Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem, which can help greatly reduce the number of the required traffic monitoring sensors for cost savings. In this work, we notice that traffic flow has a high correlation with road network, which was either completely ignored or simply treated as an external factor in previous works.To facilitate this problem, we propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that explicitly exploits the prior knowledge of road networks to fully learn the road-aware spatial distribution of fine-grained traffic flow. Specifically, a multi-directional 1D convolutional layer is first introduced to extract the semantic feature of the road network. Subsequently, we incorporate the road network feature and coarse-grained flow feature to regularize the short-range spatial distribution modeling of road-relative traffic flow. Furthermore, we take the road network feature as a query to capture the long-range spatial distribution of traffic flow with a transformer architecture. Benefiting from the road-aware inference mechanism, our method can generate high-quality fine-grained traffic flow maps. Extensive experiments on three real-world datasets show that the proposed RATFM outperforms state-of-the-art models under various scenarios. Our code and datasets are released at {\url{https://github.com/luimoli/RATFM}}.

翻译：从粗粒度交通流量精确推断细粒度流量是一个新兴但至关重要的问题，有助于大幅减少所需交通监测传感器的数量以实现成本节约。本工作注意到，交通流量与道路网络高度相关，而先前的研究要么完全忽略了这一点，要么仅将其视为外部因素。为解决此问题，我们提出了一种新颖的道路感知交通流量放大器（RATFM），该方法显式利用道路网络的先验知识，充分学习细粒度交通流量的道路感知空间分布。具体而言，首先引入多方向一维卷积层提取道路网络的语义特征；随后，融合道路网络特征与粗粒度流量特征，以规整道路相关交通流量的短程空间分布建模；进一步，将道路网络特征作为查询，利用Transformer架构捕获交通流量的长程空间分布。得益于道路感知推断机制，我们的方法能够生成高质量的细粒度交通流量图。在三个真实世界数据集上的大量实验表明，所提出的RATFM在多种场景下均优于现有最优模型。我们的代码和数据集已发布于{\url{https://github.com/luimoli/RATFM}}。