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Traffic prediction based on auto spatiotemporal Multi-graph Adversarial Neural Network

Author

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  • Wang, Jun
  • Wang, Wenjun
  • Liu, Xueli
  • Yu, Wei
  • Li, Xiaoming
  • Sun, Peiliang

Abstract

Traffic prediction plays an essential role in the intelligent transportation systems and has broad applications in transportation management and planning. And the key to this field is to explore the spatiotemporal information of traffic data, synchronously. Recently, various deep learning methods, such as Convolution Neural Network (CNN) and Graph Convolutional Network (GCN), have shown promising performance in traffic prediction. However, these methods cannot automatically model spatial dependencies and dynamic spatiotemporal States, and there are no constraints on the distribution of outputs. To solve the above problems, in this paper, a method of automatically obtaining spatiotemporal dependence in data, which can automatically obtain the spatiotemporal state and spatiotemporal dependency using Multi-graph Adversarial Neural Network (GAN) and is named AST-MAGCN, is proposed. The new method AST-MAGCN combines GAN and GCN, extracts spatiotemporal state of the data in real-time, and outputs the traffic forecast by GAN constraint. Lastly, the proposed method is evaluated on two real-world traffic datasets, and the experimental results show that the proposed method outperforms baseline traffic prediction methods.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:590:y:2022:i:c:s0378437121009407
    DOI: 10.1016/j.physa.2021.126736
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    Citations

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    Cited by:

    1. Zhang, Ke & Lin, Xi & Li, Meng, 2023. "Graph attention reinforcement learning with flexible matching policies for multi-depot vehicle routing problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    2. Wang, Yaguan & Qin, Yong & Guo, Jianyuan & Cao, Zhiwei & Jia, Limin, 2022. "Multi-point short-term prediction of station passenger flow based on temporal multi-graph convolutional network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Tian, Jing & Song, Xianmin & Tao, Pengfei & Liang, Jiahui, 2022. "Pattern-adaptive generative adversarial network with sparse data for traffic state estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    4. 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).
    5. Xiaoping Tian & Changkuan Zou & Yuqing Zhang & Lei Du & Song Wu, 2023. "NA-DGRU: A Dual-GRU Traffic Speed Prediction Model Based on Neighborhood Aggregation and Attention Mechanism," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    6. 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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