ARSRPE Conference Paper Database

Towards safer urban roads and roadsides: factors affecting crash risk in complex urban environments

Stephan, K, Newstead, Stuart (Peer reviewed)

Road Environment

2012

The Australian Road Safety Strategy (2011-2020) identifies the importance of assessing risk on the road network. Accident prediction models have been developed to identify characteristics of the road and roadside that affect crash risk, however, these are mainly restricted to simple road environments like rural roads and freeways. Modelling crash risk in urban areas is complicated due to the difficulty obtaining data to fully characterise complex environments. The aim of this research was to identify the characteristics of the road and roadside, surrounding environment and socio-demographic factors that affect crash risk in complex urban environments, namely, strip shopping centres. A literature review and consultation with experts resulted in a comprehensive list of data items required for measuring the influence of the road, roadside and other factors on crash risk. Strip shopping segments located on arterial roads in metropolitan Melbourne were identified and separated into midblock segments and signalised intersections. Extensive data describing the characteristics of these segments were collected from a diverse range of sources, e.g. administrative government databases (VicRoads- crashes, traffic volume, speed limit and pavement condition; Australian Bureau of Statistics- socio-demographic information; Department of Justice- liquor licensing), detailed maps, on-line image sources and digital images of arterial roads collected by ARRB for VicRoads. The quality of the collected data was thoroughly checked using secondary sources and errors rectified. Negative binomial regression was used to investigate risk factors for crashes in urban strip shopping centres. Separate models were developed for midblock and intersection crashes. In this paper, factors associated with midblock crash risk in strip shopping centres will be discussed and implications for the design of evidence-based risk assessment tools will be considered.