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

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.

Travel demand forecast of a new high-speed railway station during COVID 19

Passenger flow at a new high-speed railway station presents significant uncertainty during COVID-19, which brings a huge challenge to the daily management and operation of the station. To detect the future development trend of demand and reduce the impact of its fluctuation on the daily operation of the station, three classical forecast methods are applied to predict the passenger flow in and out of the station during workdays in this paper. Furthermore, the performance of these methods is compared by conducting a case study of Huairou South Station. The results show that the ARIMA model (autoregressive integrated moving average model) shows better performance than the neural network model and Bass model (Bass diffusion model). Finally, a revised ARIMA model is introduced to predict the passenger flow of the National Day. © 2021 IEEE.

Key Points of Railway Police Work based on Bayesian Passenger Flow Forecast Method under the Normalization of COVID-19 Prevention and Control

China has entered the stage of normalized COVID-19 prevention and control, but the pressure of overseas COVID-19 input continues to increase, and the railway COVID-19 prevention and control police work is still facing a major test. This paper analyzes the characteristics of railway passenger flow under the influence of the COVID-19 situation, establishes a passenger flow forecasting method based on Bayesian theory, and puts forward the key points of railway police work by improving the police mode, strengthening daily police, optimizing smart policing, relying on mass prevention and mass treatment, It provides a theoretical reference for the railway public security organs to effectively control the public health risks and social security risks under the normalization of COVID-19 prevention and control, effectively maintain the railway operation order, and strive to ensure the safe travel of passengers. © 2021 Kriedt Enterprises Ltd. All right reserved.