IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i19p3159-d1495112.html
   My bibliography  Save this article

Spatial–Temporal-Correlation-Constrained Dynamic Graph Convolutional Network for Traffic Flow Forecasting

Author

Listed:
  • Yajun Ge

    (Shaanxi Transportation Holding Group Co., Ltd., Xi’an 710000, China)

  • Jiannan Wang

    (Operation Management Branch of Shaanxi Transportation Holding Group Co., Ltd., Xi’an 710000, China)

  • Bo Zhang

    (Operation Management Branch of Shaanxi Transportation Holding Group Co., Ltd., Xi’an 710000, China)

  • Fan Peng

    (Operation Management Branch of Shaanxi Transportation Holding Group Co., Ltd., Xi’an 710000, China)

  • Jing Ma

    (Shaanxi Expressway Testing & Measuring Co., Ltd., Xi’an 710000, China)

  • Chenyu Yang

    (School of Economics, Renmin University of China, Beijing 100872, China)

  • Yue Zhao

    (School of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an 710048, China)

  • Ming Liu

    (School of Materials Science and Engineering, Xi’an University of Technology, Xi’an 710048, China)

Abstract

Accurate traffic flow prediction in road networks is essential for intelligent transportation systems (ITS). Since traffic data are collected from the road network with spatial topological and time series sequences, the traffic flow prediction is regarded as a spatial–temporal prediction task. With the powerful ability to model the non-Euclidean data, the graph convolutional network (GCN)-based models have become the mainstream framework for traffic forecasting. However, existing GCN-based models either use the manually predefined graph structure to capture the spatial features, ignoring the heterogeneity of road networks, or simply perform 1-D convolution with fixed kernel to capture the temporal dependencies of traffic data, resulting in insufficient long-term temporal feature extraction. To solve those issues, a spatial–temporal correlation constrained dynamic graph convolutional network (STC-DGCN) is proposed for traffic flow forecasting. In STC-DGCN, a spatial–temporal embedding encoder module (STEM) is first constructed to encode the dynamic spatial relationships for road networks at different time steps. Then, a temporal feature encoder module with heterogeneous time series correlation modeling (TFE-HCM) and a spatial feature encoder module with dynamic multi-graph modeling (SFE-DCM) are designed to generate dynamic graph structures for effectively capturing the dynamic spatial and temporal correlations. Finally, a spatial–temporal feature fusion module based on a gating fusion mechanism (STM-GM) is proposed to effectively learn and leverage the inherent spatial–temporal relationships for traffic flow forecasting. Experimental results from three real-world traffic flow datasets demonstrate the superior performance of the proposed STC-DGCN compared with state-of-the-art traffic flow forecasting models.

Suggested Citation

  • Yajun Ge & Jiannan Wang & Bo Zhang & Fan Peng & Jing Ma & Chenyu Yang & Yue Zhao & Ming Liu, 2024. "Spatial–Temporal-Correlation-Constrained Dynamic Graph Convolutional Network for Traffic Flow Forecasting," Mathematics, MDPI, vol. 12(19), pages 1-18, October.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:3159-:d:1495112
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/19/3159/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/19/3159/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Varaiya, Pravin, 2001. "Freeway Performance Measurement System, PeMS v3, Phase 1: Final Report," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt20p1j2w7, Institute of Transportation Studies, UC Berkeley.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Golob, Thomas F. & Recker, Wilfred W., 2004. "A method for relating type of crash to traffic flow characteristics on urban freeways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(1), pages 53-80, January.
    2. Golob, Thomas F. & Recker, Wilfred W. & Alvarez, Veronica M., 2003. "A Tool to Evaluate the Safety Effects of Changes in Freeway Traffic Flow," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt1kn30323, Institute of Transportation Studies, UC Berkeley.
    3. Zhu, Zheng & Li, Xinwei & Liu, Wei & Yang, Hai, 2019. "Day-to-day evolution of departure time choice in stochastic capacity bottleneck models with bounded rationality and various information perceptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 168-192.
    4. Xiaoyuan Feng & Yue Chen & Hongbo Li & Tian Ma & Yilong Ren, 2023. "Gated Recurrent Graph Convolutional Attention Network for Traffic Flow Prediction," Sustainability, MDPI, vol. 15(9), pages 1-13, May.
    5. Golob, Thomas F. & Recker, Wilfred W., 2003. "A Method for Relating Type of Crash to Traffic Flow Characteristics on Urban Freeways," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7n64466d, Institute of Transportation Studies, UC Berkeley.
    6. Zhong, Zijia & Lee, Joyoung, 2019. "The effectiveness of managed lane strategies for the near-term deployment of cooperative adaptive cruise control," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 257-270.
    7. Divya Jayakumar Nair & Flavien Gilles & Sai Chand & Neeraj Saxena & Vinayak Dixit, 2019. "Characterizing multicity urban traffic conditions using crowdsourced data," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-16, March.
    8. Varaiya, Pravin, 2001. "Freeway Performance Measurement System: Final Report," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2cx2x2s6, Institute of Transportation Studies, UC Berkeley.

    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:gam:jmathe:v:12:y:2024:i:19:p:3159-:d:1495112. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.