Short-term Metro Passenger Flow Prediction Capturing the Impact of Unplanned Events

Unplanned events present challenges for operations and management in metro systems. Short-term passenger flow prediction can help agencies to better design contingency strategies and communicate with passengers under unplanned events. Though many short-term prediction methods have been proposed in the literature, most studies focused on normal situations. The study focuses on the short-term metro passenger flow predictions under disruptions and explores novel mechanisms for capturing the impact of unplanned events and addressing the imbalanced dataset for training. Typical machine learning and deep learning methods are developed for exploration. A large-scale automatic fare collection (AFC) data and incident log data for a heavily used metro system is used for empirical studies. The analysis found that the same type of unplanned events shares a similar and consistent pattern of demand change (with respect to normal situations) at the station level. The synthetic minority over-sampling technique (SMOTE) can enrich the passenger flow observations under unplanned events and generate a balanced dataset for model training. The results show that the combination of passenger flow change ratio and the SMOTE oversampling technique enables the prediction models to learn the impact of unplanned events, and thus significantly improves the prediction accuracy under disruptions. However, the over-sampling techniques (i.e., SMOTE and replication) slightly deteriorate the prediction accuracy for passenger flow under normal situations. The findings shed insights on mechanisms for disruption impact representation and oversampling imbalanced data in model training and guide the development of short-term prediction under unplanned events.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 16p

Subject/Index Terms

Filing Info

  • Accession Number: 01763628
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-03642
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:07AM