Green and Low Carbon Transport Research Centre, Sichuan Communication Surveying and Design Institute Co., Ltd, Chengdu, China.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China.
PLoS One. 2024 Nov 22;19(11):e0314133. doi: 10.1371/journal.pone.0314133. eCollection 2024.
The purpose of this study is to explore nonlinear and threshold effects of traffic statuses and road geometries, as well as their interactions, on traffic accident severity. In contrast to earlier research that primarily defined road alignment qualitatively as straight or curved, flat or slope, this study focused on the design elements of road geometry at accident locations. Additionally, this study considers the traffic conditions on the day of the accident, rather than the average annual traffic data as previous studies have done. To achieve this, we collected road design documents, traffic-related data, and 2023 accident data from the Suining section of the G42 Expressway in China. Using this dataset, we tested the classification performance of four machine learning models, including eXtreme Gradient Boosting, Gradient Boosted Decision Tree, Random Forest, and Light Gradient Boosting Machine. The optimal Random Forest model was employed to identify the key factors infulencing traffic accident severity, and the partial dependence plot was introduced to visualize the relationship between severity and various single and two-factor variables. The results indicate that the percentage of trucks, daily traffic volume, slope length, road grade, curvature, and curve length all exhibit significant nonlinear and threshold effects on accident severity. This reveals sepecific road and traffic features associated with varying levels of accident severity along the highway section examined in this study. The findings of this study will provide data-driven recommendations for highway design and daily safety management to reduce the severity of traffic accidents.
本研究旨在探讨交通状态和道路几何形状的非线性和阈值效应，以及它们之间的相互作用对交通事故严重程度的影响。与早期主要将道路线形定性地定义为直道或弯道、平坦或斜坡的研究不同，本研究侧重于事故发生地点的道路几何形状的设计要素。此外，本研究考虑了事故发生当天的交通状况，而不是像之前的研究那样采用平均年交通数据。为此，我们收集了道路设计文件、交通相关数据以及中国 G42 高速公路遂宁段 2023 年的事故数据。使用该数据集，我们测试了四种机器学习模型的分类性能，包括极端梯度提升、梯度提升决策树、随机森林和轻量级梯度提升机。最优的随机森林模型被用于识别影响交通事故严重程度的关键因素，并且引入了部分依赖图来可视化严重程度与各种单因素和双因素变量之间的关系。结果表明，卡车比例、日交通量、坡度长度、道路坡度、曲率和曲线长度对事故严重程度均呈现出显著的非线性和阈值效应。这揭示了与研究路段上不同交通事故严重程度相关的特定道路和交通特征。本研究的发现将为高速公路设计和日常安全管理提供数据驱动的建议，以降低交通事故的严重程度。
