STLP-OD: Spatial and Temporal Label Propagation for Traffic Outlier Detection

Juhua Pu, Yue Wang, Xinran Liu, Xiangliang Zhang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


This paper focuses on the detection of non-recurrent traffic anomaly caused by unexpected or transient incidents, such as traffic accidents, celebrations, and disasters. Comparing to existing approaches, it considers the spatial and temporal propagation of traffic anomalies from one road to other neighbor roads by proposing an STLP-OD framework. The experimental results on a real data set show that the proposed approach can improve the accuracy of traffic outlier detection baselines significantly.
Original languageEnglish (US)
Pages (from-to)63036-63044
Number of pages9
JournalIEEE Access
StatePublished - 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1002000,
Science Technology and Innovation Commission of Shenzhen Municipality JCYJ20180307123659504, and the State Key Laboratory of
Software Development Environment.


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