 Artificial neural network models for airport capacity predictionSun Choi and Young Jin Kim Journal of Air Transport Management, 2021, vol. 97, issue C 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. Keywords: Artificial neural networks; Multilayer perceptron (MLP); Recurrent neural networks (RNN); Long short-term memory (LSTM); Airport capacity prediction (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jaitra:v:97:y:2021:i:c:s0969699721001277 DOI: 10.1016/j.jairtraman.2021.102146 Access Statistics for this article Journal of Air Transport Management is currently edited by Anne Graham More articles in Journal of Air Transport Management from Elsevier |
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