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.
Finding a Taxi With Illegal Driver Substitution Activity via Behavior Modelings
IEEE Transactions on Intelligent Transportation Systems ; 25 , 12 ; 20309-20319
2024-12-01
13596062 byte
Article (Journal)
Electronic Resource
English
Modeling Analysis on the Influencing Factors of Taxi Driver’s Illegal Behavior in Metropolis
Springer Verlag | 2018
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