#5361. Artificial neural network models for airport capacity prediction

August 2026publication date
Proposal available till 13-05-2025
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Journal’s subject area:
Law;
Strategy and Management;
Management, Monitoring, Policy and Law;
Transportation;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
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 20XX to 20XX. The models’ predictive performances were validated against the observed capacity of ATL in 20XX.
Keywords:
Airport capacity prediction; Artificial neural networks; Long short-term memory (LSTM); Multilayer perceptron (MLP); Recurrent neural networks (RNN)

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