Path Selection and Congestion Mitigation Strategies Based on Deep Learning and Traffic Simulation Models

Transportation demand is gradually increasing and road traffic congestion is becoming more and more serious.Traffic state prediction is one of the important bases for accurate traffic management and control.This paper investigates a traffic state prediction method based on a deep learning algorithm fusing spatio-temporal graphical convolutional networks, and explores the law of path selection decision-making of pedestrians under different traffic flow prediction and guidance strategies, and analyzes the effect of the implementation of the information guidance policy by traffic managers in realistic scenarios using evolutionary game theory.The simulation results combined with the traffic simulation model show that the traffic state prediction method proposed in this paper is more effective compared with other models.The evolution results are more reasonable when the value of the path adjustment rate in the replicated dynamic model is the inverse of the number of iterations.In the perceptual error analysis, when the value of perceptual error 1 is taken to be too large, i.e., when the perceptual error of the first type of travelers is small and small, it tends to be a deterministic choice.Finally, a traffic simulation model is implemented to validate the performance of the proposed model and propose congestion mitigation strategies.