Traffic state estimation through compressed sensing and Markov random field

Zheng, Zuduo & Su, Dongcai (2016) Traffic state estimation through compressed sensing and Markov random field. Transportation Research Part B: Methodological. (In Press)

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Abstract

This study focuses on information recovery from noisy traffic data and traffic state estimation. The main contributions of this paper are: i) a novel algorithm based on the compressed sensing theory is developed to recover traffic data with Gaussian measurement noise, partial data missing, and corrupted noise; ii) the accuracy of traffic state estimation (TSE) is improved by using Markov random field and total variation (TV) regularization, with introduction of smoothness prior; and iii) a recent TSE method is extended to handle traffic state variables with high dimension. Numerical experiments and field data are used to test performances of these proposed methods; consistent and satisfactory results are obtained.

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ID Code: 96327
Item Type: Journal Article
Refereed: Yes
Keywords: traffic state estimation, data noise, compressed sensing, compressive sensin, Markov random field, cell transmission model, total variation regularization
DOI: 10.1016/j.trb.2016.06.009
ISSN: 0191-2615
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500)
Divisions: Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: Copyright 2016 Elsevier B.V.
Deposited On: 23 Jun 2016 22:24
Last Modified: 12 Jul 2016 04:47

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