基于低秩分解的时空图神经网络交通流量预测方法
Low-Rank Decomposition Method for Spatiotemporal Graph Neural Network-Based Traffic Flow Prediction
DOI: 10.12677/ojtt.2025.144045, PDF,    科研立项经费支持
作者: 乔毅晨*, 程泽生#：青岛大学计算机科学技术，山东 青岛
关键词: 图卷积网络；低通滤波器；去噪；交通流量预测；Graph Convolutional Network； Low-Pass Filter； Denoising； Traffic Flow Prediction
摘要: 针对现有图神经网络(Graph Neural Network, GNN)模型在交通流量预测中存在的结构冗余与计算复杂度较高问题，提出一种基于谱图理论与图信号处理的GNN优化方法。通过将时空图卷积网络模型与k阶奇异值分解相结合，能够有效抑制预测过程中的高频噪声干扰，在保持模型结构简洁性的同时显著提升预测精度。基于3个公开交通流数据集的实验结果表明，所提模型在短时预测精度和长时预测稳定性上均达到或超越了当下先进模型的性能水平。
Abstract: To address the structural redundancy and high computational complexity issues inherent in existing Graph Neural Network (GNN) models for traffic flow prediction, this study proposes an optimized GNN method based on spectral graph theory and graph signal processing. By integrating a Spatiotemporal Graph Convolutional Network (STGCN) model with k-order Singular Value Decomposition (k-SVD), the proposed approach effectively reduces high-frequency noise interference during predictions while maintaining structural simplicity and significantly enhancing prediction accuracy. Experimental evaluations on three publicly available traffic flow datasets demonstrate that the proposed model achieves or surpasses the performance of state-of-the-art models in both short-term prediction accuracy and long-term prediction stability.
文章引用：乔毅晨, 程泽生. 基于低秩分解的时空图神经网络交通流量预测方法[J]. 交通技术, 2025, 14(4): 446-458. https://doi.org/10.12677/ojtt.2025.144045

参考文献

[1] Boukerche, A., Tao, Y. and Sun, P. (2020) Artificial Intelligence-Based Vehicular Traffic Flow Prediction Methods for Supporting Intelligent Transportation Systems. Computer Networks, 182, Article ID: 107484. [Google Scholar] [CrossRef] 
[2] Liu, Y., Wu, F., Liu, Z., Wang, K., Wang, F. and Qu, X. (2023) Can Language Models Be Used for Real-World Urban-Delivery Route Optimization? The Innovation, 4, Article ID: 100520. [Google Scholar] [CrossRef] [PubMed]
[3] Zhang, J., Wang, F., Wang, K., Lin, W., Xu, X. and Chen, C. (2011) Data-Driven Intelligent Transportation Systems: A Survey. IEEE Transactions on Intelligent Transportation Systems, 12, 1624-1639. [Google Scholar] [CrossRef] 
[4] Andreotti, E., Selpi and Boyraz, P. (2023) Potential Impact of Autonomous Vehicles in Mixed Traffic from Simulation Using Real Traffic Flow. Journal of Intelligent and Connected Vehicles, 6, 1-15. [Google Scholar] [CrossRef] 
[5] Lu, Q., Tettamanti, T., Hörcher, D. and Varga, I. (2019) The Impact of Autonomous Vehicles on Urban Traffic Network Capacity: An Experimental Analysis by Microscopic Traffic Simulation. Transportation Letters, 12, 540-549. [Google Scholar] [CrossRef] 
[6] Zhang, Z., Li, M., Lin, X., Wang, Y. and He, F. (2019) Multistep Speed Prediction on Traffic Networks: A Deep Learning Approach Considering Spatio-Temporal Dependencies. Transportation Research Part C: Emerging Technologies, 105, 297-322. [Google Scholar] [CrossRef] 
[7] Lee, J., Hong, B., Lee, K. and Jang, Y. (2015) A Prediction Model of Traffic Congestion Using Weather Data. 2015 IEEE International Conference on Data Science and Data Intensive Systems, Sydney, 11-13 December 2015, 81-88. [Google Scholar] [CrossRef] 
[8] Coretti Sanchez, N., Martinez, I., Alonso Pastor, L. and Larson, K. (2022) On the Simulation of Shared Autonomous Micro-Mobility. Communications in Transportation Research, 2, Article ID: 100065. [Google Scholar] [CrossRef] 
[9] Liu, Z., Liu, Y., Meng, Q. and Cheng, Q. (2019) A Tailored Machine Learning Approach for Urban Transport Network Flow Estimation. Transportation Research Part C: Emerging Technologies, 108, 130-150. [Google Scholar] [CrossRef] 
[10] Xie, P., Li, T., Liu, J., Du, S., Yang, X. and Zhang, J. (2020) Urban Flow Prediction from Spatiotemporal Data Using Machine Learning: A Survey. Information Fusion, 59, 1-12. [Google Scholar] [CrossRef] 
[11] Lv, M., Hong, Z., Chen, L., Chen, T., Zhu, T. and Ji, S. (2021) Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems, 22, 3337-3348. [Google Scholar] [CrossRef] 
[12] Peng, H., Du, B., Liu, M., Liu, M., Ji, S., Wang, S., et al. (2021) Dynamic Graph Convolutional Network for Long-Term Traffic Flow Prediction with Reinforcement Learning. Information Sciences, 578, 401-416. [Google Scholar] [CrossRef] 
[13] Sarker, I.H. (2021) Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2, Article No. 160. [Google Scholar] [CrossRef] [PubMed]
[14] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., et al. (2020) Graph Neural Networks: A Review of Methods and Applications. AI Open, 1, 57-81. [Google Scholar] [CrossRef] 
[15] Xu, K., Hu, W., Leskovec, J., et al. (2018) How Powerful Are Graph Neural Networks?
[16] Scarselli, F., Gori, M., Hagenbuchner, M., Monfardini, G., et al. (2009) The Graph Neural Network Model. IEEE Transactions on Neural Networks, 20, 61-80. [Google Scholar] [CrossRef] [PubMed]
[17] Yu, B., Yin, H. and Zhu, Z. (2018) Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, 13-19 July 2018, 3634-3640. [Google Scholar] [CrossRef] 
[18] Kipf, T.N. and Welling, M. (2016) Semi-Supervised Classification with Graph Convolutional Networks.
[19] Lea, C., Flynn, M.D., Vidal, R., Reiter, A. and Hager, G.D. (2017) Temporal Convolutional Networks for Action Segmentation and Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 21-26 July 2017, 156-165. [Google Scholar] [CrossRef] 
[20] Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., et al. (2020) T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems, 21, 3848-3858. [Google Scholar] [CrossRef] 
[21] Chung, J., Gulcehre, C., Cho, K., et al. (2014) Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.
[22] Guo, S., Lin, Y., Feng, N., Song, C. and Wan, H. (2019) Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 922-929. [Google Scholar] [CrossRef] 
[23] Li, Y., Yu, R., Shahabi, C., et al. (2017) Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.
[24] Cai, Z., Jiang, R., Yang, X., Wang, Z., Guo, D., Kobayashi, H.H., et al. (2023) MemDA: Forecasting Urban Time Series with Memory-Based Drift Adaptation. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Birmingham, 21-25 October 2023, 193-202. [Google Scholar] [CrossRef] 

为你推荐