Research On Dynamic Pricing And Ticket Allocation Of High-speed Railway Passenger Ticket Based On Big Data

In recent years,high-speed railways have developed rapidly in China,and the passenger flow of high-speed railways has increased year by year,and the competition among various modes of transportation has become increasingly fierce.The state is gradually delegating the right to set the price of passenger tickets to the railway sector.At the same time,big data technology has also been applied in various fields in recent years.Under this background,the railway department has adapted passenger flow research based on passenger ticket data to explore the market demand.The flexible and controllable fare adjustment mechanism and the construction of high-speed railway dynamic fare and ticket allocation optimization model have important theoretical significance and practical value.Based on the existing research,the research work of this paper is as follows:Firstly,this paper introduces the theory of revenue management,compares the similarities and differences between high-speed railway and aviation application revenue management,and draws the feasibility of using high-speed railway revenue management.In view of the big ticket purchase data of high-speed railway passengers,passenger flow characteristics of high-speed railway and the law of passenger ticket purchase behavior are studied,which provides suggestions for the railway department to implement revenue management to guide passengers outflow.Based on the long-short-term memory network in the artificial intelligence method,the passenger flow prediction model is established.The results are compared with the traditional seasonal variation prediction model.The results verify the validity of the long-term and short-term memory network passenger flow prediction model.Then,this paper analyzes the dynamic pricing strategy of high-speed railway and introduces two dynamic pricing strategies: continuous and discrete.It shows that for the current situation in China,the high-speed railway adopts the discrete time fare strategy should be the best choice,and study the passenger price.The sensitivity,introduced several common price response functions,and found that the Logit price response function is used to describe the changes in passenger demand and ticket price.Finally,based on the comprehensive optimization problem of ticket allocation and dynamic fare,the allocation coefficient is set according to the passenger flow prediction result,and the initial ticket allocation scheme is obtained to adapt to the fluctuation of passenger flow demand.The dynamic fare strategy is adopted for the section with residual capacity to attract potential.According to the passenger ticket,the time limit of passenger purchase time is set according to the pre-sale period,and the time point of sudden change of demand intensity is set as the fare adjustment time point.Based on the dynamic pricing method,the ticket price and ticket allocation with the change of demand intensity are established.The comprehensive optimization model,and finally through the example analysis,the results show that the optimization model can significantly increase the economic benefits of the railway sector while improving the train attendance rate.The results of this paper can be of some value for the management of high-speed railway ticket revenue.