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Incentive-Based Rebalancing of Bike-Sharing Systems

  • Samarth J. Patel
  • Robin Qiu
  • Ashkan Negahban
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

This paper proposes an incentive-based approach for rebalancing bike-sharing systems where customers are offered discount to pick up bikes from nearby stations that are expected to become full in the near future. The main contribution of this work is twofold: (1) we develop a customized station object in the Simio simulation software to facilitate modeling of bike-sharing systems and reduce the burden on the modeler by eliminating the need to code the basic functionalities of a bike station; and, (2) we develop a discrete event simulation model of a real-world bike-sharing system (CitiBike) using instances of the customized station object to evaluate the effectiveness of pickup incentives in rebalancing the system. The model is calibrated using historic data and the results confirm the effectiveness of such incentive-based rebalancing scheme. More specifically, the results suggest that while incentives help improve bike availability in general throughout the system (i.e., better balance and service), offering too many incentives can in fact reduce total profit due to decreased marginal profit per ride.

References

  1. 1.
    Citi Bike NYC. Citi Bike monthly operating reports, Citi Bike NYC. 2017. https://www.citibikenyc.com/system-data/operating-reports. Accessed 20 Oct 2017.
  2. 2.
    Schuijbroek J, Hampshire R, Van Hoeve W. Inventory rebalancing and vehicle routing in bike sharing systems. Pittsburgh, PA: Carnegie Mellon University; 2013.Google Scholar
  3. 3.
    Dell’Olio L, Angel I, Moura JL. Implementing bike-sharing systems. Proc Inst Civ Eng Munic Eng Lond. 2011;164(2):89–101.CrossRefGoogle Scholar
  4. 4.
    Martinez LM, Caetano L, Eiró T, Cruz F. An optimisation algorithm to establish the location of stations of a mixed fleet biking system: an application to the city of Lisbon. Procedia Soc Behav Sci. 2012;54:513–24.CrossRefGoogle Scholar
  5. 5.
    Prem Kumar V, Bierlaire M. Optimizing locations for a vehicle sharing system. In: Proceedings of the Swiss transport research conference. 2012. p. 1–30.Google Scholar
  6. 6.
    Lin JR, Yang TH. Strategic design of public bicycle sharing systems with service level constraints. Transp Res Part E Logist Transp Rev. 2011;47(2):284–94.CrossRefGoogle Scholar
  7. 7.
    Kaltenbrunner A, Meza R, Grivolla J, Codina J, Banchs R. Urban cycles and mobility patterns: exploring and predicting trends in a bicycle-based public transport system. Pervasive Mob Comput. 2010;6(4):455–66.CrossRefGoogle Scholar
  8. 8.
    Vogel P, Mattfeld DC. Modeling of repositioning activities in bike-sharing systems. In: Proceedings of the world conference on transport research. 2010. p. 1–13.Google Scholar
  9. 9.
    Shu J, Chou MC, Liu Q, Teo C-P, Wang I-L. Models for effective deployment and redistribution of bicycles within public bicycle-sharing systems. Oper Res. 2013;61(6):1346–59.CrossRefGoogle Scholar
  10. 10.
    Jian N, Freund D, Wiberg H, Henderson S. Simulation optimization for a large-scale bike-sharing system. In: Proceedings of the 2016 Winter simulation conference. IEEE; 2016. p. 602–13.Google Scholar
  11. 11.
    O’Mahony E, Shmoys DB. Data analysis and optimization for (citi)bike sharing. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence. 2015. p. 687–94.Google Scholar
  12. 12.
    Fricker C, Gast N. Incentives and redistribution in homogeneous bike-sharing systems with stations of finite capacity. Euro J Transp Logist. 2014;5(3):261–91.CrossRefGoogle Scholar
  13. 13.
    Patel SJ. An incentive-based rebalancing scheme for large bike-sharing systems. Master’s paper. The Pennsylvania State University; 2017.Google Scholar
  14. 14.
    Smith J, Sturrock D, Kelton D. Simio and simulation: modeling, analysis, applications. Sewickley, PA: Simio LLC; 2017.Google Scholar
  15. 15.
    Ansari M, Negahban A, Megahed FM, Smith JS. HistoRIA: a new tool for simulation input analysis. In: Proceedings of the 2014 Winter simulation conference. IEEE; 2014. p. 2702–13.Google Scholar
  16. 16.
    Negahban A, Ansari M, Smith JS. ADD-MORE: automated dynamic display of measures of risk and error. In: Proceedings of the 2016 Winter simulation conference, IEEE; 2016. p. 977–88.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Graduate Professional StudiesThe Pennsylvania State UniversityMalvernUSA

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