FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection

Taxicabs and rideshare cars nowadays are equipped with GPS devices that enable capturing a large volume of traces. These GPS traces represent the moving behavior of the car drivers. Indeed, the real-time discovery of fraud drivers earlier is a demand for saving the passenger’s life and money. For this purpose, this paper proposes a novel time-based system, namely FraudMove, to discover fraud drivers in real-time by identifying outlier active trips. Mainly, the proposed FraudMove system computes the time of the most probable path of a trip. For trajectory outlier detection, a trajectory is considered an outlier trajectory if its time exceeds the time of this computed path by a specified threshold. FraudMove employs a tunable time window parameter to control the number of checks for detecting outlier trips. This parameter allows FraudMove to trade responsiveness with efficiency. Unlike other related works that wait until the end of a trip to indicate that it was an outlier, FraudMove discovers outlier trips instantly during the trip. Extensive experiments conducted on a real dataset confirm the efficiency and effectiveness of FraudMove in detecting outlier trajectories. The experimental results prove that FraudMove saves the response time of the outlier check process by up to 65% compared to the state-of-the-art systems

Trajectory outlier detection: Algorithms, taxonomies, evaluation, and open challenges

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Learning from the Offline Trace: A Case Study of the Taxi Industry

The growth of mobile and sensor technologies today leads to the digitization of individual\u27s offline behavior. Such large-scale and fine-grained information can help better understand individual decision making. We instantiate our research by analyzing the digitized taxi trails to study the impact of information on driver behavior and economic outcome. We propose homogeneous and heterogeneous Bayesian learning models and validate them using a unique data set containing complete information on 10.6M trip records from 11,196 taxis in a large Asian city in 2009. We find strong heterogeneity in individual learning behavior and driving decisions, which significantly associate with individual economic outcome. Interestingly, our policy simulations indicate information that is noisy at individual level can become most valuable after being aggregated across various spatial and temporal dimensions. Overall, our work demonstrates the potential of analyzing the digitized offline behavioral trace to infer demand as well as to improve individual decision efficiency