Travel times are key statistics for traffic performance, policy, and management evaluation purposes. Estimating travel times from local traffic speeds collected with loops or other sensors has been a relevant and lively research area. The most widespread and arguably most flexible algorithms developed for this purpose fall into the class of trajectory methods, which reconstruct synthetic vehicle trajectories on the basis of measured spot speeds and encompass various assumptions on which speeds prevail between traffic sensors. From these synthetic trajectories, average travel times can be deduced. This paper reviews and compares a number of these algorithms against two new trajectory algorithms based on spatiotemporal filtering of speed and 1/speed (slowness). On the basis of real data (from induction loops and an automated vehicle identification system), it is demonstrated that these new algorithms are more accurate (in terms of bias and residual error) than previous algorithms, and more robust with respect to increasing amounts of missing data.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Empirical Evaluation of New Robust Travel Time Estimation Algorithms


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2010-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    A Comparison of Existing Algorithms for Travel Time Estimation

    Liu, H. / Zhang, K. / Wang, Z. et al. | British Library Conference Proceedings | 2009


    A Comparison of Existing Algorithms for Travel Time Estimation

    Liu, Hao / Zhang, Ke / Wang, Zilei et al. | ASCE | 2009


    TRAVEL EVALUATION DEVICE, TRAVEL ESTIMATION METHOD, AND TRAVEL ESTIMATION PROGRAM

    MORITA TETSUO | European Patent Office | 2016

    Free access