who come to campus frequently were the most likely passengers in a ridesharing program. While this study found females were likely passengers, the findings related to frequency of travel contradicted those of Tezcan. This contradiction could be because of large cultural and demographic differences between Istanbul, Turkey and

Columbus, Ohio, USA.

DeFrancisco et al. (2014) and Tahmasseby et al. (2014) both found that demographic and behavioral characteristics were important in determining ridesharing preferences, which is clearly supported by this study. DeFrancisco et al. specifically pointed to the importance of travelers’ mode choice prior to considering ridesharing, which was a significant factor throughout this study. Tahmasseby et al. (2014) found that individual characteristics were more important when considering passenger preferences, but rideshare program characteristics were more important for the role of driver. This result may be supported by the fact that some individuals with characteristics that might have been expected to make them likely to reject both roles in the program were likely drivers. Since the program characteristics in this study are close to ideal, there is little to deter participation as a driver. One point of disagreement between this study and

Tahmasseby et al.’s is that in this study students tend to be more likely to participate in

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both roles of the program, but in Tahmasseby et al.’s study faculty and staff members were found to be more likely to be drivers than students.

Overall it appears that this study provides worthwhile previously unidentified trends. It also confirms expected preference trends, and in general bolsters the existing literature.

6.2 Future Research

While this study provides some valuable insights into individual preferences towards a real-time employer-based ridesharing program, potential directions for further investigating these preferences could prove valuable in improving the understanding of such preferences. Firstly, there are more opportunities for study using this dataset. To further investigate the intensity of preferences, one could utilize the five-level Likert scale used for gauging likelihood in different ways. In this study, to maintain consistency with the branching scheme described in Section 2.2, respondents indicating that they were “Unsure”, “Somewhat unlikely”, or “Very likely” were considered unlikely participants. In future research, one could investigate, for instance, association of individual characteristics with the specific preference of “Very unlikely”. Additionally, one could utilize a different choice model methodology, such as an ordinal logit model, that would account for the ordinal nature of the indicated likelihood.

The responses to the questions of the branches of the survey questionnaire used in this study could be used to investigate how preferences are affected by incentives and realistic scheduling changes. In the survey questionnaire specific incentives were offered

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to encourage participation in the ideal program, so information relating to the specific values of incentives may not be particularly generalizable. Nevertheless, the collected incentives related responses could still be used to identify how individuals with varying characteristics may react to the different types of incentives and what types of incentives may be particularly effective, specifically for the role of passenger or the role of driver.

Similarly investigating the responses to scenarios involving realistic scheduling could reveal the types of changes that are considered onerous, how different characteristics may affect an individual’s preferences towards these scheduling changes, and how scheduling changes may affect potential passengers and drivers differently.

Preliminary investigations of how individual characteristics affect one’s reactions to realistic schedule changes or incentives have been carried out. Most of the results of these investigations are consistent with the results presented in Chapters 4 and 5 of this thesis. For example, characteristics that are associated with a preference toward being a likely driver in this study tend to be associated with an increased willingness to be incentivized to participate as a driver and an increased willingness to tolerate realistic scheduling changes.

In addition, some preliminary findings provide further insights worth noting. As interpreted from the combined results of the three models presented in this thesis, it is posited that older individuals might have more firmly held preferences towards ridesharing in one direction or the other, represented by one trend indicating that older individuals are more likely to reject the program even when incentivized, and another trend that older individuals who do not reject the program are more willing passengers

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and drivers than younger individuals. Preliminary investigations into incentives and realistic schedule changes are revealing that older travelers seem to be associated with those unwilling to be incentivized if they were not interested in the program as initially presented and also seem to be associated with initially interested individuals who are more tolerable of realistic scheduling changes.

Moreover, preliminary findings are also indicating that although females interested in the presented ridesharing program tend to be more likely passengers than males in the results presented in Chapter 5 of this thesis, females who were likely to participate as passengers were more easily deterred by realistic schedule changes. This result may indicate that, while potentially attracted to the societal benefits of the program, the realistic drawbacks of participating in the program could deter realized participation among females.

The methodology and models of this study could be improved by a survey designed to capture additional individual characteristics thought to be relevant when considering ridesharing preferences in this study. Questions eliciting information about income could be used to isolate the specific effects of one’s financial situation from other characteristics investigated in this study, such as affiliation, age, years on campus, and days on campus. It could also be useful to collect information specifically about the age of children to confirm or refute the assumed association of the parents’ age group and the age of their children and the corresponding tendency of travelers with likely younger children toward rejecting ridesharing.

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Regarding travel choices, a survey question eliciting information about how often someone utilizes particular modes could provide additional insights clarifying associations with mode choice and rideshare preferences found in this study. It would be interesting, for example, to investigate whether a traveler who normally drives alone, but occasionally uses transit differs from a traveler who usually takes transit, but occasionally drives alone. It may also be useful to design a survey that combines the use of incentives and realistic schedule changes in the same scenario. Obtaining stated preferences that include a combination of realistic scheduling changes and incentives may allow for a more realistic assessment of incentive levels necessary to induce participation in a realistic program.

It could also be valuable to use the findings of a study such as this one, where preferences towards ridesharing are modeled as a function of demographic and travel behavior characteristics, in combination with the explicit consideration of the geospatial constraints on a ridesharing program. Coupling information about how demographics and travel choices affect the level of interest in ridesharing with where interested travelers live in relation to other potentially interested ridesharing participants could be used to more realistically estimate participation in a planned program. Also, if further investigations of the type described in this study clearly reveal how individuals react to particular scheduling changes or delays, the geospatial constraints for matching passengers and drivers in a real-time program could be calibrated to meet preferences of individuals in a manner aimed at increasing participation.

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Finally, the quality of the trends identified in this study could be further confirmed, clarified, and refined by a pilot demonstration coupled with a revealed preference investigation. Participants in the pilot and non-participants in the community could be surveyed using a variety of tools to investigate whether the trends identified in the hypothetical real-time employer-based ridesharing program are realized in an actual program. While the ideal conditions investigated in the survey are unlikely to be replicated in an actual program, it would still be possible to investigate if the basic trends identified through stated preferences under the hypothetical program are supported by actual users’ revealed preferences.

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Amey, A., J. Attanucci, and R. Mishalani. Real-Time Ridesharing: Opportunities and Challenges in Using Mobile Phone Technology to Improve Rideshare Services. Transportation Research Record: Journal of the Transportation Research Board, 2217, pp. 103-110, 2011.

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Bruns, A. and R. Farrokhikhiavi. Need a Lift? Want to Carpool? Characteristics of Different Target Groups of Ride Sharing. Presented at the European Transport Conference, Association for European Transport. European Transport Conference 2011: Seminars, 2011.

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Cartmell, S. and J. Carter. Rise of the Real-Time Traveler: An Exploration of Trends and Innovation in Urban Mobility. ITS America, AT&T, 2015. URL: http://www.itsa.org/images/ReportImages/rise%20of%20the%20real- time%20traveler_%20its%20america%20report.pdf, accessed July 5, 2016.

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Dutzik, T., J. Inglis, P. Baxandall. Millennials in Motion: Changing Travel Habits of Young Americans and the Implications for Public Policy. U.S. PIRG Education Fund, Frontier Group, 2014. URL: http://uspirg.org/sites/pirg/files/reports/Millennials%20in%20Motion%20USPIRG.pdf, accessed August 10, 2016.

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Appendix A: Survey Questionnaire

Investigating the Potential of Employer-based “Real-Time” Ridesharing

Note to be included in the e-mail invitation to respond to the questionnaire:

Subject: OSU transportation research study

A team of Ohio State University researchers is studying factors that influence transportation choices and the prospects of ridesharing services. The sponsor of this research is US Department of Transportation. The web link that is provided at the end of this invitation takes you to a questionnaire for this study.

The OSU Statistical Consulting Service (SCS) is executing the survey on behalf of the research team. You are among the sample of OSU students, faculty, and staff selected at random to participate. The SCS will keep your identity strictly confidential by replacing your name with a number and saving all identifying information in an encrypted file on a secure SCS computer. Other than designated SCS staff, this file will not be made available to anyone, including the research team. The data from this questionnaire will be used for purely scientific research purposes and will only be reported for groups in which no individual can be identified.

Completing this questionnaire is purely voluntary, and your decision on whether to participate will not affect your standing at OSU in any way. You may skip any questions you feel uncomfortable answering and/or withdraw at any time without penalty or loss of benefits (there are no individual benefits or penalties associated with participating or not). If you decide to complete the survey, but do not wish to complete the questionnaire in a single sitting, simply close your web browser. The next time you click on the web link you will return to where you left off. Once you click on the “SUBMIT” button at the end, you will not be able to change your answers.

Completing this questionnaire should take approximately 6 to 18 minutes depending on your responses. If you need to return to a previous page in the questionnaire, use the links within the survey.

If you have any questions about the mechanics of completing the survey, or if the web link fails, please contact Ms. Barb Wohlever via e-mail at . For any questions, concerns, or complaints about the research study, please contact Prof. Rabi Mishalani via email at or at 614-292-5949.

For questions about your rights as a participant in this study or to discuss other study- related concerns or complaints with someone who is not part of the research team, you may contact Ms. Sandra Meadows in the Office of Responsible Research Practices at 1- 800-678-6251.

The following is the web link that will take you to a questionnaire for this study. 134

ENTER LINK HERE

Sincerely, Christopher Holloman, Ph.D. Director OSU Statistical Consulting Service 328 Cockins Hall

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Socio-economic characteristics

What is your affiliation at OSU during this semester (Spring 2014)? Please select only one.

 Student  Staff  Faculty

(If “Student”) Are you currently an undergraduate or graduate student?

 Undergraduate  Graduate

(If “Student”) Do you currently live in an OSU campus dorm?

 Yes  No

(If “Staff”) What is your current job designation? Please select only one.

 Administrative and Professional  Civil Service  Other (provide text box)

(If “Administrative and Professional”, “Student”, or “Faculty”) Which academic group at OSU do you currently belong to? Please select only one.

 Agriculture  Arts and Sciences  Business  Education and Human Ecology  Engineering and Architecture  Law  Medical and Health Sciences  Social Work  Centers or labs not associated with any of the above entities  Other (provide text box)

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How many years have you been at OSU? Please select only one.

 1 year or less  More than 1 year but not more than 2 years  More than 2 years but not more than 5 years  More than 5 years but not more than 15 years  More than 15 years Are you currently enrolled/employed on the main OSU campus?

 Yes  No What is your gender?

 Male  Female

Who do you live with? Please select all that apply

 No one, I live alone  Spouse / significant other  Children  Roommates  Parents / grandparents  Other (provide text box)

What age category do you belong to? Please select only one.

 Under 25  25-40  41-55  Over 55

NOTE: The following questions are only asked if did not select “No” for the “Are you currently enrolled/employed on the main OSU campus?” question. If “No” is selected, no further questions should be asked and the last, “thank you” page should be shown.

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Current Travel Behavior

During this semester (Spring 2014), did you have a car available to you in the Columbus area?

 Yes  No

During this semester (Spring 2014) please select all the modes of transportation you used to travel from your home to campus.

 Drove a car alone  Shared a car as a driver  Shared a car as a passenger  COTA bus  CABS bus  Rode a motorbike alone  Shared a motorbike as a driver  Shared a motorbike as a passenger  Bicycle  Walk  Other (provide text box)

(If “Drove a car alone” or “Shared a car as a driver”) You have indicated on some occasions you drove a car alone and/or shared a car as a driver to travel to campus. Where did you park the car? Please select all that apply.

 Surface parking lot  Garage  On-street parking  Other (provide text box)

(If “Rode a motorbike alone” or “Shared a motorbike as a driver”) You have indicated on some occasions you rode a motorbike alone and/or shared a motorbike as a driver to travel to campus. Where did you park the motorbike? Please select all that apply.

 Surface parking lot  Garage  On-street parking  Other (provide text box) (If “COTA bus”)

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You have indicated on some occasions you used COTA to travel to campus. How did you access the COTA bus stop? Please select all that apply.

 Drove auto and parked  Dropped off as an auto passenger  CABS bus  Rode motorbike and parked  Dropped off as a motorbike passenger  Bicycle  Walk  Other (provide text box)

(If “CABS bus”) You have indicated on some occasions you used CABS to travel to campus. How did you access the CABS bus stop? Please select all that apply.

 Drove auto and parked  Dropped off as an auto passenger  COTA bus  Rode motorbike and parked  Dropped off as a motorbike passenger  Bicycle  Walk  Other (provide text box)

During this semester, how many days per week are you usually on campus? Please select only one.

 0 or 1 day  2 or 3 days  4 or 5 days  6 or 7 days

Now consider a specific travel day from last week:

When was the first weekday you came to campus last week? Please select only one.

 Monday  Tuesday  Wednesday  Thursday  Friday  None of the above NOTE: The following questions up until Ridesharing Scenarios are only asked if they did not select “None of the above” for the last question. 139

Please select the mode of transportation you used to travel from your home to campus on the ___day chosen above. Please select only one.

(If “Drove a car alone” or “Shared a car as a driver”) You have indicated on the ___day chosen above you either drove a car alone or shared a car as a driver to travel to campus. Where did you park the car? Please select only one.

 Surface parking lot  Garage  On-street parking  Other (provide text box)

(If “Drove a car alone” or “Shared a car as a driver”) How much time elapsed from when you got out of the car until you got to your final destination on campus (such as office, library, or classroom)? Please select only one.

 Less than 5 minutes  5 minutes to 9 minutes  10 minutes to 19 minutes  20 minutes to 29 minutes  30 minutes or more

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(If “Drove a car alone” or “Shared a car as a driver”) From the time you got out of the car until the time you got to your final destination which of the following consumed the most of that time? Please select only one.

 CABS (including time spent waiting for the bus)  Walking  Roughly equal time spent between CABS and walking  Other (provide text box)

(If “Rode a motorbike alone” or “Shared a motorbike as a driver”) You have indicated on some occasions you rode a motorbike alone or shared a motorbike as a driver to travel to campus. Where did you park the motorbike? Please select only one.

 Surface parking lot  Garage  On-street parking  Other (provide text box)

(If “Rode a motorbike alone” or “Shared a motorbike as a driver”) How much time elapsed from when you got off of the motorbike until you got to your final destination on campus (such as office, library, or classroom)? Please select only one.

 Less than 5 minutes  5 minutes to 9 minutes  10 minutes to 19 minutes  20 minutes to 29 minutes  30 minutes or more

(If “Rode a motorbike alone” or “Shared a motorbike as a driver”) From the time you got off of the motorbike until the time you got to your final destination which of the following consumed the most of that time? Please select only one.

 CABS (including time spent waiting for the bus)  Walking  Roughly equal time spent between CABS and walking  Other (provide text box)

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(If “COTA bus”) You have indicated on ___day you used COTA to travel to campus. How did you access the COTA bus stop? Please select only one.

 Drove auto and parked  Dropped off as an auto passenger  CABS bus  Rode motorbike and parked  Dropped off as a motorbike passenger  Bicycle  Walk  Other (provide text box)

(If “CABS bus”) You have indicated on ___day you used CABS to travel to campus. How did you access the CABS bus stop? Please select only one.

 Drove auto and parked  Dropped off as an auto passenger  COTA bus  Rode motorbike and parked  Dropped off as a motorbike passenger  Bicycle  Walk  Other (provide text box)

Continue to consider your trip from home to campus on ___day. When did you leave home to come to campus on the ___day chosen above? Please select only one.

 Before 5:00 am  5:00 am to 5:59 am  6:00 am to 6:29 am  6:30 am to 6:59 am  7:00 am to 7:29 am  7:30 am to 7:59 am  8:00 am to 8:29 am  8:30 am to 8:59 am  9:00 am to 9:29 am  9:30 am to 11:59 pm  After 12:00 pm

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For the ___day chosen above did you stop somewhere before arriving at your final destination on campus? Please select only one.

 Yes  No

(If chose “No”) How long did it take you to get from your home to your final destination on campus on the ___day chosen above? Please select only one.

 Less than 10 minutes  10 minutes to 19 minutes  20 minutes to 29 minutes  30 minutes to 39 minutes  40 minutes to 49 minutes  50 minutes to 59 minutes  More than 60 minutes

(If chose “Yes”) How long did it take you to get from your home to your final destination on campus on the ___day chosen above (including the additional time you spent when you stopped)? Please select only one.

 Less than 10 minutes  10 minutes to 19 minutes  20 minutes to 29 minutes  30 minutes to 39 minutes  40 minutes to 49 minutes  50 minutes to 59 minutes  More than 60 minutes

(If chose “Yes”) How long would it have taken you to get from your home to your final destination on campus on the ___day chosen above (without the additional time you spent when you stopped)? Please select only one.

 Less than 10 minutes  10 minutes to 19 minutes  20 minutes to 29 minutes  30 minutes to 39 minutes  40 minutes to 49 minutes  50 minutes to 59 minutes  More than 60 minutes

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(If chose “Yes”) What was your purpose for the stop? Please select all that apply.

 Socializing  Stopping for food/beverage  Shopping  Exercising  Dropping off/picking up family/other passengers  Other (provide text box)

(If chose “Socializing”) Was the socializing stop on campus or off campus? Please select only one.

 On campus  Off campus

(If chose “Stopping for food/beverage”) Was the stopping for food/beverage stop on campus or off campus? Please select only one.

 On campus  Off campus

(If chose “Shopping”) Was the shopping stop on campus or off campus? Please select only one.

 On campus  Off campus

(If chose “Exercising”) Was the exercising stop on campus or off campus? Please select only one.

 On campus  Off campus

(If chose “Dropping off/picking up family/other passengers”) Was the dropping off family/other passengers stop on campus or off campus? Please select only one.

 On campus  Off campus

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(If chose “Other”) Was the “other” stop on campus or off campus? Please select only one.

 On campus  Off campus

Continue to consider the ___day chosen above. Now consider your return trip, when leaving campus for your home (the final time you left campus that day). When did you depart campus for your home on that day? Please select only one.

 Before 12:00 pm  12:00 pm to 4:29 pm  4:30 pm to 4:59 pm  5:00 pm to 5:29 pm  5:30 pm to 5:59 pm  6:00 pm to 6:29 pm  6:30 pm to 6:59 pm  7:00 pm to 7:29 pm  After 7:30 pm

For the ___day chosen above did you stop somewhere before arriving at your home from campus? Please select only one.

 Yes  No

(If chose “No”) How long did it take you to get from campus to your home on the ___day chosen above? Please select only one.

 Less than 10 minutes  10 minutes to 19 minutes  20 minutes to 29 minutes  30 minutes to 39 minutes  40 minutes to 49 minutes  50 minutes to 59 minutes  More than 60 minutes

145

(If chose “Yes”) How long did it take you to get from campus to your home on the ___day chosen above (including the additional time you spent when you stopped)? Please select only one.

 Less than 10 minutes  10 minutes to 19 minutes  20 minutes to 29 minutes  30 minutes to 39 minutes  40 minutes to 49 minutes  50 minutes to 59 minutes  More than 60 minutes

(If chose “Yes”) How long would it have taken you to get from campus to your home on the ___day chosen above (without the additional time you spent when you stopped)? Please select only one.

 Less than 10 minutes  10 minutes to 19 minutes  20 minutes to 29 minutes  30 minutes to 39 minutes  40 minutes to 49 minutes  50 minutes to 59 minutes  More than 60 minutes

(If chose “Yes”) What was your purpose for the stop? Please select all that apply.

 Socializing  Stopping for food/beverage  Shopping  Exercising  Picking up/dropping off family/other passengers  Other (provide text box)

(If chose “Socializing”) Was the socializing stop on campus or off campus? Please select only one.

 On campus  Off campus

146

(If chose “Stopping for food/beverage”) Was the stopping for food/beverage stop on campus or off campus? Please select only one.

 On campus  Off campus

(If chose “Shopping”) Was the shopping stop on campus or off campus? Please select only one.

 On campus  Off campus

(If chose “Exercising”) Was the exercising stop on campus or off campus? Please select only one.

 On campus  Off campus

(If chose “Dropping off/picking up family/other passengers”) Was the dropping off family/other passengers stop on campus or off campus? Please select only one.

 On campus  Off campus

(If chose “Other”) Was the “other” stop on campus or off campus? Please select only one.

 On campus  Off campus

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Ridesharing Scenarios

(If answered “No” to: Do you currently live in an OSU campus dorm? (pg 2) AND If did not answer “Walk” or “Other” to: During this semester (Spring 2014) please select all the modes of transportation you used to travel to campus (pg 3)).

Consider the following hypothetical scenarios assuming they are taking place this semester (Spring 2014):

A free program becomes available that gives OSU affiliated individuals the chance to share rides to and from OSU. You enter limited information into a database where your privacy is protected. The program matches drivers and passengers.

As a passenger, you would be picked up from your home by an OSU affiliated individual whenever you choose. Also, you would not have to walk any farther than you usually do to get to your campus destination. In addition, you are guaranteed a ride home at a time of your choosing.

1. Please rate how likely you are to participate in the above program at least once a week as a passenger. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

If you were a driver, rather than a passenger, in this ridesharing program, you would pick up another OSU affiliated individual at the same time you usually depart home. The individual lives less than 5 minutes away. You would drop the individual off wherever you park on campus.

2. Please rate how likely you are to participate in the above program at least once a week as a driver. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

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Branch 1 (If “Unsure” or “Somewhat unlikely” or “Very unlikely” for 1) You answered ____ to being a passenger at least once a week in the above mentioned free program. Which of the following incentives might make you change your response to “Somewhat likely” or “Very likely”? Please select all that apply.

 Sufficient parking discount  Sufficient discount on day-to-day purchases  None of the above

(If “Sufficient parking discount”) You answered that a sufficient parking discount would make you “Somewhat likely” or “Very likely” for you to participate as a passenger in the ridesharing program.

Among the following options what is the minimum parking discount that would make you “Somewhat likely” or “Very likely” to participate as a passenger? Please select only one.

 20% discount on your monthly parking fees  40% discount on your monthly parking fees  60% discount on your monthly parking fees  80% discount on your monthly parking fees  Greater than 80% discount on your monthly parking fees

149

(If “Sufficient discount on day-to-day purchases”) You answered that a sufficient discount on day-to-day purchases would make you “Somewhat likely” or “Very likely” for you to participate as a passenger in the ridesharing program.

Among the following options what is the minimum discount on day-to-day purchases that would make you “Somewhat likely” or “Very likely” to participate as a passenger? Please select only one.

 Receive $10 per month in discounts on day-to-day purchases (e.g., restaurants, movies, etc.)  Receive $20 per month in discounts on day-to-day purchases (e.g., restaurants, movies, etc.)  Receive $30 per month in discounts on day-to-day purchases (e.g., restaurants, movies, etc.)  Receive $40 per month in discounts on day-to-day purchases (e.g., restaurants, movies, etc.)  Receive more than $40 per month in discounts on day-to-day purchases (e.g., restaurants, movies, etc.)

Branch 2 (If “Somewhat likely” or “Very likely” for 1) Previously you said you were ____ to participate as a passenger if you would be picked up from your home by an OSU affiliated individual whenever you choose. Also, you would not have to walk any farther than you usually do to get to your campus destination. In addition, you are guaranteed a ride home at a time of your choosing.

Now consider the case where, rather than being picked up whenever you choose, you will be picked up +/- 5 minutes of your usual departure time.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

150

(If “Somewhat likely” or “Very likely”) Consider the same scenario as just explained, except that you are picked up +/- 10 minutes of your usual departure time, rather than being picked up +/- 5 minutes of your usual departure time.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

(If “Somewhat likely” or “Very likely”) Consider the same scenario as just explained, except that you are picked up +/- 15 minutes of your usual departure time, rather than being picked up +/- 10 minutes of your usual departure time.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

(If “Somewhat likely” or “Very likely” for 1) Previously you said you were ____ to participate as a passenger if you would be picked up from your home by an OSU affiliated individual whenever you choose. Also, you would not have to walk any farther than you usually do to get to your campus destination. In addition, you are guaranteed a ride home at a time of your choosing.

Now consider the case where, rather than walking as far as you usually do to get to your campus destination, you walk an additional 5 minutes to your final destination.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

151

(If “Somewhat likely” or “Very likely”) Consider the same scenario as just explained, except that you walk an additional 10 minutes to your final destination, rather than walking an additional 5 minutes to your final destination.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

Now consider the case where, rather than your return trip occurring at a time of your choosing, it occurs 15 minutes later than your usual departure time.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

(If “Somewhat likely” or “Very likely”) Consider the same scenario as just explained, except that your return trip occurs 30 minutes later than your usual departure time, rather than your return trip occurring 15 minutes later than your usual departure time.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

152

(If “Somewhat likely” or “Very likely”) Consider the same scenario as just explained, except that your return trip occurs an hour later than your usual departure time, rather than your return trip occurring 30 minutes later than your usual departure time.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

Branch 3 (If “Unsure” or “Somewhat unlikely” or “Very unlikely” for 2) You answered ____ to being a driver at least once a week in the above mentioned free program. Which of the following incentives would make you change your response to “Somewhat likely” or “Very likely”? Please select all that apply.

 Sufficient parking discount  Sufficient discount on day-to-day purchases  Sufficient cash payment  Convenient, designated parking spot  None of the above

(If “Sufficient parking discount”) You answered that a sufficient parking discount would make you “Somewhat likely” or “Very likely” for you to participate as a driver in the ridesharing program.

Among the following options what is the minimum parking discount that would make you “Somewhat likely” or “Very likely” to participate as a driver? Please select only one.

153

Among the following options what is the minimum discount on day-to-day purchases that would make you “Somewhat likely” or “Very likely” to participate as a driver? Please select only one.

(If “Sufficient cash payment”) You answered that a sufficient cash payment would make you “Somewhat likely” or “Very likely” for you to participate as a driver in the ridesharing program.

Among the following options what is the minimum cash payment that would make you “Somewhat likely” or “Very likely” to participate as a driver? Please select only one.

 10 cents per mile for every trip in which you drove a ridesharing passenger  30 cents per mile for every trip in which you drove a ridesharing passenger  50 cents per mile for every trip in which you drove a ridesharing passenger  70 cents per mile for every trip in which you drove a ridesharing passenger  Greater than 70 cents per mile for every trip in which you drove a ridesharing passenger

154

(If “Convenient, designated parking spot”) You answered that a convenient, designated parking spot would make you “Somewhat likely” or “Very likely” for you to participate as a driver in the ridesharing program.

Among the following options what answer would make you “Somewhat likely” or “Very likely” to participate as a driver? Please select all that apply.

 Convenient, designated parking spot in existing parking location  Designated parking spot in a parking location closer to your campus destination  Other (provide text box)

Branch 4 (If “Somewhat likely” or “Very likely” for 2) Previously you said you were ____ to participate as a driver if you would pick up another OSU affiliated individual at the same time you usually depart home. The individual lives less than 5 minutes away. You would drop the individual off wherever you park on campus.

Now consider the case where, rather than the individual living less than 5 minutes away, the person lives less than 10 minutes.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

(If “Somewhat likely” or “Very likely”) Consider the same scenario as just explained, except that the individual to be picked up lives less than 15 minutes away from you, rather than less than 10 minutes.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

155

(If “Somewhat likely” or “Very likely” for 2) Previously you said you were ____ to participate as a driver if you would pick up another OSU affiliated individual at the same time you usually depart home. The individual lives less than 5 minutes away. You would drop the individual off wherever you park on campus.

Now consider the case where, rather than you picking up the individual at the same time you usually depart the individual would like to be picked up within +/- 5 minutes of when you usually depart.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

(If “Somewhat likely” or “Very likely”) Consider the same scenario as just explained, except that the individual is to be picked up +/- 10 minutes of when you usually depart, rather than +/- 5 minutes of when you usually depart.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

(If “Somewhat likely” or “Very likely”) Consider the same scenario as just explained, except that the individual is to be picked up +/- 15 minutes of when you usually depart, rather than +/- 10 minutes of when you usually depart.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

156

Now consider the case where, rather than you dropping off the individual where you normally park the individual would be dropped off within a +/- 5 minute drive from your destination.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

(If “Somewhat likely” or “Very likely”) Consider the same scenario as just explained, except that the individual is to be dropped off within +/- 10 minute drive from your destination, rather than a +/- 5 minute drive from your destination.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

(If “Somewhat likely” or “Very likely”) Consider the same scenario as just explained, except that the individual is to be dropped off within +/- 15 minute drive from your destination, rather than a +/- 10 minute drive from your destination.

Please rate how likely you are to participate in this program at least once a week. Please select only one.  Very unlikely  Somewhat unlikely  Unsure  Somewhat likely  Very likely

157

Appendix B: Variable Definitions

Table 7: Variable Definitions

Variable Name Variable Content Gender 1 if female, 0 if male Live Alone 1 if lived alone, 0 otherwise Lives with Spouse 1 if lived with spouse, 0 otherwise Lives with 1 if lived with roommates, 0 otherwise Roommates Lives with Parent or 1 if lived with parents or grandparents, 0 otherwise Grandparent Lives with Children 1 if lived with children, 0 if otherwise “Mom” 1 if lived with children AND female, 0 otherwise Single Parent 1 if lived with children AND did not live with spouse, 0 otherwise Only Lives with 1 if lived with children AND did not live with anyone else, Children 0 otherwise Undergraduate 1 if undergraduate student, 0 otherwise Student Graduate Student 1 if graduate student, 0 otherwise Faculty 1 if faculty member, 0 if otherwise Administrative & 1 if administrative & professional staff, 0 otherwise Professional Staff Civil Service Staff 1 if civil service staff, 0 otherwise Student 1 if was a graduate OR undergraduate student, 0 otherwise Faculty or Civil 1 if was a faculty OR civil service staff member Service Staff Age (Categorical) <25 1 if less than twenty-five years old, 0 otherwise 25-40 1 if between twenty-five and forty years old, 0 otherwise 41-55 1 if between forty-one and fifty-five years old, 0 otherwise >55 1 if over fifty-five years old, 0 otherwise Age (Continuous) 21 if less than twenty-five years old, 32.5 if between twenty-five and forty years old, 48 if between forty-one and fifty-five years old, and 65 if over fifty-five years old continued

159

Table 7 continued

Years on Campus ≤1 1 if one year or less on campus, 0 otherwise (Categorical) 1-2 1 if more than one year but no more than two years on campus, 0 otherwise 2-5 1 if more than two years but no more than five years on campus, 0 otherwise 5-15 1 if more than five years but no more than fifteen years on campus, 0 otherwise ≥15 1 if more than fifteen years on campus, 0 otherwise Non-Student Age <25 1 if less than twenty-five years old AND not a (Categorical) student, 0 otherwise 25-40 1 if twenty-five and forty years old AND not a student, 0 otherwise 41-55 1 if between forty-one and fifty-five years old AND not a student, 0 otherwise >55 1 if over fifty-five years old AND not a student, 0 otherwise Non-Student Age 21 if less than twenty-five years old AND not a student, (Continuous) 32.5 if between twenty-five and forty years old AND not a student, 48 if between forty-one and fifty-five years old AND not a student, and 65 if over fifty-five years old AND not a student Non-Student Years ≤1 1 if one year or less on campus AND not a On Campus student AND not a student, 0 otherwise (Categorical) 1-2 1 if more than one year but no more than two years on campus AND not a student, 0 otherwise 2-5 1 if more than two years but no more than five years on campus AND not a student, 0 otherwise 5-15 1 if more than five years but no more than fifteen years on campus AND not a student, 0 otherwise ≥15 1 if more than fifteen years on campus AND not a student, 0 otherwise Lives with Children <25 1 if less than twenty-five years old AND lives Age (Categorical) with children, 0 otherwise 25-40 1 if between twenty-five and forty years old AND lives with children, 0 otherwise 41-55 1 if between forty-one and fifty-five years old AND lives with children, 0 otherwise >55 1 if over fifty-five years old AND lives with children, 0 otherwise continued 160

Table 7 continued

Lives with Children 21 if less than twenty-five years old AND lives with Age (Continuous) children, 32.5 if between twenty-five and forty years old AND lives with children, 48 if between forty-one and fifty- five years old AND lives with children, and 65 if over fifty- five years old AND lives with children Lives with Children ≤1 1 if one year or less on campus AND lives with Years on Campus children AND lives with children, 0 otherwise (Categorical) 1-2 1 if more than one year but no more than two years on campus AND lives with children, 0 otherwise 2-5 1 if more than two years but no more than five years on campus AND lives with children, 0 otherwise 5-15 1 if more than five years but no more than fifteen years on campus AND lives with children, 0 otherwise ≥15 1 if more than fifteen years on campus AND lives with children, 0 otherwise Non-Student Lives <25 1 if less than twenty-five years old AND not a with Children Age student AND lives with children, 0 otherwise (Categorical) 25-40 1 if between twenty-five and forty years old AND not a student AND lives with children, 0 otherwise 41-55 1 if between forty-one and fifty-five years old AND not a student AND lives with children, 0 otherwise >55 1 if over fifty-five years old AND not a student AND lives with children, 0 otherwise Non-Student Lives 21 if less than twenty-five years old AND not a student with Children Age AND lives with children, 32.5 if between twenty-five and (Continuous)) forty years old AND not a student AND lives with children, 48 if between forty-one and fifty-five years old AND not a student AND lives with children, and 65 if over fifty-five years old AND not a student AND lives with children continued

161

Table 7 continued

Non-student Lives ≤1 1 if one year or less on campus AND not a with Children Years student AND lives with children, 0 otherwise on Campus 1-2 1 if more than one year but no more than two (Categorical) years on campus AND not a student AND lives with children, 0 otherwise 2-5 1 if more than two years but no more than five years on campus AND not a student AND lives with children, 0 otherwise 5-15 1 if more than five years but no more than fifteen years on campus AND not a student AND lives with children, 0 otherwise ≥15 1 if more than fifteen years on campus AND not a student AND lives with children, 0 otherwise Joint Age and Years <40 1 if less than forty years old AND less than five on Campus yrs. old years on campus & <5 yrs. on campus <40 1 if less than forty years old AND more than five yrs. old years on campus & >5 yrs. on campus >40 1 if more than forty years old AND less than five yrs. old years on campus & <5 yrs. on campus >40 1 if more than forty years old AND more than yrs. old five years on campus & >5 yrs. on campus Drove Alone 1 if drove alone sometime this semester, 0 otherwise Shared Car as Driver 1 if shared a car as a driver sometime this semester, 0 otherwise Shared Car as 1 if shared a car as a passenger sometime this semester, 0 Passenger otherwise continued

162

Table 7 continued

Rode Motorbike 1 if rode a motorbike alone sometime this semester, 0 Alone otherwise Shared Motorbike as 1 if shared a motorbike as a driver sometime this semester, Driver 0 otherwise Shared Motorbike as 1 if shared a motorbike as a passenger sometime this Passenger semester, 0 otherwise COTA 1 if used COTA sometime this semester, 0 otherwise CABS 1 if used CABS sometime this semester, 0 otherwise Bike 1 if used a bike sometime this semester, 0 otherwise Other 1 if used other transportation sometime this semester, 0 otherwise Transit User 1 if used COTA OR CABS sometime this semester, 0 if otherwise Exclusively Drove 1 if only drove alone this semester, 0 otherwise Alone Exclusively Mode 1 if only used one mode this semester, 0 otherwise User Days on Campus 0.5 if was on campus one to two days a week, 2.5 if was on campus two or three days a week, 4.5 if was on campus four or five days a week, 6.5 if on campus six or seven days a week No Car Available 1 if did not have a car available this semester , 0 if otherwise 25-40 Years Old and 1 if between twenty-five and forty years old AND lived Lives with Children with children Travel Time to <10 min. 1 if less than ten minute travel time to Campus campus, 0 otherwise (Categorical)* 10-19 min. 1 if between ten and nineteen minute travel time to campus, 0 otherwise 20 -29 min. 1 if between twenty and twenty-nine minute travel time to campus, 0 otherwise 30 -39 min. 1 if between thirty and thirty-nine minute travel time to campus, 0 otherwise 40 -49 min. 1 if between forty and forty-nine minute travel time to campus, 0 otherwise 50 -59 min. 1 if between fifty and fifty-nine minute travel time to campus, 0 otherwise >60 min. 1 if more than sixty minute travel time to campus, 0 otherwise continued

163

Table 7 continued

Travel Time from <10 min. 1 if less than ten minute travel time from Campus campus, 0 otherwise (Categorical)* 10-19 min. 1 if between ten and nineteen minute travel time from campus, 0 otherwise 20 -29 min. 1 if between twenty and twenty-nine minute travel time from campus, 0 otherwise 30 -39 min. 1 if between thirty and thirty-nine minute travel time from campus, 0 otherwise 40 -49 min. 1 if between forty and forty-nine minute travel time from campus, 0 otherwise 50 -59 min. 1 if between fifty and fifty-nine minute travel time from campus, 0 otherwise >60 min. 1 if more than sixty minute travel time from campus, 0 otherwise Travel Time to 5 if less than ten minute travel time to campus, 14.5 if Campus between ten and nineteen minutes travel time to campus, (Continuous)* 24.5 if between twenty and twenty-nine minutes travel time to campus, 34.5 if between thirty and thirty-nine minute travel time to campus, 44.5 if between forty and forty-nine minutes travel time to campus, 55.5 if between fifty and fifty-nine minutes travel time to campus, 75 if over one hour travel time to campus Travel Time from 5 if less than ten minute travel time from campus, 14.5 if Campus between ten and nineteen minutes travel time from campus, (Continuous)* 24.5 if between twenty and twenty-nine minutes travel time from campus, 34.5 if between thirty and thirty-nine minute travel time from campus, 44.5 if between forty and forty- nine minutes travel time from campus, 55.5 if between fifty and fifty-nine minutes travel time from campus, 75 if over one hour travel time from campus continued

164

Table 7 continued

Motorized Travel <10 min. 1 if less than ten minute travel time to Time to Campus campus AND did not walk or bicycle, 0 (Categorical)* otherwise 10-19 min. 1 if between ten and nineteen minute travel time to campus AND did not walk or bicycle, 0 otherwise 20 -29 min. 1 if between twenty and twenty-nine minute travel time to campus AND did not walk or bicycle, 0 otherwise 30 -39 min. 1 if between thirty and thirty-nine minute travel time to campus AND did not walk or bicycle, 0 otherwise 40 -49 min. 1 if between forty and forty-nine minute travel time to campus AND did not walk or bicycle, 0 otherwise 50 -59 min. 1 if between fifty and fifty-nine minute travel time to campus AND did not walk or bicycle, 0 otherwise >60 min. 1 if more than sixty minute travel time to campus AND did not walk or bicycle, 0 otherwise continued

165

Table 7 continued

Motorized Travel <10 min. 1 if less than ten minute travel time from Time from Campus campus AND did not walk or bicycle, 0 (Categorical)* otherwise 10-19 min. 1 if between ten and nineteen minute travel time from campus AND did not walk or bicycle, 0 otherwise 20 -29 min. 1 if between twenty and twenty-nine minute travel time from campus AND did not walk or bicycle, 0 otherwise 30 -39 min. 1 if between thirty and thirty-nine minute travel time from campus AND did not walk or bicycle, 0 otherwise 40 -49 min. 1 if between forty and forty-nine minute travel time from campus AND did not walk or bicycle, 0 otherwise 50 -59 min. 1 if between fifty and fifty-nine minute travel time from campus AND did not walk or bicycle, 0 otherwise >60 min. 1 if more than sixty minute travel time from campus AND did not walk or bicycle, 0 otherwise Motorized Travel 5 if less than ten minute travel time to campus AND did not Time to Campus walk or bicycle, 14.5 if between ten and nineteen minutes (Continuous)* travel time to campus AND did not walk or bicycle, 24.5 if between twenty and twenty-nine minutes travel time to campus AND did not walk or bicycle, 34.5 if between thirty and thirty-nine minute travel time to campus AND did not walk or bicycle, 44.5 if between forty and forty-nine minutes travel time to campus AND did not walk or bicycle, 55.5 if between fifty and fifty-nine minutes travel time to campus AND did not walk or bicycle, 75 if over one hour travel time to campus AND did not walk or bicycle continued

166

Table 7 continued

Motorized Travel 5 if less than ten minute travel time from campus AND did Time from Campus not walk or bicycle, 14.5 if between ten and nineteen (Continuous)* minutes travel time from campus AND did not walk or bicycle, 24.5 if between twenty and twenty-nine minutes travel time from campus AND did not walk or bicycle, 34.5 if between thirty and thirty-nine minute travel time from campus AND did not walk or bicycle, 44.5 if between forty and forty-nine minutes travel time from campus AND did not walk or bicycle, 55.5 if between fifty and fifty-nine minutes travel time from campus AND did not walk or bicycle, 75 if over one hour travel time from campus AND did not walk or bicycle Socializing Stop* 1 if stopped to socialize on a trip to or from campus, 0 if otherwise Food or Beverage 1 if stopped for food or beverage on a trip to or from Stop* campus, 0 if otherwise Picked-Up or 1 if stopped to drop off or pick someone up on a trip to or Dropped-Off a from campus, 0 if otherwise Passenger* Exercising Stop* 1 if stopped to exercise on a trip to or from campus, 0 if otherwise Shopping Stop* 1 if stopped to shop on a trip to or from campus, 0 if otherwise Any Stop* 1 if stopped on a trip to or from campus, 0 if otherwise No to Passenger, No 1 if was not likely to participate in the ideal ridesharing to Driver, and None scenario as a passenger AND not likely to participate as a of the Above driver AND selected none of the above when offered Incentives incentives to participate, 0 otherwise Driver 1 if was a likely rideshare participant as a driver, 0 if otherwise Passenger 1 if was a likely rideshare participant as a passenger, 0 if otherwise * Variable was based on responses about a specific recent trip made by the respondent

167