IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v606y2022ics0378437122006689.html
   My bibliography  Save this article

Multi-step ahead traffic speed prediction based on gated temporal graph convolution network

Author

Listed:
  • Feng, Huifang
  • Jiang, Xintong

Abstract

Improving the traffic efficiency of the existing roads in the city has become an important task for the traffic management department. Timely and accurate traffic prediction is the key to urban traffic control and guidance. The multi-layer automatic feature extraction and expression ability of deep learning is deeply integrated with the non-linearity, multi-modality, and spatiotemporal correlation of traffic flow. A multi-step ahead traffic speed prediction based on gated temporal graph convolution network (GT-GCN) is proposed in this paper. Firstly, a dynamic weighted graph network is constructed according to the traffic road network structure, and then a graph convolutional network is used to process the graph structure data of the traffic road network and capture the spatial characteristics of the traffic flow. Subsequently, a gated temporal convolutional network is applied to capture the short- and long-range temporal correlations of traffic flow. Finally, a multi-step ahead prediction model based on a hybrid deep learning framework GT-GCN is proposed. Experiments using real datasets show that the proposed GT-GCN has a significant multi-step predictive performance compared to the baseline models.

Suggested Citation

  • Feng, Huifang & Jiang, Xintong, 2022. "Multi-step ahead traffic speed prediction based on gated temporal graph convolution network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    • Handle: RePEc:eee:phsmap:v:606:y:2022:i:c:s0378437122006689
    DOI: 10.1016/j.physa.2022.128075
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122006689
    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.2022.128075?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. Sun, Xiaoyong & Chen, Fenghao & Wang, Yuchen & Lin, Xuefen & Ma, Weifeng, 2023. "Short-term traffic flow prediction model based on a shared weight gate recurrent unit neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    2. 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).
    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).
    4. Hou, Yue & Zhang, Di & Li, Da & Deng, Zhiyuan, 2024. "Regional traffic flow combination prediction model considering virtual space of the road network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(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:606:y:2022:i:c:s0378437122006689. 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.