IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p5811-d1108541.html
   My bibliography  Save this article

Mobile Phone Data Feature Denoising for Expressway Traffic State Estimation

Author

Listed:
  • Linlin Wu

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Guangming Shou

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Zaichun Xie

    (Hunan Provincial Communications Planning, Survey & Design Institute Co., Ltd., Changsha 410200, China)

  • Peng Jing

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

Abstract

Due to their wide coverage, low acquisition cost and large data quantity, the mobile phone signaling data are suitable for fine-grained and large-scale estimation of traffic conditions. However, the relatively high level of data noise makes it difficult for the estimation to achieve sufficient accuracy. According to the characteristics of mobile phone data noise, this paper proposed an improved density peak clustering algorithm (DPCA) to filter data noise. In addition, on the basis of the long short-term memory model (LSTM), a traffic state estimation model based on mobile phone feature data was established with the use of denoising data to realize the estimation of the expressway traffic state with high precision, fine granules, and wide coverage. The Shanghai–Nanjing Expressway was used as a case study area for method and model verification, the results of which showed that the denoising method proposed in this paper can effectively filter data noise, reduce the impact of extreme noise data, significantly improve the estimation accuracy of the traffic state, and reflect the actual traffic situation in a fairly satisfactory manner.

Suggested Citation

  • Linlin Wu & Guangming Shou & Zaichun Xie & Peng Jing, 2023. "Mobile Phone Data Feature Denoising for Expressway Traffic State Estimation," Sustainability, MDPI, vol. 15(7), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5811-:d:1108541
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/5811/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/7/5811/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qiang Liu & Jianguang Xie & Fan Ding, 2021. "A Data-Driven Feature Based Learning Application to Detect Freeway Segment Traffic Status Using Mobile Phone Data," Sustainability, MDPI, vol. 13(13), pages 1-11, June.
    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. Shukun Lai & Hongke Xu & Fumin Zou & Yongyu Luo & Zerong Hu & Huan Zhong, 2024. "Expressway Vehicle Trajectory Prediction Considering Historical Path Dependencies," Sustainability, MDPI, vol. 16(11), pages 1-24, May.

    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.

      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:jsusta:v:15:y:2023:i:7:p:5811-:d:1108541. 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.