Review of a Fuzzy Logic Based Airport Passenger Flow Prediction System
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-46778-3_22
Reference10 articles.
1. Monmousseau, P., Jarry, G., Bertosio, F., Delahaye, D., Houalla, M.: Predicting passenger flow at Charles de Gaulle airport security checkpoints. In: 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT), Singapore, 2020, pp. 1–9. IEEE (2020)
2. Chen, J., Li, J.: Airport passenger flow forecast based on the wavelet neural network model. In: Proceedings of the 2018 2nd International Conference on Deep Learning Technologies (ICDLT 2018). Association for Computing Machinery, New York, NY, USA (2018)
3. Liu, L., Chen, R.-C.: A novel passenger flow prediction model using deep learning methods. Transp. Res. Part C: Emerg. Technol. 84, 74–91 (2017)
4. Li, Z., Bil, J., Li, Z.: Passenger flow forecasting research for airport terminal based on SARIMA time series model. In: IOP Conference Series: Earth and Environmental Science, vol. 100, 1st International Global on Renewable Energy and Development (IGRED 2017). IOP Publishing Ltd., Singapore (2017)
5. Guo, X., Grushka-Cockayne, Y., De Reyck, B.: Forecasting airport transfer passenger flow using real-time data and machine learning. Manuf. Serv. Oper. Manag. 24(6), 3193–3214 (2021)
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