Traffic Change Forecast and Decision Based on Variable Structure Dynamic Bayesian Network

Abstract

The rapid development of internet of things (IoT) and in-stream big data processing technology has brought new opportunities for the research of intelligent transportation systems. Traffic forecasting has always been a key issue in the smart transportation system. Aiming at the problem that a fixed model cannot adapt to multiple environments in traffic flow prediction and the problem of model updating for data flow, a traffic flow prediction method is proposed based on variable structure dynamic Bayesian network. Based on the complex event processing and event context, this method divides historical data through context clustering and supports cluster update through online clustering of event streams. For different clustered data, a search-scoring method is used to learn the corresponding Bayesian network structure, and a Bayesian network is approximated based on a Gaussian mixture model. When forecasting online, a suitable model or combination of models is selected according to the current context for prediction.