Short-Term Passenger Flow Prediction Based on Wavelet Transform and Kernel Extreme Learning Machine

Abstract

In view of the instability and complexity of passenger flow change in urban rail transit, it is the key and the difficult point to use the prediction model to get more accurate number of short-term passenger flow. In view of this, this study proposes a hybrid forecasting model W-KELM, which combines wavelet transform (WT) and kernel extreme learning machine (KELM). The main idea of the model is to decompose passenger flow data into high-frequency and low-frequency sequences through WT and Mallat algorithm, and then use KELM approach to learn and forecast signals with different frequencies. Finally, different prediction sequences are reconstructed using WT. Through a case study of Beijing metro, we test the effectiveness of the model. The result shows that the W-KELM model has good prediction accuracy. In addition, this paper compare the prediction result of W-KELM model with those of BP neural network model, the single KELM method, and the hybrid model based on WT and BP neural network. It shows that the W-KELM model can effectively improve the prediction accuracy. Thus, providing a more accurate and real situation for monitoring and early warning of urban rail transit.