IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v590y2022ics0378437121009407.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121009407
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.126736?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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).
    3. 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).
    4. 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.
    5. 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).
    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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:590:y:2022:i:c:s0378437121009407. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.