Optimizing Rebalance Scheme for Dock-Less Bike Sharing Systems with Adaptive User Incentive

Abstract

Recently, the development of Bike Sharing Systems (BSSs) brings environmental and economic benefits to the public. However, BSSs frequently suffer from the imbalanced bike distribution, including dock-less BSSs. The underflow or overflow of bikes in a region may lead to a lower service level to BSSs or congestion to the city. In the paper, we consider rebalancing the dock-less BSS by providing users with monetary incentives. The long-term objective is to maximize the number of satisfied users who successfully complete their rides over a period of time. The operator of the dock-less BSS can not only encourage a user to rent bikes at the neighborhood of its source with a source incentive, but also incentivize them to return bikes at the neighborhood of its destination with a destination incentive. To learn the differentiated incentive price for rebalancing bikes across time and space, we extend a novel deep reinforcement learning framework for user incentive. The source and destination incentives are integrated in an adaptive way by adjusting the detour level at the source and/or destination by avoiding bike underflow and overflow. In the experiment, we evaluate our approach in comparison with two existing pricing schemes. The locations of sources and destinations are abstracted from a selected dataset from Mobike. The experiment results show that our adapted learning algorithm outperforms the original one that only considers source incentive as well as another state-of-the-art approach in maximizing the long-term number of satisfied users.