A Kalman Filter for Quasi-Dynamic o-d Flow Estimation/Updating

This paper proposes an extended Kalman filter for quasi-dynamic estimation/updating of o-d flows from traffic counts. The quasi-dynamic assumption—that is considering constant o-d shares across a reference period, whilst total flows leaving each origin may vary for each sub-period within the reference period—has been proven already realistic and effective in off-line o-d flows estimation using generalized least squares estimators. The specification of the state variables and of the corresponding transition and measurement equations of a quasi-dynamic extended Kalman filter are illustrated, and a closed-form linearization is presented under the assumption of an uncongested network and error-free assignment matrix. Results show satisfactory performance and parsimonious computational burden on real-size networks.

• Record URL:
  https://doi.org/10.1109/TITS.2018.2865610
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Marzano, Vittorio
  • Papola, Andrea
  • Simonelli, Fulvio
  • Papageorgiou, Markos
• Publication Date: 2018-11

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 3604-3612
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 19
  • Issue Number: 11
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Dynamic models; Kalman filtering; Origin and destination; Traffic estimation; Traffic flow; Traffic models
• Geographic Terms: Italy
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01690050
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Dec 27 2018 3:43PM