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

翻译：道路交通事故造成了巨大的经济和社会成本，其中少数严重事故占据了这些成本的绝大部分。预测事故严重性有助于采取主动措施提升道路安全，通过识别潜在的不安全道路状况并采取明智行动来减少严重事故的数量。本研究探讨了随机森林机器学习算法在预测事故严重性方面的有效性。该模型使用来自大都市区的事故记录数据集进行训练，并通过多种指标进行评估。通过优化超参数和特征选择来提升模型性能。结果表明，随机森林模型是预测事故严重性的有效工具，准确率超过80%。研究还识别了模型中最重要的六个变量，包括风速、气压、湿度、能见度、晴朗条件和云量。拟合模型的曲线下面积为80%，召回率为79.2%，精确率为97.1%，F1分数为87.3%。这些结果表明，所提出的模型在解释目标变量（即事故严重性等级）方面具有较高性能。总体而言，本研究提供了证据表明随机森林模型是预测事故严重性的可行且可靠的工具，可用于帮助减少美国因道路交通事故导致的死亡和受伤人数。