期刊
IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 16, 页码 12505-12517出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.3019398
关键词
Public transportation; Trajectory; Anomaly detection; Feature extraction; Clustering algorithms; Hidden Markov models; Urban areas; Anomaly detection; taxi fraud detection; trajectory
资金
- National Natural Science Foundation of China [61976051, 61702134, U19A2067, U1811463]
- National Key Research and Development Program of China [2017YFB0202201]
- Basic Research Project of Shenzhen [JCYJ20180306174743727]
The article introduces a system called FraudTrip, which effectively and efficiently detects unmetered taxi trips, solving the problem that existing detection methods cannot be applied to real-world scenarios.
A passenger is overcharged by the taxi driver is one common type of fraudulent trip, and it brings negative impacts to modern cities. Most existing fraudulent trip detection works rely on the assumption that the trip is correctly recorded by the taximeter. However, there are many taxi drivers in China carrying passengers without activating the taximeter, especially when the taxi driver is trying to overcharge the passengers. Hence, existing detection methods cannot be directly applied to such real-world scenario. In this article, we propose a system, called FraudTrip, which detects unmetered taxi trips based on a novel fraud detection algorithm and a heuristic maximum fraudulent trajectory construction algorithm. Based on the experiments on both synthetic and real-world trajectory data sets, FraudTrip can effectively and efficiently detect fraudulent trips without the help of taximeters.
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