The categorization of high-speed railway passenger value reflects the demand differentiation from passengers. This is essential for high-speed railway price strategy and the revenue optimization. This paper extracts RFM of passenger value as the core features, and analyzes the weight for each core feature based on AHP and high-speed railway expert strategy, then 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, which are high-value passengers, growth passengers, commuters, potential passengers and general passengers. This passenger value segmentation and portraits can be applied for both revenue optimization and provide better experience for passengers.

CICTP