With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similarity score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.


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    Titel :

    Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data


    Weitere Titelangaben:

    Transportation Research Record


    Beteiligte:
    Zhu, Lei (Autor:in) / Holden, Jacob R. (Autor:in) / Gonder, Jeffrey D. (Autor:in)


    Erscheinungsdatum :

    2017-01-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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