The Analysis of Catchment Areas of Metro Stations using Trajectory Data Generated by Dockless Shared Bikes

The dockless bike-sharing system is getting popular and widely used for connecting with public transportation. This study addresses questions on how the catchment areas of metro stations are influenced by the dockless bike-sharing system and what are their characteristics. The authors develop methods to process bike trajectories and to generate the bike catchment areas of metro stations. The proposed methods are applied to generate the bike catchment areas of metro stations in Shanghai as a case study. The authors then conduct analyses to answer their research questions in three aspects. First, they analyze the spatial distribution patterns of bike catchment areas and find out that the sizes of bike catchment areas increase from the city center to the suburban area. Second, using two indicators: coverage ratio and overlap degree, they examine the impact of dockless bike sharing on the catchment areas as compared with 800 m pedestrian catchment areas. The authors find the catchment coverage ratio of the central city is increased by 104% and the maximum overlap degree increases from 5 stations to 9 stations. Third, they apply regression models to explore the factors associated with the sizes of bike catchment areas. Results show that the sizes of bike catchment areas are positively associated with good metro service, frequent morning trips, diverse users, large distances to the city center and terminal stations, but negatively associated with the density of metro stations. Based on the analytical results, the authors outline the application potentials and implications for relevant planning.

• Record URL:
  https://doi.org/10.1016/j.scs.2019.101598
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2210670719300381
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22106707
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Lin, Diao
  • 0000-0002-4621-6136
  • Zhang, Yongping
  • Zhu, Ruoxin
  • 0000-0003-4552-8223
  • Meng, Liqiu
• Publication Date: 2019-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Maps; References;
• Serial:
  • Sustainable Cities and Society
  • Volume: 49
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2210-6707
  • Serial URL: http://www.sciencedirect.com/science/journal/22106707?sdc=2

Subject/Index Terms

• TRT Terms: Bicycles; Catchment areas; Data analysis; Rail transit stations; Subway stations; Vehicle sharing; Vehicle trajectories
• Geographic Terms: Shanghai (China)
• Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Terminals and Facilities;

Filing Info

• Accession Number: 01709284
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 27 2019 2:41PM