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碰撞风险及其所致伤害严重性的关键决定因素。本文证明，宾夕法尼亚州内严重的超速相关碰撞存在空间聚集趋势，并从四个热点区域提取了四个碰撞数据集。本研究进行了两次对数似然比检验，以确定按热点区域分类的超速相关碰撞是否应分别建模，结果表明分别建模是必要的。为捕捉未观测到的异质性，本文采用四个具有均值异质性的相关随机参数有序模型，探究涉及至少一辆车辆超速的碰撞严重性影响因素。总体而言，研究发现部分指标表现出空间不稳定性，包括行人碰撞、正面碰撞、限速、施工区域、光照条件（黑暗）、农村地区、老年驾驶员、闯停车标志和闯红灯。此外，酒驾、超速和未系安全带在四个区域模型中表现出相对的空间稳定性。本文为预防超速相关碰撞提供了见解，并可能促进相应碰撞伤害缓解政策的制定。