Single-vehicle crashes are more fatality-concentrated and have posed increasing challenges in traffic safety, which is of great research necessity. Tremendous previous studies have conducted relevant analysis with econometric modeling approaches, whereas the ability of non-parametric methods to predict crash severity is still smattering of knowledge. Consequently, the main objective of this paper is to conduct single-vehicle crash severity prediction with different tree-based and non-parameter models. An alternate aim is to identify the intrinsic mechanism of how contributing factors determine single-vehicle crash severity. By virtue of Grid-Search method, this paper conducted fine-tuning of different models to obtain the best performances based on five crash severity sub-datasets. For model evaluation, the accuracy indicators were calculated in training, validation and test sets, respectively. Besides, feature importance extraction was undertaken based on the results of model comparison. The finding indicated that these models didn’t exhibit a huge performance difference for crash severity prediction in the same severity level; however, the performances of the models did vary among different datasets, with an average training accuracy of 99.27 %, 96.4 %, 86.98 %, 86.84 %, 71.76 % in fatal injury, severe injury, visible injury, complaint of pain, PDO crash datasets, respectively. Additionally, it was found that in each severity dataset, the indicator urban freeways is a determinant factor that leads to the occurrence of crashes while rural freeways is more related to more severe crashes (i.e., fatal and severe crashes). This paper can provide valuable information for model selection and tuning in accident severity prediction. Future research could consider the influences that temporal instability of contributing features has on the model performances.

单车事故更集中于死亡，并在交通安全方面提出了越来越多的挑战，这在很大程度上需要进行研究。先前的大量研究已经使用计量经济学建模方法进行了相关分析，而非参数方法预测碰撞严重性的能力仍然是知识的少量散布。因此，本文的主要目的是使用不同的基于树的模型和非参数模型进行单车碰撞严重性预测。另一个目标是确定影响因素如何确定单车碰撞严重性的内在机制。借助网格搜索方法，本文基于五个碰撞严重程度子数据集对不同模型进行了微调，以获得最佳性能。为了进行模型评估，分别在训练，验证和测试集中计算准确性指标。此外，基于模型比较的结果进行了特征重要性提取。研究结果表明，在相同严重性级别下，这些模型在碰撞严重性预测上没有表现出巨大的性能差异；但是，模型的性能在不同的数据集之间确实有所不同，在致命伤害，严重伤害，可见伤害，疼痛投诉，PDO崩溃数据集中，平均训练准确性为99.27％，96.4％，86.98％，86.84％，71.76％ ， 分别。此外，发现在每个严重性数据集中，指标 研究结果表明，在相同严重性级别下，这些模型在碰撞严重性预测上没有表现出巨大的性能差异；但是，模型的性能在不同的数据集之间确实有所不同，在致命伤害，严重伤害，可见伤害，疼痛投诉，PDO崩溃数据集中，平均训练准确性为99.27％，96.4％，86.98％，86.84％，71.76％ ， 分别。此外，发现在每个严重性数据集中，指标 研究结果表明，在相同严重性级别下，这些模型在碰撞严重性预测上没有表现出巨大的性能差异；但是，模型的性能在不同的数据集之间确实有所不同，在致命伤害，严重伤害，可见伤害，疼痛投诉，PDO崩溃数据集中，平均训练准确性为99.27％，96.4％，86.98％，86.84％，71.76％ ， 分别。此外，发现在每个严重性数据集中，指标城市高速公路是导致交通事故发生的决定性因素，而农村高速公路则与更严重的交通事故（即致命和严重交通事故）更为相关。本文可以为事故严重性预测中的模型选择和调整提供有价值的信息。未来的研究可以考虑贡献特征的时间不稳定性对模型性能的影响。