Road surface temperature prediction based on gradient extreme learning machine boosting

Abstract

The expressway is extremely important to transportation, but high road-surface temperatures (RST) can cause many traffic accidents. Most of the hourly RST prediction models are based on numerical methods, but the parameters are difficult to determine. Statistical methods cannot achieve the desired accuracy. To address these problems, this paper proposes a machine learning algorithm that utilizes gradient-boosting to assemble a ReLU (rectified linear unit)/softplus Extreme Learning Machine (ELM). By using historical data from the airport and Badaling expressways collected between November 2012 and September 2014, sigmoid ELM, ReLU ELM, softplus ELM, ReLU gradient ELM boosting (GBELM) and softplus GBELM were applied for RST forecasting, RMSE (root mean squared error), PCC (Pearson Correlation Coefficient), and the accuracy of these methods were analyzed. The experimental results show that ReLU/softplus can improve the performance of traditional ELM, and gradient boosting can further improve its performance. Thus, we obtain a more accurate model that utilizes GBELM with ReLU/softplus to forecast RST. For the airport expressway, our proposed model achieves an RMSE within 3 °C, an accuracy of 81.8% and a PCC of 0.954. For the Badaling expressway, our model achieves an RMSE within 2 °C, an accuracy of 87.4% and a PCC of 0.949.