An Intelligent Assisted Decision-Making Framework for Area Control Tasks

The continuous growth of air traffic has significantly increased the workload of air traffic controllers (ATCOs), posing challenges to the efficient and safe operation of area control. This paper proposes an intelligent decision-making framework for en-route sector operations based on an analysis of the primary tasks of area controllers. Firstly, a sector altitude profile planning (APP) model is developed using multi-objective mixed-integer programming, with linearisation techniques applied to enhance solving efficiency. The multi-objective model is addressed using a hierarchical sequencing method. Secondly, a multi-aircraft conflict resolution (MACR) model is developed based on a partially observable Markov game (POMG) framework, utilising a discrete action space. The altitude profile generated by the planning model is input into the conflict resolution model as a pre-planning scenario to enable constrained, sector-based, conflict-free operations. Additionally, invalid action masking techniques are applied within the model to enhance its effectiveness and ensure operational feasibility. To address scalability issues, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is enhanced with a dynamic scaling mechanism. Scenario samples are constructed for validation at the 3D level using real airspace structures and traffic flow data. Experimental results demonstrate that the planning model reduces conflicts in 86.6% of the scenario samples and optimises flight altitudes in 65.6% of the samples. The trained multi-aircraft conflict resolution model resolves 80.52% of conflicts in the test scenarios. The average control task completion rate in joint simulation experiments reaches 97.16%. In addition, we investigate the model performance under different traffic scales. The proposed framework provides guidance for the development of intelligent decision-making systems for en-route sector operations.