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Research on traffic flow prediction method based on adaptive multi-channel graph convolutional neural networks
- 1 Hebei University of Technology
- 2 Hebei University of Technology
* Author to whom correspondence should be addressed.
Abstract
In order to address the issues of predefined adjacency matrices inadequately representing information in road networks, insufficiently capturing spatial dependencies of traffic networks, and the potential problem of excessive smoothing or neglecting initial node information as the layers of graph convolutional neural networks increase, thus affecting traffic prediction performance, this paper proposes a prediction model based on Adaptive Multi-channel Graph Convolutional Neural Networks (AMGCN). The model utilizes an adaptive adjacency matrix to automatically learn implicit graph structures from data, introduces a mixed skip propagation graph convolutional neural network model, which retains the original node states and selectively acquires outputs of convolutional layers, thus avoiding the loss of node initial states and comprehensively capturing spatial correlations of traffic flow. Finally, the output is fed into Long Short-Term Memory networks to capture temporal correlations. Comparative experiments on two real datasets validate the effectiveness of the proposed model.
Keywords
traffic flow prediction, spatio-temporal correlations, graph convolutional neural network, adaptive adjacency matrix
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Cite this article
Xu,Z.;Gu,J. (2024). Research on traffic flow prediction method based on adaptive multi-channel graph convolutional neural networks. Advances in Engineering Innovation,7,41-47.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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