Traffic data reconstruction based on Markov random field modeling

Shun Kataoka, Muneki Yasuda, Cyril Furtlehner, Kazuyuki Tanaka

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Article number025003
JournalInverse Problems
Issue number2
Publication statusPublished - 2014 Feb


  • Gaussian graphical model
  • Markov random fields
  • machine learning
  • probabilistic information processing
  • traffic data reconstruction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Signal Processing
  • Mathematical Physics
  • Computer Science Applications
  • Applied Mathematics


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