  Deep neural networks are being increasingly used for short-term traffic flow
prediction. Existing convolution-based approaches 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 fit better with convolutions, we
suggest to first aggregate traffic flows according to pre-conceived regions of
interest, then dispose to sequentially organized raster images for network
input. Extensive experiments on real-world traffic flows demonstrate that
DeFlow-Net outperforms existing solutions using standard convolutions, and
spatial partition by pre-conceived regions further enhances the performance.
Finally, we demonstrate the advantage of DeFlow-Net in maintaining spatial
autocorrelation, and reveal the impacts of partition shapes and scales on deep
traffic flow prediction.
