Catch me if you can: Detecting pickpocket suspects from large-scale transit records

Bowen Du, Chuanren Liu, Wenjun Zhou, Zhenshan Hou, Hui Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Scopus citations

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 pickpocket 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 effectiveness of our method. We also developed a prototype system with a user-friendly interface for the security personnel.

Original languageEnglish (US)
Title of host publicationKDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages87-96
Number of pages10
ISBN (Electronic)9781450342322
DOIs
StatePublished - Aug 13 2016
Event22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States
Duration: Aug 13 2016Aug 17 2016

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume13-17-August-2016

Conference

Conference22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
CountryUnited States
CitySan Francisco
Period8/13/168/17/16

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Keywords

  • Anomaly detection
  • Automated fare collection
  • Mobility patterns
  • Public safety
  • Travel behaviors

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