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:超速已被认为是增加事故风险及其导致的伤害严重性的重要决定因素。本文表明,宾夕法尼亚州内严重的超速相关事故具有空间聚集趋势,其中从四个热点区域提取了四个事故数据集。进行了两个寻味比率(LR)测试以确定是否应分别对热点区域分类的超速相关事故进行建模。结果表明需要进行分开建模。为了捕捉未知的异质性,采用了具有均值异质性的四个相关随机参数顺序模型,以探索至少涉及一辆超速车辆的事故严重程度的因素。总体而言,研究结果显示了一些指标具有空间不稳定性,包括撞击行人事故、正面碰撞事故、限速、工作区、光线条件(黑暗)、农村地区、老年驾驶员、闯红灯和闯红灯。此外,酒后驾驶、超速和未系安全带行为在四个区域模型中呈相对空间稳定性。本文为预防超速相关事故提供了启示,并有望促进相应的事故伤害缓解政策的制定。