Aircraft-Cargo Separation Rate-Driven Ground Risk–Cost Optimization for Urban Logistics UAVs

The surge in demand for urban logistics has positioned unmanned aerial vehicles (UAVs) as a vital solution for improving last mile delivery efficiency in complex urban environments. To overcome the ground safety bottleneck in the large-scale application of logistics UAVs, this paper establishes a full-chain framework of “risk characteristics–quantitative model–risk cost” that proposes an innovative dual-state risk–cost optimization model based on aircraft-cargo separation probability. The study quantifies the separation probability using the Weibull distribution, corrects the risk value assessment deviation under the traditional single fall assumption, and establishes a risk probability model for the separation state by combining the dynamic characteristic differences between the UAV body and cargo, enabling the generation of rasterized risk maps by integrating spatial parameters, including population density and shelter coefficients, to implement risk–cost conversion and support airspace management. Case verification reveals that in areas with different land uses, there are significant differences in the ground risk values and risk costs of logistics UAVs. Additionally, introducing the aircraft-cargo separation rate improves risk assessment accuracy by 57%, and the ground risk in the aircraft-cargo integrated state is 4.31 times higher than in the separated state. This study provides core theoretical support for the safe operation and route decision-making involving logistics UAVs in complex urban scenarios.