Department of Urban and Regional Planning, Charles E. Schmidt College of Science, Florida Atlantic University, 777 Glades road, SO 284, Boca Raton, FL 33431, United states.
J Safety Res. 2020 Dec;75:41-50. doi: 10.1016/j.jsr.2020.07.004. Epub 2020 Aug 8.
Many U.S. cities have adopted the Vision Zero strategy with the specific goal of eliminating traffic-related deaths and injuries. To achieve this ambitious goal, safety professionals have increasingly called for the development of a safe systems approach to traffic safety. This approach calls for examining the macrolevel risk factors that may lead road users to engage in errors that result in crashes. This study explores the relationship between built environment variables and crash frequency, paying specific attention to the environmental mediating factors, such as traffic exposure, traffic conflicts, and network-level speed characteristics.
Three years (2011-2013) of crash data from Mecklenburg County, North Carolina, were used to model crash frequency on surface streets as a function of built environment variables at the census block group level. Separate models were developed for total and KAB crashes (i.e., crashes resulting in fatalities (K), incapacitating injuries (A), or non-incapacitating injuries (B)) using the conditional autoregressive modeling approach to account for unobserved heterogeneity and spatial autocorrelation present in data.
Built environment variables that are found to have positive associations with both total and KAB crash frequencies include population, vehicle miles traveled, big box stores, intersections, and bus stops. On the other hand, the number of total and KAB crashes tend to be lower in census block groups with a higher proportion of two-lane roads and a higher proportion of roads with posted speed limits of 35 mph or less.
This study demonstrates the plausible mechanism of how the built environment influences traffic safety. The variables found to be significant are all policy-relevant variables that can be manipulated to improve traffic safety. Practical Applications: The study findings will shape transportation planning and policy level decisions in designing the built environment for safer travels.
许多美国城市都采用了“零愿景”战略,其具体目标是消除与交通相关的死亡和伤害。为了实现这一雄心勃勃的目标,安全专业人员越来越呼吁制定安全系统方法来保障交通安全。这种方法要求检查可能导致道路使用者犯错从而导致事故的宏观风险因素。本研究探讨了建成环境变量与事故频率之间的关系,特别关注环境中介因素,如交通暴露、交通冲突和网络级速度特征。
本研究使用北卡罗来纳州梅克伦堡县三年(2011-2013 年)的事故数据,根据普查区组级别建成环境变量来构建街道表面事故频率模型。使用条件自回归模型方法分别为总事故和 KAB 事故(即导致死亡(K)、丧失能力伤害(A)或非丧失能力伤害(B)的事故)开发模型,以解决数据中存在的未观测异质性和空间自相关问题。
与总事故和 KAB 事故频率都呈正相关的建成环境变量包括人口、车辆行驶里程、大型箱式商店、交叉口和公共汽车站。另一方面,在总事故和 KAB 事故数量上,具有较高比例的双车道和较低比例的限速为 35 英里/小时或以下的道路的普查区组,事故数量往往较低。
本研究展示了建成环境如何影响交通安全的可能机制。被发现具有重要意义的变量都是可以操纵的政策相关变量,以提高交通安全。
研究结果将影响交通规划和政策层面在设计更安全的出行环境时的决策。