NDLI: Catch Me If You Can: Detecting Pickpocket Suspects from Large-Scale Transit Records

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Catch Me If You Can: Detecting Pickpocket Suspects from Large-Scale Transit Records

Content Provider	ACM Digital Library
Author	Hou, Zhenshan Zhou, Wenjun Xiong, Hui Liu, Chuanren Du, Bowen
Abstract	Massive data collected by automated fare collection (AFC) systems provide opportunities for studying both personal traveling behaviors and collective mobility patterns in the urban area. Existing studies on the AFC data have primarily focused on identifying passengers' movement patterns. In this paper, however, we creatively leveraged such data for identifying thieves in the public transit systems. Indeed, stopping pickpockets in the public transit systems has been critical for improving passenger satisfaction and public safety. However, it is challenging to tell thieves from regular passengers in practice. To this end, we developed a suspect detection and surveillance system, which can identify pick-pocket suspects based on their daily transit records. Specifically, we first extracted a number of features from each passenger's daily activities in the transit systems. Then, we took a two-step approach that exploits the strengths of unsupervised outlier detection and supervised classification models to identify thieves, who exhibit abnormal traveling behaviors. Experimental results demonstrated the effective- ness of our method. We also developed a prototype system with a user-friendly interface for the security personnel.
Starting Page	87
Ending Page	96
Page Count	10
File Format	PDF MP4
ISBN	9781450342322
DOI	10.1145/2939672.2939687
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-08-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Mobility patterns Public safety Automated fare collection Travel behaviors Anomaly detection
Content Type	Audio Text
Resource Type	Article

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