Comparing regions while adjusting for differences in characteristics of sites located in those regions is valuable since it identifies possible inter-regional dissimilarities in crash risk propensities according to specific safety performance measures (e.g., crash frequency of a specific type). This paper describes a framework to benchmark different regions (neighborhoods, provinces, etc.) in terms of a selected safety performance measure. To avoid issues relating to aggregated (macro-level) data, we use disaggregate (micro-level) data to draw inferences at a macro/region-level, which is often needed for developing large-scale transportation safety and planning programs and policies. To overcome unobserved heterogeneity, we employ a multilevel Bayesian heteroskedastic Poisson lognormal model with grouped random parameters allowing heterogeneity in both mean and variance parameters. The proposed approach is illustrated through a comprehensive study of highway railway grade crossings across Canada. The results indicate that the proposed model addresses unobserved heterogeneity more efficiently and provides more insight compared to conventional random parameters models. For example, we found that as traffic exposure increases, grade crossing safety deteriorates at a higher rate in the Canadian Prairies than in the other regions. Our benchmarking framework is also affected by different model specifications. The results indicate the need for further in-depth investigations, which could help to identify possible reasons for inter-region differences in terms of specific safety indicators. This study provides valuable guidelines to Canadian transportation authorities, revealing important underlying crash mechanisms at highway railway grade crossings in Canada.

比较区域，同时调整位于那些区域的站点的特征差异，这是有价值的，因为它可以根据特定的安全性能指标（例如，特定类型的碰撞频率）识别碰撞风险倾向中可能存在的区域间差异。本文描述了一个框架，该框架根据选定的安全绩效指标对不同地区（邻里，省等）进行基准测试。为避免与汇总（宏级别）数据有关的问题，我们使用分解（微观级别）数据在宏观/区域级别上得出推论，这对于制定大规模运输安全和规划计划与政策通常是必需的。为了克服无法观察到的异质性，我们采用多级贝叶斯异方差泊松对数正态模型，该模型具有分组的随机参数，允许均值和方差参数均具有异质性。通过对整个加拿大的高速公路铁路平交道口的全面研究，说明了所建议的方法。结果表明，与常规随机参数模型相比，该模型可以更有效地解决未观察到的异质性问题，并提供更多的见识。例如，我们发现，随着交通暴露的增加，加拿大大草原的平交道口安全性恶化的速度要比其他地区高。我们的基准测试框架还受到不同模型规格的影响。结果表明有必要进行进一步的深入调查，这可以帮助根据特定的安全指标确定区域间差异的可能原因。这项研究为加拿大运输当局提供了有价值的指导，揭示了加拿大高速公路铁路平交道口的重要潜在碰撞机制。