The one-way carsharing system has been widely used in the carsharing field due to its flexibility. However, one of its main disadvantages is the imbalance between supply and pickup demand. At present, multi-source data are available for the real-time prediction of pickup demand. The multi-source data that are used for this purpose include real-time user application log data, historical order data, real-time station data, and user characteristic data. Based on these data, a demand prediction model was used to predict in real-time whether there is a pickup demand, and a demand time prediction model was applied to forecast the time at which a sharing vehicle is needed. Finally, a case study was conducted using 10 stations’ one-week field data to test the benefits of the models. The potential application of this study would effectively guide the system to formulate an active operation optimisation strategy to meet users’ demand.

中文翻译：

---

单向汽车共享系统动态取货需求的多源数据驱动预测

单向汽车共享系统以其灵活性在汽车共享领域得到了广泛的应用。然而，它的主要缺点之一是供应和皮卡需求之间的不平衡。目前，多源数据可用于皮卡需求的实时预测。用于此目的的多源数据包括实时用户应用日志数据、历史订单数据、实时站点数据和用户特征数据。基于这些数据，利用需求预测模型实时预测是否有接客需求，利用需求时间预测模型预测需要共享车辆的时间。最后，使用 10 个站点的一周现场数据进行了案例研究，以测试模型的优势。