Dynamic correlation adjacency-matrix-based graph neural networks for traffic flow prediction

Junhua Gu, Zhihao Jia*, Taotao Cai, Xiangyu Song, Adnan Mahmood

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
38 Downloads (Pure)


Modeling complex spatial and temporal dependencies in multivariate time series data is crucial for traffic forecasting. Graph convolutional networks have proved to be effective in predicting multivariate time series. Although a predefined graph structure can help the model converge to good results quickly, it also limits the further improvement of the model due to its stationary state. In addition, current methods may not converge on some datasets due to the graph structure of these datasets being difficult to learn. Motivated by this, we propose a novel model named Dynamic Correlation Graph Convolutional Network (DCGCN) in this paper. The model can construct adjacency matrices from input data using a correlation coefficient; thus, dynamic correlation graph convolution is used for capturing spatial dependencies. Meanwhile, gated temporal convolution is used for modeling temporal dependencies. Finally, we performed extensive experiments to evaluate the performance of our proposed method against ten existing well-recognized baseline methods using two original and four public datasets.

Original languageEnglish
Article number2897
Pages (from-to)1-17
Number of pages17
Issue number6
Publication statusPublished - 2 Mar 2023

Bibliographical note

Copyright the Author(s) 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • graph neural networks
  • dynamic adjacency matrix
  • multivariate time series
  • traffic prediction


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