Analysis of train derailment severity using vine copula quantile regression modeling

Abstract

Abstract Although there is a low frequency of train derailments, they have been a major concern due to their high consequences justifying the need to critically examine the severity of train derailments. Derailments may result in injury, loss of life and property, interruption of services and damage of the environment. Most derailment severity models have utilized point estimation approaches which focus on the central tendency of derailment severity outcomes. However, this approach is not reliable given the high variation in derailment severity. Thus, it is imperative to take into consideration the entire severity distribution by examining other statistics including conditional quantiles. Furthermore, derailment data has been found to exhibit tail dependence, skewness and non-normality of the marginal distributions and joint distribution of the variables. Therefore, it is not appropriate to examine their interrelationships using conventional correlation analysis. For these reasons, this paper employs vine copula quantile regression model, an interval estimation approach, to predict conditional mean and quantiles of derailment severity outcomes. This novel methodology automatically tackles prominent issues in classical quantile regression including quantile crossing at various levels and interactions between covariates. Vine copulas, which are multivariate copulas constructed hierarchically from bivariate copulas as building blocks, permit the modeling of the complex dependences between the variables. The vine copula quantile regression model was found to offer better accuracy for analyzing derailment severity at various confidence levels compared to the classical quantile regression approach. The findings provide greater comprehension of the influence of the covariates on train derailment severity.