Abstract
Traffic flow forecasting is an important application domain of machine learning. How to use the information provided by adjacent links more efficiently is a key to improving the performance of Intelligent Transportation Systems (ITS). In this paper, we build a sparse graphical model for multi-link traffic flow through the Graphical Lasso (GL) algorithm and then implement the forecasting with Neural Networks. Through a large number of experiments, we find that network-scale traffic forecasting with modeling by Graphical Lasso performs much better than previous research. Traditional approaches considered the information provided by adjacent links but did not extract the information. Thus, although they improved the performance to some extent, they did not make good use of the information. Furthermore, we summarize the theoretical analysis of Graphical Lasso algorithm. From theoretical and practical points of view, we fully verify the superiority of Graphical Lasso used in modeling for multi-link traffic flow forecasting.
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
Jordan, M.I.: Graphical Models. Statistical Science 19(1), 140–155 (2004)
Murphy, K.P.: An introduction to graphical models. Citeseer (2001)
Friedman, J., Hastie, T., Tibshirani, R.: Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3), 432–441 (2008)
Rich, C., Virginia, R.S.: Benefiting from the variables that variable selection discards. Machine Learning Research 3, 1245–1264 (2003)
Gao, Y., Sun, S.: Multi-link traffic flow forecasting using neural networks. In: The 6th International Conference on Natural Computation, vol. 1, pp. 398–401 (2010)
Meinshausen, N., Bühlmann, P.: High dimensional graphs and variable selection with the lasso. The Annals of Statistics 34(3), 1436–1462 (2006)
Yuan, M., Lin, Y.: Model election and estimation in the Gaussian graphical model. Biometrika 94(1), 19–35 (2007)
Banerjee, O., El Ghaoui, L., d’Aspremont, A.: Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. Machine Learning Research 9, 485–516 (2008)
Dahl, J., Vandenberghe, L., Roychowdhury, V.: Covariance selection for nonchordal graphs via chordal embedding. Optimization Methods and Software 23(4), 501–520 (2008)
Sun, S., Zhang, C.: The selective random subspace predictor for traffic flow forecasting. IEEE Transactions on Intelligent Transportation Systems 8(2), 367–373 (2007)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Citeseer (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, Y., Sun, S., Shi, D. (2011). Network-Scale Traffic Modeling and Forecasting with Graphical Lasso. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21090-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-21090-7_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21089-1
Online ISBN: 978-3-642-21090-7
eBook Packages: Computer ScienceComputer Science (R0)