Traffic congestion detection based on hybrid observer and GLR test

Fouzi Harrou; Abdelhafid Zeroual; Ying Sun

doi:10.23919/ACC.2018.8431387

Traffic congestion detection based on hybrid observer and GLR test

Fouzi Harrou, Abdelhafid Zeroual, Ying Sun

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	2018 Annual American Control Conference (ACC)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	604-609
Number of pages	6
ISBN (Print)	9781538654286
DOIs	https://doi.org/10.23919/ACC.2018.8431387
State	Published - Aug 17 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-12-16
Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582
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.

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10.23919/ACC.2018.8431387

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@inproceedings{6f38e33876c54fcabf3a87ae6787ced6,

title = "Traffic congestion detection based on hybrid observer and GLR test",

abstract = "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.",

author = "Fouzi Harrou and Abdelhafid Zeroual and Ying Sun",

note = "KAUST Repository Item: Exported on 2020-12-16 Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582 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.",

year = "2018",

month = aug,

day = "17",

doi = "10.23919/ACC.2018.8431387",

language = "English (US)",

isbn = "9781538654286",

pages = "604--609",

booktitle = "2018 Annual American Control Conference (ACC)",

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T1 - Traffic congestion detection based on hybrid observer and GLR test

AU - Harrou, Fouzi

AU - Zeroual, Abdelhafid

AU - Sun, Ying

N1 - KAUST Repository Item: Exported on 2020-12-16 Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582 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.

PY - 2018/8/17

Y1 - 2018/8/17

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/630556

UR - https://ieeexplore.ieee.org/document/8431387

UR - http://www.scopus.com/inward/record.url?scp=85052560931&partnerID=8YFLogxK

U2 - 10.23919/ACC.2018.8431387

DO - 10.23919/ACC.2018.8431387

M3 - Conference contribution

AN - SCOPUS:85052560931

SN - 9781538654286

SP - 604

EP - 609

BT - 2018 Annual American Control Conference (ACC)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Traffic congestion detection based on hybrid observer and GLR test

Abstract

Bibliographical note

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