New mobility technologies, such as shared mobility services and autonomous vehicles (AVs), continue to evolve. How do travelers decide whether to adopt new transportation modes or continue to use conventional modes? "Transportation Mode Choice Behavior in the Era of Autonomous Vehicles: The Application of Discrete Choice Modeling and Machine Learning" is a 2022 dissertation by Sangwan Lee of Portland State University which uses machine learning to examine this question.

Lee, who earned his PhD from the Nohad A. Toulan School of Urban Studies and Planning in 2022 working with faculty advisor Liming Wang, is now a research associate working in employment research at LX Spatial Information Research Institute, Korea Land and Geospatial Informatix Corporation in Jeonju, South Korea. He is currently working on several research topics, including autonomous logistics.

"I'm excited about the next chapter of my work in employment research because I am joining research projects about autonomous vehicles," Lee said.

Lee's dissertation consists of three papers. The first examines future market shares of each available mode of transportation in the era of AVs, factors influencing mode choice behaviors, and their marginal effects using a mixed logit model (MXL). The second uses interpretable machine learning (ML) to investigate the optimal algorithm (i.e., stochastic gradient boosting decision tree model) in greater depth, including feature importance and non-linear marginal effects. Focusing on methodology, the final paper assesses the limitations of ML when applied to transportation mode choice modeling and suggests future research directions for methodological improvements by comparing ML to discrete choice modeling (DCM).

This research contributes to three major elements of the current understanding of transportation mode choice behavior in the era of AVs and choice modeling as follows:

• First, consumers in the AV era could choose from a variety of transportation modes likely to coexist, including private AVs, shared mobility services, and conventional transportation modes. This dissertation thus makes a significant contribution by examining more comprehensive transportation mode choice behaviors and expanding demand-side discussions.
• Second, since current transportation planning efforts have relied on estimates and expectations, this dissertation contributes to the decision-making process by offering crucial underlying knowledge not currently available.
• Third, this dissertation assesses the limitations of ML for transportation mode choice modeling and suggests potential future avenues for methodological improvement.

Learn more about Sangwan Lee's background and works by visiting his ORCID profile.

Photo by CHUTTERSNAP on Unsplash

Portland State University's Transportation Research and Education Center (TREC) is home to the U.S. DOT funded National Institute for Transportation and Communities (NITC), the Initiative for Bicycle and Pedestrian Innovation (IBPI), PORTAL, BikePed Portal and other transportation grants and programs. We produce impactful research and tools for transportation decision makers, expand the diversity and capacity of the workforce, and engage students and professionals through education and participation in research.