Finding Public Transportation Community Structure Based on Large-Scale Smart Card Records in Beijing

  • Ying Long
  • Zhenjiang Shen
Part of the GeoJournal Library book series (GEJL, volume 116)


Public transportation in big cities is a crucial part of urban transportation infrastructures. Exploring the spatiotemporal patterns of public trips can help us to understand dynamic transportation patterns and the complex urban systems thus supporting better urban planning and design. The availability of large-scale smart card data (SCD) offers new opportunities to study intra-urban structure and spatial interaction dynamics. In this research, we applied the novel community detection methods from the study of complex networks to examine the dynamic spatial interaction structures of public transportation communities in the Beijing Metropolitan Area. It can help to find the ground-truth community structure of strongly connected traffic analysis zones by public transportation, which may yield insights for urban planners on land use patterns or for transportation engineers on traffic congestion. We also found that the daily community detection results using SCD are different from that using household travel surveys. The SCD results match better with the planned urban area boundary, which means that the actual operation data of publication transportation might be a good source to validate the urban planning and development.


Public transportation Smart card records Spatial interaction OD flow matrix Community detection Urban big data 


  1. Beijing Transportation Research Center. (2011). Beijing transportation annual report 2011 (In Chinese).Google Scholar
  2. Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6), 066111.CrossRefGoogle Scholar
  3. Gao, S., Liu, Y., Wang, Y., & Ma, X. (2013). Discovering spatial interaction communities from mobile phone data. Transactions in GIS, 17(3), 463–481.CrossRefGoogle Scholar
  4. Jang, W., & Yao, X. (2011). Interpolating spatial interaction data. Transactions in GIS, 15(4), 541–555.CrossRefGoogle Scholar
  5. Johnston, R., Gregory, D., & Smith, D. (1981). The dictionary of human geography. Oxford: Blackwell Reference.Google Scholar
  6. Kang, C., Zhang, Y., Ma, X., & Liu, Y. (2013). Inferring properties and revealing geographical impacts of intercity mobile communication network of China using a subnet data set. International Journal of Geographical Information Science, 27(3), 431–448.CrossRefGoogle Scholar
  7. Liu, Y., Wang, F., Xiao, Y., & Gao, S. (2012). Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai. Landscape and Urban Planning, 106(1), 73–87.CrossRefGoogle Scholar
  8. Liu, Y., Sui, Z., Kang, C., & Gao, Y. (2014). Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PloS One, 9(1), e86026.CrossRefGoogle Scholar
  9. Long, Y., & Thill, J. C. (2013). Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing. arXiv preprint. arXiv:1309.5993.Google Scholar
  10. Long, Y., Zhang, Y., & Cui, C. Y. (2012). Identifying commuting pattern of Beijing using bus smart card data. Acta Geographica Sinica, 67(10), 1339–1352.Google Scholar
  11. Manley, E. (2014). Identifying functional urban regions within traffic flow. Regional Studies, Regional Science, 1(1), 40–42.CrossRefGoogle Scholar
  12. Newman, M. E. (2004). Fast algorithm for detecting community structure in networks. Physical Review E, 69(6), 066133.CrossRefGoogle Scholar
  13. Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.CrossRefGoogle Scholar
  14. Rae, A. (2009). From spatial interaction data to spatial interaction information? Geovisualisation and spatial structures of migration from the 2001 UK census. Computers, Environment and Urban Systems, 33(3), 161–178.CrossRefGoogle Scholar
  15. Ratti, C., Sobolevsky, S., Calabrese, F., Andris, C., Reades, J., Martino, M., Claxton, R., & Strogatz, S. H. (2010). Redrawing the map of Great Britain from a network of human interactions. PloS One, 5, e14248.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ying Long
    • 1
  • Zhenjiang Shen
    • 2
    • 3
    • 4
  1. 1.Beijing Key Lab of Capital Spatial Planning and StudiesBeijing Institute of City PlanningBeijingChina
  2. 2.2C718Kanazawa University Natural Science and Technology HallKanazawaJapan
  3. 3.Tsinghua UniversityBeijingChina
  4. 4.Fuzhou UniversityFuzhouChina

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