IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v49y2019i5p310-323.html
   My bibliography  Save this article

Analytics and Bikes: Riding Tandem with Motivate to Improve Mobility

Author

Listed:
  • Daniel Freund

    (Sloan School of Management, MIT, Cambridge, Massachusetts 02142;)

  • Shane G. Henderson

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14850;)

  • Eoin O’Mahony

    (Uber Technologies Inc., San Francisco, California 94103;)

  • David B. Shmoys

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14850; Department of Computer Science, Cornell University, Ithaca, New York 14850)

Abstract

Bike-sharing systems are now ubiquitous across the United States. We have worked with Motivate, the operator of the systems in, for example, New York, Chicago, and San Francisco, to both innovate a data-driven approach to managing their day-to-day operations and provide insight on several central issues in the design of its systems. This work required the development of a number of new optimization models, characterization of their mathematical structure, and use of this insight in designing algorithms to solve them. Here, we focus on two particularly high-impact projects: an initiative to improve the allocation of docks to stations and the creation of an incentive scheme to crowdsource rebalancing. Both of these projects have been fully implemented to improve the performance of Motivate’s systems across the country; for example, the Bike Angels program in New York City yields a system-wide improvement comparable with that obtained through Motivate’s traditional rebalancing efforts at far less financial and environmental cost.

Suggested Citation

  • Daniel Freund & Shane G. Henderson & Eoin O’Mahony & David B. Shmoys, 2019. "Analytics and Bikes: Riding Tandem with Motivate to Improve Mobility," Interfaces, INFORMS, vol. 49(5), pages 310-323, September.
    • Handle: RePEc:inm:orinte:v:49:y:2019:i:5:p:310-323
    DOI: 10.1287/inte.2019.1005
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/inte.2019.1005
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2019.1005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dorit S. Hochbaum, 1994. "Lower and Upper Bounds for the Allocation Problem and Other Nonlinear Optimization Problems," Mathematics of Operations Research, INFORMS, vol. 19(2), pages 390-409, May.
    2. Schuijbroek, J. & Hampshire, R.C. & van Hoeve, W.-J., 2017. "Inventory rebalancing and vehicle routing in bike sharing systems," European Journal of Operational Research, Elsevier, vol. 257(3), pages 992-1004.
    3. Tal Raviv & Ofer Kolka, 2013. "Optimal inventory management of a bike-sharing station," IISE Transactions, Taylor & Francis Journals, vol. 45(10), pages 1077-1093.
    4. Médard de Chardon, Cyrille & Caruso, Geoffrey & Thomas, Isabelle, 2016. "Bike-share rebalancing strategies, patterns, and purpose," Journal of Transport Geography, Elsevier, vol. 55(C), pages 22-39.
    5. Felipe Caro & Christopher S. Tang, 2018. "The 3rd POMS Applied Research Challenge 2018 Awards," Production and Operations Management, Production and Operations Management Society, vol. 27(12), pages 2339-2339, December.
    6. Bruce Hajek, 1985. "Extremal Splittings of Point Processes," Mathematics of Operations Research, INFORMS, vol. 10(4), pages 543-556, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bruno Albert Neumann-Saavedra & Teodor Gabriel Crainic & Bernard Gendron & Dirk Christian Mattfeld & Michael Römer, 2020. "Integrating Resource Management in Service Network Design for Bike-Sharing Systems," Transportation Science, INFORMS, vol. 54(5), pages 1251-1271, September.
    2. Gilbert Laporte & Frédéric Meunier & Roberto Wolfler Calvo, 2018. "Shared mobility systems: an updated survey," Annals of Operations Research, Springer, vol. 271(1), pages 105-126, December.
    3. Ahmed Kheiri & Alina G. Dragomir & David Mueller & Joaquim Gromicho & Caroline Jagtenberg & Jelke J. Hoorn, 2019. "Tackling a VRP challenge to redistribute scarce equipment within time windows using metaheuristic algorithms," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 561-595, December.
    4. Saif Benjaafar & Daniel Jiang & Xiang Li & Xiaobo Li, 2022. "Dynamic Inventory Repositioning in On-Demand Rental Networks," Management Science, INFORMS, vol. 68(11), pages 7861-7878, November.
    5. Yu, Qing & Xie, Yingkun & Li, Weifeng & Zhang, Haoran & Liu, Xiaolei & Shang, Wen-Long & Chen, Jinyu & Yang, Dongyuan & Yan, Jinyue, 2022. "GPS data in urban bicycle-sharing: Dynamic electric fence planning with assessment of resource-saving and potential energy consumption increasement," Applied Energy, Elsevier, vol. 322(C).
    6. Guillermo Cabrera-Guerrero & Aníbal Álvarez & Joaquín Vásquez & Pablo A. Maya Duque & Lucas Villavicencio, 2022. "A VNS-Based Matheuristic to Solve the Districting Problem in Bicycle-Sharing Systems," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    7. Liangpeng Gao & Yanjie Ji & Xingchen Yan & Yao Fan & Weihong Guo, 2021. "Incentive measures to avoid the illegal parking of dockless shared bikes: the relationships among incentive forms, intensity and policy compliance," Transportation, Springer, vol. 48(2), pages 1033-1060, April.
    8. Negahban, Ashkan, 2019. "Simulation-based estimation of the real demand in bike-sharing systems in the presence of censoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 317-332.
    9. Silva, Maria Clara Martins & Aloise, Daniel & Jena, Sanjay Dominik, 2024. "Data-driven prioritization strategies for inventory rebalancing in bike-sharing systems," Omega, Elsevier, vol. 129(C).
    10. VERGEYLEN, Nicholas & SÖRENSEN, Kenneth & PALHAZI CUERVO, Daniel, 2018. "Solution space analysis for the bike request scheduling problem," Working Papers 2018005, University of Antwerp, Faculty of Business and Economics.
    11. Gu, Wei & Yu, Xiaoru & Zhang, Shichen & Yan, Xiangbin & Wang, Chen, 2023. "To outsource or not: Bike-share rebalancing strategies under the service quality deviation of a third party," European Journal of Operational Research, Elsevier, vol. 310(2), pages 847-859.
    12. Huang, Di & Chen, Xinyuan & Liu, Zhiyuan & Lyu, Cheng & Wang, Shuaian & Chen, Xuewu, 2020. "A static bike repositioning model in a hub-and-spoke network framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    13. Carlos M. Vallez & Mario Castro & David Contreras, 2021. "Challenges and Opportunities in Dock-Based Bike-Sharing Rebalancing: A Systematic Review," Sustainability, MDPI, vol. 13(4), pages 1-26, February.
    14. Osorio, Jesus & Lei, Chao & Ouyang, Yanfeng, 2021. "Optimal rebalancing and on-board charging of shared electric scooters," Transportation Research Part B: Methodological, Elsevier, vol. 147(C), pages 197-219.
    15. Wang, Yi-Jia & Kuo, Yong-Hong & Huang, George Q. & Gu, Weihua & Hu, Yaohua, 2022. "Dynamic demand-driven bike station clustering," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    16. Çelebi, Dilay & Yörüsün, Aslı & Işık, Hanife, 2018. "Bicycle sharing system design with capacity allocations," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 86-98.
    17. Liang Gao & Wei Xu & Yifeng Duan, 2019. "Dynamic Scheduling Based on Predicted Inventory Variation Rate for Public Bicycle System," Sustainability, MDPI, vol. 11(7), pages 1-11, March.
    18. Quan-Lin Li & Rui-Na Fan, 2022. "A mean-field matrix-analytic method for bike sharing systems under Markovian environment," Annals of Operations Research, Springer, vol. 309(2), pages 517-551, February.
    19. Chen, Qingxin & Fu, Chenyi & Zhu, Ning & Ma, Shoufeng & He, Qiao-Chu, 2023. "A target-based optimization model for bike-sharing systems: From the perspective of service efficiency and equity," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 235-260.
    20. Yuanyuan Zhang & Yuming Zhang, 2018. "Associations between Public Transit Usage and Bikesharing Behaviors in The United States," Sustainability, MDPI, vol. 10(6), pages 1-20, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orinte:v:49:y:2019:i:5:p:310-323. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.