Predicting Spatiotemporal Traffic Flow Based on Support Vector Regression and Bayesian Classifier

Recently, with the rapid development of sensor technologies, it is important to manage the large amounts of traffic data and predict the traffic condition from them. To satisfy the demand of traffic flow estimation, this paper studies 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 field in spatiotemporal domain. Based on their relations, we define cliques as combination of current cone-zone 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 showed superior accuracy than linear-based regression.