用概率模型预测60分钟内航班天气改道，提升空管效率与安全。
Handling Weather Uncertainty in Air Traffic Prediction through an Inverse Approach
- 构建3维高斯混合模型，融合气象与航班数据预测改道轨迹。
- 预测误差低于2%，在60分钟预报窗口表现稳定可靠。
- 通过可解释性分析揭示关键影响因素，助力优化空管策略。
恶劣天气，尤其是对流现象，给空中交通管理带来重大挑战，常需实时调整航路以保障安全与效率。本研究提出一种三维高斯混合模型，用于预测长达60分钟的航班航线变化，融合高分辨率气象数据（包括对流天气图和风场数据）及航班运行记录。该模型采用概率方法捕捉不确定性，准确预测航路的经度、纬度与高度变化。大量评估显示，在不同预测时长下均保持低于0.02的平均绝对百分比误差，验证了其高精度与可扩展性。结合Vanilla Gradient等可解释性技术，揭示了各特征对预测结果的贡献，为改进应对天气扰动的空管策略提供了依据。
原文摘要 · Abstract (English)
Adverse weather conditions, particularly convective phenomena, pose significant challenges to Air Traffic Management, often requiring real-time rerouting decisions that impact efficiency and safety. This study introduces a 3-D Gaussian Mixture Model to predict long lead-time flight trajectory changes, incorporating comprehensive weather and traffic data. Utilizing high-resolution meteorological datasets, including convective weather maps and wind data, alongside traffic records, the model demonstrates robust performance in forecasting reroutes up to 60 minutes. The novel 3-D Gaussian Mixture Model framework employs a probabilistic approach to capture uncertainty while providing accurate forecasts of altitude, latitude, and longitude. Extensive evaluation revealed a Mean Absolute Percentage Error below 0.02 across varying lead times, highlighting the model's accuracy and scalability. By integrating explainability techniques such as the Vanilla Gradient algorithm, the study provides insights into feature contributions, showing that they contribute to improving Air Traffic Management strategies to mitigate weather-induced disruptions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。