Predicting and Explaining Traffic Crash Severity Through Crash Feature Selection

TL;DR

This study develops an explainable machine learning framework using a large Ohio crash dataset to predict crash severity and identify key risk factors, supporting data-driven traffic safety policies.

Contribution

It introduces a transparent AutoML and explainability approach for crash severity prediction, highlighting influential features and offering a scalable, interpretable methodology.

Findings

Final model achieved 85.6% AUC-ROC on training data.

Identified 17 key predictive features across multiple categories.

Environmental and contextual factors were more influential than impairment.

Abstract

Motor vehicle crashes remain a leading cause of injury and death worldwide, necessitating data-driven approaches to understand and mitigate crash severity. This study introduces a curated dataset of more than 3 million people involved in accidents in Ohio over six years (2017-2022), aggregated to more than 2.3 million vehicle-level records for predictive analysis. The primary contribution is a transparent and reproducible methodology that combines Automated Machine Learning (AutoML) and explainable artificial intelligence (AI) to identify and interpret key risk factors associated with severe crashes. Using the JADBio AutoML platform, predictive models were constructed to distinguish between severe and non-severe crash outcomes. The models underwent rigorous feature selection across stratified training subsets, and their outputs were interpreted using SHapley Additive exPlanations (SHAP) to quantify the contribution of individual features. A final Ridge Logistic Regression model achieved an AUC-ROC of 85.6% on the training set and 84.9% on a hold-out test set, with 17 features consistently identified as the most influential predictors. Key features spanned demographic, environmental, vehicle, human, and operational categories, including location type, posted speed, minimum occupant age, and pre-crash action. Notably, certain traditionally emphasized factors, such as alcohol or drug impairment, were less influential in the final model compared to environmental and contextual variables. Emphasizing methodological rigor and interpretability over mere predictive performance, this study offers a scalable framework to support Vision Zero with aligned interventions and advanced data-informed traffic safety policy.