Passenger flow prediction at rail transit stations based on LSTM network and correlation analysis

Abstract

As residents are more and more inclined to choose rail transportation as their daily travel mode, the status of urban rail transportation is becoming more and more prominent. However, due to the level of passenger flow analysis and prediction accuracy, how to better meet passenger demand and improve operation efficiency has become a key issue to be solved by the operation department. In order to improve operational efficiency and service quality, this paper introduces weather-related data to analyze passenger flow at rail transit stations and develop short-term passenger flow prediction. Taking Beijing urban rail transit passenger flow as an example, cluster analysis and correlation analysis are conducted with corresponding weather data to verify the passenger flow characteristics in line with the actual travel. The long and short-term memory (LSTM) neural network model is selected for short-term passenger flow prediction, and weather data are added as features to improve the prediction accuracy. The results show that the passenger flow data with weather features are better fitted in the prediction, and the more accurate prediction results will play a more effective role for the relevant operation departments to schedule trains and improve the operation efficiency.