A Regression Tree Approach for Investigating the Impact of High Speed Rail on Tourists’ Choices

Francesca Pagliara, Filomena Mauriello and Lucia Russo
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Francesca Pagliara: Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
Filomena Mauriello: Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
Lucia Russo: Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy

Sustainability, 2020, vol. 12, issue 3, 1-15

Abstract: This paper provides a contribution to the international literature by applying regression tree methods to the analysis of the expected effects of the High Speed Rail project in Italy on the tourism market. This approach, as far as the author knows, has never been applied in this context. Tourism and transport information have been gathered for 99 Italian provinces during the 2006–2016 period. Tree-structured methods have been chosen as an application of regression models in which some explanatory variables are used as covariates to predict the dependent variable values on the basis of some decision rules. This approach establishes a casual effect between dependent and independent variables. The dependent variables chosen are the Italian and foreign tourists, and the number of overnights spent by Italians and foreigners. Among the independent variables are the presence of HSR, the presence of first-level airport hubs and the number of operating bases of low-cost airlines; among the attractiveness variables are the GDP, the number of attractions in a given province, the presence of the sea, the population and the percentage of unemployment. The main outcome of this study is that HSR affects the tourism market.

Keywords: high speed rail; tourists’ behavior; regression tree-methods (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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