Models for Effective Deployment and Redistribution of Shared Bicycles with Location Choices

  • Mabel C. ChouEmail author
  • Qizhang Liu
  • Chung-Piaw Teo
  • Deanna Yeo
Part of the Springer Series in Supply Chain Management book series (SSSCM, volume 6)


We develop practical OR models to support decision making in the design and management of public car-sharing or bicycle-sharing systems. We develop a network flow model with proportionality constraints to estimate the flow of bicycles within the network, and to estimate the number of trips supported by the system given an initial allocation of bicycles at each station. Furthermore, the number of docks needed at each station, to support the flow, can also be estimated. We also examine the impact of periodic redistribution of bicycles in the network to support more flows, and the location choices of bicycle stations. We conduct our numerical analysis using transit data from the train and bus operators in Singapore. Given that a substantial proportion of the passengers in the train system commute short distance – more than 16% of the passengers alight within 2 stops from the start station – this forms a latent segment of demand for the bicycle-sharing program. We argue that for the bicycle-sharing system to be most effective for this customer segment, the system must deploy the right number of bicycles at the right place, as this affects the utilization rate of the bicycles, how the bicycles circulate within the system, and also the effectiveness of any redistribution strategy. The same approach can be extended to incorporate the issue of station location choices, by incorporating the proportional flow constraints into the MIP formulation. Using a set of bus transit data, we implemented this approach to identify the ideal locations for the bicycle stations in a new town in Singapore, to support the movement of passengers from residential areas to the train station.



We thank Singapore Mass Rapid Transit and Land Transport Authority for providing the data used in this research. This research was supported in part by NUS Academic Research Fund R-314-000-078-112.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mabel C. Chou
    • 1
    Email author
  • Qizhang Liu
    • 1
  • Chung-Piaw Teo
    • 1
  • Deanna Yeo
    • 1
    • 2
  1. 1.Department of Analytics and Operations, NUS Business SchoolNational University of SingaporeSingaporeSingapore
  2. 2.GE HealthcareSingaporeSingapore

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