Belief Propagation and Bethe approximation for Traffic Prediction

Cyril Furtlehner 1 Jean-Marc Lasgouttes 2 Arnaud de la Fortelle 2, 3
1 TANC - Algorithmic number theory for cryptology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France
Abstract : We define and study an inference algorithm based on "belief propagation" (BP) and the Bethe approximation. The idea is to encode into a graph an a priori information composed of correlations or marginal probabilities of variables, and to use a message passing procedure to estimate the actual state from some extra real-time information. This method is originally designed for traffic prediction and is particularly suitable in settings where the only information available is floating car data. We propose a discretized traffic description, based on the Ising model of statistical physics, in order to both reconstruct and predict the traffic in real time. General properties of BP are addressed in this context. In particular, a detailed study of stability is proposed with respect to the a priori data and the graph topology. The behavior of the algorithm is illustrated by numerical studies on a simple traffic toy model. How this approach can be generalized to encode superposition of many traffic patterns is discussed.
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Soumis le : lundi 19 mars 2007 - 15:46:36
Dernière modification le : mercredi 27 mars 2019 - 16:41:29
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Cyril Furtlehner, Jean-Marc Lasgouttes, Arnaud de la Fortelle. Belief Propagation and Bethe approximation for Traffic Prediction. [Research Report] RR-6144, INRIA. 2007, pp.29. ⟨inria-00136657v2⟩



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