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Traffic state estimation through compressed sensing and Markov random field

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  • Zheng, Zuduo
  • Su, Dongcai

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.

Suggested Citation

  • Zheng, Zuduo & Su, Dongcai, 2016. "Traffic state estimation through compressed sensing and Markov random field," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 525-554.
    • Handle: RePEc:eee:transb:v:91:y:2016:i:c:p:525-554
    DOI: 10.1016/j.trb.2016.06.009
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