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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorZeroual, Abdelhafid
dc.contributor.authorSun, Ying
dc.date.accessioned2020-02-20T08:48:21Z
dc.date.available2020-02-20T08:48:21Z
dc.date.issued2020-01-25
dc.date.submitted2019-08-18
dc.identifier.citationHarrou, F., Zeroual, A., & Sun, Y. (2020). Traffic congestion monitoring using an improved kNN strategy. Measurement, 156, 107534. doi:10.1016/j.measurement.2020.107534
dc.identifier.doi10.1016/j.measurement.2020.107534
dc.identifier.urihttp://hdl.handle.net/10754/661586
dc.description.abstractA 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.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0263224120300713
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Measurement: Journal of the International Measurement Confederation. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Measurement: Journal of the International Measurement Confederation, [[Volume], [Issue], (2020-01-25)] DOI: 10.1016/j.measurement.2020.107534 . © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleTraffic congestion monitoring using an improved kNN 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.journalMeasurement
dc.rights.embargodate2022-01-25
dc.eprint.versionPost-print
dc.contributor.institutionFaculty of technology, University of 20 August 1955, Skikda 21000, Algeria
dc.contributor.institutionLAIG Laboratory, University of 08 May 1945, Guelma 24000, Algeria
dc.contributor.institutionCReSTiC URCA UFR SEN, University of Reims Champagne-Ardenne, Moulin de la Housse, France
kaust.personHarrou, Fouzi
kaust.personSun, Ying
dc.date.accepted2020-01-20
dc.date.published-online2020-01-25
dc.date.published-print2020-05


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