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

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Sharing Economy

Part of the book series: Springer Series in Supply Chain Management ((SSSCM,volume 6))

Abstract

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.

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Notes

  1. 1.

    Note that we have assumed all passengers will use bicycles to substitute their short distance MRT trips (within 2-Stop), upon the availability of the bicycles. We have thus actually obtained a gross over-estimate on the total volume of trips that can be substituted by bicycles. In reality, only a small percentage of the short distance passengers captured in the data will choose to use bicycles, say 10%. Therefore, all our numbers must be scaled down by a factor of 10 accordingly. In this case, we can see that for α = 40, the maximum number of bicycle docks we need to setup among all stations is no more than 80 for our system.

  2. 2.

    If we assume that the take-up rate for bicycle trip is only 10% of the full demand, then the corresponding number of docks needed will be reduced by 90%, i.e., from 700 to 70 docks.

  3. 3.

    Housing and Development Board – a statutory board of the Singapore Government responsible for public housing.

  4. 4.

    We thank Prof Gideon Weiss for pointing this out.

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Acknowledgements

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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Correspondence to Mabel C. Chou .

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Chou, M.C., Liu, Q., Teo, CP., Yeo, D. (2019). Models for Effective Deployment and Redistribution of Shared Bicycles with Location Choices. In: Hu, M. (eds) Sharing Economy. Springer Series in Supply Chain Management, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-01863-4_17

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