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Vehicle to Two‐Wheeler Accident Severity Prediction Optimization Using IGWO‐Enhanced LightGBM.

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Title: Vehicle to Two‐Wheeler Accident Severity Prediction Optimization Using IGWO‐Enhanced LightGBM.
Authors: Yi, Xiaojian1,2 (AUTHOR) csust_yxj@163.com, Hu, Lin2,3 (AUTHOR), Li, Qiqi2,3 (AUTHOR), Yan, Ruizhe2,3 (AUTHOR), Habib, Mohammad Rezwan (AUTHOR) mohabib@wiley.com
Source: Journal of Advanced Transportation. 7/8/2026, Vol. 2026, p1-12. 12p.
Subjects: Grey Wolf Optimizer algorithm, Traffic safety, Traffic accidents, Shapley Additive Explanations, Traffic accident statistics
Abstract: The growing complexity of car to two‐wheeler accident patterns underscores the urgent need for sustainable solutions in traffic safety research. Current limitations in analyzing key variables and predicting accident severity hinder the effectiveness of prevention strategies, highlighting a critical gap in achieving sustainability goals. Traditional LightGBM approaches, while valuable, require extensive hyperparameter tuning and often suffer from overfitting, compromising model accuracy and long‐term sustainability. To address these challenges, this study proposes an improved gray wolf optimization (IGWO)‐optimized LightGBM classification model, integrating sustainability principles to enhance accident severity prediction. The IGWO algorithm employs a dynamic weight allocation mechanism for fitness coefficients and a nonlinear step‐size update strategy, significantly improving convergence precision and reducing susceptibility to local optima, key factors for sustainable model performance. Experimental results demonstrate IGWO's superior convergence accuracy compared with baseline GWO (p < 0.05), validating its potential for sustainable optimization. The IGWO–LightGBM model achieves balanced parameter tuning, ensuring robustness and reproducibility, cornerstones of sustainable research. Shapley Additive exPlanations (SHAP) analysis identifies the collision type as the most influential feature (SHAP value: 1.5, 42 percent higher than others), guiding targeted interventions. Environmental and spatiotemporal analyses identify critical sustainability risks; road accidents with higher severity levels occur when adhesion coefficients drop below 0.4 during winter midnight periods. These findings advocate for prioritizing high‐conflict scenarios, low‐adhesion road maintenance, and temporal risk management to foster sustainable traffic safety. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Advanced Transportation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Vehicle to Two‐Wheeler Accident Severity Prediction Optimization Using IGWO‐Enhanced LightGBM.
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  Data: The growing complexity of car to two‐wheeler accident patterns underscores the urgent need for sustainable solutions in traffic safety research. Current limitations in analyzing key variables and predicting accident severity hinder the effectiveness of prevention strategies, highlighting a critical gap in achieving sustainability goals. Traditional LightGBM approaches, while valuable, require extensive hyperparameter tuning and often suffer from overfitting, compromising model accuracy and long‐term sustainability. To address these challenges, this study proposes an improved gray wolf optimization (IGWO)‐optimized LightGBM classification model, integrating sustainability principles to enhance accident severity prediction. The IGWO algorithm employs a dynamic weight allocation mechanism for fitness coefficients and a nonlinear step‐size update strategy, significantly improving convergence precision and reducing susceptibility to local optima, key factors for sustainable model performance. Experimental results demonstrate IGWO&#39;s superior convergence accuracy compared with baseline GWO (p &lt; 0.05), validating its potential for sustainable optimization. The IGWO–LightGBM model achieves balanced parameter tuning, ensuring robustness and reproducibility, cornerstones of sustainable research. Shapley Additive exPlanations (SHAP) analysis identifies the collision type as the most influential feature (SHAP value: 1.5, 42 percent higher than others), guiding targeted interventions. Environmental and spatiotemporal analyses identify critical sustainability risks; road accidents with higher severity levels occur when adhesion coefficients drop below 0.4 during winter midnight periods. These findings advocate for prioritizing high‐conflict scenarios, low‐adhesion road maintenance, and temporal risk management to foster sustainable traffic safety. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: &lt;i&gt;Copyright of Journal of Advanced Transportation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1155/atr/7547060
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1
    Subjects:
      – SubjectFull: Grey Wolf Optimizer algorithm
        Type: general
      – SubjectFull: Traffic safety
        Type: general
      – SubjectFull: Traffic accidents
        Type: general
      – SubjectFull: Shapley Additive Explanations
        Type: general
      – SubjectFull: Traffic accident statistics
        Type: general
    Titles:
      – TitleFull: Vehicle to Two‐Wheeler Accident Severity Prediction Optimization Using IGWO‐Enhanced LightGBM.
        Type: main
  BibRelationships:
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          Name:
            NameFull: Yi, Xiaojian
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            NameFull: Hu, Lin
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            NameFull: Li, Qiqi
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            NameFull: Yan, Ruizhe
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            NameFull: Habib, Mohammad Rezwan
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          Dates:
            – D: 08
              M: 07
              Text: 7/8/2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 01976729
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            – Type: volume
              Value: 2026
          Titles:
            – TitleFull: Journal of Advanced Transportation
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