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

Author

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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)

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.

Suggested Citation

  • Francesca Pagliara & Filomena Mauriello & Lucia Russo, 2020. "A Regression Tree Approach for Investigating the Impact of High Speed Rail on Tourists’ Choices," Sustainability, MDPI, vol. 12(3), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:910-:d:313249
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    References listed on IDEAS

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    1. Cascetta, Ennio & Papola, Andrea & Pagliara, Francesca & Marzano, Vittorio, 2011. "Analysis of mobility impacts of the high speed Rome–Naples rail link using withinday dynamic mode service choice models," Journal of Transport Geography, Elsevier, vol. 19(4), pages 635-643.
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    5. Pagliara, Francesca & Mauriello, Filomena & Garofalo, Antonio, 2017. "Exploring the interdependences between High Speed Rail systems and tourism: Some evidence from Italy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 300-308.
    6. Pagliara, Francesca & Mauriello, Filomena, 2020. "Modelling the impact of High Speed Rail on tourists with Geographically Weighted Poisson Regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 780-790.
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    3. Woraphon Yamaka & Xuefeng Zhang & Paravee Maneejuk, 2021. "Analyzing the Influence of Transportations on Chinese Inbound Tourism: Markov Switching Penalized Regression Approaches," Mathematics, MDPI, vol. 9(5), pages 1-23, March.
    4. Deng, Taotao & Gan, Chen & Du, Huiping & Hu, Yukun & Wang, Dandan, 2021. "Do high speed rail configurations matter to tourist arrivals? Empirical evidence from China's prefecture-level cities," Research in Transportation Economics, Elsevier, vol. 90(C).

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