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DCENet: A dynamic correlation evolve network for short-term traffic prediction

Author

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  • Liu, Shuai
  • Feng, Xiaoyuan
  • Ren, Yilong
  • Jiang, Han
  • Yu, Haiyang

Abstract

Graph neural networks (GNNs) have been extensively employed in traffic prediction tasks due to their excellent capturing capabilities of spatial dependence. However, the majority of GNN-based approaches tend to employ static graphs, whereas they evolve over time and vary dynamics in real-world traffic situations. It is challenging to capture the dynamic spatial–temporal evolution characteristics of traffic data. To address this problem, we propose a dynamic correlation evolve network (DCENet) for short-term traffic prediction. To be specific, we develop a dynamic correlation self-attention (DCSA) module, which captures dynamic node associations adaptively. In this way, the model acquires new node embedding features without explicitly constructing a new graph structure. Then, an evolution encoder–decoder (EED) module is built to learn the interactions of dynamic features and output future traffic states. The experiments are conducted on two real-world datasets, and the results show that the DCENet outperformers baseline models for most of the cases.

Suggested Citation

  • Liu, Shuai & Feng, Xiaoyuan & Ren, Yilong & Jiang, Han & Yu, Haiyang, 2023. "DCENet: A dynamic correlation evolve network for short-term traffic prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
  • Handle: RePEc:eee:phsmap:v:614:y:2023:i:c:s0378437123000808
    DOI: 10.1016/j.physa.2023.128525
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    References listed on IDEAS

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    1. Wang, Bowen & Wang, Jingsheng, 2022. "ST-MGAT:Spatio-temporal multi-head graph attention network for Traffic prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    2. Wang, Jun & Wang, Wenjun & Liu, Xueli & Yu, Wei & Li, Xiaoming & Sun, Peiliang, 2022. "Traffic prediction based on auto spatiotemporal Multi-graph Adversarial Neural Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
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    Citations

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    Cited by:

    1. Yang, Di & Li, Hong & Wang, Peng & Yuan, Lihong, 2024. "Multistep traffic speed prediction: A sequence-to-sequence spatio-temporal attention model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    2. Taomei Zhu & Maria Jesus Lopez Boada & Beatriz Lopez Boada, 2024. "Adaptive Graph Attention and Long Short-Term Memory-Based Networks for Traffic Prediction," Mathematics, MDPI, vol. 12(2), pages 1-18, January.
    3. Ma, Changxi & Zhao, Mingxi, 2023. "Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).

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