Rapid increase of traffic volume on urban roads over time has changed the traffic scenario globally. It has also increased the ratio of road accidents that can be severe and fatal in the worst case. To improve traffic safety and its management on urban roads, there is a need for prediction of severity level of accidents. Various machine learning models are being used for accident prediction. In this study, tree based ensemble models (Random Forest, AdaBoost, Extra Tree, and Gradient Boosting) and ensemble of two statistical models (Logistic Regression Stochastic Gradient Descent) as voting classifiers are compared for prediction of road accident severity. Significant features that are strongly correlated with the accident severity are identified by Random Forest. Analysis proved Random Forest as the best performing model with highest classification results with 0.974 accuracy, 0.954 precision, 0.930 recall and 0.942 F-score using 20 most significant features as compared to other techniques classification of road accidents severity.

中文翻译：

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基于树和集成回归算法的交通事故严重程度预测比较分析

随着时间的推移，城市道路交通量的迅速增加改变了全球的交通状况。它还增加了在最坏的情况下可能是严重和致命的道路事故的比例。为了提高城市道路的交通安全和管理水平，需要对事故的严重程度进行预测。各种机器学习模型被用于事故预测。在本研究中，比较了基于树的集成模型（随机森林、AdaBoost、Extra Tree 和梯度提升）和作为投票分类器的两个统计模型（逻辑回归随机梯度下降）的集成，以预测道路事故的严重程度。与事故严重程度密切相关的重要特征由随机森林识别。