Towards Station-Level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems

Bike sharing systems continue gaining worldwide popularity as they offer benefits on various levels, from society to environment. Given that those systems tend to be unbalanced along time, bikes are typically redistributed throughout the day to better meet the demand. Reasonably accurate demand prediction is key to effective redistribution; however, it is has received only little attention in the literature. In this paper, we focus on predicting the hourly demand for demand rentals and returns at each station of the system. The proposed model uses temporal and weather features to predict demand mean and variance. It first extracts the main traffic behaviors from the stations. These simplified behaviors are then predicted and used to perform station-level predictions based on machine learning and statistical inference techniques. We then focus on determining decision intervals, which are often used by bike sharing companies for their online rebalancing operations. Our models are validated on a two-year period of real data from BIXI Montreal. A worst-case analysis suggests that the intervals generated by our models may decrease unsatisfied demands by 30% when compared to the current methodology employed in practice.