Aviation is one of the most global industries that fosters economic growth, creates job opportunities, and also facilitates international trade and tourism. If we can model a country's air traffic flow and economic indicators ahead of time, we may be able to use it as a key decision-making tool for the management and operation process. This paper provides a broad view of Air transport, freight (million ton-km) and Air transport, passengers carried (million) data of worlds top ten aviation passenger owner countries with a particular focus on data modeling results. This study proposes a modeling method that employs both fractional calculus and Multi-Deep Assessment Methodology (M-DAM) techniques. For the application, air passengers carried, air freight data between 2000-2020 for top ten countries which are USA, China, Ireland, India, UK, Japan, Russia, Turkey, Germany and Brazil that have the worlds busiest airports were chosen. As a result, the highest modeling error was discovered to be India's air transport freight factor expressed as a percentage of 0,391642 and Germanys air passenger factor expressed as a percentage of 0,10926. The novel methodology proposed in this paper has a high accuracy in air flow indicators modeling and is being used for the first time with aviation data.
Anahtar Kelimeler: Aviation data, Mathematical Modeling, Fractional Calculus, Deep Assessment Methodology