GEORGIA INSTITUTE OF TECHNOLOGY

Under the provisions of the regulations for the degree

DOCTOR OF PHILOSOPHY

on Tuesday, June 5, 2018

1:00 PM – 3:30 PM (Eastern Time)

in Architecture East Room 214

will be held the

DISSERTATION DEFENSE

for

Fangru Wang

“Unveiling the Potential of On-demand Ride Service and Its Impact on Mode Choice and Accessibility”

The Examiners Are:

Dr. Catherine Ross (Chair)

Dr. Patricia Mokhtarian

Dr. Bruce Stiftel

Dr. Alex Karner

Ms. Jamie Cochran

Faculty and students are invited to attend this examination.

Abstract:

The recent advancement in information technologies has facilitated the emergence and growth of travel modes like ride-sourcing, car-sharing, and bike-sharing, providing travelers with unprecedentedly broad travel options. The nature of these options will significantly affect the way how people travel and engage in activities, and therefore lead to transport network impacts. Ride-sourcing, referring to app-based on-demand ride service (ODRS), exhibits similar traits of traditional taxis but provides better real-time information and lowered cost compared to taxis. The fast growth of ride-sourcing also reflects the trend known as Mobility as a Service (MaaS) and can be seen as a litmus test of connected and autonomous vehicles which will further transform the transportation landscape. This dissertation explores three main aspects of ODRS using a three-part analysis: an exploratory analysis of the role of ODRS in urban transportation, a discrete choice modeling to understand the choice of ODRS, and scenario forecasting to quantify the potential impact of ODRS on transport accessibility and equity.

The dissertation results indicate the critical role that ODRS has in serving transport-disadvantaged population and multimodal travel and filling in gaps of transit, identify the socio-demographic, built environment, and trip characteristics associated with the choice of ODRS, and reveal the substantial accessibility and equity benefits of integrating ODRS with transit. The dissertation also shows strong performance of machine learning travel mode choices and suggests the further integration of machine learning with travel demand forecasting. The findings unveil the potentials of ODRS in elevating transport benefits of the existing infrastructure and point to strategies of leveraging ODRS and autonomous vehicles to improve transport mobility, accessibility, and equity. The results also reveal challenges of realizing the benefits of ODRS and incorporating ODRS into travel demand forecasting, which will have to rely on data collection, public-private collaboration, and research and practical exploration of synergizing ODRS with other travel modes.