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    Road traffic density estimation and congestion detection with a hybrid observer-based strategy

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    Type
    Article
    Authors
    Zeroual, Abdelhafid
    Harrou, Fouzi cc
    Sun, Ying cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Environmental Statistics Group
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2018-12-31
    Online Publication Date
    2018-12-31
    Print Publication Date
    2019-04
    Permanent link to this record
    http://hdl.handle.net/10754/631659
    
    Metadata
    Show full item record
    Abstract
    Reliable detection of traffic congestion provides pertinent information for improving safety and comfort by alerting the driver to crowded roads or providing useful information for rapid decision-making. This paper addresses the problem of road traffic congestion estimation and detection from a statistical approach. First, a piecewise switched linear traffic model (PWSL)-based observer is introduced. The proposed hybrid observer (HO) estimates the unmeasured traffic density, thus reducing the cost of implementing and maintenance sensors and measurements devices. Here, the observer gains of each mode are obtained by solving a set of linear matrix inequalities. Second, a novel method for efficiently monitoring traffic congestion is proposed by combining the proposed HO with a generalized likelihood ratio (GLR) test. Also, an exponentially-weighted moving average (EWMA) filter is applied to the residual data to reduce high-frequency noise. Thus, as the EWMA filter, aggregates all of the information from past and actual samples in the decision rule, it extends the congestion detection abilities of the GLR test to the detection of incipient changes. This study shows that a better performance is achieved when GLR is applied to filtered data than to unfiltered data. The effectiveness of the proposed approach is verified on traffic data from the four-lane State Route 60 (SR-60) and the three lanes Interstate 210 (I-210) in California freeways. Results show the efficacy of the proposed HO-based EWMA-GLR method to monitor traffic congestions. Also, the proposed approach is compared to that of the conventional Shewhart and EWMA approaches and found better performance.
    Citation
    Zeroual A, Harrou F, Sun Y (2019) Road traffic density estimation and congestion detection with a hybrid observer-based strategy. Sustainable Cities and Society 46: 101411. Available: http://dx.doi.org/10.1016/j.scs.2018.12.039.
    Sponsors
    The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.
    Publisher
    Elsevier BV
    Journal
    Sustainable Cities and Society
    DOI
    10.1016/j.scs.2018.12.039
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S2210670718312332
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.scs.2018.12.039
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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