Utilizing Regression Algorithms for ATS Route Forecasts

Abstract

The air traf ic forecast methodology by FAA assumes that the route network for lights will not change. The route forecasts are, therefore, derived from the origin-destination demand by assigning the origin-destination passengers to routes based on the percentage distribution of passengers. Assuming the static nature of routes, our study employed traf ic feedback results from European airspace design evaluation tool combined with econometric modeling to forecast air traf ic on selected ATS routes in the ASEAN region. A case study involving evolution scenarios of the economy from 2004 to 2019 was used to show the importance of regression modeling and form an ATS route forecast procedure in order to illustrate current capabilities. The results of the study provide valuable insights on the ASEAN ATS route network and the future direction for efforts to prevent structure imbalances by increasing capacity or reducing demand.