Short-term passenger flow prediction for urban rail stations using learning network based on optimal passenger flow information input algorithm

Abstract

Massive short-term passenger flow prediction models of urban rail transit stations have been used in different conditions. However, researchers encountered several challenges while selecting the optimal passenger flow information input matrix and eliminating the redundant information in the original data. In this paper, we propose a learning network based on the optimal passenger flow input information algorithm (MTFLN) method. Based on the passenger flow information attribute of the predicted target station and the correlation coefficient distribution characteristics in different stages, the parameters of the optimal passenger flow information input algorithm (OPFIIA) are set reasonably. The optimal passenger flow input information matrix is also determined. We combine the training results of MTFLN method to optimize parameters of the Adam optimization algorithm-long and short-time memory network (Adam-LSTM). In the case study, the short-term passenger flow predicted values of urban rail transit stations are presented by three predicted methods including Adam-LSTM, Elman neural network and autoregressive integrated moving average (ARIMA). The presented results demonstrate that selecting the initial time correlation matrix by combining the attributes of the predicted target station and using OPFIIA method can not only effectively select the optimal passenger flow input information matrix, but also improve training efficiency and the prediction accuracy of traditional predicted model.