Abstract The new generation of bicycle-sharing is an O2O (online-to-offline) platform service that enables the users to access the bicycle with a smartphone App. This paper proposes a dynamic repositioning model with predicted demand, where the repositioning time interval is fixed. A data-driven Neural Network (NN) approach is introduced to forecast the bicycle-sharing demand. The repositioning objective function at each time interval is defined to simultaneously minimize the operator cost and penalty cost. In addition to the normal constraints in static repositioning problem, flow conservation, inventory-balance and travel time constraints are taken into account. Due to the non-deterministic polynomial-time hard (NP-hard) nature of this model, a hybrid metaheuristic approach of Adaptive Genetic Algorithm (AGA) and Granular Tabu Search (GTS) algorithm is applied to calculate the solution. Based on predicted demand, the initial repositioning plan is made by AGA statically at the beginning of study horizon, which ensures the global optimization of the first solution. As time goes on, repositioning plan is checked and updated according to the real-usage patterns using GTS algorithm, which has the advantage of high-performance local-search within a short computing time. Numerical analysis is conducted using the real cases. The simulation results reveal that the proposed methodology can effectively model the dynamic repositioning problem in response to real-time bicycle-sharing usage. The proposed methodology can be a value-added tool in enhancing the feasibility and sustainability of bicycle-sharing program.

中文翻译：

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共享单车系统中数据驱动的动态重新定位模型

摘要 新一代共享单车是一种O2O（online-to-offline）平台服务，用户可以通过智能手机App访问单车。本文提出了一种具有预测需求的动态重新定位模型，其中重新定位时间间隔是固定的。引入了数据驱动的神经网络 (NN) 方法来预测自行车共享需求。每个时间间隔的重新定位目标函数被定义为同时最小化操作员成本和惩罚成本。除了静态重定位问题中的正常约束外，还考虑了流量守恒、库存平衡和旅行时间约束。由于该模型的非确定性多项式时间困难（NP-hard）性质，自适应遗传算法 (AGA) 和粒度禁忌搜索 (GTS) 算法的混合元启发式方法用于计算解决方案。AGA根据预测需求，在研究范围开始时静态制定初始重定位计划，保证了第一个解的全局优化。随着时间的推移，使用GTS算法根据实际使用模式检查和更新重新定位计划，具有在较短的计算时间内进行高性能本地搜索的优点。数值分析是使用实际案例进行的。仿真结果表明，所提出的方法可以有效地模拟响应实时共享单车使用情况的动态重新定位问题。