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.

复飞是飞机中止的进近。我们使用主成分逻辑回归 (PCLR) 对复飞发生进行建模。这需要复飞检测、特征工程和模型估计。作为案例研究，我们考虑了约翰·肯尼迪 (JFK) 国际机场的到达情况，并根据目标航班距着陆跑道入口 5 海里时的可用信息对复飞发生进行建模。PCLR 模型基于主成分分析 (PCA)，用于分析存在多重共线性的数据。该模型提供了复飞发生与主成分 (PC) 协变量之间的经验关系的表示，其中包括飞行进近特征、飞机特性、飞行前线间距、地面操作、复飞集群效应、机场和天气状况。我们使用因子载荷分析来揭示变量与其形成的 PC 之间的关系。PC 的系数估计也通过矩阵运算转换回原始变量的尺度。反事实分析被用来评估不同特征的重要性，并揭示进近稳定性、飞行前道间距、离场交通和天花板是影响复飞发生的最显着因素。