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<p>This study develops a systematic analysis of Level Crossing configurations combining road and rail factors. It provides a decision-support framework for prioritizing safety equipment deployment at railway level crossings using explainable machine learning and robust clustering. Accident risk at level crossings emerges from the interaction of heterogeneous infrastructural, operational, and environmental variables, whose combined effects cannot be captured through single-factor analysis. Rather than focusing solely on prediction, the methodology combines dimensionality reduction, clustering algorithms, supervised classification, and SHAP-based explainability to identify homogeneous crossing typologies and classify them according to structural risk profiles. Separate analyses of road and rail characteristics enable clearer identification of their interdependencies and improve interpretability. Results show that the highest-risk clusters are characterized by the joint presence of high volumes of road and rail traffic, dense surrounding road environments, confirming the multifactorial nature of accident risk. The cluster-based approach mitigates the stochasticity of rare events and reveals robust comparative patterns that support comparative risk profiling of level crossing infrastructure. The study demonstrates how interpretable machine learning can be structured as a decision-aid framework for exploratory risk priorisation for infrastructure management.</p>

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Prioritizing Railway Level Crossing Safety Equipment Using Stabilized and Explainable Machine Learning: A Large-Scale Study of the French Railway Network

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • Nadège Reboul,
  • Pierre-Antoine Laharotte,
  • Rim Brahim,
  • Philippe Bon,
  • Simon Collart-Dutilleul,
  • Nour-Eddin El Faouzi

摘要

This study develops a systematic analysis of Level Crossing configurations combining road and rail factors. It provides a decision-support framework for prioritizing safety equipment deployment at railway level crossings using explainable machine learning and robust clustering. Accident risk at level crossings emerges from the interaction of heterogeneous infrastructural, operational, and environmental variables, whose combined effects cannot be captured through single-factor analysis. Rather than focusing solely on prediction, the methodology combines dimensionality reduction, clustering algorithms, supervised classification, and SHAP-based explainability to identify homogeneous crossing typologies and classify them according to structural risk profiles. Separate analyses of road and rail characteristics enable clearer identification of their interdependencies and improve interpretability. Results show that the highest-risk clusters are characterized by the joint presence of high volumes of road and rail traffic, dense surrounding road environments, confirming the multifactorial nature of accident risk. The cluster-based approach mitigates the stochasticity of rare events and reveals robust comparative patterns that support comparative risk profiling of level crossing infrastructure. The study demonstrates how interpretable machine learning can be structured as a decision-aid framework for exploratory risk priorisation for infrastructure management.