Flight delay prediction has become a key focus in air traffic management, as delays highlight inefficiencies that impact overall network performance. This paper presents a lightweight large language model-based multimodal flight delay prediction, formulated from the perspective of air traffic controllers monitoring aircraft delay after entering the terminal area. The approach integrates trajectory representations with textual aeronautical information, including flight information, weather reports, and aerodrome notices, by adapting trajectory data into the language modality to capture airspace conditions. The experiments show that the model consistently achieves sub-minute prediction error by effectively leveraging contextual information related to the sources of delay, fulfilling the operational standard for minute-level precision. The framework demonstrates that linguistic understanding, when combined with cross-modality adaptation of trajectory data, enhances delay prediction. Moreover, the approach shows practicality and potential scalability for real-world operations, supporting real-time updates that refine predictions upon receiving new operational information.

翻译：航班延误预测已成为空中交通管理的关键焦点，因为延误突显了影响整体网络运行效率的不足。本文提出了一种基于轻量化大语言模型的多模态航班延误预测方法，该方法从空中交通管制员监控航空器进入终端区后延误情况的视角出发，通过将轨迹数据适配至语言模态以捕捉空域状态，将轨迹表征与文本化航空信息（包括航班信息、天气报告和机场通告）相结合。实验表明，该模型通过有效利用与延误源相关的上下文信息，持续实现亚分钟级预测误差，满足了分钟级精度的运行标准。该框架证明，语言理解与轨迹数据的跨模态适应相结合，能够提升延误预测性能。此外，该方法在实际运行中展现出实用性和潜在的可扩展性，支持实时更新机制，可在接收新运行信息时优化预测结果。