Abstract
How to model the spatial-temporal graph is a crucial problem for the accuracy of traffic forecasting. Existing GNN-based work mostly captures spatial dependencies by using a pre-defined graph for close nodes and a self-adaptive graph for distant nodes. However, the pre-defined graphs cannot accurately represent the genuine spatial dependency due to the complexity of traffic conditions. Furthermore, existing methods cannot effectively capture the spatial heterogeneity and temporal periodicity in traffic data. Additionally, small errors in each time step will greatly amplify in the long sequence prediction for a sequence-to-sequence model. To address these issues, we propose a novel framework, MASTRNN, for traffic forecasting. Firstly, a novel mask-adaptive matrix is proposed to enhance the pre-defined graph, which is learned through node embedding. Secondly, we assign identity embeddings to each node and each time step in order to capture the spatial heterogeneity and temporal periodicity, respectively. Thirdly, a multi-head attention layer is employed between the encoder and decoder to alleviate the problem of error propagation. Experimental results on three real-world traffic network datasets demonstrate that MASTRNN outperforms the state-of-the-art baselines.
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References
Zheng, C., Fan, X., Wang, C., Qi, J.: GMAN: a graph multi-attention network for traffic prediction. In: AAAI, pp. 1234–1241 (2020)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: ICLR (2018)
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks: theoretical basis and empirical results. J. Transp. Eng. 32(1), 4–24 (2020)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling. In: IJCAI (2019)
Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: AAAI (2018)
Hochreiter, S., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies (2001)
Graves, A.: Long short-term memory. Supervised sequence labelling with recurrent neural networks, pp. 37–45 (2012)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS (2014)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint: arXiv:1511.07122 (2015)
Van Den Oord, A., et al.: WaveNet: a generative model for raw audio. In: 9th ISCA Speech Synthesis Workshop, pp. 125–125 (2016)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: AAAI, pp. 1106–11115 (2021)
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: FedFormer: frequency enhanced decomposed transformer for long-term series forecasting. In: ICML, pp. 7268–27286 (2022)
Yu, B., Yin, H., Zhu, Z.: FedFormer: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: IJCAI, pp. 634–3640 (2018)
Guo, S., Lin, Y., Wan, H., Li, X., Cong, G.: Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Trans. Knowl. Data Eng. 34(11), 5415–5428 (2021)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2018)
Li, F., et al.: Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution. ACM Trans. Knowl. Discov. Data 17(1), 1–21 (2023)
Jiang, R., et al.: Spatio-temporal meta-graph learning for traffic forecasting. In: AAAI, pp. 8078–8086 (2023)
Wang, X., et al.: Traffic flow prediction via spatial temporal graph neural network. In: Proceedings of the Web Conference 2020, pp. 1082–1092 (2020)
Shao, Z., et al.: Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. Proc. VLDB Endowment 15(11), 2733–2746 (2022)
Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: SIGKDD, pp. 753–763 (2020)
Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Sign. Process. Mag. 30(3), 83–98 (2013)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
Shao, Z., Zhang, Z., Wang, F., Wei, W., Xu, Y.: Spatial-temporal identity: a simple yet effective baseline for multivariate time series forecasting. In: CIKM, pp. 4454–4458 (2022)
Liang, Y., Shao, Z., Wang, F., Zhang, Z., Sun, T., Xu, Y.: BasicTS: an open source fair multivariate time series prediction benchmark. In: International Symposium on Benchmarking, Measuring and Optimization, pp. 87–101 (2022)
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Hu, X., Zhang, S., Zhang, W., Huang, H. (2024). Mask Adaptive Spatial-Temporal Recurrent Neural Network for Traffic Forecasting. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_21
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