Abstract
With the vast availability of traffic sensing data on highway, real-time traffic flow prediction is essential part of transportation, traffic control, reports of accidents and intelligent transportation systems. To satisfy the demand of traffic flow prediction, this paper presents the method of real-time traffic flow prediction based on Bayesian classifier and support vector regression (SVR). We first model the traffic flow and its relations on the roads using 3D Markov random fields in spatiotemporal domain. Based on their relations, we define cliques as combination of current road and its neighbors. The dependencies on the defined cliques are estimated by using multiple linear regression and SVR. Finally, the traffic flow at next time stamp is predicted by finding the speed level with decreasing the energy function. To evaluate the performance of the proposed method, it was tested on traffic data obtained from Gyeongbu expressway. The experimental results showed that the approach using SVR-based estimation had superior accuracy than linear-based regression.