Traffic data reconstruction based on Markov random field modeling

Abstract

We consider the traffic data reconstruction problem. Suppose we have the\ntraffic data of an entire city that are incomplete because some road data are\nunobserved. The problem is to reconstruct the unobserved parts of the data. In\nthis paper, we propose a new method to reconstruct incomplete traffic data\ncollected from various traffic sensors. Our approach is based on Markov random\nfield modeling of road traffic. The reconstruction is achieved by using\nmean-field method and a machine learning method. We numerically verify the\nperformance of our method using realistic simulated traffic data for the real\nroad network of Sendai, Japan.\n