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Data-Driven Rebalancing Methods for Bike-Share Systems

  • Daniel FreundEmail author
  • Ashkan Norouzi-Fard
  • Alice Paul
  • Carter Wang
  • Shane G. Henderson
  • David B. Shmoys
Chapter
  • 27 Downloads

Abstract

As bike-share systems expand in urban areas, the wealth of publicly available data has drawn researchers to address the novel operational challenges these systems face. One key challenge is to meet user demand for available bikes and docks by rebalancing the system. This chapter reports on a collaborative effort with Citi Bike to develop and implement real data-driven optimization to guide their rebalancing efforts. In particular, we provide new models to guide truck routing for overnight rebalancing and new optimization problems for other non-motorized rebalancing efforts during the day. Finally, we evaluate how our practical methods have impacted rebalancing in New York City.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Daniel Freund
    • 1
    Email author
  • Ashkan Norouzi-Fard
    • 2
  • Alice Paul
    • 3
  • Carter Wang
    • 4
  • Shane G. Henderson
    • 5
  • David B. Shmoys
    • 5
  1. 1.MITCambridgeUSA
  2. 2.EPFLLausanneSwitzerland
  3. 3.Brown UniversityProvidenceUSA
  4. 4.Motivate InternationalNew YorkUSA
  5. 5.Cornell UniversityIthacaUSA

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