This paper investigates the determinants of injury severities in single-bicycle and bicycle-motor vehicle crashes by estimating correlated random parameter ordered probit models with heterogeneity in the means. This modeling approach extends the frontier of the conventional random parameters by capturing the likely correlations among the random parameters and relaxing the fixed nature of the means for the mixing distributions of the random parameters. The empirical analysis was based on a publicly available database of police crash reports in the UK using information from crashes occurred on urban and rural carriageways of Scotland between 2010 and 2018. The model estimation results show that various crash, road, location, weather, and driver or cyclist characteristics affect the injury severities for both categories of crashes. The heterogeneity-in-the-means structure allowed the incorporation of a distinct layer of heterogeneity in the statistical analysis, as the means of the random parameters were found to vary as a function of crash or driver/cyclist characteristics. The correlation of the random parameters enabled the identification of complex interactive effects of the unobserved characteristics captured by road, location and environmental factors. Overall, the determinants of injury severities are found to vary between single-bicycle and bicycle-motor vehicle crashes, whereas a number of common determinants are associated with different effects in terms of magnitude and sign. The comparison of the proposed methodological framework with less sophisticated ordered probit models demonstrated its relative benefits in terms of statistical fit, explanatory power and forecasting accuracy as well as its potential to capture unobserved heterogeneity to a greater extent.

本文通过估计均值具有异质性的相关随机参数有序概率模型，研究了单车和自行车-机动车辆碰撞事故中伤害严重程度的决定因素。这种建模方法通过捕获随机参数之间可能的相关性并放宽随机参数混合分布的均值的固定性质，扩展了传统随机参数的前沿。实证分析基于英国警方事故报告的公开数据库，使用了 2010 年至 2018 年苏格兰城乡道路上发生的事故信息。模型估计结果表明，各种事故、道路、位置、天气和驾驶员或骑自行车者的特征会影响两类碰撞的伤害严重程度。均值结构的异质性允许在统计分析中加入不同的异质性层，因为随机参数的均值被发现作为碰撞或驾驶员/骑自行车者特征的函数而变化。随机参数的相关性能够识别道路、位置和环境因素捕获的未观察到的特征的复杂交互作用。总体而言，单次自行车和自行车-机动车辆碰撞事故的伤害严重程度的决定因素有所不同，而许多常见的决定因素与程度和符号方面的不同影响有关。所提出的方法框架与不太复杂的有序概率模型的比较证明了其在统计拟合方面的相对优势，