Optimization method of Intersection signal control scheme based on heuristic algorithm

In order to maximize the operational efficiency of intersections, this paper first establishes a multi-objective function model with the maximum traffic capacity, minimum average number of stops, and minimum intersection delay as the optimization goals by studying the traffic data of instance intersections and their change laws. Then, the multi-objective optimization model is solved by heuristic algorithms such as the genetic algorithm and particle swarm algorithm in Python language, and the above algorithms are compared and analyzed to find the optimal intersection signal control scheme. Finally, taking the intersection of Zhongyang East Road and Yijing Street in Siping City as an example, the genetic algorithm and particle swarm algorithm reduced the average delay of the intersection by 32.3% and 31.4%, and the average number of stops decreased by 4.8% and 6.0%, respectively. The results show that the signal control scheme optimized based on the heuristic algorithm can reduce the delay level and average number of stops at the existing intersections, which proves the feasibility of the proposed heuristic algorithm in this paper.