Research on the Bi-level Programming Model for Ticket Fare Pricing of Urban Rail Transit based on Particle Swarm Optimization Algorithm
mode - rail, policy - fares, place - asia, economics - pricing
Ticket fare pricing; Urban rail transit; Logit model; Bi-level programming model, Particle swarm optimization algorithm
As a public service facility, the social and economic benefits of urban rail transit ticket fare are both important, so reasonable ticket fare is a key for the solid development of urban rail transit. The social and economic benefits should be taken into account under the competitive condition led by various modes of transportation in order to get an optimal strategy in ticket fare pricing of urban rail transit on the premise of meeting the service quality standard. Here, the factors considered in the ticket fares fare pricing of urban rail transit in the domestic and foreign cities are summarized, after which the Logit model of the mode split within the public transit system is established. With considering both the respective benefits of the urban rail transit company and the travellers, a bi-level programming model is established together with the solution idea to the model with the particle swarm optimization algorithm. The example demonstrates the feasibility and effectiveness of the bi-level programming model and the related measures and the particle swarm optimization algorithm is fitable for the urban rail transit fare pricing. The suggestions proposed from the result of the example are helpful for the decision making of ticket fare pricing of urban rail transit.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Xueyu, Z., & Jiaqi, Y. (2013). Research on the Bi-level Programming Model for Ticket Fare Pricing of Urban Rail Transit based on Particle Swarm Optimization Algorithm. Procedia - Social and Behavioral Sciences, Volume 96, 6 November 2013, Pages 633–642.