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- W4313030070 abstract "Traffic forecasting is fundamental to realizing intelligent transportation systems (ITS) and challenging due to the complicated spatial dependencies of traffic data and nonlinear temporal trends. Graph convolutional networks (GCNs) have been employed in the latest studies to capture the intricate spatial relationships of roadways. However, due to the intrinsic restrictions of traditional GCNs, these methods cannot represent high-order or mixed neighborhood information. The MixHop Graph WaveNet (MH-GWN), a novel graph neural network architecture for traffic forecasting, is proposed in this research. In MH-GWN, a spatial-temporal convolutional layer that effectively integrates the MixHop graph convolutional layer and the dilated causal convolutional layer is designed, which can aggregate arbitrary-order neighborhood information and model the complex spatial-temporal dependencies of traffic data. Furthermore, via stacking spatial-temporal convolutional layers, the model’s receptive field in the spatial-temporal domain can be exponentially improved. Extensive experiments on two real-world road network traffic datasets show that MH-GWN model is better than other baselines." @default.
- W4313030070 created "2023-01-06" @default.
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- W4313030070 date "2022-01-01" @default.
- W4313030070 modified "2023-09-23" @default.
- W4313030070 title "MixHop Graph WaveNet for Traffic Forecasting" @default.
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- W4313030070 doi "https://doi.org/10.1007/978-981-19-8152-4_8" @default.
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