Abstract Based on simulations in the framework of Kerner’s three-phase traffic theory, we present a methodology for the prediction of a moving bottleneck (MB) with the use of a small share of probe vehicles (floating car data – FCD) randomly distributed in traffic flow. In this methodology, a conclusion of the three-phase traffic theory has been used that in the vicinity of any bottleneck there can be observed phase transitions between free flow and synchronized flow. The presented methodology is based on the recognition of phase transition points from synchronized flow to free flow on probe vehicle trajectories. For the simulations, we have used the Kerner–Klenov microscopic stochastic traffic flow model. It has been found that the MB can be predicted even if about 1% of probe vehicles are in traffic flow. The time-function of the probability of MB prediction in dependence of the share of probe vehicles in traffic flow has been calculated. We have found that the time-dependence of the probability of MB prediction as well as the accuracy of the estimation of MB location depend considerably on the occurrence of sequences of phase transitions from free flow to synchronized flow and back from synchronized flow to free flow occurring before traffic breakdown at the MB as well as speed oscillations in synchronized flow at the MB. The methodology of MB prediction presented in the paper can be used by either automated driving vehicles or other ITS-applications for speed harmonization, collision avoidance that should increase traffic safety and comfort.

中文翻译：

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通过使用探测车预测移动瓶颈：三相交通理论框架下的仿真方法

摘要 基于 Kerner 三相交通理论框架中的模拟，我们提出了一种预测移动瓶颈 (MB) 的方法，该方法使用少量随机分布在交通中的探测车辆（浮动汽车数据 - FCD）。流。在该方法中，使用了三相交通理论的结论，即在任何瓶颈附近都可以观察到自由流动和同步流动之间的相变。所提出的方法是基于对探测车辆轨迹上从同步流到自由流的相变点的识别。对于模拟，我们使用了 Kerner-Klenov 微观随机交通流模型。已经发现，即使大约 1% 的探测车辆处于交通流中，也可以预测 MB。已经计算了与探测车辆在交通流中的份额相关的 MB 预测概率的时间函数。我们发现 MB 预测概率的时间依赖性以及 MB 位置估计的准确性在很大程度上取决于从自由流到同步流和从同步流到自由流发生的相变序列的发生在 MB 的交通中断之前以及 MB 的同步流中的速度振荡之前。论文中提出的 MB 预测方法可以被自动驾驶车辆或其他 ITS 应用程序用于速度协调、避免碰撞，从而提高交通安全和舒适度。我们发现 MB 预测概率的时间依赖性以及 MB 位置估计的准确性在很大程度上取决于从自由流到同步流和从同步流到自由流发生的相变序列的发生在 MB 的交通中断之前以及 MB 的同步流中的速度振荡之前。论文中提出的 MB 预测方法可以被自动驾驶车辆或其他 ITS 应用程序用于速度协调、避免碰撞，从而提高交通安全和舒适度。我们发现 MB 预测概率的时间依赖性以及 MB 位置估计的准确性在很大程度上取决于从自由流到同步流和从同步流到自由流发生的相变序列的发生在 MB 的交通中断之前以及 MB 的同步流中的速度振荡之前。论文中提出的 MB 预测方法可以被自动驾驶车辆或其他 ITS 应用程序用于速度协调、避免碰撞，从而提高交通安全和舒适度。