Bike-sharing systems are now ubiquitous across the U.S. We have worked with Motivate, the operator of the largest such systems, including in New York, Chicago and San Francisco, to innovate data-driven approaches for bike-sharing. With them we have developed methods to improve their day-to-day operations and also provide insight on central issues in the design of their 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.
Competing for the Wagner Prizes places you and your author team among the upper echelons of those within the O.R. and Analytics fields today.
In our presentation, 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:
Motivate has moved hundreds of docks in its systems nationwide and the Bike Angels program now aids rebalancing in San Francisco and NYC.
In NYC, Bike Angels yields improvement comparable to that obtained through Motivate’s traditional rebalancing efforts, at far less financial and environmental cost.