GPS Trajectories provide a valuable opportunity for us to understand the human behaviors of road users and detect adverse and/or malignant events. In this chapter, we intend to detect anomalous routes through an in-depth comparison between the target trace and historical “normal” ones. Specifically, a new approach is proposed to detect anomalous trajectory “on-the-fly” and find out which segments cause the anomaly. After that, we conduct a systematical analysis on nearly 43,800 anomalous trajectories derived from 7600 taxis in a month. It reveals that most anomalous trips are caused by deliberate fraudulent decisions by greedy taxi drivers. At last, a group of experiments has been conducted to evaluate the effectiveness of the isolation-based online anomalous trajectory (iBOAT) algorithm. iBOAT algorithm shows superior performance compared with the baseline methods (AUC ≥ 0.99).


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    Titel :

    iBOAT: Detecting Anomalous Trajectories On-the-Fly


    Beteiligte:
    Chen, Chao (Autor:in) / Zhang, Daqing (Autor:in) / Wang, Yasha (Autor:in) / Huang, Hongyu (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2021-04-02


    Format / Umfang :

    22 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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