IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/1928189.html
   My bibliography  Save this article

Enhancing Traffic Flow Forecasting with Deep-Learning and Noise Reduction Techniques

Author

Listed:
  • Jinqiu Zhao
  • Le Yu
  • Shuhua Wang
  • Zhonghao Zhang
  • Wei Li

Abstract

Traditional short-term traffic volume forecasting approaches make it difficult to predict the highly spatiotemporally coupled short-time traffic. To tackle the problem, this paper first proposes a variational modal algorithm (GWO-VMD) based on the optimization of the gray wolf search algorithm. It aims to decompose and reduce the noise of short-time traffic flows. Meanwhile, it reduces the intricacy of data sequences and enhances the regularity pattern. To address the insufficient utilization of spatiotemporal features, this paper presents an innovative deep-learning traffic prediction framework based on the stacking of multiple temporal trend-aware graph attention (TGA) layers and gated temporal convolution (GTC) layers, which are called trend-aware temporal graph neural network (TTGAN). TGA dynamically models the space-time relationships of traffic data, and GTC models the temporal characteristics of traffic data. The experimental findings demonstrate that the MAPE model, as presented, achieves a reduction of 9% and 2% compared to the AGCRN and GWNET models, respectively, in the domain of deep spatiotemporal graph modeling. Data decomposition and noise reduction are necessary to achieve accurate results. This model has superior performance in terms of mean absolute error (MAE), coefficient of determination (R2), and explained variance score (EVAR).

Suggested Citation

  • Jinqiu Zhao & Le Yu & Shuhua Wang & Zhonghao Zhang & Wei Li, 2024. "Enhancing Traffic Flow Forecasting with Deep-Learning and Noise Reduction Techniques," Discrete Dynamics in Nature and Society, Hindawi, vol. 2024, pages 1-19, August.
  • Handle: RePEc:hin:jnddns:1928189
    DOI: 10.1155/2024/1928189
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/ddns/2024/1928189.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/ddns/2024/1928189.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2024/1928189?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
    ---><---

    More about this item

    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:hin:jnddns:1928189. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.