Work

Research on Forecast of Rail Traffic Flow Based on ARIMA Model

Year: 2021

Type: conference-paper

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.

Source: Journal of Physics Conference Series

DOI: 10.1088/1742-6596/1792/1/012065

Authors: Shu Ying Liu, Shuo Liu, Ye Tian, Quan Long Sun, Yu Yang Tang

Institution: Anhui Xinhua University

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funders: Anhui Provincial Department of Education, Anhui Xinhua University