Vehicle trajectories clustering plays an increasingly essential role in understanding urban traffic patterns. In order to improve the clustering effect, we propose a novel clustering method integrated with multidimensional trajectory information based on ST-OPTICS clustering algorithm. This density-based algorithm utilizes spatial, temporal, road segment and direction angle information to form clusters of varying density based on spatial and temporal closeness. Furthermore, we utilize DTW to build a similarity model to measure the similarity between vehicle trajectories. Then we conduct experiments on a large-scale vehicle trajectory dataset consisting of 2172 trajectories collected from the GPS traces nearby Beijing Olympic Parks. Compared with four general clustering frameworks: DBSCAN, ST-DBSCAN, OPTICS and ST-OPTICS, we demonstrate that our method performs better than other clustering methods by evaluated on two internal cluster validity measures. Finally, we explore route choosing strategy according to the travel time of different trajectories and discover some abnormal trajectories.


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    Title :

    Spatial-Temporal Trajectory Clustering and Anomaly Analysis Based on Improved OPTICS Method


    Contributors:
    Zhang, Ke (author) / Li, Huiping (author) / Shan, Yu (author) / Li, Meng (author)

    Conference:

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


    Published in:

    CICTP 2021 ; 189-198


    Publication date :

    2021-12-14




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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