With the rapid development of urban traffic, the problem of urban traffic becomes very serious. The traditional signal lamp control method can not adaptively control the traffic signal at the intersection. The rise of reinforcement learning technologies has made traffic control and artificial intelligence closely related. This paper presents a traffic optimization algorithm and model based on SUMO simulation platform. In this model, SARSA algorithm in reinforcement learning is used to establish multi-intersection simulation model. The multi-intersection simulation model is four consecutive intersections in Guangming Road, and the experimental parameters are actually investigated. By comparing the simulation data of multiple intersections, it is found that SARSA algorithm is superior to traditional fixed timing and full induction control.
Urban traffic control optimization algorithm based on artificial intelligence
International Conference on Optics and Machine Vision (ICOMV 2022) ; 2022 ; Guangzhou,China
Proc. SPIE ; 12173
12.05.2022
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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