Traffic Flow Forecasting Using a Spatio-Temporal Bayesian Network Predictor
This addresses traffic management for urban planners, but it is incremental as it builds on existing Bayesian network methods with specific enhancements.
The paper tackled traffic flow forecasting by proposing a spatio-temporal Bayesian network predictor that incorporates spatial and temporal information, and experimental results on Beijing vehicular flow data demonstrated its effectiveness.
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.