Non-Linear Kalman Filtering Algorithms for On-Line Calibration of Dynamic Traffic Assignment Models
2006 (English)In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, Toronto, Canada, 2006, 833-838 p.Conference paper (Refereed)
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.
Dynamic Traffic Assignment (DTA), Limiting EKF (LimEKF)
Transport Systems and Logistics
IdentifiersURN: urn:nbn:se:kth:diva-88161ISBN: 978-142440094-2OAI: oai:DiVA.org:kth-88161DiVA: diva2:502150
9th International IEEE Conference on Intelligent Transportation Systems
TSC import 1990 2012-02-14. QC 201202282012-02-142012-02-142012-02-28Bibliographically approved