  In our urban life, Illegal Driver Substitution (IDS) activity for a taxi is a
grave unlawful activity in the taxi industry, possibly causing severe traffic
accidents and painful social repercussions. 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, motivated by this problem, 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) method 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.
