Effects of Geodemographic Profiles of Drivers on Their Injury Severity from Traffic Crashes Using Multilevel Mixed-Effects Ordered Logit Model

Abstract

The purpose of this study was to use a statistical model to examine the effects of various geodemographic factors on levels of driver injury severity. A driver's geodemographic profile with respect to involvement in a traffic crash consists of variables from multiple hierarchical levels such as drivers nested within crashes and crashes clustered within areas. This structure implies that driver-level observations are correlated rather than independent as assumed in many injury severity models. To capture within-group and between-group correlations in observations, a multilevel mixed-effects ordered logit model was employed. The mixed effect allows some variables to vary by observations (i.e., random parameters). The analysis was based on national traffic crash data in the United Kingdom between 2009 and 2011, consisting of 271,654 drivers from 217,523 traffic crashes occurring across 27,773 census areas. Data on area, social deprivation, census, and land use patterns were collected from multiple sources and integrated with a geographic information system framework. Results indicated that the severity of injuries sustained by urban drivers involved in crashes increased if they traveled to rural areas; the level of driver injury severity also increased if traffic crashes occurred in areas with high car ownership per capita; and drivers from more disadvan-taged areas would sustain, if all else is equal, more severe injuries. Findings from this study would be useful to the UK Department for Transport and local authorities in formulating safety policies aimed at enhancing driver education, training, and licensing programs.