Travel time information is generally nonlinear and nonstationary in a dynamic environment, and therefore no consistent tendency can be easily observed. This research developed a novel approach that combined the empirical decomposition method for speed data analysis and gray theory for travel time prediction to predict the arrival time at each stop along a bus route. In addition, sensitivity analysis was performed for different numbers of stops. With an average prediction error of less than 3.5%, the experiments showed that the proposed prediction approach, which employed both historical and real-time speed data collected from the geographic positioning system, outperformed Chou's approach, which used only historical speed data. The proposed prediction method could be readily incorporated into a cell phone–based information retrieval system that indicated bus position en route as well as its arrival times at all stops.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Travel Time Prediction Using Empirical Mode Decomposition and Gray Theory


    Subtitle :

    Example of National Central University Bus in Taiwan


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2012-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Hybrid Empirical Mode Decomposition-Neuro Model for Short-Term Travel Time Prediction on Freeways

    National Research Council (U.S.) | British Library Conference Proceedings | 2005


    Travel Time Prediction Using Singular Value Decomposition

    Nishiuma, N. / Goto, Y. / Kumazawa, H. et al. | British Library Online Contents | 2006


    Trip Mode Recommendation Using Travel Time Prediction

    Cremer, M. / Holtmann, C. / Schreiber, S. et al. | British Library Conference Proceedings | 1996