Neural network with single hidden layer for air traffic volume prediction in uncontrolled airspace

• Autorzy: Paszyński Piotr , Gnyś Piotr

Identyfikatory

DOI

10.34808/npna-h426

• Języki publikacji: EN

Abstrakty

EN

This article presents a model enabling more efficient air traffic management achieved by better data use . Appropriate resource allocation is possible if it is based on a high quality air traffic volume forecast. The proposed approach is inspired by procedures used in flow management in air traffic control. Staff planning in controlled airspace is easier because almost all operations are communicated in the submitted flight plan. Short-term prediction of the number of operations in uncontrolled airspace is a much more challenging task. It is correlated with weather parameters and moreover, it naturally fluctuates throughout the day and the season. The relationship between General Aviation (GA) traffic volume and meteorological conditions were modeled using neural network. The obtained results confirm that it is possible to use the decision support system to plan the number of operational sectors. The described results open a scientific discussion about designing tools predicting air traffic volume in uncontrolled air space. The accuracy of the model can be improved by processing data from additional sources, but it is associated with a significant increase in the complexity of the solution.

Słowa kluczowe

EN

general aviation; airspace; neural networks

PL

lotnictwo; przestrzeń powietrzna; sieci neuronowe

• Wydawca: Politechnika Gdańska
• Czasopismo: TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk
• Rocznik: 2022
• Tom: Vol. 26, No 3
• Strony: 1--7
• Opis fizyczny: Bibliogr. 14 poz., rys., tab.

Twórcy

autor

Paszyński Piotr

• Department of Computer Science, Polish-Japanese Academy of Information Technology

• https://orcid.org/0000-0003-0563-6289

autor

Gnyś Piotr

• Department of Computer Science, Polish-Japanese Academy of Information Technology

• https://orcid.org/0000-0001-7266-9537

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).