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：交通流量是提供精细化管理与控制信息不可或缺的要素。然而，受限于交通传感器的稀疏部署，获取全景流量信息并非易事。现有相关研究主要聚焦于提升特定方法的整体估计精度，却忽视了流量估计中的潜在挑战，导致在某些关键任务上表现欠佳。本文针对交通流量估计中的两个关键问题展开研究：（1）由未被检测到的交通流引发的欠定问题，以及（2）因拥堵传播导致的非平衡交通流问题。我们提出了一种基于图的深度学习方法，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，实现精确的全网络交通流量估计。具体而言，为量化欠定流估计中车速与流量间的动态非线性关系，我们基于图注意力机制构建了速度模式自适应邻接矩阵，并将其融入图卷积过程，以捕捉传感器间的非局部相关性。为衡量非平衡流的影响，我们定制了结合门控时序卷积层的时序掩码截断注意力机制，用以捕获上下游传感器间的时域异步相关性。基于真实高速公路交通流量数据集，我们将所提模型与多个基准模型进行了对比评估。实验结果表明，即使在传感器覆盖率仅为20%的情况下，该模型仍能达到高估计精度，尤其在欠定流与非平衡流位置处的性能显著优于其他基线模型。此外，我们还开展了全面的定量模型分析以验证模型设计的合理性。