Sangwan Lee of Portland State University Investigates Transportation Mode Choice in the Era of Autonomous Vehicles

Dec 21, 2022

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...

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A red car travels along a highway

Where Do Emerging Travel Modes Fit Into Regional Models?

Nov 15, 2018
Photo by Felix Tchverkin on Unsplash
Principal Investigator: Liming Wang, Portland State University
Learn more about this research by viewing related publications, open-source data, and the full Final Report on the Project Overview page.

The latest report from The National Institute for Transportation & Communities (NITC) offers help to planners seeking to incorporate emerging travel modes—including car sharing, bike sharing, ride hailing, and autonomous vehicles—into regional travel demand models. More specifically, it brings these new travel modes into the Regional Strategic Planning Model (RSPM) tool. As more people start taking advantage of new...

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Webinar: Modeling Freeway Traffic in a Mixed Environment: Connected and Human-Driven Vehicles - Terry Yang

Webinar: Modeling Freeway Traffic in a Mixed Environment: Connected and Human-Driven Vehicles

 

PRESENTATION ARCHIVE

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OVERVIEW

Although connected vehicles (CVs) will soon go beyond testbeds, CVs and human-driven vehicles (HVs) will co-exist over a long period. Hence, it is critical to consider the interactions between these two types of vehicles in traffic flow modeling. In this study, we aim to develop a macroscopic model to understand how CVs would impact HVs in the traffic stream. Grounded on the second-order traffic flow model, we study the relationships among flow, density, and speed by two sets of formulations for the groups of CVs and HVs, respectively. A set of friction factors, which indicate CVs' impact to HVs, are introduced to the speed equation for accounting CV speed impacts. Then extended Kalman Filter is employed to update both model parameters and friction factors in real-time. By using CVs trajectory data as measurements, the difference between CV average speed and overall traffic mean speed will be fully accounted. The proposed model will serve as a basis for designing CV-based traffic control function,...

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Aerial view of urban city road with cars on the road and crosswalk. Text reads: Webinar: Land Use and Transportation Policies for a Sustainable Future.

Webinar: Land Use and Transportation Policies for a Sustainable Future

 

PRESENTATION ARCHIVE

OVERVIEW

Even though there are tremendous uncertainties in the timing and evolution path of the Autonomous Vehicles (AV) technology, it may become a likely reality within most MPOs' long-range regional transportation plan horizon of twenty years. Yet a recent survey of the largest MPOs in the US indicates only one of them "even mentions driverless, automated, or autonomous vehicles in its most recent RTP". One of the uncertainties in assessing the impacts of AV is their direction: on one hand, self-driving cars could increase VMT by increasing roadway capacity, lowering costs of travel; on the other, they may reduce VMT by enabling more car-sharing, improving access to transit, eliminating the fixed costs of car ownership, and reclaiming parking space. To date, there is no suitable conceptual framework or modeling tools available to MPOs for quantitatively assessing the likely long-term effects of AV or potential policy scenarios.

This project studies the possible impacts on travel and land use of the emerging AV technology and focuses on advancing this innovative mobility option by...

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