This thesis aims to address three research topics in intelligent transportation systems
which include multi-intersection traffic light control based on stochastic flow models with
delays and blocking, optimization of mobility-on-demand systems using event-driven receding
horizon control and the optimal control of lane change maneuvers in highways for
connected and automated vehicles.
First, for the traffic light control work, we extend Stochastic Flow Models (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 multi-intersection traffic light control 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 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 second problem relates to developing an event-driven Receding Horizon Control
(RHC) scheme for a Mobility-on-Demand System (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. 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 (CBFs). Structural properties of the optimal policies which simplify the solution derivations are provided in the case of the longitudinal maneuver, leading to analytical optimal control expressions. The solutions, when they exist, are guaranteed to satisfy safety constraints for all vehicles involved in the maneuver.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/41881 |
| Date | 18 January 2021 |
| Creators | Chen, Rui |
| Contributors | Cassandras, Christos G. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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