Integrating Raster Modeling with Collision Risk Analysis to Evaluate the Capacity of Urban Low-Altitude Airspace Systems

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.