Optimization Method for Urban Traffic Signal Control Based on Digital Twin Technology

Digital twin technology is promising, and has great advantage for traffic management to study, predict, simulate and experiment, from driving physical innovation to virtual testing. The goal remains the same: to improve traffic safety, regulate traffic flow, and ease urban congestion. At present, a mature digital twin transportation model has not been established at the social level. In this paper, a method is proposed to construct a digital twin traffic model by extracting environmental features and physical entities such as digital roads, traffic lights, and vehicles, and combining the optimization algorithm to take fuel consumption as the optimization goal, so as to provide ideas for improving the efficiency of urban road traffic and the quality of traffic environment. Based on the digital twin traffic map, combined with the genetic algorithm, this paper sets the objective function to fuel consumption, sets different iteration times, and performs multiple iterative optimizations on the four-phase duration of each intersection. After the optimization is completed, modify the phase duration of the original traffic light and run the simulation experiment to output the traffic information table file, according to which the user can select the optimal duration that matches the real environment. Finally, on the basis of genetic algorithm optimization, the green wave control is used to adjust the phase difference between the signal lights of the main road to further improve the optimization quality.