A spatially disaggregate analysis of road casualties in England

Abstract

A Spatially Disaggregate Analysis of Road Casualties in England AbstractSpatially disaggregate ward level data for England is used in an analysis of various area-wide factors on road casualties. Data on 8414 wards was input into a geographic information system that contained data on land use types, road characteristics and road casualties. Demographic data on area-wide deprivation (the index of multiple deprivation) for each ward was also included.Negative binomial count data models were used to analyze the associations between these factors with traffic fatalities, serious injuries and slight injuries. Results suggest that urbanized areas are associated with fewer casualties (especially fatalities) while areas of higher employment density are associated with more casualties. More deprived areas tend to have higher levels of casualties, though not of motorized casualties (except slight injuries). The effect of road characteristics are less significant but there are some positive associations with the density of "A" and "B" level roads.

Impact of traffic congestion on road accidents: A spatial analysis of the M25 motorway in England

“…Lord and Miranda-Moreno, 2007;Aguero-Valverde and Jovanis, 2008); and the Poisson-gamma model (also known as Negative Binomial model) which is widely used in accident analysis (e.g. Shankar et al, 1995;Milton and Mannering, 1998;Abdel-Aty and Radwan, 2000;Lord, 2000;Amoros et al, 2003;Noland and Quddus, 2004;Kim et al, 2006;Haynes et al, 2007). Poisson-lognormal and Poisson-gamma models have the advantage in that they can accommodate overdispersion in accident data.…”

Excess accident risk among residents of deprived areas

“…Thus, in England, an analysis of the 8,414 wards that cover the country showed, firstly, the importance of general characteristics related to urbanisation: urbanised areas have fewer fatalities, whereas areas with higher employment density have more bodily injury accidents. But another factor is the level of deprivation in relation to a higher number of casualties (Noland and Quddus, 2004). In a more targeted manner, the relationship between the level of deprivation and pedestrian casualties has been demonstrated (Graham et al, 2005); this effect is strong for children who are victims of road accidents.…”

“…Their position within the urban area, the distance to the centre, the density of housing or jobs and the rural or urban character of the environment can lead to different risks (Noland and Quddus, 2004). This factor must be integrated into the approach to answer the previous questions and to provide an estimate of any general excess risk related to the socio-spatial characteristics of populations considered as deprived.…”

A latent variable representation of count data models to accommodate spatial and temporal dependence: Application to predicting crash frequency at intersections

“…because the CRIS does not include traffic flow information on intersection approach movements, one of the most relevant variables to explain intersection crash risk propensity (see Mountain et al, 1998, Noland and Quddus, 2004, Quddus, 2008. So, we had to locate areas in the State that At the same time, the frequency distribution indicates a long right tail, which is also easily accommodated in the proposed count framework by setting…”