IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v648y2024ics0378437124004710.html
   My bibliography  Save this article

Understanding multimodal travel mobilities of dockless bike-sharing and metro: A multilayer network analysis

Author

Listed:
  • Zhang, Hui
  • Cui, Yu

Abstract

The integrated use of dockless bike-sharing (DBS) and metro has greatly promoted the development of multimodal transportations. It is challenging to understand the travel mobilities of DBS and metro due to their complicated flow structures. In this paper, we propose an interconnected double-layer network to analyze the mobilities, in which each travel mode constructs a layer network. The studied area is divided into grids with same size. The grids are considered as nodes and trips between nodes are considered as edges. The edges between different layers are transfer trips that are found by space-time constraints. The results show that the metro trip network is connected more tightly with high efficiency than DBS trip network. Moreover, the Gini coefficient of aggregate network is more than 0.9, which is larger than metro trip network and DBS trip network. It is observed that the transfer ratios between layers are high in morning peak, but low in evening peak. The multiplex participation coefficient is adopted to measure the disparity of degree distributions of a node in different layers. It is found that passengers are distributed more evenly in the central area than suburbs between two layers. Our findings could be helpful in planning and managing multimodal transportations.

Suggested Citation

  • Zhang, Hui & Cui, Yu, 2024. "Understanding multimodal travel mobilities of dockless bike-sharing and metro: A multilayer network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 648(C).
  • Handle: RePEc:eee:phsmap:v:648:y:2024:i:c:s0378437124004710
    DOI: 10.1016/j.physa.2024.129962
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124004710
    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.2024.129962?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 search for a different version of it.

    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:648:y:2024:i:c:s0378437124004710. 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: 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.