High-Speed Railway Passenger Categorization Based on Fuzzy Clustering

The categorization of high-speed railway passenger value reflects the demand differentiation from passengers. This is essential for optimizing high-speed railway price strategy and the revenue. This paper extracts RFM of passenger value as the core features, analyzes the weight for each core feature based on AHP and high-speed railway expert strategy, and adopts fuzzy clustering algorithm for clustering analysis, finally comes out the passenger value segmentation model. Based on the passenger flow for Beijing-Shanghai high-speed railway, this paper divides the passengers into five categories including high-value passengers, growth passengers, commuters, potential passengers, and general passengers. This passenger value segmentation and portraits can be applied in revenue optimization and provide better experience for passengers.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 2469-2482
  • Monograph Title: CICTP 2020: Transportation Evolution Impacting Future Mobility

Subject/Index Terms

Filing Info

  • Accession Number: 01768163
  • Record Type: Publication
  • ISBN: 9780784483053
  • Files: TRIS, ASCE
  • Created Date: Dec 9 2020 3:04PM