Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper adopts the multiagent system approach to develop distributed unsupervised traffic responsive signal control models, where each agent in the system is a local traffic signal controller for one intersection in the traffic network. The first multiagent system is developed using hybrid computational intelligent techniques. Each agent employs a multistage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multiagent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN). The problem of real-time traffic signal control is especially challenging if the agents are used for an infinite horizon problem, where online learning has to take place continuously once the agent-based traffic signal controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District of Singapore has been developed using PARAMICS microscopic simulation program. Simulation results show that the hybrid multiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases. Using the hybrid NN-based multiagent system, the mean delay of each vehicle was reduced by 78% and the mean stoppage time, by 85% compared to the existing traffic signal control algorithm. The promising results demonstrate the efficacy of the hybrid NN-based multiagent system in solving large-scale traffic signal control problems in a distributed manner


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Neural Networks for Real-Time Traffic Signal Control


    Beteiligte:
    Srinivasan, D. (Autor:in) / Min Chee Choy, (Autor:in) / Cheu, R.L. (Autor:in)


    Erscheinungsdatum :

    01.09.2006


    Format / Umfang :

    961314 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Neural Networks for Real-Time Traffic Signal Control

    Srinivasan, D. | Online Contents | 2006


    REAL TIME TRAFFIC SIGNAL CONTROL SYSTEM

    LEE DONG JU / KIM DAE SIG | Europäisches Patentamt | 2016

    Freier Zugriff

    Traffic Signal Real Time Moding Control Method

    MENG WEIPING | Europäisches Patentamt | 2018

    Freier Zugriff

    Models for Real-Time Traffic Adaptive Signal Control

    Bullen, A. G. R. / Memon, G. Q. | British Library Conference Proceedings | 1997


    Evolving to Real-Time Adaptive Traffic Signal Control

    Venglar, S. / Urbanik, T. | British Library Conference Proceedings | 1995