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
Md Abu Sufian Jayasree Varadarajan
This research investigates road traffic accident severity in the UK, using a
combination of machine learning, econometric, and statistical methods on
historical data. We employed various techniques, including correlation
analysis, regression models, GMM for error term issues, and time-series
forecasting with VAR and ARIMA models. Our approach outperforms naive
forecasting with an MASE of 0.800 and ME of -73.80. We also built a random
forest 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 and
MAE of 0.087. Factor Analysis identified key variables, and we used SHAP for
Explainable AI, highlighting influential factors like Driver_Home_Area_Type and
Road_Type. Our study enhances understanding of accident severity and offers
insights for evidence-based road safety policies.