Road accidents have significant economic and societal costs, with a small
number of severe accidents accounting for a large portion of these costs.
Predicting accident severity can help in the proactive approach to road safety
by identifying potential unsafe road conditions and taking well-informed
actions to reduce the number of severe accidents. This study investigates the
effectiveness of the Random Forest machine learning algorithm for predicting
the severity of an accident. The model is trained on a dataset of accident
records from a large metropolitan area and evaluated using various metrics.
Hyperparameters and feature selection are optimized to improve the model's
performance. The results show that the Random Forest model is an effective tool
for predicting accident severity with an accuracy of over 80%. The study also
identifies the top six most important variables in the model, which include
wind speed, pressure, humidity, visibility, clear conditions, and cloud cover.
The fitted model has an Area Under the Curve of 80%, a recall of 79.2%, a
precision of 97.1%, and an F1 score of 87.3%. These results suggest that the
proposed model has higher performance in explaining the target variable, which
is the accident severity class. Overall, the study provides evidence that the
Random Forest model is a viable and reliable tool for predicting accident
severity and can be used to help reduce the number of fatalities and injuries
due to road accidents in the United States

这项研究调查了使用随机森林机器学习算法预测事故严重程度的有效性，在大型都市地区的事故记录数据集上训练模型并使用各种指标进行评估，结果显示随机森林模型能够有效地预测事故严重程度，准确率超过 80%，并确定了模型中最重要的变量，包括风速、气压、湿度、可见度、晴朗状况和云量，这些结果表明，提出的模型在解释目标变量 —— 事故严重程度类别方面具有更高的性能，因此可以帮助减少美国道路事故造成的死亡和伤害数量。