Open Access
| Issue |
ITM Web Conf.
Volume 88, 2026
The 2026 International Conference on Artificial Intelligence, Big Data and Computer Science (AIBDCS 2026)
|
|
|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 4 | |
| Section | Artificial Intelligence, Big Data and Computer Science | |
| DOI | https://doi.org/10.1051/itmconf/20268801005 | |
| Published online | 27 July 2026 | |
- W. Cheng, Rail transit “fourth city”--Chengdu metro enters the era of large line network. Transp. Constr. Manage. (06), 124–126 (2020) [Google Scholar]
- D. XI, Study on the Influencing Factors of Mass Passenger Flow in Metro Stations and Optimization of Operation Organization. Transp. Manage. World (32), 1–3 (2025) [Google Scholar]
- X. Zhu, Research on Price Forecasting of Small Agricultural Products in China Based on ARIMA Model--Taking the Market Price of Chili Pepper as an Example. Price Theory Pract. (12), 226–230+295 (2025) [Google Scholar]
- M. Liao, On the practical application of ARIMA mathematical model. Proc. Int. Forum Acad. Res. Educ. Sci. Dev. (IX) (2025) [Google Scholar]
- Q. Wang, Characteristics of spatial and temporal distribution of passenger flow and ARIMA passenger flow prediction in Wuhan-Hamzhou Intercity Railway. Railw. Oper. Technol. 32(01), 22–26 (2026) [Google Scholar]
- Chengdu Rail Transit Group, Daily passenger flow. (2025) www.chengdurail.com [Google Scholar]
- X. Nie, D. Wu, J. Jing, et al., Analysis of short-time inbound passenger flow prediction and influencing factors of subway based on LightGBM and SHAP. Railw. Econ. Res. (05), 50–55 (2023) [Google Scholar]
- Y. Liu, Analysis of the impact of sensitivity factors on urban rail transit passenger traffic. Transp. Eng. 17(03), 14–18 (2017) [Google Scholar]
- Y. Zhou, L. Zhou, Y. Le, Research on short-time passenger flow prediction of urban rail transit based on combined model. Proc. Int. Conf. Eng. Bus. Manage. (EBM2010) (2010) [Google Scholar]
- Z. Lv, P. Wang, Highway traffic volume prediction based on ARIMA-LSTM. Proc. 42nd China Control Conf. (15) (2023) [Google Scholar]
- Y. C. Yu, S. Li, Z. Liu, Comparison of passenger flow prediction methods at railroad stations based on ARIMA and LSTM. J. Shanghai Univ. Eng. Technol. 38(03), 278–283 (2024) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.