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.
翻译:无人驾驶航空器系统(无人驾驶航空器系统)作业的挑战之一是操作对地面人员风险最小的无人驾驶飞机,这项研究的目的是通过纳入人口密度的局部变化,界定和衡量人口风险等风险。与以往的研究不同,我们使用高分辨率事实上的人口数据,而不是居民人口数据,以反映人口分布的局部特征。此外,我们根据空气空间管理中的人口风险,分析缓解措施的影响。我们利用人口风险和可接受的安全水平,设定了有限的空气空间。对首尔研究区进行的设想分析,韩国提供了一套关于受限制空气空间的突发性差异的更丰富的调查结果。在白天,商业区周围有许多受限制的空域,但居住区周围很少。此外,我们观察到基于居住人口和事实人口分布的人口风险而限制空气空间的差别。这些调查结果证实,在评估和减轻与无人驾驶空间系统作业相关的人口风险时,必须准确考虑人口密度。对人口密度进行精确估计,同时评估人口分布的多参数。