Adaptive traffic signal control is the process by which the timing of a traffic signal is continuously adjusted based on the changing arrival patterns of vehicles at an intersection, usually with the goal of optimizing a given measure of effectiveness. Herein, a methodology is developed in which the characteristics of a traffic signal cycle are optimized at the conclusion of every phase based on the arrival times of vehicles to an intersection, using stopped delay as the measure of effectiveness. This optimization is solved using metaheuristic search procedures, namely tabu search, and embedded in an algorithm in which current vehicle arrival times are detected, arrival patterns over a specified horizon are predicted, the traffic signal timing is optimized, and the timings are sent to a traffic signal controller. The methodology is shown to provide improvement in performance for a number of intersection configurations and traffic regimes over traditional forms of traffic signal control, and the metaheuristic search is demonstrated to significantly reduce the computation time for a solution as compared with other search procedures. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/24348 |
| Date | 29 April 2014 |
| Creators | Shenoda, Michael |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | electronic |
| Rights | Copyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works. |
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