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)


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.


traffic flow forecasting kernel regression Mahalanobis metric Euclidean metric gradient descent 


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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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