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Principal graph embedding convolutional recurrent network for traffic flow prediction

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Abstract

As an essential part of traffic management, traffic flow prediction attracts worldwide attention to intelligent traffic systems (ITSs). Complicated spatial dependencies due to the well-connected road networks and time-varying traffic dynamics make this problem extremely challenging. Recent works have focused on modeling this complicated spatial-temporal dependence through graph neural networks with a fixed weighted graph or an adaptive adjacency matrix. However, fixed graph methods cannot address data drift due to changes in the road network structure, and adaptive methods are time consuming and prone to be overfitting because the learning algorithm thoroughly optimizes the adaptive matrix. To address this issue, we propose a principal graph embedding convolutional recurrent network (PGECRN) for accurate traffic flow prediction. First, we propose the adjacency matrix graph embedding (AMGE) generation algorithm to solve the data drift problem. AMGE can model the distribution of spatiotemporal series after data drift by extracting the principal components of the original adjacency matrix and performing an adaptive transformation. At the same time, it has fewer parameters, alleviating overfitting. Then, except for the essential spatial correlations, traffic flow data are also temporally dynamic. We utilize temporal variation by integrating gated recurrent units (GRU) and AMGE to comprise the proposed model. Finally, PGECRN is evaluated on two real-world highway datasets, PeMSD4 and PeMSD8. Compared with the existing baselines, the better prediction accuracy of our model shows that it can accurately and efficiently model traffic flow.

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  1. https://pems.dot.ca.gov/

  2. https://pytorch.org/

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Author information

Authors and Affiliations

  1. School of Software Engineering, Tongji University, Shanghai, China

    Yang Han, Shengjie Zhao, Hao Deng & Wenzhen Jia

  2. Key Laboratory of Embedded System and Service Computing, Shanghai, China

    Shengjie Zhao

  3. Engineering Research Center of Key Software Technologies for Smart City Perception and Planning, Ministry of Education, Shanghai, China

    Shengjie Zhao

Authors
  1. Yang Han
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  2. Shengjie Zhao
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  3. Hao Deng
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  4. Wenzhen Jia
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Corresponding author

Correspondence to Shengjie Zhao.

Ethics declarations

This work is supported in part by the National Key Research and Development Project under Grant 2020YFB2103900, in part by the National Natural Science Foundation of China under Grant 61936014, in part by the Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX0100, in part by the Shanghai Science and Technology Innovation Action Plan Project No. 22511105300, and in part by the Fundamental Research Funds for the Central Universities.

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Appendix: A Baseline parameter settings

Appendix: A Baseline parameter settings

We list the experimental parameter settings for the baseline methods as follows:

  • HA: Historical Average, which models traffic flow as a seasonal process, and uses the weighted average of previous seasons as the prediction. The period used is 1 week, and the prediction is based on aggregated data from previous weeks.

  • SVR: Linear Support Vector Regression; the penalty term C = 1, and the number of historical observations is 12.

  • GRU: Gated Recurrent Unit, which has 16 hidden layers; each layer contains 512 units. The model is trained with a batch size of 64, learning rate of 0.001, and MAE as the loss function.

  • LSTM: Long-Short Term Memory with 16 hidden layers; each layer contains 512 units. The model is trained with a batch size of 64, learning rate of 0.001, and MAE as the loss function.

  • T-GCN: Temporal Graph Convolutional Network, the learning rate is set to 0.001, the batch size to 32, the number of training epochs to 5000, and hidden units to 100.

  • STGCN: The channels of the three layers in the ST-Conv block are 64, 16, and 64. Both the graph convolution kernel size K and temporal convolution kernel size Kt are set to 3.

  • ASTGCN: The Chebyshev polynomial K and the kernel size along the temporal dimension are set to 3. All graph convolution layers and temporal convolution layers use 64 convolution kernels.

  • DCRNN: Diffusion Convolutional Recurrent Neural Network encoder and decoder contain two recurrent layers. In each recurrent layer, there are 64 units, the initial learning rate is 0.01. The maximum steps of random walks is set to 3.

  • Graph WaveNet: Graph WaveNet uses eight layers with a sequence of dilation factors 1, 2, 1, 2, 1, 2, 1, 2. The diffusion step in the graph convolution layer is set to 2. Node embeddings are randomly initialized by a uniform distribution with a size of 10. The model uses the Adam optimizer with an initial learning rate of 0.001.

  • AGCRN: Adaptive Graph Convolutional Recurrent Network has two layers, and 64 hidden units for all AGCRN cells. The embedding dimension is 10. The model is optimized with the L1 Loss and Adam optimizer for 100 epochs, and the learning rate is set to 0.003.

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Han, Y., Zhao, S., Deng, H. et al. Principal graph embedding convolutional recurrent network for traffic flow prediction. Appl Intell 53, 17809–17823 (2023). https://doi.org/10.1007/s10489-022-04211-x

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