Search Results (230)

Rural tourism is a significant driver of socio-economic development in rural areas. However, current offerings are often characterized by monotonous experiences, homogenized products, and a lack of cultural depth, failing to meet tourists’ growing demand for immersive engagement. While some scholars have adopted a spatial perception perspective, a visitor-centered approach remains scarce, with limited focus on methods for analyzing cultural perception. This study takes Dangjia Village, a renowned cultural tourism destination in Hancheng, China, as a case study. By scraping online reviews from travel and social platforms, we employ LDA topic modeling, textual semantic analysis, and IPA to investigate the characteristics and preferences of tourists’ cultural perception. The findings reveal that: (1) Tourists’ cultural perception of Dangjia Village includes three dimensions: History and Culture, Architecture and Culture, and Local Products and Culture. (2) Positive sentiments outweigh negative ones in tourist evaluations. (3) The History and Culture dimension received the highest levels of both attention and satisfaction. Architecture and Culture attracted the least attention but relatively high satisfaction, while Local Products and Culture garnered considerable attention yet the lowest satisfaction. Originating from a visitor perception perspective, this study explores cultural perception characteristics, providing insights for the high-quality utilization and optimization of cultural tourism resources in such villages. Full article

Background: Schedule reliability in container liner services is essential for the efficiency of maritime and inland transport, terminal operations, and the overall supply chain. Disruptions to vessel schedules can trigger a series of disruptions at other points, generating additional operational costs for carriers, terminal operators, inland transport providers, and ultimately, for importers, exporters, and end consumers. Methods: The research paper combines literature reviews and shipping company data. A qualitative analysis contains specific causes of vessel delays and corrective actions used to realign schedules with the pro forma plan. The analysis was expanded to include transport of cargo in containers from origin to the final inland destination. Results: Disruption factors are identified and classified by their place of occurrence: (1) inland transport, (2) anchorage, (3) ports, and (4) navigation between ports. The research produced several new disruptive factors previously not identified and published. It has been confirmed that port congestion acts as the principal cause of delay in liner service. Conclusions: The findings indicate that while the number and complexity of disruptive factors are increasing due to global and regional dynamics, the range of recovery measures remains narrow. A deeper understanding of these causes enables more effective prevention, aiming to minimize supply chain disruptions and costs and increase the reliability of door-to-door container transport. Full article

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

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

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

This study investigates the economic role of wine tourism in Nemea, Greece, a prominent Protected Designation of Origin (PDO) wine-producing region. Employing a mixed-methods approach, the research combines interviews with local stakeholders and a structured post-wine-tasting visitor survey to assess wine tourism’s contribution to local development. A two-step multivariate analysis, incorporating Multiple Correspondence Analysis and Hierarchical Cluster Analysis, reveals five distinct visitor profiles differing in spending behaviour, familiarity with the destination, and engagement patterns. While high-spending visitors support winery revenues, their limited local integration reduces their broader developmental impact. Conversely, younger and repeat domestic visitors offer more dispersed economic benefits through overnight stays, gastronomy, and cultural participation. In addition, local stakeholders highlight the region’s viticultural identity and growing tourism interest as strengths but also note persistent weaknesses such as inadequate infrastructure, limited coordination, and underdeveloped visitor services. The study concludes that visitor segmentation offers actionable insights for enhancing wine tourism’s developmental role. Targeted strategies tailored to specific visitor types are essential for improving integration with the local economy. These findings contribute to ongoing discussions on how wine tourism can act as a lever for inclusive, sustainable rural development in traditional wine regions. Full article

Vehicle kilometers traveled (VKT) is a critical metric in transportation and environmental research. However, conventional VKT estimation approaches frequently fail to capture the complexity of route selection and spatiotemporal dynamics of individual road users. This study presents a framework for accurately estimating the total VKT using high-resolution trajectory data obtained from a commercial navigation system. To address the structural limitations of conventional origin destination matrix-based models, such as the modifiable areal unit problem, representative routes were identified based on cumulative travel distance coverage. A novel metric, coverage of estimated travel (CET), was introduced to quantify the explanatory capacity of these routes in approximating total travel distance. Representative routes were selected to maximize CET, and the resulting VKT estimates were validated against national statistical yearbook data. Robustness was further evaluated using mean absolute percentage error, correlation analysis, paired t-tests, and bootstrap-based confidence intervals. The results indicated that as few as five representative routes accounted for over 80% of the total estimated VKT, exhibiting strong agreement with the national statistics after temporal adjustment. These findings demonstrate that trajectory data can serve as a practical alternative to traditional methods, offering higher spatial resolution and enabling dynamic traffic analyses that support transportation policy and environmental planning. Full article

Large language-model chatbots such as ChatGPT and DeepSeek are quickly gaining traction as an easy, first-stop tool for trip planning because they offer instant, conversational advice that once required sifting through multiple websites or guidebooks. Yet little is known about the biases that shape the destination suggestions these systems provide. This study conducts a controlled, persona-based audit of the two models, generating 6480 recommendations for 216 traveller profiles that vary by origin country, age, gender identity and trip theme. Six observable bias families (popularity, geographic, cultural, stereotype, demographic and reinforcement) are quantified using tourism rankings, Hofstede scores, a 150-term cliché lexicon and information-theoretic distance measures. Findings reveal measurable bias in every bias category. DeepSeek is more likely than ChatGPT to suggest off-list cities and recommends domestic travel more often, while both models still favour mainstream destinations. DeepSeek also points users toward culturally more distant destinations on all six Hofstede dimensions and employs a denser, superlative-heavy cliché register; ChatGPT shows wider lexical variety but remains strongly promotional. Demographic analysis uncovers moderate gender gaps and extreme divergence for non-binary personas, tempered by a “protective” tendency to guide non-binary travellers toward countries with higher LGBTQI acceptance. Reinforcement bias is minimal, with over 90 percent of follow-up suggestions being novel in both systems. These results confirm that unconstrained LLMs are not neutral filters but active amplifiers of structural imbalances. The paper proposes a public-interest re-ranking layer, hosted by a body such as UN Tourism, that balances exposure fairness, seasonality smoothing, low-carbon routing, cultural congruence, safety safeguards and stereotype penalties, transforming conversational AI from an opaque gatekeeper into a sustainability-oriented travel recommendation tool. Full article

The COVID-19 pandemic disrupted tourism systems worldwide, particularly ecologically sensitive and tourism-dependent regions such as the Galapagos Islands. This study investigated the impact of the pandemic on profiles of tourists visiting Santa Cruz Island by comparing an analysis from 2019 to data we gathered in 2021. Using survey-based data and cluster analysis, we identified significant shifts in tourist origin, travel modalities, and expenditure patterns. Results showed a marked increase in domestic tourism, with Ecuadorians becoming the dominant visitor group during the pandemic, primarily favoring land-based tourism and shorter stays. In contrast, international tourists remained present in niche, higher-spending segments associated with cruise-based and multi-island itineraries. These findings highlight a temporary yet meaningful transformation in the tourism dynamic, driven by changes in risk perception, economic factors, and policy restrictions. The emergence of these segments underscores the need for adaptive destination management strategies that align with sustainability goals, conservation priorities, and socioeconomic resilience. We also demonstrated the value of structured surveys as a cost-effective tool for evidence-based decision-making in resource-constrained settings. 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

During the last decades, there have been increasing concerns in public health debates regarding the production and consumption of red meat, considering connections between the occurrence of nutrition transition and an increase in the prevalence of chronic noncommunicable diseases. The consumption of red meat has been linked to adverse health outcomes; however, current evidence reveals controversies regarding the intake of diverse red meats. In addition, barriers to meat consumption include sanitary legislation linked to foodborne diseases connected to livestock, whilst governments of diverse countries provide incentives for its production and export worldwide. Thus, the objective of the present study was to investigate the evolution in the global trade of processed and unprocessed red meat from 1986 to 2023, using network analysis. Data on the trade of red meat between pairs of 216 countries were obtained from the Food and Agriculture Organization Database (FAOSTAT). The dataset, comprising the mean annual volume of processed and unprocessed red meat exchanged from reporting countries (origin) to partner countries (destination), was used to map global trade networks of red meats and identify global trends in red meat consumption according to country income level. The results indicate substantial intensification in the global trade of processed (0.202 in 1986 to 0.453 kg per capita in 2023) and unprocessed red meat (1.415 in 1986 to 3.315 Kg per capita in 2023). The volume of trade of unprocessed red meat remains greater than the volume processed red meat; yet, the findings indicate a threefold increase in the average weighted degree of processed red meat trade (0.002 to 0.006) from 1986 until 2023, whilst unprocessed red meat showed a twofold increase (0.009 to 0.019). The results raise public health concerns regarding the long-term consequences of consuming processed foods with high sodium and fat content. Additionally, the global trade of red meat showed fluctuations in periods of major foodborne outbreaks related to meat consumption, particularly during the 1990s. The findings of the study highlight strategies at the national level to advance food system transformations towards improvements in public health, nutrition, and sustainability. 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

This paper presents an initial contribution to a broader research initiative focused on the aggregate calibration of travel demand sub-models using low-cost and widely accessible data. Specifically, this first phase investigates methods and algorithms for the aggregate calibration of destination choice models, with the objective of assessing the possible utilization of an external observed matrix, eventually derived from opportunistic data. It can be hypothesized that such opportunistic data may originate from processed mobile phone data or result from the application of data fusion techniques that produce an estimated observed trip matrix. The calibration problem is formulated as a simulation-based optimization task and its implementation has been tested using a small-scale network, employing an agent-based model with a nested demand structure. A range of optimization algorithms is implemented and tested in a controlled experimental environment, and the effectiveness of various objective functions is also examined as a secondary task. Three optimization techniques are evaluated: Simultaneous Perturbation Stochastic Approximation (SPSA), Particle Swarm Optimization (PSO), and Adaptive Moment Estimation (ADAM). The application of the ADAM optimizer in this context represents a novel contribution. A comparative analysis highlights the strengths and limitations of each algorithm and identifies promising avenues for further investigation. The findings demonstrate the potential of the proposed framework to advance transportation modeling research and offer practical insights for enhancing transport simulation models, particularly in data-constrained settings. Full article

Historically, traditional transportation planning has promoted public policies focused on building and maintaining infrastructure for private cars to improve travel efficiency. This approach presents a significant challenge for cities in the Global South due to their unique socioeconomic conditions and urban development patterns. Dedicated public transport infrastructure can make better use of the road network by moving more people and reducing congestion. Beyond its environmental benefits, it also provides the population with greater accessibility, creating new development opportunities. This study uses Mexicali, Mexico, a medium-sized city with dispersed urban growth and a high dependence on cars, as a case study. The goal is to identify the relationship between the supply of public bus routes and actual work-related commuting patterns. The methodology considers that, given the scarcity of economic resources and prior studies in the Global South, using Geographic Information Systems (GIS) for the spatial analysis of travel is a key tool for redesigning more inclusive and sustainable public transport systems. Specifically, this study utilized origin–destination survey data from 14 urban areas to assess modal coverage, work-related commuting patterns, and the spatial distribution of employment centres. The findings reveal a marked misalignment between the existing public transport network and the population’s travel needs, particularly in marginalized areas. Users face long travel times, multiple transfers, low service frequency, and limited connectivity to key employment areas. This configuration reinforces an exclusionary urban structure, with negative impacts on equity, modal efficiency, and sustainability. The study concludes that GIS-based spatial analysis generates sufficient evidence to redesign the public transport system and reorient urban mobility policy toward sustainability and social inclusion. Full article

One practical approach to reduce GHG emissions is to shift from driving to modes with lower emissions, such as cycling. One key component of supporting cycling is the quality and quantity of cycling infrastructure. This study analyzes the relationship between the quality (or comfort) and quantity of bicycle infrastructure, the likelihood of cycling, and the emissions. The first objective of this study is to analyze the influence of various variables on cycling choice using an interpretable ensemble learning approach. Second, a scenario-based analysis is applied to examine the influence of various policy scenarios (related to cycling infrastructure) on the transportation life cycle GHG emissions. Using origin–destination survey data from Montreal and Laval, Canada, policy modelling results suggest that without current cycling infrastructure, cycling mode share would be 5.3% less, driving mode share would be 4% higher, and GHG emissions would be 10.2% higher among all trips of a reasonable cycling distance starting from home. Then, policy scenarios modelling for this subset of trips suggests that improving the quality of bikeways, increasing their quantity, and reducing the trip distances by 25% can reduce the GHG emissions by 3.9%, 6.6%, and 29.3%, and increase the number of cycling trips by 8.1%, 14%, and 24.4%, respectively. Full article