Analytics and Bikes: Riding Tandem with Motivate to Improve Mobility

Daniel Freund (), Shane G. Henderson (), Eoin O’Mahony () and David B. Shmoys ()
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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

Interfaces, 2019, vol. 49, issue 5, 310-323

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.

Keywords: transportation; optimization; inventory; sharing economy (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:49:y:2019:i:5:p:310-323

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