Investigating cargo capacity determinants in desertic airports using Bayesian Network (BN) analysis

Abstract

Since the inauguration of Kuwait International Airport in 1960, Kuwait's transition from an oil-based economy to a key aviation industry player in the region has been marked. This study investigates the cargo capacity determinants at Kuwait International Airport (KIA) from 2011 to 2021, employing Bayesian Network (BN) analysis. It dissects aviation data into two five-year periods to discern time-sensitive influences on the aviation network, focusing on scheduled and non-scheduled cargo flights. The study tests economic factors like GDP, population, jet fuel prices, inflation rates, and exchange rates. Results indicate that GDP and exchange rate are pivotal for cargo capacity throughout the decade. In the first five years, the exchange rate solely influenced cargo capacity, while in the latter half, both the exchange rate and inflation were significant factors. Additionally, cargo capacity was indirectly impacted by fuel prices through inflation. A sensitivity analysis reveals the varying impacts on cargo capacity: inflation had the highest sensitivity, causing a 36 % increase in cargo capacity for a 7.5–15.5 % inflation rate increase during 2016–2021. Exchange rates and fuel prices followed with 32 % and 14 % sensitivity, respectively. The study also notes that changes in GDP and exchange rates had relatively minor effects on cargo capacity in the first half of the decade. This abstract summarizes the research's aim, methods, and crucial findings, emphasizing their relevance in understanding cargo capacity dynamics at KIA amidst Kuwait's economic evolution.