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Open AccessArticle

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

Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
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Sustainability 2020, 12(3), 910; https://doi.org/10.3390/su12030910
Received: 17 December 2019 / Revised: 16 January 2020 / Accepted: 24 January 2020 / Published: 26 January 2020
(This article belongs to the Special Issue High Speed Rail and Tourism)
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. View Full-Text
Keywords: high speed rail; tourists’ behavior; regression tree-methods high speed rail; tourists’ behavior; regression tree-methods
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Pagliara, F.; Mauriello, F.; Russo, L. A Regression Tree Approach for Investigating the Impact of High Speed Rail on Tourists’ Choices. Sustainability 2020, 12, 910.

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