Optimality versus run time for isolated signalized intersections |
| |
| Institution: | 1. University of Florida, PO Box 116585, Gainesville, FL 32611-6585, United States;2. University of Virginia, 351 McCormick Road, Charlottesville, VA 22904, United States;3. Florida Atlantic University, 777 Glades Road, Building 36, Room 225, Boca Raton, FL 33431, United States;4. Leidos, Inc., 11251 Roger Bacon Drive, Reston, VA 20190, United States;1. Insight Centre for Data Analytics, University College Cork, Cork, Ireland;2. Department of Business Administration, İzmir University of Economics, İzmir, Turkey;3. Department of Computer Engineering, İzmir University of Economics, İzmir, Turkey;4. Department of Industrial Engineering, İzmir University of Economics, İzmir, Turkey;1. University College Dublin, School of Civil, Structural and Environmental Engineering, Newstead Building, Belfield, Dublin 4, Ireland;2. Monash University, Department of Civil Engineering, Clayton Campus, Victoria 3800, Australia;1. Institute for Risk and Reliability, Leibniz University Hannover, Germany;2. Dept. of Civil Engineering, Santa Maria University, Valparaiso, Chile;3. Institute for Risk and Uncertainty, University of Liverpool, Liverpool L69 3GH, UK;4. School of Civil Engineering & Shanghai Institute of Disaster Prevention and Relief, Tongji University, China;5. Department of Civil Engineering and Engineering Mechanics, Columbia University, USA;1. Department of Structural Engineering, Tongji University, Shanghai 200092, China;2. State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China |
| |
| Abstract: | Simulation-based optimization of traffic signal timing has become pervasive and popular, in the field of traffic engineering. When the underlying simulation model is well-trusted and/or well-calibrated, it is only natural that typical engineers would want their signal timing optimized using the judgment of that same model. As such, it becomes important that the heuristic search methods typically used by these optimizations are capable of locating global optimum solutions, for a wide range of signal systems. However off-line and real-time solutions alike offer just a subset of the available search methods. The result is that many optimizations are likely converging prematurely on mediocre solutions. In response, this paper compares several search methods from the literature, in terms of both optimality (i.e., solution quality) and computer run times. Simulated annealing and genetic algorithm methods were equally effective in achieving near-global optimum solutions. Two selection methods (roulette wheel and tournament), commonly used within genetic algorithms, exhibited similar effectiveness. Tabu searching did not provide significant benefits. Trajectories of optimality versus run time (OVERT) were similar for each method, except some methods aborted early along the same trajectory. Hill-climbing searches always aborted early, even with a large number of step-sizes. Other methods only aborted early when applied with ineffective parameter settings (e.g. mutation rate, annealing schedule). These findings imply (1) today’s products encourage a sub-optimal “one size fits all” approach, (2) heuristic search methods and parameters should be carefully selected based on the system being optimized, (3) weaker searches abort early along the OVERT curve, and (4) improper choice of methods and/or parameters can reduce optimization benefits by 22–33%. |
| |
| Keywords: | Traffic signal timing Simulation-based optimization Genetic algorithm Simulated annealing Heuristic method Tabu search |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
