  The ubiquitous deployment of monitoring devices in urban flow monitoring
systems induces a significant cost for maintenance and operation. A technique
is required to reduce the number of deployed devices, while preventing the
degeneration of data accuracy and granularity. In this paper, we present an
approach for inferring the real-time and fine-grained crowd flows throughout a
city based on coarse-grained observations. This task exhibits two challenges:
the spatial correlations between coarse- and fine-grained urban flows, and the
complexities of external impacts. To tackle these issues, we develop a model
entitled UrbanFM which consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs that uses a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influence of different external factors. This structure provides outstanding
effectiveness and efficiency for small scale upsampling. However, the
single-pass upsampling used by UrbanFM is insufficient at higher upscaling
rates. Therefore, we further present UrbanPy, a cascading model for progressive
inference of fine-grained urban flows by decomposing the original tasks into
multiple subtasks. Compared to UrbanFM, such an enhanced structure demonstrates
favorable performance for larger-scale inference tasks.
