A Study On Theories And Methods Of Special High Speed Railway Line Revenue Management

Revenue management is to strike a balance between the limited market supply and the changing market demand through adopting certain mechanisms and strategies so as to maximize revenue/profits of the company. It aims to predict customer behavior at the micro level and maximize revenue by optimizing the price and accessibility of products.Revenue management originates from American aircraft industry in the late 1970s. After 40 years'development, revenue management has been widely applied in aircrafts; hotel industries and so on and especially reaped significant benefits in aircraft industry. As a great number of special high speed railway lines are being constructed and put into operation in recent years, the competition between aircraft, railway and highway is more intensive than ever before. Under such circumstance, studying the relevant theories of special high speed railway line revenue management is of great importance to increase competitive strength as well as revenue in Chinese railway industry.Firstly, this paper makes a general analysis on the relevant theoretical components of revenue management and the feasibility of applying revenue management to special high speed railway line in Chinese railway industry. Based on such analysis, it is concluded that special high speed railway line transportation is in line with the basic condition of application of revenue management and has a high similarity with air transportation. Therefore it is practical to apply revenue management to special high speed railway line.Secondly, in terms of demand prediction, the author tries to predict passenger demand in a way of combining grey prediction with Markov prediction. Considering that grey prediction does not have a high accuracy on those predictions of high random fluctuation and the demand for railway passenger transport has relatively high randomness, Markov prediction is superior to grey prediction in aspect of accuracy. Based on this point, this paper combines these two kinds of prediction together so as to raise the prediction accuracy to a new level and to provide effective data basis for dynamic pricing. In the last place, in terms of dynamic pricing, this paper considers first the dynamic pricing of direct special high speed railway line and establishes a bi-level dynamic pricing programming which is based on maximize revenue. This dynamic pricing first takes the passenger arrival rate and purchase rate into main consideration. Then it takes account of those with more than one stop stations in it and establishes three-level programming of dynamic pricing based on maximized revenue. It takes the special high speed railway line revenue maximization as the upper level programming; the user balance model chosen by passengers'modes of transportation is regarded as the middle level programming and passenger ticket sales process as the lowest level programming. This dynamic pricing system considers the ticketing rules on the basis of passenger arrival rate and purchase rate. In these two dynamic pricing programming, all solving are made through Genetic algorithms and algorithm examples are also given.