基于因果分析和相似日选择的共享单车需求量预测组合模型

共享单车的需求量预测是优化车辆系统布局、实现车辆合理调度的基础。为了提高共享单车需求量预测模型的精度，建立了基于格兰杰因果分析和相似日选择的组合预测模型，研究了时间和天气因素对共享单车出行需求的影响。应用格兰杰因果检验方法，筛选出影响共享单车需求量变化的关键天气指标。然后，基于天气特征向量的灰色关联度指标，提取待预测日各时段的相似日样本集。综合随机森林回归、支持向量回归等机器学习算法，建立了Stacking策略的组合预测模型，对区域分时共享单车需求量进行预测。最后，对北京市共享单车用户的骑行数据进行实例分析。结果表明相较单个机器学习预测模型，提出的组合预测模型的平均绝对百分比误差下降了9.1%，提高了共享单车短时需求预测的科学性和准确性，可为实际车辆调度提供参考依据。

A combined model for forecasting shared bikes demand based on causal analysis and similar day selection

Short-term demand of shared bikes forecasting plays an important role in optimizing the layout of free-floating bike sharing systems and bikes rebalancing. To improve the accuracy of demand forecasting methods for the emerging shared bikes business, this study establishes a combined forecasting model based on Stacking strategy and examines the impact of temporal variables and weather factors on shared bikes demand. In particular, the Granger causality test is used to identify the key weather indicators that cause demand fluctuations. We extract the set of similar samples for each period of the day for prediction based on the grey correlation index of weather variables. The Stacking strategy is then introduced to integrate random forest, support vector regression, and other machine learning algorithms for establishing a combined forecasting model to predict the short-term demand in different regions. Finally, using free-floating bike sharing data in Beijing, the proposed combined model is tested. The prediction results of combined model demonstrate that the mean absolute percentage error decreased by 9.1% compared with the single prediction model, thus improving the accuracy of the short-term demand forecast of shared bikes and providing useful information for bike relocation.