The authors analyze traffic crashes involving trees (a subset of run-off-road collisions that tend to be especially deadly) to identify what factors make them more severe. They use a gradient-boosting classifier (CatBoost) to predict crash severity, SHAP values to explain which factors matter most, logistic regression to validate the findings, and SHAP interaction plots to find combined effects. Not wearing a seatbelt is the strongest predictor—unrestrained occupants are nearly 3× more likely to have severe outcomes—followed by vehicle age, speeding, and driver impairment.
Main takeaways:
- Restraint non-use is the dominant risk factor: unrestrained occupants are nearly three times more likely to experience fatal or incapacitating injuries, largely due to ejection risk
- Vehicle age, speeding violations, and driver impairment all substantially increase severity through reduced crashworthiness, higher impact forces, and impaired control
- Key risk interactions emerge: poor lighting with older vehicles, speeding with poor lighting, no restraints with older vehicles, and wet roads with speeding all show additive effects
- SHAP values (a model-agnostic explanation method) and logistic regression coefficients largely agree on factor importance, cross-validating the findings
- Results support targeted interventions like enhanced seat belt enforcement, speed management, and roadside hazard mitigation