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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

5 Scopus Citations
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Author(s)

  • Xiangjie Kong
  • Juntao Wang
  • Zehao Hu
  • Yuwei He
  • Guojiang Shen

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)19210-19231
Journal / PublicationIEEE Internet of Things Journal
Volume11
Issue number11
Online published18 Mar 2024
Publication statusPublished - 1 Jun 2024

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.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review