In our urban life, Illegal Driver Substitution (IDS) activity for a taxi is a grave unlawful activity in the taxi industry. Currently, the IDS activity is manually supervised by law enforcers, i.e., law enforcers empirically choose a taxi and inspect it. The pressing problem of this scheme is the dilemma between the limited number of law-enforcers and the large volume of taxis. In this paper, we propose a computational method that helps law enforcers efficiently find the taxis which tend to have the IDS activity. Firstly, our method converts the identification of the IDS activity to a supervised learning task. Secondly, two kinds of taxi driver behaviors, i.e., the Sleeping Time and Location (STL) behavior and the Pick-Up (PU) behavior are proposed. Thirdly, the multiple scale pooling on self-similarity is proposed to encode the individual behaviors into the universal features for all taxis. Finally, a Multiple Component-Multiple Instance Learning (MC-MIL) is proposed to handle the deficiency of the behavior features and to align the behavior features, simultaneously. Extensive experiments on a real-world data set shows that the proposed behavior features have a good generalization ability across different classifiers, and the proposed MC-MIL method suppresses the baseline methods.


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    Titel :

    Finding a Taxi With Illegal Driver Substitution Activity via Behavior Modelings


    Beteiligte:
    Pang, Junbiao (Autor:in) / Ayub Sabir, Muhammad (Autor:in) / Wang, Zuyun (Autor:in) / Hu, Anjing (Autor:in) / Yang, Xue (Autor:in) / Yu, Haitao (Autor:in) / Huang, Qingming (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    13596062 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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