利用探测车轨迹估计排队长度和交通量的各种方法
Various methods for queue length and traffic volume estimation using probe vehicle trajectories
摘要：
互联车辆技术的快速发展和叫车服务的出现使得能够收集大量的探测车辆轨迹数据。由于其大规模的特点，在交通网络的性能评价方面，轨迹数据已经成为广泛使用的固定位置传感器的潜在替代品。具体而言，对于交通量和队列长度估计，现有文献中的大多数基于轨迹数据的方法要么需要探测车辆的高市场渗透率来识别冲击波，要么需要关于队列长度分布和渗透率的先验信息，这在现实世界中可能是不可行的。为了克服现有方法的局限性，本文提出了一系列基于概率论的新方法。该方法利用探测车在队列中的停靠位置，建立探测车通过率的单变量方程并求解。一旦获得了穿透率，就可以用它来预测总的排队长度和总的交通量。使用模拟数据和真实世界的数据的验证结果表明，该方法将是足够准确的援助在交叉口的性能措施和交通信号控制，即使当探测车辆的渗透率是非常低的。
英文摘要：
The rapid development of connected vehicle technology and the emergence of ride-hailing services have enabled the collection of a tremendous amount of probe vehicle trajectory data. Due to the large scale, the trajectory data have become a potential substitute for the widely used fixed-location sensors in terms of the performance measures of transportation networks. Specifically, for traffic volume and queue length estimation, most of the trajectory data based methods in the existing literature either require high market penetration of the probe vehicles to identify the shockwave or require the prior information about the queue length distribution and the penetration rate, which may not be feasible in the real world. To overcome the limitations of the existing methods, this paper proposes a series of novel methods based on probability theory. By exploiting the stopping positions of the probe vehicles in the queues, the proposed methods try to establish and solve a single-variable equation for the penetration rate of the probe vehicles. Once the penetration rate is obtained, it can be used to project the total queue length and the total traffic volume. The validation results using both simulation data and real-world data show that the methods would be accurate enough for assistance in performance measures and traffic signal control at intersections, even when the penetration rate of the probe vehicles is very low.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
