Exploiting Structure for Control of Traffic Flow Networks

Tuesday, February 16, 2016
330 Gross Hall -  12:00 - 1:00PM


Sam Coogan, PhD

Assistant Professor, University of California, Los Angeles (UCLA)


Advances in wireless sensing and cyber-physical systems afford new opportunities for control of transportation networks to improve safety and efficiency. These systems exhibit complex global behavior such as large-scale congestion caused by interactions throughout the network, and ongoing advancements in automated vehicles and infrastructure operations will further influence 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 viewed as 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.

Biographical Sketch:
Sam Coogan is an Assistant Professor in the Electrical Engineering Department at the University of California, Los Angeles (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 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 focus is on developing scalable tools for verification and design of networked cyber-physical systems, and his research has applied these tools to create efficient and intelligent transportation systems.