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Railway Revenue Management: Overview and Models (Operations Research)

Author

Listed:
  • Alexander Armstrong

    (Department of Management Science, Lancaster University Management School)

  • Joern Meissner

    (Department of Management Science, Lancaster University Management School)

Abstract

The railway industry offers similar revenue management opportunities to those found in the airline industry. The railway industry caters for the delivery and management of cargo as well as the transport of passengers. Unlike the airline industry, the railway industry has seen relatively little attention to revenue management problems. We provide an overview of the published literature for both passenger and freight rail revenue management. We include a summary of the some the available models and include some possible extensions. From the existing literature and talks with industry, it is clear that that there is room to exploit revenue management techniques in the railway industry, an industry that has revenues of $60 billion in the US and promises huge growth in Europe in the forthcoming years.

Suggested Citation

  • Alexander Armstrong & Joern Meissner, 2010. "Railway Revenue Management: Overview and Models (Operations Research)," Working Papers MRG/0019, Department of Management Science, Lancaster University, revised Jul 2010.
    • Handle: RePEc:lms:mansci:mrg-0019
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    References listed on IDEAS

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    Cited by:

    1. Haque, Md Tabish & Hamid, Faiz, 2023. "Social distancing and revenue management—A post-pandemic adaptation for railways," Omega, Elsevier, vol. 114(C).
    2. Bart Van Riessen & Judith Mulder & Rudy R. Negenborn & Rommert Dekker, 2021. "Revenue management with two fare classes in synchromodal container transportation," Flexible Services and Manufacturing Journal, Springer, vol. 33(3), pages 623-662, September.
    3. Xu, Guangming & Zhong, Linhuan & Hu, Xinlei & Liu, Wei, 2022. "Optimal pricing and seat allocation schemes in passenger railway systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    4. Wang, Xinchang, 2016. "Stochastic resource allocation for containerized cargo transportation networks when capacities are uncertain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 334-357.
    5. Bilegan, Ioana C. & Crainic, Teodor Gabriel & Wang, Yunfei, 2022. "Scheduled service network design with revenue management considerations and an intermodal barge transportation illustration," European Journal of Operational Research, Elsevier, vol. 300(1), pages 164-177.
    6. Haque, Md Tabish & Hamid, Faiz, 2022. "An optimization model to assign seats in long distance trains to minimize SARS-CoV-2 diffusion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 104-120.
    7. Jin Qin & Wenxuan Qu & Xuanke Wu & Yijia Zeng, 2019. "Differential Pricing Strategies of High Speed Railway Based on Prospect Theory: An Empirical Study from China," Sustainability, MDPI, vol. 11(14), pages 1-17, July.
    8. Wang, Xinchang & Wang, Hua & Zhang, Xiaoning, 2016. "Stochastic seat allocation models for passenger rail transportation under customer choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 95-112.
    9. Wang, Hua & Wang, Xinchang & Zhang, Xiaoning, 2017. "Dynamic resource allocation for intermodal freight transportation with network effects: Approximations and algorithms," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 83-112.
    10. Michael Siebert & David Ellenberger, 0. "Validation of automatic passenger counting: introducing the t-test-induced equivalence test," Transportation, Springer, vol. 0, pages 1-15.
    11. Robenek, Tomáš & Azadeh, Shadi Sharif & Maknoon, Yousef & de Lapparent, Matthieu & Bierlaire, Michel, 2018. "Train timetable design under elastic passenger demand," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 19-38.
    12. Wuyang Yuan & Lei Nie & Xin Wu & Huiling Fu, 2018. "A dynamic bid price approach for the seat inventory control problem in railway networks with consideration of passenger transfer," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-23, August.
    13. Xu, Guangming & Liu, Yihan & Gao, Yihan & Liu, Wei, 2023. "Integrated optimization of train stopping plan and seat allocation scheme for railway systems under equilibrium travel choice and elastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    14. Li, Dongjun & Islam, Dewan Md Zahurul & Robinson, Mark & Song, Dong-Ping & Dong, Jing-Xin & Reimann, Marc, 2024. "Network revenue management game in the railway industry: Stackelberg equilibrium, global optimality, and mechanism design," European Journal of Operational Research, Elsevier, vol. 312(1), pages 240-254.
    15. Klein, Robert & Koch, Sebastian & Steinhardt, Claudius & Strauss, Arne K., 2020. "A review of revenue management: Recent generalizations and advances in industry applications," European Journal of Operational Research, Elsevier, vol. 284(2), pages 397-412.
    16. Michael Siebert & David Ellenberger, 2020. "Validation of automatic passenger counting: introducing the t-test-induced equivalence test," Transportation, Springer, vol. 47(6), pages 3031-3045, December.
    17. Jin Qin & Xiqiong Li & Kang Yang & Guangming Xu, 2022. "Joint Optimization of Ticket Pricing Strategy and Train Stop Plan for High-Speed Railway: A Case Study," Mathematics, MDPI, vol. 10(10), pages 1-17, May.

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    More about this item

    Keywords

    operations research; marketing; network economics; business economics; railway; revenue management; pricing; passenger; freight; models;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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