A probabilistic framework for road traffic reconstruction and prediction based on incomplete data

Victorin Martin 1 Jean-Marc Lasgouttes 2 Cyril Furtlehner 3
3 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : We present some new developments in probabilistic road traffic modeling. The problem at stake is real-time prediction of travel times from floating car data (FCD) coming from probe vehicles. We tackle it using a probabilistic model based on an Ising model, well known in statistical physics, and real-time predictions are computed using the Belief Propagation (BP) algorithm. The Ising model estimation requires only pairwise statistics, which is compatible with the use of FCD data. The behavior of the method is illustrated by a numerical experiment on a space-time highway network.
Document type :
Conference papers
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https://hal.inria.fr/hal-01094376
Contributor : Jean-Marc Lasgouttes <>
Submitted on : Friday, December 12, 2014 - 11:09:26 AM
Last modification on : Monday, November 12, 2018 - 11:02:40 AM

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  • HAL Id : hal-01094376, version 1

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Victorin Martin, Jean-Marc Lasgouttes, Cyril Furtlehner. A probabilistic framework for road traffic reconstruction and prediction based on incomplete data. Actes du GERI SMRT 2011, 2011, Marne-la-Vallée, France. pp.91-96. ⟨hal-01094376⟩

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