Exploring pre-crash gate violations behaviors of drivers at highway-rail grade crossings using a mixed multinomial logit model

Abstract

The highway-rail grade crossings (HRGCs) across the United States have been experiencing about 2500 crashes each year. Previous studies analyzed crash frequencies and fatalities; however, factors pertaining to drivers’ gate violation behaviors are little known. Also, applied methodologies for gate violation behaviors analysis did not consider their heterogeneity across regions. This study uses 20-year of crash data (1999–2018) to evaluate pre-crash drivers’ behaviors at HRGCs. A mixed multinomial logit model was developed to associate such behaviors with demographic factors, vehicle characteristics, temporal and environmental factors, as well as crossing-related factors. The study results indicated a high intra-class correlation coefficient which signifies the importance of including the random-effect parameter in the model. Further, the study found that male drivers are more likely to drive around the gate, while older drivers are more likely to stop and proceed before a train has passed. Furthermore, compared to trucks, all other vehicle types are more likely to drive around the gate. The influence of train speed, vehicle occupancy, visibility, among others, on drivers’ pre-crash behaviors, is also presented. Understanding the impact of these factors on pre-crash behaviors may assist in improving the motorist’s safety at the highway-rail grade crossings across the United States.

Keywords:

• Highway-rail grade crossings
• driver behaviors
• gate violations
• mixed multinomial logit model
• intra-class correlation

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data used in this study are available in the FRA Office of Safety Analysis. The following link can be used to access the data https://safetydata.fra.dot.gov/OfficeofSafety/Default.aspx

Additional information

Funding

This study received no funding from any source.