Post-Recession Recovery in US Air Transport: Forecasting with a Temporal Causal Model - Maja Ozmec-Ban; Ružica Škurla Babić; Vladimir Vasić; Dajana Bartulović (Aerospace)
This study examines the causal relationship between air transport demand and socio-economic indicators, with a focus on post-recession recovery dynamics. Using monthly data from 1990 to 2022, the research explores causality between air transport indicators and socio-economic indicators, based on which a temporal causal model for forecasting is created. A temporal causal model, integrating air transport and socio-economic metrics, is introduced to improve passenger air transport forecasting. Forecasted values from the temporal causal model and an ARIMA model were compared with actual realized traffic to assess the quality and accuracy of the models. Based on the comparison, the temporal causal model enables instant analysis of circumstances and causes in dynamic environments, as well as reliable forecasting of upcoming intermediate periods. This research contributes to airlines and other air transport stakeholders by delivering a reliable short-term forecasting tool for informed decision-making and sustainable growth.