Real-time multi-sensor multi-source network data fusion using dynamic traffic assignment models

Huang, E; Antoniou, C; Wen, Y; Ben-Akiva, M; Lopes, J; Bento, J

dc.contributor.author	Huang, E	en
dc.contributor.author	Antoniou, C	en
dc.contributor.author	Wen, Y	en
dc.contributor.author	Ben-Akiva, M	en
dc.contributor.author	Lopes, J	en
dc.contributor.author	Bento, J	en
dc.date.accessioned	2014-03-01T02:51:53Z
dc.date.available	2014-03-01T02:51:53Z
dc.date.issued	2009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35727
dc.subject	Dynamic Traffic Assignment	en
dc.subject	Empirical Validation	en
dc.subject	Root Mean Square Error	en
dc.subject	Sensor Fusion	en
dc.subject	Simulation and Modeling	en
dc.subject	State Estimation	en
dc.subject	Data Fusion	en
dc.subject	Pattern Search	en
dc.subject	Real Time	en
dc.title	Real-time multi-sensor multi-source network data fusion using dynamic traffic assignment models	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ITSC.2009.5309859	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ITSC.2009.5309859	en
heal.publicationDate	2009	en
heal.abstract	This paper presents a model-based data fusion framework that allows systematic fusing of multi-sensor multi-source traffic network data at real-time. Using simulation-based Dynamic Traffic Assignment (DTA) models, the framework seeks to minimize the inconsistencies between observed network data and the model estimates using a variant of the Hooke-Jeeves Pattern Search. An empirical validation is provided on the Brisa A5 Inter-City	en
heal.journalName	International Conference on Intelligent Transportation	en
dc.identifier.doi	10.1109/ITSC.2009.5309859	en