Traffic congestion detection based on hybrid observer and GLR test

Fouzi Harrou; Abdelhafid Zeroual; Ying Sun

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Traffic congestion detection based on hybrid observer and GLR test

Fouzi Harrou, Abdelhafid Zeroual and Ying Sun

The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.604

01/01/2018

Abstract

Likelihood ratio

Parameter estimation

Traffic congestion

Traffic flow

Traffic models

Traffic volume

Conference Title: 2018 Annual American Control Conference (ACC) Conference Start Date: 2018, June 27 Conference End Date: 2018, June 29 Conference Location: Milwaukee, WI, USA This paper introduces an effective approach for detecting road traffic congestion. This approach uses a hybrid observer (HO) that exploits both the flexibility and simplicity of the piecewise switched linear model to estimate the traffic density parameter and employs a generalized likelihood ratio (GLR) test to detect traffic congestion. We evaluated the HO-GLR with real data from a segment of the four-lane State Route 60 (SR-60) highway in southern California. Results show that the HO-GLR approach is suitable for traffic congestion monitoring.

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Title: Traffic congestion detection based on hybrid observer and GLR test
Creators - without role: Fouzi Harrou
Abdelhafid Zeroual
Ying Sun
Publication Details: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.604
Publisher: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Identifiers: 9944271208331
Academic Unit: King Abdullah University of Science & Technology
Language: English
Resource Type: Conference proceeding