Evaluation of the impact of Covid-19 on air traffic volume in Turkish airspace using artificial neural networks and time series

Autor: Nurullah Gultekin, Sibel Acik Kemaloglu
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-023-33784-x
Popis: Abstract In early 2020, the aviation sector was one of the business lines adversely affected by the Covid 19 outbreak that affected the whole world. As a result, some countries imposed travel restrictions. Following these restrictions, air traffic density has decreased significantly worldwide. Since air traffic density directly affects almost all operations in air transportation, analyzing these data is very essential. For this purpose, SARIMA models, one of the linear time series models, and multilayer perceptron model (MLP), one of the artificial neural network methods suitable for nonlinear modeling, were applied to the air traffic data regarding Turkish airspace between 2010 and 2019, and the actual air traffic density was compared with the forecasts obtained from these analyses. It is considered that the results of this study are essential for organizations conducting aviation operations to take into consideration while doing future planning.
Databáze: Directory of Open Access Journals
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