Urban Traffic Information Estimation and Prediction
Project ImageData Integration: Combines GPS, video surveillance, public transit, and other sources to create a comprehensive data framework.
Prediction Model: Utilizes variable-order Markov models (VOMM) and dynamic rule mining for short-term multi-step traffic forecasts.
Challenges Addressed: Tackles issues like discontinuity in traffic mode transitions and complexity in multi-layer network couplings.
Outcome: Improves accuracy and efficiency in predicting urban traffic patterns, enabling proactive management strategies.
Yunwen XU
Associate Professor
Yunwen Xu is an Associate Professor at the Department of Automation, Shanghai Jiao Tong University. She earned her Ph.D. and M.S. in Control Science and Engineering from Shanghai Jiao Tong University and her B.S. in Automation from Nanjing University of Science and Technology. She was a visiting scholar at Purdue University and has held postdoctoral and research positions at Shanghai Jiao Tong University. Her research focuses on predictive control applications, intelligent transportation systems, robot control, embedded predictive control, and intelligent control of complex systems. She has published extensively in top journals and conferences and has been involved in several significant research projects.