Abstract Queue length is one of the most important performance measures for signalized intersections. With recent advancements in connected vehicles and intelligent mobility technologies, utilizing vehicle trajectory data to estimate queue length has received considerable attentions. However, most of the existing methods are based on some assumptions, such as known arrival patterns and/or high penetration rates. Besides, existing models would probably be unstable or invalid under sparse trajectory environment. Hence, license plate recognition (LPR) data is introduced in this study to fuze with the vehicle trajectory data, and then, a lane-based queue length estimation method is proposed. First, by matching the LPR data with probe vehicle data, the two-dimensional probability density distribution of discharge headway and stop-line crossing time of various kinds of vehicles, i.e., queued and nonqueued vehicle for undersaturated condition and twice-queued and once-queued vehicle for oversaturated condition, can be calibrated. Then, the Bayesian theory is adopted to derive the lane-based queue length for undersaturated condition as well as the initial queue for oversaturated condition with the largest possibility, respectively. Where probe vehicle trajectories, if existed, will provide the boundaries for the estimated queue lengths. Finally, the performance of the proposed method is evaluated using both simulation and empirical data. Simulation results show that the proposed method could produce accurate estimates of queue lengths for both undersaturated and oversaturated conditions and can achieve reliable estimates even under low penetration rate (3%). Empirical results show that the proposed method outperforms an existing method using probe vehicle trajectories only.

中文翻译：

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融合车牌识别数据和车辆轨迹数据，用于信号交叉口基于车道的队列长度估计

摘要 队列长度是信号交叉口最重要的性能指标之一。随着互联车辆和智能移动技术的最新进展，利用车辆轨迹数据来估计队列长度受到了相当多的关注。然而，大多数现有方法都基于一些假设，例如已知的到达模式和/或高渗透率。此外，现有模型在稀疏轨迹环境下可能会不稳定或无效。因此，本研究引入车牌识别（LPR）数据与车辆轨迹数据融合，然后提出一种基于车道的队列长度估计方法。首先，通过将 LPR 数据与探测车辆数据进行匹配，可以标定各种车辆的排行车头时距和停车线穿越时间的二维概率密度分布，即欠饱和条件下的排队和非排队车辆以及过饱和条件下的二次排队和一次排队车辆。然后，采用贝叶斯理论分别推导出欠饱和条件下的基于车道的队列长度以及最大可能性的过饱和条件下的初始队列。探测车辆轨迹（如果存在）将提供估计队列长度的边界。最后，使用模拟和经验数据评估所提出方法的性能。仿真结果表明，所提出的方法可以对欠饱和和过饱和条件下的队列长度进行准确估计，并且即使在低渗透率（3％）下也可以实现可靠的估计。实证结果表明，所提出的方法优于仅使用探测车辆轨迹的现有方法。