考虑因素异质影响的直升机事故严重性分析

The study explores the contributing factors affecting the injury severity of helicopter accidents based on the modeling exercises that capture unobserved heterogeneity. The data include 1241 helicopter accident records collected from the National Transportation Safety Board (NTSB) database, categorizing the accident information into temporal, flight, pilot, helicopter, and meteorological factors. Afterward, the study uses Chi-square tests to examine the statistical differences of factors' probability distributions across injury severities (property-damage-only, minor injury, and serious injury or fatal). Several mixed Logit models are developed to model the injury severity, which set the random parameters with diverse priori densities. Through a meticulous comparison, the optimal model is selected based on data-fitting metrics. Further, the estimates of this model are used to analyze the effects of the contributing factors on injury severities and their heterogenous effects. The results indicate that unobserved heterogeneity can be found in modeling the injury severity of helicopter accidents. The mixed Logit model with independent normal and multivariate normal random parameters outperformances others in data-fitting. Among the factors, encountering IMC, loss of control in flight, fuel issues, low altitude operation or external loading (including contact with powerlines or trees), maneuvering or operation, approaching, elder pilots, pilots with helicopter instrument qualification, and accidents involving fire or explosion significantly increase the probability of serious injuries or fatalities. For the factor's heterogenous effects, the coefficients of weekend flight, elder pilots, and the use of 3-point restraints can be modeled as random parameters.