Models and Applications of Tourists' Travel Behavior
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53 869 Ft (51 303 Ft + 5% VAT)
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53 869 Ft
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Product details:
- Publisher Elsevier
- Date of Publication 18 April 2025
- ISBN 9780443265938
- Binding Paperback
- No. of pages222 pages
- Size 229x152 mm
- Weight 450 g
- Language English 700
Categories
Long description:
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.
- 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
Table of Contents:
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
