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May 31, 2026Quality & QuantityOpen Access

Interpretable and robust tree-based methodology for imbalanced classification in driving safety assessment

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Authors

GVGiulia VannucciFederico II University HospitalADA D’AmbrosioFederico II University HospitalRSRoberta SicilianoFederico II University Hospital

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Implication

Randomized trial identifies causal relationships influencing safety-critical driving outcomes, suggesting improved risk assessment methods.

Key Points

  • The study aims to uncover causal relationships between human behaviors, external factors, and safety-critical driving events.
  • Analyzed the SHRP2 naturalistic driving database for context-specific driving situations.
  • Employed a three-step framework incorporating multiple correspondence analysis and classification trees.
  • Utilized random forests and global sensitivity analysis to evaluate variable importance in driving safety.
  • Identified latent behavioral typologies influencing the likelihood of crashes or near-crashes.
  • Demonstrated that contextual factors significantly affect the risk of safety-critical driving events.
  • Provided actionable insights for improving road safety through a transparent and interpretable framework.

Cite This Study

Vannucci et al. (2026) studied this question.

synapsesocial.com/papers/6a1bd1f65783ba022b6fd58fhttps://doi.org/10.1007/s11135-026-02877-w
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