Research On The Revenue Optimization Of High-speed Railway Based On Passenger Behavior Analysis

With continuous construction of new high-speed railway,more and more people choose railway as their primary traffic way.When facing the competition of aviation,railway revenue and service optimization based on current freight capacity has become an important project for railway operator.This paper analyses the advantages and limitation of revenue optimization management in railway by using experience from aviation and domestic railway industry for reference and creates revenue model based on passenger behaviors.The research states the theory and method of revenue optimization by analysing passenger behaviors,market segments,short-term passenger flow forecasting and spatial distribution forecasting of high-speed railway,price-strategy optimization and seat management under multiple classes price strategy to provide scientific and reasonable decision-making reference for railway operator.The paper uses literature search,data statistics,model creation and deductive analysis for research method to discuss the revenue optimization management of high-speed railway from following four aspects:1.First of all,the paper segments high-speed passenger market based on RFM passenger value model,AHP step analysis and soft clustering method,then calculates increased travel probability under price discount by using disaggregate model calculations.Finally analyses passenger travel characteristic by using statistical data and proposes revenue management method.2.The paper analyses influencing factor of passenger flow,timing characteristic,and space characteristic,then combined with influencing factor of passenger flow,creats short-time passenger flow forecasting model based on stochastic forest regression,and recorder importance of influencing factors.Finally it forecasts spatial distribution ofhigh-speed railway by using the combination of gray-scale model and double constrained gravity model.The forecasting of passenger timing and space make revenue management take proper price strategy and seat management to maximum revenue.3.It creates market-class pricing model and differential pricing model based on current pricing strategy,current problem and industry competition.In addition,it reserches dynamic discount model in presale period with ticket return risk probability model to make selling period for every discount policy.Pricing strategy is the reflection of revenue management for high-speedrail way.4.It designs optimization model of single train with multiple stop and multiple train with different stop based on the multiple-class pricing strategy.By using particle swarm optimization algorithm to calculate seat stock control and compare it with single price strategy which validates multiple class pricing strategy and appropriate seat stock management will increase train revenue when passenger flow is in lower grade.Seat stock management based on pricing strategy is the reflection of revenue management for high-speedrail way.