Abstract
In this paper, we apply a new method to forecast short-term traffic flows. It is kernel regression based on a Mahalanobis metric whose parameters are estimated by gradient descent methods. Based on the analysis for eigenvalues of learned metric matrices, we further propose a method for evaluating the effectiveness of the learned metrics. Experiments on real data of urban vehicular traffic flows are performed. Comparisons with traditional kernel regression with the Euclidean metric under two criterions show that the new method is more effective for short-term 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
William, B.M.: Modeling and Forecasting Vehicular Traffic Flow as a Seasonal Stochastic Time Series Process. Ph.D. Dissertation, Univ. Virginia, Charlottesville, VA (1999)
Moorthy, C.K., Ratcliffe, B.G.: Short Term Traffic Forecasting Using Time Series Methods. Transp. Plan. Technol. 12, 45–56 (1988)
Lee, S., Fambro, D.B.: Application of Subsets Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting. Transp. Res. Rec. 1678, 179–188 (1999)
Hall, J., Mars, P.: The Limitations of Artificial Neural Networks for Traffic Prediction. In: Proc. 3rd IEEE Symp. Computers and Communications, Athens, Greece, pp. 8–12 (1998)
Okutani, I., Stephanedes, Y.J.: Dynamic Prediction of Traffic Volume through Kalman Filter Theory. Transp. Res., Part B: Methodol. 18, 1–11 (1984)
Yu, G., Hu, J., Zhang, C., Zhuang, L., Song, J.: Short-Term Traffic Flow Forecasting based on Markov Chain Model. In: Proc. IEEE Intelligent Vehicles Symp., Columbus, OH, pp. 208–212 (2003)
Müller, K.R., Smola, A.J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.: Predicting Time Series with Support Vector Machines. In: Proc. Int. Conf. Artificial Neural Networks, pp. 999–1004 (1997)
Sun, S., Zhang, C.: A Bayesian Network Approach to Traffic Flow Forecasting. IEEE Trans. Intell. Transp. Syst. 7, 124–132 (2006)
Lam, W.H.K., Xu, J.: Estimation of AADT from Short Period Counts in Hong Kong—A Comparison between Neural Network Method and Regression Analysis. J. Adv. Transp. 34, 249–268 (2000)
Smith, B.L., Williams, B.M., Oswald, R.K.: Comparison of Parametric and Nonparametric Models for Traffic Flow Forecasting. Transp. Res., Part C: Emerg. Technol. 10, 303–321 (2002)
Davis, G.A., Nihan, N.L.: Non-Parametric Regression and Short-Term Freeway Traffic Forecasting. J. Transp. Eng. 177, 178–188 (1991)
Weinberger, K.Q., Tesauro, G.: Metric Learning for Kernel Regression. In: Proc. 11th Int. Conf. Artificial Intelligence and Statistics, Omnipress, Puerto Rico, pp. 608–615 (2007)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, New York (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, S., Chen, Q. (2008). Kernel Regression with a Mahalanobis Metric for Short-Term Traffic Flow Forecasting. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-88906-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88905-2
Online ISBN: 978-3-540-88906-9
eBook Packages: Computer ScienceComputer Science (R0)