aceMachine learning and SHAP-based explainability for accident severity prediction: evidence from a strategic road network in a developing country
Objective This study aims to develop an Explainable Artificial Intelligence (XAI) framework to classify and predict road accident severity on a strategic road network in a developing country. The research focuses on identifying the primary risk factors contributing to fatal and serious injuries to support "Vision Zero" policies. Methods Three machine learning algorithms-Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN)-were trained using a dataset of traffic accidents from federal highways in Per…
A Random Forest model trained on unbalanced highway crash data predicted accident severity with high accuracy, and SHAP analysis exposed the main drivers of lethality for engineers and policymakers.
The evidence comes from one highway network in Pernambuco, Brazil, and the abstract reports model performance rather than any intervention outcome, so its transfer to other settings and real safety gains is not shown.
Evidence
- Peer-reviewedTraffic Injury Prevention2026-10-07
How should this claim be treated?
Truvace Impact Record TRV-2026-1771, v1: “Machine learning and SHAP-based explainability for accident severity prediction: evidence from a strategic road network in a developing country.” Truvace, 2026-10-18. /record/TRV-2026-1771 (accessed at citation time). sha256 05d08faf3ddaa4e0…
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