# 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

arXiv: https://arxiv.org/abs/2309.13483 · HTML: https://mlsanity.com/paper/2309.13483/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 51 references that are in the map.
- First posted on arXiv 23 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)

## Abstract (arXiv)

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.