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.