Variable speed limit (VSL) control can enhance traffic efficiency, reduce accident risks, and improve speed harmonization. Traditional methods like rule-based and proactive approaches face limitations in adaptability, robustness, or computational complexity. Reinforcement Learning (RL) offers potential by enabling self-learning in complex environments to optimize traffic management. This paper proposes an improved Soft Actor-Critic (SAC) algorithm, incorporating multi-objective rewards, factorized action space, and weighted state mechanism to enhance VSL control. Using a Cellular Automation (CA) model to simulate driver behavior, effectiveness of VSL were evaluated in two cases: (1) varying driver compliance rates, and (2) truck-to-passenger ratios. Results show higher compliance significantly reduces bottleneck dissipation time, increases average speed, and lowers collision probability, with optimization improving as truck ratios decline.


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    Title :

    Variable speed limit control method for highways based on an improved SAC algorithm


    Contributors:
    Zhou, Yi (editor) / Wu, Jianqing (editor) / Xie, Xing (author) / Shi, Junqing (author) / Ruan, Junhui (author) / Ruan, Sui (author)

    Conference:

    International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2024) ; 2024 ; Lanzhou, China


    Published in:

    Proc. SPIE ; 13645 ; 136450P


    Publication date :

    2025-06-16





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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