With China’s low-altitude economy becoming a strategic emerging industry, the rapid growth of UAV applications demands higher efficiency in low-altitude airspace utilization and safety management. However, existing studies lack unified grid division standards and refined methods to evaluate capacity for complex urban low-altitude airspace. This study is devoted to developing a methodology for determining safe distances and assessing the throughput capacity of transport systems. The work is based on a multi-criteria assessment that takes into account four significant indicators. The application of the Pareto optimization principle made it possible to identify the most effective compromise solutions. A collision probability model with random UAV(Unmanned Aerial Vehicle) headings was proposed to determine safety separations, and a grid capacity simulation model with saturation judgment and convergence verification was established. The optimal grid granularity was identified as 20 m. Safety separations for DJI M300RTK, Mavic 3Pro, and Air 3S were 104 m, 86 m, and 47 m, respectively. Saturated capacity stabilized within 106–116 s, with stable values of 1.022, 0.961, and 1.023 drones/min for the three UAV models. The results of the study contain key conclusions about traffic capacity and suggest ways to optimize it. Conclusions: This study provides a theoretical framework for airspace resource optimization and UAV path planning, offering quantifiable benchmarks to evaluate and manage urban low-altitude airspace.

This study investigates the construction of a topological network for resilient low-altitude vertiports. Addressing the issue of excessive network redundancy often caused by maximizing algebraic connectivity in traditional topology optimization problems, we employ algebraic connectivity—a key spectral metric—as a measure of network topology resilience. The objective function employs normalized algebraic connectivity that simultaneously considers total network distance, achieving an effective trade-off between global fault tolerance and construction costs at the model level. To address this challenging combinatorial optimization problem, the Gray Wolf Optimizer (GWO) algorithm is mechanistically enhanced. Experiments demonstrate that the proposed method achieves superior performance in key metrics such as objective function value and total network distance, significantly enhancing network resilience while controlling construction costs. For the optimized network topology solutions, simulations of six failure modes for nodes and edges analyze the response characteristics of the vertiport network’s maximum connected subgraph proportion and global efficiency during the gradual removal of nodes and edges. Results demonstrate that the designed vertiport topology network exhibits robust resilience. It maintains high connectivity and global efficiency under both random attacks and degree-based targeted node attacks, showcasing strong engineering applicability.