Chen Jianxiong, Wang Jingtao, Li Fan, Zou Lin
Institute of Data Science and Behavior Science, Civil Aviation Flight University of China, Guanghan, 618307, China.
Sci Rep. 2025 Aug 19;15(1):30455. doi: 10.1038/s41598-025-13232-8.
Ensuring airspace safety is crucial for the efficient operation of aviation, especially at high-altitude airports where challenging weather and geographic conditions significantly increase operational complexity. With the widespread use of ADS-B data, flight monitoring has improved, providing detailed insights into aircraft trajectories. This study investigates the impact of weather variations on trajectory patterns in Terminal Maneuvering Area (TMA) at high-altitude airports during the rainy season, applying daily clustering analysis of ADS-B data combined with meteorological conditions, while further exploring the synergistic effects of other contributing factors such as air traffic control strategies and traffic density. After data preprocessing, a clustering algorithm was used to identify trajectory patterns and detect outlier trajectories. The results show that wind direction is a key factor influencing trajectory cluster changes, with significant shifts observed when wind direction approaches or exceeds certain thresholds. This research introduces a novel method for analyzing trajectory changes in the rainy season at high-altitude airports, offering valuable insights for optimizing flight path planning and enhancing airspace safety management under complex weather conditions.
确保空域安全对于航空的高效运行至关重要，特别是在高海拔机场，那里具有挑战性的天气和地理条件会显著增加运营复杂性。随着自动相关监视-广播（ADS-B）数据的广泛使用，飞行监控得到了改善，能够提供有关飞机轨迹的详细信息。本研究调查了雨季期间高海拔机场终端机动区（TMA）天气变化对轨迹模式的影响，应用ADS-B数据的每日聚类分析并结合气象条件，同时进一步探索空中交通管制策略和交通密度等其他影响因素的协同效应。经过数据预处理后，使用聚类算法识别轨迹模式并检测异常轨迹。结果表明，风向是影响轨迹聚类变化的关键因素，当风向接近或超过某些阈值时会观察到显著变化。本研究引入了一种分析高海拔机场雨季轨迹变化的新方法，为在复杂天气条件下优化飞行路径规划和加强空域安全管理提供了有价值的见解。
