Analytics and bikes: riding tandem with Motivate to improve mobility. Freund, D., Henderson, S. G., O'Mahony, E., & Shmoys, D. B. INFORMS Journal on Applied Analytics, 49(5):310–323, 2019.
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Paper abstract bibtex 3 downloads
Paper abstract bibtex 3 downloads 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 innovate a data-driven approach to managing both their day-to-day operations and to provide insight on several central issues in the design of its systems. This work required the development of a number of new optimization models, characterizing their mathematical structure, and using 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 to that obtained through Motivate's traditional rebalancing efforts, at far less financial and environmental cost.
@article{frehenomashm19,
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 innovate a data-driven approach to managing both their day-to-day operations and to provide insight on several central issues in the design of its systems. This work required the development of a number of new optimization models, characterizing their mathematical structure, and using 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 to that obtained through Motivate's traditional rebalancing efforts, at far less financial and environmental cost.},
author = {Daniel Freund and Shane G. Henderson and Eoin O'{M}ahony and David B. Shmoys},
date-added = {2018-11-07 19:52:28 +0000},
date-modified = {2020-04-29 14:09:16 -0400},
journal = {{INFORMS} Journal on Applied Analytics},
number = {5},
pages = {310--323},
title = {Analytics and bikes: riding tandem with {M}otivate to improve mobility},
url_paper = {pubs/RidingTandemWithMotivate.pdf},
volume = {49},
year = {2019}}Downloads: 3
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This work required the development of a number of new optimization models, characterizing their mathematical structure, and using 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. 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