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<p>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.</p>

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Impact of rainy season on approach trajectories in high-altitude airport terminal maneuvering area: a clustering analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月19日

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  • Jianxiong Chen,
  • Jingtao Wang,
  • Fan Li,
  • Lin Zou

摘要

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.