The optimization of traffic light control systems is at the heart of work in traffic management. Many of the solutions considered to design efficient traffic signal patterns rely on controllers that use pre-timed stages. Such systems are unable to identify dynamic changes in the local traffic flow and thus cannot adapt to new traffic conditions. An alternative, novel approach proposed by computer scientists in order to design adaptive traffic light controllers relies on the use of intelligent agents. The idea is to let autonomous entities, named agents, learn an optimal behavior by interacting directly in the system. By using machine learning algorithms based on the attribution of rewards according to the results of the actions selected by the agents, we can obtain a control policy that tries to optimize the urban traffic flow. In this paper, we explain how we designed an intelligent agent that learns a traffic light control policy. We also compare this policy with results from an optimal pre-timed controller.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Urban traffic control based on learning agents


    Beteiligte:
    Gregoire, P.L. (Autor:in) / Desjardins, C. (Autor:in) / Laumonier, J. (Autor:in) / Chaib-Draa, B. (Autor:in)


    Erscheinungsdatum :

    2007


    Format / Umfang :

    6 Seiten, 16 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Urban Traffic Control Based on Learning Agents

    Gregoire, Pierre-Luc / Desjardins, Charles / Laumonier, Julien et al. | IEEE | 2007


    Urban traffic signal control using reinforcement learning agents

    Balaji, P.G. / German, X. / Srinivasan, D. | IET | 2010


    An Urban Traffic Control System Based on Mobile Multi-Agents

    Li, Zhenjiang / Wang, Fei-Yue / Miao, Qinghai et al. | IEEE | 2006


    Using intelligent agents for dynamic urban traffic control systems

    Roozemond, D. A. / Association for European Transport | British Library Conference Proceedings | 1999


    Urban traffic control method based on reinforcement learning

    YUAN YANWEI / KUANG XIAOYA / SHI HONGFANG et al. | Europäisches Patentamt | 2024

    Freier Zugriff