Speeding has been acknowledged as a critical determinant in increasing the risk of crashes and their resulting injury severities. This paper employs Global Moran’s I coefficient and local Getis – Ord G* indexes to systematically account for the spatial distribution feature of speeding-related crashes, study the global spatial pattern of speeding-related crashes, and identify severe crash cluster districts. The findings demonstrate that severe speeding-related crashes within the state of Pennsylvania have a spatial clustering trend, where four crash datasets are extracted from four hotspot districts. Two log-likelihood ratio (LR) tests were conducted to determine whether speeding-related crashes classified by hotspot districts should be modeled separately. The results suggest that separate modeling is necessary. To capture the unobserved heterogeneity, four correlated random parameter order models with heterogeneity in means are employed to explore the factors contributing to crash severity involving at least one vehicle speeding.


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    Titel :

    Investigating the spatial heterogeneity of factors influencing speeding-related crash severities using correlated random parameter order models with heterogeneity-in-means


    Weitere Titelangaben:

    R. YUAN ET AL.
    TRANSPORTATION LETTERS


    Beteiligte:
    Yuan, Renteng (Autor:in) / Ding, Shengxuan (Autor:in) / Fang, Zhiheng (Autor:in) / Gu, Xin (Autor:in) / Xiang, Qiaojun (Autor:in)

    Erschienen in:

    Transportation Letters ; 16 , 9 ; 989-1001


    Erscheinungsdatum :

    20.10.2024


    Format / Umfang :

    13 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch