Using hierarchical tree-based regression model to predict train-vehicle crashes at passive highway-rail grade crossings.
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.
Yan
Xuedong
X
Southeastern Transportation Center (STC), The University of Tennessee, Suite 309, Conference Center Building, Knoxville, TN 37996-4133, USA. xyan1@utk.edu
Richards
Stephen
S
Su
Xiaogang
X
D000059:Accidents / Q000706:statistics & numerical data*; D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data*; D000465:Algorithms; D003430:Cross-Sectional Studies; D004779:Environment Design / Q000592:standards; D006801:Humans; D011890:Railroads; D012044:Regression Analysis; D012449:Safety; D017751:Safety Management; D012926:Social Control, Formal; D018709:Statistics, Nonparametric