SE PhD Final Defense of Rui Chen


ABSTRACT: This thesis addresses three research topics in intelligent transportation systems including multi-intersection Traffic Light Control(TLC) based on Stochastic Flow Models (SFMs) with delays and blocking, optimization of Mobility-on-Demand Systems (MoDS) using event-driven Receding Horizon Control (RHC) and the optimal control of lane change maneuvers in highways for connected and automated vehicles.

First, for the TLC work, we extend SFMs, used for a large class of discrete event and hybrid systems, by including the delays which typically arise in flow movements, as well as blocking effects due to space constraints. We apply this framework to the multiintersection TLC problem by including transit delays for vehicles moving from one intersection to the next and possible blocking between two intersections. Using Infinitesimal Perturbation Analysis (IPA) for this SFM with delays and possible blocking, we derive new on-line gradient estimates of several congestion cost metrics with respect to the controllable green and red cycle lengths. The IPA estimators in conjunction with a standard gradient-based algorithm are used to iteratively adjust light cycle lengths to improveperformance.

The second problem relates to developing an event-driven RHC scheme for a MoDS in a transportation network where vehicles may be shared to pick up and drop off passengers so as to minimize a weighted sum of passenger waiting and traveling times. Viewed as a discrete event system, the event-driven nature of the controller significantly reduces the complexity of the vehicle assignment problem, thus enabling its real-time implementation.

Finally, optimal control policies are derived for a Connected Automated Vehicle (CAV) cooperating with neighboring CAVs in order to implement a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes. For the first phase, the maneuver time subject to safety constraints and subsequently the associated energy consumption of all cooperating vehicles in this maneuver are optimized. Structural properties of the optimal policies are provided. For the second phase, time and energy are jointly optimized based on three different solution methods including a real-time approach based on Control Barrier Functions.

COMMITTEE: ADVISORChristos G. Cassandras, SE, ECE; Ioannis Ch. Paschalidis, SE, ECE; Pirooz Vakili, SE, ME; Sean Andersson, SE, ME; CHAIR: Robert Tron, SE

When 10:00 am on Tuesday, October 6, 2020
Location Zoom