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

This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for the first problem by adapting DBSCAN , k nearest neighbors (kNN) , and feature selection (FS) . DBSCAN-GTO first applies DBSCAN to derive the micro clusters , which are considered as potential candidates. A pruning strategy based on density computation measure is then suggested to find the group of trajectory outliers. kNN-GTO recursively derives the trajectory candidates from the individual trajectory outliers and prunes them based on their density. The overall process is repeated for all individual trajectory outliers. FS-GTO considers the set of individual trajectory outliers as the set of all features, while the FS process is used to retrieve the group of trajectory outliers. The proposed algorithms are improved by incorporating ensemble learning and high-performance computing during the detection process. Moreover, we propose a general two-phase-based algorithm for detecting the deviation points, as well as a version for graphic processing units implementation using sliding windows. Experiments on a real trajectory dataset have been carried out to demonstrate the performance of the proposed approaches. The results show that they can efficiently identify useful patterns represented by group of trajectory outliers, deviation points, and that they outperform the baseline group detection algorithms.

Vessel Trajectory Reconstruction Based on Functional Data Analysis Using Automatic Identification System Data

This study provides an automatic shipping-route construction method using functional data analysis (FDA), which analyzes information about curves, such as multiple data points over time. The proposed approach includes two steps: outlier detection and shipping-route construction. This study uses automatic-identification system (AIS) data for the experiments. The effectiveness of the proposed method is demonstrated through case studies, wherein our approach is compared with the Mahalanobis distance method for trajectory-outlier detection, and the performance of vessel trajectory reconstruction is compared with that of a density-based approach. The proposed method improves understanding of vessel-movement dynamics, thereby improving maritime monitoring and security.