Optimization model of urban road signal lights based on non dominated sorting genetic algorithm

This article explores the optimization of urban traffic flow control. On the basis of data preprocessing, the K-Means clustering analysis algorithm was used to divide a day into several time periods and estimate the traffic flow at the intersections of Jingzhong Road and Weizhong Road. The use of non dominated sorting genetic algorithm for signal light optimization significantly improved the average speed of vehicles. A random forest model for identifying parade vehicles was established using vehicle characteristics such as threshold setting and turnover rate. A classifier was used to train and classify the vehicles, and the parade vehicles for each day of the May Day Golden Week were calculated. Finally, the effectiveness of the control measures was evaluated based on indicators such as the average speed, delay time, and queue length of vehicles.