Ordinal Logistic Regression Modeling Research on Decreasing Perceived Metro Transfer Time

This study newly develops two Ordinal Logistic Regression (OLR) models to explore effective ways to save Perceived Transfer Time (PTT) of metro passengers, in view of the difficulty of improving the infrastructure of a metro station. It is found that the PTT will be effectively decreased if the transfer walking congestion is released to be acceptable. Moreover, the congestion on the platform should be eliminated for reducing the PTT. In addition, decreasing the actual transfer waiting time to less than 5.00 min will evidently decrease the PTT. In future works, the effectiveness of the newly developed OLR models needs to be validated in a further and improved by applying them to study the PTT of metro passengers in different cities.

• Record URL:
  https://doi.org/10.1007/978-981-15-0644-4
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9789811506437
• Supplemental Notes:
  • © Springer Nature Singapore Pte Ltd. 2020.
• Corporate Authors:

  Springer Singapore

  152 Beach Road
  Singapore,     189721
• Authors:
  • Feng, Xuesong
  • Hua, Weixin
  • Niu, Xuejun
• Conference:
  • 9th International Conference on Green Intelligent Transportation Systems and Safety
  • Location: Guilin , China
  • Date: 2018-7-1 to 2018-7-3
• Publication Date: 2020-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1-8
• Monograph Title: Green, Smart and Connected Transportation Systems: Proceedings of the 9th International Conference on Green Intelligent Transportation Systems and Safety
• Serial:
  • Lecture Notes in Electrical Engineering
  • Volume: 617
  • Publisher: Springer
  • ISSN: 1876-1100

Subject/Index Terms

• TRT Terms: Passenger traffic; Perception; Rapid transit; Traffic congestion; Transfers; Waiting time
• Subject Areas: Operations and Traffic Management; Public Transportation; Railroads; Safety and Human Factors;

Filing Info

• Accession Number: 01899638
• Record Type: Publication
• ISBN: 9789811506437
• Files: TRIS
• Created Date: Nov 17 2023 11:25AM