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    Investigating articulated heavy-vehicle crashes in western Australia using a spatial approach

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    Authors
    Gudes, O.
    Varhol, Richard
    Sun, Q.
    Meuleners, Lynn
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Gudes, O. and Varhol, R. and Sun, Q. and Meuleners, L. 2017. Investigating articulated heavy-vehicle crashes in western Australia using a spatial approach. Accident Analysis and Prevention. 106: pp. 243-253.
    Source Title
    Accident Analysis and Prevention
    DOI
    10.1016/j.aap.2017.05.026
    ISSN
    0001-4575
    School
    School of Public Health
    URI
    http://hdl.handle.net/20.500.11937/54640
    Collection
    • Curtin Research Publications
    Abstract

    Recent developments in Western Australia's economy including widespread traffic congestion as well as road safety issues are increasingly becoming prominent. Previous studies relied on traditional statistical methods to investigate patterns and characteristics of motor vehicle crashes. Although useful, statistical analysis alone is incapable of providing a spatial context and is therefore unable to associate existing crash characteristics with a spatial distribution. Aims To identify concentrations or “hotspots” of articulated heavy vehicle crashes in WA between the years 2001–2013, by using a spatial analysis approach. Methods Spatial modelling and spatio-temporal analytical methods such as Emerging Hotspots were used to identify emerging hotspots on specific roads in Western Australia using the Integrated Road Information System (IRIS). Results The results suggest that the majority of articulated heavy vehicles crashes occurred in the vicinity or within the Perth metropolitan area. Based on spatial-temporal trend analyses, our findings highlight some regions that are emerging as areas of interest. Discussion This study was one of the first attempts to adopt a spatial analysis approach in studying heavy-vehicle crashes in Western Australia. Applying spatial methodologies to road safety data has the potential of obtaining previously undiscovered insights, which can be extended further, and provide future avenues to research in this field.

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