Passenger Flow Prediction Method for Rail Transit Stations Based on Empirical Mode Decomposition and K-nearest Neighbors

ABSTRACT

A passenger flow time series forecasting method based on empirical mode decomposition (EMD) and K-nearest neighbor nonparametric regression (KNN) was proposed. Based on the principle of EMD and KNN algorithm, the EMD-KNN combined algorithm flow was constructed on the basis of improving KNN prediction method. According to the characteristics that the time series trend of passenger flow has changed obviously due to the influence of COVID-19 epidemic situation in the example stations. BP structural breakpoint detection method was used to identify three structural breakpoints, and the time series segment with the closest passenger flow change trend to the forecast day was selected for empirical mode decomposition. The decomposed sequences were reorganized into high-frequency, low-frequency and trend sequences, and then the K-nearest neighbor algorithm considering weight was used to predict, and the final prediction results were obtained by superposition, and compared with the prediction results of single KNN algorithm and ARIMA model. The results show that the prediction accuracy of EMD-KNN combination algorithm is higher than that of single KNN algorithm and ARIMA model, and it can effectively capture the changing trend of passenger flow. © 2022, Editorial Department of Journal of Wuhan University of Technology. All right reserved.