Uncertainty-Aware Scenario-Adaptive Feature Ensemble Learning Framework for Runway Occupancy Time Prediction

Runway occupancy time (ROT) is a key indicator affecting runway capacity, arrival spacing control, and airport surface operational safety. ROT is jointly influenced by multi-source heterogeneous factors, including aircraft performance, speed profiles, taxi distance, runway exits, meteorological conditions, and airport operational states. However, existing methods still face challenges in simultaneously modeling feature–expert adaptation, sample-level expert reliability, and predictive uncertainty. To address these issues, this paper proposes an uncertainty-aware scenario-adaptive feature ensemble learning framework, termed U-SAFE. The framework achieves expert-specific adaptation of multi-source features through shared-specific feature assignment and adaptive feature gating, constructs four heterogeneous experts including CatBoost, TabNet, TCN, and NGBoost, and designs a dynamic Softmax fusion mechanism based on predictive uncertainty, historical reliability, and scenario matching degree. A residual calibration module is further introduced to correct fusion bias and provide uncertainty estimates. Experiments were conducted on 15,030 landing samples collected from Xi’an Xianyang, Chengdu Tianfu, and Guangzhou Baiyun airports. The results show that U-SAFE achieves MAE, RMSE, and MAPE values of 2.97 s, 3.92 s, and 5.96%, respectively, on the overall test set, outperforming conventional models, including individual base learners and traditional stacking. Airport-specific testing, cross-airport generalization, scenario-based robustness analysis, and ablation studies further demonstrate stable predictive performance under the evaluated settings and verify the effectiveness of the key modules. Uncertainty analysis shows that U-SAFE obtains an NLL of 2.78, a PICP of 94.6%, and an MPIW of 15.6 s, indicating that it can provide reliable and compact prediction intervals while maintaining high predictive accuracy. These results demonstrate that U-SAFE can effectively support accurate ROT prediction and uncertainty-aware decision-making in complex airport operating environments. Nevertheless, the present findings should be interpreted within the scope of the three-airport dataset and the February 2025–May 2026 observation window. Complete airport topology and human-related operational factors are not explicitly represented, while prospective temporal validation and deployment-level inference latency remain to be further evaluated.