Abstract: Largescale mobility data can be collected from mobile phones, car navigation systems, road surveillance cameras, and loop sensors. Turning such mobility data into knowledge can provide insights about our city and empower the city to be more intelligent.

This talk presents how to utilize mobility data and advanced learning methods for traffic signal control. First, we examine the existing traffic signal control system and discuss why today we have the opportunity for a potential breakthrough in traffic signal control. Second, the talk presents our recent research results in traffic signal control via deep reinforcement learning. We demonstrate how the classical transportation methods can be integrated to guide our reinforcement learning approach. Finally, we would like to discuss the open challenges in this research topic and share the experience in the field experiments of controlling traffic signals in the city of Hangzhou.

Bio: Dr. Zhenhui (Jessie) Li is a tenured associate professor of Information Sciences and Technology at the Pennsylvania State University. She is Haile family early career endowed professor. Prior to joining Penn State, she received her PhD degree in Computer Science from University of Illinois UrbanaChampaign in 2012, where she was a member of data mining research group. Her research has been focused on mining spatialtemporal data with applications in transportation, ecology, environment, social science, and urban computing. She is a passionate interdisciplinary researcher and has been actively collaborating with crossdomain researchers. She has served as organizing committee or senior program committee of many conferences including KDD, ICDM, SDM, CIKM, and SIGSPATIAL. She has received NSF CAREER award, junior faculty excellence in research, and George J. McMurtry junior faculty excellence in teaching and learning award. She is currently taking sabbatical leave (20182019) at Hangzhou China to conduct city brain research. She is looking for interns to join her research team in Hangzhou. To learn more, please visit her homepage: https://faculty.ist.psu.edu/jessieli