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

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## Abstract

**:**

## 1. Introduction

## 2. HSR and Tourism: Is There a Link?

## 3. The Methodology

## 4. The Case Study

- ItalianTourist: no. of Italian Tourists, i.e., number of arrivals from other Italian provinces travelling for both tourism and business tourism purposes (Italian Census, ISTAT, www.istat.it);
- ForeignTourist: no. of Foreign Tourists; number of arrivals from other countries travelling for both tourism and business tourism purposes (Italian Census, ISTAT, www.istat.it);
- Overnights_Italian: no. of nights spent in tourist installations by Italian tourists (nights—hundreds of thousands—Census data);
- Overnights_Foreign: no. of nights spent in tourist installations by Foreign tourists (hundreds of thousands—Census data).

- Transportation systems variables
- HSR is a dummy variable assuming Value 1 if the HSR is present, 0 if otherwise;
- HUB2 is a dummy variable assuming Value 1 if the airport is not a first level hub; 0 if otherwise;
- LowCost: no. of operating bases of low-cost airlines.

- Attractiveness variables
- GDP is the Gross Domestic Product of the province (Italian Census, ISTAT, www.istat.it);
- Attraction: is the no. of activities in a given province (sum of museums, historical sites, etc., information collected through different websites);
- Sea is a dummy variable assuming Value 1 if the province is close to the sea; 0 if otherwise;
- POP is the number of inhabitants in a given province (hundreds of thousands—Census data);
- Unemployment: percentage of unemployed in a given province (Census data).

## 5. Results and Discussion

## 6. Conclusions and Further Perspectives

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**An example of a flow-chart structure of a regression tree. Each node contains means, standard deviations, number of observations, percentage of observations w.r.t. the total number of observations, and predicted values of the dependent variable. Source: Authors’ elaboration adapted from IBM SPSS Advanced Statistics 20.

Tourism Trend | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Italians | 172.4 | 171.2 | 166.5 | 165.5 | 165.5 | 166.4 | 158.2 | 155.0 | 180.9 | 190.2 | 203.5 | 209.3 |

Foreigners | 142.3 | 148.3 | 147.8 | 145.8 | 151.6 | 161.1 | 164.9 | 167.9 | 176.8 | 182.6 | 199.4 | 210.7 |

Total | 314.7 | 319.5 | 314.3 | 311.3 | 317.1 | 327.5 | 323.1 | 322.9 | 357.7 | 372.8 | 402.9 | 420.7 |

Variable | Minimum | Maximum | Mean | Std. Dev. |
---|---|---|---|---|

ItalianTourist | 0.33 | 122.90 | 19.59 | 23.12 |

ForeignTourist | 0.08 | 252.92 | 17.26 | 38.44 |

Overnights_Italian | 0.08 | 894.21 | 87.24 | 129.33 |

Overnights_Foreign | 0.10 | 1,463.61 | 79.99 | 204.19 |

HSR | ||||

POP | 0.86 | 43.40 | 5.87 | 6.29 |

Low-Cost | 0.00 | 19.00 | 2.00 | 4.64 |

HUB2 | ||||

GDP | 1.60 | 138.40 | 15.84 | 19.49 |

Unemployment | 2.10 | 269.58 | 22.55 | 29.99 |

Sea | ||||

Attract | 49.00 | 1,981.00 | 526.77 | 372.88 |

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**MDPI and ACS Style**

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.
https://doi.org/10.3390/su12030910

**AMA Style**

Pagliara F, Mauriello F, Russo L. A Regression Tree Approach for Investigating the Impact of High Speed Rail on Tourists’ Choices. *Sustainability*. 2020; 12(3):910.
https://doi.org/10.3390/su12030910

**Chicago/Turabian Style**

Pagliara, Francesca, Filomena Mauriello, and Lucia Russo. 2020. "A Regression Tree Approach for Investigating the Impact of High Speed Rail on Tourists’ Choices" *Sustainability* 12, no. 3: 910.
https://doi.org/10.3390/su12030910