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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
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 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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Contributor : Cyril Furtlehner Connect in order to contact the contributor
Submitted on : Thursday, December 18, 2014 - 2:58:15 PM
Last modification on : Tuesday, July 5, 2022 - 8:38:50 AM

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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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