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

Network-wide short-term passenger flow prediction is critical for the operation and management of metro systems. However, it is challenging due to the inherent non-stationarity, nonlinearity, and spatial–temporal dependencies within passenger flow. To tackle these challenges, this paper introduces a hybrid model called multi-scale dynamic propagation spatial–temporal network (MSDPSTN). Specifically, the model employs multivariate empirical mode decomposition to jointly decompose the multivariate passenger flow into multi-scale intrinsic mode functions. Then, a set of dynamic graphs is developed to reveal the passenger propagation law in metro networks. Based on the representation, a deep learning model is proposed to achieve multistep passenger flow prediction, which employs the dynamic propagation graph attention network with long short-term memory to extract the spatial–temporal dependencies. Extensive experiments conducted on a real-world dataset from Chengdu, China, validate the superiority of the proposed model. Compared to state-of-the-art baselines, MSDPSTN reduces the mean absolute error, root mean squared error, and mean absolute percentage error by at least 3.243%, 4.451%, and 4.139%, respectively. Further quantitative analyses confirm the effectiveness of the components in MSDPSTN. This paper contributes to addressing inherent features of passenger flow to enhance prediction performance, offering critical insights for decision-makers in implementing real-time operational strategies.

• Record URL:
  • https://doi.org/10.1111/mice.13214
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/10939687
• Supplemental Notes:
  • © 2024 The Authors. Computer-Aided Civil and Infrastructure Engineering published by Wiley Periodicals LLC on behalf of Editor.
• Authors:
  • Huang, Hao
  • Mao, Jiannan
  • Kang, Leilei
  • Lu, Weike
  • Zhang, Sijia
  • Liu, Lan
• Publication Date: 2024-9

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; Maps; References; Tables;
• Pagination: pp 2596-2618
• Serial:
  • Computer-Aided Civil and Infrastructure Engineering
  • Volume: 39
  • Issue Number: 17
  • Publisher: Blackwell Publishing
  • ISSN: 1093-9687
  • Serial URL: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-8667

Subject/Index Terms

• TRT Terms: Machine learning; Multivariate analysis; Passenger traffic; Predictive models; Subway stations
• Identifier Terms: Chengdu Metro
• Subject Areas: Data and Information Technology; Operations and Traffic Management; Planning and Forecasting; Public Transportation; Railroads;

Filing Info

• Accession Number: 01929992
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 11 2024 9:54AM