Prediction model of rail transit passenger flow in rain and snow weather

Abstract:

For improving the rail transit passenger flow prediction models under rain and snow weather conditions, the passenger flow data of Harbin Metro Line 1 from December 2017 to January 2019 was studied, and the indicators of passenger flow benchmark and passenger flow deviation rate were introduced to quantify the passenger flow of rail transit. The fluctuation rule of rail transit passenger flow under rain and snow weather conditions was analyzed, and a WI-LSTM prediction model was proposed based on the temporal and spatial fluctuation of rail transit passenger flow in rain and snow weather. The mean absolute error (MAE), root mean square error (RMSE), and mean relative error (MRE) were used as the evaluation indexes of the prediction model. The proposed model was compared with SARIMA prediction model, support vector machine (SVR) prediction model, and the LSTM prediction model without considering rain and snow weather. Results show that the WI-LSTM model considering rain and snow weather could make full use of the fluctuation rule of rail transit passenger flow in rain and snow weather, and achieved higher accuracy and reliability than the other three prediction models. The proposed WI-LSTM model further improves the accuracy of rail transit passenger flow forecast in rain and snow weather, and can provide data support for the operation and management of rail transit enterprises.