考虑总体分布时空特征的无人机系统作战数据驱动风险分析
Data-driven risk analysis of unmanned aircraft system operations considering spatiotemporal characteristics of population distribution
摘要：
无人驾驶飞机系统（UAS）操作的挑战之一是在对地面人员的风险最小的情况下操作无人驾驶飞机。本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。与以往的研究不同，我们使用高分辨率的事实人口数据而不是居住人口数据来反映总体分布的时空特征。此外，我们分析了在空域管理的背景下，人口风险的缓解措施的影响。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。在韩国首尔的研究区域的情景分析提供了一个更丰富的研究结果在限制空域的时空差异。在白天，商业区周围有许多受限制的空域，但住宅区周围很少。此外，我们观察到限制空域之间的差异，基于人口风险来自居民人口和实际人口。这些发现证实了在评估和减轻与无人机系统操作相关的人口风险时准确考虑人口密度的重要性。敏感性分析还揭示了在用多个参数值的组合估计群体风险时，需要精确地估计群体密度。所提出的方法捕捉时空特征的总体分布时，评估与无人机相关的人口风险。
英文摘要：
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.
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