| The travel demand and the travel requirements of Chinese citizens have been growing with the development of China economic and the refinement of transport infrastructure,which means a higher service quality of transport service requirements are needed.The competitiveness of China railway services is facing serve challenges due to the relatively simple and out-of-date pricing strategy.On the one hand,static ticket fare and fixed tickets in the pre-sale period cannot mobilize the vitality of the passenger transport market,which is not conducive to the exploration of potential revenue.On the other hand,the ticket prices of trains of the same class with the same OD are the same,which does not reflect the differences in departure time and travel time,etc.It will cause the unbalanced distribution of passenger flow and lower revenue.This research has introduced revenue management theory and studied the pricing optimization and ticket allocation of China High-speed railway service which includes model introduction and the solution to the model.The main content of this thesis contains several factors:Firstly,the applicable conditions of revenue management and the characteristics of high-speed railway transportation organization has been summarized.Besides,the thesis has discussed the feasible path of introducing revenue management into high-speed railway from two control strategies based on price and quantity.The pricing and ticket allocation problems has been abstracted and the optimization objectives has been defined.The thesis has formed the theoretical framework of these problems.Then,the thesis has studied the dynamic pricing and ticket allocation problem based on single train service with static and fixed ticket prices in the pre-sale period.With the regularity of passenger purchase behavior known,the pricing and ticket allocation model is introduced to increasing the income of the train service and to refine to passenger purchase flow.Aiming at maximizing the revenue of the train,a nonlinear integer programming model is constructed,and the model is solved by means of particle swarm optimization.The results show that,the dynamic adjustment scheme of fare and ticket allocation can improve train revenue and attendance.Before the capacity is saturated,fare adjustments can effectively boost revenue.In addition,the effect of increasing revenue is more significant in the market where demand fluctuates significantly.In the end,the thesis has introduced a nonlinear integer programming model under the setting of trains at similar service level which service for the same set of an Origin-Destination flow.The object function is the maximum overall interests of railway and passengers.For train differences,several key service difference factors,such as train running time,departure time and other factors,are considered.In order to maximum the overall interests,develop differentiated fares and tickets for different trains.The results show that,the optimization scheme can not only improve the transport revenue,but also reduce the travel cost of passengers and improve the overall efficiency.For the segment with high demand of passenger flow,adopting optimization scheme can achieve better results.Figure:23,table:23,reference:70. |
