Non-linear kalman filtering algorithms for on-line calibration of dynamic traffic assignment models

Antoniou, C; Ben-Akiva, M; Koutsopoulos, HN

dc.contributor.author	Antoniou, C	en
dc.contributor.author	Ben-Akiva, M	en
dc.contributor.author	Koutsopoulos, HN	en
dc.date.accessioned	2014-03-01T02:44:07Z
dc.date.available	2014-03-01T02:44:07Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31690
dc.subject	Dynamic Traffic Assignment	en
dc.subject	extended kalman filter	en
dc.subject	Linear Extension	en
dc.subject	State Space Model	en
dc.subject	unscented kalman filter	en
dc.subject	kalman filter	en
dc.subject.other	Dynamic Traffic Assignment (DTA)	en
dc.subject.other	Limiting EKF (LimEKF)	en
dc.subject.other	Algorithms	en
dc.subject.other	Extended Kalman filters	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Online systems	en
dc.subject.other	Traffic congestion	en
dc.title	Non-linear kalman filtering algorithms for on-line calibration of dynamic traffic assignment models	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ITSC.2006.1706847	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ITSC.2006.1706847	en
heal.identifier.secondary	1706847	en
heal.publicationDate	2006	en
heal.abstract	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-linear extensions need to be considered. In this paper, three extensions to the Kalman Filter algorithm 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 best algorithm, but vastly superior computational performance. © 2006 IEEE.	en
heal.journalName	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC	en
dc.identifier.doi	10.1109/ITSC.2006.1706847	en
dc.identifier.spage	833	en
dc.identifier.epage	838	en