Urban Mobility Institute, Key Laboratory of Road and Traffic Engineering, Ministry of Education at Tongji University, College of Transportation Engineering, Tongji University, China.
Accid Anal Prev. 2021 Aug;158:106153. doi: 10.1016/j.aap.2021.106153. Epub 2021 May 23.
Due to the burgeoning demand for freight movement, freight related road safety threats have been growing substantially. In spite of some research on the factors influencing freight truck-related crashes in major cities, the literature offers limited evidence about the effects of the built environment on the occurrence of those crashes by injury severity. This article uses data from the Los Angeles region in 2010-2019 to explore the relationships between the built environment factors and the spatial distribution of freight truck-related crashes using XGBoost and SHAP methods. Results from the XGBoost model show that variables related to the built environment, in particular demographics, land uses and road network, are highly correlated to freight truck related crashes of all three injury types. The SHAP value plots further indicate the important nonlinear relationships between independent variables and dependent variables. This study also emphasizes the differences in modeling mechanisms between the XGBoost model and traditional statistical models. The findings will help transport planners develop operational measures for resolving the emerging freight truck related traffic safety problems in local communities.
由于货运需求的蓬勃发展，与货运相关的道路安全威胁也在大幅增加。尽管有一些研究探讨了影响主要城市货运卡车相关事故的因素，但文献对于建成环境对这些事故严重程度的影响提供的证据有限。本文使用 2010-2019 年洛杉矶地区的数据，通过 XGBoost 和 SHAP 方法，探索了建成环境因素与货运卡车相关事故的空间分布之间的关系。XGBoost 模型的结果表明，与建成环境相关的变量，特别是人口统计学、土地利用和道路网络，与所有三种伤害类型的货运卡车相关事故高度相关。SHAP 值图进一步表明了自变量和因变量之间的重要非线性关系。本研究还强调了 XGBoost 模型和传统统计模型在建模机制上的差异。研究结果将有助于交通规划者制定解决当地社区新兴货运卡车相关交通安全问题的运营措施。
