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Speeding has been acknowledged as a critical determinant in increasing therisk of crashes and their resulting injury severities. This paper demonstratesthat severe speeding-related crashes within the state of Pennsylvania have aspatial clustering trend, where four crash datasets are extracted from fourhotspot districts. Two log-likelihood ratio (LR) tests were conducted todetermine whether speeding-related crashes classified by hotspot districtsshould be modeled separately. The results suggest that separate modeling isnecessary. To capture the unobserved heterogeneity, four correlated randomparameter order models with heterogeneity in means are employed to explore thefactors contributing to crash severity involving at least one vehicle speeding.Overall, the findings exhibit that some indicators are observed to be spatialinstability, including hit pedestrian crashes, head-on crashes, speed limits,work zones, light conditions (dark), rural areas, older drivers, running stopsigns, and running red lights. Moreover, drunk driving, exceeding the speedlimit, and being unbelted present relative spatial stability in four districtmodels. This paper provides insights into preventing speeding-related crashesand potentially facilitating the development of corresponding crash injurymitigation policies.

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