Abstract
In this paper expanded Bayesian network method for short-term traffic flow forecasting in case of incomplete data is proposed. Expanded Bayesian network model is constructed to describe the causal relationship among traffic flows, and then the joint probability distribution between the cause and effect nodes with dimension reduced by Principal Component Analysis (PCA) is approximated through Gaussian Mixture Model (GMM). The parameters of the GMM are learned through Competitive EM algorithm. Experiments show that the expanded Bayesian network method is appropriate and effective for short-term traffic flow forecasting with incomplete data.
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Zhang, C., Sun, S., Yu, G. (2004). Short-Term Traffic Flow Forecasting Using Expanded Bayesian Network for Incomplete Data. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_151
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DOI: https://doi.org/10.1007/978-3-540-28648-6_151
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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