Predicting Bike Usage for New York City's Bike Sharing System

Abstract

Bike sharing systems consist of a fleet of bikes placed in a network of docking stations. These bikes can then be rented and returned to any of the docking stations after usage. Pre-dicting unrealized bike demand at locations currently without bike stations is important for effectively designing and ex-panding bike sharing systems. We predict pairwise bike de-mand for New York City’s Citi Bike system. Since the system is driven by daily commuters we focus only on the morning rush hours between 7:00 AM to 11:00 AM during weekdays. We use taxi usage, weather and spatial variables as covari-ates to predict bike demand, and further analyze the influence of precipitation and day of week. We show that aggregating stations in neighborhoods can substantially improve predic-tions. The presented model can assist planners by predicting bike demand at a macroscopic level, between pairs of neigh-borhoods.