Collaborative Optimization of Dynamic Pricing and Seat Allocation for High-Speed Railways: An Empirical Study from China

Xuanke Wu, Jin Qin, Wenxuan Qu, Yijia Zeng, Xia Yang

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

In order to improve the high-speed trains' service levels and increase their market shares, the Chinese high-speed railway (HSR) enterprise is reforming its ticket pricing strategy. A collaborative model that incorporates seat allocation decision into HSR dynamic pricing problem based on the revenue management theory is proposed, in which the objective is to maximize the total ticket revenue of enterprise under the constrains of price ceilings. A two-stage algorithm is developed to solve practical problems. The first stage solves the optimal price problem, and the second is to obtain the optimal seat allocation decisions. Finally, a case study based on the actual ticket data of Beijing-Shanghai HSR in China is implemented to show the effectiveness of the proposed approach, for which the results show that compared with the fixed price case, the revenue improvement ranges from 4.47% to 4.95% by using dynamic pricing strategy. Also, the case analysis shows that dynamic pricing strategy will lead to an increase in short-haul demands whereas a decrease in long-haul demands.

Original languageEnglish
Article number8846744
Pages (from-to)139409-139419
Number of pages11
JournalIEEE Access
Volume7
DOIs
StatePublished - 2019
Externally publishedYes

Funding

This work was supported in part by the Science and Technology Research and Development Program of China Railway under Grant N2018X009, in part by the Funds of National Railway Administration under Grant KF2019-005-B, and in part by the Fundamental Research Funds for the Central Universities of Central South University under Grant 2018zzts509.

Keywords

  • collaborative optimization
  • dynamic pricing
  • High-speed railway (HSR)
  • revenue management (RM)
  • seat allocation

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