Behavioral and Traffic-Related Determinants of Road Crash Severity: Supporting the Safety Dimension of Sustainable Transportation Systems
Journal
Sustainability
ISSN
2071-1050
Date Issued
2026
Abstract
Road crashes undermine the sustainability of transportation systems by generating substantial
human, social, and economic costs. Identifying the factors associated with severe
crash outcomes is therefore essential for supporting safer and more sustainable mobility, in
accordance with Vision Zero and Sustainable Development Goals 3.6 and 11.2. This study
investigates the influence of behavioral, environmental, and infrastructural factors on road
crash severity through a comparative evaluation of three modeling approaches: Ordinal
Logistic Regression (OLR), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting
(CatBoost). The analysis was based on 5194 police-reported crash records from Cyprus
covering the period 2015–2024. The dataset included crash severity outcomes together
with traffic violations and roadway and environmental conditions. The results identified
speeding, substance use, and improper turning maneuvers as influential predictors of
crash severity. Among the evaluated models, CatBoost achieved the strongest overall
predictive performance, obtaining the highest accuracy (0.52), weighted area under the
curve (AUC) (0.72), weighted precision (0.50), and weighted recall (0.52), while CatBoost
and XGBoost tied for the highest weighted F1-score (0.48). CatBoost’s overall performance
reflects its ability to efficiently process categorical variables and capture nonlinear relationships
without extensive preprocessing. The findings highlight the dominant role of driver
behavior in determining crash severity and demonstrate how the comparative modeling
framework can serve as an analytical decision-support tool for identifying and monitoring
crash-severity risk factors, prioritizing targeted interventions, and supporting progress
towards safer and more sustainable transportation systems.
human, social, and economic costs. Identifying the factors associated with severe
crash outcomes is therefore essential for supporting safer and more sustainable mobility, in
accordance with Vision Zero and Sustainable Development Goals 3.6 and 11.2. This study
investigates the influence of behavioral, environmental, and infrastructural factors on road
crash severity through a comparative evaluation of three modeling approaches: Ordinal
Logistic Regression (OLR), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting
(CatBoost). The analysis was based on 5194 police-reported crash records from Cyprus
covering the period 2015–2024. The dataset included crash severity outcomes together
with traffic violations and roadway and environmental conditions. The results identified
speeding, substance use, and improper turning maneuvers as influential predictors of
crash severity. Among the evaluated models, CatBoost achieved the strongest overall
predictive performance, obtaining the highest accuracy (0.52), weighted area under the
curve (AUC) (0.72), weighted precision (0.50), and weighted recall (0.52), while CatBoost
and XGBoost tied for the highest weighted F1-score (0.48). CatBoost’s overall performance
reflects its ability to efficiently process categorical variables and capture nonlinear relationships
without extensive preprocessing. The findings highlight the dominant role of driver
behavior in determining crash severity and demonstrate how the comparative modeling
framework can serve as an analytical decision-support tool for identifying and monitoring
crash-severity risk factors, prioritizing targeted interventions, and supporting progress
towards safer and more sustainable transportation systems.
Affiliation
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