Using hierarchical tree-based regression model to predict train–vehicle crashes at passive highway-rail grade crossings

Abstract

This paper applies a nonparametric statistical method, hierarchical tree-based regression (HTBR), to explore train–vehicle crash prediction and analysis at passive highway-rail grade crossings. Using the Federal Railroad Administration (FRA) database, the research focuses on 27 years of train–vehicle accident history in the United States from 1980 through 2006. A cross-sectional statistical analysis based on HTBR is conducted for public highway-rail grade crossings that were upgraded from crossbuck-only to stop signs without involvement of other traffic-control devices or automatic countermeasures. In this study, HTBR models are developed to predict train–vehicle crash frequencies for passive grade crossings controlled by crossbucks only and crossbucks combined with stop signs respectively, and assess how the crash frequencies change after the stop-sign treatment is applied at the crossbuck-only-controlled crossings. The study results indicate that stop-sign treatment is an effective engineering countermeasure to improve safety at the passive grade crossings. Decision makers and traffic engineers can use the HTBR models to examine train–vehicle crash frequency at passive crossings and assess the potential effectiveness of stop-sign treatment based on specific attributes of the given crossings.