Identification of Major Road Influence Area Using NDS Data

R. Thapa, S. Hallmark, Nicole Oneyear, O. Smadi
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引用次数: 1

Abstract

Crashes at rural intersection make up almost one-third of rural crashes. Many studies have focused on the minor stream driver since they are typically the ones who initiate the sequence of events leading to a crash, such as failure to yield to traffic control. However, the actions of the mainline road driver can influence crash outcome and severity. For instance, an alert major street driver can take the necessary maneuvers to avoid a crash or lessen the severity.This study used NDS data to assess the number of major approach drivers who demonstrate a measureable response to an upcoming intersection. A binary model was developed to relate response point to roadway, driver, and environmental characteristics. The result from this study showed that about 32% of mainline drivers at the high speed rural minor street stop controlled intersections showed a measurable response. The majority of drivers responded 80 to 240 meters upstream of the intersection. The relationship between other characteristics and response was also modeled.Results can be used to indicate where drivers react to an upcoming minor street intersection which can inform sign and countermeasure placement. Additionally it demonstrates a method to which could be used to assess rural intersection countermeasures. For instance, a number of agencies in the US are utilizing intelligent transportation system countermeasures such as intersection collision warning systems. Understanding where drivers are likely to respond can help in placing these types of countermeasures.Results also have implications for connected and autonomous vehicles. If application developers understand how a mainline driver reacts to the presence of an intersection, it can guide warning systems for the minor approach vehicle. For instance, detecting a change in speed of the major approach driver could signify the mainline driver is aware of the minor street vehicle while lack of response could trigger an alert for the minor street driver. This is particularly helpful in assessing on-coming vehicle speed and gap selection are problematic for drivers at minor stop-controlled approach.
利用NDS数据识别主要道路影响区域
农村十字路口的交通事故几乎占农村交通事故的三分之一。许多研究都集中在小流量司机身上,因为他们通常是导致撞车的一系列事件的始作俑者,比如不服从交通管制。然而,主干道驾驶员的行为会影响碰撞的结果和严重程度。例如,一个警觉的主要街道司机可以采取必要的机动来避免撞车或减轻严重程度。本研究使用NDS数据来评估对即将到来的十字路口表现出可测量反应的主要进场司机的数量。建立了一个二元模型,将响应点与道路、驾驶员和环境特征联系起来。研究结果表明,在高速农村小街站控制路口,约32%的干线司机表现出可测量的响应。大多数司机在十字路口上游80到240米的地方做出了反应。其他特征与反应之间的关系也被建模。结果可以用来指示司机对即将到来的小十字路口的反应,这可以通知标志和对策的放置。并给出了一种可用于农村交叉口对策评估的方法。例如,美国的一些机构正在利用智能交通系统的对策,如路口碰撞预警系统。了解司机可能会在哪里做出反应,有助于部署这些类型的对策。研究结果也会对联网和自动驾驶汽车产生影响。如果应用程序开发人员了解干线驾驶员对十字路口的反应,就可以指导对次要接近车辆的警告系统。例如,检测到主要接近司机的速度变化可能表明干线司机意识到次要街道车辆,而缺乏响应可能触发对次要街道司机的警报。这在评估迎面而来的车辆速度和间隙选择方面特别有帮助,而间隙选择对于小型停车控制方法的驾驶员来说是有问题的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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