Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to limited deployment of traffic sensors, obtaining full-scale volume information is far from easy. Existing works on this topic primarily focus on improving the overall estimation accuracy of a particular method and ignore the underlying challenges of volume estimation, thereby having inferior performances on some critical tasks. This paper studies two key problems with regard to traffic volume estimation: (1) underdetermined traffic flows caused by undetected movements, and (2) non-equilibrium traffic flows arise from congestion propagation. Here we demonstrate a graph-based deep learning method that can offer a data-driven, model-free and correlation adaptive approach to tackle the above issues and perform accurate network-wide traffic volume estimation. Particularly, in order to quantify the dynamic and nonlinear relationships between traffic speed and volume for the estimation of underdetermined flows, a speed patternadaptive adjacent matrix based on graph attention is developed and integrated into the graph convolution process, to capture non-local correlations between sensors. To measure the impacts of non-equilibrium flows, a temporal masked and clipped attention combined with a gated temporal convolution layer is customized to capture time-asynchronous correlations between upstream and downstream sensors. We then evaluate our model on a real-world highway traffic volume dataset and compare it with several benchmark models. It is demonstrated that the proposed model achieves high estimation accuracy even under 20% sensor coverage rate and outperforms other baselines significantly, especially on underdetermined and non-equilibrium flow locations. Furthermore, comprehensive quantitative model analysis are also carried out to justify the model designs.

翻译：交通流量是提供细粒度信息以支持交通管理与控制不可或缺的要素。然而，由于交通传感器部署有限，获取全路网规模的流量信息并非易事。现有相关研究主要侧重于提升特定方法的整体估计精度，却忽视了流量估计所面临的固有挑战，导致其在某些关键任务上表现欠佳。本文研究交通流量估计中的两个核心问题：（1）未检测通行行为导致的欠定交通流；（2）拥堵传播引发的非平衡交通流。我们提出一种基于图的深度学习方法，该方法能够以数据驱动、无模型且相关性自适应的方式解决上述问题，并实现精确的全路网交通流量估计。具体而言，为量化交通速度与流量之间的动态非线性关系以估计欠定流，我们基于图注意力机制构建了一种速度模式自适应邻接矩阵，并将其集成至图卷积过程中，以捕捉传感器间的非局部相关性。为衡量非平衡流的影响，我们定制了一种结合门控时间卷积层的时序掩码裁剪注意力机制，以捕捉上下游传感器之间的时间异步相关性。随后，我们在真实高速公路交通流量数据集上评估所提模型，并对比若干基准模型。实验表明，即使传感器覆盖率低至20%，所提模型仍能实现高估计精度，并在欠定流与非平衡流位置处显著优于其他基线方法。此外，我们还进行了全面的定量模型分析以验证模型设计的合理性。