The rapid expansion of urban air mobility operations demands adaptive airspace management approaches that transcend traditional static sectorization. This paper proposes an integrated framework for dynamic low-altitude airspace partitioning and management strategy optimization by fusing graph neural networks with spatial cognitive science. Urban low-altitude airspace is modeled as an attributed weighted directed graph encoding spatial adjacency, traffic flow coupling, and environmental constraints. A spatial cognitive constraint system is developed, quantifying boundary discriminability, shape complexity, and hierarchical cognitive load as differentiable optimization terms. A cognition-enhanced graph attention network architecture with spatiotemporal feature aggregation is designed to generate end-to-end partition assignments, with an explicit cognitive attention modulation mechanism that steers message-passing toward perceptually coherent neighborhoods. A reinforcement learning module, formalized as a Markov decision process with clearly defined state, action, and reward spaces, fine-tunes the partition and management strategy outputs through proximal policy optimization. The joint training scheme co-optimizes sector boundaries with capacity allocation, priority sequencing, and conflict alert policies through multi-task learning with curriculum scheduling. Experimental results on both synthetic simulation data and real-world ADS-B trajectory data from the OpenSky Network demonstrate that the proposed method achieves an airspace utilization rate of 81.2 ± 1.4% and reduces the flight conflict rate to 3.1 ± 0.5 per 100 flight-hours, representing a 46.6% improvement over vanilla graph attention baselines and outperforming advanced spatiotemporal GNN baselines including STGCN and DCRNN. Statistical significance is confirmed via Wilcoxon signed-rank tests ( p < 0.01). A small-scale human-in-the-loop study with eight certified air traffic management researchers points to a roughly 37% reduction in operator decision response time, a signal we read as preliminary rather than conclusive given the modest sample. Scalability tests indicate that model inference alone stays feasible up to 10,000-node airspace graphs within the reconfiguration window, though we are careful to note that the complete operational pipeline has not yet been integrated end-to-end.
Zhang et al. (Mon,) studied this question.