Monitoring road traffic congestion using a macroscopic traffic model and a statistical monitoring scheme

Handle URI:
http://hdl.handle.net/10754/625367
Title:
Monitoring road traffic congestion using a macroscopic traffic model and a statistical monitoring scheme
Authors:
Zeroual, Abdelhafid; Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 ) ; Messai, Nadhir
Abstract:
Monitoring vehicle traffic flow plays a central role in enhancing traffic management, transportation safety and cost savings. In this paper, we propose an innovative approach for detection of traffic congestion. Specifically, we combine the flexibility and simplicity of a piecewise switched linear (PWSL) macroscopic traffic model and the greater capacity of the exponentially-weighted moving average (EWMA) monitoring chart. Macroscopic models, which have few, easily calibrated parameters, are employed to describe a free traffic flow at the macroscopic level. Then, we apply the EWMA monitoring chart to the uncorrelated residuals obtained from the constructed PWSL model to detect congested situations. In this strategy, wavelet-based multiscale filtering of data has been used before the application of the EWMA scheme to improve further the robustness of this method to measurement noise and reduce the false alarms due to modeling errors. The performance of the PWSL-EWMA approach is successfully tested on traffic data from the three lane highway portion of the Interstate 210 (I-210) highway of the west of California and the four lane highway portion of the State Route 60 (SR60) highway from the east of California, provided by the Caltrans Performance Measurement System (PeMS). Results show the ability of the PWSL-EWMA approach to monitor vehicle traffic, confirming the promising application of this statistical tool to the supervision of traffic flow congestion.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Zeroual A, Harrou F, Sun Y, Messai N (2017) Monitoring road traffic congestion using a macroscopic traffic model and a statistical monitoring scheme. Sustainable Cities and Society. Available: http://dx.doi.org/10.1016/j.scs.2017.08.018.
Publisher:
Elsevier BV
Journal:
Sustainable Cities and Society
Issue Date:
19-Aug-2017
DOI:
10.1016/j.scs.2017.08.018
Type:
Article
ISSN:
2210-6707
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. We would like to thank the reviewers of this article for their insightful comments, which helped us to greatly improve its quality.
Additional Links:
http://www.sciencedirect.com/science/article/pii/S2210670717302810
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorZeroual, Abdelhafiden
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorSun, Yingen
dc.contributor.authorMessai, Nadhiren
dc.date.accessioned2017-08-21T06:28:05Z-
dc.date.available2017-08-21T06:28:05Z-
dc.date.issued2017-08-19en
dc.identifier.citationZeroual A, Harrou F, Sun Y, Messai N (2017) Monitoring road traffic congestion using a macroscopic traffic model and a statistical monitoring scheme. Sustainable Cities and Society. Available: http://dx.doi.org/10.1016/j.scs.2017.08.018.en
dc.identifier.issn2210-6707en
dc.identifier.doi10.1016/j.scs.2017.08.018en
dc.identifier.urihttp://hdl.handle.net/10754/625367-
dc.description.abstractMonitoring vehicle traffic flow plays a central role in enhancing traffic management, transportation safety and cost savings. In this paper, we propose an innovative approach for detection of traffic congestion. Specifically, we combine the flexibility and simplicity of a piecewise switched linear (PWSL) macroscopic traffic model and the greater capacity of the exponentially-weighted moving average (EWMA) monitoring chart. Macroscopic models, which have few, easily calibrated parameters, are employed to describe a free traffic flow at the macroscopic level. Then, we apply the EWMA monitoring chart to the uncorrelated residuals obtained from the constructed PWSL model to detect congested situations. In this strategy, wavelet-based multiscale filtering of data has been used before the application of the EWMA scheme to improve further the robustness of this method to measurement noise and reduce the false alarms due to modeling errors. The performance of the PWSL-EWMA approach is successfully tested on traffic data from the three lane highway portion of the Interstate 210 (I-210) highway of the west of California and the four lane highway portion of the State Route 60 (SR60) highway from the east of California, provided by the Caltrans Performance Measurement System (PeMS). Results show the ability of the PWSL-EWMA approach to monitor vehicle traffic, confirming the promising application of this statistical tool to the supervision of traffic flow congestion.en
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. We would like to thank the reviewers of this article for their insightful comments, which helped us to greatly improve its quality.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S2210670717302810en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Sustainable Cities and Society. 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 Sustainable Cities and Society, [, , (2017-08-19)] DOI: 10.1016/j.scs.2017.08.018 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectTraffic congestionen
dc.subjectMacroscopic traffic modelen
dc.subjectStatistical monitoringen
dc.subjectQuality control charten
dc.titleMonitoring road traffic congestion using a macroscopic traffic model and a statistical monitoring schemeen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalSustainable Cities and Societyen
dc.eprint.versionPost-printen
dc.contributor.institutionCReSTiC-URCA UFR SEN, University of Reims Champagne-Ardenne, Moulin de la Housse, BP 1039, 51687 Reims Cedex 2, Franceen
dc.contributor.institutionLAIG Laboratory, University of 08 May 1945, Guelma 24000, Algeriaen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.