01657865

Component

This paper focuses on pricing for high-speed rail (HSR) passenger fares when the HSR operator has multiple objectives. While many previous studies have focused on calculating optimal fares over railway networks, none of them has focused on characterizing the trade-offs between multiple objectives over a railway network under multi-modal competition. We use a bi-level programming model for multi-criteria HSR fare optimization over a linear network. The upper level model is a competitive game process among different transportation operators while the lower level model is a logit model of interaction between passengers and operators. We obtain and compare two single-criterion solutions and four multi-criteria solutions on a variety of metrics.Our results based on a case study of Beijing-Shanghai HSR linear network provide several new insights. We find that the fares under profit-maximizing solutions are consistently higher than those under passenger welfare-maximizing solutions. Lexicographic optimization of profit and passenger welfare shows considerable improvements over either of the single-criterion pricing solutions. Additionally, we derive the Pareto Frontier between the criteria of profit and passenger welfare to enable the decision makers to choose the best trade-offs. We also find that small changes in fares can lead to a significant improvement in passenger welfare at no loss of profitability under multi-criteria solutions.

This paper was sponsored by TRB committee ABE20 Standing Committee on Transportation Economics.

18-02732

English

7p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Competition; High speed rail; Linear systems; Multimodal transportation; Networks; Optimization; Pricing

Economics; Policy; Railroads

Transportation Research Board Annual Meeting 2018 Paper #18-02732

TRIS, TRB, ATRI

Jan 8 2018 10:39AM