An Ising Model for Road Traffic Inference

Cyril Furtlehner 1
1 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 review some properties of the ''belief propagation'' algorithm, a distributed iterative map, used to perform Bayesian inference and present some recent work where this algorithm serves as a starting point to encode observation data into a probabilistic model and to process large scale information in real time. A natural approach is based on the linear response theory and various recent instantiations are presented. We will focus on the particular situation where the data have many different statistical components, representing a variety of independent patterns. As an application, the problem of reconstructing and predicting traffic states based on floating car data is then discussed.
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Submitted on : Thursday, October 18, 2012 - 5:38:19 PM
Last modification on : Thursday, April 5, 2018 - 12:30:12 PM
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  • HAL Id : hal-00743351, version 1



Cyril Furtlehner. An Ising Model for Road Traffic Inference. Xavier Leoncini and Marc Leonetti. From Hamiltonian Chaos to Complex Systems: a Nonlinear Physics Approach, Springer, 2012. ⟨hal-00743351⟩



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