Flight Delay Prediction via Cross-Modality Adaptation of Large Language Models and Aircraft Trajectory Representation
This addresses inefficiencies in air traffic management for controllers, though it appears incremental as it adapts existing methods to a specific domain.
The paper tackles flight delay prediction by integrating aircraft trajectory data with textual aeronautical information using a lightweight large language model, achieving sub-minute prediction error to meet operational standards.
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.