A study on an enhanced method for joint extraction of control intent and navigation information for smart city airport operations.

To address the critical challenge of bidirectional dependency modeling in air traffic control (ATC) communications - a gap identified in state-of-the-art joint extraction models like CII - this study proposes CII-BERT, an enhanced framework integrating BERT's contextual representation with task-specific DNN layers. Motivated by the need to reduce aviation risks caused by instruction misinterpretation, we define "flight safety" through two quantifiable metrics: conflict probability reduction and instruction error rate. Our approach uniquely employs continuous pre-training with domain-adapted augmentation strategies (synonym substitution and random block exchange), enabling robust learning of ATC-specific syntax and terminologies. Evaluated on a diverse control directive dataset (6,000 + samples across 7 operational scenarios), CII-BERT achieves 99.44% intent recognition accuracy and 99.23% information extraction accuracy - outperforming CII by 1.71 and 1.58%, respectively. Crucially, error analysis confirms a 37% reduction in high-risk edge cases (e.g., ambiguous altitude commands) compared to baseline models. This demonstrates CII-BERT's potential to enhance smart airport operations not only in safety but also in controller workload optimization and runway efficiency.