A Short-Term Traffic Flow Prediction Method Based on Kernel Extreme Learning Machine

Abstract

Real-time and accurate traffic flow prediction plays an important role in ITS (Intelligent Transport System). Extreme learning machine (ELM) has proven to be an efficient and effective learning paradigm for a wide field. With the method of kernel function instead of the hidden layer, Kernel-ELM overcame the problem of variation caused by randomly assigned weights. In order to improve the accuracy of traffic flow prediction, this paper introduces a kernel extreme learning machine(KELM)-based forecasting method. We have implemented KELM to forecast real-time traffic flow in the data of Nanning in South China. The results indicate that the KELM method can generate a more accurate prediction and provide better performance on extremely fast learning speed compared with other state-of-art algorithms.