Shin Eun Jin
Department of Public Administration and Graduate School of Governance, Sungkyunkwan University, 25-2 Sungkyunkwan-ro Hoam hall 50908, Jongno-gu, Seoul 03063, Republic of Korea.
J Safety Res. 2024 Feb;88:244-260. doi: 10.1016/j.jsr.2023.11.012. Epub 2023 Dec 2.
Despite evidence showing higher fatality rates in freight-related crashes, there has been limited exploration of their spatial distribution and factors associated with such distribution. This gap in the literature primarily stems from the focus of existing studies on micro-level factors predicting the frequency or severity of injuries in freight crashes. The present study delves into the factors contributing to freight crashes at the neighborhood level, particularly focusing on different types of freight crashes: collisions involving a freight vehicle and a passenger vehicle, crashes between freight vehicles, and freight vehicle-non-motorized crashes.
This study analyzes traffic crash data from the urbanized region of Seoul, collected between 2016 and 2019. To effectively deal with spatial autocorrelation and model different types of crashes in a unified framework, a Bayesian multivariate conditional autoregressive model was employed.
Findings show substantial differences in the factors associated with various types of freight crashes. The predictors for crashes between freight vehicles diverge significantly from those for freight vehicle-non-motorized crashes. Crashes between freight vehicles are relatively more influenced by road network structure, while freight crashes involving non-motorized users are relatively more affected by the built environment and freight facilities than the other crash types examined. Freight vehicle-passenger vehicle crashes fall into an intermediate category, sharing most predictors with either of the other two types of freight crashes.
The findings of this study offer valuable lessons for transportation practitioners and policymakers. They can guide the formulation of effective land use policies and infrastructure planning, specifically designed to address the unique characteristics of different types of freight crashes.
尽管有证据表明与货运相关的撞车事故死亡率更高，但对其空间分布以及与这种分布相关的因素的探索却很有限。文献中的这一空白主要源于现有研究侧重于预测货运撞车事故中受伤频率或严重程度的微观层面因素。本研究深入探讨了邻里层面导致货运撞车事故的因素，特别关注不同类型的货运撞车事故：涉及货运车辆和乘用车的碰撞、货运车辆之间的撞车事故以及货运车辆与非机动车的撞车事故。
本研究分析了2016年至2019年期间收集的首尔城市化地区的交通事故数据。为了有效处理空间自相关并在统一框架中对不同类型的撞车事故进行建模，采用了贝叶斯多元条件自回归模型。
研究结果表明，与各类货运撞车事故相关的因素存在显著差异。货运车辆之间撞车事故的预测因素与货运车辆与非机动车撞车事故的预测因素有很大不同。货运车辆之间的撞车事故相对受道路网络结构的影响更大，而涉及非机动车使用者的货运撞车事故相比其他所研究的撞车类型，相对更受建成环境和货运设施的影响。货运车辆与乘用车的撞车事故属于中间类别，与其他两种类型的货运撞车事故中的任何一种都共享大多数预测因素。
本研究的结果为交通从业者和政策制定者提供了宝贵的经验教训。它们可以指导制定有效的土地利用政策和基础设施规划，特别是针对不同类型货运撞车事故的独特特征而设计的规划。
