Title :
A Novel Approach to Forecast Weakly Regular Traffic Status
Author :
Zhang, Yang ; Liu, Yuncai
Author_Institution :
Inst. of Image Process. & Pattern Recognition, Shanghai Jiao Tong Univ., Shanghai
Abstract :
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.
Keywords :
automated highways; least squares approximations; road traffic; support vector machines; LS-SVM; intelligent transportation system; least squares support vector machine; weakly regular traffic status forecasting; Intelligent transportation systems; Least squares methods; Performance analysis; Predictive models; Quadratic programming; Robust stability; Support vector machines; Telecommunication traffic; Traffic control; Training data;
Conference_Titel :
Intelligent Transportation Systems, 2008. ITSC 2008. 11th International IEEE Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2111-4
Electronic_ISBN :
978-1-4244-2112-1
DOI :
10.1109/ITSC.2008.4732561
