IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v657y2025ics0378437124007428.html

Metro short-term section passenger flow inherent patterns extraction and prediction: A novel denoising-based method

Author

Listed:
  • Xie, Xuewen
  • Wang, Yonggang
  • Jiang, Yanlin
  • Cheng, Yanqiu
  • Peng, Hui

Abstract

Eliminating the random noise is the key issue to be solved to deeply explore the inherent patterns of passenger flow fluctuation and apply accurate prediction. This paper proposes a multi-stage data denoising based short-term section passenger flow patterns extraction and prediction method. Coarse-grained denoising is performed by adaptive soft thresholding of the Empirical Wavelet Transform (EWT) model, and the component attributes are categorized based on the Gramian Angular Summation Field (GASF) images and Determinism (DET) indicator. The periodic inherent patterns of passenger flow fluctuation are analysed by recomposing the deterministic components, and finally fine-grained denoising and trend prediction are achieved by the Gate Recurrent Unit (GRU) algorithm. Numerical experimental results show that the multi-stage denoising model outperforms the noise-balance based time-domain method and accurately identifies periodic fluctuation patterns. Moreover, the proposed EWT-GRU model in this paper significantly improves the accuracy of section passenger flow prediction.

Suggested Citation

  • Xie, Xuewen & Wang, Yonggang & Jiang, Yanlin & Cheng, Yanqiu & Peng, Hui, 2025. "Metro short-term section passenger flow inherent patterns extraction and prediction: A novel denoising-based method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 657(C).
    • Handle: RePEc:eee:phsmap:v:657:y:2025:i:c:s0378437124007428
    DOI: 10.1016/j.physa.2024.130233
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124007428
    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.2024.130233?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Tang, Jinjun & Chen, Xinqiang & Hu, Zheng & Zong, Fang & Han, Chunyang & Li, Leixiao, 2019. "Traffic flow prediction based on combination of support vector machine and data denoising schemes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    2. Chen, Xinqiang & Chen, Huixing & Yang, Yongsheng & Wu, Huafeng & Zhang, Wenhui & Zhao, Jiansen & Xiong, Yong, 2021. "Traffic flow prediction by an ensemble framework with data denoising and deep learning model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    3. Zhou, Yuyang & Zheng, Shuyan & Hu, Zhonghui & Chen, Yanyan, 2022. "Metro station risk classification based on smart card data: A case study in Beijing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    4. Quanchao Chen & Di Wen & Xuqiang Li & Dingjun Chen & Hongxia Lv & Jie Zhang & Peng Gao, 2019. "Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-18, September.
    5. Wu, Xingtang & Dong, Hairong & Tse, Chi Kong & Ho, Ivan W.H. & Lau, Francis C.M., 2018. "Analysis of metro network performance from a complex network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 553-563.
    6. Chen, Mu-Chen & Wei, Yu, 2011. "Exploring time variants for short-term passenger flow," Journal of Transport Geography, Elsevier, vol. 19(4), pages 488-498.
    7. Qingwen Ma & Sihan Liu & Xinyu Fan & Chen Chai & Yangyang Wang & Ke Yang, 2020. "A Time Series Prediction Model of Foundation Pit Deformation Based on Empirical Wavelet Transform and NARX Network," Mathematics, MDPI, vol. 8(9), pages 1-14, September.
    8. Huang, Haichao & Chen, Jingya & Sun, Rui & Wang, Shuang, 2022. "Short-term traffic prediction based on time series decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    9. Jie Luo & Ping Huang & Qiyuan Peng, 2023. "A multi-output deep learning model based on Bayesian optimization for sequential train delays prediction," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 11(5), pages 705-731, September.
    10. Wu, Weitiao & Li, Peng & Liu, Ronghui & Jin, Wenzhou & Yao, Baozhen & Xie, Yuanqi & Ma, Changxi, 2020. "Predicting peak load of bus routes with supply optimization and scaled Shepard interpolation: A newsvendor model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    11. Li, Peng & Wu, Weitiao & Pei, Xiangjing, 2023. "A separate modelling approach for short-term bus passenger flow prediction based on behavioural patterns: A hybrid decision tree method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Ma, Changxi & Zhao, Mingxi, 2025. "Urban rail transit passenger flow prediction using large language model under multi-source spatiotemporal data fusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 675(C).

    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. 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).
    2. Maosheng Li & Chen Zhang, 2024. "An Urban Metro Section Flow Forecasting Method Combining Time Series Decomposition and a Generative Adversarial Network," Sustainability, MDPI, vol. 16(2), pages 1-19, January.
    3. Liu, Yang & Song, Yaolun & Zhang, Yan & Liao, Zhifang, 2022. "WT-2DCNN: A convolutional neural network traffic flow prediction model based on wavelet reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    4. Wu, Jiaxin & Zhou, Xubing & Peng, Yi & Zhao, Xiaojun, 2022. "Recurrence analysis of urban traffic congestion index on multi-scale," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    5. Huang, Hai-chao & Chen, Jing-ya & Shi, Bao-cun & He, Hong-di, 2023. "Multi-step forecasting of short-term traffic flow based on Intrinsic Pattern Transform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    6. Ma, Changxi & Zhao, Mingxi & Huang, Xiaoting & Zhao, Yongpeng, 2024. "Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    7. Huang, Haichao & Chen, Jingya & Sun, Rui & Wang, Shuang, 2022. "Short-term traffic prediction based on time series decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    8. Noguchi, Hiroki & Fuse, Masaaki, 2020. "Rethinking critical node problem for railway networks from the perspective of turn-back operation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    9. Junjie Fu & Xinqiang Chen & Shubo Wu & Chaojian Shi & Huafeng Wu & Jiansen Zhao & Pengwen Xiong, 2020. "Mining ship deficiency correlations from historical port state control (PSC) inspection data," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-19, February.
    10. Zhang, Weibin & Zha, Huazhu & Zhang, Shuai & Ma, Lei, 2023. "Road section traffic flow prediction method based on the traffic factor state network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    11. Hong, Liu & Ye, Bowen & Yan, Han & Zhang, Hui & Ouyang, Min & (Sean) He, Xiaozheng, 2019. "Spatiotemporal vulnerability analysis of railway systems with heterogeneous train flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 725-744.
    12. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    13. Oksana Mandrikova & Yuryi Polozov & Nataly Zhukova & Yulia Shichkina, 2022. "Approximation and Analysis of Natural Data Based on NARX Neural Networks Involving Wavelet Filtering," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    14. Ma, Changxi & Zhang, Bowen & Li, Shukai & Lu, Youpeng, 2024. "Urban rail transit passenger flow prediction with ResCNN-GRU based on self-attention mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    15. Shao, Feng & Shao, Hu & Wang, Dongle & Lam, William H.K., 2024. "A multi-task spatio-temporal generative adversarial network for prediction of travel time reliability in peak hour periods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    16. Hang Shen & Lin Li & Haihong Zhu & Yu Liu & Zhenwei Luo, 2021. "Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective," Land, MDPI, vol. 11(1), pages 1-28, December.
    17. Zhai, Cong & Wu, Weitiao, 2021. "A continuous traffic flow model considering predictive headway variation and preceding vehicle’s taillight effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    18. Lu, Xijin & Ma, Changxi & Qiao, Yihuan, 2021. "Short-term demand forecasting for online car-hailing using ConvLSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    19. Libiao Chen & Qiang Ren & Juncheng Zeng & Fumin Zou & Sheng Luo & Junshan Tian & Yue Xing, 2023. "CSFPre: Expressway key sections based on CEEMDAN-STSGCN-FCM during the holidays for traffic flow prediction," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-22, April.
    20. He, Shanshan & Wang, Qiao & Chen, Juan & Ding, Heng & Ma, Jian, 2025. "Efficiency paradox and robustness of elevator-assisted evacuation in a deeply buried subway station," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 680(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:657:y:2025:i:c:s0378437124007428. 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: 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.