Volume 3 · Issue 4 (2026)
10.66521/2938-9933-2026043002
The Collaborative Optimization of High-Speed Rail Ticket Pricing and Capacity Considering Passenger Classification
Dongyao Qi
Lanzhou Jiaotong University, Lanzhou, China
Corresponding Author: Dongyao Qi (1419089464@qq.com)
Abstract: Based on RP (Revealed Preference) and SP (Stated Preference) survey data, this paper utilizes the latent class model to segment high-speed rail passengers, identifying their preferences for different service attributes of the train, such as travel time, departure periods, and ticket prices, and quantifying these preferences. By incorporating revenue management principles and aiming to maximize the overall revenue from multiple trains, a collaborative optimization model for high-speed rail ticket pricing and capacity is developed. The simulated annealing algorithm is employed to solve the model. Finally, a case study is conducted using the high-speed rail route from Xi'an North to Changsha South. The results indicate that, compared to the traditional fixed ticket pricing model, the proposed optimization scheme effectively increases the total railway ticket revenue and provides a feasible reference for the formulation of a flexible pricing mechanism for high-speed rail tickets.
Keywords: High-Speed Rail; Latent Class Model; Dynamic Pricing; Ticket Allocation; Simulated Annealing Algorithm
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