The iRAP methodology supports a proactive, data-driven approach to road safety that aligns closely with the Safe System. Rather than waiting for crashes to accumulate, iRAP identifies risk based on road design and operating conditions—allowing action to be taken before collisions occur. This is increasingly important as collision data has become less predictive over time; serious road trauma is now more sparsely distributed across networks, making traditional blackspot/hotspot analysis less effective due to statistical noise and phenomena like Regression to the Mean.
iRAP overcomes these limitations by systematically assessing road infrastructure every 100 meters, capturing over 50 attributes that influence crash likelihood and severity. These are used to generate Star Ratings for different user groups—vehicle occupants, motorcyclists, pedestrians, and cyclists—across relevant crash types.
Each route or network is calibrated using route or network level information about historical collisions, so effectively the model is being told the rough shape of the casualty issue on the route or network. Then the Fatal and Serious Injuries expected at each 100m location is estimated based on the calibration, the Star Rating Score (reflecting individual risk) and the user flows (exposure). These FSI estimates inform a model-generated investment plan, offering a starting point for road safety engineers to prioritise upgrades. Practitioners can then refine these plans using local standards, constraints, and knowledge through iRAP’s Route Review tool, ultimately developing tailored User Defined Investment Plans. This structured approach not only supports the development of compelling business cases but also enables governments and agencies to systematically treat infrastructure risk and move toward the long-term goal of eliminating death and serious injury on our roads.
