Data-driven risk analysis of unmanned aircraft system operations considering spatiotemporal characteristics of population distribution

TL;DR

This study introduces a method to assess UAS operation risks by incorporating high-resolution, spatiotemporal population data, enabling more accurate and dynamic airspace management strategies.

Contribution

It uses de facto population data instead of residential data to better reflect real-time population distribution for risk analysis.

Findings

Restricted airspace varies between commercial and residential areas during daytime.

Population risk-based restrictions differ when using de facto versus residential populations.

Precise population density estimation is crucial for accurate risk assessment.

Abstract

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.