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

    Authors

    X. D. Yan; S. Richards;X. G. Su

    Comments

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    Abbreviated Journal Title

    Accid. Anal. Prev.

    Keywords

    Grade crossing; Hierarchical tree-based regression; Annual crash; frequency; Vehicle-train crashes; Crossbucks; Stop signs; INTERFACES; COLLISIONS; EXAMPLE; Ergonomics; Public, Environmental & Occupational Health; Social; Sciences, Interdisciplinary; Transportation

    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. (C) 2009 Elsevier Ltd. All rights reserved.

    Journal Title

    Accident Analysis and Prevention

    Volume

    42

    Issue/Number

    1

    Publication Date

    1-1-2010

    Document Type

    Article

    Language

    English

    First Page

    64

    Last Page

    74

    WOS Identifier

    WOS:000272482100009

    ISSN

    0001-4575

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