# How the Pandemic Changes the Factors Influencing Aircraft Utilization: The Case of Korea
## 1 Introduction  
We verify a hypothesis that the Pandemic changed the importance of factors influencing aircraft utilization by airlines and a hypothesis that the Pandemic also changedthe importance of factors influencing the business models. This study explores how socio-economic factors affecting aircraft utilization shifted during the COVID-19 Pandemic, across airline business models. Aircraft utilization is a critical factor in generating airline revenue ([PERSON], [PERSON], [PERSON], [PERSON], & [PERSON] [1]), and understanding whether and how the importance of influencing factors changes based on the combination of the business model and the pre-/post-Pandemic periods is crucial for comprehending supply related decision-making by airlines. Some studies have focused on optimizing aircraft operation according to the fleet composition ([PERSON], [PERSON] et al. [2]). However, there has been no research on aircraft operation in relation to changes in external environment influencing the decision-making of airlines. In particular, understanding how the factors influencing aircraft utilization have changed due to external environmental changes since the Pandemic would provide significant implications for the air transport industry. As societal and economic uncertainty increases, airlines experience greater difficulty in establishing plans for aircraft utilization by route and realize the importance of comparative advantage strategies. However, time series analysis and similar methods, which assume the balance of supply and demand, are limited in establishing the plans and strategies for aircraft utilization in situations where uncertainty spreads rapidly and extensively. Earlier research ([PERSON] [3]; [PERSON] et al. [4]) focused on factors influencing aircraft utilization. Although previous studies have offered valuable insights into airline recovery and demand disruptions in the post-COVID-19 context, the specific structural and operational changes brought about by the Pandemic remain underexplored. We analyzed factors grouping variables that influence the strategy for aircraft utilization by classifying the business models into pre- and post-Pandemic. It is known that certain variables have a significant influence on air travel demand ([PERSON] [5]; [PERSON] et al. [6]). Aircraft utilization is influenced by various factors in a complex way, as it depends on air travel demand, which is sensitive to specific variables, and a supplier's decision-making regarding the external environment. It is necessary to review the traditional understanding of aircraft utilization, given the significant impact of the Pandemic on the aviation industry. We classified and reviewed the factors influencing aircraft utilization based on the Pandemic period and business models, using the SHAP methodology and the Random Forest model for exploratory data analysis. It is well known that short-term economic indicators, exchange rates, and similar factors influence both air travel demand factors (e.g., passengers, tariffs, convenience-related factors, etc.) and supply factors (e.g., traffic rights, infrastructure, slot-related factors, etc.) ([PERSON] et al. [7]; [PERSON] al. [8]). It was anticipated that such external factors would influence aircraft utilization even during the Pandemic. However, it was assumed that each indicator would affect aircraft utilization in levels and methods different from those pre-Pandemic. To address the heightened uncertainty and nonlinear dynamics introduced by the COVID-19 Pandemic, machine learning methods such as Random Forest and SHAP provide robust tools for modeling complex relationships and identifying key variables, complementing traditional econometric approaches. This study performed exploratory data analysis using SHAP analysis ([PERSON], & [PERSON] [9]; [PERSON] et al. [10]) and Random Forest analysis ([PERSON] [11]), rather than confirmatory data analysis of aircraft utilization, causality, or correlation of independent variables, to identify external influencing factors and verify changes.