THE RELATIONSHIP BETWEEN TRUCK ACCIDENTS AND GEOMETRIC DESIGN OF ROAD SECTIONS - POISSON VERSUS NEGATIVE BINOMIAL REGRESSIONS
THE RELATIONSHIP BETWEEN TRUCK ACCIDENTS AND GEOMETRIC DESIGN OF ROAD SECTIONS - POISSON VERSUS NEGATIVE BINOMIAL REGRESSIONS
复制标题
DOI：
10.1016/0001-4575(94)90038-8
复制
发表时间：
1994-08-01
影响因子：
5.9
通讯作者：
MIAOU, SP
中科院分区：
文献类型：
--
作者：
MIAOU, SP
本文评估了泊松和负二项式 (NB) 回归模型在建立卡车事故与路段几何设计之间关系方面的性能。考虑三种类型的模型：泊松回归、零膨胀泊松 (ZIP) 回归和 NB 回归。最大似然（ML）方法用于估计这些模型的未知参数。还检查了另外两个用于估计 NB 回归模型中的色散参数的可行估计器：矩估计器和基于回归的估计器。这些模型和估计量的评估依据是：(i) 估计的回归参数，(ii) 整体拟合优度，(iii) 估计的跨路段卡车事故发生的相对频率，(iv) 对短路段的敏感性，以及 (v) 估计的卡车事故发生总数。来自公路安全信息系统的数据用于检查这些模型在建立此类关系方面的性能。评估结果表明，应谨慎使用基于矩和基于回归的方法估计的NB回归模型。此外，在 ML 方法下，所有三个模型的估计回归参数都非常一致，并且就跨路段卡车事故涉及的估计相对频率而言，没有任何特定模型优于其他两个模型。建议使用泊松回归模型作为建立关系的初始模型。如果发现事故数据的过度离散程度为中度或高度，则可以探索 NB 和 ZIP 回归模型。总体而言，当数据表现出过多的零时，例如，ZIP 回归模型似乎是一个重要的候选模型。由于少报。然而，ZIP 模型的解释可能很困难。
This paper evaluates the performance of Poisson and negative binomial (NB) regression models in establishing the relationship between truck accidents and geometric design of road sections. Three types of models are considered: Poisson regression, zero-inflated Poisson (ZIP) regression, and NB regression. Maximum likelihood (ML) method is used to estimate the unknown parameters of these models. Two other feasible estimators for estimating the dispersion parameter in the NB regression model are also examined: a moment estimator and a regression-based estimator. These models and estimators are evaluated based on their (i) estimated regression parameters, (ii) overall goodness-of-fit, (iii) estimated relative frequency of truck accident involvements across road sections, (iv) sensitivity to the inclusion of short road sections, and (v) estimated total number of truck accident involvements. Data from the Highway Safety Information System are employed to examine the performance of these models in developing such relationships. The evaluation results suggest that the NB regression model estimated using the moment and regression-based methods should be used with caution. Also, under the ML method, the estimated regression parameters from all three models are quite consistent and no particular model outperforms the other two models in terms of the estimated relative frequencies of truck accident involvements across road sections. It is recommended that the Poisson regression model be used as an intial model for developing the relationship. If the overdispersion of accident data is found to be moderate or high, both the NB and ZIP regression models could be explored. Overall, the ZIP regression model appears to be a serious candidate model when data exhibit excess zeros, e.g. due to underreporting. However, the interpretation of the ZIP model can be difficult.