Woodruff School of Mechanical Engineering
Exploiting Structure for Control of Transportation Networks
Dr. Sam Coogan
University of California(UCLA), Electrical Engineering Department
Tuesday, November 8, 2016 at 11:00:00 AM
Van Leer Building, Room 218
Dr. Fumin Zhang
Advances in wireless sensing and cyber-physical systems afford new opportunities for controlling transportation networks to improve safety and efficiency. These systems exhibit complex global behavior such as large-scale congestion caused by interactions throughout the network. Moreover, ongoing advancements in automated and connected vehicles will further complicate traffic flow dynamics and alter the global network behavior. Motivated by these challenges, this talk will elucidate structural properties of transportation networks that enable scalable analysis and control synthesis techniques. First, we exploit intrinsic properties of traffic flow dynamics to derive a new structural property for transportation networks. This mixed monotonicity property is an extension of the classical notion of monotonicity in dynamical systems. We will show that mixed monotonicity enables efficient finite state abstraction of traffic flow dynamics, which allows for correct-by-construction synthesis of control strategies. Next, we will develop a data-driven traffic predictive control scheme using a low-rank decomposition technique to learn trends in historical data that are then used to make real-time predictions of traffic flow for control. This data-driven approach relies on high resolution measurements obtained from wireless traffic-flow sensors.
Sam Coogan is an Assistant Professor in the Electrical Engineering Department at UCLA. He received the B.S. degree in Electrical Engineering from Georgia Tech (2010), and the M.S. and Ph.D. degrees in Electrical Engineering from the University of California, Berkeley (2012 and 2015). In 2015, he was a postdoctoral research engineer at Sensys Networks, a wireless traffic sensing company, and in 2012 he was a research intern at NASA's Jet Propulsion Lab. He received the Eli Jury Award from UC Berkeley EECS in 2016 for outstanding achievement in the area of systems, communications, control, or signal processing, the Leon O. Chua Award from UC Berkeley EECS in 2014 for outstanding achievement in an area of nonlinear science, and the best student paper award at the 2015 Hybrid Systems: Computation and Control conference. His research focuses on developing scalable tools for verification and design of networked cyber-physical systems and especially focuses on creating efficient and intelligent transportation systems.