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A dynamic graph deep learning model with multivariate empirical mode decomposition for network‐wide metro passenger flow prediction

Author(s): (School of Transportation and Logistics Southwest Jiaotong University Chengdu China)
(Department of Civil and Environmental Engineering University of Wisconsin–Madison Wisconsin Madison USA)
(School of Transportation and Logistics Southwest Jiaotong University Chengdu China)
(School of Rail Transportation Soochow University Soochow China)
(School of Transportation and Logistics Southwest Jiaotong University Chengdu China)
(School of Transportation and Logistics Southwest Jiaotong University Chengdu China)
Medium: journal article
Language(s): English
Published in: Computer-Aided Civil and Infrastructure Engineering
DOI: 10.1111/mice.13214
Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1111/mice.13214.
  • About this
    data sheet
  • Reference-ID
    10784694
  • Published on:
    20/06/2024
  • Last updated on:
    20/06/2024
 
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