Fine-grained urban flow inference (FUFI) is a crucial transportation service
aimed at improving traffic efficiency and safety. FUFI can infer fine-grained
urban traffic flows based solely on observed coarse-grained data. However, most
of existing methods focus on the influence of single-scale static geographic
information on FUFI, neglecting the interactions and dynamic information
between different-scale regions within the city. Different-scale geographical
features can capture redundant information from the same spatial areas. In
order to effectively learn multi-scale information across time and space, we
propose an effective fine-grained urban flow inference model called UrbanMSR,
which uses self-supervised contrastive learning to obtain dynamic multi-scale
representations of neighborhood-level and city-level geographic information,
and fuses multi-scale representations to improve fine-grained accuracy. The
fusion of multi-scale representations enhances fine-grained. We validate the
performance through extensive experiments on three real-world datasets. The
resutls compared with state-of-the-art methods demonstrate the superiority of
the proposed model.

通过自我监督对比学习，UrbanMSR 模型利用多尺度表示的邻近层级和城市层级地理信息，融合多尺度表示以提高细粒度准确性，从而有效地学习跨时空的多尺度信息，并在三个真实数据集上进行了广泛实验证实了该模型的优越性。