In the presented case study, travel times for passenger cars (PC) and heavy goods vehicles (HGV) were predicted with a data-driven, hybrid approach, using historical traffic data of the entire high-ranking Austrian road network. In case flow data were available, travel time was predicted with a Kernel predictor searching for similar speed-density patterns. In case of missing flow data, travel time was predicted with deviations from typical historical speed time series. The performed steps in pre-processing traffic data, the hybrid prediction method as well as the results for selected road sections are described and analysed.


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    Titel :

    A data-driven approach for travel time prediction on motorway sections


    Beteiligte:
    Heilmann, B. (Autor:in) / Koller, H. (Autor:in) / Asamer, J. (Autor:in) / Reinthaler, M. (Autor:in) / Aleksa, M. (Autor:in) / Breuss, S. (Autor:in) / Richter, G. (Autor:in)


    Erscheinungsdatum :

    01.11.2014


    Format / Umfang :

    144900 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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