Accident Analysis and Prevention Modelling area-wide count outcomes with spatial correlation and heterogeneity: An analysis of London crash data
Accident Analysis and Prevention Modelling area-wide count outcomes with spatial correlation and heterogeneity: An analysis of London crash data
复制标题
DOI：
--
复制
发表时间：
2008
影响因子：
4.2
通讯作者：
M. Quddus
中科院分区：
文献类型：
--
作者：
M. Quddus
诸如负二项（NB）回归模型等计数模型通常被用于建立区域范围内的交通事故与影响因素之间的关系。由于事故数据是参照空间中的点（即位置）收集的，所以区域层面的事故观测值之间存在空间依赖性。尽管NB模型能够考虑到社区之间未观测到的异质性（由于模型中变量缺失）的影响，但这类模型可能无法考虑空间相关性区域。因此，采用一种能同时考虑相邻单元之间的空间依赖性和不相关异质性的计量经济模型是至关重要的。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）适用于较高空间聚合水平（如州、县等）的经典空间模型；（ii）适用于所有空间单元，特别是较小规模区域聚合的贝叶斯层次模型。因此，本文的主要目的是利用非空间模型（如NB模型）和空间模型，建立区域范围内不同交通伤亡情况与选区特征相关的影响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市区的633个人口普查选区。选区层面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了细分。分析表明，不同的选区层面因素对交通伤亡的影响各不相同。研究结果还表明，贝叶斯层次模型在建立区域范围内交通事故与该区域道路基础设施、社会经济和交通状况相关的影响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考虑空间依赖性和不相关异质性。
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 spatialpatternoftrafficcrashes,twotypesofspatialmodelsmaybeemployed:(i)classicalspatialmodels for higher levels of spatial aggregation such as states, counties, etc. and (ii) Bayesian hierarchical models forallspatialunits,especiallyforsmallerscalearea-aggregations.Therefore,theprimaryobjectivesofthis paper is to develop a series of relationships between area-wide different traffic casualties and the con- tributing 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 suggestthatBayesianhierarchicalmodelsaremoreappropriateindevelopingarelationshipbetweenarea-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.