IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v61y2014icp68-83.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554513001725
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2013.10.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. Huseyin Topaloglu, 2009. "Using Lagrangian Relaxation to Compute Capacity-Dependent Bid Prices in Network Revenue Management," Operations Research, INFORMS, vol. 57(3), pages 637-649, June.
    4. Lawrence R. Weatherford, 1997. "Using Prices More Realistically as Decision Variables in Perishable-Asset Revenue Management Problems," Journal of Combinatorial Optimization, Springer, vol. 1(3), pages 277-304, October.
    5. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    6. 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.
    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.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, October.
    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. 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.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    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. Sanjana Hossain & Md. Sami Hasnine & Khandker Nurul Habib, 2021. "A latent class joint mode and departure time choice model for the Greater Toronto and Hamilton Area," Transportation, Springer, vol. 48(3), pages 1217-1239, June.
    2. Xuemei Fu & Zhicai Juan, 2017. "Accommodating preference heterogeneity in commuting mode choice: an empirical investigation in Shaoxing, China," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(4), pages 434-448, May.
    3. 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.
    4. Khakdaman, Masoud & Rezaei, Jafar & Tavasszy, Lóránt A., 2020. "Shippers’ willingness to delegate modal control in freight transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    5. Zhou, Heng & Norman, Richard & Xia, Jianhong(Cecilia) & Hughes, Brett & Kelobonye, Keone & Nikolova, Gabi & Falkmer, Torbjorn, 2020. "Analysing travel mode and airline choice using latent class modelling: A case study in Western Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 187-205.
    6. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    7. 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.
    8. Richartz, P. Christoph & Abdulai, Awudu & Kornher, Lukas, 2020. "Attribute Non Attendance and Consumer Preferences for Online Food Products in Germany," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 69(1), March.
    9. Meissner, Joern & Strauss, Arne, 2012. "Network revenue management with inventory-sensitive bid prices and customer choice," European Journal of Operational Research, Elsevier, vol. 216(2), pages 459-468.
    10. Nicolas Houy & François Le Grand, 2015. "The Monte Carlo first-come-first-served heuristic for network revenue management," Working Papers halshs-01155698, HAL.
    11. Hackbarth, André & Madlener, Reinhard, 2016. "Willingness-to-pay for alternative fuel vehicle characteristics: A stated choice study for Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 89-111.
    12. Singh, Jyotsna & Homem de Almeida Correia, Gonçalo & van Wee, Bert & Barbour, Natalia, 2023. "Change in departure time for a train trip to avoid crowding during the COVID-19 pandemic: A latent class study in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    13. Morlotti, Chiara & Birolini, Sebastian & Malighetti, Paolo & Redondi, Renato, 2023. "A latent class approach to estimate air travelers’ propensity toward connecting itineraries," Research in Transportation Economics, Elsevier, vol. 99(C).
    14. Abenoza, Roberto F. & Ettema, Dick F. & Susilo, Yusak O., 2018. "Do accessibility, vulnerability, opportunity, and travel characteristics have uniform impacts on the traveler’s experience?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PA), pages 38-51.
    15. Wolinetz, Michael & Axsen, Jonn, 2017. "How policy can build the plug-in electric vehicle market: Insights from the REspondent-based Preference And Constraints (REPAC) model," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 238-250.
    16. Siliang Luan & Qingfang Yang & Zhongtai Jiang & Huxing Zhou & Fanyun Meng, 2022. "Analyzing Commute Mode Choice Using the LCNL Model in the Post-COVID-19 Era: Evidence from China," IJERPH, MDPI, vol. 19(9), pages 1-26, April.
    17. Howard, Gregory & Roe, Brian E., 2013. "Stripping Because You Want to Versus Stripping Because the Money is Good: A Latent Class Analysis of Farmer Preferences Regarding Filter Strip Programs," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149821, Agricultural and Applied Economics Association.
    18. Angel Bujosa & Antoni Riera & Robert Hicks, 2010. "Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 47(4), pages 477-493, December.
    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. Stephane Hess, 2014. "Latent class structures: taste heterogeneity and beyond," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 14, pages 311-330, Edward Elgar Publishing.

    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:eee:transe:v:61:y:2014:i:c:p:68-83. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.