FraudTrip: Taxi Fraudulent Trip Detection From Corresponding Trajectories
Y. Ding, Wenyi Zhang, Xibo Zhou, Qing Liao, Qiong Luo, Lionel M. Ni
Abstract
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.