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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Urban traffic control optimization algorithm based on artificial intelligence


    Contributors:
    Liu, Chong (author)

    Conference:

    International Conference on Optics and Machine Vision (ICOMV 2022) ; 2022 ; Guangzhou,China


    Published in:

    Proc. SPIE ; 12173


    Publication date :

    2022-05-12





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Artificial intelligence techniques for urban traffic control

    Bielli, Maurizio / Ambrosino, Giorgio / Boero, Marco et al. | Elsevier | 1991


    Traffic control using artificial intelligence

    Simmerville,F. / Bright,J. / Castle Rock Consultants,GB | Automotive engineering | 1990


    Artificial Intelligence-Based Smart Traffic Control System

    Tiwari, Amit Kumar / Pandey, Raghvendra Kumar / Singh, Saharsh et al. | Springer Verlag | 2024


    Artificial intelligence traffic flow control device

    WU QIANNAN | European Patent Office | 2021

    Free access

    Urban road traffic intelligent early warning system based on artificial intelligence

    WANG ZHONG / LI TONG / ZHU QIANG et al. | European Patent Office | 2024

    Free access