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Artificial neural network models for airport capacity prediction

Author

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  • Choi, Sun
  • Kim, Young Jin

Abstract

This paper proposes artificial neural network models to predict the arrival/departure capacity of airports. Multilayer perceptron (MLP), recurrent neural networks (RNN), and long short-term memory (LSTM) models have been trained using capacity and meteorological data from Hartsfield–Jackson Atlanta International Airport (ATL) from 2013 to 2017. The models’ predictive performances were validated against the observed capacity of ATL in 2018. The qualitative and quantitative analysis of the trained models confirmed that the artificial neural networks approach is effective in predicting airport capacity. In addition, the transferability of the models for Boston Logan International Airport (BOS) is examined. Capacity prediction performance for BOS measures the transferability of the models trained with the ATL data. MLP showed good transferability without taking any other measures, and RNN and LSTM were able to predict the BOS capacity well after fine-tuning.

Suggested Citation

  • Choi, Sun & Kim, Young Jin, 2021. "Artificial neural network models for airport capacity prediction," Journal of Air Transport Management, Elsevier, vol. 97(C).
    • Handle: RePEc:eee:jaitra:v:97:y:2021:i:c:s0969699721001277
    DOI: 10.1016/j.jairtraman.2021.102146
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    Cited by:

    1. Tayfun Uyanık & Yunus Yalman & Özcan Kalenderli & Yasin Arslanoğlu & Yacine Terriche & Chun-Lien Su & Josep M. Guerrero, 2022. "Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel," Mathematics, MDPI, vol. 10(22), pages 1-21, November.
    2. Tayfun Uyanık & Nur Najihah Abu Bakar & Özcan Kalenderli & Yasin Arslanoğlu & Josep M. Guerrero & Abderezak Lashab, 2023. "A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study," Energies, MDPI, vol. 16(13), pages 1-20, June.
    3. Marta Skiba & Barbara Dutka & Mariusz Młynarczuk, 2021. "MLP-Based Model for Estimation of Methane Seam Pressure," Energies, MDPI, vol. 14(22), pages 1-12, November.
    4. Khan, Waqar Ahmed & Chung, Sai-Ho & Eltoukhy, Abdelrahman E.E. & Khurshid, Faisal, 2024. "A novel parallel series data-driven model for IATA-coded flight delays prediction and features analysis," Journal of Air Transport Management, Elsevier, vol. 114(C).
    5. Cheng-Hong Yang & Borcy Lee & Pey-Huah Jou & Yu-Fang Chung & Yu-Da Lin, 2023. "Analysis and Forecasting of International Airport Traffic Volume," Mathematics, MDPI, vol. 11(6), pages 1-19, March.

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