This paper presents an experimental comparison of several statistical machine learning methods for short-term prediction of travel times on road segments. The comparison includes linear regression, neural networks, regression trees, k-nearest neighbors, and locally-weighted regression, tested on the same historical data. In spite of the expected superiority of non-linear methods over linear regression, the only non-linear method that could consistently outperform linear regression was locally-weighted regression. This suggests that novel iterative linear regression algorithms should be a preferred prediction methods for large-scale travel time prediction.


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    Titel :

    Univariate short-term prediction of road travel times


    Beteiligte:
    Nikovski, D. (Autor:in) / Nishiuma, N. (Autor:in) / Goto, Y. (Autor:in) / Kumazawa, H. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    190592 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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