By 2050, two-thirds of the global population will live in cities, intensifying challenges like traffic congestion, pollution, and inequality. In this talk, I’ll present our innovations that carry the potential to advance the efficiency and adaptability of future cities. First, I'll introduce our recent progress in mixed-autonomy traffic control. Next, I'll showcase how we reconstruct citywide traffic dynamics using sparse mobile data, laying the groundwork for large-scale mixed traffic management. Finally, I'll explore future research directions at the intersection of intelligent transportation systems and urban innovations.
Weizi Li is an Assistant Professor of Teaching in the Department of Computer Science and Engineering at the University of California, Riverside. Previously, he was an Assistant Professor of Computer Science at the University of Tennessee, Knoxville, and a Postdoctoral Fellow at MIT. He received his Ph.D. in Computer Science from the University of North Carolina at Chapel Hill. His research interests include intelligent transportation systems, robotics, reinforcement learning, and multi-agent simulation.