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.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
William, B.M.: Modeling and Forecasting Vehicular Traffic Flow as a Seasonal Stochastic Time Series Process. Doctoral Dissertation. University of Virginia, Charlottesville (1999)
Yu, E.S., Chen, C.Y.R.: Traffic Prediction Using Neural Networks. In: Proceedings of IEEE Global Telecommunications Conference, vol. 2, pp. 991–995 (1993)
Moorthy, C.K., Ratcliffe, B.G.: Short Term Traffic Forecasting Using Time Series Methods. Transportation Planning and Technology 12, 45–56 (1988)
Okutani, I., Stephanedes, Y.J.: Dynamic Prediction of Traffic Volume through Kalman Filter Theory. Transportation Research, Part B 18B, 1–11 (1984)
Chrobok, R., Wahle, J., Schreckenberg, M.: Traffic Forecast Using Simulations of Large Scale Networks. In: Proceedings of IEEE Intelligent Transportation Systems Conference, pp. 434–439 (2001)
Davis, G.A., Nihan, N.L.: Non-Parametric Regression and Short-Term Freeway Traffic Forecasting. Journal of Transportation Engineering, 178–188 (1991)
Yin, H.B., Wong, S.C., Xu, J.M., Wong, C.K.: Urban Traffic Flow Prediction Using a Fuzzy-Neural Approach. Transportation Research, Part C 10, 85–98 (2002)
Yu, G.Q., Hu, J.M., Zhang, C.S., Zhuang, L.K., Song, J.Y.: Short-Term Traffic Flow Forecasting Based on Markov Chain Model. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 208–212 (2003)
Chang, S.C., Kim, R.S., Kim, S.J., Ahn, B.H.: Traffic-Flow Forecasting Using a 3-Stage Model. In: Proceedings of IEEE Intelligent Vehicle Symposium, pp. 451–456 (2000)
Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Kohavi, R., John, M.: Wrappers for Feature Selection. Artificial Intelligence, 273–324 (1997)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York (2001)
Zhang, B.B., Zhang, C.S., Yi, X.: Competitive EM Algorithm for Finite Mixture Models. Pattern Recognition 37, 131–144 (2004)
Sun, S.L., Zhang, C.S., Yu, G.Q., Lu, N.J., Xiao, F.: Bayesian Network Methods for Traffic Flow Forecasting with Incomplete Data. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 419–428. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, S., Zhang, C., Zhang, Y. (2005). Traffic Flow Forecasting Using a Spatio-temporal Bayesian Network Predictor. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_43
Download citation
DOI: https://doi.org/10.1007/11550907_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
Online ISBN: 978-3-540-28756-8
eBook Packages: Computer ScienceComputer Science (R0)