Unplanned events present significant challenges for operations and management in metro systems. Short-term ridership prediction can help agencies to better design contingency strategies under unplanned events. Though many short-term prediction methods have been proposed in the literature, most studies focused on typical situations or planned events. The study develops methods for the short-term metro ridership prediction under unplanned events. It explores event impact representation mechanisms and deals with the imbalanced data training problem in building the prediction model under unplanned events. Typical machine learning and deep learning methods are developed for exploration. A large-scale automatic fare collection (AFC) dataset and event record data for a heavily used metro system are used for empirical studies. The analysis found that the same type of unplanned event shares a similar and consistent demand change pattern (with respect to the demand under typical situations) at the station level. The synthetic minority oversampling technique (SMOTE) can enrich the ridership observations under unplanned events and generate a balanced dataset for model training. Given the occurrence of unplanned events, the results show that a combination of demand change ratio and the SMOTE oversampling technique enables the prediction models to learn the impact of unplanned events and improve the prediction accuracy under unplanned events. However, the oversampling methods (i.e., SMOTE and replication) slightly deteriorate the prediction accuracy for ridership under normal conditions. The findings provide insights into mechanisms for disruption impact representation and oversampling imbalanced data in model training, and guide the development of models for short-term prediction under unplanned events.

意外事件给地铁系统的运营和管理带来了重大挑战。短期客流量预测可以帮助机构更好地设计意外事件下的应急策略。尽管文献中提出了许多短期预测方法，但大多数研究都集中在典型情况或计划事件上。该研究开发了在意外事件下进行短期地铁客流量预测的方法。它探索了事件影响表示机制，并解决了在计划外事件下构建预测模型时数据训练不平衡的问题。典型的机器学习和深度学习方法是为探索而开发的。大量使用的地铁系统的大规模自动收费 (AFC) 数据集和事件记录数据用于实证研究。分析发现，同一类型的计划外事件在站点级别具有相似且一致的需求变化模式（相对于典型情况下的需求）。合成少数过采样技术（SMOTE）可以丰富计划外事件下的乘客量观察，并为模型训练生成平衡的数据集。考虑到意外事件的发生，结果表明，需求变化率和 SMOTE 过采样技术的结合使预测模型能够学习意外事件的影响并提高意外事件下的预测准确性。然而，过采样方法（即 SMOTE 和复制）在正常条件下略微降低了对乘客量的预测精度。