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T3e Webinar Overview

Autonomous Vehicle Assignment and Routing in Congested Transportation Networks

Date: Thursday, October 25, 2018
Time: 1:00 PM – 2:00 PM ET
Cost: All T3e webinars are free of charge.
PDH: 1.0 View PDH Policy

T3e Webinars are brought to you by the Intelligent Transportation Systems (ITS) Professional Capacity Building Program (PCB) of the U.S. Department of Transportation’s (U.S. DOT) ITS Joint Program Office (JPO). The purpose of this webinar series is to provide a platform for students to share their research findings. References in this webinar to any specific commercial products, processes, or services, or the use of any trade, firm, or corporation name is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by the U.S. DOT.


Ridesharing services have been growing since the start of network service companies in recent years, and will be further enhanced with the deployment of autonomous vehicles in the future. When the daily trip request pattern is stable based on the steady population composition, economy, and city infrastructure in a long time period, one fundamental question is what the corresponding dynamic traffic condition will be if all vehicles are autonomous and can be dispatched in a centralized way. This presentation will discuss how to optimally assign the available vehicles from different depots to satisfy those stable trip requests, while considering the road congestion endogenously incurred by the interactions of those dynamically assigned vehicles.

Learning Objectives

The audience will learn about:

  • Modeling the process of dispatching autonomous vehicles to pick up and drop off passengers with trip location and time window requests;
  • Capturing the road congestion incurred by those dispatched vehicles in space-time networks; and
  • Developing algorithms to solve the vehicle routing and assignment problems to show the dynamic traffic patterns in future transportation systems.

Target Audience

The target audience includes graduate students, researchers, transportation modelers, and other practitioners who are interested in vehicle routing and dynamic traffic assignment problems.


Xuesong Zhou, Ph.D., Associate Professor in Civil Engineering at Arizona State University
Xuesong Zhou Dr. Xuesong Zhou serves as Associate Professor at the School of Sustainable Engineering and the Built Environment at Arizona State University (ASU). His research work focuses on dynamic traffic assignment, traffic estimation and prediction, large-scale routing, and rail scheduling. Dr. Zhou is currently an Associate Editor of Transportation Research Part C, an Associate Executive Editor-in-Chief of Urban Rail Transit, an Associate Editor of Networks and Spatial Economics, and an Editorial Board Member of Transportation Research Part B. He was the formal Chair of INFORMS (Institute for Operations Research and the Management Sciences) Rail Application Section (2016), and the Co-Chair of the IEEE (Institute of Electrical and Electronics Engineers) ITS Society Technical Committee on Traffic and Travel Management, as well as a subcommittee chair of the TRB (Transportation Research Board) Committee on Transportation Network Modeling (ADB30).


Jiangtao Liu, Ph.D., Post-doctoral Research Associate in Civil Engineering at Arizona State University
Jiangtao Liu Dr. Jiangtao Liu received a B.S. in Traffic and Transportation from Southwest Jiaotong University, Chengdu, China in 2008 and a Master in Transportation Planning and Management from Beijing Jiaotong University, Beijing, China in 2010. He recently graduated with a Ph.D. in Civil Engineering from Arizona State University in 2018. He is currently a post-doctoral research associate in the School of Sustainable Engineering and the Built Environment at Arizona State University. Dr. Liu has published several papers on vehicle routing and control, transportation service network design, as well as dynamic traffic assignment and simulation. His current research mainly focuses on future urban mobility optimization and simulation.


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