Traffic simulations have traditionally used strictly deterministic, differential-equation-based, models of driving processes. Human decision making is, however, a linguistically structured rule-based process where desired outcomes are only approximately defined. The nature of human reasoning is thus better captured by fuzzy-inference logic, which this project uses to model driving behavior. individualized fuzzy inference models of different types of drivers that have been developed and incorporated into the TRAF-NETSIM microscopic traffic flow simulation environment. Users can now choose this fuzzy inference based driver decision model as an option when running TRAF-NETSIM to simulate more realistic vehicle motion accuracy.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen


    Exportieren, teilen und zitieren



    Titel :

    Fuzzy Inference-Based Driver Decision Processes and Traffic Flow Simulation


    Beteiligte:
    S. Kikuchi (Autor:in)

    Erscheinungsdatum :

    1996


    Format / Umfang :

    32 pages


    Medientyp :

    Report


    Format :

    Keine Angabe


    Sprache :

    Englisch




    Modeling the Driver for Traffic Flow Simulation

    Stang, N. E. / Transportation Association of Canada | British Library Conference Proceedings | 1995


    Multi-Agent and Fuzzy Inference Based Framework for Urban Traffic Simulation

    IKIDID, Abdelouafi / Abdelaziz, El Fazziki | IEEE | 2019


    Fuzzy Inference Rule based Neural Traffic Light Controller

    Mir, Aqeela / Hassan, Ali | British Library Conference Proceedings | 2018


    INFERENCE OF TRAFFIC FLOW

    ERIC CHRISTPHER SJOBERG / ROBIN CARLSON | Europäisches Patentamt | 2020

    Freier Zugriff

    Freeway Traffic Congestion Identification Based on Fuzzy Logic Inference

    Peng, Ming-Long / Liang, Xin-Rong / Dong, Chao-Jun et al. | Tema Archiv | 2013