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Title: Socioeconomic and sociodemographic inequalities and their association with road traffic injuries
Authors: PIRDAVANI, Ali 
BRIJS, Kris 
KOCHAN, Bruno 
Issue Date: 2017
Source: Journal of Transport & Health, 4, p. 152-161
Status: In Press
Abstract: 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.
Notes: Pirdavani, A (reprint author), Hasselt Univ, Fac Engn Technol, Agoralaan, BE-3590 Diepenbeek, Belgium.
Keywords: crash prediction; socioeconomic status; traffic safety; traffic analysis zone; activity-based transportation models
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ISSN: 2214-1405
e-ISSN: ****-****
DOI: 10.1016/j.jth.2016.12.001
ISI #: 000403125400018
Rights: (c) 2016 Elsevier Ltd All rights reserved.
Category: A1
Type: Journal Contribution
Validations: ecoom 2018
Appears in Collections:Research publications

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