Crash frequency prediction models have been an important subject of safety research that unveils a relationship between crash occurrences and their influencing factors. Recently, the hourly-based refined-scale crash frequency analysis becomes attractive since it holds the benefits of introducing time-varying explanatory information (e.g. traffic volume and operating speed). However, crash frequency data with short time intervals possess the analytical issues of excessive zeros and unobserved heterogeneity. In this study, a marginalized random effects hurdle negative binomial (MREHNB) model was, for the first time, developed in which the hurdle modelling structure handles the excessive zeros issue and site-specific random effect terms capture the factors associated with unobserved heterogeneity. Moreover, the marginalized inference approach was introduced here to obtain the marginal mean inference for the overall population rather than subject-specific estimations. Empirical analyses were conducted based on data from the Shanghai urban expressway system, and the MREHNB model was compared with the HNB (hurdle negative binomial) and the REHNB (random effects hurdle negative binomial) model. In terms of model goodness-of-fits, REHNB and MREHNB model showed substantial improvement compared to the HNB model while there was no distinct difference between the REHNB and MREHNB models. However, as for the estimated parameters, the MREHNB model provided better inference precisions. Furthermore, the MREHNB model provided interesting findings for the crash contributing factors, for example, higher ratios of local vehicles within the traffic volume would enhance the probability of crash occurrence; and a non-linear relationship was concluded between traffic volume and crash frequency with the moderate level of volume held the highest crash occurrence probability. Finally, in-depth analyses about the modeling results and the model technique were discussed. The results will assist in designing more efficient control strategies for near real-time traffic management.

碰撞频率预测模型已经成为安全研究的重要课题，它揭示了碰撞发生及其影响因素之间的关系。近来，基于小时的精细规模的碰撞频率分析变得有吸引力，因为它具有引入随时间变化的解释信息（例如交通量和运行速度）的好处。然而，具有短时间间隔的碰撞频率数据具有过大的零和未观察到的异质性的分析问题。在这项研究中，首次出现了边缘化的随机效应栏负二项式（MREHNB）模型，其中跨栏模型结构处理了过多的零问题，并且特定于站点的随机效应项捕获了与未观察到的异质性相关的因素。此外，此处引入了边缘化推断方法，以获得总体总体的边缘均值推断，而不是针对特定主题的估计。基于上海城市高速公路系统的数据进行了实证分析，并将MREHNB模型与HNB（障碍负二项式）模型和REHNB（随机影响障碍负二项式）模型进行了比较。在模型拟合优度方面，与HNB模型相比，REHNB和MREHNB模型显示出显着改进，而REHNB和MREHNB模型之间没有明显差异。但是，关于估计参数，MREHNB模型提供了更好的推断精度。此外，MREHNB模型提供了有关碰撞影响因素的有趣发现，例如，在交通量内更高比例的本地车辆将增加碰撞发生的可能性；并得出交通量与碰撞频率之间的非线性关系，中等水平的碰撞发生概率最高。最后，对建模结果和建模技术进行了深入分析。结果将有助于为近实时流量管理设计更有效的控制策略。在交通量内提高本地车辆的比例将增加发生撞车的可能性；并得出交通量与碰撞频率之间的非线性关系，中等水平的碰撞发生概率最高。最后，对建模结果和建模技术进行了深入分析。结果将有助于为近实时流量管理设计更有效的控制策略。在交通量内提高本地车辆的比例将增加发生撞车的可能性；并得出交通量与碰撞频率之间的非线性关系，中等水平的碰撞发生概率最高。最后，对建模结果和建模技术进行了深入分析。结果将有助于为近实时流量管理设计更有效的控制策略。