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dc.contributor.authorZeroual, Abdelhafid
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.date.accessioned2019-03-14T14:24:50Z
dc.date.available2019-03-14T14:24:50Z
dc.date.issued2018-12-31
dc.identifier.citationZeroual 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.
dc.identifier.issn2210-6707
dc.identifier.doi10.1016/j.scs.2018.12.039
dc.identifier.urihttp://hdl.handle.net/10754/631659
dc.description.abstractReliable 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.
dc.description.sponsorshipThe 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.
dc.publisherElsevier BV
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S2210670718312332
dc.subjectHybrid observer
dc.subjectIntelligent transportation systems
dc.subjectMonitoring traffic congestion
dc.subjectStatistical monitoring schemes
dc.titleRoad traffic density estimation and congestion detection with a hybrid observer-based strategy
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
dc.identifier.journalSustainable Cities and Society
dc.contributor.institutionCReSTiC URCA UFR SEN, University of Reims Champagne-Ardenne, Moulin de la Housse, BP 1039 51687 Reims Cedex 2, , France
dc.contributor.institutionLAIG Laboratory, University of 08 May 1945, Guelma, 24000, , Algeria
dc.contributor.institutionUniversity of 20 August 1954, Skikda, 21000, , Algeria
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582
dc.date.published-online2018-12-31
dc.date.published-print2019-04


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