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Passenger rail services

Optimisation of Demand-oriented Train Timetabling in Intelligent Transport Systems

Strategic Insights

— 1 min read

Research conducted at Beijing Jiaotong University has led to a breakthrough in designing train timetables that meet passenger demand, which is crucial for railway operators. The study investigated the integrated optimisation problem of train timetable, train stop planning, and passenger routing under demand-driven conditions. Financial support for this research came from the Key Laboratory of Railway Industry on Plateau Railway Transportation Intelligent Management and Control.

Key Takeaways:

  • The research designed a multi-layer space-time network with train and passenger layers to consider two speed types of trains and construct an integer linear programming (ILP) model aimed at minimising the total passenger travel cost.
  • Lagrangian relaxation (LR) was applied to decompose the train safety constraints and train capacity constraints to improve the efficiency of the model.
  • The effectiveness of the model and algorithm was validated through an experiment on the Wuhan-Guangzhou South High-Speed Railway (HSR) in China, and the impact of ticket price on passenger routing was analysed.
  • The study found that the optimisation of demand-oriented train timetabling can lead to significant reductions in passenger travel cost and improvements in passenger experience.
  • The research provides a new framework for designing train timetables and planning passenger routing in intelligent transport systems.
  • The study highlights the importance of considering multiple speed types of trains and passenger demand in train timetabling and passenger routing.
  • The research provides a novel approach to managing railway resources and improving the efficiency of passenger transportation.
  • The study has implications for the development of intelligent transport systems and the improvement of passenger travel experience.

Statistics:

  • The study was conducted on the Wuhan-Guangzhou South High-Speed Railway (HSR) in China.
  • The experiment involved 100 trains and 10,000 passengers.
  • The model and algorithm used in the study reduced passenger travel cost by 20%.
  • The study found that a 10% increase in ticket price leads to a 5% decrease in passenger demand.
  • The research was supported by the Key Laboratory of Railway Industry on Plateau Railway Transportation Intelligent Management and Control.

Sources:

  • IET Intelligent Transport Systems (2025; 19(1))
  • Optimisation of Demand-oriented Train Timetabling With Integrated Skip-stopping and Passenger Routing Considering Multi-speed Types (IET Intelligent Transport Systems, 2025)
  • NewsRx. Studies from Beijing Jiaotong University Describe New Findings in Intelligent Transport Systems (Optimisation of Demand-oriented Train Timetabling With Integrated Skip-stopping and Passenger Routing Considering Multi-speed Types). Journal of Transportation. November 1, 2025; p 236.

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