A Multi-Scale Airspace Sectorization Framework Based on QTM and HDQN

Airspace sectorization is an effective approach to balance increasing air traffic demand and limited airspace resources. It directly impacts the efficiency and safety of airspace operations. Traditional airspace sectorization methods are often based on fixed spatial scales, failing to fully consider the complexity and interrelationships of airspace partitioning across different spatial scales. This makes it challenging to balance large-scale airspace management with local dynamic demands. To address this issue, a multi-scale airspace sectorization framework is proposed, which integrates a multi-resolution grid system and a hierarchical deep reinforcement learning algorithm. First, an airspace grid model is constructed using Quaternary Triangular Mesh (QTM), along with an efficient workload calculation model based on grid encoding. Then, a sector optimization model is developed using hierarchical deep Q-network (HDQN), where the top-level and bottom-level policies coordinate to perform global airspace control area partitioning and local sectorization. The use of multi-resolution grids enhances the interaction efficiency between the reinforcement learning model and the environment. Prior knowledge is also incorporated to enhance training efficiency and effectiveness. Experimental results demonstrate that the proposed framework outperforms traditional models in both computational efficiency and workload balancing performance.