Modeling go-around occurrence using principal component logistic regression

Abstract

Abstract A go-around is an aborted approach of an aircraft. We model go-around occurrence using Principal Component Logistic Regression (PCLR). This entails go-around detection, feature engineering, and model estimation. As a case study, we consider John F. Kennedy (JFK) International Airport arrivals, and model go-around occurrence based on information available when the subject flight is five nautical miles from its landing runway threshold. The PCLR model is based on Principal Component Analysis (PCA) for analyzing data that suffer from multi-collinearity. The model provides a representation of the empirical relationship between go-around occurrence and Principal Components (PCs) covariates, which encompass flight approach features, aircraft characteristics, flight lead-trail spacing, surface operation, go-around clustering effect, airport and weather conditions. We use factor loading analysis to reveal the relationship between variables and the PCs they formed. Coefficient estimates of PCs are also transformed back to the scale of the original variables by matrix operations. A counterfactual analysis is employed to assess the importance of different features, and reveals that the stability of an approach, flight lead-trail spacing, departure traffic, and ceiling are the most salient factors affecting go-around occurrence.