Research on short-term passenger flow prediction of urban rail transit based on ARIMA algorithm
Year: 2024
Type: article
Abstract: The continuous growth of urban population has led to an increasing demand for urban public transport among citizens. In response, many cities have implemented urban rail transit systems to alleviate internal traffic congestion. Accurate short-term passenger flow prediction is crucial for the efficient operation of intelligent subway systems. Therefore, it is essential to establish or select a suitable model for predicting the short-term passenger flow of subways. This study utilizes ten days of travel data from the Beijing subway between May 1 and May 10, 2019. After conducting preliminary preprocessing on the 10-day subway appearance data, an ARIMA prediction model is established for initial forecasting. The accuracy of passenger volume forecast is evaluated by analyzing the variance between predicted values and actual values at specific time intervals, with results indicating that the ARIMA model demonstrates strong predictive capability.
DOI: 10.54097/5sx1j906
Author: Zhihan Liao
Institution: Tianjin University of Technology and Education
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4401667240