HAL CCSD
Optimal linear estimator of origin-destination flows with redundant data
Meunier, Frédéric
Laboratoire Ville, Mobilité, Transport (LVMT ) ; Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)
International audience
ISSN: 0254-5330
EISSN: 1572-9338
Annals of Operations Research
Springer Verlag
hal-00722262
https://enpc.hal.science/hal-00722262
https://enpc.hal.science/hal-00722262
Annals of Operations Research, 2010, 181 (1), pp.709-722. ⟨10.1007/s10479-010-0784-0⟩
DOI: 10.1007/s10479-010-0784-0
info:eu-repo/semantics/altIdentifier/doi/10.1007/s10479-010-0784-0
en
Laplacian of graph
Linear estimator
Minimal variance
Multiflow
OD matrix
[INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO]
info:eu-repo/semantics/article
Journal articles
Suppose given a network endowed with a multiflow. We want to estimate some quantities connected with this multiflow, for instance the value of an s-t flow for one of the sources-sinks pairs s-t, but only measures on some arcs are available, at least on one s-t cocycle (set of arcs having exactly one endpoint in a subset X of vertices with saX and ta parts per thousand X). These measures, supposed to be unbiased, are random variables whose variances are known. How can we combine them optimally in order to get the best estimator of the value of the s-t flow? This question arises in practical situations when the OD matrix of a transportation network must be estimated. We will give a complete answer for the case when we deal with linear combinations, not only for the value of an s-t flow but also for any quantity depending linearly from the multiflow. Interestingly, we will see that the Laplacian matrix of the network plays a central role.
2010