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.

每年的交通事故不仅给世界造成一百万人的生命损失，还造成了金钱损失，尤其是在海湾合作委员会地区。这种情况导致需要检查导致交通事故严重程度的潜在风险因素。本文采用了三种数据挖掘模型对交通事故严重程度相关的风险因素进行了综合分析。使用的模型之一是决策树，用于检查潜在风险因素之间的相关性。其他应用的模型是贝叶斯网络和线性支持向量机。结果证实，与驾驶员和乘客相比，行人是最脆弱的道路使用者。男性驾驶员和前排座位乘客更容易受到严重或致命的伤害。同样，年长的驾驶员遭受重伤或致命伤害的几率更高。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。使用安全带可以减轻伤害程度。关于所应用模型的性能，与其他模型相比，贝叶斯网络在预测变量方面更为准确。