Traffic state estimation through compressed sensing and Markov random field

• Compressed sensing (CS) for data denoising and information recovery.
• Markov random field (MRF) for simplifying traffic flow model.
• A total variation (TV) regularization for estimating traffic states.
• The TSE algorithm developed in this paper outperforms the two benchmarking algorithms.
• A recently developed TSE method is extended to estimate traffic states with high dimension.

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.