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Chapter 9 Demand Models for Greek Passenger Shipping

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  • 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
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    References listed on IDEAS

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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    2. Walker, Joan & Ben-Akiva, Moshe, 2002. "Generalized random utility model," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 303-343, July.
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    1. MarÅ¡enka Marksel & Polona Tominc & Stane BožiÄ nik, 2017. "Cruise passengers’ expenditures," Tourism Economics, , vol. 23(4), pages 890-897, June.
    2. 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.
    3. 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.
    4. 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.
    5. 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.

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