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A latent class choice based model system for railway optimal pricing and seat allocation

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  • Hetrakul, Pratt
  • Cirillo, Cinzia

Abstract

In this paper, discrete choice methods in the form of multinomial logit and latent class models are proposed to explain ticket purchase timing of passenger railway. The choice model and demand functions are incorporated into a revenue optimization problem which jointly considers pricing and seat allocation. The framework provides insightful policy implications in term of fare and capacity distribution derived from actual passenger behavior. It shows that accepting short-haul demand provides greater revenue than long-haul demand using the same capacity. Revenue improvement ranges from 16.24% to 24.96% in multinomial logit models and from 13.82% to 21.39% in latent class models respectively.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:transe:v:61:y:2014:i:c:p:68-83
    DOI: 10.1016/j.tre.2013.10.005
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    References listed on IDEAS

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    1. Bhat, Chandra R., 1998. "Accommodating variations in responsiveness to level-of-service measures in travel mode choice modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(7), pages 495-507, September.
    2. 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.
    3. Teichert, Thorsten & Shehu, Edlira & von Wartburg, Iwan, 2008. "Customer segmentation revisited: The case of the airline industry," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(1), pages 227-242, January.
    4. Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, May.
    6. Wen, Chieh-Hua & Lai, Shan-Ching, 2010. "Latent class models of international air carrier choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(2), pages 211-221, March.
    7. Junyi Shen, 2009. "Latent class model or mixed logit model? A comparison by transport mode choice data," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2915-2924.
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    Cited by:

    1. 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.
    2. repec:eee:transb:v:99:y:2017:i:c:p:83-112 is not listed on IDEAS
    3. Chiou, Yu-Chiun & Liu, Chia-Hsin, 2016. "Advance purchase behaviors of air tickets," Journal of Air Transport Management, Elsevier, vol. 57(C), pages 62-69.
    4. Xu, Xin-yue & Liu, Jun & Li, Hai-ying & Jiang, Man, 2016. "Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 130-148.
    5. 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.
    6. Tsai, Tsung-Hsien, 2016. "Homogeneous service with heterogeneous products: Relationships among airline ticket fares and purchase fences," Journal of Air Transport Management, Elsevier, vol. 55(C), pages 164-175.
    7. repec:eee:jaitra:v:64:y:2017:i:pa:p:91-99 is not listed on IDEAS

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