  Recently, forecasting the crowd flows has become an important research topic,
and plentiful technologies have achieved good performances. As we all know, the
flow at a citywide level is in a mixed state with several basic patterns (e.g.,
commuting, working, and commercial) caused by the city area functional
distributions (e.g., developed commercial areas, educational areas and parks).
However, existing technologies have been criticized for their lack of
considering the differences in the flow patterns among regions since they want
to build only one comprehensive model to learn the mixed flow tensors.
Recognizing this limitation, we present a new perspective on flow prediction
and propose an explainable framework named ST-ExpertNet, which can adopt every
spatial-temporal model and train a set of functional experts devoted to
specific flow patterns. Technically, we train a bunch of experts based on the
Mixture of Experts (MoE), which guides each expert to specialize in different
kinds of flow patterns in sample spaces by using the gating network. We define
several criteria, including comprehensiveness, sparsity, and preciseness, to
construct the experts for better interpretability and performances. We conduct
experiments on a wide range of real-world taxi and bike datasets in Beijing and
NYC. The visualizations of the expert's intermediate results demonstrate that
our ST-ExpertNet successfully disentangles the city's mixed flow tensors along
with the city layout, e.g., the urban ring road structure. Different network
architectures, such as ST-ResNet, ConvLSTM, and CNN, have been adopted into our
ST-ExpertNet framework for experiments and the results demonstrates the
superiority of our framework in both interpretability and performances.
