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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    Title :

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


    Contributors:
    Heilmann, B. (author) / Koller, H. (author) / Asamer, J. (author) / Reinthaler, M. (author) / Aleksa, M. (author) / Breuss, S. (author) / Richter, G. (author)


    Publication date :

    2014-11-01


    Size :

    144900 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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