Research on Forecast of Rail Traffic Flow Based on ARIMA Model
Shu Ying Liu, Shuo Liu, Ye Tian, Quan Sun, Yu Yang Tang · Journal of Physics Conference Series · 2021
Abstract With the rapid economic development, subway-based rail transit is spreading all over the country, and efficient prediction of rail passenger flow is the key to alleviating traffic pressure. In view of the time-series characteristics of subway passenger flow data, the author uses the simulation results to show that the ARIMA model has higher accuracy and better effect in predicting the rail transit flow.