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A Kalman filter approach for exploiting bluetooth traffic data when estimating time-dependent OD matrices

Author
Barcelo, J.; Lídia Montero; Bullejos, M.A.; Serch, O.; Carmona, C.
Type of activity
Journal article
Journal
Journal of intelligent transportation systems: technology, planning, and operations
Date of publication
2013-05-17
Volume
17
Number
2
First page
123
Last page
141
DOI
https://doi.org/10.1080/15472450.2013.764793 Open in new window
Project funding
1) TRA2011-27791-C03-02 ROBUSTEZ, RECUPERABILIDAD Y CONGESTIÓN EN REDES DE TRANSPORTE PUBLICO
SIMETRIA – Modelos de simulación para la evaluación de escenarios multimodales de transportes globales y regionales
Repository
http://hdl.handle.net/2117/26824 Open in new window
URL
http://cats.informa.com/PTS/go?t=rl&o=oa&m=764793 Open in new window
Abstract
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 consi...
Citation
Barcelo, J. [et al.]. A Kalman filter approach for exploiting bluetooth traffic data when estimating time-dependent OD matrices. "Journal of intelligent transportation systems: technology, planning, and operations", 17 Maig 2013, vol. 17, núm. 2, p. 123-141.
Keywords
ATIS, ATMS, Estimation, ICT, Kalman Filter Prediction, Time-Dependent Origin–Destination Matrices
Group of research
CRAHI - Center of Applied Research in Hydrometeorology
IMP - Information Modelling and Processing
inLab FIB

Participants