论文标题
无人飞机系统操作的数据驱动风险分析考虑了人口分布的时空特征
Data-driven risk analysis of unmanned aircraft system operations considering spatiotemporal characteristics of population distribution
论文作者
论文摘要
无人飞机系统（UAS）行动的挑战之一是操作一架无人飞机，对地面上的人风险很小。这项研究的目的是通过纳入人口密度的时空变化来定义和衡量人口风险等风险。与以前的研究不同，我们使用高分辨率的人口数据，而不是居民人口数据来反映人口分布的时空特征。此外，我们在空域管理背景下根据人口风险分析了缓解措施的影响。我们通过使用人口风险和可接受的安全水平来设置限制空域。韩国首尔研究区域的方案分析提供了有关限制空域时空差异的更丰富的发现。在白天，商业区周围有许多限制空间，但在居民区周围很少。此外，我们观察到基于居民人口和事实上人口的人口风险限制空域之间的差异。这些发现证实了在评估和减轻与UAS操作相关的人口风险时准确考虑人口密度的重要性。灵敏度分析还表明，当估计种群风险与多个参数值的组合时，需要精确估计人口密度。提出的方法在评估与UAS相关的人口风险时捕获了人口分布的时空特征。
One of the challenges of Unmanned Aircraft System (UAS) operations is to operate an unmanned aircraft with minimal risk to people on the ground. The purpose of this study is to define and measure such risks as population risk, by incorporating spatiotemporal changes in population density. Unlike previous studies, we use high-resolution de facto population data instead of residential population data to reflect the spatiotemporal characteristics of population distribution. Furthermore, we analyze the impact of mitigation measures based on population risk in the context of airspace management. We set a restricted airspace by using population risk and an acceptable level of safety. Scenario analysis of the study area in Seoul, South Korea provides a richer set of findings regarding spatiotemporal differences in restricted airspace. During the daytime, there are many restricted airspaces around commercial areas, but few around residential areas. Additionally, we observe the difference between restricting airspace based on population risk derived from the residential population and from the de facto population. These findings confirm the importance of accurately considering population density when assessing and mitigating the population risk associated with UAS operations. Sensitivity analysis also reveals the need to precisely estimate population density when estimating population risk with combinations of multiple parameter values. The proposed approach captures spatiotemporal characteristics of population distribution when assessing the population risk associated with UAS.
