The introduction of connected vehicles, connected and automated vehicles, and advanced infrastructure sensors will allow the collection of microscopic metrics that can be used for better estimation and prediction of traffic performance. This study examines the use of disturbance metrics in combination with the macroscopic metrics usually used for the estimation of traffic safety and mobility. The disturbance metrics used are the number of oscillations and a measure of disturbance durations in the time exposed time to collision. The study investigates using the disturbance metrics in data clustering for better off-line categorization of traffic states. In addition, the study uses machine-learning based classifiers for the recognition and prediction of the traffic state and safety in real-time operations. The study also demonstrates that the disturbance metrics investigated are significantly related to crashes. Thus, this study recommends the use of these metrics as part of decision tools that support the activation of transportation management strategies to reduce the probability of traffic breakdown, ease traffic disturbances, and reduce the probability of crashes.

引入互联车辆，互联和自动车辆以及先进的基础设施传感器将允许收集微观指标，这些指标可用于更好地估计和预测交通性能。这项研究考察了干扰指标与通常用于估算交通安全性和机动性的宏观指标的结合使用。所使用的干扰度量是振荡次数和在暴露于碰撞时间内的干扰持续时间的量度。该研究调查了在数据聚类中使用干扰度量来更好地对交通状态进行离线分类。此外，该研究使用基于机器学习的分类器来识别和预测实时操作中的交通状态和安全。这项研究还表明，所研究的干扰指标与碰撞密切相关。因此，本研究建议将这些度量标准用作决策工具的一部分，以支持激活交通管理策略，以减少交通事故的可能性，缓解交通干扰并减少撞车的可能性。