大语言模型用于单步与多步飞行轨迹预测
Large Language Models for Single-Step and Multi-Step Flight Trajectory Prediction
AI总结：
本研究率先将飞行轨迹预测重新构建为语言建模问题，通过提取ADS-B数据特征构建提示数据集并微调大语言模型，实验证明LLMs在单步和多步预测中显著优于传统方法，但高推理延迟仍是实时应用的挑战。
摘要：
飞行轨迹预测是航空领域一项关键的时间序列任务。尽管深度学习方法已展现出巨大潜力，但大语言模型（LLMs）在该领域的应用仍待深入探索。本研究率先将LLMs应用于飞行轨迹预测，将其重新构建为一个语言建模问题。具体而言，我们从ADS-B飞行数据中提取代表飞机位置和状态的特征，构建基于提示的数据集，其中轨迹航点被转换为语言标记。随后，利用该数据集对LLMs进行微调，使其能够学习复杂的时空模式以实现准确预测。综合实验表明，与传统方法相比，LLMs在单步和多步预测中均取得了显著的性能提升，其中LLaMA-3.1模型实现了最高的总体准确率。然而，LLMs较高的推理延迟给实时应用带来了挑战，凸显了在这一极具前景的方向上进一步研究的必要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
