Maghelal Praveen, Ali Abdulrahim Haroun, Azar Elie, Jayaraman Raja, Khalaf Kinda
Faculty of Resilience, Rabdan Academy, Abu Dhabi, United Arab Emirates.
Industrial and Systems Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.
Heliyon. 2023 Oct 5;9(10):e20694. doi: 10.1016/j.heliyon.2023.e20694. eCollection 2023 Oct.
The World Health Organization (WHO) identifies road traffic injuries as a global health problem. The Eastern-Mediterranean region is particularly suffering from low traffic safety levels, recording the third highest death per capita ratio in the world. It is critical to evaluate and understand the causes of crashes and their severity levels as a first step to devising policies that aim to reduce these causes. Previous studies examining the frequency or severity of crashes present important limitations that motivate the need for the current work. While these studies have investigated the relation of contributing factors to severity of crashes, not until recently the importance of these factors are bring investigated. Even then, less research have explored various Machine Learning models and none in the middle-eastern region. This is critical because the WHO report concludes that the chances of dying in a traffic crash in this region are second only to Africa per 100000 population. This is a first study analyzing the severity of vehicle-to-vehicle crashes among drivers in the United Arab Emirates. Traffic Crash Data was obtained from the Abu Dhabi Police, which consisted of 11,400 observations during the period 2014-2017. Machine learning algorithms, including gradient boosting (GB), support vector machines (SVM), and random forest (RF), were trained and tested to predict crash severity and extract (using feature analysis) its determinants. The models were evaluated using two performance metrics: prediction accuracy and F1-scores. The RF model outperformed both GB and SVM, with the confusion matrix of RF reporting a better prediction for all four crash severity classes. The feature importance analysis indicates that the age of car, age of the injured, and the age of the initiator have the highest effect on severity, which is an important finding as the listed factors were rarely considered in previous studies. Vehicle and road characteristics such as vehicle class, crash type, and lighting are slightly associated with the severity. Consistent with other studies, gender was the least essential predictor of severity. Recommendations are finally provided to the Abu Dhabi Department of Municipalities and Transport (AD-DMT) authority to guide the development of road safety policies and countermeasures to mitigate the occurrence and severity of crashes.
世界卫生组织（WHO）将道路交通伤害确定为一个全球性的健康问题。东地中海地区尤其深受交通安全水平低下之苦，其人均死亡率在世界上排名第三。作为制定旨在减少这些事故成因的政策的第一步，评估和了解撞车事故的成因及其严重程度至关重要。以往研究撞车事故的频率或严重程度存在重要局限性，这促使开展当前这项工作。虽然这些研究调查了促成因素与撞车事故严重程度的关系，但直到最近才开始研究这些因素的重要性。即便如此，探索各种机器学习模型的研究较少，中东地区尚无此类研究。这一点至关重要，因为世卫组织的报告得出结论，该地区每10万人口中死于交通事故的几率仅次于非洲。这是第一项分析阿拉伯联合酋长国驾驶员之间车辆碰撞事故严重程度的研究。交通事故数据取自阿布扎比警方，涵盖2014年至2017年期间的11400条观测数据。对包括梯度提升（GB）、支持向量机（SVM）和随机森林（RF）在内的机器学习算法进行了训练和测试，以预测撞车事故的严重程度并（通过特征分析）提取其决定因素。使用预测准确率和F1分数这两个性能指标对模型进行评估。RF模型的表现优于GB和SVM，RF的混淆矩阵对所有四个撞车事故严重程度类别都给出了更好的预测。特征重要性分析表明，汽车使用年限、伤者年龄和引发事故者年龄对严重程度的影响最大，这是一个重要发现，因为在以往研究中很少考虑所列因素。车辆类别、撞车类型和照明等车辆及道路特征与严重程度有轻微关联。与其他研究一致，性别是严重程度最不重要的预测因素。最后向阿布扎比市政和运输部（AD-DMT）当局提出了建议，以指导道路安全政策和对策的制定，从而减少撞车事故的发生并减轻其严重程度。
