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dc.contributor.advisorFu, Michael Cen_US
dc.contributor.authorHowell, William Caseyen_US
dc.date.accessioned2006-09-12T05:44:14Z
dc.date.available2006-09-12T05:44:14Z
dc.date.issued2006-07-12en_US
dc.identifier.urihttp://hdl.handle.net/1903/3779
dc.description.abstractWe develop simulation optimization algorithms for determining the traffic light signal timings for an isolated intersection and a network of two-signalized intersections modeled as single-server queues. Both problem settings consider traffic flowing in one direction. The system performance is estimated via stochastic discrete-event simulation. In the first problem setting, we examine an isolated intersection. We use smoothed perturbation analysis to derive both left-hand and right-hand gradient estimators of the queue lengths with respect to the green/red light lengths within a signal cycle. Using these estimators, we are able to apply stochastic approximation, which is a gradient-based search algorithm. Next we extend the problem to the case of a two-light intersection, where there are two additional parameters that we must estimate the gradient with respect to: the green/red light lengths within a signal cycle at the second light and the offset between the two light signals. Also, the number of queues increases from two to five. We again derive both left-hand and right-hand gradient estimators of the all queue lengths with respect to the three aforementioned parameters. As before, we are able to apply gradient-based search based on stochastic approximation using these estimators. Next we reexamine the two aforementioned problem settings. However, this time we are solely concerned with optimization; thus, we model the intersections using three different stochastic fluid models, each incorporating different degrees of detail. From these new models, we derive infinitesimal perturbation analysis gradient estimators. We then implement these estimators on the underlying discrete-event simulation and are able to apply gradient-based search based on stochastic approximation using these estimators. We perform numerical experiments to test the performance of the three gradient estimators and also compare these results with finite-difference estimators. Optimization for both the one-light and two-light settings is carried out using the gradient estimation approaches.en_US
dc.format.extent700750 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleSimulation Optimization of Traffic Light Signal Timings via Perturbation Analysisen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentApplied Mathematics and Scientific Computationen_US
dc.subject.pqcontrolledMathematicsen_US
dc.subject.pqcontrolledTransportationen_US
dc.subject.pqcontrolledOperations Researchen_US
dc.subject.pquncontrolledPerturbation Analysisen_US
dc.subject.pquncontrolledSimulationen_US
dc.subject.pquncontrolledTraffic Signal Timingsen_US
dc.subject.pquncontrolledStochastic Fluid Modelsen_US
dc.subject.pquncontrolledTraffic Controlen_US
dc.subject.pquncontrolledTraffic Flow Modelsen_US


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