  Deep neural networks are being increasingly used for short-term traffic flow
prediction, which can be generally categorized as convolutional (CNNs) or graph
neural networks (GNNs). CNNs are preferable for region-wise traffic prediction
by taking advantage of localized spatial correlations, whilst GNNs achieves
better performance for graph-structured traffic data. When applied to
region-wise traffic prediction, CNNs typically partition an underlying
territory into grid-like spatial units, and employ standard convolutions to
learn spatial dependence among the units. However, standard convolutions with
fixed geometric structures cannot fully model the nonstationary characteristics
of local traffic flows. To overcome the deficiency, we introduce deformable
convolution that augments the spatial sampling locations with additional
offsets, to enhance the modeling capability of spatial nonstationarity. On this
basis, we design a deep deformable convolutional residual network, namely
DeFlow-Net, that can effectively model global spatial dependence, local spatial
nonstationarity, and temporal periodicity of traffic flows. Furthermore, to
better fit with convolutions, we suggest to first aggregate traffic flows
according to pre-conceived regions or self-organized regions based on traffic
flows, then dispose to sequentially organized raster images for network input.
Extensive experiments on real-world traffic flows demonstrate that DeFlow-Net
outperforms GNNs and existing CNNs using standard convolutions, and spatial
partition by pre-conceived regions or self-organized regions further enhances
the performance. We also demonstrate the advantage of DeFlow-Net in maintaining
spatial autocorrelation, and reveal the impacts of partition shapes and scales
on deep traffic flow prediction.
