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

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