Description:

 
Abstract:
 
Despite decades of research, signalized intersections remain a major urban bottleneck and traffic signal timing in practice is suboptimal. Signal timing algorithms must address two challenges: performance under uncertainty in future demand and turning proportions, and real-time computation. One possible approach is max-pressure signal timing. By modeling the traffic network as a Markov decision process, max-pressure control is mathematically proven to maximize throughput under uncertainty using Lyapunov drift. Nevertheless, the control itself is easy to compute with the technical difficulty relegated to the mathematical analysis of throughput properties. Recent work on max-pressure signal timing has integrated some practicalities of traffic signal timing into the mathematical control and analyses, such as cyclical phase selection, pedestrian phases, signal coordination, transit signal priority, and limited deployment. Moreover, simulation results comparing max-pressure control against current signal timings in Hennepin County corridors suggest significant improvements from using max-pressure control. This seminar will introduce max-pressure control and then present recent work on bridging the mathematical theory with the practice of signal timing towards implementation on public roads.
 
Bio:
 
Michael W. Levin is an Associate Professor in the Department of Civil, Environmental, and Geo- Engineering at the University of Minnesota. He received a B.S. degree in Computer Science and a Ph.D. degree in Civil Engineering from The University of Texas at Austin in 2013 and 2017, respectively. Dr. Levin is a member of the Network Modeling Committee (AEP40) of the Transportation Research Board and is on the editorial board of Transportation Research Part B: Methodological. His work has been published in top journals including Transportation Science, Transportation Research Part C: Emerging Technologies, and IEEE Transactions on Intelligent Transportation Systems and has received several awards, including the 2019 Ryuichi Kitamura Award and the 2016 Milton Pikarsky Award from the Council of University Transportation Centers. His research focuses on traffic flow and network modeling of connected autonomous vehicles and intelligent transportation systems.