论文标题
交通事故严重性预测的基于树和结合的回归算法的比较分析
Comparison Analysis of Tree Based and Ensembled Regression Algorithms for Traffic Accident Severity Prediction
论文作者
论文摘要
随着时间的推移，城市道路上的交通量迅速增加已改变了全球的交通情况。它还提高了在最坏情况下可能是严重和致命的道路事故比率。为了改善城市道路上的交通安全及其管理，需要预测严重程度的事故。各种机器学习模型被用于事故预测。在这项研究中，将基于树木的集合模型（随机森林，Adaboost，Extra Tree和梯度提升）和两个统计模型（逻辑回归随机梯度下降）的合奏进行了投票分类器的比较，以预测道路事故的严重性。与事故严重程度密切相关的重要特征是通过随机森林确定的。分析证明了随机森林是最佳性能模型，具有0.974精度，0.954精度，0.930召回和0.942 F-评分，使用20个最重要的功能，与其他技术事故严重性分类相比，使用了20个最重要的功能。
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.
