Mobile Trajectory Anomaly Detection : Taxonomy, Methodology, Challenges, and Directions
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 19210-19231 |
Journal / Publication | IEEE Internet of Things Journal |
Volume | 11 |
Issue number | 11 |
Online published | 18 Mar 2024 |
Publication status | Published - 1 Jun 2024 |
Link(s)
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. © 2024 IEEE.
Research Area(s)
- Accidents, digital twin, edge intelligence, federated learning, Internet of Vehicles (IoV), Mobile trajectory anomaly, Reviews, Roads, Sensors, Trajectory, Urban areas, Videos
Citation Format(s)
Mobile Trajectory Anomaly Detection: Taxonomy, Methodology, Challenges, and Directions. / Kong, Xiangjie; Wang, Juntao; Hu, Zehao et al.
In: IEEE Internet of Things Journal, Vol. 11, No. 11, 01.06.2024, p. 19210-19231.
In: IEEE Internet of Things Journal, Vol. 11, No. 11, 01.06.2024, p. 19210-19231.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review