Current Urban Traffic Control (UTC) systems rely heavily on inductive loop detectors. The emergence of Connected Vehicles (CV) opens new possibilities for improving signal control, while reducing the need for loop detectors. However, for low penetration rates, the CV measurements are sporadic and thus difficult to exploit by existing UTC systems. In this paper, a methodology that enables cycle-to-cycle traffic state estimation and prediction based on limited CV measurements is presented. Furthermore, the proposed formulation enables fusion of CV with other data sources and their integration in any UTC system. The developed Extended Observer (EO) is a discrete-time, variable-dimension implementation of the Extended Kalman Filter. It does not require loop detectors and is independent of the type of signal control. The evaluation focuses on the queue length estimation. Especially in oversaturation, the EO outperforms the CV measurements for all examined penetration rates. Moreover, the highest benefit is observed for the lowest penetration rates. The results show that for oversaturation and low penetration rates the EO improves the CV measurements 13-31%. In addition, the EO is tested with an adaptive UTC system by feeding the fused estimation from CV and camera measurements. The error in queue length estimation from the EO is significantly lower than the error based on stochastic arrivals. Additionally, the results show a reduction in delays at the examined signal and the complete intersection. Overall, this paper sheds light on the potential benefits from enhancing limited CV measurements in order to contribute immediately to current UTC systems.

中文翻译：

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基于联网车辆有限测量的城市交通控制扩展观察器

当前的城市交通控制 (UTC) 系统严重依赖电感环路检测器。联网车辆 (CV) 的出现为改进信号控制开辟了新的可能性，同时减少了对环路检测器的需求。然而，对于低渗透率，CV 测量是零星的，因此很难被现有的 UTC 系统利用。在本文中，提出了一种基于有限 CV 测量值实现周期间交通状态估计和预测的方法。此外，提议的公式可以将 CV 与其他数据源融合，并将它们集成到任何 UTC 系统中。开发的扩展观察器 (EO) 是扩展卡尔曼滤波器的离散时间、可变维度实现。它不需要环路检测器并且与信号控制的类型无关。评估侧重于队列长度估计。尤其是在过饱和情况下，EO 在所有检查的渗透率方面都优于 CV 测量值。此外，最低的渗透率观察到了最高的好处。结果表明，对于过饱和和低渗透率，EO 将 CV 测量值提高了 13-31%。此外，通过提供来自 CV 和相机测量的融合估计，使用自适应 UTC 系统对 EO 进行了测试。从 EO 估计队列长度的误差明显低于基于随机到达的误差。此外，结果显示检查信号和完整交叉路口的延迟减少。总的来说，本文阐明了增强有限 CV 测量的潜在好处，以便立即为当前的 UTC 系统做出贡献。