Estimating link-dependent Origin-Destination matrices from sample trajectories and traffic counts
Gabriel Michau, Pierre Borgnat, Nelly Pustelnik, Patrice Abry, Alfredo Nantes, Edward Chung
Abstract
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.
BibTeX
@inproceedings{icassp2015_estimatinglinkde,
title = {Estimating link-dependent Origin-Destination matrices from sample trajectories and traffic counts},
author = {Gabriel Michau and Pierre Borgnat and Nelly Pustelnik and Patrice Abry and Alfredo Nantes and Edward Chung},
booktitle = {ICASSP 2015},
year = {2015}
}