Research On Ticket Fare Dynamic Optimization Method For High-speed Railway

On the premise that the infrastructure and transport capacity remain unchanged, the optimization of ticket fare is the key to further improve the utilization of transportation resources and increase the income of High-speed Railway. Based on the shortcomings of the current railway ticket fare system in our country, this dissertation studies the optimization method of ticket fare for High-speed Railway. According to different situation, different ticket fare optimization problems are proposed and solved.The main work of this dissertation can be concluded as follows.1. On the theoretical level, the ticket fare optimization problem could be reduced to supply and demand matching problem, of which the supply is transportation service, the demand is passengers, and the ticket fare is a key element between them. By analyzing the supply, demand and the matching relationship between them, the definition of ticket fare dynamic optimization problem and the boundry of this study are clear. The formal models are built according to different situation, and the theoretical framework is formed then.2. On the premise that the service attributes of trains between a particular origin and destination (OD) have little difference, the method of dynamically adjusting the ticket fare of a train is studied. Two problems need to be solved, one is when to adjust ticket fare, the other one is how much to adjust ticket fare. Based on the statistical analysis of the passenger ticket data, the regular of passengers' purchasing ticket is obtained. A method for determining the time of fare adjustment according to this regular is proposed. On the basis of the dynamic pricing theory of revenue management, the fare optimization model is established, which could be transformed into convex programming and solved directly by Lagrange multiplier method. Our method is verified by a case.3. On the premise of fixed fares, the problem of how to determine different prices for different trains between the same OD is studied. First, the demand distribution mode under certain fares is studied. Based on the analysis of passenger choice behavior, the method of passenger flow distribution with elastic demand is put forward. Then, a bi-level programming model is built, of which the upper level program is the problem of maximizing railway department revenue, and the lower level program is the passenger flow distribution problem. A method for calculating the derivative of passenger flow volume to ticket fare is presented, based of which a heuristic algorithm is designed to solve the bi-level programming model. Both the model and algorithm are verified by the case.4. The problem of how to dynamically determine different prices for different trains are studied. The dynamic programming method is used to model and solve the complex decision-making process. The state is expressed by the number of sold tickets,and random refund information is included in state transition equation. An optimality equation is built using the maximum expected revenue as value function. It's proved that the optimal solution of deterministic problem in Chapter 3 could be used as the approximation of value function. Then the post-decision state variable is introduced and an approximate dynamic programming algorithm based on post-decision state is proposed to solve our problem, which is verified by a case.