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A Survey on Urban Traffic Anomalies Detection Algorithms

Djenouri, Youcef; Belhadi, Asma; Lin, Jerry Chun-Wei; Djenouri, Djamel; Cano, Alberto

Authors

Youcef Djenouri

Asma Belhadi

Jerry Chun-Wei Lin

Alberto Cano



Abstract

© 2019 IEEE. This paper reviews the use of outlier detection approaches in urban traffic analysis. We divide existing solutions into two main categories: flow outlier detection and trajectory outlier detection. The first category groups solutions that detect flow outliers and includes statistical, similarity and pattern mining approaches. The second category contains solutions where the trajectory outliers are derived, including off-line processing for trajectory outliers and online processing for sub-trajectory outliers. Solutions in each of these categories are described, illustrated, and discussed, and open perspectives and research trends are drawn. Compared to the state-of-the-art survey papers, the contribution of this paper lies in providing a deep analysis of all the kinds of representations in urban traffic data, including flow values, segment flow values, trajectories, and sub-trajectories. In this context, we can better understand the intuition, limitations, and benefits of the existing outlier urban traffic detection algorithms. As a result, practitioners can receive some guidance for selecting the most suitable methods for their particular case.

Citation

Djenouri, Y., Belhadi, A., Lin, J. C., Djenouri, D., & Cano, A. (2019). A Survey on Urban Traffic Anomalies Detection Algorithms. IEEE Access, 7, 12192-12205. https://doi.org/10.1109/access.2019.2893124

Journal Article Type Article
Acceptance Date Jan 1, 2018
Online Publication Date Jan 15, 2019
Publication Date Jan 15, 2019
Deposit Date Jan 21, 2020
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 7
Pages 12192-12205
DOI https://doi.org/10.1109/access.2019.2893124
Keywords General Engineering; General Materials Science; General Computer Science
Public URL https://uwe-repository.worktribe.com/output/5199816
Publisher URL https://ieeexplore.ieee.org/document/8612931

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(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.





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