Factors affecting bike-sharing behaviour in Beijing: price, traffic congestion, and supply chain

  • Lei Li
  • Yue Liu
  • Yanwu SongEmail author
S.I. : OR for Sustainability in Supply Chain Management


The bike-sharing boom is receiving growing attention with societies becoming more aware of active non-motorized traffic modes and their importance. However, the usage of this transport mode remains low in China, raising several concerns. The development of the bike-sharing behaviour can help to achieve sustainable development goals. Thus, this study aims to explore the impact of price, traffic congestion, and supply chain on bike-sharing selection behaviour. We employ big data technology to accurately research and evaluate usage behaviour towards bike-sharing apps in Beijing over a 4-day period. First, we administer a preliminary questionnaire survey across the country to explore factors influencing the choice of bike-sharing. Second, we perform a panel model regression to analyse the degree of impact by major factors on bike-sharing usage. The results revealed that price, traffic congestion, and supply chain affect the choice of bike-sharing and the degree of each factor’s impact differs by time of day. Drawing on these findings, we offer the following suggestions to increase the usage amount of bike-sharing and improve the operating efficiency of bike-sharing companies: (1) design time-based charging strategies (2) reasonably limit the number of shared bikes as per time of day (3) establish effective layouts for bicycle sites (4) account for maintenance time, and (5) use methods other than price wars to increase usage.


Big data Bike-sharing Panel model Supply chain Green city SDG 



This work was partially supported by the National Natural Science Foundation of China (Grant No. 71874120); Humanity and Social Science Youth Foundation of Ministry of Education of China (Grant No. 16YJC630051); Major Program of Social Science by the Tianjin Education Commission (2017JWZD02); Major Subject of Scientific and Technological Development Strategy Research Plan of Tianjin (17ZLZLZF02000); Philosophy and Social Sciences Planning Foundation of Tianjin (Grant No. TJGL16-016); Research Fund for Peiyang Young Scholars Program (Grant No. 2018XRX-0023); and the Public Resource Center of Tianjin University. The authors acknowledge and are grateful to those who supported this research.


  1. Arellano, M. (2003). Panel data econometrics. Oxford: Oxford University Press.CrossRefGoogle Scholar
  2. Baltagi, B. H. (2009). A companion to econometric analysis of panel data (Vol. 5, pp. 747–754). Hoboken: Wiley.Google Scholar
  3. Benson, J., & Gatchalian, A. N. (2014). BikeShare business model for Cal Poly-San Luis Obispo. San Luis Obispo: California Polytechnic State University.Google Scholar
  4. Birdsall, Michelle. (2014). Bikesharing in full bloom. Institute of Transportation Engineers. ITE Journal, 84(2), 28.Google Scholar
  5. Demaio, P. (2014). Bike-sharing: History, impacts, models of provision, and future. The Journal of Public Transportation, 12(4), 41–56.CrossRefGoogle Scholar
  6. El-Assi, W., Mahmoud, M. S., & Habib, K. N. (2017). Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation, 44(3), 589–613.CrossRefGoogle Scholar
  7. Gudivada, V. N., Baezayates, R. A., & Raghavan, V. V. (2015). Big data: Promises and problems. IEEE Computer, 48(3), 20–23.CrossRefGoogle Scholar
  8. Guo, Y., Zhou, J., Wu, Y., & Liz, Z. (2017). Identifying the factors affecting bike-sharing usage and degree of satisfaction in Ningbo, China. PLoS ONE, 12(9), e0185100.CrossRefGoogle Scholar
  9. Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing. Information Systems, 47(1), 98–115.CrossRefGoogle Scholar
  10. Hausman, J. A., & Taylor, W. E. (1981). Panel data and unobservable individual effects. Econometrica, 49, 1377–1398.CrossRefGoogle Scholar
  11. Høgevold, M. N., Svensson, G., Rodriguez, R., & Eriksson, D. (2019). Relative importance and priority of TBL elements on the corporate performance. Management of Environmental Quality, 30(3), 609–623.CrossRefGoogle Scholar
  12. Hsiao, C. (2003). Analysis of panel data. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  13. Li, L., Hao, T., & Chi, T. (2017). Evaluation on China’s forestry resources efficiency based on big data. Journal of Cleaner Production, 142, 513–523.CrossRefGoogle Scholar
  14. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al. (2011). Big data: The next frontier for innovation, competition, and productivity. New York: McKinsey Global Institute.Google Scholar
  15. Pouyanfar, S., Yang, Y., Chen, S. C., Shyu, M. L., & Iyengar, S. S. (2018). Multimedia big data analytics: A survey. ACM Computing Surveys, 51(1), 1–34.CrossRefGoogle Scholar
  16. Shaheen, S. A., Guzman, S., & Zhang, H. (2010). Bikesharing in Europe, the Americas, and Asia: Past, present, and future. Transportation Research Record, 2143, 159–167.CrossRefGoogle Scholar
  17. Shaheen, S. A., Zhang, H., Martin, E., & Guzman, S. (2011). China’s Hangzhou public bicycle: Understanding early adoption and behavioral response to bikesharing. Transportation Research Record, 2247, 33–41.CrossRefGoogle Scholar
  18. Vogel, P., Greiser, T., & Mattfeld, D. C. (2011). Understanding Bike-sharing systems using data mining: Exploring activity patterns. Procedia—Social and Behavioral Sciences, 20(6), 514–523.CrossRefGoogle Scholar
  19. Wigan, M. R., & Clarke, R. (2013). Big data’s big unintended consequences. Computer, 46(6), 46–53.CrossRefGoogle Scholar
  20. Yahya, B. (2017). Overall bike effectiveness as a sustainability metric for bike sharing systems. Sustainability, 9(11), 2070.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Management and EconomicsTianjin UniversityTianjinChina
  2. 2.School of InternetAnhui UniversityHefeiChina

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