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This research investigates road traffic accident severity in the UK, using acombination of machine learning, econometric, and statistical methods onhistorical data. We employed various techniques, including correlationanalysis, regression models, GMM for error term issues, and time-seriesforecasting with VAR and ARIMA models. Our approach outperforms naiveforecasting with an MASE of 0.800 and ME of -73.80. We also built a randomforest classifier with 73% precision, 78% recall, and a 73% F1-score.Optimizing with H2O AutoML led to an XGBoost model with an RMSE of 0.176 andMAE of 0.087. Factor Analysis identified key variables, and we used SHAP forExplainable AI, highlighting influential factors like Driver_Home_Area_Type andRoad_Type. Our study enhances understanding of accident severity and offersinsights for evidence-based road safety policies.

Enhancing Prediction and Analysis of UK Road Traffic Accident Severity  Using AI: Integration of Machine Learning, Econometric Techniques, and Time  Series Forecasting in Public Health Research