The structured and networked planning of urban low-altitude air routes is an effective approach to alleviating the shortage of low-altitude airspace resources and ensuring operational safety. Considering the actual requirements of route deployment and traffic flow in urban low-altitude logistics scenarios, this paper proposes a low-altitude “trunk-feeder” route network planning method based on combined clustering, with the layout of takeoff and landing sites and actual operational traffic volumes as input conditions. Initially, the optimal orientation of trunk route was determined using principal component analysis (PCA) and numerical optimization, respectively. Meanwhile, the quality of the clustering results was evaluated using a Gaussian distribution model and a geometric similarity model, and four differentiated route network clustering schemes were constructed. Secondly, a comprehensive evaluation system encompassing the total route network length, construction costs, and route intersection points was established to assess the performance of the four clustering methods. Finally, the feasibility and effectiveness of the method were validated using Shaoxing City as a case study. The results show that, compared with the initial planning scheme, the total construction cost of the route network decreases by 21.39% and the number of route intersections decreases by 34.41% under the high-density scenario. Under the medium-density scenario, the total construction cost decreases by 36.44% and the number of route intersections decreases by 16.92%. The findings indicate that the proposed “principal component analysis-geometric similarity” combined clustering method adopted in this paper can significantly improve the safety and cost-effectiveness of low-altitude operations, and can provide theoretical foundations and technical support for the scientific planning of urban low-altitude traffic systems.

With the growing demand for unmanned aerial vehicle (UAV) operations in urban low-altitude airspace, the structure of vertiport approach and departure airspace will become a critical factor in capacity limitation and safe operations. To address the core bottleneck of high-density operations in future Urban Air Mobility (UAM), this research investigates an optimal design method for UAV approach and departure airspace structures at vertiports. A structured multi-layer funnel-shaped hybrid airspace model is constructed, and an airspace efficiency optimization function is designed. Genetic algorithms are employed to optimize the airspace parameters. The airspace optimization design was calculated within urban obstacle spaces. Through comparative analysis with six typical airspace configurations in terms of operational efficiency, and under different obstacle environments and UAV operational intervals, it is found that the designed airspace demonstrates significant balance in overall performance, while the design algorithm maintains stable effectiveness across various conditions. The results indicate that strategies relying simply on enlarging airspace dimensions to enhance capacity involve clear performance trade-offs. In contrast, a relatively compact funnel-shaped structure achieves coordinated optimization across multiple metrics by eliminating radial gradients and simplifying flight paths. The proposed genetic algorithm-based optimal design method for UAV approach and departure structures in urban obstacle spaces can effectively identify the optimal parameter combinations, ensuring the safety of UAV operations in urban environments and improving the operational efficiency of vertiport airspace.