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Towards Sustainable Road Safety: Feature-Level Interpretation of Injury Severity in Poland (2015–2024) Using SHAP and XGBoost
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Towards Sustainable Road Safety: Feature-Level Interpretation of Injury Severity in Poland (2015–2024) Using SHAP and XGBoost
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Towards Sustainable Road Safety: Feature-Level Interpretation of Injury Severity in Poland (2015–2024) Using SHAP and XGBoost
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Journal Article
Towards Sustainable Road Safety: Feature-Level Interpretation of Injury Severity in Poland (2015–2024) Using SHAP and XGBoost
2025
Overview
This study investigates the severity of injuries sustained by over seven million participants involved in road traffic incidents in Poland between 2015 and 2024, with a view to supporting sustainable mobility and the United Nations Sustainable Development Goals. Road safety is a crucial dimension of sustainable development, directly linked to public health, urban liveability, and the socio-economic costs of transportation systems. Using a harmonised participant-level dataset, this research identifies key demographic, behavioural, and environmental factors associated with injury outcomes. A novel five-level injury severity variable was developed by integrating inconsistent records on fatalities and injuries. Descriptive analyses revealed clear seasonal and weekly patterns, as well as substantial differences by participant type and driving licence status. Pedestrians and passengers faced the highest risk, with fatality rates more than five times higher than those of drivers. An XGBoost classifier was trained to predict injury severity, and SHAP analysis was applied to interpret the model’s outputs at the feature level. Participant role emerged as the most important predictor, followed by driving licence status, vehicle type, lighting conditions, and road geometry. These findings provide actionable insights for sustainable road safety interventions, including stronger protection for pedestrians and passengers, stricter enforcement against unlicensed driving, and infrastructural improvements such as better lighting and safer road design. By combining machine learning with interpretability tools, this study offers an analytical framework that can inform evidence-based policies aimed at reducing crash-related harm and advancing sustainable transport development.
