IDEAS home Printed from https://ideas.repec.org/a/eee/retrec/v21y2007i1p297-322.html
   My bibliography  Save this article

Chapter 9 Demand Models for Greek Passenger Shipping

Author

Listed:
  • Polydoropoulou, Amalia
  • Litinas, Nikolaos

Abstract

Passenger shipping in Greece is an important sector of the economy and holds a significant share of the transport market for the movements of residents, business, and tourist travellers. This is more so in the Aegean archipelagos where shipping constitutes the main alternative for passenger travel from/to most of the islands, complemented in the case of the bigger islands by air travel. Over the last few years, the Greek passenger shipping industry has undergone several changes including the introduction of fast high-technology ships and a growth in the volume of passenger transport. These conditions have created an environment that is more competitive, demanding, and dynamic, making the application of advanced analysis techniques for forecasting the demand for travel of critical strategic importance. This chapter developed a methodological demand modelling framework based on understanding the choice behaviour of individuals addressing the competition and/or complementarity of ships and aeroplanes. The methodology was applied to a case study on the competition between passenger shipping and aviation in the Northern Aegean region, based on the analysis of individual choice data collected on an annual base for 2001-2005. The models developed included a Multinomial Logit Model with dependent variable, the choice among different types of shipping lines and airlines. The estimation results showed that travel times and travel costs of the alternative modes play significant role in the choice of the alternatives. Furthermore, socio-economic characteristics such as age, education level, purpose of trip, and experience represented by the prior frequency of travel with the specific mode were also found significant. In addition, response bias indicators were applied to capture the propensity of respondents to justify their prior choices and their travel-related constraint to be accompanied by their car in their trip. The models developed were used to calculate value of times for the alternative modes. These models can be also used for prediction of the market shares for the different alternative modes. The methodology and modelling results presented in this chapter offer a unique paradigm for applying innovative techniques of Decision-Making Theory that can be used by policy-makers and service providers to offer services better tailored to the passengers needs.

Suggested Citation

  • Polydoropoulou, Amalia & Litinas, Nikolaos, 2007. "Chapter 9 Demand Models for Greek Passenger Shipping," Research in Transportation Economics, Elsevier, vol. 21(1), pages 297-322, January.
    • Handle: RePEc:eee:retrec:v:21:y:2007:i:1:p:297-322
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0739-8859(07)21009-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Walker, Joan & Ben-Akiva, Moshe, 2002. "Generalized random utility model," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 303-343, July.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    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. MarÅ¡enka Marksel & Polona Tominc & Stane BožiÄ nik, 2017. "Cruise passengers’ expenditures," Tourism Economics, , vol. 23(4), pages 890-897, June.
    2. Konstantinos Rigas, 2012. "Connecting Island Regions – A Qualitative Approach to the European Experience," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 62(3-4), pages 30-53, July - De.
    3. Saeed, Naima & Larsen, Odd I., 2010. "An application of cooperative game among container terminals of one port," European Journal of Operational Research, Elsevier, vol. 203(2), pages 393-403, June.
    4. Anna Dłużewska & Andrea Giampiccoli, 2021. "Enhancing island tourism's local benefits: A proposed community‐based tourism‐oriented general model," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(1), pages 272-283, January.
    5. Juan Gabriel Brida & Daniel Bukstein & Emiliano Tealde, 2012. "Exploring cruise ship passengers’ spending patterns in two Uruguayan ports of call," Documentos de Investigación 76, Universidad ORT Uruguay. Facultad de Administración y Ciencias Sociales.

    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. Potoglou, Dimitris & Palacios, Juan & Feijoo, Claudio & Gómez Barroso, Jose-Luis, 2015. "The supply of personal information: A study on the determinants of information provision in e-commerce scenarios," 26th European Regional ITS Conference, Madrid 2015 127174, International Telecommunications Society (ITS).
    2. Aude Ridier & Caroline Roussy & Karim Chaib, 2021. "Adoption of crop diversification by specialized grain farmers in south-western France: evidence from a choice-modelling experiment," Review of Agricultural, Food and Environmental Studies, Springer, vol. 102(3), pages 265-283, September.
    3. Chorus, Caspar & van Cranenburgh, Sander & Daniel, Aemiro Melkamu & Sandorf, Erlend Dancke & Sobhani, Anae & Szép, Teodóra, 2021. "Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence," Mathematical Social Sciences, Elsevier, vol. 109(C), pages 28-44.
    4. Zgheib, Najib & Abou-Zeid, Maya & Kaysi, Isam, 2020. "Modeling demand for ridesourcing as feeder for high capacity mass transit systems with an application to the planned Beirut BRT," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 70-91.
    5. Kim, Seheon & Rasouli, Soora, 2022. "The influence of latent lifestyle on acceptance of Mobility-as-a-Service (MaaS): A hierarchical latent variable and latent class approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 304-319.
    6. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    7. John V. Colias & Stella Park & Elizabeth Horn, 2021. "Optimizing B2B product offers with machine learning, mixed logit, and nonlinear programming," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(3), pages 157-172, September.
    8. Jegnie, Alemken & Hailu, Atakelty & Burton, Michael P., 2017. "Boat-based and other recreational fishing in Western Australia: Analysis of site choice, access values and bag limit effects," Working Papers 257167, University of Western Australia, School of Agricultural and Resource Economics.
    9. María J. Alonso-González & Oded Cats & Niels van Oort & Sascha Hoogendoorn-Lanser & Serge Hoogendoorn, 2021. "What are the determinants of the willingness to share rides in pooled on-demand services?," Transportation, Springer, vol. 48(4), pages 1733-1765, August.
    10. Malte Welling & Ewa Zawojska & Julian Sagebiel, 2022. "Information, Consequentiality and Credibility in Stated Preference Surveys: A Choice Experiment on Climate Adaptation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 82(1), pages 257-283, May.
    11. Daina, Nicolò & Sivakumar, Aruna & Polak, John W., 2017. "Modelling electric vehicles use: a survey on the methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 447-460.
    12. Ghader, Sepehr & Carrion, Carlos & Zhang, Lei, 2019. "Autoregressive continuous logit: Formulation and application to time-of-day choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 240-257.
    13. repec:ags:aare05:139316 is not listed on IDEAS
    14. María J. Alonso-González & Oded Cats & Niels van Oort & Sascha Hoogendoorn-Lanser & Serge Hoogendoorn, 0. "What are the determinants of the willingness to share rides in pooled on-demand services?," Transportation, Springer, vol. 0, pages 1-33.
    15. Macea, Luis F. & Cantillo, Victor & Arellana, Julian, 2018. "Influence of attitudes and perceptions on deprivation cost functions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 112(C), pages 125-141.
    16. Choi, Andy S., 2011. "Implicit prices for longer temporary exhibitions in a heritage site and a test of preference heterogeneity: A segmentation-based approach," Tourism Management, Elsevier, vol. 32(3), pages 511-519.
    17. Kassahun, Habtamu Tilahun & Jacobsen, Jette Bredahl & Nicholson, Charles F., 2020. "Revisiting money and labor for valuing environmental goods and services in developing countries," Ecological Economics, Elsevier, vol. 177(C).
    18. Joan L. Walker & Moshe Ben-Akiva, 2011. "Advances in Discrete Choice: Mixture Models," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 8, Edward Elgar Publishing.
    19. Paolo Bragolusi & Chiara D’Alpaos, 2021. "The Willingness to Pay for Residential PV Plants in Italy: A Discrete Choice Experiment," Sustainability, MDPI, vol. 13(19), pages 1-13, September.
    20. Mauersberger, Felix, 2019. "Thompson Sampling: Endogenously Random Behavior in Games and Markets," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203600, Verein für Socialpolitik / German Economic Association.
    21. Kim, Jinhee & Rasouli, Soora & Timmermans, Harry, 2014. "Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: Application to intended purchase of electric cars," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 71-85.

    More about this item

    Statistics

    Access and download statistics

    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:retrec:v:21:y:2007:i:1:p:297-322. 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/620614/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.