Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management. Partial observability of vehicle flows complicates this task despite the availability of two privacy-preserving data sources: (i) aggregated vehicle counts from loop detectors near stop lines, and (ii) aggregated floating car data (aFCD) that provide segment-wise average speed measurements. However, how to integrate these sources with differing spatial and temporal resolutions for queue length estimation is rather unclear. Addressing this question, we present Q-Net: a queue estimation framework built upon a state-space formulation. This design addresses key challenges in queue modeling, such as violations of traffic conservation assumptions. Q-Net follows the Kalman predict-update structure and maintains physical interpretability in both the state evolution and measurement models. Q-Net uses an AI-augmented Kalman filter to learn time-varying gain dynamics from data. The framework supports real-time implementation and improves spatial transferability by grouping aFCD measurements into fixed-size local groups, making the number of learnable parameters independent of section length. Evaluations on urban main roads in Rotterdam, the Netherlands, show that Q-Net outperforms baseline methods, tracks queue formation and dissipation accurately, and mitigates aFCD-induced delays. By combining data efficiency, interpretability, real-time applicability, and spatial transferability, Q-Net makes accurate queue length estimation possible without costly sensing infrastructure like cameras or radar.

翻译：在信号交叉口估计队列长度是交通管理中长期存在的挑战。尽管有两种保护隐私的数据源可用，即（i）来自停车线附近环形检测器的聚合车辆计数，以及（ii）提供路段平均速度测量的聚合浮动车数据，但车辆流的部分可观测性使该任务变得复杂。然而，如何整合这些具有不同时空分辨率的数据源以进行队列长度估计尚不明确。针对这一问题，我们提出Q-Net：一种基于状态空间建模的队列估计框架。该设计解决了队列建模中的关键挑战，例如交通守恒假设的违反。Q-Net遵循卡尔曼预测-更新结构，并在状态演化和测量模型中保持物理可解释性。Q-Net使用AI增强卡尔曼滤波器从数据中学习时变增益动态。该框架支持实时实现，并通过将聚合浮动车数据测量值分组为固定大小的局部组来提高空间可迁移性，使得可学习参数数量独立于路段长度。在荷兰鹿特丹城市主干道上的评估表明，Q-Net优于基线方法，能够准确追踪队列的形成和消散，并缓解聚合浮动车数据引起的延迟。通过结合数据效率、可解释性、实时适用性和空间可迁移性，Q-Net使得无需昂贵的传感基础设施（如摄像头或雷达）即可实现准确的队列长度估计。