Research On The Collaborative Optimization Of High-speed Railway Passenger Fare And Ticket Quantity Under Competitive Conditions

With the increasingly fierce competition among various modes of transportation in the passenger transport market and the diversified and personalized development of passenger travel demands,the single fixed fare and fare allocation mechanism has been unable to effectively adjust the balance between fixed capacity and actual demand,and there has been an imbalance in the utilization of seat capacity between different trains at the same point,resulting in the waste of high-speed rail capacity resources and the loss of revenue.Under the background of deepening the reform of the railway system,China’s high-speed railway urgently needs to use the concept of revenue management,fully consider the actual situation and passenger travel needs,use market-oriented means to adjust the relationship between supply and demand,improve service levels,further improve market competitiveness,and ultimately maximize revenue.This paper mainly studies the comprehensive optimization of high-speed railway train fares and fares from the following aspects:(1)On the basis of consulting the research literature and application results on revenue management and passenger travel selection behavior at home and abroad,the influencing factors of passenger travel choice behavior are summarized,and the feasibility of the basic theory and method of high-speed railway utilization revenue management in China is analyzed from the aspects of the theoretical basis of revenue management,practical experience and characteristics of China’s high-speed railway transportation organization.(2)Based on the market research results,the high-speed passenger transport market is subdivided by using the potential category model,taking the BeijingShanghai corridor high-speed passenger transport market as an example,and taking the age,monthly income,travel purpose,cost source,travel distance,and most important attributes as the explicit variables,and dividing passengers into three categories: planned business type,conventional leisure type and mixed economy type;and analyzing different types of passengers between modes of transportation and highspeed railway Selection behavior characteristics between multiple trains of the same OD.(3)Take the number of train stops and train schedule in each section are selected as the evaluation indicators of train service level,and the K-Means clustering algorithm is used to divide the service level of trains between the same OD,and the passenger travel utility function is constructed to describe the selection behavior of different types of passengers between modes of transportation,and OD is based on the discrete selection model The demand for passenger flow between the rooms is distributed among the various modes of transport.Then,a collaborative optimization model of differential fare and ticket amount of trains with the goal of optimizing the revenue of high-speed railway tickets is constructed,and an artificial swarm algorithm is designed to solve the model problem.Empirical results show that under competitive conditions,the implementation of differentiated fares for trains of different service classes according to the selection behavior of different types of passengers and the service level of trains can effectively improve ticket revenue and train load factor.