  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}}.
