Intersection Signal Control Approach Based on PSO and Simulation

This paper applies fuzzy theory and machine learning in the process of intersection signal control. It provides a fuzzy traffic signal control approach based on Particle Swarm Optimization for intersection signal control. Through fuzzy classifying traffic flow in under control intersection and adjacent intersection, this paper puts decision schemes of signal control in different conditions as rule-set into knowledge-database. It applies PSO to improve the rule-sets in traffic signal control process, so the control model has the self-learning ability. After programming the simulation program of this control model and simulating, this paper compares the control effect of this new approach with the traditional fuzzy control method. The result of simulating illustrates that the effect of the model is obviously better than the traditional ones.