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

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    08715411.pdf
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    Description:
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    Type
    Article
    Authors
    Pu, Juhua
    Wang, Yue
    Liu, Xinran
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/655896
    
    Metadata
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    Abstract
    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.
    Citation
    Pu, J., Wang, Y., Liu, X., & Zhang, X. (2019). STLP-OD: Spatial and Temporal Label Propagation for Traffic Outlier Detection. IEEE Access, 7, 63036–63044. doi:10.1109/access.2019.2916853
    Sponsors
    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.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Access
    DOI
    10.1109/ACCESS.2019.2916853
    Additional Links
    https://ieeexplore.ieee.org/document/8715411/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8715411
    ae974a485f413a2113503eed53cd6c53
    10.1109/ACCESS.2019.2916853
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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