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Optimizing Bike-Share Repositioning: Networked Inventory Management with Spatiotemporal Modeling

In a bike-sharing system, demand loss is primarily due to out-of-stock stations. One solution to tackle this problem is to rebalance the bike inventory of the stations through repositioning. In a large-scale bike-sharing system, bike repositioning typically works in two steps: The platform generates the reposition tasks, and then the operators execute those tasks. In this paper, we focus on the problem of determining the reposition tasks, which is a core problem for the platform operations. We model the bike-sharing system as a networked inventory system and propose a select-and-match method to generate optimal repositions, seamlessly combining inventory management and spatiotemporal value learning techniques. We compute the optimal (s, S) policy to select the stations with excess and deficit bike stocks. Subsequently, we solve a minimum cost maximum flow problem for rebalance assignment while considering the long-term effects of repositioning, characterized by a bike transition value. We design a spatiotemporal value network to learn the values. We evaluate the performance of the proposed method in a simulator based on real bike-sharing data, and the results demonstrate the superiority of our approach over the other alternatives.