A model framework for discovering the spatio-temporal usage patterns of public free-floating bike-sharing system

Public bike-sharing has gained much attention with the tide of sharing economy. Empowered by modern technologies (e.g., global positioning system (GPS) devices and smartphone-based APPs), a new generation of free-floating bike-sharing systems has recently become popular. Usage data generated by such systems produce rich information. This study presents a model framework to explore the spatio-temporal usage patterns of free-floating shared bikes using the usage data. The framework includes modules of probability fitting, Random Forest, a cluster-based time-domain analysis, and a visualization toolset. A case study is discussed based on the usage data from Mobike, one of the largest operating bike-sharing systems in Shanghai (China). The daily usage dynamics is modeled using log-normal distributions. Random Forest is adopted to explore the impact of factors on the usage frequency in different districts. It is found that residential area, park & green area, and population size are the top three factors influencing the frequency. Particularly, usage near metro stations is delved using the hierarchical clustering method, resulting in three typical usage modes. Visualization analysis is demonstrated to understand the time-varying flow patterns and the spatial distribution of shared bikes. This study improves the authors' understanding of the usage patterns of this emerging transport mode and provides insights for the promotion and dynamic deployment of the bike-sharing system in urban areas.

• Record URL:
  • https://doi.org/10.1016/j.trc.2019.04.006
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X18306673
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Du, Yuchuan
  • Deng, Fuwen
  • Liao, Feixiong
  • 0000-0002-8911-0788
• Publication Date: 2019-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 39-55
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 103
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Bicycle travel; Bicycles; Case studies; Global Positioning System; Mathematical models; Mobile applications; Spatial analysis; Urban areas; Vehicle sharing
• Identifier Terms: Mobike
• Uncontrolled Terms: Free-floating bike sharing; Temporal analysis
• Geographic Terms: Shanghai (China)
• Subject Areas: Operations and Traffic Management; Pedestrians and Bicyclists; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01705934
• Record Type: Publication
• Files: TRIS
• Created Date: May 28 2019 9:43AM