IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i4p857-d1061205.html
   My bibliography  Save this article

A Literature Review of Railway Pricing Based on Revenue Management

Author

Listed:
  • Xueyi Guan

    (School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China)

  • Jin Qin

    (School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
    Rail Data Research and Application Key Laboratory of Hunan Province, Changsha 410075, China)

  • Chenghui Mao

    (School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China)

  • Wenliang Zhou

    (School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
    Rail Data Research and Application Key Laboratory of Hunan Province, Changsha 410075, China)

Abstract

In recent decades, railway passenger transport enterprises have been exploring numerous operation and management strategies to improve service quality and market competitiveness of railway passenger transport so as to ensure that the interests of railway passenger transport enterprises are maximized when taking social welfare into account. However, there are still shortcomings in the current research with respect to determining the pricing mechanism and formulating a reasonable price. This paper systematically reviews the scientific literature related to railway pricing, focusing on the application of basic price methods, mathematical programming methods, and data-driven methods in railway pricing, with the hope of proposing an innovative direction to solve existing problems. The main subjects involved in the formulation of railway pricing are passenger groups and transportation companies. The research can be conducted from four broad aspects: passenger demand, passenger time value, market segmentation, and the equilibrium relationship between rail service supply and passenger demand. On the basis of absorbing and summarizing the strengths and weaknesses of previous studies, this paper puts forward suggestions for improvement and innovative directions which will help promote railway passenger transport services from the perspective of pricing, thereby enhancing the sustainability of railway transport.

Suggested Citation

  • Xueyi Guan & Jin Qin & Chenghui Mao & Wenliang Zhou, 2023. "A Literature Review of Railway Pricing Based on Revenue Management," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:857-:d:1061205
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/4/857/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/4/857/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A. Ciancimino & G. Inzerillo & S. Lucidi & L. Palagi, 1999. "A Mathematical Programming Approach for the Solution of the Railway Yield Management Problem," Transportation Science, INFORMS, vol. 33(2), pages 168-181, May.
    2. Enjian Yao & Qirong Yang & Yongsheng Zhang & Xun Sun, 2013. "A Study on High-Speed Rail Pricing Strategy in the Context of Modes Competition," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-6, December.
    3. 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.
    4. Hetrakul, Pratt & Cirillo, Cinzia, 2014. "A latent class choice based model system for railway optimal pricing and seat allocation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 68-83.
    5. Kalyan Talluri & Garrett van Ryzin, 2004. "Revenue Management Under a General Discrete Choice Model of Consumer Behavior," Management Science, INFORMS, vol. 50(1), pages 15-33, January.
    6. Nejib Ben-Khedher & Josephine Kintanar & Cecile Queille & William Stripling, 1998. "Schedule Optimization at SNCF: From Conception to Day of Departure," Interfaces, INFORMS, vol. 28(1), pages 6-23, February.
    7. Yin, Jiateng & D’Ariano, Andrea & Wang, Yihui & Yang, Lixing & Tang, Tao, 2021. "Timetable coordination in a rail transit network with time-dependent passenger demand," European Journal of Operational Research, Elsevier, vol. 295(1), pages 183-202.
    8. Jili Kong & Ziyu Chen & Xiaoping Liu, 2022. "A Review of Logistics Pricing Research Based on Game Theory," Sustainability, MDPI, vol. 14(17), pages 1-20, August.
    9. Hetrakul, Pratt & Cirillo, Cinzia, 2013. "Accommodating taste heterogeneity in railway passenger choice models based on internet booking data," Journal of choice modelling, Elsevier, vol. 6(C), pages 1-16.
    10. Gabriel R. Bitran & Stephen M. Gilbert, 1996. "Managing Hotel Reservations with Uncertain Arrivals," Operations Research, INFORMS, vol. 44(1), pages 35-49, February.
    11. You, Peng-Sheng, 2008. "An efficient computational approach for railway booking problems," European Journal of Operational Research, Elsevier, vol. 185(2), pages 811-824, March.
    12. Yan Han & Wanying Li & Shanshan Wei & Tiantian Zhang, 2018. "Research on Passenger’s Travel Mode Choice Behavior Waiting at Bus Station Based on SEM-Logit Integration Model," Sustainability, MDPI, vol. 10(6), pages 1-23, June.
    13. 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).
    14. Ozbay, Kaan & Yanmaz-Tuzel, Ozlem, 2008. "Valuation of travel time and departure time choice in the presence of time-of-day pricing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(4), pages 577-590, May.
    15. Gabriel R. Bitran & Susana V. Mondschein, 1995. "An Application of Yield Management to the Hotel Industry Considering Multiple Day Stays," Operations Research, INFORMS, vol. 43(3), pages 427-443, June.
    16. Gabriel R. Bitran & Susana V. Mondschein, 1997. "Periodic Pricing of Seasonal Products in Retailing," Management Science, INFORMS, vol. 43(1), pages 64-79, January.
    17. Xu, Guangming & Liu, Wei & Wu, Runfa & Yang, Hai, 2021. "A double time-scale passenger assignment model for high-speed railway networks with continuum capacity approximation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    18. Kasilingam, R. G., 1997. "Air cargo revenue management: Characteristics and complexities," European Journal of Operational Research, Elsevier, vol. 96(1), pages 36-44, January.
    19. Beuthe, Michel & Jourquin, Bart & Geerts, Jean-François & Koul à Ndjang' Ha, Christian, 2001. "Freight transportation demand elasticities: a geographic multimodal transportation network analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(4), pages 253-266, August.
    20. Jin Qin & Yijia Zeng & Xia Yang & Yuxin He & Xuanke Wu & Wenxuan Qu, 2019. "Time-Dependent Pricing for High-Speed Railway in China Based on Revenue Management," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    21. Su, Min & Luan, Weixin & Sun, Tianyao, 2019. "Effect of high-speed rail competition on airlines’ intertemporal price strategies," Journal of Air Transport Management, Elsevier, vol. 80(C), pages 1-1.
    22. Jinzi Zheng & Jun Liu, 2016. "The Research on Ticket Fare Optimization for China’s High-Speed Train," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-8, August.
    23. Bharill, Rohit & Rangaraj, Narayan, 2008. "Revenue management in railway operations: A study of the Rajdhani Express, Indian Railways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(9), pages 1195-1207, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jingjing Cao & Tianyi Guo & Yan Chen, 2023. "Modeling Government Subsidy Strategies for Railway Carriers: Environmental Impacts and Industry Competition," Mathematics, MDPI, vol. 11(14), pages 1-26, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Haque, Md Tabish & Hamid, Faiz, 2023. "Social distancing and revenue management—A post-pandemic adaptation for railways," Omega, Elsevier, vol. 114(C).
    2. 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).
    3. Xiang Zhao & Xinghua Shan & Jinfei Wu, 2023. "The Impact of Seat Resource Fragmentation on Railway Network Revenue Management," Networks and Spatial Economics, Springer, vol. 23(1), pages 135-177, March.
    4. Ikeda, Shunnosuke & Nishimura, Naoki & Sukegawa, Noriyoshi & Takano, Yuichi, 2023. "Prescriptive price optimization using optimal regression trees," Operations Research Perspectives, Elsevier, vol. 11(C).
    5. Xu, Guangming & Zhong, Linhuan & Liu, Wei & Guo, Jing, 2024. "A flexible train composition strategy with extra-long trains for high-speed railway corridors with time-varying demand," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    6. 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).
    7. Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
    8. Yu Wang & Xinghua Shan & Hongye Wang & Junfeng Zhang & Xiaoyan Lv & Jinfei Wu, 2022. "Ticket Allocation Optimization of Fuxing Train Based on Overcrowding Control: An Empirical Study from China," Sustainability, MDPI, vol. 14(12), pages 1-12, June.
    9. Hu, Qiying & Wei, Yihua & Xia, Yusen, 2010. "Revenue management for a supply chain with two streams of customers," European Journal of Operational Research, Elsevier, vol. 200(2), pages 582-598, January.
    10. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    11. Chiou, Yu-Chiun & Liu, Chia-Hsin, 2016. "Advance purchase behaviors of air passengers: A continuous logit model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 474-484.
    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. 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.
    14. Jin Qin & Yijia Zeng & Xia Yang & Yuxin He & Xuanke Wu & Wenxuan Qu, 2019. "Time-Dependent Pricing for High-Speed Railway in China Based on Revenue Management," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    15. 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.
    16. Barry C. Smith & Dirk P. Günther & B. Venkateshwara Rao & Richard M. Ratlife, 2001. "E-Commerce and Operations Research in Airline Planning, Marketing, and Distribution," Interfaces, INFORMS, vol. 31(2), pages 37-55, April.
    17. Pak, K. & Dekker, R. & Kindervater, G.A.P., 2003. "Airline Revenue Management with Shifting Capacity," Econometric Institute Research Papers ERS-2003-091-LIS, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Zhan, Shuguang & Wong, S.C. & Lo, S.M., 2020. "Social equity-based timetabling and ticket pricing for high-speed railways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 165-186.
    19. Syed Asif Raza & Rafi Ashrafi & Ali Akgunduz, 2020. "A bibliometric analysis of revenue management in airline industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(6), pages 436-465, December.
    20. Wenliang Zhou & Ziyu Zou & Naijie Chai & Guangming Xu, 2023. "Optimization of Differential Pricing and Seat Allocation in High-Speed Railways for Multi-Class Demands: A Chinese Case Study," Mathematics, MDPI, vol. 11(6), pages 1-17, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:857-:d:1061205. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.