Traffic data reconstruction based on Markov random field modeling

Kataoa Shun 1 Yasuda Muneki 2 Cyril Furtlehner 3 Kazuyuki Tanaka 1
3 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : We consider the traffic data reconstruction problem. Suppose we have the traffic data of an entire city that are incomplete because some road data are unobserved. The problem is to reconstruct the unobserved parts of the data. In this paper, we propose a new method to reconstruct incomplete traffic data collected from various sensors. Our approach is based on Markov random field modeling of road traffic. The reconstruction is achieved by using a mean-field method and a machine learning method. We numerically verify the performance of our method using realistic simulated traffic data for the real road network of Sendai, Japan.
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Inverse Problems, IOP Publishing, 2014, 30 (2), pp.15. 〈10.1088/0266-5611/30/2/025003〉
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https://hal.inria.fr/hal-01096947
Contributeur : Cyril Furtlehner <>
Soumis le : jeudi 18 décembre 2014 - 14:58:15
Dernière modification le : jeudi 5 avril 2018 - 12:30:12

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Kataoa Shun, Yasuda Muneki, Cyril Furtlehner, Kazuyuki Tanaka. Traffic data reconstruction based on Markov random field modeling. Inverse Problems, IOP Publishing, 2014, 30 (2), pp.15. 〈10.1088/0266-5611/30/2/025003〉. 〈hal-01096947〉

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