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

Abdelhafid Zeroual, Fouzi Harrou, Ying Sun, Nadhir Messai

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)494-510
Number of pages17
JournalSustainable Cities and Society
StatePublished - Aug 19 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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.


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