Civil Engineering Department, College of Engineering and Petroleum, Kuwait University, Kuwait.
Civil Engineering Department, College of Engineering and Petroleum, Kuwait University, Kuwait.
ISA Trans. 2020 Nov;106:213-220. doi: 10.1016/j.isatra.2020.06.018. Epub 2020 Jun 24.
Traffic accidents are costing the world more than a million lives yearly alongside monetary losses, especially in the Gulf Cooperation Council region. This situation raised the need to examine potential risk factors contributing to traffic accident severities. In this paper, three data mining models were applied to provide a comprehensive analysis of risk factors related to traffic accidents' severities. One of the used models was a decision tree to examine the correlations between potential risk factors. The other applied models were Bayesian Network and linear Support Vector Machine. The results confirmed that pedestrians were the most vulnerable road users compared to drivers and passengers. Male drivers and front seat-passengers were more exposed to severe or fatal injury. Similarly, elderly drivers had higher odds of having severe or fatal injuries. Road classifications and accident types were also considered significant variables related to traffic accidents' injuries. Utilizing seat belt could lessen the level of injury. Regarding the performance of the applied models, Bayesian network was more accurate in predicting the variables compared to other models.
交通事故每年在全球造成超过 100 万人死亡和经济损失，尤其是在海湾合作委员会地区。这种情况促使我们有必要研究导致交通事故严重程度的潜在风险因素。本文应用了三种数据挖掘模型，对与交通事故严重程度相关的风险因素进行了全面分析。其中一个使用的模型是决策树，用于检验潜在风险因素之间的相关性。另外两个应用的模型是贝叶斯网络和线性支持向量机。结果证实，与司机和乘客相比，行人是最脆弱的道路使用者。男性司机和前排乘客更容易受到重伤或致命伤。同样，老年司机重伤或致命伤的几率更高。道路分类和事故类型也被认为是与交通事故伤害相关的重要变量。系安全带可以降低受伤程度。就应用模型的性能而言，贝叶斯网络在预测变量方面比其他模型更准确。
