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The Impact of Seat Resource Fragmentation on Railway Network Revenue Management

Author

Listed:
  • Xiang Zhao

    (China Academy of Railway Sciences)

  • Xinghua Shan

    (China Academy of Railway Sciences)

  • Jinfei Wu

    (China Academy of Railway Sciences)

Abstract

The previous literature of railway revenue management (RM) ignores the negative impact of the problem of fragmented seat resources (PFSR) on passenger transport income. A single train is characterized by continuous transport of multiple segments. Under the condition that a given seat number is assigned to each random arriving customer during the pre-sale period, the remaining seat resource of each rail leg of the train may be distributed on different seats in a fragmented way. When a customer wants to purchase a long-distance transport product, because the remaining seat resource of each rail leg may be not in the same seat, the train cannot provide a service to the customer. This will result in lost customer demand and wasted seat resources. This paper mainly studies the impact of PFSR on railway RM, and a new seat control method is proposed to avoid the revenue loss caused by PFSR. Based on the case study of a real high speed railway (HSR) network, PFSR causes an average revenue loss of 3.95% for passenger transport. The influence of the number of train segments, the size of customer demand and passenger refund rate on PFSR is studied.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:netspa:v:23:y:2023:i:1:d:10.1007_s11067-022-09581-w
    DOI: 10.1007/s11067-022-09581-w
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    1. 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.
    2. Sumit Kunnumkal & Kalyan Talluri, 2019. "Choice Network Revenue Management Based on New Tractable Approximations," Transportation Science, INFORMS, vol. 53(6), pages 1591-1608, November.
    3. 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.
    4. 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.
    5. Garrett van Ryzin & Gustavo Vulcano, 2017. "Technical Note—An Expectation-Maximization Method to Estimate a Rank-Based Choice Model of Demand," Operations Research, INFORMS, vol. 65(2), pages 396-407, April.
    6. Wang, Yadong & Meng, Qiang & Du, Yuquan, 2015. "Liner container seasonal shipping revenue management," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 141-161.
    7. Saito, Taiga & Takahashi, Akihiko & Koide, Noriaki & Ichifuji, Yu, 2019. "Application of online booking data to hotel revenue management," International Journal of Information Management, Elsevier, vol. 46(C), pages 37-53.
    8. Chaoxu Tong & Huseyin Topaloglu, 2014. "On the Approximate Linear Programming Approach for Network Revenue Management Problems," INFORMS Journal on Computing, INFORMS, vol. 26(1), pages 121-134, February.
    9. 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.
    10. 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.
    11. Julien Guillen & Pierre Ruiz & Umberto Dellepiane & Ludovica Maccarrone & Raffaele Maccioni & Alessandro Pinzuti & Enrico Procacci, 2019. "Europcar Integrates Forecasting, Simulation, and Optimization Techniques in a Capacity and Revenue Management System," Interfaces, INFORMS, vol. 49(1), pages 1-40, January.
    12. 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.
    13. Dan Zhang & Daniel Adelman, 2009. "An Approximate Dynamic Programming Approach to Network Revenue Management with Customer Choice," Transportation Science, INFORMS, vol. 43(3), pages 381-394, August.
    14. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.
    15. Shinjini Pandey & Goutam Dutta & Harit Joshi, 2017. "Survey on Revenue Management in Media and Broadcasting," Interfaces, INFORMS, vol. 47(3), pages 195-213, June.
    16. Qian Liu & Garrett van Ryzin, 2008. "On the Choice-Based Linear Programming Model for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 288-310, October.
    17. Li, Zhi-Chun & Sheng, Dian, 2016. "Forecasting passenger travel demand for air and high-speed rail integration service: A case study of Beijing-Guangzhou corridor, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 397-410.
    18. Daniel Adelman, 2007. "Dynamic Bid Prices in Revenue Management," Operations Research, INFORMS, vol. 55(4), pages 647-661, August.
    19. 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.
    20. Püschel, Tim & Schryen, Guido & Hristova, Diana & Neumann, Dirk, 2015. "Revenue management for Cloud computing providers: Decision models for service admission control under non-probabilistic uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 637-647.
    21. 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.
    22. Dimitris Bertsimas & Sanne de Boer, 2005. "Simulation-Based Booking Limits for Airline Revenue Management," Operations Research, INFORMS, vol. 53(1), pages 90-106, February.
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