Journal of Visualization

, Volume 22, Issue 6, pp 1177–1192 | Cite as

Finding communities in bicycle sharing system

  • XiaoYing ShiEmail author
  • Yang Wang
  • Fanshun Lv
  • Wenhui Liu
  • Dewen Seng
  • Fei Lin
Regular Paper


The bicycle sharing system (BSS) provides a more sustainable transport paradigm in big cities. The recorded cycling trajectories can be used to detect human movement patterns. Community detection methods have been used to study BSS from a complex network perspective. However, the previous used modularity-based methods not only ignored the interdependencies of bicycle flows in the system, but also suffered from the problem of resolution limit. The in-depth analysis of detection results is also lacked. In this paper, we propose an interactive visual analytics system to detect the cycling communities of bicycle sharing system. Different kinds of community detection algorithms are adopted for finding station clusters; multiple inter-linked views are designed to visualize properties of the detected substructures from different perspectives. The real bicycle sharing dataset in Hangzhou is used for analysis, which demonstrates the effectiveness of our method. By using the system, analyzers can compare the cluster results generated by different algorithms, investigate the reason of the partition results based on different metrics, and find the relationship among human activity communities and the city subregional structures. This study provides insights into using bicycle sharing data to reveal human travel pattern and BSS usage pattern, which potentially aids in developing urban planning policies.

Graphic abstract


Bicycle sharing system Community detection Visual analysis Complex network Geographical visualization 



This work was supported by the National Natural Science Foundation of China (Grant Nos. 61602141, 61603119, 61703127).


  1. Arenas A, Díaz-Guilera A, Pérez-Vicente CJ (2006) Synchronization reveals topological scales in complex networks. Phys Rev Lett 96(11):114102CrossRefGoogle Scholar
  2. Austwick MZ, O’Brien O, Strano E et al (2013) The structure of spatial networks and communities in bicycle sharing systems. PLoS ONE 8(9):e74685CrossRefGoogle Scholar
  3. Blondel VD, Guillaume JL, Lambiotte R et al (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10:P10008CrossRefGoogle Scholar
  4. Borgnat P, Fleury E, Robardet C et al (2009) Spatial analysis of dynamic movements of Vélo’v, Lyon’s shared bicycle program. In: Proceedings of ECCS’09. Complex Systems SocietyGoogle Scholar
  5. Borgnat P, Abry P, Flandrin P et al (2011) Shared bicycles in a city: a signal processing and data analysis perspective. Adv Complex Syst 14(3):415–438CrossRefGoogle Scholar
  6. Borgnat P, Robardet C, Abry P et al (2013) A dynamical network view of lyon’s vélo’v shared bicycle system. Dyn Complex Netw 2:267–284Google Scholar
  7. Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):066111CrossRefGoogle Scholar
  8. El-Assi W, Mahmoud MS, Habib KN (2017) Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto. Transportation 44(3):589–613CrossRefGoogle Scholar
  9. Etienne C, Latifa O (2014) Model-based count series clustering for bike sharing system usage mining: a case study with the Vélib’system of Paris. ACM Trans Intell Syst Technol 5(3):39CrossRefGoogle Scholar
  10. Faghih-Imani A, Eluru N (2016) Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: a case study of New York CitiBike system. J Transp Geogr 54:218–227CrossRefGoogle Scholar
  11. Fishman E (2016) Bikeshare: a review of recent literature. Transp Rev 36(1):92–113CrossRefGoogle Scholar
  12. Fortunato S, Hric D (2016) Community detection in networks: a user guide. Phys Rep 659:1–44MathSciNetCrossRefGoogle Scholar
  13. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826MathSciNetCrossRefGoogle Scholar
  14. Good BH, de Montjoye YA, Clauset A (2010) Performance of modularity maximization in practical contexts. Phys Rev E 81(4):046106MathSciNetCrossRefGoogle Scholar
  15. Goodman A, Cheshire J (2014) Inequalities in the London bicycle sharing system revisited: impacts of extending the scheme to poorer areas but then doubling prices. J Transp Geogr 41:272–279CrossRefGoogle Scholar
  16. Jin D, Liu D, Yang B et al (2011) Ant colony optimization with a new random walk model for community detection in complex networks. Adv Complex Syst 14(05):795–815MathSciNetCrossRefGoogle Scholar
  17. Lancichinetti A, Fortunato S (2009) Community detection algorithms: a comparative analysis. Phys Rev E 80(5):056117CrossRefGoogle Scholar
  18. Lancichinetti A, Fortunato S (2011) Limits of modularity maximization in community detection. Phys Rev E 84(6):066122CrossRefGoogle Scholar
  19. Nair R, Miller-Hooks E, Hampshire RC et al (2013) Large-scale vehicle sharing systems: analysis of Vélib’. Int J Sustain Transp 7(1):85–106CrossRefGoogle Scholar
  20. Newman MEJ (2004) Analysis of weighted networks. Phys Rev E 70(5):056131CrossRefGoogle Scholar
  21. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113CrossRefGoogle Scholar
  22. O’brien O, Cheshire J, Batty M (2014) Mining bicycle sharing data for generating insights into sustainable transport systems. J Transp Geogr 34:262–273CrossRefGoogle Scholar
  23. Oliveira GN, Sotomayor JL, Torchelsen RP et al (2016) Visual analysis of bike-sharing systems. Comput Graph 60:119–129CrossRefGoogle Scholar
  24. Pons P, Latapy M (2006) Computing communities in large networks using random walks. J Graph Algorithms Appl 10(2):191–218MathSciNetCrossRefGoogle Scholar
  25. Ricci M (2015) Bike sharing: a review of evidence on impacts and processes of implementation and operation. Res Transp Bus Manag 15:28–38CrossRefGoogle Scholar
  26. Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci 105(4):1118–1123CrossRefGoogle Scholar
  27. Shaheen S, Zhang H, Martin E et al (2011) China’s Hangzhou public bicycle: understanding early adoption and behavioral response to bikesharing. Transp Res Record J Transp Res Board 2247(1):33–41CrossRefGoogle Scholar
  28. Shi C, Yan Z, Wang Y et al (2010) A genetic algorithm for detecting communities in large-scale complex networks. Adv Complex Syst 13(1):3–17MathSciNetCrossRefGoogle Scholar
  29. Shi X, Yu Z, Chen J et al (2018) The visual analysis of flow pattern for bicycle sharing system. J Vis Lang Comput 45:51–60CrossRefGoogle Scholar
  30. Sobolevsky S, Campari R, Belyi A et al (2014) General optimization technique for high-quality community detection in complex networks. Phys Rev E 90(1):012811CrossRefGoogle Scholar
  31. Vogel M, Hamon R, Lozenguez G et al (2014) From bicycle sharing system movements to users: a typology of Vélo’v cyclists in Lyon based on large-scale behavioural dataset. J Transp Geogr 41:280–291CrossRefGoogle Scholar
  32. Wood J, Slingsby A, Dykes J (2011) Visualizing the dynamics of London’s bicycle-hire scheme. Cartogr Int J Geograph Inf Geovis 46(4):239–251Google Scholar
  33. Wu J, Wang L, Li W (2018) Usage patterns and impact factors of public bicycle systems: comparison between city center and suburban district in Shenzhen. J Urban Plan Dev 144(3):04018027CrossRefGoogle Scholar
  34. Yan Y, Tao Y, Xu J et al (2018) Visual analytics of bike-sharing data based on tensor factorization. J Vis 21(3):495–509CrossRefGoogle Scholar
  35. Zhang Y, Thomas T, Brussel M et al (2017) Exploring the impact of built environment factors on the use of public bikes at bike stations: case study in Zhongshan, China. J Transp Geogr 58:59–70CrossRefGoogle Scholar
  36. Zhou X (2015) Understanding spatiotemporal patterns of biking behavior by analyzing massive bike sharing data in Chicago. PLoS ONE 10(10):e0137922CrossRefGoogle Scholar

Copyright information

© The Visualization Society of Japan 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHangzhou Dianzi UniversityHangzhouChina
  2. 2.Key Laboratory of Complex Systems Modeling and SimulationMinistry of EducationHangzhouChina

Personalised recommendations