支持向量机
事故(哲学)
预测建模
卷积神经网络
计算机科学
交通事故
随机森林
毒物控制
人工智能
机器学习
运输工程
工程类
医学
哲学
环境卫生
认识论
作者
Nihal Abuzinadah,Turki Aljrees,Xuqing Chen,Muhammad Umer,Omar Aboulola,Shahzaib Tahir,Ala’ Abdulmajid Eshmawi,Khaled Alnowaiser,Imran Ashraf
标识
DOI:10.1177/03611981231220656
摘要
Although there have been improvements in traffic safety measures, the frequency of traffic accidents continues to persist. Developing countries experience a significant impact from traffic accidents with respect to fatalities and property damage. Traffic accidents happen for multiple reasons, involving traffic conditions, driving violations, driver misjudgments, and so forth. Severe casualties may lead to fatalities; therefore, accident severity prediction might help reduce the chances of fatalities. This research makes use of a U.S. road accident dataset that contains the most relevant 32 factors related to accidents. For obtaining accurate prediction of traffic accident severity, this research proposes a solution based on an ensemble of random forest and support vector classifiers that is trained using deep convoluted features. Features are extracted from the road accident dataset using a convolutional neural network (CNN). The performance of models using original features and CNN features is analyzed that shows the superiority of convoluted features. Experimental results involving the use of several well-known machine learning models indicate that the proposed model can obtain an accuracy of 99.99% for traffic accident severity prediction. The efficacy of the proposed model is validated against existing state-of-the-art approaches.
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