Workdiamond2021

Research on Forecast of Rail Traffic Flow Based on ARIMA Model

Shu Ying Liu · Shuo Liu · Ye Tian · Quan Sun · Yu Yang Tang

29 citations0/0 files storedJournal of Physics Conference Series

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Forecasting Techniques and Applications Railway Systems and Energy Efficiency Traffic Prediction and Management Techniques

Abstract

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.

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2023Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method

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