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Air and HST Multimodal Products. A Segmentation Analysis for Policy Makers

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Abstract

Different solutions for the integration of high-speed rail (HSR) and air transport could be implemented, ranging from very basic integration to more sophisticated systems that include ticket and handling integration. This paper uses two statistical techniques, name cluster analysis and discrete choice models in order to investigate how different market segments have a determining influence in being more proactive to change to HSR for the second leg in multimodal trips. A discrete choice experiment is conducted to better understand passengers’ preferences. We obtain a number of clusters and estimate flexible choice models, taking into account the panel nature of stated preference data. We obtain a range of willingness-to-pay values for service quality attributes, finding results that can be used to infer policy conclusions about the real attractiveness of the Air-HSR integrated alternative. In this respect, clusters and schedule coordination, which reduces connecting time, are crucial to explain HSR attractiveness.

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Notes

  1. Air and HSR integration has affected the competition for different air transport markets and the size of the catchment areas of the airports. The network integration increases the number of potential markets and travellers enjoy from a more ample set of transport alternatives.

  2. More details about the choice experiment can be found in Román and Martín (2014).

  3. In fact, these variables are also relevant for any trip that involves any transfer or connection, e.g. air-air alternative. However, as it can be seen in what follows, the level of quality provided by one transport mode or operator is usually better in case of no coordination between transport modes.

  4. These authors suggest that sometimes the type of heterogeneity obtained in preferences is dependent on the type of search that is performed and propose a structured way for exploring preference heterogeneity via the deterministic and/or stochastic component of the utility.

  5. To obtain the confidence interval we adapted the asymptotic t-test proposed in Armstrong et al. (2001) based on the null hypothesis \( {H}_0:\left[\frac{\partial {U}_j}{\partial {X}_j}-{WTP}_X\frac{\partial {U}_j}{\partial {X}_j}\right]=0 \), with WTP X being the true value of the WTP for improving X. Gatta et al. (2015) provide an interesting reference where different methods to calculate WTP confidence intervals for finite sample are compared.

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Acknowledgements

This research was financially supported by the Project E 20/08 from the public call “Integración del Transporte Aéreo y Alta Velocidad Ferroviaria: Impactos sobre Accesibilidad y Medio Ambiente, Ministerio de Fomento. Proyectos de I+D+I de la acción estratégica en Energía y Cambio Climático, 2009–2010; by the Free University of Bolzano project “Tourism and economic growth: the role of transportations and spatial contiguity”, Research Funds 2013; and by the ANII (Uruguay). Authors would like to thank the participants to the workshop “Métodos Dinámicos en Economía del Turismo y del Transporte”, (CURE-UDELAR, Maldonado-Uruguay, 4 -6 December 2014) and anonymous referees for their very valuable comments.

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Correspondence to Raffaele Scuderi.

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Brida, J.G., Martín, J.C., Román, C. et al. Air and HST Multimodal Products. A Segmentation Analysis for Policy Makers. Netw Spat Econ 17, 911–934 (2017). https://doi.org/10.1007/s11067-017-9352-3

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