Unplanned Disruption Analysis in Urban Railway Systems Using Smart Card Data

Metro system disruptions are a big concern due to their impacts on safety, service quality, and operating efficiency. A better understanding of system performance and passenger behavior under unplanned disruptions is critical for efficient decision making, effective customer communication, and identifying potential improvements. However, few studies explore disruption impacts on individual passenger behavior, and mostly collect data manually. Due to survey limitations, this study examines the potential of automated data to analyze unplanned disruption impacts comprehensively. The authors propose a systematic approach to evaluate disruption impacts on system performance and passenger behavior using automated fare collection (AFC) data. The approach proposes various metrics and inference methods to evaluate performance from perspectives of train operations, information provision (customer communication), and bridging strategy (use of shuttle services to connect stations impacted by an incident). The proposed approach is demonstrated using data from a major metro system. The results highlight the ability of AFC data to provide new insights for unplanned disruption analysis that are difficult to extract from traditional data collection methods.


  • English

Media Info

  • Media Type: Web
  • Features: Figures; References; Tables;
  • Pagination: 19p

Subject/Index Terms

Filing Info

  • Accession Number: 01763577
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-04106
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:06AM