Mobile Trajectory Anomaly Detection: Taxonomy, Methodology, Challenges, and Directions
Xiangjie Kong, Juntao Wang, Zehao Hu, Yuwei He, Xiangyu Zhao, Guojiang Shen
Abstract
The growing number of cars on city roads has led to an increase in traffic accidents, highlighting the need for traffic safety measures. Mobile trajectory anomaly detection is an important area of research that can identify unusual patterns or trajectories in urban environments and provide timely warnings to drivers to avoid accidents. However, there is a significant lack of research on the analysis of vehicle trajectory anomalies. To address this gap, we provide a comprehensive review of currently published papers on anomalous trajectories, highlighting important research trends and future directions. Besides, we innovatively classify trajectory anomalies into vehicle-based anomalies and driver-based anomalies according to whether they are caused by the driver’s behavior or not. The study further examines the existing challenges associated with analyzing anomalous trajectories and assesses the currently available solutions.