Study on the Tourist Tickets Pricing of Urban Rail Transit

Abstract

A bi-level programming model of rail tourist ticket pricing is set up in this paper. The upper planning aims at maximizing the revenue of rail transit enterprises. The lower planning considers the economy, rapidity, comfort and other costs of tourists’ trips, consider the minimum general travel expenses for tourists as the target, to achieve a pricing program which can balance the interests of both rail transit operators and tourists. And a hybrid particle swarm optimization and genetic algorithm is designed to solve the problem. The parameters are set according to the current situation of Beijing rail transit and tourism, then the result of model is evaluated, which shows that the model and the algorithm are effective.