Multimodal Feature Fusion with Ensemble Learning for Automatic Allocation Scheme Matching in General Aviation Tower Control

The increasing complexity of general aviation flight training poses significant challenges to tower controllers, who must reconcile safety separation standards with training efficiency under diverse and often unexpected conflict scenarios. This study proposes a BERT-BiLSTM-XGBoost multimodal feature fusion ensemble learning model that automatically matches optimal allocation schemes based on predicted conflict characteristics, thereby providing real-time decision support for tower controllers. The framework operates in three stages. First, a BERT model extracts high-level semantic features from textual data—including aircraft status descriptions, controller characteristics, pilot profiles, and airspace constraints—by leveraging bidirectional contextual encoding. Second, a Bidirectional Long Short-Term Memory (BiLSTM) network captures temporal dynamic features from sequential aircraft operation data. Third, the extracted semantic and temporal feature vectors are fused and fed into an XGBoost classifier, which performs the final allocation scheme matching. The XGBoost algorithm additionally provides feature importance scores, supporting the traceability requirements of tower control decision-making. Experimental results demonstrate that the proposed model achieves a matching accuracy of 97.2%, substantially outperforming baseline methods including Naive Bayes, linear SVM, and LightGBM. Moreover, the model reduces controller decision-making time by 75–80% in simulated tower control tests, validating its practical utility as an effective decision-support tool for general aviation tower operations.