Abstract
Traffic flow forecasting is an important issue in the field of Intelligent Transportation Systems. Due to practical limitations, traffic flows recorded can be partially missing or unavailable. In this case few methods can deal with forecasting successfully. In this paper two methods based on the concept of Bayesian networks are originally proposed to cope with this matter. A Bayesian network model and a two-step Bayesian network model are constructed respectively to describe the causal relationship among traffic flows, and then the joint probability distribution between the cause and effect nodes with its dimension reduced by Principal Component Analysis is approximated through a Gaussian Mixture Model. The parameters of the Gaussian Mixture Model are learned through the Competitive EM algorithm. Experiments show that the proposed Bayesian network methods are applicable and effective for traffic flow forecasting with incomplete data.
Keywords
- Root Mean Square Error
- Probability Density Function
- Bayesian Network
- Gaussian Mixture Model
- Forecast Performance
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Sun, S., Zhang, C., Yu, G., Lu, N., Xiao, F. (2004). Bayesian Network Methods for Traffic Flow Forecasting with Incomplete Data. In: Boulicaut, JF., Esposito, F., Giannotti, F., Pedreschi, D. (eds) Machine Learning: ECML 2004. ECML 2004. Lecture Notes in Computer Science(), vol 3201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30115-8_39
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DOI: https://doi.org/10.1007/978-3-540-30115-8_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23105-9
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