Pareto-improving seat allocation for high-speed railway networks with equilibrium flows

Guangming Xu, Yihan Gao, Wei Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

This paper develops optimization models for Pareto-improving seat allocation schemes in high-speed railway (HSR) networks, where time-dependent demand and equilibrium travel choices are incorporated. Under the Pareto-improving seat allocation scheme, at least some passengers are strictly better off, and no passenger is worse off, when compared to the situation with no seat allocation scheme. In particular, two scenarios with fixed and elastic passenger demands are considered. The passenger travel choice equilibrium conditions are taken as constraints for the seat allocation optimization model, which can be further reformulated into a mixed-integer linear programming (MILP) with the help of linearization, relaxation and outer-approximation techniques. The optimal solution then can be obtained by solving the proposed MILP. The numerical examples on two real-world regional HSR systems are presented to illustrate the proposed Pareto-improving seat allocation optimization approach.

Original languageEnglish
Article number104261
JournalTransportation Research Part C: Emerging Technologies
Volume154
DOIs
Publication statusPublished - Sept 2023

Keywords

  • High-speed railway
  • MILP
  • Pareto-improving
  • Seat allocation
  • Time-dependent demand

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Management Science and Operations Research

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