Traffic data reconstruction based on Markov random field modeling

Shun Kataoka, Muneki Yasuda, Cyril Furtlehner, Kazuyuki Tanaka

研究成果: Article査読

6 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号025003
ジャーナルInverse Problems
30
2
DOI
出版ステータスPublished - 2014 2

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • 信号処理
  • 数理物理学
  • コンピュータ サイエンスの応用
  • 応用数学

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