Quantifying the Effect of Area Deprivation on Child Pedestrian Casualties by Using Longitudinal Mixed Models to Adjust for Confounding, Interference and Spatial Dependence

Graham, Daniel J.; McCoy, Emma J.; Stephens, David A.

doi:10.1111/j.1467-985x.2012.01071.x

Cited by 19 publications

(14 citation statements)

References 45 publications

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“…Analyses of the link between casualties and deprivation either use information on the socioeconomic status of victims or they take an area-based approach. The typical procedure is to test for association between spatial variation in casualty counts and area levels of deprivation (Graham et al, 2013). Laflamme and Diderichsen (2000) mention two different area-based designs.…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic and sociodemographic inequalities and their association with road traffic injuries

Pirdavani

Daniëls

Vlierden

et al. 2017

Journal of Transport & Health

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Increasing evidence suggests that neighborhood-based measures of socioeconomic status are correlated with traffic injury. The main objective of this study is to determine the differences in associations between predictive variables and injury crashes (i.e. including injury and fatal crashes). This study makes a novel contribution by establishing the association between traffic casualties and socio-demographic, socioeconomic characteristics, traffic exposure data and road network variables, at the neighborhood-level while categorized by different genders and transport mode; 'car driver', 'car passenger' and 'active mode users' (i.e. pedestrians and cyclists). In this study an activity-based transportation model called FEATHERS (Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS) is utilized to produce exposure measures. Exposure measures are in the form of production/attraction trips for several traffic analysis zones (TAZ) in Flanders, Belgium. Analyzing crashes at a neighborhood-level provides important information that enables us to compare traffic safety of different neighborhoods. This information is used to identify safety problems in specific zones and consequently, implementing safety interventions to improve the traffic safety condition. This can be carried out by associating casualty counts with a number of factors (i.e. developing crash prediction models) which have macro-level characteristics, such as socio-demographic and network level exposure. The results indicate that socioeconomic variables are differently associated with casualties of different travel modes and genders. For instance, income level of residence of a TAZ is a significant predictor of male car driver injury crashes while it does not significantly contribute to the prediction of female car driver injury crashes.

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Section: Introductionmentioning

confidence: 99%

Socioeconomic and sociodemographic inequalities and their association with road traffic injuries

Pirdavani

Daniëls

Vlierden

et al. 2017

Journal of Transport & Health

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“…Barnes, Chatterton, & Longhurst, 2019), traffic collisions (e.g. Graham, McCoy, & Stephens, 2013), and transport-related social exclusion (DfT 2014;Lucas 2012). The number of people potentially affected is 16,000 (4% of population).…”

Section: Resultsmentioning

confidence: 99%

A Scoping Indicator Identifying Potential Impacts of All-Inclusive MaaS Taxis on Other Modes in Manchester

2020

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We present a spatial scoping indicator of the potential impacts of an all-inclusive taxi service as part of a plausible but hypothetical Mobility as a Service (MaaS) package in Manchester UK. Our indicator identifies the number of people in each area who have some potential for mode shift from walking, cycling, public transport or car commuting to an all-inclusive taxi service within a MaaS package. We find the method quick and straightforward. In our case study, potential to shift from walk to an all-inclusive taxi service within a MaaS package is almost double the potential shift away from car, which represents a risk of increased car use.

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“…Traffic flows have thus far not been accounted for. Using the LSOA census data, and drawing on previous research, population and employment can be used to create proxy variables for traffic flows: proximate population density (PPD); and proximate employment density (PED) ( 34 ). These are derived using a gravity-model-type approach to estimate the amount of activity generated by each LSOA.…”

Section: Methodsmentioning

confidence: 99%

Use of Open Data to Assess Cyclist Safety in London

Collins¹,

Graham

2019

Transportation Research Record: Journal of the Transportation R

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This study develops a predictive model for cycling collisions in London. Specifically, the effects of bus lanes, parking or loading facilities, and multilane roads on the risk of cycling collisions are considered. To the best of the authors’ knowledge, this is the first such predictive collision model that develops covariates to measure the characteristics of different types of road infrastructure within zones. A kernel density estimator is used to identify 90 collision hotspots. Each hotspot is populated with information regarding the highway infrastructure within it. A multiple linear regression model tests for the statistical significance of the infrastructure variables. Bus lanes, multilane roads, and 30-mph speed limits are found to affect cycle collision counts, whereas junction density has the largest impact on collision density. Speed limits of 20 mph affect collision counts to a lesser degree than 30 mph, indicating potential safety improvement from reducing speed limits. One-way roads are found to reduce the risk of collisions, along with the provision of priority junctions. This infers that other junction types, such as roundabouts and signalized junctions, present a higher collision risk. The models produce conflicting results on parking or loading provision. The models are expanded to include sociodemographic variables, such as population and employment. The combined model offers no performance improvement over the infrastructure-only model, although a potential link between public transport provision and reducing cycle collisions warrants further investigation.

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Quantifying the Effect of Area Deprivation on Child Pedestrian Casualties by Using Longitudinal Mixed Models to Adjust for Confounding, Interference and Spatial Dependence

Cited by 19 publications

References 45 publications

Socioeconomic and sociodemographic inequalities and their association with road traffic injuries

Socioeconomic and sociodemographic inequalities and their association with road traffic injuries

A Scoping Indicator Identifying Potential Impacts of All-Inclusive MaaS Taxis on Other Modes in Manchester

Use of Open Data to Assess Cyclist Safety in London

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