Control of traffic light timing using probability distribution genetic algorithm and finite state machine

Peichen Zhang; Mingyin Zou; Xiaohan Li; Yuming Huang; Bingfeng Ge; Kewei Yang

doi:10.1117/12.2639045

17 May 2022 Control of traffic light timing using probability distribution genetic algorithm and finite state machine

Peichen Zhang, Mingyin Zou, Xiaohan Li, Yuming Huang, Bingfeng Ge, Kewei Yang

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 122590P (2022) https://doi.org/10.1117/12.2639045
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

With traffic congestion gradually becoming the heart problem of many cities, the traffic congestion phenomenon usually occurs at the intersection of large traffic flow. Traffic lights are the key to solving the problem of traffic congestion. To reasonably schedule traffic lights and alleviate traffic congestion at intersections, this paper first constructed a vehicle road traffic simulation system based on finite state machine, which can simulate and calculate the total score of vehicles under any scheduling scheme. Then, a genetic algorithm based on probability distribution is designed to optimize the scheduling scheme. Finally, the validity of the model and algorithm is verified by a case study. Compared with the traditional genetic algorithm, the genetic algorithm based on probability distribution can speed up the time more than 7 times and can calculate the convergence scheduling scheme more quickly. shorten the running time more than 7 times and has a faster convergence speed.

Citation Download Citation

Peichen Zhang, Mingyin Zou, Xiaohan Li, Yuming Huang, Bingfeng Ge, and Kewei Yang "Control of traffic light timing using probability distribution genetic algorithm and finite state machine", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 122590P (17 May 2022); https://doi.org/10.1117/12.2639045

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