MRF model based real‐time traffic flow prediction with support vector regression

Abstract

A novel statistical model for real-time traffic flow prediction is proposed. In this study, a 3D Markov random field (MRF) is used to model the temporal dynamics of the traffic flow measured by VDS sensor network. Then, the spatial and temporal relations between roads at a given location are represented by the 3D graph using cliques, then its structure is determined by clique parameters. Here, a support vector regression is adopted for estimating the correlation parameters. The technique is applied to actual traffic flow data from Gyeongbu expressway, South Korea. The experiments demonstrate that the proposed method can predict the traffic flow with an accuracy of 85.6%, which improves 17.3% of the existing state-of-the-art method.