IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i3p620-d1047312.html
   My bibliography  Save this article

Outlier Detection of Crowdsourcing Trajectory Data Based on Spatial and Temporal Characterization

Author

Listed:
  • Xiaoyu Zheng

    (Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China)

  • Dexin Yu

    (Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China
    College of Navigation, Jimei University, Xiamen 361021, China)

  • Chen Xie

    (Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China)

  • Zhuorui Wang

    (Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China)

Abstract

As an emerging type of spatio-temporal big data based on positioning technology and navigation devices, vehicle-based crowdsourcing data has become a valuable trajectory data resource. However, crowdsourcing trajectory data has been collected by non-professionals and with multiple measurement terminals, resulting in certain errors in data collection. In these cases, to minimize the impact of outliers and obtain relatively accurate trajectory data, it is crucial to detect and clean outliers. This paper proposes an efficient crowdsourcing trajectory outlier detection (CTOD) method that detects outliers from the trajectory sequence data in both spatial view and temporal view. Specifically, we first use the adaptive spatial clustering algorithm based on the Delaunay triangulation (ASCDT) algorithm to remove the location offset points in the trajectory sequence. After that, based on the most basic attributes of the trajectory points, a 6-dimensional movement feature vector is constructed for each point as an input. The feature-rich trajectory sequence data is reconstructed using the proposed temporal convolutional network autoencoder (TCN-AE), and the Squeeze-and-Excitation (SE) channel attention mechanism is introduced. Finally, the effectiveness of the CTOD method is experimentally verified.

Suggested Citation

  • Xiaoyu Zheng & Dexin Yu & Chen Xie & Zhuorui Wang, 2023. "Outlier Detection of Crowdsourcing Trajectory Data Based on Spatial and Temporal Characterization," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:620-:d:1047312
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/3/620/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/3/620/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Tan Li & Che-Heng Fung & Him-Ting Wong & Tak-Lam Chan & Haibo Hu, 2023. "Functional Subspace Variational Autoencoder for Domain-Adaptive Fault Diagnosis," Mathematics, MDPI, vol. 11(13), pages 1-18, June.

    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:jmathe:v:11:y:2023:i:3:p:620-:d:1047312. 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: 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.