贝叶斯网络
决策树
可能性
运输工程
毒物控制
计算机科学
工程类
逻辑回归
环境卫生
机器学习
医学
作者
Sharaf AlKheder,Fahad AlRukaibi,Ahmad Aiash
标识
DOI:10.1016/j.isatra.2020.06.018
摘要
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.
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