Jam-absorption driving with a car-following model

Jam-absorption driving with a car-following model
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DOI:
10.1016/j.physa.2015.03.036
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发表时间:
2015-09
影响因子:
3.3
通讯作者:
Y. Taniguchi;Ryosuke Nishi;Takahiro Ezaki;K. Nishinari
Y. Taniguchi;Ryosuke Nishi;Takahiro Ezaki;K. Nishinari
中科院分区:
物理与天体物理2区
文献类型:
--
作者:
Y. Taniguchi;Ryosuke Nishi;Takahiro Ezaki;K. Nishinari

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阻塞吸收驾驶(JAD)是指由单个车辆动态改变其车头时距以消除交通阻塞的行为。由于其不规则的运动,执行JAD的汽车干扰了跟随它的其他汽车,并且这些干扰可能发展成为所谓的二次交通堵塞。JAD的基本理论(Nishi et al. 2013)没有考虑汽车的加速或交通流的稳定性。本文通过引入跟车行为,在JAD中实现了这些元素。以往大量关于交通流不稳定性的研究表明,即使在一个密度低于临界密度的区域中,如果扰动的初始值很大,扰动也会增长。根据这些先前的研究,我们预计,由JAD引起的扰动,如果他们是足够小的,不会增长成为二次交通堵塞。使用微观跟车模型,我们数值证实了遵守该模型的流的稳定性取决于JAD扰动的大小。在此基础上的知识,数值结果表明,参数区域存在JAD允许交通堵塞被删除,而不会造成二次交通堵塞。此外,JAD对模型中的加速度参数以及车辆跟随模型的选择具有鲁棒性。
Jam-absorption driving (JAD) refers to the action performed by a single car to dynamically change its headway to remove a traffic jam. Because of its irregular motion, a car performing JAD perturbs other cars following it, and these perturbations may grow to become the so-called secondary traffic jams. A basic theory for JAD (Nishi et al. 2013) does not consider accelerations of cars or the stability of traffic flow. In this paper, by introducing car-following behaviors, we implement these elements in JAD. Numerous previous studies on the instability of traffic flow showed that even in a region whose density is below a critical density, perturbation may grow if its initial magnitude is large. According to these previous studies, we expect that the perturbations caused by JAD, if they are sufficiently small, do not grow to become secondary traffic jams. Using a microscopic car-following model, we numerically confirmed that the stability of a flow obeying the model depends on the magnitude of JAD perturbations. On the basis of this knowledge, numerical results indicate that parameter regions exist where JAD allows traffic jams to be removed without causing secondary traffic jams. Moreover, JAD is robust against a parameter of acceleration in the model, as well as the choice of car-following models.