Networked Traffic Signal Control (NTSC) is a fundamental component of Intelligent Transportation Systems (ITS) and the broader vision of smart city development. While a plethora of intelligent strategies have been developed, the Sim2Real challenge often impedes their full realization. In response, this paper introduces the Parallel Learning-based Adaptive Network for Traffic Signal Control (PLANT) as a foundation model for NTSC. We employ the Wasserstein GAN with Gradient Penalty (WGAN-GP) to generate a wide range of artificial scenarios for robust PLANT training. Further, the Transformer-based Cooperation Mechanism (TCM) is integrated as the primary learner within PLANT, facilitating effective capture of traffic dynamics and knowledge accumulation. This knowledge is readily transferable to real-world applications through meticulous fine-tuning, equipping PLANT to adapt and evolve in alignment with shifting transportation paradigms. Our empirical study on the Hangzhou road network demonstrates PLANT's superiority over both traditional and emerging DRL-based approaches, emphasizing its viability as a potential foundation model for NTSC.


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    Titel :

    Parallel Learning Based Foundation Model for Networked Traffic Signal Control


    Beteiligte:
    Zhao, Chen (Autor:in) / Dai, Xingyuan (Autor:in) / Chen, Yuanyuan (Autor:in) / Lin, Yilun (Autor:in) / Lv, Yisheng (Autor:in) / Wang, Fei-Yue (Autor:in)


    Erscheinungsdatum :

    2023-09-24


    Format / Umfang :

    1131564 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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