Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems

As an innovative mobility strategy, public bikesharing has grown dramatically worldwide. Though providing convenient, low-cost and environmental-friendly transportation, the unique features of bike-sharing systems give rise to problems to both users and operators. The primary issue among these problems is the uneven distribution of bikes caused by the everchanging usage and (available) supply. This bike unbalance issue necessitates efficient bike rebalancing strategies, which depends highly on bike mobility modeling and prediction. In this paper, a trace-driven simulation-based prediction approach is proposed by simultaneously taking user mobility demand and real-time status of stations into consideration. We extensively evaluated the performance of our design with the dataset from one of the world’s largest public bike-sharing system (BSS) in Hangzhou, China, which owns more than 2800 stations. Evaluation results show an 85 percentile relative error of 0.6 for checkout and 0.4 for check-in prediction. Preliminary results on how the predictions can be used for bike rebalancing are also provided. We believe this new mobility modeling and prediction approach can improve the bike-sharing system operation algorithm design and pave the way for the rapid deployment and adoption of bike-sharing systems across the globe.