Travel Plans Research Articles

Decoding the allure of Nanling National Park: a study on the correlation between environmental responsibility and anticipated tourist behavior from the perspective of scarcity

Amid China’s push for ecological civilization and the development of national parks, Guangdong Nanling National Park (proposed) has restricted access since 2018 for reasons of ecological protection. This study examines how the park’s inaccessibility influences tourists’ perceived scarcity and how this scarcity affects their pro-environmental intentions and future travel plans. Using a survey of 310 interested tourists who were unable to visit the park, the findings show that perceived scarcity increases environmental responsibility and future visitation interest. Additionally, destination attachment mediates the relationship between perceived scarcity and these intentions. This study enriches the application of scarcity theory in tourism and environmental psychology, offering destination marketers insights into leveraging scarcity psychology, especially when access restrictions may be lifted, to boost public engagement and sustainable tourism development.

Forecasting Trends in Foreign Tourism by Machine Learning

Tourists frequently use online search engines for travel planning, making search data a valuable predictor of future tourism volume. This study employs machine learning to analyse the predictive power of keyword search data for forecasting tourist arrivals, incorporating a lag time between searches and arrivals. The dataset is collected and prepared from two sources: a search engine and government agencies, covering the years 2014-2019, to be analysed by machine learning. The SARIMA model effectively forecasts trends in keyword searches and tourist numbers, while SVM (Support Vector Machine) and Random Forest outperform other methods in predicting arrivals. This research supports tourism operators and stakeholders in planning for future tourists, utilising the obtained keywords to enhance visibility in tourist searches through SEO.

Meta-learning few-shot prediction of electric vehicle charging time with parallel CNN-Transformer networks

ABSTRACT Accurate prediction of electric vehicle (EV) charging time is a critical technical challenge for optimizing user travel planning and the management of charging infrastructure. However, existing data-driven methods face significant limitations when dealing with the heterogeneity of vehicle types, the complexity of charging patterns, and the scarcity of labeled data. To address these issues, this paper proposes an innovative prediction framework based on meta-learning. We first construct a cross-vehicle charging feature dataset, in which continuous current parameters are discretized into charging levels to reduce modeling complexity. A parallel network architecture integrating a Convolutional Neural Network (CNN) and a Transformer is then developed, coupled with the Model-Agnostic Meta-Learning (MAML) algorithm to enable rapid adaptation to new vehicle types under limited data conditions. Experimental results demonstrate that the proposed method exhibits robust performance across various vehicle models and charging scenarios, achieving a maximum root mean square error of 0.20 h and a mean absolute percentage error of 6.08%. This study provides a novel approach to EV charging time prediction in few-shot settings.

How Heidi helps: designing for inspiration in digital travel assistants

Abstract The emerging trend of inspirational Digital Travel Assistants (DTAs) is rapidly gaining prominence in the tourism industry yet remains underexplored in academic literature. This paper examines how inspirational DTAs can be designed to facilitate travel inspiration through autonomous and efficient co-creation at scale. Grounded in the literature on co-creation within the domain of travel planning (Schmidt-Rauch and Schwabe 2014) and the framework of customer inspiration (Thrash and Elliot 2003), this study employs a design science research (DSR) methodology to explore how to offer personalized travel inspiration through co-creation, catering to the evolving needs of modern travellers. We present an artefact of an inspirational DTA, named Heidi, as one example of how to facilitate this. Heidi was developed by Swiss International Airlines (part of the Lufthansa Group) in collaboration with Google. The evaluation was conducted via semi-structured interviews with three members of the HEIDI initiative and eleven users engaging with the design artefact. This research makes three contributions. First, we demonstrate a means–end relationship between co-creation (means) and inspiration (end) to conceptualise inspirational DTAs. Second, we propose actionable design principles for the development of inspirational DTAs. Lastly, this study extends the academic discourse on information systems in travel advice from IT-enabled human agent setups to IT-enabled virtual agent configurations, positioning inspirational DTAs as a novel paradigm in the evolving landscape of travel inspiration.

Risk perception, intended sexual behaviours and potential associated risks for sexually transmissible infections acquisition among Australian travellers: A cross-sectional study.

Changes in travellers' sexual behaviours, driven by opportunities during travel, contribute to the acquisition of sexually transmissible infections (STIs). However, research on travellers' risk perception, intentions regarding new sexual partners, and engagement in behaviours that may place them at potential risk of STI acquisition remains limited. This study aims to assess the risk perception and intended sexual behaviours associated with STI acquisition among Australian travellers. A cross-sectional online survey was conducted from July 2023 to August 2024 among Australian residents (aged ≥18 years) planning to travel overseas within six months. The survey was distributed in five Travel Medicine Alliance (TMA) clinics and at the Gladstone Road Medical Centre (GRMC) clinic. Socio-demographics, travel plans, sexual intentions, STI risk perception, and patterns of intended sexual behaviours were collected. Subgroup analysis was performed on participants travelling without a partner to estimate their intention to engage in new sexual encounters. Of the 205 respondents, 172 (83.9%) attended the TMA clinics and 33 (16.1%) the GRMC clinic. The median age was 42.5 years (interquartile range 28-56 years), 51% (n = 105) were female. Nearly a quarter (22.7%, n = 29) intended to engage in sexual activity with new partners while travelling. Among these, 72.4% perceived their risk of contracting STIs as low, despite reporting intended sexual behaviours linked to STI acquisition risk such as no intention to use condoms (28.0%), intention to engage in sexual relationships with sex workers (24.1%), and plans to undergo post-travel STI testing (34.6%). Nearly half (41.4%), identified a need for better STI-related pre-travel information. A substantial proportion of surveyed travellers intended to engage in sexual activity with a new sexual partner while travelling, with many underestimating their STI risk and demonstrating intended sexual behaviours that increase their likelihood of STI acquisition. These findings underscore the need for comprehensive sexual health counselling during pre-travel consultations, with a focus on STI risk awareness, preventive strategies, and post-travel STI screening.

Restricción de fronteras durante la pandemia del COVID-19 en América Latina y el Caribe

Introduction: This study analyzed the effect of travel restrictions, including closure of borders and the cancellation of international flights in Latin America and the Caribbean, as one of the early measures implemented to control the spread of COVID-19 virus. Methodology: Literature review examining governmental and scientific databases as well as news-press publications. The material obtained was organized according to the dates of the first cases of COVID-19, notifications informing closure of borders, flights suspension, and travel rebound. The data was crosschecked with the reports of the passengers stranded in Latin American countries highlighting governments' roles in response to the pandemic. Results: Travel restrictions and border control measures affected several passengers returning to their country of origin and/or legal residence. Passengers experienced overwhelming challenges impacting their socioeconomic status, families, health, and employment. Discrimination was also a topic addressed. Many citizens of Latin American countries stranded in various Latin American regions were forced to restructure their travel plans and spend several months in places other than their homes. Conclusions: The early measures had several unintended consequences in the lives of countless individuals, despite their effectiveness in decreasing the transmission of the virus

Travel Virtual Assistant or Untrusted Advisor? Developing a Typology of Resistance to AI‐Generated Travel Advice

ABSTRACTMany travelers remain hesitant to rely on generative AI for travel planning, despite its growing presence in tourism services. While most existing studies emphasize adoption, this study shifts attention to the relatively underexplored issue of resistance. Drawing on Innovation Resistance Theory (IRT) and qualitative data from a developing country, we identify five core barriers to AI‐generated travel advice: usage, value, risk, image, and tradition. We propose a typology of traveler resistance comprising rejecters, postponers, and opinion leaders, each defined by distinct motivations, levels of engagement, and patterns of skepticism. Our findings show that resistance is not fixed but shaped by cultural norms, social context, and personal identity. In rethinking resistance as a situated practice rather than a static outcome, the study extends IRT within tourism research and offers practical guidance for designing AI‐based travel services that are culturally attuned, trust‐oriented, and responsive to the social meanings embedded in travel planning.

A critical assessment of the role of mapping in Ohio Safe Routes to School travel plans

A critical assessment of the role of mapping in Ohio Safe Routes to School travel plans

What happens when forced migrants and transit state actors meet? Encounters at decision nodal points during the migration journey

This study investigates the interactions of forced migrants with state actors in transit countries at critical decision nodal points (DNPs) along their journey—defined as turning points where migrants make decisions about resuming mobility or altering the course of their journey. Granted official legal power by the state, transit state actors operate on the ground in various ways using their power. Nevertheless, their roles in the decision-making as experienced by forced migrants have been studied very little. We examine the journey narratives of forced migrants originating from Eritrea and Sudan. We quantitatively identify and analyze spaces of mobility, immobility and DNPs, within their trajectories. The findings demonstrate that state actors in transit countries can serve as integral and concrete journey components that shape migration outcomes. The study concludes by presenting two insights into (i) the directionality of influence (enabling versus halting journey plans) and (ii) the encounter space (direct-official spaces versus indirect spaces).

Security Analysis of the Lombok Tourism Android Application Using Penetration Testing (Pentesting) Methods Based on the OWASP Mobile Top 10-2024 Framework

Android has become the most widely used operating system for mobile devices, playing a crucial role in supporting the tourism sector. As tourism in Indonesia grows, the demand for quick and easy access to information for travel planning has increased. However, concerns about the security of user data in Android applications have emerged. This study focuses on penetration testing of tourism-related Android applications in Lombok to identify vulnerabilities, particularly based on the OWASP Top 10 Mobile Risks. Using static analysis with the Mobile Security Framework (MobFS), two critical vulnerabilities were identified: Insecure Data Storage and Insufficient Cryptography. Penetration testing revealed that although there was a risk related to insecure data storage, no sensitive user data was found in the application's database. The application was also found to use outdated encryption (CBC with PKCS7 padding), which could expose it to padding oracle attacks. This research emphasizes the need for robust security measures in mobile applications within the tourism sector.

THE EFFECT OF EARTHQUAKE ON THE TOURISM SECTOR: CASE OF TURKIYE 1999 AND 2023

Perceptions of tourism risk and decision-making processes are influenced by various factors, including security concerns, socioeconomic and demographic characteristics such as age, gender, education, religion, income, tourism motivation, and personal experiences. The effect of disaster events on tourism is shaped by the events’ magnitude, frequency, and the destination’s ability to adapt and recover. This study investigates how the 1999 Marmara and 2023 Kahramanmaraş earthquakes influenced Türkiye’s tourism sector, using a qualitative methodology supported by secondary data. Tourism statistics from the 1999 Marmara earthquake show a sharp decline in international tourist arrivals—from 9,752,697 in 1998 to 7,487,285 in 1999. Tourism revenues also dropped significantly, with estimated losses reaching $3 million due to canceled tours. A 25% decline in tourism was noted specifically during August and September 1999. In contrast, the 2023 earthquake did not result in a significant reduction in tourism activity. One reason for the stability in 2023 is that the earthquake-affected regions—such as Kahramanmaraş, Hatay, Adıyaman, Malatya, Gaziantep, and others—are geographically distant from major tourist destinations like Antalya, Bodrum, and Muğla. However, public perception plays a critical role. If tourists are unaware that their intended destinations are unaffected, it could lead to declines regardless of safety. To enhance recovery and build resilience, several strategies are proposed. First, rebuilding critical infrastructure—roads, bridges, and public spaces—is essential. Second, social media should be used effectively to promote unaffected tourist destinations. Targeted campaigns should highlight attractions based on specific tourist interests, such as family travel, adventure, or gastronomy. Collaborations with platforms like Booking.com, TripAdvisor, and Expedia can further ease travel planning. Moreover, media engagement is vital. Positive coverage can help reshape the destination image. Journalists and bloggers should be provided with accurate, timely information about recovery efforts and safe areas. Despite effective recovery from the 2023 event, challenges remain in establishing clear causal relationships between crises and structural changes in tourism flows. Structural break analysis, while helpful, is limited by time-specific data and cannot fully isolate tourism behavior triggers.

Application of Natural Language Processing and LSTM in A Travel Chatbot for Medan City

The tourism sector plays a vital role in economic growth and regional development. Medan, a major city in North Sumatra, offers rich religious, historical, and cultural attractions. However, fragmented and inconsistent information presents challenges for both tourists and destination managers, often complicating travel planning. To address this issue, this study proposes the development of an AI-based chatbot aimed at enhancing the tourism experience in Medan. By integrating Natural Language Processing (NLP) and Long Short-Term Memory (LSTM), the chatbot is designed to deliver accurate, contextual, and conversational responses tailored to users' tourism-related queries. It was trained on a comprehensive dataset compiled from various sources concerning Medan’s tourism. The training ran over 100 epochs, achieving an accuracy of 84.31% and a loss of 0.7594. Validation testing yielded an accuracy of 77.14% and a loss of 2.4233, indicating good generalization to unseen data. End-to-end testing with 312 queries covering all defined intents resulted in a testing accuracy of 75.64%, confirming the model’s practical effectiveness. The findings demonstrate that the chatbot can accurately interpret user input, classify information, and enhance user interaction. supports the digital transformation of Medan’s tourism sector by introducing a reliable, AI-driven tool for seamless travel planning and engagement.

MaaS adoption intention among car commuters and Park&Ride users in suburban areas of Madrid Region

This paper investigates the motivations behind travel app adoption intention among regular car drivers from suburban areas of Madrid Region, focusing on two groups of commuters: daily Park&Ride users (combining car with public transport) and car-only users who drive for the whole length of their trip (231 respondents in each group). Propensity score matching was employed to ensure groups’ comparability in terms of socioeconomic characteristics, while ordinal logit models were used to detect key drivers of adoption intention. Adoption intention was high in both groups (though stronger among P&R users) and determined by gain motives (reductions in travel cost, reliable travel time estimations). Also, it increased among respondents making longer trips. For P&R users key determinants were being female and technophilia. These findings highlight the need for reliable multimodal travel planners and advocate for a wider perspective on transport policies that can mutually reinforce each other, like Mobility-as-a-Service and P&R.

Investigating customers’ typical longitudinal behavioural responses in a large-scale mobility-as-a-service trial

Abstract Mobility as a Service (MaaS) aims to reduce the complexity of travel planning and execution for multimodal services. Understanding customer behaviour in real market conditions is crucial for designing and maintaining effective MaaS strategies. However, there is limited empirical data on customers actual subscription patterns, and few studies explore longitudinal behaviour in stable MaaS environments. This study narrows these knowledge gaps based on trial data collected from a MaaS trial at the University of Queensland (UQ), in Brisbane, Australia, with a focus on customers’ MaaS subscriptions during the first semester of 2023 (from 20 February to 17 June). A sequence analysis reveals that there are seven types of representative subscription patterns in customers’ longitudinal sequences. Each pattern significantly differs in terms of the temporal distribution of daily membership states, primarily around the duration of the subscription and the variation of mobility bundle choices over time. Overall, within the MaaS bundles on offer in this trial, customers are more likely to subscribe to mobility bundles only with unlimited allowances for public transport service in the long run, while only a minority of customers subscribe to multimodal mobility bundles. A multinomial logit (MNL) model based on the seven subscription patterns also confirms the important roles of customer demographics, motivations for joining the MaaS trial, built environment characteristics surrounding home locations, and trip booking characteristics, in shaping the heterogeneous subscription patterns. The findings are insightful for MaaS operators/practitioners to design bundle offerings, thus retaining customer subscriptions. The proposed research framework can also be used to construct customer market segments or to plan future bundle offerings in MaaS markets.

Characterising and Comparing the Sleep Characteristics and Behaviours of Female and Male Soccer Players: A Cross-Sectional Survey of an Elite Soccer Club.

The aim of this cross-sectional study was to evaluate the sleep characteristics and behaviours of senior male, senior female, and under 21 (U21) male elite soccer players using athlete-specific questionnaires. During the preseason/early season period, 74 players from the English Premier League (n = 26, age 26 ± 5 y), Women's Super League (n = 22, age 25 ± 5 y), and English Premier League 2 (n = 26, age 19 ± 1 y) completed the validated Athlete Sleep Screening Questionnaire (ASSQ) to obtain a sleep difficulty score (SDS) and the Athlete Sleep Behaviour Questionnaire (ASBQ) to obtain a global score and individual behaviours. We found that sleep difficulty scores were higher in senior females (5.9 ± 1.9) than senior males (4.1 ± 1.7) and U21 males (4.3 ± 1.2) (p ≤ 0.006), but no severe clinical problems were noted. Global sleep behaviour scores from the ASBQ were worse in senior males (37.9 ± 6.5) and senior females (40.6 ± 7.1) than male U21 players (33.6 ± 4.7) (p ≤ 0.021). Senior players consumed more alcohol and stimulants and felt that travel disrupted sleep; females went to bed thirstier, woke more for the bathroom, and ruminated more prior to sleep (all p < 0.05). In conclusion, senior female players reported more sleep difficulties than male senior and male U21 players. Behaviours such as pre-bed rumination, nutrition, and travel plans could be targeted to improve sleep quality in soccer players. Study limitations include data drawn from a single club in the off-season.

Find Your Way: An IOS-Based Travel Planning Application with Route Optimization Using Agile Methodology

Aims: This study aimed to develop Find Your Way, an iOS-based travel planning application that addressed user needs through the Agile development approach. Study Design: The study applied Agile methodology, consisting of six iterative phase such as planning, design, development, testing, deployment, and review, executed over two development cycles to refine the application based on user feedback. Place and Duration of Study: The research was conducted in Indonesia over a five-month period, from December 2024 to April 2025. Methodology: Researchers conducted interviews with four participants aged 18–35 to gather functional requirements. The data were analyzed using thematic analysis to identify key user needs. These needs guided the design and implementation of the application using Swift and MVVM Clean Architecture. The core features developed included route optimization using Particle Swarm Optimization (PSO), destination recommendations, saving and managing travel routes, and customizable map settings. Testing was conducted using blackbox and whitebox methods. The app was deployed via TestFlight using CI/CD with Xcode Cloud. In the review phase, 30 users aged 18–25 completed a User Acceptance Test (UAT) to evaluate the app's usability and performance. Results: The study produced Find Your Way, a travel planning application tailored to user-identified functional needs. Five key features were implemented, including destination information, location and route recommendations, as well as storage and map display settings. The application achieved a UAT score of 97.33%, indicating a high level of user satisfaction. Testing confirmed the application's functionality and program logic through blackbox and whitebox methods. Conclusion: The Find Your Way application was successfully developed and met user functional requirements. The high UAT score of 97.33% demonstrated that the app effectively fulfilled its intended purpose, validating the Agile development approach used in this study.

How does ChatGPT-generated information influence travel planning? The mediating role of inspiration

Purpose This study aims to explore the impact of ChatGPT’s information quality on travel plans, drawing on the stimuli-organism-response framework and the inspiration transmission model. Design/methodology/approach Participants were recruited in online groups on Chinese social media platforms, and they were required to have experiences of having searched for travel information using ChatGPT before or during their travels. Partial least squares-structural equation modeling was used to validate the model. Findings The findings indicated that reliability, relevance and quickness all exerted a significant positive influence on travel planning behavior. However, conciseness does not have a significant effect on travel planning behavior. The two stages of inspiration – inspired-by and inspired-to – acted as a sequential mediating mechanism in these relationships. Perceived privacy risk negatively moderated the impact of inspired-by on inspired-to, as well as the influence of inspired-to on travel planning behavior. Originality/value This research reveals the impact of ChatGPT’s information quality on traveler inspiration. And the authors also investigate the differences in the impact of the dimensions of information quality on travel planning behavior. The findings contribute to a comprehensive understanding of the influence mechanisms of ChatGPT information. Furthermore, this study offers practical insights for destinations to apply ChatGPT rationally in guiding tourist inspiration and behavior.

Unravelling the Digital Journey: Exploring the Impact of Social Media on Tourist Behaviour in the Digital Age

This research explores how social media shapes the way people plan and experience their travels in today's digital age. By collecting responses from 200 participants and reviewing various sources, we aim to understand how platforms like Instagram, Facebook, and Twitter influence tourists' destination choices, travel plans, and itinerary changes. We'll also look at how different generations use social media to plan their trips, providing valuable insights for travel businesses and marketers. While we expect to find that social media has a significant impact—both positive and negative—on how people travel, we'll also address concerns about the authenticity of online information and the effects of commercialization. Ultimately, this study seeks to show the importance of leveraging social media to foster sustainable tourism and enhance the travel experience for everyone.

Understanding international travelers’ health risk perceptions, preferences, and decisions: a segmentation analysis

BackgroundThis study assesses international travelers’ risk perceptions and travel decisions related to three recent emerging diseases. Travelers can facilitate the spread of emerging infectious diseases and their decision-making on where to travel is influenced by outbreaks. These feedback loops can potentially impact the tourism economy. Often, travelers’ judgment and actions towards a risk are based on their perceptions.MethodWe conducted two surveys, using constructs from the Health Belief Model, with 747 individuals who had recently traveled to selected Latin American countries, and who had heard about Zika virus, chikungunya, and/or COVID-19. Using segmentation analysis, the respondents were grouped based on their risk perception level (i.e., low, medium and high), and we tested the differences between groups for different constructs of the model.ResultsWe found a significant difference between the risk perception groups for most of the sociodemographic factors, as well as for the purpose of the trip, regarding travel preferences. Personal experience with a disease and perceived efficacy towards diverse protective measures also differed between groups. Higher risk perception was related to reporting more changes in past travel plans, and higher likelihood of future travel avoidance if facing different risk scenarios in a tourism destination.ConclusionsIncluding the concepts of risk perception, sociodemographic factors, previous experience, and efficacy can help better explain the individual behavior of international travelers. These findings can inform tailored and more effective mitigation and management strategies to promote safe travel and prevent disease spread in the event of a future outbreak.

Travel Planner System for Sri Lanka

Abstract The Sri Lanka Travel Planner is an intelligent web platform that revolutionizes travel planning by integrating flights, hotels, and activities in one place. Using AI-powered route optimization and personalized recommendations, it solves key pain points like fragmented information and time-consuming itinerary creation.With dynamic pricing updates and rich multimedia content, it reduces planning time by 40% while improving itinerary satisfaction. Machine learning algorithms tailor suggestions based on budget, interests, and travel duration. Initial tests show 87% accuracy in price predictions and significant user time savings.