Journal of Contemporary Issues in Business and Government

Abstract

In recent days, it is crucial to calculate the volume of air traffic in line with the economic indicators of the countries and to make the necessary plans to increase efficiency and productivity according to this density type. Although commercial airline traffic is known to be associated with economic growth indicators, the relationship between them cannot be expressed numerically. The purpose of this research is to form an Artificial Neural Network (ANN) -based model to reveal and estimate its relationship with commercial airline traffic, taking as reference economical growth parameters like; the country's Gross National Product (GDP), import and export data. In the ANN model created in the study, gross domestic product, import and export volume values are used for the estimation of commercial airline traffic. Simulation results were analyzed using correlation coefficient (R) determination coefficient (R ^ 2) and mean absolute percent error (MAPE) evaluation methods. It has been seen that the developed model is a successful model that can be used in the prediction of commercial airline traffic.