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Detecting Unmetered Taxi Rides from Trajectory Data
Zhou, Xibo; Ding, Ye; Peng, Fengchao; Luo, Qiong; Ni, Lionel M.; Nie, JY; Obradovic, Z; Suzumura, T; Ghosh, R; Nambiar, R; Wang, C; Zang, H; BaezaYates, R; Hu, X; Kepner, J; Cuzzocrea, A; Tang, J; Toyoda, M
2017
Conference Name2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
Pages530-535
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
PublisherIEEE
AbstractTaxi fraud has become a serious problem in many large cities, where passengers are overcharged by taxi drivers in various ways. Researchers have developed a number of methods to detect taxi frauds with the assumption that fraudulent trips, among normal trips, are recorded by taximeters. In this paper, different from the previous work, we identify a new type of taxi fraud called unmetered taxi rides, where taxi drivers carry passengers without activating the taximeters. Since these fraudulent rides are not recorded by taximeters, previous detection approaches cannot directly apply to them. Hence, we propose a novel fraud detection system specifically designed for unmetered taxi rides. Our system uses a learning model to detect unmetered trajectory segments that are similar to metered rides, and introduces a heuristic algorithm to construct maximum fraudulent trajectories from the trajectory dataset. We have conducted detailed experiments on real-world datasets, and the results show that the proposed system can detect unmetered taxi rides effectively and efficiently.
Keywordtaxi fraud detection trajectory anomaly detection
URLView the original
Indexed ByCPCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS IDWOS:000428073700067
The Source to ArticleWOS
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Zhou, Xibo,Ding, Ye,Peng, Fengchao,et al. Detecting Unmetered Taxi Rides from Trajectory Data[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:530-535.
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