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Root Cause Analysis of Traffic Anomalies Using Uneven Diffusion Model

Huang, Guang-Li, Deng, Ke, Ren, Yongli and Li, Jianxin 2019, Root Cause Analysis of Traffic Anomalies Using Uneven Diffusion Model, IEEE Access, vol. 7, pp. 16206-16216, doi: 10.1109/ACCESS.2019.2893997.

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Title Root Cause Analysis of Traffic Anomalies Using Uneven Diffusion Model
Author(s) Huang, Guang-Li
Deng, Ke
Ren, Yongli
Li, JianxinORCID iD for Li, Jianxin orcid.org/0000-0002-9059-330X
Journal name IEEE Access
Volume number 7
Start page 16206
End page 16216
Total pages 11
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Place of publication Piscataway, N.J.
Publication date 2019-01-21
ISSN 2169-3536
2169-3536
Keyword(s) Root cause analysis
traffic anomalies
spatiotemporal causal relationship
visible outlier index
uneven diffusion model
Summary Detection and analysis of traffic anomalies are important for the development of intelligent transportation systems. In particular, the root causes of traffic anomalies in road networks as well as their propagation and influence to the surrounding areas are highly meaningful. The root cause analysis of traffic anomalies aims to identify those road segments, where the traffic anomalies are detected by the traffic statuses significantly deviating from the usual condition and are originated due to incidents occurring in those roads such as traffic accidents or social events. The existing methods for traffic anomaly root cause analysis detect all traffic anomalies first and then apply, implicitly or explicitly, specified causal propagation rules to infer the root cause. However, these methods require reliable detection techniques to accurately identify all traffic anomalies and extensive domain knowledge of city traffic to specify plausible causal propagation rules in road networks. In contrast, this paper proposes an innovative and integrated root cause analysis method. The proposed method is featured by 1) defining a visible outlier index as the probabilistic indicator of traffic anomalies/disturbances and 2) automatically learning spatiotemporal causal relationship from historical data to build an uneven diffusion model for root cause analysis. The accuracy and effectiveness of the proposed method have been demonstrated by experiments conducted on a trajectory dataset with 2.5 billion location records of 27 266 taxies in Shenzhen city.
Language eng
DOI 10.1109/ACCESS.2019.2893997
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2013, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30135701

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.