Flight delay prediction has become a key focus in air traffic management (ATM), as delays reflect inefficiencies in the system. This paper proposes LLM4Delay, a large language model (LLM)-based framework for predicting flight delays from the perspective of air traffic controllers monitoring aircraft after they enter the terminal maneuvering area (TMA). LLM4Delay is designed to integrate textual aeronautical information, including flight data, weather reports, and aerodrome notices, together with multiple trajectories that model airspace conditions, forming a comprehensive delay-relevant context. By jointly leveraging comprehensive textual and trajectory contexts via instance-level projection, an effective cross-modality adaptation strategy that maps multiple instance-level trajectory representations into the language modality, the framework improves delay prediction accuracy. LLM4Delay demonstrates superior performance compared to existing ATM frameworks and prior time-series-to-language adaptation methods. This highlights the complementary roles of textual and trajectory data while leveraging knowledge from both the pretrained trajectory encoder and the pretrained LLM. The proposed framework enables continuous updates to predictions as new information becomes available, indicating potential operational relevance.

翻译：航班延误预测已成为空中交通管理（ATM）领域的关键问题，因为延误反映了系统运行效率的低下。本文提出LLM4Delay——一种基于大语言模型（LLM）的框架，从空管监控飞机进入终端机动区（TMA）后的视角进行航班延误预测。LLM4Delay旨在整合文本航空信息（包括飞行数据、气象报告和机场通告）与多种用于建模空域状况的轨迹数据，形成全面的延误相关上下文。通过实例级投影联合利用全面的文本与轨迹上下文——一种将多个实例级轨迹表征映射到语言模态的有效跨模态自适应策略，该框架提升了延误预测精度。相比现有ATM框架及时间序列到语言的先验自适应方法，LLM4Delay展现出卓越性能。这凸显了文本与轨迹数据的互补作用，同时充分利用了预训练轨迹编码器与预训练大语言模型的知识。所提框架能够随着新信息的获取持续更新预测结果，表明了其潜在的操作实用性。