The study proposes and estimates a new econometric framework for analysing crash count events labeled as the Mixed Grouped Response Ordered Logit Count model. The proposed framework relates the crash count propensity to the observed counts directly while also accommodating for heteroscedasticity and unobserved heterogeneity. The proposed model is demonstrated by using Traffic Analysis Zone level bicycle crash count data for the Island of Montreal. The model framework employs a comprehensive set of exogenous variables − accessibility measures, exposure measures, built environment, road network characteristics, sociodemographic and socioeconomic characteristics. Further, we also compare the performance of the proposed model to the most commonly used negative binomial model and the generalized ordered logit count model by generating a comprehensive set of measures to evaluate model performance and data fit. The alternative modeling approaches considered for the comparison exercise include: (1) negative binomial model without parameterized overdispersion, (2) negative binomial model with parameterized overdispersion, and (3) mixed negative binomial model with parameterized overdispersion, (4) generalized ordered logit count model and (5) mixed generalized ordered logit count model, (6) grouped response ordered logit count model without parameterized variance, (7) grouped response ordered logit count model with parameterized variance and (8) mixed grouped response ordered logit count model with parameterized variance. The comparison exercise clearly highlights that the proposed mixed grouped response ordered logit count model with parameterized variance relative to the mixed negative binomial model with parameterized overdispersion offers either equivalent or superior data fit across various measures in the current study context. The fit measures for comparing the predictive performance also indicate that the proposed grouped response model offers better predictions both at the aggregate and disaggregate levels. Overall, the results from this comparison exercise points out that the grouped response ordered logit count model is a promising alternate econometric framework for examining crash count events.

该研究提出并估计了一个新的计量经济学框架，用于分析标记为“混合分组响应排序的Logit Count”模型的崩溃计数事件。提出的框架将崩溃计数的倾向性直接与观察到的计数相关联，同时还考虑了异方差和未观察到的异质性。通过使用“交通分析区域”级别的蒙特利尔岛自行车碰撞计数数据演示了该模型。该模型框架采用了一组全面的外生变量-可达性度量，暴露度量，建筑环境，道路网络特征，社会人口统计学和社会经济特征。进一步，我们还通过生成一套评估模型性能和数据拟合的综合措施，将建议的模型的性能与最常用的负二项式模型和广义有序logit计数模型进行比较。为进行比较而考虑的替代建模方法包括：（1）没有参数化超分散的负二项式模型；（2）参数化超分散的负二项式模型；（3）参数化超分散的混合负二项式模型；（4）广义有序对数模型和（5）混合广义有序Logit计数模型，（6）无参数化方差的分组响应有序Logit计数模型，（7）具有参数化方差的分组响应排序对数计数模型，以及（8）具有参数化方差的混合分组响应有序Logit计数模型。比较练习清楚地表明，相对于参数化过度分散的混合负二项式模型，所建议的具有参数化方差的混合分组响应排序对数计数模型提供了在当前研究背景下跨各种度量的等效或优良数据。用于比较预测性能的拟合指标还表明，建议的分组响应模型在总体和分解水平上均提供了更好的预测。总体而言，此比较练习的结果指出，分组响应排序的logit计数模型是用于检查崩溃计数事件的有希望的替代计量经济学框架。