Analysis of U.S. freight-train derailment severity using zero-truncated negative binomial regression and quantile regression

Abstract

Derailments are the most common type of freight-train accidents in the United States. Derailments cause damage to infrastructure and rolling stock, disrupt services, and may cause casualties and harm the environment. Accordingly, derailment analysis and prevention has long been a high priority in the rail industry and government. Despite the low probability of a train derailment, the potential for severe consequences justify the need to better understand the factors influencing train derailment severity. In this paper, a zero-truncated negative binomial (ZTNB) regression model is developed to estimate the conditional mean of train derailment severity. Recognizing that the mean is not the only statistic describing data distribution, a quantile regression (QR) model is also developed to estimate derailment severity at different quantiles. The two regression models together provide a better understanding of train derailment severity distribution. Results of this work can be used to estimate train derailment severity under various operational conditions and by different accident causes. This research is intended to provide insights regarding development of cost-efficient train safety policies.