Data Analysis and Optimization for (Citi)Bike Sharing

Bike-sharing systems are becoming increasingly prevalent in urban environments. They provide a low-cost, environmentally-friendly transportation alternative for cities. The management of these systems gives rise to many optimization problems. Chief among these problems is the issue of bicycle rebalancing. Users imbalance the system by creating demand in an asymmetric pattern. This necessitates action to put the system back in balance with the requisite levels of bicycles at each station to facilitate future use. In this paper, we tackle the problem of maintaing system balance during peak rush-hour usage as well as rebalancing overnight to prepare the system for rush-hour usage. We provide novel problem formulations that have been motivated by both a close collaboration with the New York City bike share (Citibike) and a careful analysis of system usage data. We analyze system data to discover the best placement of bikes to facilitate usage. We solve routing problems for overnight shifts as well as clustering problems for handling mid rush-hour usage. The tools developed from this research are currently in daily use at NYC Bike Share LLC, operators of Citibike.