Abstract
Accurate Traffic Prediction is a challenging task in intelligent transportation due to the spatial-temporal aspects of road networks. The traffic of a road network can be affected by long-distance or long-term dependencies where existing methods fall short in modeling them. In this paper, we introduce a novel framework known as Spatial-Temporal Multi-Granularity Framework (STMGF) to enhance the capture of long-distance and long-term information of the road networks. STMGF makes full use of different granularity information of road networks and models the long-distance and long-term information by gathering information in a hierarchical interactive way. Further, it leverages the inherent periodicity in traffic sequences to refine prediction results by matching with recent traffic data. We conduct experiments on two real-world datasets, and the results demonstrate that STMGF outperforms all baseline models and achieves state-of-the-art performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bonald, T., Charpentier, B., Galland, A., Hollocou, A.: Hierarchical graph clustering using node pair sampling. arXiv preprint arXiv:1806.01664 (2018)
Chen, C., Petty, K., Skabardonis, A., Varaiya, P., Jia, Z.: Freeway performance measurement system: mining loop detector data. Transp. Res. Rec. 1748(1), 96–102 (2001)
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)
Grossberg, S.: Recurrent neural networks. Scholarpedia 8(2), 1888 (2013)
Guo, K., Hu, Y., Sun, Y., Qian, S., Gao, J., Yin, B.: Hierarchical graph convolution network for traffic forecasting. In: Proceedings of AAAI, vol. 35, pp. 151–159 (2021)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of AAAI, vol. 33, pp. 922–929 (2019)
Guo, S., Lin, Y., Wan, H., Li, X., Cong, G.: Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE TKDE 34(11), 5415–5428 (2021)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Jagadish, H.V., et al.: Big data and its technical challenges. Commun. ACM 57(7), 86–94 (2014)
Jiang, J., Han, C., Zhao, W.X., Wang, J.: PDFormer: propagation delay-aware dynamic long-range transformer for traffic flow prediction. arXiv preprint arXiv:2301.07945 (2023)
Lan, S., Ma, Y., Huang, W., Wang, W., Yang, H., Li, P.: DSTAGNN: dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In: ICML, pp. 11906–11917. PMLR (2022)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)
Lippi, M., Bertini, M., Frasconi, P.: Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE T-ITS 14(2), 871–882 (2013)
Medsker, L.R., Jain, L.: Recurrent neural networks. Des. Appl. 5(64–67), 2 (2001)
Sahili, Z.A., Awad, M.: Spatio-temporal graph neural networks: a survey. arXiv preprint arXiv:2301.10569 (2023)
Shao, Z., et al.: Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. arXiv preprint arXiv:2206.09112 (2022)
Shin, Y., Yoon, Y.: PGCN: progressive graph convolutional networks for spatial-temporal traffic forecasting. arXiv preprint arXiv:2202.08982 (2022)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14, 199–222 (2004)
Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: Proceedings of AAAI, vol. 34, pp. 914–921 (2020)
Vaswani, A., et al.: Attention is all you need. NIPS 30 (2017)
Wang, X., et al.: Traffic flow prediction via spatial temporal graph neural network. In: Proceedings of the Web Conference 2020, pp. 1082–1092 (2020)
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 SIGKDD, pp. 753–763 (2020)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)
Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
Acknowledgement
This work was supported by Shandong Provincial Natural Science Foundation (No ZR2022QF114).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhao, Z., Yuan, H., Jiang, N., Chen, M., Liu, N., Li, Z. (2024). STMGF: An Effective Spatial-Temporal Multi-granularity Framework for Traffic Forecasting. In: Onizuka, M., et al. Database Systems for Advanced Applications. DASFAA 2024. Lecture Notes in Computer Science, vol 14850. Springer, Singapore. https://doi.org/10.1007/978-981-97-5552-3_16
Download citation
DOI: https://doi.org/10.1007/978-981-97-5552-3_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5551-6
Online ISBN: 978-981-97-5552-3
eBook Packages: Computer ScienceComputer Science (R0)