How Bike Sharing Can Be More Efficient

Identifying the point at which no additional bikes should be moved is critical; for that, we cannot rely on our intuition but must instead take a data-driven approach.

With that in mind, my colleagues and I—David B. Shmoys and Shane G. Henderson of Cornell University; Hangil Chung, a Cornell graduate; and Eoin O’Mahony, a Cornell PhD—set out to develop sophisticated algorithms and advanced analytics to tackle the novel challenges of bike-share operations.

One area in which our methods were applied was the Bike Angels program that our industry collaborators at Motivate (now part of Lyft) run in cities across the country. As part of the program, cyclists earn points by renting or returning bikes at certain high-need stations. Initially, the program awarded points for the same pattern of stations every morning, and a different fixed pattern each afternoon.