Abstract
Accurate short-term traffic forecasting plays a key role in various intelligent mobility operations and management systems. Traffic flows have potential spatio-temporal correlations that cannot be identified by extracting the spatio-temporal patterns of traffic data separately. Furthermore, the problem of missing traffic data leads to the inability to train accurate models with sufficient data. Developing traffic prediction models with small training data is still a problem to be solved. In this paper, we study short-term traffic forecasting tasks and propose a method based on deep spatio-temporal domain adaptation. The experimental results show that our deep spatio-temporal domain adaptation model has better performance.
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References
Guevara, L., Auat Cheein, F.: The role of 5G technologies: challenges in smart cities and intelligent transportation systems. Sustainability 12(16), 6469 (2020)
Li, F., et al.: Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution. ACM Trans. Knowl. Discov. Data 17(1), 9:1–9:21 (2023)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: The 6th International Conference on Learning Representations (ICLR). OpenReview.net (2018)
Meena, G., Sharma, D., Mahrishi, M.: Traffic prediction for intelligent transportation system using machine learning. In: The 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), pp. 145–148. IEEE (2020)
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 the AAAI Conference on Artificial Intelligence, vol. 34, no. 1, pp. 914–921 (2020)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27, pp. pp. 3104–3112 (2014)
Wang, X., et al.: Traffic flow prediction via spatial temporal graph neural network. In: Proceedings of the Web Conference 2020, pp. 1082–1092. Association for Computing Machinery (2020)
Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664–672 (2003)
Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 753–763. ACM (2020)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI), pp. 1907–1913 (2019)
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (2018)
Zhang, Q., Chang, J., Meng, G., Xiang, S., Pan, C.: Spatio-temporal graph structure learning for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 1, pp. 1177–1185 (2020)
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, no. 1, pp. 1234–1241 (2020)
Acknowledgments
This work was supported in part by the Scientific & Technological Innovation 2030 - “New Generation AI” Key Project (No. 2021ZD0114001; No. 2021ZD0114000), and the Science and Technology Commission of Shanghai Municipality (No. 21511102200).
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Wang, Z., Li, B. (2024). Traffic Flow Prediction Based on Deep Spatio-Temporal Domain Adaptation. In: Strauss, C., Amagasa, T., Manco, G., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2024. Lecture Notes in Computer Science, vol 14911. Springer, Cham. https://doi.org/10.1007/978-3-031-68312-1_8
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