To address the trade-off between safety levels and operational efficiency in the Bay Area, this study proposes a Multi-Stand Grouped Operations method based on deep reinforcement learning under the consideration of the safety domain. The full-process operation of aircraft within the Bay Area is analyzed to identify key operational spots. Safety domains are then established based on path conflicts arising from aircraft movements and safety conflicts caused by minimum separation distances and wake vortex effects. These domains are used to define corresponding safe operating spaces and construct an optimized operational model for the Bay Area. A multi-agent reinforcement learning algorithm is employed to solve the model, deriving an optimized stand allocation plan and Multi-Stand Grouped Operations strategy. To evaluate the effectiveness of the optimization, real flight data from the northwest Bay Area of Terminal 2 at Guangzhou Baiyun Airport are used for validation. Compared to the original stand allocation scheme, the optimized stand allocation and Multi-Stand Grouped Operations strategy reduce aircraft delay times by 62.45%, demonstrating that the proposed model effectively enhances operational efficiency in the Bay Area.

Dynamic evaluation of airport terminal capacity is critical for efficient operations, yet it remains challenging due to the complex interplay of spatial and temporal factors. Existing approaches often handle spatial connectivity and temporal fluctuations separately, limiting their predictive power under rapidly changing conditions. Here the ST-GTNet (Spatio-Temporal Graph Transformer Network) is presented, a novel deep learning model that integrates Graph Convolutional Networks (GCNs) with a Transformer architecture to simultaneously capture spatial interdependencies among airport gates and temporal patterns in operational data. To ensure interpretability and efficiency, a feature selection mechanism guided by XGBoost and SHAP (Shapley Additive Explanations) is incorporated to identify the most influential features. This unified spatio-temporal framework overcomes the limitations of conventional methods by learning spatial and temporal dynamics jointly, thereby enhancing the accuracy of dynamic capacity predictions. In a case study at a large international airport with a U-shaped corridor terminal, the ST-GTNet delivered robust and reliable capacity forecasts, validating its effectiveness in a complex real-world scenario. These findings highlight the potential of the ST-GTNet as a powerful tool for dynamic airport capacity evaluation and management.