Could free-floating bikeshare weed out station-based bikeshare? Analyzing the relationship between two bikeshare systems from bivariate flow clustering

Against the backdrop of the strong market expansion of free-floating bikeshare systems (FFBS), the future of government-funded station-based bikeshare system (SBBS) is a matter of controversy. Merely relying on point density analysis proves to be inadequate in reflecting the flow characteristic, this paper employs a flow clustering analysis to investigate the relationship between SBBS and FFBS. To recognize both bikeshare flow clusters with inhomogeneous density and shape in less time, the authors propose a two-step network-constrained bivariate flow clustering method that organically combines multiplex-network community detection and bivariate flow clustering method. The performance and applicability of the method in flow clustering detection is exemplified by the SBBS and FFBS systems in Nanjing. The results indicate that though FFBS outnumbers SBBS in terms of bikes, there are still specific flow clusters where SBBS performs better. Spatiotemporal patterns of flow clusters reveal that about one-third of flow clusters dominated by FFBS move from the metro station to the business district during the morning peak, while more SBBS-dominated flow clusters (55.4%) move from the residence to the metro station. Conversely, during the evening peak, flow clusters are observed in the opposite direction. Nevertheless, if the difference in bike numbers is significant, SBBS will be at a disadvantage. It is necessary for SBBS to prioritize resource allocation towards target groups and advantageous areas to prevent decentralized development. Their findings contribute to a more profound comprehension of the interplay between SBBS and FFBS, thereby offering more informed recommendations for strategically aligning the functions of bikeshare systems.

• Record URL:
  • https://doi.org/10.1016/j.jtrangeo.2024.103941
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0966692324001509
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09666923
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Liu, Xize
  • Chen, Wendong
  • Chen, Xuewu
  • Chen, Jingxu
  • Cheng, Long
• Publication Date: 2024-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: 103941
• Serial:
  • Journal of Transport Geography
  • Volume: 118
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0966-6923
  • Serial URL: http://www.elsevier.com/locate/jtrangeo

Subject/Index Terms

• TRT Terms: Bicycle travel; Business models; Market share; Subsidies; Vehicle sharing
• Subject Areas: Data and Information Technology; Finance; Pedestrians and Bicyclists; Planning and Forecasting;

Filing Info

• Accession Number: 01927646
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 20 2024 4:17PM