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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/7002

    Título
    Reinforcement learning for Traffic Signal Control: Comparison with commercial systems
    Autor
    Cabrejas Egea, Álvaro
    Zhang, Raymond
    Walton, Neil
    Publicado en
    R-Evolucionando el transporte
    Editorial
    Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
    Fecha de publicación
    2021-07
    ISBN
    978-84-18465-12-3
    DOI
    10.36443/10259/7002
    Descripción
    Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos
    Abstract
    In recent years, Intelligent Transportation Systems are leveraging the power of increased sensory coverage and available computing power to deliver data-intensive solutions achieving higher levels of performance than traditional systems. Within Traffic Signal Control (TSC), this has allowed the emergence of Machine Learning (ML) based systems. Among this group, Reinforcement Learning (RL) approaches have performed particularly well. Given the lack of industry standards in ML for TSC, literature exploring RL often lacks comparison against commercially available systems and straightforward formulations of how the agents operate. Here we attempt to bridge that gap. We propose three different architectures for RL based agents and compare them against currently used commercial systems MOVA, SurTrac and Cyclic controllers and provide pseudo-code for them. The agents use variations of Deep Q-Learning (Double Q Learning, Duelling Architectures and Prioritised Experience Replay) and Actor Critic agents, using states and rewards based on queue length measurements. Their performance is compared in across different map scenarios with variable demand, assessing them in terms of the global delay generated by all vehicles. We find that the RL-based systems can significantly and consistently achieve lower delays when compared with traditional and existing commercial systems.
    Palabras clave
    Tráfico
    Traffic
    Infraestructuras
    Infrastructures
    Materia
    Ingeniería civil
    Civil engineering
    Transportes
    Transportation
    Tecnología
    Technology
    URI
    http://hdl.handle.net/10259/7002
    Versión del editor
    https://doi.org/10.36443/9788418465123
    Relacionado con
    http://hdl.handle.net/10259/6490
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