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：交通量估算是一个不可或缺的要素,为交通管理和控制提供精确的信息。然而,由于交通传感器的部署有限,获得完整的数量信息远非易事。关于这个专题的现有工作主要侧重于提高特定方法的总体估计准确性,忽视数量估算的基本挑战,从而忽视了数量估算的基本挑战,从而在一些关键任务上表现较差。本文件研究交通量估算方面的两个关键问题:(1) 交通流量因未察觉到的移动而变化不足,(2) 交通流量不均匀,交通流量不均匀是因为拥堵的传播。在这里,我们展示了基于图表的深度学习方法,可以提供一种数据驱动的、无模型和相关性的全面适应方法,解决上述问题,并进行准确的网络范围流量估算。特别是,为了量化交通速度和流量估算不足的动态之间的动态和非线性关系,根据图表的注意发展并纳入图表模型变异过程,以捕捉到传感器之间的非本地的关联。为了衡量非平衡流动的影响,甚至测量时间偏差的准确度和剪接的综合关注度,特别在时间偏差的基线值下,我们所展示的行距的行进速度和上下游的轨数据基比,在随后的轨测测测测测测算中,我们所测测测测测测测测测算的轨道的轨道下,在一系列测测测算的轨道上,在一系列测测测测测测测测算的轨道下,在一系列测算的轨道上,在一系列测路基底路基底路基底的轨道上,在一系列测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测算、测</s>
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