Search Results (1,026)

This study examines the intention of Greek tourists who visit national touristic destinations to adopt Artificial Intelligence (AI) chatbots in the tourism sector. Using the UTAUT2 model as a framework, data were collected through a closed-ended questionnaire and analyzed with correlation and regression methods to identify the main drivers and barriers to this adoption. Results show that specific factors such as performance expectancy, hedonic motivation, and perceived innovativeness significantly and positively influence chatbot usage, emphasizing the role of usefulness, enjoyment, and innovation in shaping user acceptance. Conversely, factors such as inconvenience, habit, and difficulty of use negatively affect adoption, indicating the importance of overcoming usability challenges and resistance to change. These findings highlight the need for the development of accessible and engaging chatbot systems and underscore the value of continuous technological improvements. The study concludes that adopting AI-driven solutions can help tourism providers personalize services, improve operational efficiency, and enhance customer satisfaction, fostering sustainable competitiveness in the sector. Full article

The National Research Council Canada (NRC) is actively engaged in the development of an advanced autonomy system for the Bell 412 helicopter. This system’s capabilities extend to the execution of complex missions, such as arctic resupply missions. In an arctic resupply mission, the helicopter autonomously delivers supplies to a remote arctic base. During the mission it performs tasks such as takeoff, navigation, obstacle avoidance, and precise landing at its destination, all while minimizing the need for pilot intervention. The complexity of this autonomy system necessitates the inclusion of a high-level supervisory controller. This controller plays a critical role in monitoring mission progress, interacting with system components, and efficiently allocating resources. Conventionally, supervisory controllers are embedded within monolithic programs, lacking transparent state flows. This causes system modification and testing to be a significant challenge. In our research, we present an innovative approach and methodology to develop supervisory controllers for autonomous aircraft on the example of the NRC Bell 412. Using the Discrete Event System Specification (DEVS) formalism and the Cadmium simulation engine, we effectively address the challenges above. We discuss the entire development process for a state-based, event-driven supervisory controller for autonomous rotorcraft using the NRC’s Bell-412 autonomy system as a comprehensive case study. This process includes modeling, implementation, verification, validation, testing, and deployment. It incorporates a simulation phase, in which the supervisor integrates with components within a Digital Twin of the Bell 412, and a real-time operations phase, where the supervisor becomes an integral part of the actual Bell 412 helicopter. Our method outlines the smooth transition between these phases, ensuring a seamless and efficient process. Full article

Rural poverty in Indonesia remains a complex issue involving various aspects. West Manggarai, East Nusa Tenggara, is a national tourist destination and a significant focus of national development, yet poverty rates remain very high. Therefore, this study developed a Composite Poverty Index (CPI) using the Sustainable Rural Livelihoods Approach (SRLA) to illustrate the complexity of rural deprivation in West Manggarai Regency. The CPI was developed by normalizing eighteen validated indicators across five livelihood capitals—human, social, natural, physical, and financial. These indicators were then classified using a Likert-type scale, and their weights were determined through the Analytic Hierarchy Process (AHP) to produce village-level CIP scores. The results show that most villages fall into the “Moderate” category (CIP: 0.40–0.60), reflecting chronic but not extreme deprivation. Spatial inequalities are evident, particularly in access to education, infrastructure, clean water, financial services, and ecological resources. Remote villages recorded higher CIP scores. Natural and economic capital were weakest, while human and social capital performed relatively well. Therefore, poverty alleviation in West Manggarai requires an integrated strategy tailored to local spatial conditions and livelihood capital. Full article

Vision is a critical component of daily life, and its loss significantly hinders an individual’s ability to navigate, particularly when using public transportation systems. To address this challenge, this paper introduces a novel approach for accurately identifying bus route numbers and destinations, designed to assist visually impaired individuals in navigating urban transit networks. Our system integrates object detection, image enhancement, and Optical Character Recognition (OCR) technologies to achieve reliable and precise recognition of bus information. We employ a custom-trained You Only Look Once version 8 (YOLOv8) model to isolate the front portion of buses as the region of interest (ROI), effectively eliminating irrelevant text and advertisements that often lead to errors. To further enhance accuracy, we utilize the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to improve image resolution, significantly boosting the confidence of the OCR process. Additionally, a post-processing step involving a pre-defined list of bus routes and the Levenshtein algorithm corrects potential errors in text recognition, ensuring reliable identification of bus numbers and destinations. Tested on a dataset of 120 images featuring diverse bus routes and challenging conditions such as poor lighting, reflections, and motion blur, our system achieved an accuracy rate of 95%. This performance surpasses existing methods and demonstrates the system’s potential for real-world application. By providing a robust and adaptable solution, our work aims to enhance public transit accessibility, empowering visually impaired individuals to navigate cities with greater independence and confidence. Full article

Türkiye’s role in the global lemon trade was evaluated using the Constant Market Share (CMS) method to assess changes in export competitiveness across major destination markets. The CMS framework decomposes export performance into three components—market share effect, commodity composition effect, and commodity adaptation effect—which, respectively, represent competitiveness, product–market alignment, and structural responsiveness. Trade data for the ten largest importing countries, representing over 80% of Türkiye’s lemon exports, were analyzed to identify the drivers of export growth and structural change. Results show a sharp decline in competitiveness during the COVID-19 disruption, followed by a partial recovery in markets such as Iraq, Poland, Russia, and Azerbaijan. Persistent structural rigidities were identified in several Eastern European and Gulf markets, indicating limited responsiveness to shifting import demand. The findings highlight the need for flexible production systems, improved alignment of export structures with market requirements, and strategic partnerships to sustain long-term competitiveness in the global citrus sector. Full article

The increasing use of UAV swarms for collaborative autonomous missions presents significant challenges in coordination, safety, and scalability, especially during dynamic formation reconfigurations. This study introduces the Magnetic Swarm Reconfiguration (MSR) protocol, a fully distributed navigation method that enables UAV swarms to transition smoothly and safely between geometric formations. MSR achieves this by combining two main components: first, it employs the Hungarian algorithm to compute an optimal assignment of UAVs to target positions within the new formation, thereby minimizing trajectory overlap and interference; second, it utilizes virtual magnetic attraction and repulsion forces for real-time navigation, drawing each UAV toward its assigned destination while dynamically repelling nearby agents to avoid collisions. To evaluate the performance of the MSR protocol, six representative formation transitions were simulated across swarm sizes of up to 100 UAVs. Results show that MSR reduces reconfiguration time significantly compared to existing methods, maintains strict safety standards by achieving minimal to zero collisions, and supports fully decentralized and simultaneous maneuvering. The scalability and robustness of the MSR protocol make it suitable for complex, large-scale swarm operations requiring rapid and reliable formation changes. Full article

Providing age-friendly metro service substantially enhances the elderly’s mobility and well-being. Despite recent progress in user profiling and mobility prediction, the prediction of the elderly’s metro travel patterns remains limited. To fill this gap, this study proposes a framework integrating user profiling and knowledge graph embedding to predict the elderly’s activity types at metro trip destinations, utilizing 180,143 smart card records and 885,072 points of interest (POI) records from Chongqing, China in 2019. First, an elderly metro travel profile (EMTP) tag system is developed to capture the elderly’s spatiotemporal metro travel behaviors and preferences. Subsequently, an elderly metro travel knowledge graph (EMTKG) is constructed to support semantic reasoning, transforming the activity types prediction problem into a knowledge graph completion problem. To solve the completion problem, the Temporal and Non-Temporal ComplEx (TNTComplEx) model is introduced to embed entities and relations into a complex vector space and distinguish between time-sensitive and time-insensitive behavioral patterns. Fact plausibility within the graph is evaluated by a scoring function. Numerical experiments validate that the proposed model outperforms the best-performing baselines by 13.37% higher Accuracy@1 and 52.40% faster training time per epoch, and ablation studies further confirm component effectiveness. This study provides an enlightening and scalable approach for enhancing age-friendly metro system service. Full article

Digital transformation has re-engineered tourism marketing and how destination branding competes for tourist attention, yet scholarship offers little systematic quantification of these changes. Drawing on 160 peer-reviewed studies published between 1990 and 2025, we combine grounded-theory thematic synthesis with a random-effect meta-analysis of 60 datasets to trace branding performance across five technological eras (pre-Internet and brochure era: to mid-1990s; Web 1.0: 1995–2004; Web 2.0: 2004–2013; mobile first: 2013–2020; AI-XR: 2020–2025). Results reveal three structural shifts: (i) dialogic engagement replaces one-way promotion, (ii) credibility migrates to user-generated content, and (iii) artificial intelligence–driven personalisation reconfigures relevance, while mobile and virtual reality marketing extend immersion. Meta-analytic estimates show the strongest gains for engagement intentions (g = 0.57), followed by brand awareness (g = 0.46) and image (g = 0.41). Other equity dimensions (attitudes, loyalty, perceived quality) also improved on average, but to a lesser degree. Visual, UGC-rich, and influencer posts on highly interactive platforms consistently outperform brochure-style content, while robustness checks (fail-safe N, funnel symmetry, leave-one-out) confirm stability. We conclude that digital tools amplify, rather than replace, co-creation, credibility, and context. By fusing historical narrative with statistical certainty, the study delivers a data-anchored roadmap for destination marketers, researchers, and policymakers preparing for the AI-mediated decade ahead. Full article

The present study explores the determinants of tourist satisfaction within the context of wellness tourism in Greece, an emerging segment of the tourism industry that emphasizes holistic well-being. The aim was to assess the overall satisfaction of wellness tourists, identify the relative importance and performance of satisfaction dimensions, and offer insights for service improvement. A structured questionnaire was administered to 487 wellness tourists during the summer of 2024, and the data were analyzed using descriptive statistics and the Multicriteria Satisfaction Analysis (MUSA) method. The results revealed a high overall satisfaction level (90.4%), with physical and spiritual well-being activities contributing most significantly to the satisfaction structure. In contrast, mind well-being activities scored the lowest in satisfaction, despite being rated highly in importance, suggesting a service gap. Improvement analysis indicated that mental and spiritual well-being activities are high-impact, low-effort areas for enhancement. Demographic data further highlighted that wellness tourists are typically young, educated and economically active women. The findings suggest the need for more personalized, holistic offerings and point to the potential integration of wellness and medical tourism services. The study offers practical implications for wellness providers and destination managers and identifies future research directions related to satisfaction dynamics and health-oriented tourism strategies. Full article

This study investigates how traditional versus modern Chinese ink painting styles in tourism advertisements affect viewers’ visual attention, aesthetic evaluations, and tourism intentions. Using eye-tracking experiments combined with surveys and interviews, the researchers conducted a mixed-design experiment with 80 Chinese college students. Results indicate that traditional ink-style advertisements attracted longer total fixation durations, higher aesthetic evaluations, and stronger cultural resonance in natural landscape contexts, while modern ink-style advertisements captured initial attention more quickly and performed better aesthetically in urban settings. Qualitative analyses further revealed cultural familiarity and aesthetic resonance underpinning preferences for traditional style, whereas modern style mainly attracted attention through novelty and creativity. These findings expand Cultural Schema Theory and the aesthetic processing model within advertising research, suggesting practical strategies for tourism advertising to match visual styles appropriately with destination types and audience characteristics to enhance promotional effectiveness. Full article

One type of transportation system developed in several cities is the Bus Rapid Transit (BRT) system. BRT systems are influenced by various factors, and route planning is one of the most important ones, which involves aspects such as route design, bus schedules, and passenger load. BRT systems can generate certain service data, which can be useful for calculating passenger load. However, these service data are insufficient to accurately predict future passenger loads. Processes such as origin–destination matrix analysis are required, which are time-consuming and not suitable in most cases. This paper proposes a machine learning (ML) model that allows predicting passenger load at the key stations of a BRT system. An exploration of datasets from several BRT systems was performed for a particular use case. Open data on the Transmilenio BRT system from Bogotá (Colombia) was determined as the source. The obtained results showed that the model using the Long-Short Term Memory (LSTM) algorithm obtained the best results in the metrics using one of the two generated datasets. However, the initial results were not satisfactory enough, so it was necessary to use a hyperparameter-tuning tool and vary the range of dates in the dataset to improve the respective metrics. Full article

In today’s dynamic tourism industry, shaped by globalization and digitalization, understanding destination competitiveness is crucial for crafting sustainable development policies. This paper explores Romania’s competitive advantage as a tourist destination through both theoretical and practical perspectives. The present research aims to diagnose Romania’s level of competitiveness by identifying tourist attributes perceived as relevant by visitors and evaluating their performance relative to other similar European destinations. A quantitative questionnaire-based survey was conducted to achieve this goal. The survey included 235 respondents, gathered through non-probability convenience and snowball sampling. Romania’s competitiveness was assessed using the Competitive Importance-Performance Analysis (CIPA) method, which allowed for the strategic mapping of the country’s position based on the relative performance of essential attributes. These attributes included cultural heritage, the diversity of natural landscapes, the digitalization of tourism services, and staff hospitality. The results highlighted that Romania possesses significant strengths in natural landscapes, gastronomy, accommodation quality, and outdoor activities. However, the study identified major negative gaps in critical areas such as service digitalization, tourist staff attitude, and the quality of cultural events. These findings underscore a latent competitive advantage based on authentic resources, which is currently underexploited from the perspective of modern management and infrastructure. The practical implications of this research provide a solid basis for optimizing tourism marketing policies, efficient resource allocation, and strengthening Romania’s positioning as an authentic, sustainable, and competitive destination within the European landscape. Full article

Since statistics show a growing trend in blindness and visual impairment, the development of navigation systems supporting Blind and Visually Impaired People (BVIP) must be urgently addressed. Guiding BVIP to a desired destination across indoor and outdoor settings without relying on a pre-installed infrastructure is an open challenge. While numerous solutions have been proposed by researchers in recent decades, a comprehensive navigation system that can support BVIP mobility in mixed and unprepared environments is still missing. This study proposes a novel navigation system that enables BVIP to request directions and be guided to a desired destination across heterogeneous and unprepared settings. To achieve this, the system applies Computer Vision (CV)—namely an integrated Structure from Motion (SfM) pipeline—for tracking the user and exploits Building Information Modelling (BIM) semantics for planning the reference path to reach the destination. Audio Augmented Reality (AAR) technology is adopted for directional guidance delivery due to its intuitive and non-intrusive nature, which allows seamless integration with traditional mobility aids (e.g., white canes or guide dogs). The developed system was tested on a university campus to assess its performance during both path planning and navigation tasks, the latter involving users in both blindfolded and sighted conditions. Quantitative results indicate that the system computed paths in about 10 milliseconds and effectively guided blindfolded users to their destination, achieving performance comparable to that of sighted users. Remarkably, users in blindfolded conditions completed navigation tests with an average deviation from the reference path within the 0.60-meter shoulder width threshold in 100% of the trials, compared to 75% of the tests conducted by sighted users. These findings demonstrate the system’s accuracy in maintaining navigational alignment within acceptable human spatial tolerances. The proposed approach contributes to the advancement of BVIP assistive technologies by enabling scalable, infrastructure-free navigation across heterogeneous environments. Full article

Automated landmark recognition represents a cornerstone technology for advancing smart tourism systems, cultural heritage documentation, and enhanced visitor experiences. Contemporary deep learning methodologies have substantially transformed the accuracy and computational efficiency of destination classification tasks. Addressing critical gaps in existing approaches, we introduce an enhanced Samarkand_v2 dataset encompassing twelve distinct historical landmark categories with comprehensive environmental variability. Our methodology incorporates a systematic multi-threshold pixel intensification strategy, applying graduated enhancement transformations at intensity levels of 100, 150, and 225 to accentuate diverse architectural characteristics spanning from fine-grained textural elements to prominent reflective components. Four independent YOLO11 architectures were trained using original imagery alongside systematically enhanced variants, with optimal epoch preservation based on validation performance criteria. A key innovation lies in our intelligent selective ensemble mechanism that conducts exhaustive evaluation of model combinations, identifying optimal configurations through data-driven selection rather than conventional uniform weighting schemes. Experimental validation demonstrates substantial performance gains over established baseline architectures and traditional ensemble approaches, achieving exceptional metrics: 99.24% accuracy, 99.36% precision, 99.40% recall, and 99.36% F1-score. Rigorous statistical analysis via paired t-tests validates the significance of enhancement strategies, particularly demonstrating effectiveness of lower-threshold transformations in capturing architectural nuances. The framework exhibits remarkable resilience across challenging conditions including illumination variations, structural occlusions, and inter-class architectural similarities. These achievements establish the methodology’s substantial potential for practical smart tourism deployment, automated heritage preservation initiatives, and real-time mobile landmark recognition systems, contributing significantly to the advancement of intelligent tourism technologies. Full article

With the development of dynamic wireless power transfer (DWPT) technology, the introduction of wireless charging lanes (WCLs) in traffic systems is seen as a promising trend for electrified transportation. Though there has been extensive discussion about the planning and allocation of WCLs in different situations, studies on traffic control models for WCLs are relatively lacking. Thus, this paper aims to design a coordinated optimization strategy for managing electric vehicle (EV) traffic on an expressway network, which integrates a corridor traffic flow model with a wireless power transmission model. Two components are considered in the control objective: the total energy increased for the EVs and the total number of EVs served by the expressway, over the problem horizon. By setting the trade-off coefficients for these two objectives, our model can be used to achieve mixed optimization of WCL traffic management. The decisions include metering of different on-ramps as well as routing plans for different groups of EVs defined by origin/destination pairs and initial SOC levels. The control problem is formulated as a novel linear programming model, rendering an efficient solution. Numerical examples are used to verify the effectiveness of the proposed traffic control model. The results show that with the properly designed traffic management strategy, a notable increase in charging performance can be achieved by compromising slightly the traffic performance while maintaining overall smooth operation throughout the expressway system. Full article