School of Transportation, Southeast University, 2 Si pai lou, Nanjing, 210096, PR China.
Accid Anal Prev. 2021 Apr;153:106034. doi: 10.1016/j.aap.2021.106034. Epub 2021 Feb 26.
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.
单车事故的致死率更高，对道路安全构成了越来越大的挑战，因此非常有必要进行相关研究。此前已有大量研究采用计量经济学建模方法进行了相关分析，而非参数方法预测事故严重程度的能力仍知之甚少。因此，本文的主要目的是采用不同的基于树的和非参数模型对单车事故严重程度进行预测。另一个目的是确定各因素对单车事故严重程度的影响机制。本文通过网格搜索法对不同模型进行微调，以基于五个事故严重程度子数据集获得最佳性能。对于模型评估，分别在训练集、验证集和测试集中计算准确性指标。此外，还基于模型比较的结果进行了特征重要性提取。结果表明，这些模型在同一严重程度水平下对事故严重程度的预测没有显著的性能差异；然而，模型在不同数据集之间的性能存在差异，致命伤害、严重伤害、可见伤害、疼痛投诉、PDO 事故数据集的平均训练准确率分别为 99.27%、96.4%、86.98%、86.84%、71.76%。此外，还发现，在每个严重程度数据集，城市高速公路指标是导致事故发生的决定因素，而农村高速公路与更严重的事故（即致命和严重事故）更为相关。本文可为事故严重程度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各特征时间不稳定性对模型性能的影响。
