Short-term Passenger Flow Prediction of Urban Rail Transit based on ARIMA Model
DOI:
https://doi.org/10.54097/sxg7er77Keywords:
Urban rail transit; ARIMA model; passenger flow prediction.Abstract
The urban economy is growing quickly, the city is getting bigger and the problem of traffic congestion is getting worse. As the primary means of transferring passengers, urban rail transit is an essential component of it. Therefore, developing urban rail transit is an important way to alleviate traffic congestion in large and medium-sized cities in the country. However, as rail transit grows, the number of lines grows daily and the volume of passengers increases quickly, putting strain on the urban transportation system to handle the increased volume of passengers. Consequently, it is now necessary to find a solution to the problem of how to quickly estimate future passenger flow based on past rail transit data, assist the operation management department in carrying out preventive work ahead of time and enable it to accomplish safe and orderly operation. Based on the Beijing Line 10 passenger flow, this study forecasts the passenger flow for the next few days by using the ARIMA model to uncover the travel rules hidden in the huge amount of concealed data. The forecast findings aid in the logical optimization of rail transportation, hence raising the level of service quality.
Downloads
References
[1] Song Zhenyang. Research on Short-term Inbound Passenger Flow Forecast of Urban Rail Station Based on Spatial and Temporal Correlation of Passenger Flow. Beijing Jiaotong University, 2022.
[2] Zhang Wanning. Research on Short-term Prediction of Urban Rail Transit Inbound Passenger Flow Based on Deep Learning. Dalian Jiaotong University, 2023.
[3] Huang Shuting. Research on short-term passenger flow Prediction of subway based on CNN-LSTM deep neural network. East China Jiaotong University, 2023.
[4] Li Boyuan. Research on Short-term Passenger Flow Prediction of Urban Rail Transit. Lanzhou Jiaotong University, 2023.
[5] Shi Xuerong, Wang Peh-hui, Liu Dong-Jie. Rail transit passenger flow prediction and visualization based on deep neural network. Electronic technology and Software engineering, 2020, 19: 182-185.
[6] LI Wei, SUI Liying, ZHOU Min, et al. Short-term passenger flow forecast for urban rail transit based on multi-source data. Journal on Wireless Communications and Networking, 2021, 1: 1-13.
[7] WILLIAMSBM, HOELLA. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of transportation engineering, 2003, 129(6): 664-672.
[8] NI Jie, YU Li, JIN Xiaonan, Forecast and research of urban rail transit passenger flow based on ARIMA model. Intelligent Computer and Applications, 2021.
[9] Cao Yang, Sun Ya, Lin Li. Short-term Passenger Inbound Flow Prediction for Urban Rail Transit Based on CNN-LSTM. Traffic and transportation, 2024.
[10] Li Shuqing, Li Wei, Liu Yaohong, Ma Bo. Short-Term Inbound Passenger Flow Prediction of Model Rail Transit Based on Combined Deep Learning. School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing, China, 2024.
Downloads
Published
Conference Proceedings Volume
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.