共享单车是城市交通的重要组成部分。共享单车用户需求的随机性导致其时空需求不均衡，甚至出现“借车难”现象，无法满足高峰时段的用户需求。因此出现高频用户高频出行时段到临近站点借车的现象，这意味着存在隐性需求。对于隐性需求，首先应用租借数和归还数刻画站点状态变化，通过挖掘临近站点用户出行情况判断参考站点的临界状态，建立基于站点状态变化图和需求判定模型确定站点的隐性需求。进而依据站点真实需求，建立长短期记忆（long short-term memory, LSTM）网络预测模型，并建立基于真实需求的共享单车区域调度模型。该模型以成本最小为目标，通过遗传算法求解得到调度成本最小的路径，为基于真实需求开展平衡调度提供了参考。结果显示，在相近的运输成本下，真实需求下的调度方式能在一定程度上缓解用户借车难的问题，从而减少高频用户的流失。

Shared bikes represent a crucial component of urban transportation. The randomness of user demand for shared bikes with fixed piles leads to unbalanced demand in time and space, and even the difficulty in renting a bike, which cannot meet the user demand during peak hours. Therefore, high-frequency users frequently travel to nearby stations to rent a bike for serving, which means that there are implicit demands. As for the hidden demand, firstly, the state changes of the site were described by the rental number and the return number, and the critical state of the reference site was determined by mining the user travel conditions of nearby sites. The hidden demand of the site was determined based on the site state change diagram and the demand judgment model. Then, according to the real needs of the site, the long short-term memory(LSTM) network prediction model was established, and the regional scheduling model of shared bicycles based on the real needs was established. The model takes the cost minimization as the goal, and obtains the path with minimum scheduling cost through genetic algorithm, which provides a reference for balanced scheduling based on real demand. The results demonstrate that, when transportation costs are similar, the scheduling method under real demand can alleviate the problem of users' difficulty in renting a bike, thereby reducing the loss of high-frequency users.