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.

翻译：超速已被公认为增加事故风险及其伤害严重程度的关键因素。本文表明，宾夕法尼亚州内严重超速相关事故呈现空间聚类趋势，从四个热点区域提取了四组事故数据集。通过两个对数似然比检验，判定按热点区域分类的超速事故是否应分别建模。结果表明，分别建模是必要的。为捕捉未观测到的异质性，采用四个具有均值异质性的相关随机参数排序模型，分析至少涉及一辆超速车辆的事故严重程度影响因素。总体而言，研究发现部分指标存在空间不稳定性，包括：碰撞行人事故、正面碰撞事故、限速值、施工区域、光照条件（黑暗）、乡村区域、高龄驾驶员、闯停车标志以及闯红灯。此外，醉酒驾驶、超速行驶及未系安全带在四个区域模型中呈现相对空间稳定性。本文为预防超速相关事故及制定相应事故伤害缓解政策提供了重要参考依据。