TY - GEN
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 -
