Optimization of Differential Pricing and Seat Allocation in High-Speed Railways for Multi-Class Demands: A Chinese Case Study

Abstract

There are many options for passengers choosing to travel by train. In order to maximize price revenue requests, railway companies must consider the differences between passenger types in the process of formulating ticket strategies. This study firstly subdivides passenger demand according to the latent class model based on Stated Preference (SP) and Revealed Preference (RP) survey data, then the passenger’s preference for train service attributes is identified. Based on the prospect theory, logit model and passenger flow transfer model, the final passenger flow assignment result is obtained. A differential pricing and seat allocation model aiming at maximizing price revenue is established, and a simulated annealing (SA) algorithm is designed to solve it. The results of the case show that the proposed model can increase revenue by 3.52% and by 1.02% compared with the result under single fares and without demand division.