Knowledge Embedding-Assisted Multi-Exemplar Learning Particle Swarm Optimization for Traffic Signal Timing Optimization

Traffic signal timing optimization (TSTO) has aroused extensive attention, which aims to optimize the signal timing to improve the service capability of intersections. In recent years, particle swarm optimization (PSO) has played a vital role in optimization field. However, the PSO may get stuck in the local optimum when handling TSTO. In this paper, we propose a multi-exemplar learning PSO (MEL-PSO) algorithm, which enhances the exploration capability of the algorithm by letting particles have more opportunities to learn from more exemplars. Moreover, a knowledge embedded solution generating (KESG) strategy is proposed by exploiting the characteristic of input traffic volume distribution as pre-knowledge, which helps MEL-PSO generate an initial population covering promising search space. Furthermore, in order to make MEL-PSO suitable for different kinds of saturation situations of the intersection, we adopt multiple indicators to measure the performance of the signal timing scheme. Comparison experiments for validating the performance of MEL-PSO are carried out on a single intersection in both undersaturated and oversaturated traffic flow conditions. Experimental results show that MEL-PSO outperforms the classic numerical timing method, random timing method, and some other PSO-based algorithms.