Drivers can sustain serious injuries in traffic accidents. In this study,
traffic crashes on Florida's Interstate-95 from 2016 to 2021 were gathered, and
several classification methods were used to estimate the severity of driver
injuries. In the feature selection method, logistic regression was applied. To
compare model performances, various model assessment matrices such as accuracy,
recall, and area under curve (AUC) were developed. The Adaboost algorithm
outperformed the others in terms of recall and AUC. SHAP values were also
generated to explain the classification model's results. This analytical study
can be used to examine factors that contribute to the severity of driver
injuries in crashes.

通过对佛罗里达洲 2016 年至 2021 年之间的 95 号州际公路上的交通事故进行研究，使用了几种分类方法来估计驾驶员受伤的严重程度，并应用特征选择方法进行了逻辑回归。通过准确率、召回率和曲线下面积等多种模型评估方法，比较了模型性能，Adaboost 算法在召回率和曲线下面积方面表现优异。同时生成了 SHAP 值以解释分类模型的结果，这项分析研究可以用于检查导致交通事故中驾驶员受伤严重程度的因素。