Traffic management model for vehicle re-routing and traffic light control based on Multi-Objective Particle Swarm Optimization

Abstract

In this paper, a novel traffic management model is presented, which simultaneously optimizes vehicle re-routing and traffic light control to alleviate traffic congestion and limit the effects of incidents on traffic flow based on Multi-Objective Particle Swarm Optimization (MOPSO) method. Once a co ngested road is predicted, our proposed Multi-Objective Traffic Light Control is then applied to optimize signal timing which takes the maximization of traffic flow on the edge where the incident takes place and the minimization of the average junction waiting time as two objectives. To improve the performance and sensitivity of MOPSO algorithm, we used Q-Learning algorithm to grant to each agent of the swarm the ability of selecting appropriate MOPSO parameters adapted to the structure of the problem. At the same time, when the situation of the traffic flow starts to become more serious, we adopt a novel Multi-Objective Vehicle Re-routing strategy for assigning alternatives routes to cars before entering the congested road, in order to perform dynamic load balancing. Vehicle re-routing is also optimized by MOPSO to simultaneously find the shortest and least popular path. The obtained results from the simulation using SUMO, a well-known microscopic traffic simulator, confirm the efficiency of the proposed system.