Traffic state estimation through compressed sensing and Markov random field

Zuduo Zheng and Dongcai Su

Transportation Research Part B: Methodological, 2016, vol. 91, issue C, 525-554

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.

Keywords: Traffic state estimation; Data noise; Compressed sensing; Compressive sensing; Markov random field; Cell transmission model; Total variation regularization (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1016/j.trb.2016.06.009

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