标题： 交通事故频率模型的互补拟合程序 显示英文标题

标题： Complementary Goodness of Fit Procedure for Crash Frequency Models

摘要： 本文提出了一种用于评估基于夏威夷州双向双车道（TLTW）州级道路的 roadway departure（RwD）事故频率数据估计的广义线性模型（GLM）拟合优度的新方法。该方法使用了十年的RwD事故数据（包括所有严重程度）以及可以按路段聚合的道路特征（如交通、几何形状和库存数据库）。所评估的三种估计方法包括：负二项分布（NB）、零膨胀负二项分布（ZINB）和广义线性混合模型-负二项分布（GLMM-NB）。结果显示，这三种方法在预测的平均事故频率狭窄范围内以及观测值与预测值的平均事故频率方面都能提供非常好的拟合效果。该方法补充了其他用于模型选择的统计量，如赤池信息准则（AIC）、贝叶斯信息准则（BIC）和对数似然值，对于不含随机效应的模型，它与这些统计量一致，但对于GLMM-NB模型则有所不同。该方法可以通过清晰展示事故频率模型的拟合情况来辅助模型选择，并允许计算类似于线性回归中使用的伪R²值。建议进一步研究该方法在评估GLMM-NB模型中随机效应数量与拟合优度之间的权衡时的适用性，使用更合适的数据集以避免收敛问题。 显示英文摘要

摘要： This paper presents a new procedure for evaluating the goodness of fit of Generalized Linear Models (GLM) estimated with Roadway Departure (RwD) crash frequency data for the State of Hawaii on two-lane two-way (TLTW) state roads. The procedure is analyzed using ten years of RwD crash data (including all severity levels) and roadway characteristics (e.g., traffic, geometry, and inventory databases) that can be aggregated at the section level. The three estimation methods evaluated using the proposed procedure include: Negative Binomial (NB), Zero-Inflated Negative Binomial (ZINB), and Generalized Linear Mixed Model-Negative Binomial (GLMM-NB). The procedure shows that the three methodologies can provide very good fits in terms of the distributions of crashes within narrow ranges of the predicted mean frequency of crashes and in terms of observed vs. predicted average crash frequencies for those data segments. The proposed procedure complements other statistics such as Akaike Information Criterion, Bayesian Information Criterion, and Log-likelihood used for model selection. It is consistent with those statistics for models without random effects, but it diverges for GLMM-NB models. The procedure can aid model selection by providing a clear visualization of the fit of crash frequency models and allowing the computation of a pseudo R2 similar the one used in linear regression. It is recommended to evaluate its use for evaluating the trade-off between the number of random effects in GLMM-NB models and their goodness of fit using more appropriate datasets that do not lead to convergence problems.