Travel time prediction is essential for the development of advanced traveler information systems. In this paper, we apply support vector regression (SVR) for travel-time predictions and compare its results to the other baseline travel-time prediction methods using real highway traffic data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that support vector regression performs well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can reduce significantly both relative mean errors and root mean squared errors of predicted travel times. We demonstrate the feasibility of applying SVR in travel-time prediction and prove that SVR is applicable and perform well for traffic data analysis.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Travel time prediction with support vector regression


    Contributors:


    Publication date :

    2003-01-01


    Size :

    339516 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Travel-Time Prediction With Support Vector Regression

    Wu, C.-H. / Ho, J.-M. / Lee, D. T. et al. | British Library Conference Proceedings | 2004


    Travel-time prediction with support vector regression

    Chun-Hsin Wu, / Jan-Ming Ho, / Lee, D.T. | IEEE | 2004


    Travel Time Prediction with Support Vector Regression

    Wu, C.-H. / Wei, C.-C. / Su, D.-C. et al. | British Library Conference Proceedings | 2003


    Urban Arterial Travel Time Prediction Using Support Vector Regression

    Philip, Anna Mary / Ramadurai, Gitakrishnan / Vanajakshi, Lelitha | Springer Verlag | 2018


    Bus travel time prediction model with ν - support vector regression

    Wang, Jing-nan / Chen, Xu-mei / Guo, Shu-xia | IEEE | 2009