In order to improve the simulation effect of complex traffic conditions, based on machine learning algorithms, this paper builds a simulation model. Starting from the macroscopic traffic flow LWR theory, this paper introduces the process of establishing the original CTM mathematical model, and combines it with machine learning algorithms to improve it, and establishes the variable cell transmission model VCTM ordinary transmission, split transmission, and combined transmission mathematical expressions. Moreover, this paper establishes a road network simulation model to calibrate related simulation parameters. In addition, this paper combines the actual needs of complex traffic conditions analysis to construct a complex traffic simulation control model based on machine learning, and designs a hybrid microscopic traffic simulation system architecture to simulate all relevant factors of complex road conditions. Finally, this paper designs experiments to verify the performance of the simulation model. The research results show that the simulation control model of complex traffic conditions constructed in this paper has certain practical effects.
City-scale traffic simulation from digital footprints
