Nonlinear 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	2014-03-01T01:26:44Z
dc.date.available	2014-03-01T01:26:44Z
dc.date.issued	2007	en
dc.identifier.issn	1524-9050	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18207
dc.subject	Dynamic traffic assignment (DTA)	en
dc.subject	Extended Kalman filter (EKF)	en
dc.subject	Limiting extended Kalman filter (LimEKF)	en
dc.subject	Nonlinear optimization	en
dc.subject	On-line calibration	en
dc.subject	Unscented Kalman filter (UKF)	en
dc.subject.classification	Engineering, Civil	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Dynamic traffic assignment (DTA)	en
dc.subject.other	Limiting extended Kalman filter (LimEKF)	en
dc.subject.other	Nonlinear optimization	en
dc.subject.other	On-line calibration	en
dc.subject.other	Unscented Kalman filter (UKF)	en
dc.subject.other	Algorithms	en
dc.subject.other	Extended Kalman filters	en
dc.subject.other	Mathematical models	en
dc.subject.other	Nonlinear filtering	en
dc.subject.other	Optimization	en
dc.subject.other	Robustness (control systems)	en
dc.subject.other	Traffic control	en
dc.title	Nonlinear Kalman filtering algorithms for on-line calibration of dynamic traffic assignment models	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TITS.2007.908569	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TITS.2007.908569	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	An online calibration approach that jointly estimates demand and supply parameters of dynamic traffic assignment (DTA) systems is presented and empirically validated through an extensive application. The problem can be formulated as a nonlinear state-space model. Because of its nonlinear nature, the resulting model cannot be solved by the Kalman filter, and therefore, nonlinear extensions need to be considered. The following three extensions to the Kalman filtering algorithm are presented: 1) the extended Kalman filter (EKF); 2) the limiting EKF (LimEKF); and 3) the unscented Kalman filter. The solution algorithms are applied to the on-line calibration of the state-of-the-art DynaMIT DTA model, and their use is demonstrated in a freeway network in Southampton, U.K. The LimEKF shows accuracy that is comparable to that of the best algorithm but with vastly superior computational performance. The robustness of the approach to varying weather conditions is demonstrated, and practical aspects are discussed. © 2006 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Intelligent Transportation Systems	en
dc.identifier.doi	10.1109/TITS.2007.908569	en
dc.identifier.isi	ISI:000251589900010	en
dc.identifier.volume	8	en
dc.identifier.issue	4	en
dc.identifier.spage	661	en
dc.identifier.epage	670	en