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Kernel Regression with a Mahalanobis Metric for Short-Term Traffic Flow Forecasting

  • Shiliang Sun
  • Qiaona Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

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.

Keywords

traffic flow forecasting kernel regression Mahalanobis metric Euclidean metric gradient descent 

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References

  1. 1.
    William, B.M.: Modeling and Forecasting Vehicular Traffic Flow as a Seasonal Stochastic Time Series Process. Ph.D. Dissertation, Univ. Virginia, Charlottesville, VA (1999)Google Scholar
  2. 2.
    Moorthy, C.K., Ratcliffe, B.G.: Short Term Traffic Forecasting Using Time Series Methods. Transp. Plan. Technol. 12, 45–56 (1988)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Okutani, I., Stephanedes, Y.J.: Dynamic Prediction of Traffic Volume through Kalman Filter Theory. Transp. Res., Part B: Methodol. 18, 1–11 (1984)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Sun, S., Zhang, C.: A Bayesian Network Approach to Traffic Flow Forecasting. IEEE Trans. Intell. Transp. Syst. 7, 124–132 (2006)CrossRefGoogle Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Davis, G.A., Nihan, N.L.: Non-Parametric Regression and Short-Term Freeway Traffic Forecasting. J. Transp. Eng. 177, 178–188 (1991)CrossRefGoogle Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, New York (2000)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shiliang Sun
    • 1
  • Qiaona Chen
    • 1
  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina

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