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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Univariate short-term prediction of road travel times


    Contributors:
    Nikovski, D. (author) / Nishiuma, N. (author) / Goto, Y. (author) / Kumazawa, H. (author)


    Publication date :

    2005-01-01


    Size :

    190592 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Univariate Short-Term Prediction of Road Travel Times

    Nikovski, D. N. / Nishiuma, N. / Yukio, G. et al. | British Library Conference Proceedings | 2005


    The Short-term Prediction of Link Travel Times in Signal Controlled Road Networks

    Anderson, J. M. / Bell, M. G. H. / Sayers, T. M. et al. | British Library Conference Proceedings | 1995


    Short-term travel time prediction

    Zhang, Xiaoyan | Online Contents | 2003


    Customized Pretrip Prediction of Freeway Travel Times for Road Users

    Wouters, Jacorien A. A. / Chan, Kin-Fai / Kolkman, Joost et al. | Transportation Research Record | 2005


    Customized Pretrip Prediction of Freeway Travel Times for Road Users

    Wouters, Jacorien / Chan, Kin-Fai / Kolkman, Joost et al. | Transportation Research Record | 2005