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 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.
Sprache
Verlagsort
Zielgruppe
Für Beruf und Forschung
Für höhere Schule und Studium
Produkt-Hinweis
Broschur/Paperback
Klebebindung
Maße
Höhe: 228 mm
Breite: 160 mm
Dicke: 11 mm
Gewicht
ISBN-13
978-0-443-26593-8 (9780443265938)
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Schweitzer Klassifikation
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. 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. 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.
Autor*in
Associate professor in Transport Engineering at the Department of Civil, Architectural and Environmental Engineering of the University of Naples Federico II
Full professor in Statistics for Social Sciences in the Department of Economics and Statistics at the University of Naples Federico II.
Assistant professor in Road Design and Safety at the University of Naples Federico II
1. Role of Transport in Tourists' Behavior
2. Literature Review on Transport and Toruists' Travel Choices
3. Theoretical Approach for Modeling Tourists' Travel Behavior
4. Descriptive Approach for Modeling Tourists' Travel Behavior
5. Statistical Approach for Modeling for Tourists' Travel Behavior
6. Choice Models Based on Stated Preference (SP) Data
7. Choice Models Based on Revealed Preference (RP) Data
8. Machine Learning and Tourism
9. Uncovering Patterns in Tourist Behavior through Machine Learning Methods: Naive Bayes, ANN, SVM and Random Forest
