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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2018, IEEE.
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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
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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