ABSTRACT: Objective: Previous studies on crash modeling at highway–rail grade crossings were aimed at exploring the factors that are likely to increase the crash frequencies at highway–rail grade crossings. In recent years, modeling driver's injury severity at highway–rail grade crossings has received interest. Because there were substantial differences among different weather conditions for driver's injury severity, this study attempts to explore the impact of weather influence on driver injury at highway–rail grade crossing. Method: Utilizing the most recent 10 years (2002–2011) of highway–rail grade crossing accident data, this study applied a mixed logit model to explore the determinants of driver injury severity under different weather conditions at highway–rail grade crossing. Results: Analysis results indicate that drivers' injury severity at highway–rail grade crossings is strongly different for different weather conditions. It was found that the factors significantly impacting driver injury severity at highway–rail grade crossings include motor vehicle speed, train speed, driver's age, gender, area type, lighting condition, highway pavement, traffic volume, and time of day. Conclusions: The findings of this study indicate that crashes are more prevalent if vehicle drivers are driving at high speed or the oncoming trains are high speed. Hence, a reduction in speed limit during inclement weather conditions could be particularly effective in moderating injury severity, allowing more reaction time for last-minute maneuvering and braking in moments before impacts. In addition, inclement weather-related crashes were more likely to occur in open areas and highway–rail grade crossings without pavement and lighting. Paved highway–rail grade crossings with installation of lights could be particularly effective in moderating injury severity.
All Science Journal Classification (ASJC) codes
- Safety Research
- Public Health, Environmental and Occupational Health
- highway–rail grade crossings
- injury severity
- mixed logit model
- weather influence