Information about public transport travel time is a key indicator of service performance, and is valued by passengers and operators. Among many different approaches, Support Vector Machines (SVM) has recently gained attention in predicting bus travel times. The training process of SVMs involves solving a quadratic programming problem which is slow when dealing with large training data. This paper proposes a Least Squares SVM (LS-SVM) method that expedites the training process by simplifying the quadratic programming problem using a linear regression technique. Also, to ensure the accuracy of the prediction results, a Genetic Algorithm (GA) is used to determine the optimal set of model parameters. The GA based LS-SVM approach is tested using real-world travel time data from a bus route in Melbourne, Australia. The comparison of the results in this paper to those obtained in a previous study using artificial neural networks shows that the proposed method produces more accurate results.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A genetic algorithm-based support vector machine for bus travel time prediction


    Contributors:


    Publication date :

    2015-06-01


    Size :

    323840 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    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

    Chun-Hsin Wu, / Chia-Chen Wei, / Da-Chun Su, et al. | IEEE | 2003


    Travel Time Prediction with Support Vector Regression

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


    Travel-Time Prediction With Support Vector Regression

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


    A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction

    Shiquan Zhong / Juanjuan Hu / Shuiping Ke et al. | DOAJ | 2015

    Free access