Bayesian Sequential Learning and Decision Making in Bike-Sharing Systems

Abstract

In this article, we introduce modeling strategies for sequentially learning various types of demand uncertainty in bike-share networks and propose methods for optimal station inventory management. Our approach is motivated by a real bike-share network in Seoul, South Korea, with 40,000 bikes over a network of 2500 stations covering 25 municipal districts. In doing so, we consider novel Bayesian state space models that are suitable for fast and efficient learning of dynamically evolving system parameters for both intra-day and inter-week planning horizons. Our proposed approach provides an overall solution for operation managers where sequential parameter updating, demand prediction, and inventory decision making are addressed simultaneously and is straightforward to implement for the end-user. We illustrate how our approach can be applied to a large metropolitan area like Seoul and discuss practical implementation insights.