RedPacketBike: A Graph-Based Demand Modeling and Crowd-Driven Station Rebalancing Framework for Bike Sharing Systems

Bike-sharing systems have been deployed globally. One of the key issues for high-quality bike-sharing systems is to rebalance city-wide stations to maintain bike availability. Traditional strategies, such as repositioning bikes by trucks and volunteers based on historical riding records, usually operate in fixed paths and limited capacities, lacking the flexibility to cope with the highly dynamic and context dependent riding demands, and usually suffer from high costs and long delays. In this work, we propose RedPacketBike, an incentive-driven, crowd-based station rebalancing framework to effectively recruit participants from hybrid fleets (e.g., volunteer riders and hired trucks) based on the accurate forecast of bike demand leveraging deep learning techniques. First, we propose a spatiotemporal clustering method to extract bike demand hotspots from fluctuating bike usage data. Then, we build a context-aware deep neural network named BikeNet to forecast the trends of bike demand hotspots, simultaneously modeling the spatial correlations by graph convolution networks (GCN), the temporal dependencies by long short-term memory networks (RNN), and the contextual factors by autoencoders (AE). Finally, we propose a reinforcement-learning-based method to find optimal station rebalancing schemes by generating station rebalancing tasks with an integer linear programming (ILP) algorithm and allocating tasks to participants from hybrid fleets with dynamic incentive designs and reward expectations. Experiments using real-world bike-sharing system data collected from Citi Bike in New York City and Mobike in Xiamen City validate the performance of our framework, achieving a demand forecast error below 4.171 measured in MAE, and a 17.2% improvement of station availability by simulations with real-world parameter settings, outperforming the state-of-the-art baselines.