Models and Applications of Tourists’ Travel Behavior
- 1st Edition - March 14, 2025
- Imprint: Elsevier
- Authors: Francesca Pagliara, Massimo Aria, Filomena Mauriello
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 6 5 9 3 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 6 5 9 2 - 1
Models and Applications of Tourists’ Travel Behavior offers an exhaustive overview of various approaches to modeling tourists’ travel behavior, aiding readers in selecting the most… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteModels and Applications of Tourists’ Travel Behavior offers an exhaustive overview of various approaches to modeling tourists’ travel behavior, aiding readers in selecting the most suitable theoretical approach based on the available data. The book bridges traditional travel behavior theories and tourist studies, introducing specific tourist contexts in travel demand modeling. It transcends theoretical understanding, providing practical insights for choosing the right model and data source. It covers theoretical, descriptive, and statistical approaches to modeling, discussing choice models based on both Stated Preference Data and Revealed Preference Data.
The book starts by exploring the role of transport in tourist travel behavior and employs a comprehensive literature review to establish a foundational understanding. The concluding chapters delve into machine learning methods, emphasizing the modeling of transport in tourism, including mode choice, waiting time, and delay modeling. This resource is beneficial for educators, students, and researchers alike, providing a solid foundation for future model development.
- Includes the latest advances in methodologies, such as machine learning algorithms, mixed methods, and how to leverage big data to complement traditional regression models
- Compares the pros and cons of each method to help with choosing the appropriate model for each scenario
- Covers all modes of transportation while uniquely focusing on the tourist context in the modeling process
Post-graduate and PhD level students, higher academic levels and researchers in travel and tourism, tourism management, transport management and other related fields, University courses in tourism, transportation engineering, transport economics, social statistics, social sciences, territorial and urban planning and many others
- Models and Applications of Tourists’ Travel Behavior
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Contributors
- About the authors
- Preface
- Acknowledgments
- Chapter 1 Role of transport in tourists’ behavior
- Abstract
- 1.1 Introduction
- 1.2 Transportation’s key role in modern tourism
- 1.3 Transportation: A tool or integral element in tourism?
- 1.4 Transportation’s historical impact on tourism evolution
- 1.5 Transportation demand: The link between tourism and mobility
- 1.6 Exploring derived demand in tourism-related transportation
- 1.7 Policy implications in tourism transportation
- 1.8 Tourism transportation: Connecting destinations and experience
- 1.9 A catalyst for tourism promotion
- 1.10 Transportation: Enhancing mobility and elevating experiences in tourism
- 1.11 Transportation: Bridging cultures and driving tourism development
- 1.12 Conclusion
- References
- Chapter 2 Transportation choice in tourism and travel: A bibliometrics literature review
- Abstract
- 2.1 Introduction
- 2.2 Methodology
- 2.3 Findings
- 2.4 Conclusions
- References
- Chapter 3 Theoretical approach for modeling tourists' travel behavior
- Abstract
- 3.1 Introduction
- 3.1.1 General introduction
- 3.1.2 Macroclassification of existing models based on the field of development
- 3.1.3 Chi-square & Kruskal-Wallis H-tests
- 3.1.4 Regression models
- 3.2 Conclusions
- References
- Chapter 4 Descriptive approach for modeling tourists’ travel behavior
- Abstract
- 4.1 Introduction
- 4.2 Factors influencing tourists’ choice of transport
- 4.2.1 Economic factors
- 4.2.2 Environmental factors
- 4.2.3 Technological factors
- 4.2.4 Destination-specific factors
- 4.2.5 Psychological and perceptual factors
- 4.3 The importance of statistics for the study of tourists’ choices
- 4.4 Theory about mobility decision-processes
- 4.5 Understanding the determinants of tourist transport choices
- 4.6 Leisure traffic
- 4.7 Discussion
- References
- Chapter 5 Statistical approach for modeling for tourists' travel behavior
- Abstract
- 5.1 Tourist behaviors and Poisson distribution
- 5.1.1 Generalized estimating equations (GEEs)
- 5.1.2 Geographically weighted Poisson regression (GWPR)
- 5.1.3 Goodness of fit of the models
- 5.1.4 The case study
- 5.2 SEM (structural equation model)
- 5.2.1 Partial least squares path modeling (PLS-PM)
- References
- Chapter 6 Modeling tourist decision-making through discrete choice analysis: Theoretical approaches
- Abstract
- 6.1 Discrete choice analysis: Definition and significance
- 6.1.1 Historical model development and applications
- 6.2 Random utility models
- 6.2.1 Assumptions and implications
- 6.3 Probability distributions in discrete choice
- 6.4 Model extensions
- 6.4.1 Conditional logit model
- 6.4.2 Nested logit model
- 6.4.3 Mixed logit model
- 6.5 Data collection methods and applications in discrete choice analysis
- 6.5.1 Stated preferences method
- 6.5.2 Revealed preferences method
- 6.6 Comparing stated and revealed preferences methods
- 6.7 Hybrid choice models
- 6.8 Conclusion
- References
- Chapter 7 Discrete choice models: Practical examples
- Abstract
- 7.1 Applications of revealed preference in empirical research
- 7.1.1 Tourist flows and spatial behavior (Lew et al., 2014)
- 7.1.2 The determinants of tourist use of public transport at the destination (Gutiérrez and Miravet, 2016)
- 7.1.3 What prompts tourists to become public transportation users at their destination? The case of a Mediterranean city (Miravet et al., 2021)
- 7.1.4 Tourists’ intra-destination visits and transport mode: A bivariate probit model (Masiero and Zoltan, 2013)
- 7.1.5 Factors affecting tourists’ public transport use and areas visited at destinations (Le-Klähn et al., 2015)
- 7.1.6 Sustainable mobility at tourist destinations: The relevance of habits and the role of policies (Zamparini and Vergori, 2021)
- 7.2 Applications of stated preference in empirical research
- 7.2.1 Road pricing in National Parks: A case study in the Yorkshire Dales National Park (Steiner and Bristow, 2000)
- 7.2.2 Estimating visitors’ travel mode choices along the Bear Lake Road in Rocky Mountain National Park (Pettebone et al., 2011)
- 7.2.3 Assessing the effects of access policies on travel mode choices in an alpine tourist destination (Orsi and Geneletti, 2014)
- 7.2.4 Preferences for sustainable mobility in natural areas: The case of Teide National Park (González et al., 2019)
- 7.2.5 Stated preferences of tourists for eco-efficient destination planning options (Kelly et al., 2007)
- 7.2.6 Demand for park shuttle services—A stated-preference approach (Shiftan et al., 2006)
- 7.3 Conclusion
- References
- Chapter 8 Machine learning and tourism: Uncovering patterns in tourist behavior through machine learning methods
- Abstract
- Acknowledgments
- 8.1 Introduction
- 8.2 Machine learning
- 8.2.1 Definition of machine learning
- 8.2.2 Brief history of machine learning
- 8.2.3 Machine learning techniques
- 8.3 Machine learning and tourism
- 8.3.1 Methodological aspects
- 8.3.2 Deeper understanding of travelers/visitors
- 8.3.3 Enhancing efficient operation, competitiveness of tourism companies, supply side by machine learning
- 8.3.4 Current topics in academic tourism research addressing machine learning
- 8.4 Case study
- 8.4.1 Destination in focus: Veszprém-Balaton 2023 European Capital of Culture
- 8.4.2 Data and methods
- 8.4.3 Results
- 8.5 Conclusions
- References
- Glossary
- Index
- Edition: 1
- Published: March 14, 2025
- No. of pages (Paperback): 222
- No. of pages (eBook): 250
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780443265938
- eBook ISBN: 9780443265921
FP
Francesca Pagliara
Francesca Pagliara is Associate Professor in Transport Engineering at the Department of Civil, Architectural and Environmental Engineering of the University of Naples Federico II. She has been Visiting Professor at several European and non-European Universities and has participated in several research projects. Her main fields of research are the wider socio-economic impacts of high-speed rail systems, the analysis and quantification of the impact of the transportation system on the tourism market, public engagement in the transportation decision-making process, Transit Oriented Development Policies and Integrated Land-use/Transport models. She is currently Adjunct Professor at the International Railway Transportation Research Center of Silk Road of the Beijing Jiaotong University. She is author of academic books and of more than 100 papers.
Affiliations and expertise
Associate professor in Transport Engineering at the Department of Civil, Architectural and Environmental Engineering of the University of Naples Federico IIMA
Massimo Aria
Massimo Aria is Full Professor in Statistics for Social Sciences in the Department of Economics and Statistics at the University of Naples Federico II and has a Ph.D. in Computational Statistics. His main research fields are bibliometrics, science mapping, systematic literature reviews, statistical survey, and machine learning. He led several statistical surveys and data science projects in the economic, social, tourism, and healthcare fields. Massimo Aria is the author of over 100 scientific articles published in international journals and co-author of the Bibliometrix R-Package and its Biblioshiny web app which are considered the most complete and user-friendly science mapping software.
Affiliations and expertise
Full professor in Statistics for Social Sciences in the Department of Economics and Statistics at the University of Naples Federico II.FM
Filomena Mauriello
Filomena Mauriello is an Assistant Professor at the University of Naples Federico II. She obtained two PhDs, the first in "Engineering of hydraulic transport and territorial systems", the second in "Computational statistics." Her research focuses on Road Safety, regarding two main areas: study of drivers' behavior by analyzing the continuous driving profiles obtained through experiments in the simulated field or the real field; and analysis of road accidents through econometric and machine learning techniques. Recently, her research focused on the study of vulnerable users, such as pedestrians, motorcyclists and cyclists.
Affiliations and expertise
Assistant professor in Road Design and Safety at the University of Naples Federico IIRead Models and Applications of Tourists’ Travel Behavior on ScienceDirect