The energy-saving low-frequency floating car data has increasingly attracted significantly attention in traffic management, but the low-frequency GPS data has adverse effects on map-matching in urban road networks. Therefore, this paper develops a new spatial-temporal path filter-based and route choice model aided map-matching (FCMM) algorithm that enhances the map-matching of low-frequency positioning data on an urban road map. We introduce filters based on spatial and temporal analyses, which consider real-time traffic status to eliminate unreasonable paths between adjacent candidate points, calculate the heuristic information, and establish the candidate graph. Considering the driver path preference, we further use the route choice model estimated from from real drive data to assess each candidate path. The algorithm was evaluated using ground truth data, and the results of the experiment show that the proposed FCMM algorithm outperforms the baseline methods in both effectiveness and efficiency under the condition of low sampling rates.


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    Titel :

    Path Inference Filter and Route Choice Model Aided Map-Matching for Low-Frequency GPS Data


    Beteiligte:
    Xiao, Pinghui (Autor:in) / Fang, Jie (Autor:in) / Xu, Mengyun (Autor:in) / Liu, Zhijia (Autor:in)

    Kongress:

    21st COTA International Conference of Transportation Professionals ; 2021 ; Xi’an, China


    Erschienen in:

    CICTP 2021 ; 145-157


    Erscheinungsdatum :

    14.12.2021




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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