Title

Using Hierarchical Tree-Based Regression Model To Predict Train-Vehicle Crashes At Passive Highway-Rail Grade Crossings

Keywords

Annual crash frequency; Crossbucks; Grade crossing; Hierarchical tree-based regression; Stop signs; Vehicle-train crashes

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. © 2009 Elsevier Ltd. All rights reserved.

Publication Date

1-1-2010

Publication Title

Accident Analysis and Prevention

Volume

42

Issue

1

Number of Pages

64-74

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.aap.2009.07.003

Socpus ID

71549153767 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/71549153767

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