A state space model (SSM), based on spatial and temporal Bayesian conditional independencies between traffic variables, is developed to correct/update directly measured route travel times using point-based traffic flow data. In this model, lag-free current route travel time estimates can be obtained by adjusting the lagged travel time measurements in accordance with the traffic flow measurements taken during the interval between the times the observed vehicles pass the upstream and downstream license plate readers (LPRs). The model has advantages of heightened confidence, improved accuracy, and enhanced robustness over models relying on either section-based or point-based data alone.


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    Titel :

    Integration of Section- and Point-Based Data for Online Arterial Route Travel Time Estimation


    Beteiligte:
    Xie, Chi (Autor:in) / Shuldiner, Paul W. (Autor:in)

    Kongress:

    Eighth International Conference on Applications of Advanced Technologies in Transportation Engineering (AATTE) ; 2004 ; Beijing, China



    Erscheinungsdatum :

    13.05.2004




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Integration of Section- and Point-Based Data for Online Arterial Route Travel Time Estimation

    Xie, C. / Shuldiner, P. W. / ASCE | British Library Conference Proceedings | 2004




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