[1]MENG Xiangfu,XIE Weipeng,CUI Jiangyan.Research on traffic flow prediction method based on spatial-temporal dynamic graph convolutional network[J].CAAI Transactions on Intelligent Systems,2025,20(4):776-786.[doi:10.11992/tis.202402012]
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Research on traffic flow prediction method based on spatial-temporal dynamic graph convolutional network
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
20
Number of periods:
2025 4
Page number:
776-786
Column:
学术论文—机器学习
Public date:
2025-08-05
- Title:
- Research on traffic flow prediction method based on spatial-temporal dynamic graph convolutional network
- Keywords:
- traffic flow; spatio-temporal data; hybrid model; attention mechanism; spatio-temporal dynamic graph; graph convolutional neural network; recurrent neural network; deep learning
- CLC:
- TP302.7
- DOI:
- 10.11992/tis.202402012
- Abstract:
- To address the limitations of existing traffic flow prediction methods in modeling spatio-temporal data and capturing dynamic spatial correlations, a spatio-temporal dynamic graph network(STDGNet) is proposed. This model adopts an encoder-decoder architecture with an embedding layer and utilizes a dynamic graph generation module to uncover potential spatio-temporal relationships from a data-driven perspective, reconstructing the dynamic correlation graph of nodes at each time step. The embedding layer employs a spatio-temporal adaptive embedding method to model the intrinsic spatio-temporal relationships and temporal information of traffic data. The encoder part uses a spatio-temporal memory attention mechanism to model spatio-temporal features from a global perspective. The decoder part incorporates a graph convolution module into a recurrent neural network to simultaneously capture temporal and spatial dependencies and output future traffic conditions. Experimental results show that, compared with the optimal baseline model decoupled dynamic spatial-temporal graph neural network(D2STGNN), the proposed model reduces mean absolute error by an average of 1.63% and reduces model training time by nearly 2.5 times. This study effectively improves the accuracy and efficiency of traffic flow prediction, providing strong support for the development of intelligent transportation systems.
- References:
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1900-01-01