Abstract: In order to ease the difficulty of public bicycles’ borrowing and returning, a query system of public bicycles with WeChat interface was designed, which used WeChat platform to obtain data and used cloud platform to process data. Firstly, travel input was obtained by WeChat and the users’ travel demand for each period of time was fitted according to historical data. And then, according to the travel input, the information of around sites was extracted by the cloud platform, and the fitting data was used to predict the remained vehicles available to borrow when users arrived at the around sites; meanwhile, a sorting algorithm was provided to system users for site selection in order to guide users’ reasonable travel. Finally, taking a public bicycle network in Wuhan as an example, a query system of public bicycles was designed, whose functions included the site bicycle number query and route recommendation. And the improvement of forecasting information and traditional information on the problem was comprised.

Key words: traffic and transportation engineering, public bicycle, micro-letter, cloud platform, predictive information, ranking site selection

摘要： 为缓解公共自行车借还车难现象，基于微信平台获取数据及云平台处理数据，设计了以微信为界面的公共自行车查询系统。首先通过微信获取出行输入，根据历史数据拟合用户分时段的出行需求，再由云平台根据输入调用周围站点信息，运用拟合数据预测用户到达周围站点时的可借还车辆数，并通过排序算法返回促进系统供需平衡的用户站点选择优劣方案，引导用户合理出行。最后以武汉市公共自行车网络为例，设计了包含站点车辆数查询，选择路线推荐等功能的公共自行车查询系统，并比较了预测信息与传统信息对问题的改善程度。

关键词: 交通运输工程, 公共自行车, 微信, 云平台, 预测信息, 选择站点排序