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Traffic flow forecasting based on augmented multi-component recurrent graph attention network
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DOI：10.1080/19427867.2025.2450577.png)
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Accurate real-time traffic flow forecasting has been a challenge due to the complex spatial-temporal dependencies and uncertainties associated with the dynamic changes in traffic flow. To overcome this problem, a traffic flow forecasting model based on an Augmented Multi-Component Recurrent Graph Attention Network (AMR-GAT) is proposed in this paper to model the spatial-temporal correlations and periodic offset of traffic flows. This paper introduces an augmented multi-component module to address periodic temporal offset in traffic flow forecasting. It proposes an encoder-decoder architecture combining 1D convolution and LSTM via a Temporal Correlation Learner (TCL) to capture temporal characteristics, while a Graph Attention Network (GAT) handles spatial features. TCL and GAT are integrated to manage spatial-temporal correlations, and the decoder uses TCL and convolutional neural networks to generate high-dimensional representations based on spatial-temporal sequences. Experiments on two datasets demonstrate superior prediction performance of the proposed AMR-GAT model.
Keyword:
Traffic flow forecasting
graph attention networks
augmented multi-component
spatial-temporal correlation
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3.3
论文数:
928
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NEUROCOMPUTINGIF6.5
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