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Non-Linear Kalman Filtering Algorithms for On-Line Calibration of Dynamic Traffic Assignment Models
Department of Civil Engineering, North-eastern University.
2006 (English)In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, Toronto, Canada, 2006, 833-838 p.Conference paper, Published paper (Refereed)
Abstract [en]

The problem of on-line calibration of Dynamic Traffic Assignment (DTA) models is receiving increasing attention from researchers and practitioners. The problem can be formulated as a non-linear state-space model. Because of its nonlinear nature, the resulting model cannot be solved by the Kalman Filter and therefore non-linearextensions need to be considered. In this paper, three extensions to the Kalman Filteralgorithm are presented: Extended Kalman Filter (EKF), Limiting EKF (LimEKF), and Unscented Kalman Filter (UKF). The solution algorithms are applied to the calibration of the state-of-the-art DynaMIT-R DTA model and their use is demonstrated in a freeway network in Southampton, U.K. The LimEKF shows accuracy comparable to that of the bestalgorithm, but vastly superior computational performance. 

Place, publisher, year, edition, pages
Toronto, Canada, 2006. 833-838 p.
Keyword [en]
Dynamic Traffic Assignment (DTA), Limiting EKF (LimEKF)
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-88161ISBN: 978-142440094-2 (print)OAI: oai:DiVA.org:kth-88161DiVA: diva2:502150
Conference
9th International IEEE Conference on Intelligent Transportation Systems
Note
TSC import 1990 2012-02-14. QC 20120228Available from: 2012-02-14 Created: 2012-02-14 Last updated: 2012-02-28Bibliographically approved

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CiteExportLink to record
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