The topic of traffic signal control (TSC) for urban intersection networks has been a key research area for many years, but comprehensive comparisons among state-of-the-art methodologies remain limited. This calls for a more exhaustive approach to benchmarking TSC techniques. This study builds upon previous TSC benchmarking work by evaluating the performance of both reinforcement learning (RL) and model-based TSC algorithms across a spectrum of demand levels. We adapt new metrics for in-depth simulation analysis and ground our investigation in a real-world urban arterial scenario. Our results unveil a noteworthy observation: the performance of tested algorithms is inconsistent when evaluated under diverse demand levels and across different metrics. This highlights the importance of implementing multidimensional evaluations in future TSC studies for a more nuanced understanding of their performance.


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    Titel :

    Evaluation of Traffic Signal Control at Varying Demand Levels: A Comparative Study


    Beteiligte:
    Zhang, Zhivao (Autor:in) / Quinones-Grueiro, Marcos (Autor:in) / Barbour, William (Autor:in) / Zhang, Yuhang (Autor:in) / Biswas, Gautam (Autor:in) / Work, Daniel (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    1258419 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch