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dc.contributor.authorBarceló Bugeda, Jaime
dc.contributor.authorMontero Mercadé, Lídia
dc.contributor.authorBullejos, Manuel
dc.contributor.authorSerch, Oriol
dc.contributor.authorCarmona Bautista, Carlos
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2013-02-11T18:40:49Z
dc.date.available2013-02-11T18:40:49Z
dc.date.created2012
dc.date.issued2012
dc.identifier.citationBarcelo, J. [et al.]. A Kalman filter approach for the estimation of time dependent OD matrices exploiting bluetooth traffic data collection. A: Transportation Research Board Annual Meeting. "TRB 91st Annual Meeting Compendium of Papers DVD". WASHINGTON: 2012, p. 1-16.
dc.identifier.urihttp://hdl.handle.net/2117/17642
dc.description.abstractTime-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models, microscopic and mesoscopic traffic simulators are relevant examples of such models, traditionally used to assist in the design and evaluation of Traffic Management and Information Systems (ATMS/ATIS). Dynamic traffic models can also be used to support real-time traffic management decisions. The typical approaches to the time-dependent OD estimation have been based either on ad hoc heuristics using mathematical programming approaches, or on Kalman-Filtering. The advent of the new Information and Communication Technologies (ICT), makes available new types of traffic data of higher quality and accuracy allowing for new modeling hypothesis leading to more computationally efficient algorithms. Ad hoc procedures based on Kalman Filtering, explicitly exploiting traffic data available from Bluetooth sensors, have been designed and implemented successfully and the numerical results of the computational experiments are discussed for freeway and network test sites.
dc.format.extent16 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Simulació
dc.subject.lcshSimulation methods
dc.subject.lcshKalman filtering
dc.subject.otherBluetooth technology
dc.subject.otherData collection
dc.subject.otherDynamic traffic assignment
dc.subject.otherKalman filtering
dc.subject.otherOrigin and destination
dc.subject.otherReal time information
dc.subject.otherTime dependence
dc.subject.otherTraffic data
dc.subject.otherTraffic models
dc.titleA Kalman filter approach for the estimation of time dependent OD matrices exploiting bluetooth traffic data collection
dc.typeConference report
dc.subject.lemacKalman, Filtratge de
dc.subject.lemacSimulació, Mètodes de
dc.contributor.groupUniversitat Politècnica de Catalunya. PROMALS - Grup de Recerca en Programació Matemática, Logística i Simulació
dc.identifier.doihttp://trid.trb.org/view.aspx?id=1130522
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::90 Operations research, mathematical programming::90C Mathematical programming
dc.relation.publisherversionhttp://amonline.trb.org/pap@PaperNo=12-3843
dc.rights.accessOpen Access
drac.iddocument9455431
dc.description.versionPostprint (published version)
upcommons.citation.authorBarcelo, J.; Montero, L.; Bullejos, M.; Serch, O.; Carmona, C.
upcommons.citation.contributorTransportation Research Board Annual Meeting
upcommons.citation.pubplaceWASHINGTON
upcommons.citation.publishedtrue
upcommons.citation.publicationNameTRB 91st Annual Meeting Compendium of Papers DVD
upcommons.citation.startingPage1
upcommons.citation.endingPage16


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