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Revisiting the application of simultaneous perturbation stochastic approximation towards signal timing optimization
| 题名： | Revisiting the application of simultaneous perturbation stochastic approximation towards signal timing optimization |
| 正文语种： | 英文 |
| 作者： | David K. Hale1, Constantinos Antoniou2, Byungkyu Brian Park3, Jiaqi Ma4, Lei Zhang5,Alexander Paz6 |
| 作者单位： | 1Leidos, Inc. 11251 Roger Bacon Drive, Reston, VA 20190 2Technical University of Munich, Department of Civil, Geo and Environmental Engineering, Chair of Transportation Systems Engineering, Athens, Greece 3University of Virginia, 351 McCormick Rd, Charlottesville, VA 22904 4Leidos, Inc. 11251 Roger Bacon Drive, Reston, VA 20190 5Mississippi State University, Mississippi State, MS 39762 6University of Nevada, Las Vegas, 4505 Maryland Parkway, PO Box 454015, Las Vegas, NV 89154-4015 |
| 关键词： | adaptive signal control; genetic algorithm; heuristic methods; simultaneous perturbation stochastic approximation; traffic signal timing optimization |
| 摘要： | Simultaneous Perturbation Stochastic Approximation (SPSA) has gained favor as an efficient optimization method for calibrating computationally intensive, “black box” traffic flow simulations. Few recent studies have investigated the efficiency of SPSA for traffic signal timing optimization. It is important for this to be investigated, because significant room for improvement exists in the area of signal optimization. Some signal timing methods and products perform optimization very quickly, but deliver mediocre solutions. Other methods and products deliver high-quality solutions, but at a very slow rate. When using commercialized desktop signal timing products, engineers are often forced to choose between speed and solution quality. Real-time adaptive control products, which must optimize timings within seconds on a cycle-by-cycle basis, have limited time to reach a high-quality solution. The existing literature indicates that SPSA provides the potential for upgrading both off-line and on-line solutions alike, by delivering high-quality solutions within seconds. This article describes an extensive set of optimization tests involving SPSA and genetic algorithms (GAs). The final results suggest that GA was slightly more efficient than SPSA. Moreover, the results suggest today's signal timing solutions could be improved significantly by incorporating GA, SPSA, and “playbooks” of preoptimized starting points. However, it may take another 5–10 years before our computers become fast enough to simultaneously optimize coordination settings (i.e., cycle length, phasing sequence, and offsets) at numerous intersections, using the most powerful heuristic methods, at speeds that are compatible with real-time adaptive solutions. |
| 出版年： | 2018 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | 22 |
| 期： | 5 |
| 页码： | 365-375 |
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