Cross-National Statistical Analysis of Multi-Rotor Unmanned Aircraft Accidents: Causal Factors, Flight Phases, and Temporal Trends (2016–2022)

Abstract

Multi-rotor unmanned aircraft systems (UAS) are now pervasive, yet quantitative evidence on how and why they fail remains fragmented across heterogeneous national reporting systems. This study analyses 319 multi-rotor UAS occurrences (2016–2022) coded from three official sources: the U.S. SAFECOM system (122), the Australian Transport Safety Bureau database (159) and the U.K. Air Accidents Investigation Branch reports (38). Each occurrence was assigned a primary causal factor from a twelve-factor taxonomy and a flight phase (take-off, en route, landing). Analyses comprised distributional estimation with Wilson confidence intervals, chi-squared association tests with permutation p-values for sparse tables, Cochran–Armitage trend tests, and correspondence analysis. Human factors (23.2%, 95% CI 18.9–28.1) and data-link problems (21.0%, CI 16.9–25.8) dominated, and 74.6% of occurrences arose en route—a phase profile opposite to that of manned aviation. Cause and phase were significantly associated (permutation p < 0.001, Cramér’s V = 0.305): all take-off occurrences were technological, none human-related, and battery failures clustered in landing (42%). Causal profiles differed markedly between reporting systems (p < 0.0001, V = 0.338), cautioning against naive pooling, and data-link problems nearly tripled from 11.9% (2016–17) to 32.9% (2021–22). Findings inform operator training, link redundancy, battery management and reporting standardisation.