Comparison of railway passenger flow forecast methods based on ARIMA and LSTM

Accurate passenger flow forcast is the basis of the optimization of passenger transportation organization and an effective way to improve operational safety and transportation efficiency. Taking the annual passenger flow data of Jiangmen East Railway Station as research object, and the ARIMA time series model and the LSTM neural network model were constructed respectively. From the perspectives of forecast accuracy, calculation speed, error index evaluation, and model adaptability, the differences in passenger flow forecast results of two forecast models were analyzed. The results show that the LSTM model has better forecast accuracy and fitting accuracy, and the ARIMA model has faster calculation speed. This study result can provide significant practical implications for the selection of passenger flow forecast methods.