Citi Bike demand predictive model for Brooklyn

The booming bike-sharing programs in US metropolitan centers are one of the few tech-enabled innovations in urban transportation of the last decade that, despite being less notorious than automated vehicles or delivery drones, has actually been successfully implemented, integrated with other forms of transportation, and grown steadily in number of programs and stations, propelled by its widespread popularity with urban residents.

However, one of the main challenges these systems confront is their reliance in a fleet of trucks to redistribute the bikes across stations and counterbalance the natural aggregated flow of people’s origin-destination trips throughout the city. The resulting dispersion can rapidly disrupt the system’s operations: when a dock station gets completely full or completely empty it is rendered unusable.

The goal of this project is to develop a space-time regression model that can predict the use of New York City’s Citi Bike bike share system in the borough of Brooklyn and help plan the daily operations of bike redistribution by forecasting the demand by hour through the following week. These models allow for the logistical operation of Citi Bike to move bikes from stations with low forecasted demand, but more than half of their docks occupied, to stations with high forecasted demand on the shortest distance possible and on-time, expanding the actual dock capacity of the highly used stations.

The complete project report can be found here.