Human cost, machine insight: A data-driven analysis of Australian road crashes

Abstract

In Australia, road crash injuries continue to be a serious public health issue. Machine learning is used in this study to analyse injury data from road crashes between 2011 and 2021 that was taken from the national hospitalized injury database. We investigate how the number of injuries and duration of stay for road users are affected by variables such as gender, age, seasonal variation, collision type, and location (urban vs. regional). Road safety measures are informed by patterns and relationships found in the data by machine learning models. Hospitalizations have been trending upward between 2011 and 2019, with a pause in 2020 due to COVID-19 lockdowns. In all categories, men sustain more injuries than women, though the number varies according to age and geography. The type of road user also affects collision patterns. The time-series projections demonstrate that the goal of zero fatalities in 2050 will not be achieved under the business-as-usual scenario. The findings highlight the necessity of focused interventions predicated on collision trends and demographics. This includes better infrastructure design, increased surveillance, and customized safety measures.