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A Novel Approach to Forecast Weakly Regular Traffic Status

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2 Author(s)
Yang Zhang ; Inst. of Image Process. & Pattern Recognition, Shanghai Jiao Tong Univ., Shanghai ; Yuncai Liu

How to accurately predict traffic data with weak regularity is difficult for various forecasting models. In this paper, least squares support vector machines (LS-SVMs) are proposed to deal with such a problem. It is the first time to apply the technique and analyze the forecast performance in the field. For comparison purpose, other three baseline predictors are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising.

Published in:

Intelligent Transportation Systems, 2008. ITSC 2008. 11th International IEEE Conference on

Date of Conference:

12-15 Oct. 2008