Most existing reinforcement learning-based traffic signal control approaches overlook vehicle-specific information in the state representation. This study addresses this gap by integrating real-time driving style information into the deep reinforcement learning (DRL) framework. We introduce a model-based framework that captures real-time driving styles and converts them into Intelligent Driver Model (IDM) parameters. Our proposed method demonstrates superior performance across various reinforcement algorithms and traffic flow scenarios, with statistical tests confirming a significant reduction in average queue length. The contributions of this paper can be summarized as follows: 1) proposing a model-based method for real-time driving style recognition, significantly reducing the requirements for trajectory data duration and computational resources, and 2) proposing a new state variable called transformed occupancy ( $o^{*}$ ) that allows the DRL-based traffic signal controller to be trained with driving style information, thereby enhancing the performance of the traffic signal control system. The proposed framework is so flexible that other car-following models, machine learning algorithms, and various downstream tasks can be incorporated.


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    Title :

    Real-Time Driving Style Integration in Deep Reinforcement Learning for Traffic Signal Control


    Contributors:
    Xu, Tu (author) / Pang, Yuqi (author) / Zhu, Yongdong (author) / Ji, Wei (author) / Jiang, Rui (author)


    Publication date :

    2025-08-01


    Size :

    1609499 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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