Traffic state estimation through compressed sensing and Markov random field

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.

• Record URL:
  • https://doi.org/10.1016/j.trb.2016.06.009
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0191261516303939
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Zheng, Zuduo
  • Su, Dongcai
• Publication Date: 2016-9

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 525-554
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 91
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: Markov processes; Traffic data; Traffic models
• Uncontrolled Terms: Cell transmission models; Traffic state estimation
• Subject Areas: Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01608861
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 29 2016 11:12AM