关键词: 仿真优化；SUMO 仿真；Webster 算法；遗传算法

Abstract: As a key node of road network, the optimization of signal timing at urban intersections is very important to alleviate traffic congestion. In order to solve the problem that the traditional Webster algorithm has insufficient adaptability in dynamic traffic scenes, this study proposes a single intersection signal timing optimization method based on genetic algorithm (GA). The micro-traffic model of Qingtan West Road intersection is built by SUMO simulation platform, and the real traffic flow characteristics are restored by Krauss car-following model, and several groups of comparative experiments are set up to verify the performance of the algorithm. The results show that the average delay of vehicles at intersections is reduced to 29.84 s (down by 12.31%) and the queue length is shortened to 30.29 m (down by 10.44%), which is significantly better than the optimization effect of Webster algorithm (average delay is 30.30 s/decrease by 10.96%. The queue length is 30.91 m/ decrease by 8.61%). In addition, through floating-point coding and adaptive search mechanism, the genetic algorithm can still achieve the global optimal solution under complex phase constraints, and its traffic capacity is improved to 0.81 saturation, which is 11.0% better than the original scheme. This study proves that genetic algorithm has stronger adaptability and robustness in dynamic traffic signal optimization, which provides a theory for fine management and control of urban intersections.

Key words: simulation optimization; SUMO simulation; Webster algorithm; genetic algorithm