  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 traffic sensors. Our approach is based on Markov random
field modeling of road traffic. The reconstruction is achieved by using
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.
