# How the Pandemic Changes the Factors Influencing Aircraft Utilization: The Case of Korea
## 3 Methodology  
First, this research conducted a basic analysis of how the domestic route utilization rate has changed since the Pandemic. The analysis used the explanatory variable as an independent variable and the number of seats supplied domestically as the dependent variable to reliably explore the factors influencing changes in aircraft utilization rate by route. The number of seats supplied refers to the total number of seats airlines or flights can offer during a specific period. It can also be defined as the number of seats available for sale on a specific route, determined by the operational schedule of airlines. The number of seats supplied is an important index for the operation of airlines and seems to be essential for balancing supply and demand. Next, this research intended to derive the importance of factors for the SHapley Additive exPlanations (SHAP) and the Random Forest analyses, which are methodologies for grouping factor analysis. Finally, the research sought to compare the influences on the international and domestic routes pre- and post-Pandemic. The research landscape, as illustrated in Figure 1, is presented as follows. In terms of the dataset, the number of seats supplied was derived from the Tower-Log data of Korea, and a ratio (%) of the number of seats supplied for international routes was calculated based on each plane number by refining the number of seats supplied by date from 2018 to 2023. The following indexes were set as the socio-economic variables: exchange rate (KRW/USD, KRW/RMB, KRW/100 JPY, KRW/EUR); stock price indexes (KOSPI and KOSDAQ indexes); and business indexes (leading index, coincident index, lagging index). SHAP is a methodology that analyzes the importance and influence of each factor in a group, based on the Shapley Value in game theory. The Shapley Value calculates the fair contribution of each participant and is defined as a value obtained by adding up the contribution of each characteristic in all possible combinations ([PERSON], [PERSON] et al. [44]). Figure 1: Research landscape. The present research aimed to derive the importance of factors influencing aircraft utilization demand by air route with the SHAP methodology. ([PERSON], & [PERSON] [45]) The LightGBM ensemble model ([PERSON], [PERSON] et al. [46]), which forms the basis of the SHAP methodology, used the number of seats supplied, relating to aircraft utilization by air route, as the input variable. It was designed to be a research model that evaluates performance by incorporating stock price, exchange rate and other socio-economic variables to be learning data. Figure 2 illustrates an example of how the SHAP methodology can be applied. \[\phi_{i}=\sum_{S\subseteq F\backslash i}\frac{|S|!(|F|-|S|-1)!}{|F|!}[f_{S \cup i}(x_{S\cup i})-f_{S}(x_{S})]\]  
\(\phi_{i}\) = Shapley value for \(i\)-th data;  
\(F\) = Complete set;  
\(S\) = All remaining subsets of the complete set excluding the \(i\)-th data;  
\(f_{S\cup i}(x_{S\cup i})\) = Total contribution including the \(i\)-th data;  
\(f_{S}(x_{S})\) = Contribution of the remaining subsets excluding the \(i\)-th data. The Random Forest is an ensemble learning-based model composed of multiple decision trees used for classifying and regressing specific events. It is primarily applied to solve classification and regression problems ([PERSON] [47]). Each tree is built using random samples of data. One key advantage of the Random Forest is the Permutation Importance function, which measures variable importance by observing changes in model performance when variable values are randomly shuffled. If performance decreases significantly, the variable is considered important ([PERSON], [PERSON], [PERSON], [PERSON], & [PERSON] [48]). The predictive value of the Random Forest model is expressed in the following formula ([PERSON], [PERSON] [11]). Figure 3 presents a representative structure of the Random Forest regression model.