Abstract
Due to the particularity of “Tourist chartered Buses, Liner Buses and Dangerous Goods Transport Vehicles” (“TLD Vehicles”), traffic accidents will bring serious losses. Therefore, traffic flow prediction for “TLD Vehicles” has become an urgent need for traffic management departments. Different from the ordinary traffic flow prediction problem, the traffic flow for “TLD Vehicles” has the characteristics of sparsity in the spatial dimension. The ordinary spatial feature extraction method will capture useless node information and affect the prediction accuracy. The traffic data of “TLD Vehicles” has significant periodic characteristics in the time dimension. Most of the traditional traffic prediction methods extract time characteristics through artificially set cycles, which has certain limitations. In this paper, a Period Extraction model for Traffic Flow Prediction is proposed to solve the problem of data sparsity and insufficient periodic feature extraction ability of traffic flow prediction. The model uses sparse graph convolution combined with Transformer to extract spatial features, uses sequence decomposition and auto-correlation attention mechanism to extract temporal features, and obtains prediction results through stacked spatio-temporal modules. The experimental results show that the proposed algorithm can extract the traffic f low data feature information with stable characteristics more effectively and improve the accuracy of the network model for traffic prediction.
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References
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 922–929 (2019)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)
Han, L., Du, B., Sun, L., Fu, Y., Lv, Y., Xiong, H.: Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 547–555 (2021)
Li, Y., Shahabi, C.: A brief overview of machine learning methods for short-term traffic forecasting and future directions. Sigspatial Special 10(1), 3–9 (2018)
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting (2017). arXiv preprint arXiv:1709.04875
Yao, H., Liu, Y., Wei, Y., Tang, X., Li, Z.: Learning from multiple cities: a meta-learning approach for spatial-temporal prediction. In: The World Wide Web Conference, pp. 2181–2191 (2019)
Polson, N., Sokolov, V.: Bayesian particle tracking of traffic flows. IEEE Trans. Intell. Transp. Syst. 19(2), 345–356 (2017)
Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L.: Predicting taxi-passenger demand using streaming data. IEEE Trans. Intell. Transp. Syst. 14(3), 1393–1402 (2013)
Ren, S., Yang, B., Zhang, L., Li, Z.: Traffic speed prediction with convolutional neural network adapted for non-linear spatio-temporal dynamics. In: Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, pp. 32–41 (2018)
Gong, Y., Li, Z., Zhang, J., Liu, W., Yi, J.: Potential passenger flow prediction: a novel study for urban transportation development. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 4020–4027 (2020)
Tan, H., Wu, Y., Shen, B., Jin, P.J., Ran, B.: Short-term traffic prediction based on dynamic tensor completion. IEEE Trans. Intell. Transp. Syst. 17(8), 2123–2133 (2016)
Yu, H.F., Rao, N., Dhillon, I.S.: Temporal regularized matrix factorization for high-dimensional time series prediction. Advances in neural information processing systems 29 (2016)
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)
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Zhao, L., et al.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848–3858 (2019)
Acknowledgements
This work was supported in part by the Hebei Province Innovation Capability Enhancement Plan Project under Grant 22567603H, in part by S&T Program of Hebei under Grant 20310801D, in part by research project (the fourth batch) of KT12 section of the new line of Rongwu Expressway.
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Wang, Q. et al. (2024). Period Extraction for Traffic Flow Prediction. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14489. Springer, Singapore. https://doi.org/10.1007/978-981-97-0798-0_29
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DOI: https://doi.org/10.1007/978-981-97-0798-0_29
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