Search Results (3,281)

Rainfall-induced cut-slope failures are one of the main causes of traffic disruptions in road networks, consuming 30–50% of annual road maintenance budgets. Therefore, it is crucial to analyze how traffic disruptions, resulting from cut-slope failures, impact the overall operation of road networks. In addition, as climate change alters the precipitation patterns, the frequency of these phenomena is expected to increase. For these reasons, it is essential to develop a methodology, from a risk perspective, to understand and assess how cut-slope failures impact the normal operation of road networks. This article introduces a methodology to assess the risk of traffic disruption caused by rainfall-induced cut-slope failure, in terms of Origin–Destination travel time increases. The methodology comprises three stages: (1) modeling the rainfall hazard, (2) estimating the road network’s vulnerability to slope instability, and (3) quantifying risk through resulting travel time increases. A case study was performed on a road network highly vulnerable to cut-slope failure in the Biobío Region of southern Chile. The analysis using the GIS-based software revealed that rainfalls lasting more than 12 h increase average travel times by 20%, with maximum increases of about 40% for 24 h rainfalls, affecting travel between the main cities in the Biobio region and the Concepción metropolitan area. These results may be critical for decision-makers to identify highly exposed and vulnerable road sections in order to recommend effective mitigation strategies to reduce the risk of cut slope failures. Full article

This study aims to examine the determinants of tourist continuance intention in halal tourism in Indonesia and extend the Theory of Planned Behavior (TPB) model by incorporating sustainable tourist citizenship behavior (STCB) and tourists’ emotional events and past halal experiences to provide a rounded understanding of Muslim tourists’ revisit intentions. This quantitative study employed partial least squares structural equation modeling (PLS-SEM) to analyze the data collected from 500 Muslim tourists who visited halal destinations in West Sumatra. The findings reveal that their STCB, attitudes, subjective norms, and perceived behavioral control significantly influence their continuance intention. Moreover, the empirical findings indicate that tourists’ emotional events and past halal experiences positively affect the TPB constructs, further strengthening the behavioral outcomes. All the proposed hypotheses were supported by the model and highlight the critical roles of psychological, experiential, and behavioral factors in shaping tourist loyalty. The results of this study contribute to the theoretical advancement of halal tourism behavior and offer practical suggestions for destination management to enhance sustainable engagement and repeat visitation among Muslim travelers. Full article

This study investigates how multidimensional value and experience quality shape satisfaction and loyalty in shopping tourism. We extend the QVSL tradition by (i) specifying three hedonic value dimensions (entertainment, exploration, escapism), (ii) differentiating functional value into performance-oriented and money-saving facets, and (iii) incorporating epistemic value and experience quality as additional antecedents. We also model immediate behavioral outcomes (i.e., money spent and time spent) and test involvement as a moderating condition. Using path analysis on data from 413 mainland Chinese tourists in Japan, findings confirm that entertainment, functional value (for performance and money), epistemic value, and experience quality enhance shopping satisfaction. Functional values, epistemic value, and satisfaction drive destination loyalty. Money and time spent are additional outcomes of satisfaction. Involvement moderates the link between satisfaction and money spent. These insights offer strategic implications for Destination Marketing Organizations (DMOs) and retailers to optimize shopping environments and employee services, increasing tourist satisfaction, loyalty, and both time and money spent in the competitive shopping tourism market. Limitations include the cross-sectional design and the use of composite-indicator path analysis; future research could apply longitudinal or full SEM approaches, broaden contexts, and test additional constructs. Full article

Active transport (AT) offers an effective and sustainable strategy to address physical inactivity, reduce traffic congestion, and mitigate environmental challenges. However, participation in AT among young adults (YA) aged 18–25 remains low, leading to public health issues. This review synthesises evidence on how traffic stress (TS), built environment (BE) features, and socioecological factors interact to shape AT behaviour among YA, a relationship that remains insufficiently understood. We systematically reviewed 173 peer-reviewed studies (2015–2025) from Web of Science (WoS), PubMed, and Scopus, following the PRISMA 2020 guidelines. Thematic analysis, bibliometric mapping, and meta-synthesis informed the impact of TS, the Level of Traffic Stress (LTS), the 5Ds of BE, and the Socioecological Model (SEM) on AT among YA. The findings show that high TS, characterised by unsafe road conditions, high-speed motor traffic, and inadequate walking or cycling facilities, consistently reduces AT participation. In contrast, supportive BE features, including street connectivity, land-use diversity, and destination accessibility, increase AT by reducing TS while enhancing safety and comfort. Socioecological factors, including self-efficacy, social norms, and peer support, further mediate these effects. This review introduces two novel metrics: Daily Traffic Stress (DTS), a time-sensitive measure of cumulative daily TS exposure, and the Stress-to-Step Ratio (SSR), a step-based index that standardises how stress exposures translate into AT behaviour. By integrating environmental and psychosocial domains, it offers a theoretical contribution as well as a practical foundation for targeted, multilevel policies to increase AT among YA and foster healthier, more equitable urban mobility. Full article

Traditional passenger transport corridor identification methods fail to effectively capture the spatiotemporal dynamic characteristics of passenger flows in complex urban rail transit networks. This study proposes a novel passenger transport corridor identification method based on Origin–Destination (OD) clustering. The method enables more accurate identification of passenger groups with similar travel patterns and distributions through a customized clustering similarity function; simultaneously, it can obtain OD pairs with actual physical significance through OD clustering as the source of basic units for identifying passenger transport corridors. By analyzing the spatial distribution of passenger transport corridor constituent units (clustered ODs), the method determines whether the passenger transport corridor is a cross-line corridor. The method is validated using Beijing’s urban rail transit system as a case study, employing the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm with optimal parameters (eps = 0.46, minpts = 980), identifying 21 clusters and ultimately determining six passenger transport corridors, including four cross-line and two non-cross-line types. Furthermore, this study conducted sensitivity analysis on the eps parameter using 80 test configurations to examine its impact on clustering effectiveness metrics, validating the method’s stability. The results demonstrate that the identified corridors exhibit high passenger flow concentration characteristics and accurately reflect passengers’ transfer demands between different lines. This research provides a theoretical foundation for integrated public transportation connectivity and supports sustainable urban development through improved operational efficiency and reduced operational costs. Full article

Contemporary digital technologies have become key instruments in enhancing the security and sustainability of tourist destinations. This study explores the role of digital solutions such as smart surveillance systems, mobile applications, artificial intelligence, and the Internet of Things (IoT) in strengthening tourist safety and supporting long-term sustainable development. The theoretical framework is based on the Norm Activation Model (NAM), employing the constructs of Awareness of Consequences, Ascription of Responsibility, Personal Norms, and Behavioral Intention, expanded by the construct of Sustainability Outcomes. This research was conducted as a comparative case study of Almaty (Kazakhstan) and Belgrade (Serbia), using a structured questionnaire and quantitative analysis. The findings indicate that tourists’ perceptions of security, mediated by digital technologies, significantly shape their behavioural intentions and contribute to sustainable destination outcomes. The study provides theoretical implications for the advancement of the NAM in tourism, as well as practical guidelines for destination managers in developing a safe and sustainable environment. Full article

Tourism demand forecasting has evolved into a wide variety of models, including time-series models that incorporate economic, environmental, and behavioral factors. Macao, one of the world’s most profitable gaming destinations, finds that gaming revenue is highly related to tourist arrivals. A forecast model for gaming tourism is essential for accurately predicting tourist arrivals. The challenge with ARIMA-type models is optimizing parameter selection in order to improve the accuracy of tourism demand forecasts. In this study, an enhanced version of SARIMAX, called SARIMAX-E, was developed to identify the most effective parameter combinations. By integrating data related to gaming revenue, weather, transportation, currency exchange rate, holidays, and seasonality into a single forecast model, this study examined the performance of different forecasting models, including the proposed SARIMAX-E model; ARIMA-type models (ARIMA, SARIMA, ARIMAX); and machine learning models (Transformer, LTSM, Random Forests, XGBoost). The results showed that the ARIMA family of models, including SARIMAX-E, ARIMAX, and SARIMA, was particularly well suited to tourism demand forecasting, as its members consistently ranked among the top performers in terms of error metrics. By applying multi-step predictions, LSTM outperforms most conventional approaches. Compared with all other models, the SARIMAX-E performed the best after applying the additional parameter grid. Full article

This study explores tourist motivations through a mixed-methods approach, combining qualitative coding of open-ended responses with quantitative network analysis. By examining why vacationing is important, we identified eight motivation categories including Physical & Mental Renewal, Social Bonding, and Novelty & Adventure. Network analysis revealed significant co-occurrence patterns between motivations, challenging traditional push–pull frameworks by demonstrating that travelers simultaneously hold multiple, sometimes paradoxical desires. Demographic comparisons showed that women emphasize relaxation and rejuvenation, while men prioritize novelty and exploration. Age-related differences revealed younger travelers seek adventure and personal growth, while middle-aged participants valued family time and relaxation. Our findings demonstrate how tourist motivations function as interconnected constellations rather than isolated factors. By highlighting tensions such as comfort versus sustainability, digital detox versus connectivity, and novelty versus familiarity, the study illustrates how motivational paradoxes can inform destination management strategies. These results offer practical guidance for DMOs, particularly in contexts of overtourism where repositioning is needed, and for new destinations seeking to differentiate themselves in a competitive global market. Framing motivations within these broader transformations—post-pandemic regeneration, sustainability debates, and digital lifestyle shifts—enhances the relevance of our contribution to both scholarship and practice. Full article

Tourist reviews provide essential insights into travellers experiences and public perceptions of destinations. In Kazakhstan, however, sentiment analysis, particularly using ensemble learning, remains underexplored for evaluating such reviews. This study proposes a hybrid stacking ensemble for sentiment analysis of English-language tourist reviews about Kazakhstan, integrating four complementary approaches: VADER, TextBlob, Stanza, and Local Context Focus Mechanism with Bidirectional Encoder Representations from Transformers (LCF-BERT). Each model contributes distinct analytical capabilities, including lexicon-based polarity detection, rule-based subjectivity evaluation, generalised star-rating estimation, and contextual aspect-oriented sentiment classification. The evaluation utilised a cleaned dataset of 11,454 TripAdvisor reviews collected between February 2022 and June 2025. The ensemble aggregates model outputs through majority and weighted voting strategies to enhance robustness. Experimental results (accuracy 0.891, precision 0.838, recall 0.891, and F1-score 0.852) demonstrate that the proposed method KazSATR outperforms individual models in overall classification accuracy and exhibits superior capacity for aspect-level sentiment detection. These findings underscore the potential of the hybrid ensemble as a practical and scalable tool for the tourism sector in Kazakhstan. By leveraging multiple analytical paradigms, the model enables tourism professionals and policymakers to better understand traveller preferences, identify service strengths and weaknesses, and inform strategic decision-making. The proposed approach contributes to advancing sentiment analysis applications in tourism research, particularly in underrepresented geographic contexts. Full article

The welfare of farmed hybrid striped bass remains largely unaddressed in U.S. aquaculture, despite the species’ economic significance and the scale of production. Physical handling during grading and inspection not only causes stress and increased incidence of injury, but also results in unmarketable fish and significant financial loss for producers. To address these issues, we present a prototype system that uses directed water currents to leverage the fish’s natural rheotactic behavior, enabling directed movement between tank regions without the need for direct physical contact. Our design allows for early identification of malformed individuals, who otherwise face prolonged suffering and starvation, so they can be humanely culled. In a small pilot study, we observed that fish moved into the destination region more frequently and with less behavioral variability when exposed to a directed current, suggesting this method as a viable alternative to traditional handling. While the system requires further refinement and testing at scale, these preliminary results offer a promising step toward ethical, commercially viable, and low-stress fish sorting systems in commercial aquaculture. Full article

Adventure tourism has established itself as a growing sector that integrates physical activity, interaction with nature, and cultural exchange. Understanding the heterogeneity of demand is crucial for designing effective and sustainable destination management strategies. Despite the global growth of adventure tourism, there is a scarcity of empirical studies analyzing the motivations, segmentation, and loyalty of tourists in emerging coastal destinations. This study contributes to filling this gap by providing evidence from the case of Santa Elena, Ecuador. This study examines the motivations, market segmentation, and loyalty of adventure tourists in Santa Elena, an emerging coastal destination in Ecuador. Based on a survey of 318 visitors and using exploratory factor analysis (EFA) and k-means cluster segmentation, five motivational dimensions were identified: learning, social, biosecurity, relaxation, and competence-mastery. The results revealed two distinct segments: (i) Relaxation seekers, primarily motivated by rest and stress reduction, and (ii) multi-motivation tourists, with high levels of motivation across all dimensions. This latter group showed greater loyalty, evidenced by the intention to return, recommend, and spread a positive image of the destination. The study contributes to academic knowledge by proposing a motivation-based segmentation model that integrates emerging dimensions such as biosecurity and offers practical implications for the sustainable management of adventure destinations. It recommends designing differentiated tourism products that cater to dominant motivations, thereby strengthening competitiveness and contributing to the sustainability of tourism in emerging contexts. Full article

Artificial intelligence (AI) is profoundly transforming heritage tourism through the incorporation of technological solutions that reconfigure the ways in which cultural heritage is conserved, interpreted, and experienced. This article presents a critical and systematic review of current AI applications in this field, with a special focus on their impact on destination management, the personalization of tourist experiences, universal accessibility, and the preservation of both tangible and intangible assets. Based on an analysis of the scientific literature and international use cases, key technologies such as machine learning, computer vision, generative models, and recommendation systems are identified. These tools enable everything from the virtual reconstruction of historical sites to the development of intelligent cultural assistants and adaptive tours, improving the visitor experience and promoting inclusion. This study also examines the main ethical, technical, and epistemological challenges associated with this transformation, including algorithmic surveillance, data protection, interoperability between platforms, the digital divide, and the reconfiguration of heritage knowledge production processes. In conclusion, this study argues that AI, when implemented in accordance with principles of responsibility, sustainability, and cultural sensitivity, can serve as a strategic instrument for ensuring the accessibility, representativeness, and social relevance of cultural heritage in the digital age. However, its effective integration necessitates the development of sector-specific ethical frameworks, inclusive governance models, and sustainable technological implementation strategies that promote equity, community participation, and long-term viability. Furthermore, this article highlights the need for empirical research to assess the actual impact of these technologies and for the creation of indicators to evaluate their effectiveness, fairness, and contribution to the Sustainable Development Goals. Full article

This study focuses on the nonlinear departure dynamics of spacecraft from the Near Rectilinear Halo Orbit (NRHO) to the outer regions of Selenocentric Space. By carefully selecting the combination of orbital parameters and the order of the evaluation process, it becomes possible to precisely identify the divergence moment and to reliably classify the subsequent dynamical space. An empirical divergence detection algorithm is proposed by integrating multiple parameters derived from multi-body dynamical models, including gravitational potentials and related quantities. In an applied analysis using this method, it is found that the majority of perturbed trajectories diverge into the outer Earth–Moon Vicinity, while transfers into the inner Earth–Moon Vicinity are relatively limited. Furthermore, transfers to Heliocentric Space are found to be dependent not on the magnitude of the initial perturbation but on the geometric configuration of the Sun, Earth, and Moon during the transfer phase. The investigation of the Sun’s initial phase reveals a rotationally symmetric structure in the perturbation distribution within the Sun–Earth–Moon system, as well as localized conditions under which the destination space varies significantly depending on the initial state. Identifying the divergence moment allows for comparative evaluation of the spacecraft’s nonlinear dynamical state, providing valuable insights for the development of safe and efficient transfer strategies from selenocentric orbits, including those originating from the NRHO. Full article

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article

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