Applying multiple kernel learning and support vector machine for solving the multicriteria and nonlinearity problems of traffic flow prediction

SUMMARY

This article proposes to develop a prediction model for traffic flow using kernel learning methods such as support vector machine (SVM) and multiple kernel learning (MKL). Traffic flow prediction is a dynamic problem owing to its complex nature of multicriteria and nonlinearity. Influential factors of traffic flow were firstly investigated; five-point scale and entropy methods were employed to transfer the qualitative factors into quantitative ones and rank these factors, respectively. Then, SVM and MKL-based prediction models were developed, with the influential factors and the traffic flow as the input and output variables. The prediction capability of MKL was compared with SVM through a case study. It is proved that both the SVM and MKL perform well in prediction with regard to the accuracy rate and efficiency, and MKL is more preferable with a higher accuracy rate when under proper parameters setting. Therefore, MKL can enhance the decision-making of traffic flow prediction. Copyright © 2012 John Wiley & Sons, Ltd.