Bike sharing systems have been widely deployed around the world in recent years. A core problem in such systems is to reposition the bikes so that the distribution of bike supply is reshaped to better match the dynamic bike demand. When the bike-sharing company or platform is able to predict the revenue of each reposition task based on historic data, an additional constraint is to cap the payment for each task below its predicted revenue. In this paper, we propose an incentive mechanism called {\em TruPreTar} to incentivize users to park bicycles at locations desired by the platform toward rebalancing supply and demand. TruPreTar possesses four important economic and computational properties such as truthfulness and budget feasibility. Furthermore, we prove that even when the payment budget is tight, the total revenue still exceeds or equals the budget. Otherwise, TruPreTar achieves 2-approximation as compared to the optimal (revenue-maximizing) solution, which is close to the lower bound of at least $\sqrt{2}$ that we also prove. Using an industrial dataset obtained from a large bike-sharing company, our experiments show that TruPreTar is effective in rebalancing bike supply and demand and, as a result, generates high revenue that outperforms several benchmark mechanisms.

翻译：共享单车系统近年来在全球范围内得到广泛部署。这类系统的核心问题之一是重新调度自行车，以重塑自行车供给分布，从而更好地匹配动态的自行车需求。当共享单车公司或平台能够基于历史数据预测每项重新调度任务的收益时，一个额外约束便是将每项任务的支付上限设定为其预测收益。本文提出了一种名为TruPreTar的激励机制，旨在激励用户将自行车停放在平台期望的位置，以平衡供需。TruPreTar具备四种重要的经济和计算特性，如诚实性和预算可行性。此外，我们证明即使支付预算紧张，总收益仍等于或超过预算。否则，TruPreTar相较于最优（收益最大化）方案可实现2-近似，这接近于我们同样证明的至少√2的下界。通过从一家大型共享单车公司获得的工业数据集，实验表明TruPreTar在平衡自行车供需方面有效，并且因此产生的高收益超过了几种基准机制。