A Traffic Prediction Method Based on Complex Event Processing and Adaptive Bayesian Networks

Recently Complex Event Processing (CEP) is widely used in many areas to support real time event processing. In some applications events should be prevented proactively before they occur. In this paper we propose a high accuracy traffic prediction method based on complex event processing and Adaptive Bayesian networks. A learning algorithm using search-and-score is proposed to learn the Bayesian network structure. Bayesian Model Averaging is used to address the problem of model uncertainty. The experiments on traffic simulation show that this predictive complex event processing method has good accuracy and acceptable performance.