Li Jin, Using Queuing Models to Design Connected and Autonomous Transportation Systems
In this webinar, Professor Li Jin discusses a class of design problems related to connected and autonomous transportation systems (CATS) using queuing models. Classical queuing models have been extensively used for conventional transportation systems. However, their application in CATS has not been well understood. To address this challenge, Professor Jin’s work builds on classical queuing models and develops novel models that can be directly used to provide solutions for CATS. He shows that the coordination of vehicle platoons in mixed-autonomy can be formulated as an M/D/1 queuing process. Second, he uses an M/D/1 queuing model with a switch-over cost to study the behavior of signal-free intersections. Third, he considers a class of model data-independent control policies for Jackson queuing networks, which can model a cluster of inter-connected intersections. Finally, he synthesizes queuing models and security game models to study the impact of malicious attacks on CATS, with a Question and Answer at the end.
Li Jin (金力) is an Assistant Professor in the Department of Civil and Urban Engineering at the New York University Tandon School of Engineering. He is also affiliated with the C2SMART Department of Transportation Center. His research focuses on developing resilient control algorithms for cyber-physical systems with guarantees of efficiency in nominal settings, robustness against random perturbations, and survivability under strategic disruptions. The theoretical foundation of his work includes the stochastic process and control theory. Specific applications of his work include connected and autonomous vehicles, automated highways, urban operations research, and air traffic control. Li joined NYU in Fall 2018 as a tenure-track assistant professor. He received B.Eng. in Mechanical Engineering from Shanghai Jiao Tong University in 2011, M.S. in Mechanical Engineering from Purdue University in 2012, and Ph.D. in Transportation from the Massachusetts Institute of Technology in 2018.