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Abstract <p>Glare from both low sun angles under daytime conditions and headlight exposure under nighttime conditions degrades driver visibility and remains a persistent challenge for safe roadway operation, yet the mechanisms governing outcome differentiation under glare conditions across complex roadway, vehicle, and driver interactions remain insufficiently understood. Prior studies have primarily relied on econometric or conventional machine learning approaches, limiting their ability to capture nonlinear relationships and deliver transparent, decision-oriented insights. This study analyzes 18,073 glare-related traffic records from Texas from 2017 to 2024 and evaluates four tabular deep learning architectures (TabR, MITRA, TabTransformer, and ARM-Net) to model three discrete outcome levels under glare conditions. A hybrid SMOTEENN resampling strategy is applied to address class imbalance, and SHapley Additive exPlanations (SHAP) are used to provide class-level and instance-level interpretability. Results show that transformer-based tabular models consistently outperform attention-based and residual baselines in predictive performance and outcome separability, while explainability analysis highlights restraint use, airbag deployment status, lighting environment, roadway context, speed regime, and driver demographic characteristics as the dominant contributors to outcome differentiation. Comparison with tree-based baselines further indicates that, although ensemble methods achieve competitive overall accuracy, the leading tabular deep learning architectures offer stronger sensitivity to fatal crash outcomes and support instance-level explainability through SHAP. These findings demonstrate the value of combining tabular deep learning with explainable artificial intelligence to support transparent, scalable modeling of glare-affected traffic outcomes and to inform targeted, data-driven strategies for mitigating glare-related transportation risks.</p> Graphical abstract <p></p>

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Predicting Glare-Related Traffic Outcomes with Transformer-Based Explainable Tabular Deep Learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • Shriyank Somvanshi,
  • Anika Baitullah,
  • Sharif Ahmed Rafat,
  • Subasish Das

摘要

Abstract

Glare from both low sun angles under daytime conditions and headlight exposure under nighttime conditions degrades driver visibility and remains a persistent challenge for safe roadway operation, yet the mechanisms governing outcome differentiation under glare conditions across complex roadway, vehicle, and driver interactions remain insufficiently understood. Prior studies have primarily relied on econometric or conventional machine learning approaches, limiting their ability to capture nonlinear relationships and deliver transparent, decision-oriented insights. This study analyzes 18,073 glare-related traffic records from Texas from 2017 to 2024 and evaluates four tabular deep learning architectures (TabR, MITRA, TabTransformer, and ARM-Net) to model three discrete outcome levels under glare conditions. A hybrid SMOTEENN resampling strategy is applied to address class imbalance, and SHapley Additive exPlanations (SHAP) are used to provide class-level and instance-level interpretability. Results show that transformer-based tabular models consistently outperform attention-based and residual baselines in predictive performance and outcome separability, while explainability analysis highlights restraint use, airbag deployment status, lighting environment, roadway context, speed regime, and driver demographic characteristics as the dominant contributors to outcome differentiation. Comparison with tree-based baselines further indicates that, although ensemble methods achieve competitive overall accuracy, the leading tabular deep learning architectures offer stronger sensitivity to fatal crash outcomes and support instance-level explainability through SHAP. These findings demonstrate the value of combining tabular deep learning with explainable artificial intelligence to support transparent, scalable modeling of glare-affected traffic outcomes and to inform targeted, data-driven strategies for mitigating glare-related transportation risks.

Graphical abstract