  Speeding has been acknowledged as a critical determinant in increasing the
risk of crashes and their resulting injury severities. This paper demonstrates
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.
Overall, the findings exhibit that some indicators are observed to be spatial
instability, including hit pedestrian crashes, head-on crashes, speed limits,
work zones, light conditions (dark), rural areas, older drivers, running stop
signs, and running red lights. Moreover, drunk driving, exceeding the speed
limit, and being unbelted present relative spatial stability in four district
models. This paper provides insights into preventing speeding-related crashes
and potentially facilitating the development of corresponding crash injury
mitigation policies.
