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

TL;DR

This study combines machine learning, econometrics, and time series analysis to improve prediction and understanding of UK road traffic accident severity, providing valuable insights for policy-making.

Contribution

It introduces an integrated approach using multiple advanced techniques to enhance accident severity prediction and analysis, surpassing traditional methods.

Findings

Random forest classifier achieved 73% precision and 78% recall.

XGBoost model with RMSE of 0.176 and MAE of 0.087.

Key variables influencing severity include Driver_Home_Area_Type and Road_Type.

Abstract

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.