Mobile Trajectory Anomaly Detection: Taxonomy, Methodology, Challenges, and Directions

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.

I. Introduction

The increasing number of vehicles in urban areas has led to a rise in driving risks and the occurrence of traffic accidents. Consequently, ensuring traffic safety has become a paramount concern. Detecting abnormal trajectories and promptly notifying nearby drivers represent a potential solution to mitigate these risks. Anomaly trajectory detection, a critical research area within the broader field of anomaly detection, focuses on identifying uncommon trajectory patterns within urban environments. Within the domain of traffic management, the detection of abnormal trajectories assumes a pivotal role in the identification and prediction of atypical traffic events that have the potential to result in accidents or severe congestion.