Proactive Road Safety
Using Data to Predict and Prevent Road Trauma
"Road traffic crashes result in the deaths of approximately 1.19 million people around the world each year and leave between 20 and 50 million people with non-fatal injuries."
World Health Organisation, Road Traffic Injuries
~1,200
Annual Deaths in Australia
~40,000
Serious Injuries Annually
$30 Billion
Annual Cost to Economy
The Challenge: Moving Beyond Reactive Measures
Road safety has traditionally relied on analysing past incidents to implement policy. While effective to a point, this reactive approach reaches a plateau. To continue reducing the national road toll, a deeper understanding of the social and community factors that influence risky road behaviour is required.
Our research aims to identify small, at-risk cohorts of road users by analysing how community characteristics like socio-economic disadvantage, residential mobility, and ethnic heterogeneity influence the frequency and severity of accidents. This project is a National Road Safety Action Grants Program initiative, funded by the Australian Government.
Our Approach: A Whole-of-Society Data Model
We are developing a comprehensive model that fuses multiple open data sources to build a complete picture of communities and their road safety challenges. By combining statistics and machine learning, we can identify hidden relationships between community structure and traffic incidents. This allows for the creation of proactive, niche-target strategies instead of broad, demographic-based policies.
Interactive Data Fusion Model
Crash Data
Type, severity, location, and time of incidents.
Census Data
Socio-Economic Indexes for Areas (SEIFA), mobility.
Crime Incidents
Rates of drug-related crime and other social indicators.
AI Model
Identifies At-Risk Cohorts
The Impact: Data-Driven Prevention
Our modelling provides a more complete profile of driver behaviour than standard approaches. The outcomes can identify regions where, for example, drug-related activity is associated with poor traffic outcomes, allowing for targeted interventions.
This research aligns with the National Road Safety Strategy 2021-30, which prioritises improved outcomes for Aboriginal and Torres Strait Islander people. We use an Indigenous-specific SEIFA indicator in our modelling to better understand and address the higher burden of road trauma in these communities. The project uses data from Queensland to serve as a proof-of-concept for analysis at the national level.
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