Abstract
As travellers commonly perceive available transport modes (e.g. planes and ships) as substitutes, forcing transportation providers into competition for the same routes, this chapter analyses the effects of intermodal competition on the time series of passenger flows. Our conceptual framework suggests negative correlations of arrivals, both within and across transport modes, during low-season periods, and positive correlations during high-season periods. Using daily passenger arrivals at the airport and seaport of Olbia, from 2005 to 2008, and Threshold-VAR models to test these suggestions, the findings support our conceptual framework.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
See De Witte et al. (2013) for a recent survey of the empirical literature on modal choice.
- 2.
While all port arrivals are from Italy, a third of airport arrivals are from abroad. Sardinia has three international airports (Alghero Airport, Olbia Costa Smeralda Airport and Cagliari Elmas Airport) and seven ports (Porto Torres, Olbia, Golfo Aranci, Arbatax, Santa Teresa Gallura, Palau and Cagliari). Most of the passengers directed to Costa Smeralda arrive at Olbia airport and port, i.e. those that we analyse in our research.
- 3.
Considering the whole time period (2006–2008), the ratio between airport arrivals and total arrivals is about 36 %. However, this ratio is significantly higher during the summer and at the weekend. This result may suggest that tourists, who travel mostly during the summer, prefer aeroplanes over ships for their travel. The opposite applies to non-tourists.
- 4.
Our threshold variable is stationary according to the results of ADF, PP and KPSS tests. We considered lagged values of airport and port arrivals as alternative threshold variables. Since, in our application, results are unaffected by the choice of the threshold variables, we continue our analysis using total arrivals.
- 5.
In the text, we comment on the economic difference between the coefficients. This comparison is possible as the scale of the two dependent variables after the logarithmic transformation is approximately the same. With regard to the statistical significance (tests are not reported), we found a statistical difference between the coefficients in the airport equation but no statistical difference between the coefficients in the port equation.
References
Abed SY, Ba-Fail AO, Jasimuddin SM (2001) An econometric analysis of international air travel demand in Saudi Arabia. J Air Transport Manage 7:143–148
Albalate D, Bel G, Fageda X (2015) Competition and cooperation between high-speed rail and air transportation services in Europe. J Transport Geogr 42:166–174
Behrens C, Pels E (2012) Intermodal competition in the London–Paris passenger market: high-speed rail and air transport. J Urban Econ 71:278–288
Bilotkach V, Fageda X, Flores-Fillol R (2010) Scheduled service versus personal transportation: the role of distance. Reg Sci Urban Econ 40:60–72
Castellani M, Mussoni M, Pattitoni P (2011) Air passenger flows: evidence from Sicily and Sardinia. Almatourism – J Tourism Cult Territorial Dev 1:16–28
Chen MC, Wie Y (2011) Exploring time variants for short-term passenger flow. J Transport Geogr 19:488–498
De Witte A, Hollevoet J, Dobruszkes F et al (2013) Linking modal choice to motility: a comprehensive review. Transport Res Part A Policy Pract 49:329–341
Divino JA, McAleer M (2010) Modelling and forecasting daily international mass tourism to Peru. Tour Manage 31:846–854
Haldrup N, Hylleberg S, Pons G et al (2007) Common periodic correlation features and the interaction of stocks and flows in daily airport data. J Bus Econ Stat 25:21–32
Hansen B (1999) Testing for linearity. J Econ Surv 13:551–576
Ivaldi M, Vibes C (2008) Price competition in the intercity passenger transport market: a simulation model. J Transport Econ Policy 42:225–254
Jorge-Calderón JD (1997) A demand model for scheduled airline services on international European routes. J Air Transport Manage 3:23–35
Lo MC, Zivot E (2001) Threshold co-integration and nonlinear adjustment to the law of one price. Macroecon Dyn 5:533–576
Marazzo M, Scherre R, Fernandes E (2010) Air transport demand and economic growth in Brazil: a time series analysis. Transport Res Part E Logistics Transport Rev 46:261–269
Mas-Colell A, Whinston MD, Green JR (1995) Microeconomic theory. Oxford University Press, New York, NY
Park Y, Ha HK (2006) Analysis of the impact of high-speed railroad service on air transport demand. Transport Res Part E Logistics Transport Rev 42:95–104
Rigas K (2009) Boat or airplane? Passengers’ perceptions of transport services to islands The example of the Greek domestic leisure market. J Transport Geogr 17:396–401
Song H, Li G (2008) Tourism demand modelling and forecasting—a review of recent research. Tour Manage 29:203–220
Tsay RS (1998) Testing and modeling multivariate threshold models. J Am Stat Assoc 93:1188–1202
Tsay RS (2005) Analysis of financial time series. Wiley, Hoboken, NJ
Tsekeris T (2011) Greek airports: efficiency measurement and analysis of determinants. J Air Transport Manage 17:140–142
Tsui WHK, Ozer Balli H, Gilbey A et al (2014) Forecasting of Hong Kong airport’s passenger throughput. Tour Manage 42:62–76
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Castellani, M., Pattitoni, P., Zirulia, L. (2016). Intermodal Competition and Temporal Interdependencies in Passenger Flows: Evidence from the Emerald Coast. In: Matias, Á., Nijkamp, P., Romão, J. (eds) Impact Assessment in Tourism Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-14920-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-14920-2_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14919-6
Online ISBN: 978-3-319-14920-2
eBook Packages: Economics and FinanceEconomics and Finance (R0)