TEDDN combines learned time embeddings, a sigmoid disentangle gate, channel attention, and residual dynamic graph convolution to forecast 60-minute traffic flow on four PEMS datasets.
IEEE Transactions on Knowledge and Data Engineering 35(9), 9168 –9180 (2022)
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A Time-Enhanced Data Disentanglement Network for Traffic Flow Forecasting
TEDDN combines learned time embeddings, a sigmoid disentangle gate, channel attention, and residual dynamic graph convolution to forecast 60-minute traffic flow on four PEMS datasets.