IMPROVING ROAD SAFETY: SUPERVISED MACHINE LEARNING ANALYSIS OF FACTORS INFLUENCING CRASH SEVERITY

. Road traffic crash severity is shaped by a complex interplay of human, vehicular, environmental, and infrastructural factors. While machine learning (ML) has shown promise in analyzing crash data, gaps remain in model interpretability and region-specific insights, particularly for the UK context. This study addresses these gaps by evaluating supervised ML models-Decision Tree, Support Vector Machine (SVM), and LightGBM-to predict crash severity using 2022 UK accident data. The research emphasizes interpretability through SHapley Additive exPlanations (SHAP) to identify critical factors influencing severity outcomes. Results demonstrate that LightGBM outperforms other models in predictive performance, with police officer attendance at the scene, speed limits, and the number of vehicles involved emerging as pivotal determinants of severity. The analysis reveals that higher speed limits and single-vehicle collisions correlate with severe outcomes, while police presence may mitigate accident severity. However, the study acknowledges limitations, including dataset constraints. By integrating ML with post-hoc interpretability techniques, this work advances actionable insights for policymakers to prioritize road safety interventions, such as optimizing enforcement strategies and revising speed regulations. The findings underscore the potential of interpretable ML frameworks to enhance understanding of crash dynamics and inform targeted safety measures, contributing to global efforts to reduce traffic-related fatalities and injuries.