Estimating link-dependent Origin-Destination matrices from sample trajectories and traffic counts

In transport networks, Origin-Destination matrices (ODM) are classically estimated from road traffic counts whereas recent technologies grant also access to sample car trajectories. One example is the deployment in cities of Bluetooth scanners that measure the trajectories of Bluetooth equipped cars...

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Bibliographic Details
Published in2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 5480 - 5484
Main Authors Michau, G., Borgnat, P., Pustelnik, N., Abry, P., Nantes, A., Chung, E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2015
Subjects
Bluetooth
convex optimization
Estimation
inference on network
inverse problem
Inverse problems
OD matrices
Roads
Trajectory
urban transport
Vehicles
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Summary:In transport networks, Origin-Destination matrices (ODM) are classically estimated from road traffic counts whereas recent technologies grant also access to sample car trajectories. One example is the deployment in cities of Bluetooth scanners that measure the trajectories of Bluetooth equipped cars. Exploiting such sample trajectory information, the classical ODM estimation problem is here extended into a link-dependent ODM (LODM) one. This much larger size estimation problem is formulated here in a variational form as an inverse problem. We develop a convex optimization resolution algorithm that incorporates network constraints. We study the result of the proposed algorithm on simulated network traffic.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2015.7179019