Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and Flows
Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and Flows
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DOI：
10.1155/2017/4629792
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发表时间：
2017-01-01
影响因子：
2.3
通讯作者：
Drabicki, Arkadiusz
中科院分区：
文献类型：
--
作者：
Kucharski, Rafab;Drabicki, Arkadiusz
本文提出了一种由点流速推算宏观体积延迟函数(VDF)的新方法。与典型的VDF估计方法相反，它还允许在超临界交通条件下估计速度，当速度和流量因拥堵(高密度交通流)而下降时。我们利用众所周知的基本图解的流体动力学关系，从测得的时间平均速度和流量推导出所谓的准密度。这允许用具有类似于BPR的典型VDF的形状的准密度的单调递减函数的速度来表示VDF估计问题。这样，我们就可以使用实际观察到的速度，并提出宏观的VDF，在超临界条件下也能真实地再现实际速度。以华沙市感应环路系统半年的测量为例说明了所提出的方法，该系统测量了500多万辆汽车的交通流量和瞬时速度。虽然该方法没有克服静态宏观交通模型的基本局限性，即不能描述动态交通现象，如排队、溢出、波传播、容量下降等，但我们设法将VDF的拟合优度从27%的R-2提高到72%，最重要的是，对于超临界条件也是如此。由于这一点，交通拥堵的宏观交通模型可以更真实地再现与经验观测一致。
This paper proposes a new method to estimate the macroscopic volume delay function (VDF) from the point speed-flow measures. Contrary to typical VDF estimation methods it allows estimating speeds also for hypercritical traffic conditions, when both speeds and flow drop due to congestion (high density of traffic flow). We employ the well-known hydrodynamic relation of fundamental diagram to derive the so-called quasi-density from measured time-mean speeds and flows. This allows formulating the VDF estimation problem with a speed being monotonically decreasing function of quasi-density with a shape resembling the typical VDF like BPR. This way we can use the actually observed speeds and propose the macroscopic VDF realistically reproducing actual speeds also for hypercritical conditions. The proposed method is illustrated with half-year measurements from the induction loop system in city of Warsaw, which measured traffic flows and instantaneous speeds of over 5 million vehicles. Although the proposed method does not overcome the fundamental limitations of static macroscopic traffic models, which cannot represent dynamic traffic phenomena like queue, spillback, wave propagation, capacity drop, and so forth, we managed to improve the VDF goodness-of-fit from R-2 of 27% to 72% most importantly also for hypercritical conditions. Thanks to this traffic congestion in macroscopic traffic models can be reproduced more realistically in line with empirical observations.