Collaborative optimisation of resource capacity allocation and fare rate for high-speed railway passenger transport. (August 2019)

• Record Type: Journal Article
• Authors: Yan, Zhen-ying; Li, Xiao-juan; Han, Bao-ming
• Is Part Of: Journal of rail transport planning & management. Volume 10(2019)
• Page Start: 23
• Page End: 33
• Publication Date: 2019-08
• Subjects: High-speed railway -- Resource capacity allocation -- Pricing -- Particle swarm optimisation; Railroads -- Periodicals; Railroads -- Planning -- Periodicals; Railroads -- Management -- Periodicals; Railroads; Railroads -- Management; Railroads -- Planning; Periodicals; 385.068
• DOI: 10.1016/j.jrtpm.2019.05.001
• Languages: English
• ISSNs: 2210-9706

Abstract

Abstract: The reasonable pricing of high-speed railway tickets and the optimal allocation of transport resource capacity can not only enhance competitiveness in the transport market, but also reasonably coordinate the revenue of the enterprise and utilities to passengers. This study uses price signals to adjust resource capacity allocation; and develops a co-optimisation model of resource capacity allocation and fare rates of high-speed trains in different train operation routes. The developed model aims at the comprehensive optimisation of railway enterprise's revenue and passengers' travel benefits, with the ratio of supply-demand and the floating rate of the fare as the main constraints. The Particle Swarm Optimisation (PSO) algorithm is applied to obtain the seat resource allocation scheme and the optimal fare rate for each train operation route. Finally, the case analysis is carried out to test the model and the algorithm. Based on a statistical analysis of actual ticket sale data of the Beijing-Shanghai high-speed railway for a certain month, the optimal unit fare and optimal seat resource allocation scheme are obtained to meet the corresponding passenger demand. The case analysis shows that after optimisation by the proposed method, the total value of the objective function is 2.04% higher than that before optimisation. Graphical abstract: Image 1 Highlights: An allocation method of transportation resources regulated by the price signal. A co-optimization model of refined resource allocation and differential pricing. The PSO algorithm is designed to solve the model. The total value of enterprise revenue and passenger utility is increased by 2.04%.