Urban traffic signal control based on genetic algorithm

Urban traffic signal control only controls individual intersections with high traffic flow, ignoring the correlation of traffic in the area, which leads to poor delay time and capacity of intersections. To optimize the above problems, urban traffic signal control based on genetic algorithm is studied. The migration learning principle combined with fuzzy rules is used to detect the passage status of the intersection. The delay time and capacity of the intersection are targeted, and an urban traffic signal control model is developed. After improving the crossover variation of the genetic algorithm, the control model is solved and the traffic signal control scheme is obtained. The experimental results on the urban road network show that the total delay time in the urban area is optimized by at least 7.5% after applying the genetic algorithm, and the communication capacity is significantly improved, which enhances the traffic efficiency of the urban road network.