Open Access
| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 03016 | |
| Number of page(s) | 7 | |
| Section | Large Language Models, Generative AI, and Multimodal Learning | |
| DOI | https://doi.org/10.1051/itmconf/20268403016 | |
| Published online | 06 April 2026 | |
- F. J. Li, Q. Zong. The Forecasting of the Elevator Traffic Flow Time Series Based on ARIMA and GP. Adv. Mater. Res., 2074(588-589): 1466–1471 (2012). DOI:10.4028/www.scientific.net/AMR.588-589.1466 [Google Scholar]
- Q. Xi, W. Zhang, M. Du, et al. Passenger flow forecasting approaches for urban rail transit: a survey. Int. J. Gen. Syst., 52(8): 919–947 (2023). DOI:10.1080/03081079.2023.2231133 [Google Scholar]
- J. Tang, Y. Zhu, S. Yang, et al. Predicting Urban Traffic Under Extreme Weather by Deep Learning Method with Disaster Knowledge. Appl. Sci., 15(17): 9848 (2025). DOI:10.3390/APP15179848 [Google Scholar]
- Q. Zhao, X. Feng, L. Zhang, et al. Research on Short-Term Passenger Flow Prediction of LSTM Rail Transit Based on Wavelet Denoising. Mathematics, 11(19): 4204 (2023). DOI:10.3390/MATH11194204 [Google Scholar]
- Y. Liu, Z. Liu, R. Jia. DeepPF: A deep learning based architecture for metro passenger flow prediction. Transp. Res. Part C, 101: 18–34 (2019). DOI:10.1016/j.trc.2019.01.027 [Google Scholar]
- M. A. H. E. Mohammed, R. A. H. Dynamic linear models to predict bike availability in a bike sharing system. Int. J. Sustain. Transp., 14(3): 232–242 (2020). DOI:10.1080/15568318.2019.1611976 [Google Scholar]
- L. Vera, A. A. R. Predicting hourly origin–destination demand in bike sharing systems using hurdle models: Lisbon case study. Case Stud. Transp. Policy, 9(4): 1836–1848 (2021). DOI:10.1016/J.CSTP.2021.10.003 [Google Scholar]
- A. Behroozi, A. Edrisi. Predicting travel demand of a bike sharing system using graph convolutional neural networks. Public Transp., 17(1): 1–37 (2024). DOI:10.1007/S12469-024-00371-W [Google Scholar]
- D. Chen, C. Xiong, M. Zhong. Improved LSTM Based on Attention Mechanism for Short-term Traffic Flow Prediction. Proceedings of the 10th International Conference on Information Science and Technology (ICIST). Bath, London, and Plymouth, UK. 2020. 71-76. DOI:10.1109/ICIST49303.2020.9202045 [Google Scholar]
- X. Wei, K. S. The Comparison Between ARIMA and Long Short-term Memory for Highway Traffic Flow Prediction. J. East. Asia Soc. Transp. Stud., 13: 1817–1834 (2020). DOI:10.11175/easts.13.1817 [Google Scholar]
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