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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 01976729 |
| DOI: | 10.1155/atr/7547060 |
