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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Kernel Regression with a Mahalanobis Metric for Short-Term Traffic Flow Forecasting
verfasst von : Shiliang Sun, Qiaona Chen
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2008
Verlag: Springer Berlin Heidelberg
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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.