Driving behaviors at intersections are complex. At intersections, drivers face more traffic events than elsewhere and are thus exposed to more potential errors with safety consequences. Drivers make real-time responses in a stochastic manner. This study used hidden Markov models (HMMs) to model the driving behavior of through-going vehicles on major roads at intersections. Observed vehicle movement data were used to estimate the model. A single HMM was used to cluster movements when vehicles were close to the intersection. The reestimated clustered HMMs could more accurately predict vehicle movements compared with traditional car-following models.


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    Titel :

    Modeling Pipeline Driving Behaviors


    Untertitel :

    Hidden Markov Model Approach


    Weitere Titelangaben:

    Transportation Research Record


    Beteiligte:
    Zou, Xi (Autor:in) / Levinson, David M. (Autor:in)


    Erscheinungsdatum :

    2006-01-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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