Region-based Evaluation Particle Swarm Optimization with Dual Solution Libraries for Real-time Traffic Signal Timing Optimization

Abstract

Traffic signal timing optimization (TSTO) is a significant topic in the smart city. However, there are two challenges when solving TSTO. Firstly, it often uses time-consuming simulation software to evaluate candidate solutions, therefore it is an expensive optimization problem. Secondly, as the traffic flow changes rapidly in TSTO, providing a timing scheme to respond to the change immediately is difficult. To address the above challenges, we propose a region-based evaluation particle swarm optimization algorithm (REPSO) with dual solution libraries, which has three novel designs. First, two solution libraries are built for undersaturated and oversaturated traffic flow states, respectively, which can be used to fast provide a signal timing scheme for a traffic flow in real-time. Second, a knowledge-assisted initialization strategy is proposed and adopted to assist the initialization of new solutions based on the knowledge in the two solution libraries. Third, a region-based evaluation strategy is proposed to reduce the number of fitness evaluations, which can also greatly reduce the construction time of the solution libraries. The performance of REPSO is validated by comparing with six signal timing methods in both undersaturated and oversaturated traffic flow states, showing the better general performance of REPSO.