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Dynamic pricing optimization for high-speed railway based on passenger flow assignment.

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Optimizing high-speed rail ticket pricing using dynamic strategies enhances operational efficiency and market competitiveness. This approach balances corporate revenue with passenger travel benefits, improving overall service.

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Area of Science:

  • Operations Research
  • Transportation Science
  • Econometrics

Background:

  • High-speed rail (HSR) faces challenges in balancing operational efficiency and market competitiveness.
  • Optimizing ticket pricing to align with dynamic supply-demand is crucial for HSR.
  • Understanding passenger demand and train supply is key to effective pricing.

Purpose of the Study:

  • To develop a multi-objective dynamic pricing model for high-speed rail.
  • To maximize both corporate revenue and passenger travel benefits.
  • To provide a practical framework for HSR ticket price management.

Main Methods:

  • Constructed a space-time service network based on train timetables.
  • Developed generalized cost and travel utility formulas incorporating various passenger costs.
  • Proposed a multi-objective dynamic pricing model solved by heuristic algorithms and accurate passenger flow assignment.

Main Results:

  • Demonstrated the practicability of dynamic pricing adjustment strategies for HSR.
  • Analyzed revenue impacts under different ticket price adjustment ranges.
  • Evaluated optimal ticket prices for different train classes and origin-destination levels.

Conclusions:

  • Dynamic pricing, considering train classification, is effective for HSR.
  • The proposed model offers a valuable reference for HSR ticket price management.
  • Balancing revenue and passenger benefits through dynamic pricing improves HSR operations.