Traffic State Estimation from Aggregated Measurements with Signal Reconstruction Techniques

The estimation of the state of traffic provides a detailed picture of the conditions of a traffic network based on limited traffic measurements and, as such, plays a key role in intelligent transportation systems. Most of the existing state estimation algorithms are based on Kalman filtering and its variants, which, starting from the current estimate, predict the future state and then correct it on the basis of new measurements. Most often, traffic measurements are aggregated over multiple time steps, and this procedure raises the question of how to best use this information for state estimation. A standard approach that performs the correction only at the time step when the aggregated measurement is received is suboptimal. Reconstructing the high-resolution measurements from the aggregated ones and using them to correct the state estimates at every time step are proposed. Several reconstruction techniques from signal processing, including kernel regression and a reconstruction approach based on convex optimization, were considered. The proposed approach was evaluated on real-world NGSIM data collected at Interstate 101, located in Los Angeles, California. Experimental results show that signal reconstruction leads to more accurate traffic state estimation as compared with the standard approach for dealing with aggregated measurements.

• Record URL:
  • https://doi.org/10.3141/2315-13
• Summary URL:
  • http://scholar.google.com/scholar_lookup?title=Traffic+State+Estimation+from+Aggregated+Measurements+with+Signal+Reconstruction+Techniques&author=V.+Coric&author=N.+Djuric&author=S.+Vucetic&publication_year=2012
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780309263146
• Authors:
  • Coric, Vladimir
  • Djuric, Nemanja
  • Vucetic, Slobodan
• Publication Date: 2012

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 121–130
• Monograph Title: Traffic Flow Theory and Characteristics 2012, Volume 1
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Issue Number: 2315
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Intelligent transportation systems; Traffic estimation; Traffic measurement; Traffic signals
• Identifier Terms: Next Generation Simulation program
• Uncontrolled Terms: Real world data; Traffic reconstruction; Traffic state estimation
• Geographic Terms: Los Angeles (California)
• Subject Areas: Highways; Operations and Traffic Management; I73: Traffic Control;

Filing Info

• Accession Number: 01373452
• Record Type: Publication
• ISBN: 9780309263146
• Report/Paper Numbers: 12-4415
• Files: TRIS, TRB, ATRI
• Created Date: Jun 22 2012 1:13PM