An adaptive spatiotemporal dynamic graph convolutional network for traffic prediction

Traffic prediction, as a core technology of Intelligent Transportation Systems, plays a pivotal role in dynamic road network optimization and urban travel planning. However, the complex spatiotemporal characteristics of transportation networks pose significant challenges to precisely capturing their...

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Vydané v:Scientific reports Ročník 15; číslo 1; s. 27098 - 16
Hlavní autori: Xiao, Zhiguo, Shen, Qi, Li, Changgen, Li, Dongni, Liu, Qian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 25.07.2025
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Accuracy
Architecture
Dynamic graph generation
Embedding
Forecasting
Gated recurrent unit
Graph convolutional network
Humanities and Social Sciences
Information processing
Machine learning
multidisciplinary
Neural networks
Predictions
Science
Science (multidisciplinary)
Temporal variations
Time series
Traffic control
Traffic flow
Traffic prediction
Trends
Dynamic graph generation
Gated recurrent unit
Traffic prediction
Graph convolutional network
ISSN:2045-2322, 2045-2322
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Popis
Shrnutí:Traffic prediction, as a core technology of Intelligent Transportation Systems, plays a pivotal role in dynamic road network optimization and urban travel planning. However, the complex spatiotemporal characteristics of transportation networks pose significant challenges to precisely capturing their dynamic patterns. Existing methods predominantly rely on predefined static adjacency matrices and employ separate processing of spatial and temporal features, failing to adequately explore the intrinsic coupling relationships between them. To address these limitations, we propose an adaptive spatiotemporal dynamic graph convolutional network (AST-DGCN) for traffic prediction. Under the encoder-decoder architecture, the proposed model leverages node embedding techniques to extract high-dimensional features, generating time-evolving adaptive graphs through self-attention mechanisms. Concurrently, the model synergistically integrates dynamic graphs with gated recurrent units to achieve joint modeling of complex spatiotemporal dependencies. Furthermore, it introduces a dual-layer encoder-decoder residual correction module that effectively compensates for prediction errors, substantially enhancing forecasting accuracy. Experimental results on four public traffic datasets demonstrate that the AST-DGCN model achieves significant performance advantages over baseline methods across three critical evaluation metrics: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), thereby fully validating its superior predictive capabilities and competitive advantages.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-12261-7