Subtrajectory Anomaly Detection Method based on Stay Area Recognition

The analysis and processing of trajectory data can reveal the motion law of moving objects and dig out the hidden information related to it. Irregular or abnormal movements of moving objects generate anomalous trajectory data, and the appearance of anomalous data often means that there is a special situation, which implies more meaningful information. Rapid and accurate detection of abnormal trajectories can serve applications such as traffic analysis and accident detection. In most application scenarios, the trajectory anomaly detection method needs to pay attention to the anomaly of the trajectory segments, while the existing methods do not fully consider the local anomaly of the trajectory, and the detection results have certain shortcomings. This paper proposes a subtrajectory anomaly detection method based on stay area recognition, which aims at the problem that traditional trajectory anomaly detection methods do not fully consider the local anomaly of the trajectory. This method first designs a density-based stay point detection algorithm to detect the stay points in the trajectory set, that is to say, establishes the initial cluster by finding the core point, expands the current cluster with the points in the neighborhood of the core point, and detects the stay points by determining whether the time interval in the cluster satisfies the time conditions or not. Second, it identifies the stay areas according to the set of stay points and segments each trajectory after taking any two stay areas as a pair of start and end areas. Third, the subtrajectory set is grouped according to the start and end regions of the subtrajectories after segmentation. Finally, for the subtrajectories in each group, a subtrajectory anomaly detection algorithm is designed to detect anomalous spatial subtrajectories and anomalous spatiotemporal subtrajectories. Compared with traditional anomaly detection methods on real trajectory datasets, the experimental results show that the proposed method can effectively detect abnormal sub-trajectories, and the running time is significantly shorter than that of the TRAOD method, and the detection accuracy is up to 23.9% higher than that of the TRAOD method. Compared with the ATDC and iBAT methods, the F1 score is significantly improved, with the highest improvement rates of 7.8% and 16.1%, respectively. The above experimental results verify the effectiveness and practicability of the proposed method. The trajectory anomaly detection method described in this study can provide effective decision-making information for transportation and management departments and provide a new solution for vehicle trajectory data mining. © 2023 Journal of Geo-Information Science. All rights reserved.