Junaid Muhammad, Jiang Chaozhe, Alotaibi Saleh, Wang Tong, Almarhab Yahya
School of Transportation and Logistics, Southwest Jiaotong University (SWJTU), Chengdu, China.
Civil and Environmental Engineering Department, Faculty of Engineering-Rabigh Branch, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
Sci Rep. 2025 Sep 2;15(1):32317. doi: 10.1038/s41598-025-16477-5.
Road traffic crashes claim around 1.19 million lives annually worldwide, with over half of the fatalities involving vulnerable road users (VRUs). While several studies have explored the risk factors associated with specific categories of VRUs in Pakistan, research focusing on VRUs collectively, considering all categories and their unique safety challenges, remains limited. This study aims to examine the influence of various risk factors on the severity of injuries resulting from crashes involving VRUs, using a three-year dataset (2021-2023). The study evaluated the effectiveness of six boosting-based ensemble machine learning classifiers across multiple evaluation metrics. The findings indicated that boosting with decision stumps outperformed extreme gradient boosting, light gradient boosting, histogram-based gradient boosting, categorical boosting, and adaptive boosting in terms of recall, F-score, and accuracy. The partial dependence plots demonstrated that VRUs aged 55 years or older, collisions with other VRU groups, involvement of vans and heavy vehicles, rainy weather, the COVID-19 period, and the existence of painted medians increase the likelihood of severe injury in crashes involving VRUs. The pairwise SHAP interaction plot also supported these findings by illustrating that the interaction between different vehicle types (vans and heavy vehicles), adverse weather conditions, and VRU crashes during the COVID-19 lockdown period elevates the risk of severe crashes. Based on the study findings, several policy recommendations were proposed, including implementing education and awareness programs, developing strategies to manage mixed traffic, and improving road infrastructure to enhance safety for all VRU groups.
全球范围内，道路交通事故每年造成约119万人死亡，其中超过半数的死亡涉及道路弱势群体（VRU）。虽然已有多项研究探讨了巴基斯坦特定类别的道路弱势群体相关的风险因素，但针对所有类别及其独特安全挑战的道路弱势群体进行综合研究仍然有限。本研究旨在利用一个三年期数据集（2021 - 2023年），考察各类风险因素对涉及道路弱势群体的撞车事故中受伤严重程度的影响。该研究在多个评估指标上评估了六种基于提升的集成机器学习分类器的有效性。研究结果表明，在召回率、F值和准确率方面，使用决策树桩的提升方法优于极端梯度提升、轻梯度提升、基于直方图的梯度提升、分类提升和自适应提升。部分依赖图表明，55岁及以上的道路弱势群体、与其他道路弱势群体群体的碰撞、面包车和重型车辆的参与、雨天、新冠疫情期间以及有涂漆的中央分隔带的存在，会增加涉及道路弱势群体的撞车事故中严重受伤的可能性。成对SHAP交互图也支持了这些发现，它表明不同车辆类型（面包车和重型车辆）、恶劣天气条件以及新冠疫情封锁期间道路弱势群体的撞车事故之间的相互作用会增加严重撞车事故的风险。基于研究结果，提出了多项政策建议，包括实施教育和宣传计划、制定管理混合交通的策略以及改善道路基础设施，以提高所有道路弱势群体群体的安全性。
