An Ensembled RBF Extreme Learning Machine to Forecast Road Surface Temperature

Abstract

At present, high road surface temperature (RST) is threatening the safety of expressway transportation. It can lead to accidents and damages to road, accordingly, people have paid more attention to RST forecasting. Numerical methods on RST prediction are often hard to obtain precise parameters, whereas statistical methods cannot achieve desired accuracy. To address these problems, this paper proposes GBELM-RBF method that utilizes gradient boosting to ensemble Radial Basis Function Extreme Learning Machine. To evaluate the performance of the proposed method, GBELM-RBF is compared with other ELM algorithms on the datasets of airport expressway and Badaling expressway during November 2012 and September 2014. The root mean squared error (RMSE), accuracy and Pearson Correlation Coefficient (PCC) of these methods are analyzed. The experimental results show that GBELM-RBF has the best performance. For airport expressway dataset, the RMSE is less than 3, the accuracy is 78.8% and PCC is 0.94. For Badaling expressway dataset, the RMSE is less than 3, the accuracy is 81.2% and PCC is 0.921.