TY - GEN
T1 - Catch me if you can
T2 - 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
AU - Du, Bowen
AU - Liu, Chuanren
AU - Zhou, Wenjun
AU - Hou, Zhenshan
AU - Xiong, Hui
N1 - Funding Information:
This research was supported in part by National Natural Science Foundation of China (No. 51408018), National Natural Science Foundation of China (No. 71329201), National High Technology Research and Development Program (863, 2013AA01A601)
Publisher Copyright:
© 2016 ACM.
PY - 2016/8/13
Y1 - 2016/8/13
N2 - 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.
AB - 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.
KW - Anomaly detection
KW - Automated fare collection
KW - Mobility patterns
KW - Public safety
KW - Travel behaviors
UR - http://www.scopus.com/inward/record.url?scp=84984985657&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84984985657&partnerID=8YFLogxK
U2 - 10.1145/2939672.2939687
DO - 10.1145/2939672.2939687
M3 - Conference contribution
AN - SCOPUS:84984985657
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 87
EP - 96
BT - KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 13 August 2016 through 17 August 2016
ER -