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    Road network state estimation using random forest ensemble learning

    Road network state estimation using random forest ensemble learning


    Network-scale travel time prediction not only enables traffic management centers (TMC) to proactively implement traffic management strategies, but also allows travelers make informed decisions about route choices between various origins and destinations. In this paper, a random forest estimator was proposed to predict travel time in a network. The estimator was trained using two years of historical travel time data for a case study network in St. Louis, Missouri. Both temporal and spatial effects were considered in the modeling process. The random forest models predicted travel times accurately during both congested and uncongested traffic conditions. The computational times for the models were low, thus useful for real-time traffic management and traveler information applications.

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    Titel :

    Road network state estimation using random forest ensemble learning

    Beteiligte:
    Hou, Yi (Autor:in) / Edara, Praveen (Autor:in) / Chang, Yohan (Autor:in)
    Erschienen in:

    2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) ; 1-6

    Erscheinungsdatum :

    2017-10-01

    Format / Umfang :

    880070 byte

    ISBN :

    978-1-5386-1526-3 , 978-1-5386-1525-6

    ISSN :

    2153-0017

    DOI :

    https://doi.org/10.1109/ITSC.2017.8317743

    Medientyp :

    Aufsatz (Konferenz)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

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