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A Kalman filter approach for exploiting bluetooth traffic data when estimating time-dependent OD matrices
Barceló Bugeda, Jaime; Montero Mercadé, Lídia; Bullejos, Manuel; Serch Muni, Oriol; Carmona, Carlos
Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa; Universitat Politècnica de Catalunya. Facultat d'Informàtica de Barcelona (FIB); Universitat Politècnica de Catalunya. PROMALS - Grup de Recerca en Programació Matemática, Logística i Simulació
Time-dependent origin–destination (OD) matrices are essential input for dynamic traffic models such as microscopic and mesoscopic traffic simulators. Dynamic traffic models also support real-time traffic management decisions, and they are traditionally used in the design and evaluation of advanced traffic traffic management and information systems (ATMS/ATIS). Time-dependent OD estimations are typically based either on Kalman filtering or on bilevel mathematical programming, which can be considered in most cases as ad hoc heuristics. The advent of the new information and communication technologies (ICT) provides new types of traffic data with higher quality and accuracy, which in turn allows new modeling hypotheses that lead to more computationally efficient algorithms. This article presents ad hoc, Kalman filtering procedures that explicitly exploit Bluetooth sensor traffic data, and it reports the numerical results from computational experiments performed at a network test site.
Peer Reviewed
Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Simulació
ATIS
ATMS
Estimation
ICT
Kalman Filter Prediction
Time-Dependent Origin–Destination Matrices
Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/submittedVersion
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