Flight Time Prediction of Arrival Air Traffic Flows Using Time-Based Airspace Model Applying Machine-Learning Methods

Author:

Nishida TakuyaORCID,Itoh EriORCID

Publisher

Springer Nature Singapore

Reference15 articles.

1. Japan Aircraft Development Corporation (JADC) Homepage, http://www.jadc.jp/files/topics/184_ext_01_0.pdf. Last accessed 25 July 2023

2. IATA PressRelease No:44, https://www.iata.org/en/pressroom/2023-releases/2023-07-06-01/. Last accessed 25 July 2023

3. Ayhan, S., Costas, P., Samet, H.: Predicting estimated time of arrival for commercial flights. In: KDD ‘18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 33–42. Machinery, New York (2018)

4. Hasevoets, N., Conroy, P.: Arrival Manager- Implementation guidelines and lessons learned. EUROCOMTROL, Brussels

5. Morikawa, N., Itoh, E.: Machine learning methods ensuring both performance and interpretability of estimating aircraft arrival times. In: ICAS Proceeding 33rd Congress of the International Council of the Aeronautical Sciences, vol. 9, pp. 6455–6466. Stockholm (2022)

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