An Ising Model for Road Traffic Inference

  • Cyril Furtlehner
Part of the Nonlinear Systems and Complexity book series (NSCH, volume 5)


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.


Ising Model Road Segment Markov Random Field Constraint Satisfaction Problem Nonnegative Matrix Factorization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This gives me the occasion to express my warm thanks to my colleagues Victorin Martin and Jean-Marc Lasgouttes with whom it is a pleasure to collaborate on the main subjects discussed in this review. I am also grateful to Anne Auger, Yufei Han, Fabrice Marchal and Fabien Moutarde, for many aspects mentioned in this work concerning ongoing projects. This work was supported by the grant ANR-08-SYSC-017 from the French National Research Agency.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.INRIA Saclay – LRI, Bat. 490Université Paris-SudOrsayFrance

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