This paper explores short duration disturbances in the traffic stream that are large enough to impact the traffic dynamics and disrupt stationarity when establishing the fundamental diagram, FD, but small enough that they are below the resolution of conventional vehicle detector data and cannot be seen using conventional methods. This empirical research develops the Exclusionary Vehicle Aggregation method (EVA) to extract high fidelity time series data from conventional loop detectors and then extends the method to measure the standard deviation of headways in a given fixed time sample, stdevh. Using loop detector data spanning 18 years and five sites, all of the sites show that samples with low stdevh tend towards a triangular FD while samples with high stdevh tend towards a concave FD that falls inside the triangular FD. The stdevh is also shown to be strongly correlated with the duration of the longest headway within the sample. The presence of a long headway means the state is perceptively different over the sample and thus, the measurement is non-stationary. A review of the earliest FD literature by Greenshields finds strong supporting evidence for these trends. Collectively, the loop detector and historical FD results span over 75 years of empirical traffic data.

中文翻译：

---

微观不连续性破坏了流体动力学和连续交通流模型


本文探讨了交通流中的短时干扰，这些干扰大到足以影响交通动力学并在建立基本图 FD 时破坏平稳性，但又足够小，以至于它们低于传统车辆探测器数据的分辨率，并且无法使用传统方法看到。这项实证研究开发了排他车辆聚合方法 （EVA） 来从传统的循环检测器中提取高保真时间序列数据，然后扩展该方法以测量给定固定时间样本 stdevh 中车头的标准差。使用跨越 18 年和 5 个地点的环探测器数据，所有地点都表明，低 stdevh 的样品倾向于三角形 FD，而高 stdevh 的样品倾向于落在三角形 FD 内的凹 FD。stdevh 也被证明与样本中最长间隔的持续时间密切相关。长间隔的存在意味着样品上的状态明显不同，因此，测量是非平稳的。Greenshields 对最早的 FD 文献的回顾发现了这些趋势的有力支持证据。总的来说，环路检测器和历史 FD 结果跨越了超过 75 年的经验流量数据。