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    Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling

    245820.pdf (1.586Mb)
    Access Status
    Open access
    Authors
    Xia, Jianhong (Cecilia)
    Murphy, A.
    Date
    2016
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Murphy, A. and Xia, J. 2016. Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling. International Journal of Crashworthiness. 21 (6): pp. 614-626.
    Source Title
    International Journal of Crashworthiness
    DOI
    10.1080/13588265.2016.1209823
    ISSN
    1754-2111
    School
    Department of Spatial Sciences
    URI
    http://hdl.handle.net/20.500.11937/44505
    Collection
    • Curtin Research Publications
    Abstract

    Driving along any rural road within Western Australia involves some level of uncertainty about encountering an animal whether it is wildlife, farm stock or domestic. This level of uncertainty can vary depending on factors such as the surrounding land use, water source, geometry of the road, speed limits and signage. This paper aims to model the risk of animal–vehicle crashes (AVCs) on a segmented highway. A hierarchical Bayesian model involving multivariate Poisson lognormal regression is used in establishing the relationship between AVCs and the contributing factors. Findings of this study show that farming on both sides of a road, a mixture of farming and forest roadside vegetation and roadside vegetation have significant positive effect on AVCs, while speed limits and horizontal curves indicate a negative effect. AVCs consist of both spatial- and segment-specific contributions, even though the spatial random error does not dominate model variability. Segment 15 is identified as the highest risk segment and its nearby segments also exhibit high risk.

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