SCCGCN: A Skip-Connection Coupled Graph Convolutional Network with Dynamic Fusion Attention Mechanism for Traffic Flow Prediction
Pages 19 - 23
Abstract
Traffic flow demand prediction is crucial for optimizing traffic resources and improving urban transportation. Given the complexity of real-world traffic data, effective modeling of spatiotemporal dynamics is essential. This paper introduces a novel traffic flow prediction method using a Skip-connection Coupled Graph Convolutional Network (SCCGCN). Our approach includes Attention Fusion Mechanism to capture information from three types of periodic data, a Skip-connection Coupled Gated Recurrent Unit (SCGRU) to learn historical node information dynamically, and local and global graph attention mechanisms to capture spatial dependencies. Our model outperforms state-of-the-art methods across multiple real-world datasets.
References
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Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926.
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Jiang, W., & Luo, J. (2022). Graph neural network for traffic forecasting: A survey. Expert systems with applications, 207, 117921.
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Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., ... & Li, H. (2019). T-GCN: A temporal graph convolutional network for traffic prediction. IEEE transactions on intelligent transportation systems, 21(9), 3848-3858.
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Wang, S., Chen, X., Ma, D., Wang, C., Wang, Y., Qi, H., ... & Liu, M. (2023). MIANet: Multi-level temporal information aggregation in mixed-periodicity time series forecasting tasks. Engineering Applications of Artificial Intelligence, 121, 106175.
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Yang, L., Zhang, Y., & Zuo, J. (2021, September). An attention-based spatial-temporal traffic flow prediction method with pattern similarity analysis. In 2021 IEEE international intelligent transportation systems conference (ITSC) (pp. 3710-3717). IEEE.
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Index Terms
- SCCGCN: A Skip-Connection Coupled Graph Convolutional Network with Dynamic Fusion Attention Mechanism for Traffic Flow Prediction
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Published In
August 2024
443 pages
ISBN:9798400710353
DOI:10.1145/3697467
Copyright © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 November 2024
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IoTML 2024
IoTML 2024: 2024 4th International Conference on Internet of Things and Machine Learning
August 9 - 11, 2024
Nanchang, China
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