Title: Traffic Flow Forecasting Using a Spatio-Temporal Bayesian Network Predictor,
Abstract: A novel predictor for traffic flow forecasting, namely spatio-temporal
Bayesian network predictor, is proposed. Unlike existing methods, our approach
incorporates all the spatial and temporal information available in a
transportation network to carry our traffic flow forecasting of the current
site. The Pearson correlation coefficient is adopted to rank the input
variables (traffic flows) for prediction, and the best-first strategy is
employed to select a subset as the cause nodes of a Bayesian network. Given the
derived cause nodes and the corresponding effect node in the spatio-temporal
Bayesian network, a Gaussian Mixture Model is applied to describe the
statistical relationship between the input and output. Finally, traffic flow
forecasting is performed under the criterion of Minimum Mean Square Error
(M.M.S.E.). Experimental results with the urban vehicular flow data of Beijing
demonstrate the effectiveness of our presented spatio-temporal Bayesian network
predictor.