High-speed railway (HSR) is recognized as a green transportation mode with lower energy consumption and less pollution emission than other transportation. At present, China has the largest HSR network globally, but the maximum revenue of railway transportation corporations has not been realized. In order to make HSR achieve a favorable position within the fierce competition in the market, increase corporate revenue, and achieve the sustainable development of HSR and railway corporations, we introduce the concept of revenue management in HSR operations and propose an innovative model to optimize the price and seat allocation for HSR simultaneously. In the study, we formulate the optimization problem as a mixed-integer nonlinear programming (MINLP) model, which appropriately captures passengers’ choice behavior. To reduce the computational complexity, we further transform the proposed MINLP model into an equivalent model. Finally, the effectiveness of both the proposed model and solution algorithm are tested and validated by numerical experiments. The research results show that the model can flexibly adjust the price and seat allocation of the corresponding ticketing period according to the passenger demand, and increase the total expected revenue by 5.92% without increasing the capacity.

Based on the single pricing method of the high-speed railway (HSR) in China, a pricing strategy without flexibility leads to the problem of extreme fluctuations in passenger flow and difficulty in increasing revenue. In order to achieve sustainable development of the HSR from the perspective of pricing, in this study, we divided the passenger market according to the different factors affecting passengers' choice behavior, maximized ticket sales revenue with expected travel cost as the reference point, and used prospect theory to construct a differentiated pricing model under elastic demand. A simulated annealing algorithm was used to solve this model under two passenger flow intensities. Taking the Beijing-Shanghai corridor as an example for analysis, the results show that differential pricing can be implemented on the basis of passenger decision-making, and price reductions at off-peak periods will attract passenger flow which will increase ticket sales revenue by 10.41%. During the peak period, prices can be increased to maintain passenger flow, and ticket sales revenue will increase by 7.98%. We also found that increasing passenger expectations have a greater impact on ticket sales. This study provides theoretical and methodological support for the sustainable development of the HSR.

In order to improve the high-speed trains' service levels and increase their market shares, the Chinese high-speed railway (HSR) enterprise is reforming its ticket pricing strategy. A collaborative model that incorporates seat allocation decision into HSR dynamic pricing problem based on the revenue management theory is proposed, in which the objective is to maximize the total ticket revenue of enterprise under the constrains of price ceilings. A two-stage algorithm is developed to solve practical problems. The first stage solves the optimal price problem, and the second is to obtain the optimal seat allocation decisions. Finally, a case study based on the actual ticket data of Beijing-Shanghai HSR in China is implemented to show the effectiveness of the proposed approach, for which the results show that compared with the fixed price case, the revenue improvement ranges from 4.47% to 4.95% by using dynamic pricing strategy. Also, the case analysis shows that dynamic pricing strategy will lead to an increase in short-haul demands whereas a decrease in long-haul demands.INDEX TERMS High-speed railway (HSR), dynamic pricing, seat allocation, collaborative optimization, revenue management (RM).