In traffic safety studies, there are almost inevitable concerns about unobserved heterogeneity. As a feasible alternative to current methods, this article proposes a novel crash count model that can address asymmetry and multimodality in the data. Specifically, a Bayesian random parameters model with flexible discrete densities for the regression coefficients is developed, employing a Dirichlet process prior. The approach is illustrated on the Ontario Highway 401, which is one of the busiest North American highways. The results indicate that the proposed model better captures the underlying structure of the data compared to conventional models, improving predictive power examined based on pseudo Bayes factors. Interestingly, the model can identify sites (highway segments, intersections, etc.) with similar risk factor profiles, those that manifest similarity in the heterogeneous effects of their site characteristics (e.g., traffic flow) on traffic safety, providing useful insight towards designing effective countermeasures.

在交通安全研究中，几乎不可避免地要关注未观察到的异质性。作为当前方法的可行替代方案，本文提出了一种新颖的崩溃计数模型，该模型可以解决数据中的不对称性和多模态问题。具体地，使用Dirichlet过程预先开发了具有灵活的离散密度的回归系数的贝叶斯随机参数模型。该方法在安大略高速公路401上进行了说明，该高速公路是北美最繁忙的高速公路之一。结果表明，与常规模型相比，所提出的模型更好地捕获了数据的底层结构，从而提高了基于伪贝叶斯因子检验的预测能力。有趣的是，该模型可以识别具有相似风险因素特征的地点（高速公路路段，交叉路口等），