相关实验视频
通过混合LSTM和变压器模型预测多个跑道配置,以实现高效的飞行操作
Xinglong Wang1, Linning Liu1,2, Songlin Li3
1Key Laboratory of Internet of Aircrafts, Civil Aviation University of China, Tianjin, 300300, China.
Scientific reports
|October 30, 2025
概括
本研究介绍了一种LSTM-Transformer模型,用于预测机场跑道配置,整合天气和飞行数据. 这种新的方法提高了空中交通管制员的操作效率和决策支持.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 航空航天工程 航空航天工程
背景情况:
- 由于天气和交通波动,用于跑道配置的传统手动决策是低效的.
- 这导致机场资源未得到充分利用,运营效率下降.
研究的目的:
- 开发一个多源数据融合框架,用于预测机场跑道配置.
- 提高民用航空中跑道配置决策的准确性和效率.
主要方法:
- 确定时间,气象和飞行操作数据作为关键预测指标.
- 开发了一种具有新型矢量编码的长短期记忆转换器 (LSTM-Transformer) 预测模型.
- 使用哈茨菲尔德-杰克逊亚特兰大国际机场的模拟数据集验证了模型.
主要成果:
- 采用矢量编码和端到端推理的LSTM-Transformer模型实现了最佳的预测准确性.
- 显著优于传统的LSTM和变压器模型.
- 在八种实验配置中表现出卓越的性能.
结论:
- 拟议的LSTM-变压器模型在跑道配置预测方面取得了重大进展.
- 为空中交通管制员提供实际的决策支持,增强机场运营.
- 显示为现实世界机场管理的广泛适用性.
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