Advances in Engineering Innovation

Advances in Engineering Innovation

Vol. 7, 25 April 2024


Open Access | Article

Research on traffic flow prediction method based on adaptive multi-channel graph convolutional neural networks

Zhengzheng Xu * 1 , Junhua Gu 2
1 Hebei University of Technology
2 Hebei University of Technology

* Author to whom correspondence should be addressed.

Advances in Engineering Innovation, Vol. 7, 41-47
Published 25 April 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Zhengzheng Xu, Junhua Gu. Research on traffic flow prediction method based on adaptive multi-channel graph convolutional neural networks. AEI (2024) Vol. 7: 41-47. DOI: 10.54254/2977-3903/7/2024066.

Abstract

In order to address the issues of predefined adjacency matrices inadequately representing information in road networks, insufficiently capturing spatial dependencies of traffic networks, and the potential problem of excessive smoothing or neglecting initial node information as the layers of graph convolutional neural networks increase, thus affecting traffic prediction performance, this paper proposes a prediction model based on Adaptive Multi-channel Graph Convolutional Neural Networks (AMGCN). The model utilizes an adaptive adjacency matrix to automatically learn implicit graph structures from data, introduces a mixed skip propagation graph convolutional neural network model, which retains the original node states and selectively acquires outputs of convolutional layers, thus avoiding the loss of node initial states and comprehensively capturing spatial correlations of traffic flow. Finally, the output is fed into Long Short-Term Memory networks to capture temporal correlations. Comparative experiments on two real datasets validate the effectiveness of the proposed model.

Keywords

traffic flow prediction, spatio-temporal correlations, graph convolutional neural network, adaptive adjacency matrix

References

1. Chang, H., Lee, Y., Yoon, B., & Baek, S. (2012). Dynamic near-term traffic flow prediction: systemoriented approach based on past experiences. Intelligent Transport Systems Iet, 6(3), p.292-305.

2. Zhao-Sheng, Y., Yuan, W., & Qing, G. (2006). Short-term traffic flow prediction method based on svm. Journal of Jilin University.

3. Fang, M., Tang, L., Yang, X., Chen, Y., & Li, Q. (2021). Ftpg: a fine-grained traffic prediction method with graph attention network using big trace data. IEEE Transactions on Intelligent Transportation Systems, PP(99), 1-13.

4. Yin, X., Wu, G., Wei, J., Shen, Y., Qi, H., & Yin, B. (2020). Deep learning on traffic prediction: methods, analysis and future directions.

5. Qu, L., Lyu, J., Li, W., Ma, D., & Fan, H. (2021). Features injected recurrent neural networks for short-term traffic speed prediction. Neurocomputing, 451, 290-304.

6. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.

7. Méndez, M., Montero, C., Núñez, M. (2022). Using Deep Transformer Based Models to Predict Ozone Levels. In 14th Asian Conference on Intelligent Information and Database Systems, 169-182.

8. Wang, Y., Lv, Z., Sheng, Z., Sun, H., & Zhao, A. (2022). A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the covid-19 pandemic. Advanced engineering informatics.

9. Zhao, L., Song, Y., Zhang, C., Liu, Y., & Li, H. (2019). T-GCN: a temporal graph convolutional network for traffic prediction.IEEE Transactions on Intelligent Transportation Systems, PP(99), 1-11.

10. Xiao, G., Wang, R., Zhang, C., & Ni, A. (2020). Demand prediction for a public bike sharing program based on spatio-temporal graph convolutional networks. Multimedia Tools and Applications(5).

11. Li, M., & Zhu, Z. (2021). Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting. National Conference on Artificial Intelligence.

12. Song, C., Lin, Y., Guo, S., & Wan, H. (2020). Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. (Vol.34, pp.914-921).

13. Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs, 30: 1025-1035.

14. Gong, Jun, Qi, Lin, Liu, & Mingyue, et al. (2013). Forecasting urban traffic flow by SVR. Proceedings of the Chinese Control and Decision Conference, 981-984.

15. Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. International Conference on Learning Representations.

16. Wu, Z., Pan, S., Long, G., Jiang, J., & Zhang, C. (2019). Graph wavenet for deep spatial-temporal graph modeling, 1907-1913.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
ISBN (Print)
ISBN (Online)
Published Date
25 April 2024
Series
Advances in Engineering Innovation
ISSN (Print)
2977-3903
ISSN (Online)
2977-3911
DOI
10.54254/2977-3903/7/2024066
Copyright
25 April 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated