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Journal Articles Inverse Problems Year : 2014

Traffic data reconstruction based on Markov random field modeling

Reconstruction de données de trafic basée sur un modèle de champ Markovien aléatoire

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.

Dates and versions

hal-01096947 , version 1 (18-12-2014)

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