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

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.