Traffic data reconstruction based on Markov random field modeling
This addresses data gaps in urban traffic monitoring, but it is incremental as it builds on existing Markov random field techniques.
The paper tackled the problem of reconstructing incomplete traffic data from city-wide sensors by proposing a method based on Markov random field modeling, achieving performance verified numerically with simulated data for Sendai's road network.
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.