From Canopy to Collision: A Hybrid Predictive Framework for Identifying Risk Factors in Tree-Involved Traffic Crashes
Authors: Abdul Azim, Ahmed Hossain, Soumyadip Maitra et al.
Summary
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
Relevance
No connection to my language model persona or behavioral installation research. This is a straightforward applied machine learning study for traffic safety, using SHAP for interpretability in a completely different domain.
Abstract
arXiv:2605.06684v1 Announce Type: new Abstract: Tree-involved crashes represent a critical subset of run-off-road (ROR) collisions, often resulting in fatal or severe injuries due to high-energy impacts. This study develops a comprehensive analytical framework to identify and quantify risk factors contributing to crash severity in tree-involved collisions using the Crash Report Sampling System (CRSS) database spanning 2020-2023. The modeling framework follows a multi-step process. First, a machine learning based classification model (CatBoost) identifies key factors associated with binary crash injury severity (KA: fatal or incapacitating injury versus BC: non-incapacitating or possible injury). Second, SHapley Additive exPlanations (SHAP) tool is used to quantify and visualize the marginal effects of top influential factors on crash severity. Third, a binary logistic regression model estimates factor effects and validates SHAP-derived importance measures. Finally, SHAP interaction plots examine the combined effects of key contributing factors. Results reveal restraint non-use as the most influential predictor, with unrestrained occupants nearly three times more likely to experience severe outcomes due to ejection risk. Vehicle age, speeding violations, and driver impairment demonstrate substantial effects, reflecting reduced crashworthiness, increased impact forces, and reduced control capabilities. Critical interactions emerge between lighting conditions and vehicle age, speeding and lighting conditions, restraint use and vehicle age, and road surface and speeding, demonstrating additive risk effects with specific interactions. These findings provide critical insights for targeted safe system-based interventions, including enhanced seat belt enforcement, speed management in reduced visibility conditions, and vehicle fleet modernization.