用轨迹数据检测飞机单发滑行，助力减排评估
On the Detection of Aircraft Single Engine Taxi using Deep Learning Models
- 基于飞行轨迹特征，用深度学习识别单发滑行
- 仅用公开系统数据即可准确识别，无需机载密钥
- 适合航空环保研究者与空管数据分析人员
航空业对全球运输至关重要，但地面运行（如滑行）的碳排放压力日益增大。单发滑行（SET）作为一种提升燃油效率和可持续性的技术，其效益评估受限于专用数据稀缺，通常仅运营商可获取。本文提出一种新深度学习方法，利用空客A320的私有快速访问记录（QAR）数据，将滑入阶段的地面运动标注为SET或常规滑行，同时仅依赖公开监视系统（如ADS-B或地面雷达）可获取的轨迹特征。结果表明，仅通过地面运动模式即可推断出SET行为，为未来使用非专有数据源的研究铺平道路。该方法展示了深度学习在提升SET检测精度方面的潜力，有助于更全面地评估其环境影响。
原文摘要 · Abstract (English)
The aviation industry is vital for global transportation but faces increasing pressure to reduce its environmental footprint, particularly CO2 emissions from ground operations such as taxiing. Single Engine Taxiing (SET) has emerged as a promising technique to enhance fuel efficiency and sustainability. However, evaluating SET's benefits is hindered by the limited availability of SET-specific data, typically accessible only to aircraft operators. In this paper, we present a novel deep learning approach to detect SET operations using ground trajectory data. Our method involves using proprietary Quick Access Recorder (QAR) data of A320 flights to label ground movements as SET or conventional taxiing during taxi-in operations, while using only trajectory features equivalent to those available in open-source surveillance systems such as Automatic Dependent Surveillance-Broadcast (ADS-B) or ground radar. This demonstrates that SET can be inferred from ground movement patterns, paving the way for future work with non-proprietary data sources. Our results highlight the potential of deep learning to improve SET detection and support more comprehensive environmental impact assessments.
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。