Quddus Mohammed A
Transport Studies Group, Department of Civil and Building Engineering, Loughborough University, Leicestershire, United Kingdom.
Accid Anal Prev. 2008 Jul;40(4):1486-97. doi: 10.1016/j.aap.2008.03.009. Epub 2008 Apr 15.
Count models such as negative binomial (NB) regression models are normally employed to establish a relationship between area-wide traffic crashes and the contributing factors. Since crash data are collected with reference to location measured as points in space, spatial dependence exists among the area-level crash observations. Although NB models can take account of the effect of unobserved heterogeneity (due to omitted variables in the model) among neighbourhoods, such models may not account for spatial correlation areas. It is then essential to adopt an econometric model that takes account of both spatial dependence and uncorrelated heterogeneity simultaneously among neighbouring units. In studying the spatial pattern of traffic crashes, two types of spatial models may be employed: (i) classical spatial models for higher levels of spatial aggregation such as states, counties, etc. and (ii) Bayesian hierarchical models for all spatial units, especially for smaller scale area-aggregations. Therefore, the primary objectives of this paper is to develop a series of relationships between area-wide different traffic casualties and the contributing factors associated with ward characteristics using both non-spatial models (such as NB models) and spatial models and to identify the similarities and differences among these relationships. The spatial units of the analysis are the 633 census wards from the Greater London metropolitan area. Ward-level casualty data are disaggregated by severity of the casualty (such as fatalities, serious injuries, and slight injuries) and by severity of the casualty related to various road users. The analysis implies that different ward-level factors affect traffic casualties differently. The results also suggest that Bayesian hierarchical models are more appropriate in developing a relationship between area-wide traffic crashes and the contributing factors associated with the road infrastructure, socioeconomic and traffic conditions of the area. This is because Bayesian models accurately take account of both spatial dependence and uncorrelated heterogeneity.
诸如负二项式（NB）回归模型之类的计数模型通常用于建立区域范围内交通事故与促成因素之间的关系。由于事故数据是参照空间中的点所测量的位置来收集的，因此区域层面的事故观测值之间存在空间依赖性。尽管NB模型可以考虑邻里间未观测到的异质性（由于模型中遗漏变量所致）的影响，但此类模型可能无法考虑空间相关性区域。因此，采用一种能够同时考虑相邻单元间空间依赖性和不相关异质性的计量经济学模型至关重要。在研究交通事故的空间模式时，可以采用两种类型的空间模型：（i）适用于州、县等较高空间聚合层次的经典空间模型，以及（ii）适用于所有空间单元，尤其是较小规模区域聚合的贝叶斯层次模型。因此，本文的主要目标是使用非空间模型（如NB模型）和空间模型来建立区域范围内不同交通伤亡与与病房特征相关的促成因素之间的一系列关系，并识别这些关系之间的异同。分析的空间单元是大伦敦都会区的633个人口普查区。区一级的伤亡数据按伤亡严重程度（如死亡、重伤和轻伤）以及与各类道路使用者相关的伤亡严重程度进行分类。分析表明，不同的区一级因素对交通伤亡的影响不同。结果还表明，贝叶斯层次模型在建立区域范围内交通事故与与该地区道路基础设施、社会经济和交通状况相关的促成因素之间的关系时更为合适。这是因为贝叶斯模型能够准确地同时考虑空间依赖性和不相关异质性。
