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 th...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/1d28c8c23fcc450391917d475996d8f4 |
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| Sumario: | 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 <i>FraudMove</i>, to discover fraud drivers in real-time by identifying outlier active trips. Mainly, the proposed <i>FraudMove</i> 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. <i>FraudMove</i> employs a tunable time window parameter to control the number of checks for detecting outlier trips. This parameter allows <i>FraudMove</i> to trade responsiveness with efficiency. Unlike other related works that wait until the end of a trip to indicate that it was an outlier, <i>FraudMove</i> discovers outlier trips instantly during the trip. Extensive experiments conducted on a real dataset confirm the efficiency and effectiveness of <i>FraudMove</i> in detecting outlier trajectories. The experimental results prove that <i>FraudMove</i> saves the response time of the outlier check process by up to 65% compared to the state-of-the-art systems. |
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