关键词: 智能交通系统, 交通流量预测, 复杂事件处理, 变结构动态贝叶斯网络

Abstract: The rapid development of Internet of things (IoT) and in-stream big data processing technology brings new opportunity to intelligent transportation system (ITS). Traffic flow prediction is the key issue of ITS. In traffic flow prediction, one fixed model cannot get excellent performance under different environments. Predicting models should also be updated according to data stream. In order to resolve these problems, this paper proposes a traffic prediction method based on structure varying dynamic Bayesian network. Based on complex event processing, this method uses context clustering to partition historical data and uses online clustering to support the update of clusters. A search-score method is used to learn the structure of Bayesian network and Gaussian mixture model is used for approximate inference of Bayesian network. When predicting at run time, appropriate model or model composition are selected based on current context. The experiments on both real and simulation data show that the proposed  method has better performance than popular methods currently used.

Key words: intelligent transportation system, traffic flow prediction, complex event processing, structure varying dynamic Bayesian network