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

Authors:
Shiliang Sun;Qiaona Chen
Affiliations:
Department of Computer Science and Technology, East China Normal University, Shanghai, China 200241;Department of Computer Science and Technology, East China Normal University, Shanghai, China 200241
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

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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.