Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Prediction

@article{Zeng2021ModelingSN,
  title={Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Prediction},
  author={Wei Zeng and Chengqiao Lin and Kang Liu and Juncong Lin and Anthony K. H. Tung},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.12010}
}
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… 

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