Research on GMM-Based Approach Pattern Recognition and Quantitative Analysis Algorithm for Flight in Airport Terminal Areas

With the rapid growth in demand for air transportation, the efficiency evaluation of aircraft approaches in airport terminal areas has become an increasingly critical focus. This study aims to develop an efficiency evaluation algorithm for aircraft approaches in airport terminal areas based on flight big data. Initially, the study constructs a multidimensional evaluation index system that includes fuel efficiency, time efficiency, and distance efficiency using Quick Access Recorder (QAR) data. Based on this index system, a detailed analysis of Boeing 777 flights at three typical international airports—Beijing Capital, Guangzhou Baiyun, and London Heathrow—is conducted. Subsequently, a hierarchical evaluation model and a quantitative scoring algorithm based on the Gaussian Mixture Model (GMM) are innovatively proposed. This method demonstrates advantages in capturing the diversity and complexity of aircraft approach patterns and has been validated with approach phase data for Boeing 737 flights at several international and domestic airports. The findings reveal that, despite having a higher overall fuel consumption, Heathrow Airport exhibits higher efficiency in descent profile management in the Boeing 777 analysis. In contrast, Beijing Capital Airport, with lower total fuel consumption, shows room for optimization in its descent profile due to longer level flight phases. The application of the GMM model further confirms this finding and provides a quantitative basis for evaluating efficiency across different airports. This study offers new perspectives and methodologies for evaluating and optimizing operational efficiency in airport terminal areas, with significant practical implications for improving air traffic management.