The present work aims to expand the application of machine learning models in predicting and identifying traffic flow data and provide a reference for the scheduling and management of shared traffic against the Coronavirus Disease 2019 (COVID-19) pandemic. First, a time segmentation-based prediction model is proposed considering the classification superiority of Support Vector Machine (SVM) and combining the Optimal Segmentation Algorithm (OSA), denoted as OSA-SVM. Second, an algorithm for generating a shared traffic flow sequence is proposed based on the historical data of shared traffic flow. Finally, a shared traffic flow moment identification model is constructed based on the label propagation algorithm and the Random Forest (RF) model. Comparative analysis suggests that the OSA-SVM regression prediction model can accurately fit the fluctuations caused by the shared traffic flow data; however, its overall effect is not good, with deviation from the actual traffic sequence. Introducing historical data for weighting processing improves the goodness-of-fit of the regression prediction model significantly, maintaining at the level of 0.66–0.71 after one week. The stochastic gradient descent algorithm can provide a better weighted processing effect. The RF model shows the best recognition effect for the shared traffic data stream compared with other models, presenting an excellent performance in dealing with the imbalance and instability problems. The proposed model and algorithm have outstanding prediction and recognition accuracy in shared traffic scheduling, playing an active role in traffic control during COVID-19 prevention and control.

目前的工作旨在扩大机器学习模型在预测和识别交通流量数据中的应用，并为针对 2019 年冠状病毒病 (COVID-19) 大流行的共享交通的调度和管理提供参考。首先，考虑支持向量机(SVM)的分类优势，结合最优分割算法(OSA)，提出了一种基于时间分割的预测模型，记为OSA-SVM。其次，基于共享交通流的历史数据，提出了一种生成共享交通流序列的算法。最后，基于标签传播算法和随机森林（RF）模型构建了共享交通流时刻识别模型。对比分析表明，OSA-SVM回归预测模型能够准确拟合共享交通流数据引起的波动；但整体效果不佳，与实际交通顺序有偏差。引入历史数据进行加权处理，显着提高了回归预测模型的拟合优度，一周后维持在0.66-0.71的水平。随机梯度下降算法可以提供更好的加权处理效果。与其他模型相比，RF模型对共享交通数据流的识别效果最好，在处理不平衡和不稳定问题方面表现出色。所提出的模型和算法在共享交通调度中具有突出的预测和识别精度，