SE PhD Final Oral Defense of Yanfeng Geng

TITLE: OPTIMIZATION METHODS FOR INTELLIGENT TRANSPORTATION SYSTEMS IN URBAN SETTINGS ABSTRACTt: Intelligent Transportation Systems (ITS) are those utilizing advanced infrastructure, synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. This dissertation focuses on studying two important topics of ITS: Smart Parking (SP) and Traffic Light Control (TLC). Both are viewed as dynamic optimization problems in stochastic hybrid system environments. The first part of this dissertation focuses on describing a novel “Smart Parking’’ system for an urban environment. As opposed to simply providing parking information to drivers in Parking Guidance Information (PGI) systems, the proposed approach is to assign and reserve an optimal parking space based on a driver’s cost function that combines proximity to destination and parking cost. This is accomplished by solving a Mixed Integer Linear Programming (MILP) problem at each decision point defined over a sequence of time instants. The solution of each MILP problem is an optimal allocation based on current state information, and is updated at the next decision point with a guarantee that there is no resource reservation conflict and that no driver is ever assigned a resource with a higher than this driver’s current cost function value. Based on simulation results, compared to uncontrolled parking processes or state-of-the-art guidance-based systems, this system reduces the average time to find a parking space and the parking cost, while the overall parking capacity is more efficiently utilized. An in-door laboratory testbed is described to demonstrate the functionality of a system prototype. A full implementation in a garage is also discussed where this system has been tested in real time. In the second part, the traffic light control problem is addressed by viewing it as a stochastic hybrid system and developing a Stochastic Flow Model (SFM) for it. Using the theory of Infinitesimal Perturbation Analysis (IPA), online gradient estimates of a cost metric are derived with respect to the controllable green and red cycle lengths. The IPA estimators obtained require counting traffic light switchings and estimating car flow rates only when specific events occur. The estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. The method is first applied to a single-intersection TLC problem, and then extended to multiple intersections with blocking. Simulation results are included to illustrate the approach and demonstrate the improved performance over predefined traffic light cycles. COMMITTEE: Advisor: Christos Cassandras, SE/ECE; Michael Caramanis, SE/ME; Calin Belta, SE/ME; Ioannis Paschalidis, SE/ECE; Chair: Hua Wang, SE/ME

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