Predicting Road Accident Severity and Identifying High-Risk Areas Using Tree-based Learning: A Case Study from the UK

Road accidents are a pressing global issue, often resulting in injuries ranging from minor to fatal. This study leverages machine learning algorithms to predict the severity and location of road accidents, with the aim of enhancing road safety measures. Utilizing accident data from the UK, we developed predictive models that classify the severity of accidents on a scale from 1 to 5, where 1 represents the least severe and 5 represents the most severe outcomes. The model’s predictions have been integrated into a user-friendly application, providing visual insights into accident severity and highlighting high-risk areas on a map. By identifying and visualizing accident-prone locations, this study offers a valuable tool for authorities to implement targeted interventions, potentially reducing the frequency and impact of road accidents. The findings underscore the potential of machine learning in proactive road safety management, contributing to efforts that could save lives and minimize the societal costs associated with road accidents.