报告时间：2023年12月11日（星期一）9:00
报告地点：三立苑324会议室
报 告 人：Xuegang (Jeff) Ban 教授
工作单位：University of Washington
举办单位：汽车与交通工程学院
报告简介：
Urban traffic system is multiscale in both spatial and temporal domains. Traffic at each scale has its own behavior and dynamics, while interacting with traffic at other scales. In this research, we propose a general multiscale modeling and control framework for urban traffic, motivated by connected and automated vehicles (CAVs). We propose several key concepts for multiscale modeling, such as the consistency of states between different scales, to establish the stability of the control scheme, as well as a model predictive control based solution method. We share simulation results of applying the multiscale control method to the Downtown Seattle network and some thoughts on how this may be extended to larger urban networks.
报告人简介：
Dr. Xuegang (Jeff) Ban is the William and Marilyn Conner Endowed Professor with the Department of Civil and Environmental Engineering at the University of Washington. He received his B.S. and M.S. in Automotive Engineering from Tsinghua University in China, and his M.S. in Computer Sciences and Ph.D. in Civil Engineering (Transportation) from the University of Wisconsin at Madison. His research interests are in Transportation Network System Modeling and Simulation, and Urban Traffic Modeling and Control. His recent work focuses on applying optimization, control, and ML/AI methods to the understanding and modeling of emerging technologies/systems in transportation such as CAVs and New Mobility Services. Dr. Ban is an Associate Editor of Transportation Research Part C, IEEE Transactions on Intelligent Transportation Systems, and Journal of Intelligent Transportation Systems. He received the 2011 CAREER Award from the National Science Foundation (NSF), and the New Faculty Award from the Council of University Transportation Centers (CUTC) and the American Road & Transportation Builders Association (ARTBA) in 2012. He was also one of the recipients of the Finalist of Franz Edelman Award in 2017 by INFORMS.