The construction industry stands out for its higher incidence of accidents in comparison to other sectors. A causal analysis of the accidents is necessary for effective prevention. In this study, we propose a data-driven causal analysis to find significant factors of fatal construction accidents. We collected 14,318 cases of structured and text data of construction accidents from the Construction Safety Management Integrated Information (CSI). For the variables in the collected dataset, we first analyze their patterns and correlations with fatal construction accidents by statistical analysis. In addition, machine learning algorithms are employed to develop a classification model for fatal accidents. The integration of SHAP (SHapley Additive exPlanations) allows for the identification of root causes driving fatal incidents. As a result, the outcome reveals the significant factors and keywords wielding notable influence over fatal accidents within construction contexts.

PURPOSES : This study empirically analyzes the determinants of fatal accidents based on raw data on traffic accidents occurring in Chungnam in 2020. METHODS : Regression models based on theoretical arguments for fatal traffic accidents are estimated using a binomial logit model. RESULTS : The prediction model for fatal accidents is affected by the degree of urbanization of the region, month and day of the accident, type of accident, and type of law violation. In addition, speeding or illegal U-turns among law violations appear more likely to result in fatal accidents. The road surface conditions at the time of the accident do not show a significant difference in the probability of fatality among traffic accidents. However, the probability of a fatal accident is rather lower in case of a snowy road; this is plausible, as drivers tend to drive more carefully in bad weather conditions. CONCLUSIONS : Among traffic accidents, fatal accidents appear to be affected by the time and place of the accident, type of accident, and weather conditions at the time of the accident. These analysis results suggest policy implications for reducing fatal accidents and can be used as a basis for establishing related policies.