Temporal instability analysis of fatal commercial fishing vessel incidents: A correlated random-parameter model with heterogeneity in means
利用美国商业渔船事故数据库23年数据，构建均值异质性的相关随机参数Logit模型，分析致命事故影响因素的时间变化，发现天气和未发求救信号持续增加死亡风险，而EPIRB效果因船龄、天气等而异。
● Temporal analysis of fatal commercial fishing incidents is conducted using CFID data. ● Correlated random parameters logit model with heterogeneity in means is developed. ● Global and pairwise tests confirm strong temporal instability in covariate effects. ● Weather and absent mayday calls consistently increase fatality risk across multiple periods. ● EPIRB effects show heterogeneity shaped by vessel age, weather, and struck event. Fatal commercial fishing vessel incidents remain a critical global safety challenge, yet empirical understanding of their underlying determinants is limited by strong unobserved heterogeneity, correlated risk mechanisms, and temporal instability in covariate effects. This study examines how the influence of contributory factors has changed over time using 23 years of data from the U.S. Commercial Fishing Incident Database. A correlated random-parameter logit model with heterogeneity in means is developed to capture unobserved heterogeneity, parameter correlation, and context-dependent variability in risk effects. Temporal instability is assessed through both global and pairwise likelihood ratio tests across five sub-periods. The results demonstrate significant temporal non-stationarity. Weather-related conditions and the absence of a mayday call consistently increase fatality risk across multiple periods. In earlier years, capsizing events and human factors were more influential, reflecting the prominent role of vessel stability and crew performance in early-stage incident outcomes. The use of an EPIRB to send a mayday signal appears as a random parameter in several periods, and its effect varies with ship age, weather conditions, and struck events, indicating that latent operational and behavioral factors shape its effectiveness. Overall, the proposed model achieves superior statistical fitting, improved interpretability, and richer behavioral insights compared with fixed-parameter or standard random-parameter models. The findings highlight the need for time-sensitive and risk-adaptive safety interventions, with recommendations to strengthen communication reliability, emergency preparedness, and context-specific safety management in the commercial fishing sector.