This paper introduces SLATO as a set of artificial intelligence traffic congestion mitigation and timing optimization methods that support manual training. The ideas of this paper include built-in traffic flow and micro-simulation models, real-time reception of traffic status data, and the acceptance of manually set parameters for network structure characteristics and traffic control demands. It uses artificial intelligence methods to conduct real-time traffic status analysis and decides whether to extend the phase time, adopting a second-by-second signal timing method. The paper first elaborates on the technical framework of SLATO, detailing the four main intelligent processing systems within the framework: state perception, dual-loop optimization, effect evaluation, and phase operation, and then explains the operating mechanism and principles of SLATO. As a key technical difficulty, the paper discusses urban-level traffic congestion control strategies separately, combining SLATO's distributed node optimization and urban-level central coordination dual-loop optimization logic. Finally, the paper briefly discusses the current research and application progress of SLATO, as well as its development prospects.


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    Title :

    SLATO: An Urban Level Artificial Intelligence Traffic Signal Timing Optimization Technology


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Jia, Limin (editor) / Wang, Yanhui (editor) / Easa, Said (editor) / Ma, Daosong (author) / He, Maolin (author) / Wang, Shuang (author)

    Conference:

    International Conference on SmartRail, Traffic and Transportation Engineering ; 2024 ; Chongqing, China October 25, 2024 - October 27, 2024



    Publication date :

    2025-07-19


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Artificial Intelligence Approach for Optimizing Traffic Signal Timing on an Urban Road Network

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