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  • A Crash Severity-Based Black Spot Identification Model

    Author(s)
    Da Costa, Stephen
    Qu, Xiaobo
    Parajuli, Partha Mani
    Griffith University Author(s)
    Da Costa, Stephen
    Qu, Xiaobo
    Year published
    2015
    Metadata
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    Abstract
    The objective of this research is to develop a new black spot model incorporating the severity of crashes with high consistency. Crash history from 2005 to 2009 of the Ipswich Motorway, in Australia, was used to identify black spots by using simple ranking and empirical Bayes approach. The results of these analyses were then compared via two consistency tests. These tests revealed that for the simple ranking method and the empirical Bayes method, the crash type analysis was more consistent than the crash frequency analysis and the collective risk analysis. Further research into the validity of the proposed indicator would ...
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    The objective of this research is to develop a new black spot model incorporating the severity of crashes with high consistency. Crash history from 2005 to 2009 of the Ipswich Motorway, in Australia, was used to identify black spots by using simple ranking and empirical Bayes approach. The results of these analyses were then compared via two consistency tests. These tests revealed that for the simple ranking method and the empirical Bayes method, the crash type analysis was more consistent than the crash frequency analysis and the collective risk analysis. Further research into the validity of the proposed indicator would be recommended due to its incorporation of the crash severity and the high consistency it demonstrated in this study, both of which are of great benefit from a practical point of view.
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    Journal Title
    Journal of Transportation Safety & Security
    Volume
    7
    Issue
    3
    DOI
    https://doi.org/10.1080/19439962.2014.911230
    Subject
    Civil engineering
    Transport engineering
    Transportation, logistics and supply chains
    Publication URI
    http://hdl.handle.net/10072/66168
    Collection
    • Journal articles

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