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    Traffic congestion monitoring using an improved kNN strategy

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    Name:
    MEAS-D-19-02550R1.pdf
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    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Harrou, Fouzi cc
    Zeroual, Abdelhafid
    Sun, Ying cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Environmental Statistics Group
    Statistics Program
    Date
    2020-01-25
    Online Publication Date
    2020-01-25
    Print Publication Date
    2020-05
    Embargo End Date
    2022-01-25
    Submitted Date
    2019-08-18
    Permanent link to this record
    http://hdl.handle.net/10754/661586
    
    Metadata
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    Abstract
    A systematic approach for monitoring road traffic congestion is developed to improve safety and traffic management. To achieve this purpose, an improved observer merging the benefits of a piecewise switched linear traffic (PWSL) modeling approach and Kalman filter (KF) is introduced. The PWSL-KF observer is utilized as a virtual sensor to emulate the traffic evolution in free-flow mode. In the proposed approach, residuals from the PWSL-KF model are used as the input to k-nearest neighbors (kNN) schemes for congestion detection. Here, kNN-based Shewhart and exponential smoothing schemes are designed for discovering the traffic congestions. The proposed detectors merge the desirable properties of kNN to appropriately separating normal from abnormal features and the capability of the monitoring schemes to better identify traffic congestions. In addition, kernel density estimation has been utilized to set nonparametric control limits of the proposed detectors and compared them with their parametric counterparts. Tests on traffic measurements from the four-lane State Route 60 in California freeways show the effectiveness of the PWSL-KF-based kNN methods in supervising traffic congestions.
    Citation
    Harrou, F., Zeroual, A., & Sun, Y. (2020). Traffic congestion monitoring using an improved kNN strategy. Measurement, 156, 107534. doi:10.1016/j.measurement.2020.107534
    Publisher
    Elsevier BV
    Journal
    Measurement
    DOI
    10.1016/j.measurement.2020.107534
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0263224120300713
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.measurement.2020.107534
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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