Stiles Jonathan, Kar Armita, Lee Jinhyung, Miller Harvey J
Department of Geography, The Ohio State University, Columbus, OH.
Department of Geography and Environment, Western University, London, Canada.
Transp Res Rec. 2023 Apr;2677(4):15-27. doi: 10.1177/03611981211044454. Epub 2021 Sep 23.
Stay-at-home policies in response to COVID-19 transformed high-volume arterials and highways into lower-volume roads, and reduced congestion during peak travel times. To learn from the effects of this transformation on traffic safety, an analysis of crash data in Ohio's Franklin County, U.S., from February to May 2020 is presented, augmented by speed and network data. Crash characteristics such as type and time of day are analyzed during a period of stay-at-home guidelines, and two models are estimated: (i) a multinomial logistic regression that relates daily volume to crash severity; and (ii) a Bayesian hierarchical logistic regression model that relates increases in average road speeds to increased severity and the likelihood of a crash being fatal. The findings confirm that lower volumes are associated with higher severity. The opportunity of the pandemic response is taken to explore the mechanisms of this effect. It is shown that higher speeds were associated with more severe crashes, a lower proportion of crashes were observed during morning peaks, and there was a reduction in types of crashes that occur in congestion. It is also noted that there was an increase in the proportion of crashes related to intoxication and speeding. The importance of the findings lay in the risk to essential workers who were required to use the road system while others could telework from home. Possibilities of similar shocks to travel demand in the future, and that traffic volumes may not recover to previous levels, are discussed, and policies are recommended that could reduce the risk of incapacitating and fatal crashes for continuing road users.
应对新冠疫情的居家政策将高流量的干道和高速公路转变为流量较低的道路,并减少了高峰出行时段的拥堵。为了解这种转变对交通安全的影响,本文对美国俄亥俄州富兰克林县2020年2月至5月的撞车数据进行了分析,并结合了速度和网络数据。在居家指导期间,分析了撞车类型和时间等撞车特征,并估计了两个模型:(i)一个将日流量与撞车严重程度相关联的多项逻辑回归模型;(ii)一个将平均道路速度的增加与严重程度的增加以及撞车致死可能性相关联的贝叶斯分层逻辑回归模型。研究结果证实,流量较低与更严重的后果相关。利用应对疫情的契机,探讨了这种影响的机制。结果表明,较高的速度与更严重的撞车事故相关,早高峰期间观察到的撞车事故比例较低,拥堵时发生的撞车事故类型有所减少。还指出,与醉酒和超速相关的撞车事故比例有所增加。这些研究结果的重要性在于,对于那些必须使用道路系统而其他人可以在家远程工作的 essential workers 存在风险。讨论了未来出行需求可能受到类似冲击以及交通流量可能无法恢复到先前水平的可能性,并建议采取政策以降低持续使用道路的用户发生致残和致命撞车事故的风险。