Current flight procedure design primarily relies on expert experience, lacking a systematic approach to comprehensively balance safety, route simplification, and environmental impact. To address this challenge, this paper proposes a reinforcement-learning-based method that leverages carefully crafted reward engineering to achieve an optimized flight procedure design, effectively considering safety, route simplicity, and environmental friendliness. To further enhance performance by tackling the low sampling efficiency in the replay buffer, we introduce a multi-objective sampling strategy based on the Pareto frontier, integrated with the soft actor–critic (SAC) algorithm. Experimental results demonstrate that the proposed method generates executable flight procedures in the BlueSky open-source flight simulator, successfully balancing these three conflicting objectives, while achieving a 28.6% increase in convergence speed and a 4% improvement in comprehensive performance across safety, route simplification, and environmental impact compared to the baseline algorithm. This study offers an efficient and validated solution for the intelligent design of flight procedures.

In air traffic systems, aircraft trajectories between airports are monitored by the radar networking system forming dynamic air traffic flow. Accurate airport arrival flow prediction is significant in implementing large-scale intelligent air traffic flow management. Despite years of studies to improve prediction precision, most existing methods only focus on a single airport or simplify the traffic network as a static and simple graph. To mitigate this shortage, we propose a hybrid neural network method, called Dynamic Multi-graph Convolutional Spatial-Temporal Network (DMCSTN), to predict network-level airport arrival flow considering the multiple operation constraints and flight interactions among airport nodes. Specifically, in the spatial dimension, a novel dynamic multi-graph convolutional network is designed to adaptively model the heterogeneous and dynamic airport networks. It enables the proposed model to dynamically capture informative spatial correlations according to the input traffic features. In the temporal dimension, an enhanced self-attention mechanism is utilized to mine the arrival flow evolution patterns. Experiments on a real-world dataset from an ATFM system validate the effectiveness of DMCSTN for arrival flow forecasting tasks.

Current flight procedure design primarily relies on expert experience, lacking a systematic approach to comprehensively balance safety, route simplification, and environmental impact. To address this challenge, this paper proposes a reinforcement learning-based method that leverages carefully crafted reward engineering to achieve an optimized flight procedure design, effectively considering safety, route simplicity, and environmental friendliness. To further enhance performance by tackling the low sampling efficiency in the Replay Buffer, we introduce a multi-objective sampling strategy based on the Pareto frontier, integrated with the Soft Actor-Critic (SAC) algorithm. Experimental results demonstrate that the proposed method generates executable flight procedures in the BlueSky open-source flight simulator, successfully balancing these three conflicting objectives, while achieving a 28.6% increase in convergence speed and a 4% improvement in comprehensive performance across safety, route simplification, and environmental impact compared to the baseline algorithm. This study offers an efficient and validated solution for the intelligent design of flight procedures.