Publisher
Springer Nature Switzerland
Reference19 articles.
1. Wei, Y., Chen, M.C.: Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp. Res. Part C Emerg. Technol. 21(1), 148–162 (2012)
2. New York State Open Data, MTA Subway Hourly Ridership Beginning February 2020, https://data.ny.gov/resource/wujg-7c2s.json, Last accessed 2023/08/15
3. Toqué, F., Khouadjia, M., Come, E., Trepanier, M., Oukhellou, L.: Short- and long-term forecasting of multimodal transport passenger flows with machine learning methods. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 560–566. IEEE (2017)
4. Zhu, K., Xun, P., Li, W., Li, Z., Zhou, R.: Prediction of passenger flow in urban rail transit based on big data analysis and deep learning. IEEE Access. 7, 142272–142279 (2017)
5. Gallo, M., De Luca, G., D’Acierno, L., Botte, M.: Artificial neural networks for forecasting passenger flows on metro lines. Sensors. 19(15), 3424 (2019)