An operational spatiotemporal prediction framework for sector congestion in air traffic management

En-route sector congestion poses a critical challenge to air traffic safety, operational efficiency, and resource-efficient airspace operation in high-density airspace. Because sector operations are spatially networked, locally emerging congestion may propagate through adjacent or upstream grid areas, leading to large-scale delays, increased fuel consumption, and elevated controller workload. This study proposes an operational spatiotemporal prediction framework for 15-minute-ahead sector congestion warning. First, processed grid-level operational states are converted into three congestion levels using K-means++ clustering based on six direct observable operational-state indicators. Second, a hybrid GNN-BiLSTM-Cross-Attention model predicts future congestion levels and local T2 hotspots from historical 5-minute grid-window sequences, without using future observed features. Third, SHAP/SHEP-style post-hoc grouped attribution is applied after inference to distinguish external triggering risk from congestion-propagation risk and to decompose external contributions from weather, restricted areas, controller workload, and wake intensity. A case study using 2023 processed grid-level operational data from the ZGGGAR15 high-altitude en-route sector achieves TEST@VAL Weighted F1 = 0.9773, L2-F1 = 0.9602, Precision = 0.9545, Recall = 0.9660, and AP = 0.9767 for rare high-congestion warning.The results indicate that the proposed framework provides accurate and spatially interpretable predictions with potential decision-support value for proactive air traffic flow management.