A highly efficient framework for outlier detection in urban traffic flow-Reference-Cited by-同舟云学术

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A highly efficient framework for outlier detection in urban traffic flow

Published:2021-08-17 Issue:12 Volume:15 Page:1494-1507
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET intell. transp. syst

Author:

Wang Xing¹²^ORCID,Zeng Ruihao³^ORCID,Zou Fumin⁴,Huang Faliang⁵,Jin Biao¹²

Affiliation:

1. College of Computer and Cyber Security Fujian Normal University Fuzhou China

2. Digital Fujian Institute of Big Data Security Technology Fuzhou China

3. International College of Chinese Studies Fujian Normal University Fuzhou China

4. Fujian Key Laboratory of Automotive Electronic and Electrical Drive Technology Fujian University of Technology Fuzhou China

5. School of Computer and Information Engineering Nanning Normal University Nanning China

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/itr2.12109

Reference38 articles.

1. There and back again: Outlier detection between statistical reasoning and data mining algorithms

2. Djenouri Y. Zimek A.&Chiarandini M.: Outlier detection in urban traffic flow distributions. In 2018 IEEE International Conference on Data Mining (ICDM) pp. 935–940. IEEE Piscataway NJ (2018)

3. Trajectory Outlier Detection for Traffic Events: A Survey

4. A Survey on Urban Traffic Anomalies Detection Algorithms

5. Djenouri Y.&Zimek A.: Outlier detection in urban traffic data. In Proceedings of the 8th International Conference on Web Intelligence Mining and Semantics pp. 1–12. IEEE Piscataway NJ (2018)

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