On-line calibration of traffic prediction models

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

dc.contributor.author	Antoniou, C	en
dc.contributor.author	Ben-Akiva, M	en
dc.contributor.author	Koutsopoulos, H	en
dc.date.accessioned	2014-03-01T02:49:40Z
dc.date.available	2014-03-01T02:49:40Z
dc.date.issued	2004	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34679
dc.subject	extended kalman filter	en
dc.subject	Prediction Accuracy	en
dc.subject	State Space Model	en
dc.subject	Traffic Prediction	en
dc.subject	unscented kalman filter	en
dc.title	On-line calibration of traffic prediction models	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ITSC.2004.1398876	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ITSC.2004.1398876	en
heal.publicationDate	2004	en
heal.abstract	A methodology for the on-line calibration of the speed-density relationship is formulated as a flexible state-space model. Applicable solution approaches are discussed and three of them (extended Kalman filter (EKF), iterated EKF, and unscented Kalman filter (UKF) are selected and presented in detail. An application of the methodology with freeway sensor data from two networks in Europe and the U.S.	en
heal.journalName	International Conference on Intelligent Transportation	en
dc.identifier.doi	10.1109/ITSC.2004.1398876	en