Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems

Metro systems play an important role in meeting the demand for urban transportation in large cities. The understanding of passenger route choice is critical for public transit management. The wide deployment of automated fare collection (AFC) systems opens up a new opportunity. However, only each trip's tap-in and tap-out time stamp and stations can be directly obtained from AFC system records; the train and route chosen by a passenger are unknown, information necessary to solve the authors' problem. While existing methods work well in some specific situations, they hardly work for complicated situations. In this paper, we propose a solution that needs no additional equipment or human involvement than the AFC systems. The authors develop a probabilistic model that can estimate from empirical analysis how the passenger flows are dispatched to different routes and trains. They validate their approach using a large-scale data set collected from the Shenzhen Metro system. The measured results provide us with useful input when building the passenger path choice model.

• Record URL:
  https://doi.org/10.1109/TITS.2016.2587864
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Authors:
  • Zhao, Juanjuan
  • Zhang, Fan
  • Tu, Lai
  • Xu, Chengzhong
  • Shen, Dayong
  • Tian, Chen
  • Li, Xiang-Yang
  • Li, Zhengxi
• Publication Date: 2017-4

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: pp 790-801
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 18
  • Issue Number: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Data mining; Mathematical models; Origin and destination; Route choice; Smart cards; Subways; Transit riders
• Geographic Terms: Shenzen, China
• Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01634023
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: May 1 2017 9:37AM