A Back-Pressure-Based Model With Fixed Phase Sequences for Traffic Signal Optimization Under Oversaturated Networks

Traffic signal control under oversaturated conditions presents a major challenge in metropolitan transportation networks. Previous works have demonstrated the ability of back-pressure methods to maximize network throughput and guarantee network stability. However, most of these methods are implemented adaptively. At present, fixed phase sequences are still widely used in traffic signal control systems. Herein, we propose a new back-pressure-based signal optimization method that combines fixed phase sequences with spatial model predictive control. First, a spatial prediction model for traffic flow was constructed to analyze the movement of vehicles between a central intersection and four peripheral intersections. Then, a multi-objective optimization model of traffic signal timing was developed with the purpose to reduce the risk of spillover and to balance the distribution of vehicles across the whole network. Next, a method based on the Multi-Objective Particle Swarm Optimization algorithm and Technique for Order Preference by Similarity to an Ideal Solution principle was used to achieve the Pareto frontier and the optimal solution. Finally, traffic simulations were performed in Paramics to assess the performance of the proposed method. The results of the simulations suggest the performance of the proposed method surpasses fixed-time control and cycle-based back-pressure schemes under oversaturated conditions.