Skip to Main content Skip to Navigation
Journal articles

Traffic data reconstruction based on Markov random field modeling

Kataoa Shun 1 Yasuda Muneki 2 Cyril Furtlehner 3 Kazuyuki Tanaka 1 
1 Graduate School of Information Sciences [Sendai]
2 Graduate School of Science and Engineering [Yamagata]
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.
Document type :
Journal articles
Complete list of metadata
https://hal.inria.fr/hal-01096947
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

Links full text

arxiv

Identifiers

Collections

EC-PARIS | CNRS | INRIA | UMR8623 | INRIA2 | LRI-AO

Citation

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⟩

Export

BibTeX TEI DC DCterms EndNote Datacite

Share

Metrics

Record views

136

 

Contact                    Legal Notice                    Personal Data