74 MODELLING HIGH SPEED RAIL IMPACT ON TOURISTS’ DESTINATION CHOICES – Contribut
74 MODELLING HIGH SPEED RAIL IMPACT ON TOURISTS’ DESTINATION CHOICES – Contributions by Francesca Pagliara
Introduction
The increase of accessibility to a given tourist destination, thanks to new interventions in the transportation system, represents an important factor for tourism development. The principle is that suitable means of transport can change the image of dead centres of tourist interest by transforming them into active places for visitors.
With the advent of new technologies, the transportation system, has experienced a change during the centuries. Specifically, rail, thanks to the High Speed Rail (HSR) deployment, is one of sectors mainly affected by their introduction. Several are the projects already realized, others are under construction, and more are in the pipeline all over the globe.
Europe is among the most visited continents in the world, and it is normal to consider that HSR projects are likely to affect tourist’s choices of a given tourist destination. HSR systems were not conceived at the beginning as a real alternative transport mode for tourists, their impact on tourism market came only later. In general, a decrease in travel time, brought by HSR, is perceived by tourists as a good motivation for choosing a destination connected with this service.
Several are the approaches able to model the impact of HSR on tourists’ behaviour. In this chapter the main ones are reported.
Discrete choice analysis : disaggregate approach
Discrete Choice Analysis (DCA) is generally used for measuring and predicting individuals’ preferences and alternatives ’choices, providing quantitative measures of the impact of different attributes related to tourism destinations, products, or services, also considering tourists’ Willingness To Pay (WTP) for different services.
DCA has found applications in different fields of research including transportation, marketing, retailing, health, and environmental economics, since it can well explain and predict preferences and individuals’ choice behaviour. Examples of DCA models in the tourism context include, for example, estimating how attractive tourists find competing destinations with respect to their attributes, on what premises they choose a transport mode for their trip or with whom they decide to travel. The fundamental concept underlying the methods is the Random Utility Model (RUM) theory, according to which a tourist chooses an alternative destination maximizing his/her utility. The utility is made of several attributes related to the tourists as well as to the destination, such as the transport accessibility. The alternative mode HSR systems can be considered as one of these attributes.
In Delaplace et al. (2014), two case studies were compared, i.e two capitals, the one of Rome in Italy (Valeri et al., 2012) and that of Paris in France. Through DCA, the main outcome of this work was that several factors influenced tourists’ choices of these destinations, like the presence of architectural si