Detecting Pickpocket Suspects from Large-Scale Public Transit Records

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

Research output: Contribution to journalArticlepeer-review

5 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 urban areas. Existing studies on AFC data have primarily focused on identifying passengers' movement patterns. However, we creatively leveraged such data for identifying pickpocket suspects. Stopping pickpockets in the public transit system has been crucial for improving passenger satisfaction and public safety. Nonetheless, in practice, it is challenging to discern thieves from regular passengers. In this paper, 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 useful features from each passenger's daily activities in the transit system. Then, we took a two-step approach that exploits the strengths of unsupervised outlier detection and supervised classification models to identify thieves, who typically exhibit abnormal traveling behaviors. Experimental results demonstrated the effectiveness of our method. We also developed a prototype system for potential uses by security personnel.

Original languageEnglish (US)
Article number8357468
Pages (from-to)465-478
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume31
Issue number3
DOIs
StatePublished - Mar 1 2019

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Keywords

  • Automated fare collection
  • anomaly detection
  • mobility patterns
  • public safety
  • travel behaviors

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