Injury-Severity Analysis of Highway-Railroad Grade Crossing Crashes Based on a Random Parameters Logit Model With Heterogeneity in Means and Variances - Lan Wu; Jiayu Zhou; Haigen Min; Gen Li (Advances in Civil Engineering)
Traffic accidents involving trucks at highway-railroad grade crossings (HRGCs) often result in catastrophic outcomes. It is crucial for transportation agencies to identify the underlying causes and develop strategies to mitigate the severity of these accidents. This study identifies the significant factors contributing to the crash injury severity involving truck and automobile drivers at HRGCs. A comprehensive dataset of 14,415 crashes collected by the Federal Railroad Administration (FRA) between 2012 and 2021 was analyzed. The multinomial logit (MNL) model, the random parameters logit model, and the random parameters logit model with heterogeneity in means and variances were constructed, and the one with the best fit and accuracy to calculate the average marginal effect of each variable, thereby quantifying their impact on injury severity at HRGCs. The findings demonstrate that the random parameters logit model with heterogeneity in means and variances enhanced the fitting and accuracy of the model over both the standard one without considering heterogeneity and the binary logit model. This improved model better captures and accounts for unobserved heterogeneity in the data. There are clear distinctions between auto and truck crashes at HRGCs, specifically regarding the factors that impact crash severity and how those factors affect the severity level of crashes. The research highlights that these divergences primarily arise from dissimilarities in the characteristics of drivers and vehicles between autos and trucks. For instance, the injury severity for auto drivers can be significantly affected by features such as stopping and then proceeding and stopping at the crossing due to malfunctions. On the other hand, factors like passive control, open spaces, the presence of barriers, and warning devices emerge as crucial factors influencing truck driver injuries. This study proposes safety improvements tailored to the specific needs of each type of crash to improve HRGC safety and decrease accident severity.