Unravel the landscape and pulses of cycling activities from a dockless bike-sharing system

Yang Xu, Dachi Chen, Xiaohu Zhang, Wei Tu, Yuanyang Chen, Yu Shen, Carlo Ratti

Research output: Journal article publicationJournal articleAcademic researchpeer-review

91 Citations (Scopus)


The recent boom of sharing economy along with its technological underpinnings have brought new opportunities to urban transport ecosystems. Today, a new mobility option that provides station-less bike rental services is emerging. While previous studies mainly focus on analyzing station-based systems, little is known about how this new mobility service is used in cities. This research proposes an analytical framework to unravel the landscape and pulses of cycling activities from a dockless bike-sharing system. Using a four-month GPS dataset collected from a major bike-sharing operator in Singapore, we reconstruct the temporal usage patterns of shared bikes at different places and apply an eigendecomposition approach to uncover their hidden structures. Several key built environment indicators are then derived and correlated with bicycle usage patterns. According to the analysis results, cycling activities on weekdays possess a variety of temporal profiles at both trip origins and destinations, highlighting substantial variations of bicycle usage across urban locations. Strikingly, a significant proportion of these variations is explained by the cycling activeness in the early morning. On weekends, the overall variations are much smaller, indicating a more uniform distribution of temporal patterns across the city. The correlation analysis reveals the role of shared bikes in facilitating the first- and last-mile trips, while the contribution of the latter (last-mile) is observed to a limited extent. Some built environment indicators, such as residential density, commercial density, and number of road intersections, are correlated with the temporal usage patterns. While others, such as land use mixture and length of cycling path, seem to have less impact. The study demonstrates the effectiveness of eigendecomposition for uncovering the system dynamics. The workflow developed in this research can be applied in other cities to understand this new-generation system as well as the implications for urban design and transport planning.

Original languageEnglish
Pages (from-to)184-203
Number of pages20
JournalComputers, Environment and Urban Systems
Publication statusPublished - May 2019


  • Bike sharing
  • Built environment
  • Eigendecomposition
  • Mobility on demand
  • Spatiotemporal analysis

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Ecological Modelling
  • Environmental Science(all)
  • Urban Studies


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