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.
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Acknowledgments
Research in this article is supported by the National Natural Science Foundation of China (No. 62177014), and Research Foundation of Hunan Provincial Education Department of China (No. 19A174, 20B222).
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Ba, B., Fu, Q., Hang, C., Jiang, Y. (2022). MixHop Graph WaveNet for Traffic Forecasting. In: Cai, Z., Chen, Y., Zhang, J. (eds) Theoretical Computer Science. NCTCS 2022. Communications in Computer and Information Science, vol 1693. Springer, Singapore. https://doi.org/10.1007/978-981-19-8152-4_8
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