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Destination recommendation systems: behavioural foundations and applications
An emerging area of study within technology and tourism focuses on the development of technologies which enable Internet users to quickly and effectively find relevant information about selected topics including travel destination, transportation, etc. This area of tourism research and development is generally referred to as destination marketing systems (DMSs) and brings together both applied and academic interests ranging from marketing and management to psychology, mathematics and computer sciences. This book provides a comprehensive synthesis of the current status of research, representing the contributions of some of the leading researchers in destination marketing systems.
eBook
How to be a digital nomad : build a successful career while travelling the world
\"You don't need to sacrifice your career to travel the world. Join the 35 million digital nomads who are living, working and exploring to the fullest. With this book, discover the incredible opportunities of digital nomadism and learn how you can travel the world while also sustaining a successful work-life. How to Be a Digital Nomad gives you everything you need to build a successful career on your terms.This book is both a practical guide and an insightful exploration of this unique lifestyle. It includes interviews with a diverse range of remote workers, telling stories that span five decades of digital nomadism, and highlights the unique opportunities this lifestyle offers you and your career. Whether you're looking for a few months away, a working gap year or an entirely new lifestyle, this book will show you how you can take control of your career while travelling the world\"-- Provided by publisher.
Book
Destination Recommendation Systems
Bringing together the work of leading researchers, this book provides a clear and accessible overview of current research on destination recommendation systems. These systems guide consumer behaviour by enabling Internet users to quickly and effectively find relevant information about travel destinations, attractions, accommodation and transportation.
Publication
Social media in travel, tourism and hospitality
Social media is fundamentally changing the way travellers and tourists search, find, read and trust, as well as collaboratively produce information about tourism suppliers and tourism destinations. Presenting cutting-edge theory, research and case studies investigating Web 2.0 applications and tools that transform the role and behaviour of the new generation of travellers, this book also examines the ways in which tourism organisations reengineer and implement their business models and operations, such as new service development, marketing, networking and knowledge management. Written by an international group of researchers widely known for their expertise in the field of the Internet and tourism, chapters include applications and case studies in various travel, tourism and leisure sectors.
eBook
Co-evolution model of traffic travel and disease transmission under limited resources
The co-evolution mechanisms between traffic mobility and disease transmission under resource constraints remain poorly understood. This study proposes a two-layer transportation network model integrating the Susceptible-Infectious-Susceptible (SIS) epidemic framework to address this gap. The model incorporates critical factors such as total medical resources, inter-network infection delays, travel willingness, and network topology. Through simulations, we demonstrate that increasing medical resources significantly reduces infection scale during outbreaks, while prolonging inter-network delays slows transmission rates but extends epidemic persistence. Complex network topologies amplify the impact of travel behavior on disease spread, and multi-factor interventions (e.g., combined resource allocation and delay extension) outperform single-factor controls in suppressing transmission. Furthermore, reducing network connectivity (lower average degree) proves effective in mitigating outbreaks, especially under low travel willingness. These findings highlight the necessity of coordinated policies that leverage resource optimization, travel regulation, and network simplification to manage epidemics. This work provides actionable insights for policymakers to design efficient epidemic control strategies in transportation-dependent societies.
Journal Article
Information and Communication Technologies for Sustainable Tourism
Sustainable development is a highly topical issue and is of critical importance to tourism as the environment is of utmost importance for the continued development and prosperity of the industry. There have been numerous texts written on sustainable tourism and the measures to mitigate and manage this but none which acknowledges Information and Communication Technologies (ICT) as a mechanism of doing so despite being an emerging area of research. ICT in this context refers to innovative tools which form an integrated system of software and networked equipment that facilitates data processing information sharing communication and the ability to search and select from an existing range of products and services for an organisation's benefits. Despite the symbiotic relationship, which exists between ICT and sustainable tourism, there has been little research, which has considered how the use of such technology can be used to make sustainable tourism development a more workable reality.
This opportune book is the first to provide a focus on the interrelationship of these two important topics demonstrating their synergies and providing insight into a new and innovative approach to managing sustainable tourism development. It considers the use of technology to reduce the negative impacts of tourism from both the demand and supply side perspectives. A critical review of a range of cutting edge technologies used by tourists and businesses to assess their usefulness in managing sustainable tourism development from the macro to the micro level is also discussed. It further integrates examples and practical applications to show how ICT can be an invaluable mechanism in the management of sustainable tourism development.
This cutting-edge volume provides a wealth of information on an important yet neglected subject. This book will be invaluable reading for students, researchers, academics and members of the tourism industry looking for new and
eBook
Using cellular device location data to estimate visitation to public lands: Comparing device location data to U.S. National Park Service’s visitor use statistics
Understanding human use of public lands is essential for management of natural and cultural resources. However, compiling consistently reliable visitation data across large spatial and temporal scales and across different land managing entities is challenging. Cellular device locations have been demonstrated as a source to map human activity patterns and may offer a viable solution to overcome some of the challenges that traditional on-the-ground visitation counts face on public lands. Yet, large-scale applicability of human mobility data derived from cell phone device locations for estimating visitation counts to public lands remains unclear. This study aims to address this knowledge gap by examining the efficacy and limitations of using commercially available cellular data to estimate visitation to public lands. We used the United States’ National Park Service’s (NPS) 2018 and 2019 monthly visitor use counts as a ground-truth and developed visitation models using cellular device location-derived monthly visitor counts as a predictor variable. Other covariates, including park unit type, porousness, and park setting (i.e., urban vs. non-urban, iconic vs. local), were included in the model to examine the impact of park attributes on the relationship between NPS and cell phone-derived counts. We applied Pearson’s correlation and generalized linear mixed model with adjustment of month and accounting for potential clustering by the individual park units to evaluate the reliability of using cell data to estimate visitation counts. Of the 38 parks in our study, 20 parks had a correlation of greater than 0.8 between monthly NPS and cell data counts and 8 parks had a correlation of less than 0.5. Regression modeling showed that the cell data could explain a great amount of the variability (conditional R-squared = 0.96) of NPS counts. However, these relationships varied across parks, with better associations generally observed for iconic parks. While our study increased our confidence in using cell phone data to estimate visitation, we also became aware of some of the limitations and challenges which we present in the Discussion.
Journal Article
Spatial Characteristics of the Tourism Flows in China: A Study Based on the Baidu Index
The characteristics of information flow, as represented by the Baidu index, reflect the pattern of tourism flows between different cities. This paper is based on the Baidu index and applies the seasonal concentration index and social network analysis (SNA) methods to study the spatial structure characteristics of tourism flows in China. The results reveal that: (1) both the search volume of the Baidu index in different cities and the online attention to different scenic areas exhibit obvious spatial heterogeneity and seasonal differences; (2) regions with strong tourism flow connections mainly occur in the areas between metropolises or among the inner cities of urban agglomerations, which are largely distributed on the southeast side of the Heihe–Tengchong Line; (3) the development of the whole tourism flow network in China is low, with an unbalanced development between tourism supply and demand, indicating that tourism resources are concentrated in a few cities and that most of the information interaction among cities occurs in core areas, while a weak interaction is observed in peripheral areas; (4) cities like Beijing and Wuhan attain obvious advantages in regard to their tourism resources, whereas other cities, including Beijing, Shanghai, Shenzhen and Guangzhou, exhibit a high demand for tourism. Moreover, tourism information flow networks are concentrated in several cities with an important role in the Chinese urban system, such as Beijing, Wuhan, and Chengdu, because they contain abundant tourism resources, well-developed transportation systems and advanced economic and societal development levels. (5) Cities such as Beijing, Lhasa, Wuhan, and Zhengzhou possess numerous advantages due to structural holes, and they thus occur at an advantageous position in the tourism flow network.
Journal Article
TransETA: transformer networks for estimated time of arrival with local congestion representation
Estimated time of arrival (ETA) is an estimate of the vehicle travel time from the origin to destination in the roadworks. From the perspective of travel planning or resource allocation, accurate ETA is significantly important. In recent years, deep learning-based methods represented by recurrent neural networks has been widely used in travel time prediction tasks, but such methods cannot effectively learn data association at different moments. At the same time, the existing methods do not effectively leverage local traffic information. Targeting these challenges, this paper proposes a new model TransETA to predict vehicle travel time. The model includes three modules: the input feature transformation module uses graph convolutional network (GCN) to extract the local congestion feature, the deep forest module mainly deals with static trajectory data, and ETA-Transformer module processes the feature extraction of dynamic trajectory data. Finally, we conducted experiments on two large trajectory datasets. The experimental results show that the proposed hybrid deep learning method, TransETA, outperforms the state-of-the-art models. On the Chengdu and Porto datasets, our proposed method shows an improvement of 6s and 9s in mean absolute error compared to the current best performing method, respectively. Also the average absolute percentage error is reduced by 2.34% and 3.64% respectively. The effectiveness of each module was approved through ablation experiments. Specifically, local congestion information representation can effectively improve the accuracy of the prediction. ETA-Transformer module is more effective in extracting spatio-temporal feature correlation than the LSTM-based method.
Journal Article