用大模型预测航班轨迹，把飞行点变语言词来学时空规律。
Large Language Models for Single-Step and Multi-Step Flight Trajectory Prediction
- 将飞行数据转为语言令牌，用大模型学习轨迹模式。
- 在单步和多步预测上均超越传统方法，LLaMA-3.1表现最优。
- 适合对精度要求高的航空轨迹研究，但实时性待优化。
飞行轨迹预测是航空领域关键的时间序列任务。尽管深度学习方法已展现出显著潜力，但大语言模型（LLMs）在此领域的应用仍较少被探索。本研究首次将大语言模型用于飞行轨迹预测，将其重构为语言建模问题。具体而言，从ADS-B飞行数据中提取表示飞机位置与状态的特征，构建基于提示的语料库，将轨迹航点转化为语言令牌。该数据集用于微调大模型，使其学习复杂的时空模式以实现精准预测。全面实验表明，相较于传统方法，大模型在单步和多步预测上均取得显著性能提升，其中LLaMA-3.1模型整体准确率最高。然而，大模型较高的推理延迟对其在实时场景中的应用构成挑战，凸显了在此方向进一步研究的必要性。
原文摘要 · Abstract (English)
Flight trajectory prediction is a critical time series task in aviation. While deep learning methods have shown significant promise, the application of large language models (LLMs) to this domain remains underexplored. This study pioneers the use of LLMs for flight trajectory prediction by reframing it as a language modeling problem. Specifically, We extract features representing the aircraft's position and status from ADS-B flight data to construct a prompt-based dataset, where trajectory waypoints are converted into language tokens. The dataset is then employed to fine-tune LLMs, enabling them to learn complex spatiotemporal patterns for accurate predictions. Comprehensive experiments demonstrate that LLMs achieve notable performance improvements in both single-step and multi-step predictions compared to traditional methods, with LLaMA-3.1 model achieving the highest overall accuracy. However, the high inference latency of LLMs poses a challenge for real-time applications, underscoring the need for further research in this promising direction.
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