Based on MOPSO Algorithm of Real-Time Traffic Signal Optimization Control for Intelligent Transportation Intersections

Abstract

In order to improve the traffic efficiency of intersections, this paper establishes a multi-objective optimization model with the minimum real-time delay time and maximum capacity as the optimization objectives by applying multi-objective particle swarm optimization algorithm, and simulates the actual intersections. By optimizing the multi-objective model, a set of Pareto optimal solution sets can be obtained, and the optimal timing scheme can be determined by using multi-attribute decision-making algorithm and real-time update of signal timing configuration parameters to achieve dynamic optimization of signal timing. The simulation model of city road intersection is established by SUMO software. By using multi-objective particle swarm optimization algorithm to optimize the intersection simulation model, the traffic capacity of the simulated intersection has been greatly improved, with the average traffic capacity increasing by about 3.69% and the average vehicle delay time decreasing by about 21.35%.