The University of Tennessee, Knoxville, United States.
The University of Tennessee, Knoxville, United States.
Accid Anal Prev. 2019 Jun;127:118-133. doi: 10.1016/j.aap.2019.01.014. Epub 2019 Mar 7.
Connected and automated vehicles have enabled researchers to use big data for development of new metrics that can enhance transportation safety. Emergence of such a big data coupled with computational power of modern computers have enabled us to obtain deeper understanding of instantaneous driving behavior by applying the concept of "driving volatility" to quantify variations in driving behavior. This paper brings in a methodology to quantify variations in vehicular movements utilizing longitudinal and lateral volatilities and proactively studies the impact of instantaneous driving behavior on type of crashes at intersections. More than 125 million Basic Safety Message data transmitted between more than 2800 connected vehicles were analyzed and integrated with historical crash and road inventory data at 167 intersections in Ann Arbor, Michigan, USA. Given that driving volatility represents the vehicular movement and control, it is expected that erratic longitudinal/lateral movements increase the risk of crash. In order to capture variations in vehicle control and movement, we quantified and used 30 measures of driving volatility by using speed, longitudinal and lateral acceleration, and yaw-rate. Rigorous statistical models including fixed parameter, random parameter, and geographically weighted Poisson regressions were developed. The results revealed that controlling for intersection geometry and traffic exposure, and accounting unobserved factors, variations in longitudinal control of the vehicle (longitudinal volatility) are highly correlated with the frequency of rear-end crashes. Intersections with high variations in longitudinal movement are prone to have higher rear-end crash rate. Referring to sideswipe and angle crashes, along with speed and longitudinal volatility, lateral volatility is substantially correlated with the frequency of crashes. When it comes to head-on crashes, speed, longitudinal and lateral acceleration volatilities are highly associated with the frequency of crashes. Intersections with high lateral volatility have higher risk of head-on collisions due to the risk of deviation from the centerline leading to head-on crash. The developed methodology and volatility measures can be used to proactively identify hotspot intersections where the frequency of crashes is low, but the longitudinal/lateral driving volatility is high. The reason that drivers exhibit higher levels of driving volatility when passing these intersections can be analyzed to come up with potential countermeasures that could reduce volatility and, consequently, crash risk.
联网和自动驾驶车辆使研究人员能够使用大数据开发新的指标，从而提高交通安全。这种大数据的出现以及现代计算机的计算能力使我们能够通过应用“驾驶波动性”的概念来量化驾驶行为的变化，从而更深入地了解瞬间驾驶行为。本文引入了一种利用纵向和横向波动性量化车辆运动变化的方法，并主动研究瞬间驾驶行为对交叉口事故类型的影响。在美国密歇根州安娜堡的 167 个交叉口中，分析了超过 2800 辆联网车辆之间传输的超过 1.25 亿条基本安全消息数据，并将其与历史碰撞和道路库存数据进行了整合。由于驾驶波动性代表了车辆的运动和控制，因此可以预期不规则的纵向/横向运动增加了碰撞的风险。为了捕捉车辆控制和运动的变化，我们通过速度、纵向和横向加速度以及偏航率来量化和使用 30 种驾驶波动性度量。开发了严格的统计模型，包括固定参数、随机参数和地理加权泊松回归。结果表明，控制交叉口几何形状和交通暴露度，并考虑未观察到的因素，车辆纵向控制的变化（纵向波动性）与追尾事故的频率高度相关。纵向运动变化较大的交叉口容易发生追尾事故的发生率较高。对于侧面碰撞和角度碰撞，以及速度和纵向波动性，横向波动性与碰撞的频率有很大的相关性。对于正面碰撞，速度、纵向和横向加速度波动性与碰撞的频率高度相关。由于从中心线偏离导致正面碰撞的风险，横向波动性较高的交叉口发生正面碰撞的风险更高。所开发的方法和波动性度量标准可用于主动识别碰撞频率低但纵向/横向驾驶波动性高的热点交叉口。当驾驶员经过这些交叉口时，他们表现出更高水平的驾驶波动性，原因可能是可以进行分析以提出潜在的对策，从而降低波动性，进而降低碰撞风险。
