Search Results (326)

Current research on evaluating tourism’s ecological efficiency using multi-source data fusion and graph neural networks has notable limitations. At the data level, integrating diverse sources is difficult due to differences in format, quality, and meaning. Data cleaning and preprocessing can lead to information loss, and relying on a single source often fails to reflect the complexity of tourism ecosystems. At the model level, traditional methods struggle to identify unreliable data and lack scientific rigor in handling expected and unexpected outcomes. These issues reduce the accuracy and practical value of evaluation results. This paper introduces a new method for assessing tourism’s ecological efficiency based on multi-source data fusion and graph neural networks. First, we integrate tourism statistics, environmental monitoring, and socio-economic data into a comprehensive dataset. Then, we apply a graph neural network (GNN) model to uncover hidden relationships and patterns, enabling a more accurate assessment of tourism’s environmental impact. The method also analyzes how tourism’s ecological efficiency varies across time and regions. We validate the method through case studies of representative tourist destinations and discuss its application in tourism planning. Regression analysis based on a single data source yields a 2020 tourism ecological efficiency score of 72. In contrast, using multi-source data fusion and GNN, the score rises to 85—an improvement of 13 points. This study offers a new approach to evaluating tourism’s ecological efficiency, enhances our understanding of tourism ecosystems, and supports sustainable tourism development. Full article

As environmental conservation and community development gain importance, ecotourism has emerged as a significant segment of the global tourism industry. However, the cultural factors that drive tourist behavior in this domain remain underexplored. This research examined how power distance belief (PDB), interacts with the type of tourism content shared on social media (conspicuous versus experiential) to influence travelers’ ecotourism intentions. To test our hypotheses, we conducted two experimental studies using a 2 (PDB: high vs. low) × 2 (tourism content type: conspicuous vs. experiential) between-subjects design. Participants for both experiments (N = 480) were recruited through an online survey platform. In the experiments, participants’ PDB was situationally primed, and tourism content type was manipulated using specifically created fictitious posts adapted from a real social media platform. Other key variables were measured using validated multi-item scales. Data were analyzed using analysis of variance (ANOVA) and moderated mediation analysis (PROCESS Model 15). The findings reveal that travelers with high PDB show higher ecotourism intentions when exposed to conspicuous content, whereas travelers with low PDB exhibit higher intentions when exposed to experiential content. This interactive effect is mediated by travelers’ social comparison motives. These findings offer novel insights into the motivations underlying ecotourism behavior by identifying distinct pathways through which social media can promote sustainable tourism behaviors, and provide practical guidance for eco-destination managers to design targeted marketing strategies that encourage sustainable tourism practices across different consumer segments. Full article

This study develops a multi-dimensional sustainable tourism optimization framework for Juneau, Alaska, integrating economic, social, and environmental dimensions to balance tourism-driven prosperity with ecological and socio-cultural integrity. Utilizing a hybrid Analytic Hierarchy Process and entropy weighting method, the model assigns robust indicator weights. Optimized via the NSGA-II algorithm, it identifies an optimal tourist threshold, achieved through a strategic tax adjustment. This policy not only sustains economic revenue at USD 325 million but also funds a critical feedback loop: revenue reinvestment into environmental conservation and social infrastructure, which stabilizes cost indices and enhances community well-being. The model’s projections show this approach significantly mitigates environmental degradation, notably glacier retreat, and alleviates social pressures such as infrastructure overload and resident dissatisfaction. A key contribution of this research is the framework’s adaptability, which was validated through its application to Barcelona, Spain. There, the framework was recalibrated with social indicators tailored to address urban overtourism, achieving substantial reductions in housing and congestion costs alongside environmental improvements, while economic recovery was maintained. Sensitivity analyses confirm the model’s stability, though data limitations and subjective weighting suggest future enhancements via real-time analytics and dynamic modeling. Key policy recommendations include dynamic tourist caps, diversified attractions, and community engagement platforms, offering scalable solutions for global tourism destinations. This framework advances sustainable tourism by providing a blueprint to decouple economic growth from ecological and social harm, ensuring the longevity of natural and cultural assets amidst climate challenges. Full article

Efficient task offloading for delay-sensitive applications, such as autonomous driving, presents a significant challenge in multi-hop Vehicular Edge Computing (VEC) networks, primarily due to high vehicle mobility, dynamic network topologies, and complex end-to-end congestion problems. To address these issues, this paper proposes a graph attention-based reinforcement learning algorithm, named GAPO. The algorithm models the dynamic VEC network as an attributed graph and utilizes a graph neural network (GNN) to learn a network state representation that captures the global topological structure and node contextual information. Building on this foundation, an attention-based Actor–Critic framework makes joint offloading decisions by intelligently selecting the optimal destination and collaboratively determining the ratios for offloading and resource allocation. A multi-objective reward function, designed to minimize task latency and to alleviate link congestion, guides the entire learning process. Comprehensive simulation experiments and ablation studies show that, compared to traditional heuristic algorithms and standard deep reinforcement learning methods, GAPO significantly reduces average task completion latency and substantially decreases backbone link congestion. In conclusion, by deeply integrating the state-aware capabilities of GNNs with the decision-making abilities of DRL, GAPO provides an efficient, adaptive, and congestion-aware solution to the resource management problems in dynamic VEC environments. Full article

Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies. Full article

Accurate and timely forecasting of cross-regional tourist flows is essential for sustainable destination management, yet existing models struggle with sparse data, complex spatiotemporal interactions, and limited interpretability. This paper presents SE-TFF, a multi-scale tourism-flow forecasting framework that couples a Squeeze-and-Excitation (SE) network with reinforcement-driven optimization to adaptively re-weight environmental, economic, and social features. A benchmark dataset of 17.8 million records from 64 countries and 743 cities (2016–2024) is compiled from the Open Travel Data repository in github (OPTD) for training and validation. SE-TFF introduces (i) a multi-channel SE module for fine-grained feature selection under heterogeneous conditions, (ii) a Top-K attention filter to preserve salient context in highly sparse matrices, and (iii) a Double-DQN layer that dynamically balances prediction objectives. Experimental results show SE-TFF attains 56.5% MAE and 65.6% RMSE reductions over the best baseline (ARIMAX) at 20% sparsity, with 0.92 × 10³ average MAE across multi-task outputs. SHAP analysis ranks climate anomalies, tourism revenue, and employment as dominant predictors. These gains demonstrate SE-TFF’s ability to deliver real-time, interpretable forecasts for data-limited destinations. Future work will incorporate real-time social media signals and larger multimodal datasets to enhance generalizability. Full article

Tourist satisfaction models typically assume that service performance dimensions carry the same weight for all travelers. Drawing on Bourdieu, we reconceptualize age, gender, and region of origin as demographic capital, durable resources that mediate how visitors decode service cues. Using a SERVPERF-based survey of 407 international travelers departing Quito (Ecuador), we test measurement invariance across six sociodemographic strata with multi-group confirmatory factor analysis. The four-factor SERVPERF core (Access, Lodging, Extra-hotel Services, Attractions) holds, yet partial metric invariance emerges: specific loadings flex with demographic capital. Gen-Z travelers penalize transport reliability and safety; female visitors reward cleanliness and empathy; and Latin American guests are the most critical of basic organization. These patterns expose a boundary condition for universalistic satisfaction models and elevate demographic capital from a descriptive tag to a structuring construct. Managerially, we translate the findings into segment-sensitive levers, visible security for youth and regional markets, gender-responsive facility upgrades, and dual eco-luxury versus digital-detox bundles for long-haul segments. By demonstrating when and how SERVPERF fractures across sociodemographic lines, this study intervenes in three theoretical conversations: (1) capital-based readings of consumption, (2) the search for boundary conditions in service-quality measurement, and (3) the shift from segmentation to capital-sensitive interpretation in emerging markets. The results position Ecuador as a critical case and provide a template for destinations facing similar performance–perception mismatches in the Global South. Full article

Nowadays, a pedestrian navigation system using a smartphone has become popular as a useful tool to reach an unknown destination. When the destination is the office of a person, a detailed map information is necessary on the target area such as the room number and location inside the building. The information can be collected from various sources including Google maps, websites for the building, and images of signs. In this paper, we propose a map information collection tool for a pedestrian navigation system. To improve the accuracy and completeness of information, it works with the four steps: (1) a user captures building and room images manually, (2) an OCR software using Google ML Kit v2 processes them to extract the sign information from images, (3) web scraping using Scrapy (v2.11.0) and crawling with Apache Nutch (v1.19) software collects additional details such as room numbers, facilities, and occupants from relevant websites, and (4) the collected data is stored in the database to be integrated with a pedestrian navigation system. For evaluations of the proposed tool, the map information was collected for 10 buildings at Okayama University, Japan, a representative environment combining complex indoor layouts (e.g., interconnected corridors, multi-floor facilities) and high pedestrian traffic, which are critical for testing real-world navigation challenges. The collected data is assessed in completeness and effectiveness. A university campus was selected as it presents a complex indoor and outdoor environment that can be ideal for testing pedestrian navigations in real-world scenarios. With the obtained map information, 10 users used the navigation system to successfully reach destinations. The System Usability Scale (SUS) results through a questionnaire confirms the high usability. Full article

Carbon transfer embodies the spatial redistribution of carbon emissions resulting from interregional economic activities and trade. In recent years, accelerated regional integration and deepening specialization within industrial chains have rendered traditional bilateral analytical frameworks inadequate for capturing the complexity of interregional carbon transfer networks. This evolving context necessitates the incorporation of spatial interaction effects to elucidate the multi-nodal and multi-pathway characteristics inherent in contemporary carbon transfer patterns. Based on the spatial interaction theoretical framework and a multiregional input–output (MRIO) model, we analyze the spatial dependence characteristics of interregional carbon transfer in China. The results reveal that interregional carbon transfer in China exhibited an upward trend from 2012 to 2017, demonstrating statistically significant positive origin dependence, destination dependence, and network dependence. The distance between regions exerts a significantly negative influence on interregional carbon transfer. Interregional carbon transfer is not merely a bilateral phenomenon; its fundamental nature is characterized as a network phenomenon. Our study demonstrates that precise regulation of the allocation of industrial land and transportation infrastructure land, strengthening the decisive role of market mechanisms in resource allocation for regional low-carbon development, and establishing interregional collaboration mechanisms for low-carbon exchange can effectively reduce the occurrence of interregional carbon transfer. These findings provide policymakers with more precise information to achieve equitable carbon emissions distribution across regions. Full article

The study explores the features of spatiotemporal circles of residents’ daily walking. Through a survey of residents’ walking activity in 16 residential communities, the walking purpose, distance, time, and speed of different residents were analyzed, and the circles of residents’ walking activities in historic and modern districts were identified. It is found that residents’ walking activities showed obvious spatiotemporal and individual differences. Walking activities on weekdays mainly focus on short distances (0.5–1 km) and short duration (5–15 min) for commuting and basic needs, while walking activities on weekends tend to be longer distances (more than 2 km) and longer duration (15–40 min) for leisure purposes. There are significant differences in distance and speed between walking activities in the historic and modern districts, with residents of the historic districts walking a smaller range but more diverse destinations, and residents of the modern districts walking to a wider range but fewer types of destinations. The study provides a scientific basis for multi-circle planning strategies of community life units, and it contributes to the localized adaptation of the “15-minute city” concept by revealing how historical and modern districts shape distinct spatiotemporal circles for walkability in Chinese cities. Full article

In the face of an unprecedented public health crisis (COVID-19), despite tourist perceptions toward cultural heritage tourism having undergone significant transformation, such transitions are increasingly viewed as opportunities to enhance sustainability practices in cultural heritage tourism worldwide. This study traces the evolution of tourist perceptions at Lijiang Old Town, a UNESCO World Heritage Site, across three stages from 2017 to 2024—before the pandemic, during the pandemic, and after the pandemic. Data were collected from major tourism platforms, yielding a comprehensive dataset of 50,022 user-generated reviews. We adopt a mixed-method framework integrating TF-IDF, Social Network Analysis (SNA), and Latent Dirichlet Allocation (LDA) to identify salient terms, semantic structures, and latent themes from large-scale unstructured textual data across time. The findings indicate that cultural heritage tourism demonstrates adaptability and resilience through significant perceptual transitions. After the pandemic, visitors increasingly prioritized cultural depth and high-quality service experiences, whereas before the pandemic, tourists focused more on cultural heritage attractions and commercial experiences. Moreover, during the pandemic period, visitor narratives reflected adaptations toward quieter, safer, and more personalized experiences, highlighting the impact of safety measures on tourism patterns. These findings demonstrate the methodological potential for dynamically monitoring perception shifts and offer empirical grounding for future perception-oriented research and sustainable cultural heritage destination management practices in cultural heritage tourism toward sustainable tourism. Full article

Modular integrated construction (MiC) is a cutting-edge approach to construction that significantly improves efficiency and reduces project timelines by prefabricating entire building modules off-site. Despite the operational benefits of MiC, the carbon footprint of its extensive supply chain remains understudied. This study develops a hybrid approach that combines multi-agent simulation (MAS) with deep learning to provide scenario-based estimations of CO₂ emissions, costs, and schedule performance for MiC supply chain. First, we build an MAS model of the MiC supply chain in AnyLogic, representing suppliers, the prefabrication plant, road transport fleets, and the destination site as autonomous agents. Each agent incorporates activity data and emission factors specific to the process. This enables us to translate each movement, including prefabricated components of construction deliveries, module transfers, and module assembly, into kilograms of CO₂ equivalent. We generate 23,000 scenarios for vehicle allocations using the multi-agent model and estimate three key performance indicators (KPIs): cumulative carbon footprint, logistics cost, and project completion time. Then, we train artificial neural network and statistical regression machine learning algorithms to captures the non-linear interactions between fleet allocation decisions and project outcomes. Once trained, the models are used to determine optimal fleet allocation strategies that minimize the carbon footprint, the completion time, and the total cost. The approach can be readily adapted to different MiC configurations and can be extended to include supply chain, production, and assembly disruptions. Full article

The present paper aims to analyze consumers’ attitudes to domestic leisure tourism, considering essential factors that affect consumer travel choices and experiences. The focus is on the attitudes to the main advantages and disadvantages of domestic leisure tourism in Bulgaria. The research is based on the outcomes of a nationally representative survey among 1003 respondents aged over 18. The results are interpreted using the Fishbein multi-attribute model. The results reveal that the customers’ attitudes toward the advantages of the Bulgarian winter (ski) and summer (sea) domestic leisure tourism are relatively higher compared to the midpoint of the interval and to their highest point. Although the score for summer domestic leisure tourism is slightly lower than that of winter tourism, Bulgarian consumers appear to be more dissatisfied with the advantages of summer (sea) domestic leisure tourism. The dissatisfaction is not only greater compared to the midpoint of the scale but also in comparison to the disadvantages associated with winter domestic leisure tourism. Overall, Bulgarian consumers are more attracted to the positive aspects of domestic winter leisure tourism and more concerned about the negative aspects of summer tourism. Full article

The rapid expansion of urban rail networks has resulted in increasingly complex passenger flow patterns, presenting significant challenges for operational management, especially during incidents and emergencies. Disruptions such as power equipment failures, trackside faults, and train malfunctions can severely impact transit efficiency and reliability, leading to congestion and cascading network effects. Existing models for predicting passenger origin–destination (OD) matrices struggle to provide accurate and timely predictions under these disrupted conditions. This study proposes a deep counterfactual inference model that improves both the prediction accuracy and interpretability of OD matrices during incidents. The model uses a dual-channel framework based on multi-task learning, where the factual channel predicts OD matrices under normal conditions and the counterfactual channel estimates OD matrices during incidents, enabling the quantification of the spatiotemporal impacts of disruptions. Our approach which incorporates KL divergence-based propensity matching enhances prediction accuracy by 4.761% to 12.982% compared to baseline models, while also providing interpretable insights into disruption mechanisms. The model reveals that incident types vary in delay magnitude, with power equipment incidents causing the largest delays, and shows that incidents have time-lag effects on OD flows, with immediate impacts on origin stations and progressively delayed effects on destination and neighboring stations. This research offers practical tools for urban rail transit operators to estimate incident-affected passenger volumes and implement more efficient emergency response strategies, advancing emergency response capabilities in smart transit systems. Full article

Tourism gentrification and lifestyle migration are reshaping both urban and rural destinations, yet no studies have examined how these trends might support regenerative tourism. This paper addresses a clear gap in the literature by being the first, to the authors’ knowledge, to explore their combined potential to contribute to regenerative outcomes. The research questions were structured using the PICOTS framework, and the review process followed the PRISMA 2020 protocol for transparency. A two-stage review design was used. First, a bibliometric analysis was conducted using Web of Science and Scopus data, applying co-occurrence mapping to identify thematic clusters. Second, an integrative literature review was performed to synthesise these findings and interpret them across spatial levels. Findings show that, while both gentrification and lifestyle migration can produce displacement and inequality, they also offer opportunities for regeneration when guided by inclusive governance, local participation, and value-based migration. The proposed multi-level framework explains how mobility-related transformations unfold at the individual, community, and policy levels. This study contributes to the field by introducing a multi-level framework that links fragmented debates, clarifies the conditions for regenerative transformation, and provides a structured approach for analysing tourism-driven socio-spatial change. Full article