Investigating Evolutions of Metro Station Functions in Shenzhen with Long-Term Smart Card Data

  • Fan Zhang
  • Kang Liu
  • Ling YinEmail author
  • Fan Zhang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1228)


The significances of urban metro stations are far more transport nodes in a city. Their surroundings are usually well developed and attract plentiful human activities, which support various functions in cities such as living and working. Investigating evolutions of metro station functions can help understand the developments of the stations as well as the whole city in a quick, low-cost, continuable and effective way. Using long-term smart card data collected from 2014 to 2018 of Shenzhen, China, this study identifies the functions of metro stations in different years, and reveals the evolution patterns of the functions over years, which is the first attempt as far as we know. The analytical results indicate that the function differentiations among stations have enlarged during those years; the cores of workplaces has shifted from Luohu to Futian and Nanshan district; the cores of residences have shifted to relatively peripheral districts such as Bao’an District, Longhua District and Longgang District; in general, the functions have evolved toward a more home-work-separation pattern. Those findings can help understand the changes of metro station functionalities, which are important clues for the governments to make better and sustainable public-transport and land-use policies.


Metro station Smart card data Function Evolution Long-term 



This research is supported by the National Natural Science Foundation of China (No. 41771441, 41901391), China Postdoctoral Science Foundation (No. 2019M653114), a grant from State Key Laboratory of Resources and Environmental Information System, the Basic Research Project of Shenzhen City (No. JCYJ20170307164104491), and the Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, and Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence.


  1. 1.
    Lu, K., Khani, A., Han, B.: A trip purpose-based data-driven alighting station choice model using transit smart card data. Complexity 2018, 1 (2018)Google Scholar
  2. 2.
    Alsger, A., Tavassoli, A., Mesbah, M., Ferreira, L., Hickman, M.: Public transport trip purpose inference using smart card fare data. Transp. Res. Part C: Emerg. Technol. 87, 123 (2018)Google Scholar
  3. 3.
    Jung, J., Sohn, K.: Deep-learning architecture to forecast destinations of bus passengers from entry-only smart-card data. IET Intell. Transp. Syst. 11, 334 (2017)Google Scholar
  4. 4.
    Tamblay, S., Galilea, P., Iglesias, P., Raveau, S., Muñoz, J.C.: A zonal inference model based on observed smart-card transactions for Santiago de Chile. Transp. Res. Part A: Policy Practice 84, 44 (2016)Google Scholar
  5. 5.
    Gan, Z., Yang, M., Feng, T., Timmermans, H.: Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations. Transportation 47, 315–336 (2018)Google Scholar
  6. 6.
    Wang, Z., Hu, Y., Zhu, P., Qin, Y., Jia, L.: Ring aggregation pattern of metro passenger trips: a study using smart card data. Physica A: Stat. Mech. Appl. 491, 471 (2018)Google Scholar
  7. 7.
    Kim, J., Corcoran, J., Papamanolis, M.: Route choice stickiness of public transport passengers: measuring habitual bus ridership behaviour using smart card data. Transp. Res. Part C: Emerg. Technol. 83, 146 (2017)Google Scholar
  8. 8.
    Ma, X., Liu, C., Wen, H., Wang, Y., Wu, Y.: Understanding commuting patterns using transit smart card data. J. Transp. Geogr. 58, 135 (2017)Google Scholar
  9. 9.
    Briand, A., Côme, E., El Mahrsi, M.K., Oukhellou, L.: A mixedture model clustering approach for temporal passenger pattern characterization in public transport. Int. J. Data Sci. Anal. 1, 37 (2016)Google Scholar
  10. 10.
    Goulet-Langlois, G., Koutsopoulos, H.N., Zhao, J.: Inferring patterns in the multi-week activity sequences of public transport users. Transp. Res. Part C: Emerg. Technol. 64, 1 (2016)Google Scholar
  11. 11.
    Long, Y., Liu, X., Zhou, J., Chai, Y.: Early birds, night owls, and tireless/recurring itinerants: an exploratory analysis of extreme transit behaviors in Beijing, China. Habitat Int. 57, 223 (2016)Google Scholar
  12. 12.
    Kieu, L.M., Bhaskar, A., Chung, E.: Passenger segmentation using smart card data. IEEE Trans. Intell. Transp. Syst. 16, 1537 (2015)Google Scholar
  13. 13.
    Zou, Q., Yao, X., Zhao, P., Wei, H., Ren, H.: Detecting home location and trip purposes for cardholders by mining smart card transaction data in Beijing subway. Transportation 45, 919 (2018)Google Scholar
  14. 14.
    Huang, J., Levinson, D., Wang, J., Zhou, J., Wang, Z.: Tracking working and living dynamics with smartcard data. Proc. Natl. Acad. Sci. 115, 12710 (2018)Google Scholar
  15. 15.
    Long, Y., Thill, J.: Combining smart card data and household travel survey to analyze workings–living relationships in Beijing. Comput. Environ. Urban Syst. 53, 19 (2015)Google Scholar
  16. 16.
    Kim, K.: Identifying the structure of cities by clustering using a new similarity measure based on smart card data. IEEE Trans. Intell. Transp. Syst. 1 (2019)Google Scholar
  17. 17.
    Maeda, T.N., Mori, J., Hayashi, I., Sakimoto, T., Sakata, I.: Comparative examination of networking clustering methods for extracting community structures of a city from public transportation smart card data. IEEE Access 7, 53377 (2019)Google Scholar
  18. 18.
    Gong, Y., Lin, Y., Duan, Z.: Exploring the spatiotemporal structure of dynamic urban space using metro smart card records. Comput. Environ. Urban Syst. 64, 169 (2017)Google Scholar
  19. 19.
    Li, Y., Wang, X., Sun, S., Ma, X., Lu, G.: Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networkings. Transp. Res. Part C: Emerg. Technol. 77, 306 (2017)Google Scholar
  20. 20.
    Yang, J., Chen, J., Le, X., Zhang, Q.: Density-oriented versus development-oriented transit investment: decoding metro station location selection in Shenzhen. Transp. Policy. 51, 93 (2016)Google Scholar
  21. 21.
    Tang, T., et al.: FISS: function identification of subway stations based on semantics mining and functional clustering. IET Intell. Transp. Syst. 12 558 (2018)Google Scholar
  22. 22.
    Wang, J., Kong, X., Rahim, A., Xia, F., Tolba, A., Al-Makhadmeh, Z.: IS2Fun: identification of subway station functions using massive urban data. IEEE Access 5, 27103 (2017)Google Scholar
  23. 23.
    El Mahrsi, M.K., Come, E., Oukhellou, L., Verleysen, M.: Clustering smart card data for urban mobility analysis. IEEE Trans. Intell. Transp. Syst. 18, 712 (2017)Google Scholar
  24. 24.
    Zhou, Y., Fang, Z., Zhan, Q., Huang, Y., Fu, X.: Inferring social functions available in the metro station area from passengers’ staying activities in smart card data. ISPRS Int. J. Geo-Inf. 6, 394 (2017)Google Scholar
  25. 25.
    Liu, K., Qiu, P., Li, M., Liu, X., Lu, F.: Exploring urban travel routes’ characteristics from a geometric perspective. Comput. Environ. Urban Syst. 74, 50–61 (2019)CrossRefGoogle Scholar
  26. 26.
    Liu, K., Gao, S., Qiu, P., Liu, X., Yan, B., Lu, F.: Road2Vec: measuring traffic interactions in urban road system from massive travel routes. ISPRS Int. J. Geo-Inf. 6, 321 (2017)Google Scholar
  27. 27.
    Xie, J., Yin, L., Mao, L.: A modeling framework for individual-based urban mobility based on data fusion. In: 2018 26th International Conference on Geoinformatics, pp. 1–6. IEEE (2018)Google Scholar
  28. 28.
    Liu, K., Gao, S., Lu, F.: Identifying spatial interaction patterns of vehicle movements on urban road networks by topic modelling. Comput. Environ. Urban Syst. 74, 50 (2019)Google Scholar
  29. 29.
    Zhao, Z., et al.: The effect of temporal sampling intervals on typical human mobility indicators obtained from mobile phone location data. Int. J. Geogr. Inf. Sci. 33, 1471 (2019)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.State Key Laboratory of Resources and Environmental Information SystemBeijingChina
  4. 4.Shenzhen Institute of Beidou Applied TechnologyShenzhenChina

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