Graduation Year

2019

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Civil and Environmental Engineering

Major Professor

Yu Zhang, Ph.D.

Co-Major Professor

Fred Mannering, Ph.D.

Committee Member

Robert Bertini, Ph.D.

Committee Member

Grisselle Centeno, Ph.D.

Committee Member

Virginia Sisiopiku, Ph.D.

Committee Member

Christian Wells, Ph.D.

Keywords

Transport and Health, Random Parameter Logit Models, Bikesharing, Ridesourcing, Shared Automated Vehicles

Abstract

New transportation technologies and shared mobility systems have not only disrupted the market but also revolutionized the way mobility is perceived. With such rapidly progressing technology, it is likely that this transformation will continue to happen and the market will evolve further. Private and public sectors have been equally engaged in this process and new policies are being formed to accommodate this growth.

The objective of this dissertation study is to investigate how shared mobility is being perceived and utilized, and how socio-demographic and health factors affect users’ behaviors and usage likelihoods. Questionnaire surveys are designed and distributed to collect data and numerous heterogeneity econometric models are estimated to uncover some of the complexities of human behaviors regarding transportation choices. Specifically, this study addresses four different aspects/models related to shared mobility adoption and use.

First, the factors influencing how often registered bikesharing users use the system and the determinants for auto-trip substitution are investigated. In addition to standard socio-demographic and travel behavior characteristics of the survey respondents, health-related questions are also included in the survey and health-related indicators are considered as explanatory variables in the estimated models. It was found that gender, age, income, household size, commute type and length, and vehicle ownership all play significant roles in bikesharing usage and modal substitution decisions. Regarding health measures, respondents’ body mass index (BMI) was also a significant predictor of bikesharing usage.

Next, likelihood of adoption of shared automated vehicles and respondents’ potential concerns of this new transportation mode are investigated. Some of the key variables playing roles in the willingness to use shared automated vehicles are ethnicity, household size, daily travel times, and vehicle crash history. With regard to concerns associated with shared automated vehicles, this study also identifies the characteristics of respondents who are more or less likely to be concerned with safety, reliability, privacy, and travel time/travel cost. The results offer initial assessment of the market and reveal how different groups of users will behave when shared automated vehicles become available to the public.

This research also investigates behaviors relating to ridesourcing usage frequency. Because ridesourcing companies have become an integral part of transportation systems, the intent of this objective is to develop a statistical model of individuals’ usage rates of ridesourcing services. The results show that age, gender, income, household size, vehicle ownership, typical parking time, and the nature of commutes all play role in determining how often an individual uses ridesourcing services. In addition, self-assessed health, high body mass index (BMI), and registration for other shared mobility services are also found to play roles in ridesourcing usage.

Lastly, peer-to-peer carsharing is investigated. The aim of this research objective is to determine the respondents’ willingness to lend their personal vehicle to the peer-to-peer ridesourcing fleet. The model estimation results reveal that gender, age, income, household composition, vehicle ownership as well as the living location and participation in other shared mobility modes all influence the likelihood to rent their personal vehicle to others. Because of the novelty of this type of carsharing model and the lack of data and literature, current study provides an early evaluation of the market and determines which factors will likely to contribute to the success of peer-to-peer carsharing.

In summary, this dissertation research applies data-driven analysis to understand perception, adoption, usage, and concerns of emerging technologies and shared mobility. Additionally, the study investigates the relationship between the area of public health and transportation, and determines how health-related variables impact transportation decisions. The outcomes provide insights to service providers, planning agencies, and policymakers.

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