Abstract
Urban Traffic Forecasting has recently seen a lot of research activity as it entails a compelling combination of multivariate temporal data with geo-spatial dependencies between multiple data collection sensors. Current top approaches to this task tend to use costly spatio-temporal pipelines, where the model complexities typically have linear dependency on the time-series length and quadratic on the number of nodes. In this paper, we propose a number of steps to dramatically improve the runtime efficiency of the traffic forecasting solutions. First, we use a temporal pooling stack prior to spatial processing to effectively eliminate the time dimension before applying the spatial components. This removes the linear dependency of the model on the length of the time series. Second, we construct learnable graph pooling blocks inside the spatial stack which progressively reduce the size of the graph and facilitate better data flow between far away nodes. Experimental results on the standard METR-LA and PEMSBAY benchmarks show that the proposed approach yields significant inference and training speedups of up to x5 in the 1-h prediction task and x27 in the 24-h prediction task, while keeping or surpassing the state-of-the-art results. Our findings call into question the need for time-consuming spatio-temporal processing blocks, used in many of latest solutions for the traffic forecasting task.
Y. Lubarsky and A. Gaissinski—Equal contribution.
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References
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. Adv. Neural. Inf. Process. Syst. 33, 17804–17815 (2020)
Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)
Dai, H., Dai, B., Song, L.: Discriminative embeddings of latent variable models for structured data. In: International Conference on Machine Learning, pp. 2702–2711. PMLR (2016)
Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: International Conference on Machine Learning, pp. 933–941. PMLR (2017)
Gao, H., Ji, S.: Graph u-nets. In: International Conference on Machine Learning, pp. 2083–2092. PMLR (2019)
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12(10), 2451–2471 (2000)
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263–1272. PMLR (2017)
Grattarola, D., Zambon, D., Bianchi, F.M., Alippi, C.: Understanding pooling in graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. (2022)
Heinrich, K., Zschech, P., Janiesch, C., Bonin, M.: Process data properties matter: introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. Decis. Support Syst. 143, 113494 (2021)
Jiang, R., et al.: DL-Traff: survey and benchmark of deep learning models for urban traffic prediction. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 4515–4525 (2021)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Lam, R., et al.: Graphcast: learning skillful medium-range global weather forecasting. arXiv preprint arXiv:2212.12794 (2022)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: International Conference on Learning Representations (ICLR 2018) (2018)
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015)
Liu, C., et al.: Graph pooling for graph neural networks: progress, challenges, and opportunities. arXiv preprint arXiv:2204.07321 (2022)
Mesquita, D., Souza, A., Kaski, S.: Rethinking pooling in graph neural networks. Adv. Neural. Inf. Process. Syst. 33, 2220–2231 (2020)
Oreshkin, B.N., Amini, A., Coyle, L., Coates, M.: FC-GAGA: fully connected gated graph architecture for spatio-temporal traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 9233–9241 (2021)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Rusek, K., Suárez-Varela, J., Almasan, P., Barlet-Ros, P., Cabellos-Aparicio, A.: Routenet: leveraging graph neural networks for network modeling and optimization in SDN. IEEE J. Sel. Areas Commun. 38(10), 2260–2270 (2020)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. In: The 28th International Joint Conference on Artificial Intelligence (IJCAI). International Joint Conferences on Artificial Intelligence Organization (2019)
Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 753–763 (2020)
Zhao, L., et al.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848–3858 (2019)
Zheng, C., Fan, X., Wang, C., Qi, J.: GMAN: a graph multi-attention network for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 1234–1241 (2020)
Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57–81 (2020)
Zhu, L.: THOP: PyTorch-OpCounter (2019). https://github.com/Lyken17/pytorch-OpCounter
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Lubarsky, Y., Gaissinski, A., Kisilev, P. (2023). Efficient Spatio-Temporal Graph Neural Networks for Traffic Forecasting. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-031-34107-6_9
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