Trivariate Copula for Modeling Barriers Crash Severity, Accounting for Policy Endogeneity

Abstract

Studies have been implemented in the literature to enhance the safety of traffic barriers by identification of the contributory factors to those crashes. However, almost all those past studies are subject to potential pitfalls of just answering the cause-and-effect question by traditional statistical methods, which fail to account for possible endogeneity. Modeling traffic barrier crash severity with traditional statistical methods might be biased as many parameters such as barrier’s types is endogenous to unseen factors including policy makers’ decisions in allocating those barriers. Not accounting for the possible endogeneity in the dataset and ignoring correlation between regressors’ error terms might result in biased or erroneous coefficients’ estimates. That is especially true in the presence of strong correlations across models’ error terms. Thus, this study was conducted to model barriers’ crash severity by taking into consideration the endogeneity and correlations across the models’ error terms. Here, the trivariate copula-based method was implemented to simultaneously model traffic barrier crash severity, shoulder width and barrier’s types, while accounting for interrelationships across the models’ error terms. The results provide strong evidence of correlations between the unseen factors to the selections of barrier’s types, shoulder width installation, and crash injury levels. For instance, we found in the presence of accounting for endogeneity and correlation between unseen factors, concrete traffic barrier type and higher shoulder width installation are negatively correlated with unseen factors contributing to severe barriers’ crashes That is despite the fact that the observed factors of those predictors were found to have a reverse impact on the severity of barriers’ crashes.