Analysis of spatial patterns can provide an efficient answer to the problem of locating global or local patterns of the spatial distribution of traffic crashes. Approximately 21% of vehicle crashes in the United States occur due to inclement weather, costing the U.S. economy more than $217.5 billion yearly. One major road winter maintenance activity is snowplow and spreading salt on the road surface to improve the driving condition. The potential for rear-end collisions or conflicts between motorists and Snowplow Trucks (SPTs) is a major safety concern. This study extensively applies Ripley’s K-function, the global Moran’s I measure and the Getis–Ord Gi* function along with Kernel Density Estimation and Network-based Kernel Density Estimations with the aim of analysing snowplow-involved crash hotspots in the state of Wyoming. The positive Moran’s I, the high z-scores and the small p values indicate that Snowplow truck crashes were spatially clustered.


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    Title :

    Identifying snowplow truck crash hotspots and spatial analysis of crashes in the mountainous roadways


    Additional title:

    I. Reza et al.
    International Journal of Crashworthiness


    Contributors:

    Published in:

    Publication date :

    2025-01-02


    Size :

    13 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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