Search Results (50)

The Camino de Santiago, a UNESCO-listed pilgrimage route, has experienced sustained growth in visitor numbers, challenging municipalities to preserve cultural integrity while ensuring service quality. This study reviews people-counting technologies and proposes a smart pilgrim management framework grounded in flux measurement systems to support data-driven and sustainable decision-making. Drawing on the smart tourism literature, the conceptual framework integrates infrared counters, mobile tracking solutions, and GPS/Wi-Fi data to generate real-time insights into pilgrim flows. A pilot simulation illustrates how these data can inform operational and strategic planning. The framework enables local authorities to monitor pedestrian movements, anticipate service demands (sanitation, accommodation, and safety), and detect overcrowding in sensitive heritage areas. By incorporating technological solutions into traditionally low-tech pilgrimage settings, municipalities can transition from reactive to proactive management approaches. The paper contributes a scalable and ethically grounded framework tailored to heritage pilgrimage routes, advancing smart tourism applications in culturally significant contexts. Full article

Jujube (Ziziphus mauritiana Lam.) is a nutritionally rich tropical fruit which is native to South Asia. In Pakistan, there are 50 varieties of jujube, and approximately 24,000 tons of jujube is harvested annually from a growing area of nearly 5000 hectares. The current study was conducted to investigate whether insect pollination affects fruit yield, and whether it influences the physical and biochemical properties of the fruit. We selected fifty inflorescences from each of the three jujube cultivars (Kheri, Desi, Ayuba) and covered them with muslin cloth to promote self-pollination. The same number was tagged to observe the impact of open pollination. Fruits from both treatments were counted on each panicle, harvested, and then subjected to lab investigation for measuring physical (fruit length, fruit width, fruit weight, and pulp weight) and biochemical (total soluble solids, titratable acidity, vitamin C, and pH) parameters. A diverse range of floral visitors, including bees, flies, wasps, moths, and butterflies, were observed on jujube, with bees being the most abundant functional group. Open pollination increased fruit set (fruits per inflorescence) by 2.3–2.5 times compared to self-pollination. Similar trends were observed in physical parameters: fruit length (1.15 to 1.30 times increase), fruit width (1.15 to 1.21 times), total weight (1.60 to 1.74 times), and pulp weight (1.55 to 1.62 times). Changes in physiological parameters, including pH, vitamin C, titratable acidity, and total soluble solids, were also observed in pollination treatments of all three varieties. Open-pollinated fruit also exhibited greater length and width, but lower firmness. These findings show the vital role of insect-mediated pollination in enhancing both the yield and quality of jujube fruit, with consistent benefits observed across multiple varieties. Full article

Artificial intelligence (AI) and big data technology were applied to assess carbon emissions in a high-tourism area in this study. In the study site, the Thalang Road in Phuket Old Town, Thailand, visitors and vehicles (including cars, motorcycles, trucks, vans, and Tuktuks) were counted using closed-circuit television (CCTV) footage and classified via the real-time detection transformer (RT-DETR) algorithm. The data were combined with records of electricity usage. From March to October 2024, 20,000 visitors per month visited the site. Electricity was the main source of carbon emissions, averaging 88 ± 11 tCO₂-eq monthly. Transport accounted for 500 ± 14 kg CO₂-eq. The average emission per visitor was calculated as 4.2 ± 0.4 kg CO₂-eq. The results showed how sustainable tourism policies and urban planning strategies need to be developed in Phuket. Based on the results, indirect emissions from the site need to be estimated. Full article

Predicting forest trail visitation is essential for sustainable management and policy development, including infrastructure planning, safety operations, and conservation. However, due to numerous informal access points and complex external influences, accurately monitoring visitor numbers remains challenging. This study applied random forest, gradient boosting, and LightGBM models with Bayesian optimization to predict daily visitor counts across six sections of the National Daegwallyeong Forest Trail, incorporating variables such as weather conditions, social media activity, COVID-19 case counts, tollgate traffic volume, and local festivals. SHAP analysis revealed that tollgate traffic volume and weekends consistently increased visitation across all sections. The impact of temperature varied by section: higher temperatures increased visitation in Kukmin Forest, whereas lower temperatures were associated with higher visitation at Seonjaryeong Peak. COVID-19 cases demonstrated negative effects across all sections. By integrating diverse variables and conducting section-level analysis, this study identified detailed visitation patterns and provided a practical basis for adaptive, section- and season-specific management strategies. These findings support flexible measures such as seasonal staffing, congestion mitigation, and real-time response systems and contribute to the advancement of data-driven regional tourism management frameworks in the context of evolving nature-based tourism demand. Full article

In this study, we employ the travel cost method to assess the viability of agro-ecotourism in Yuanshan Township, located within the fragile Lanyang River watershed. The local development of agro-ecotourism is the emerging measure that meets forest conservation and soil and water management in steep slopes and viable adaptation strategies in response to the urgent challenges posed by climate change. However, for long-term success, the self-sufficiency and livelihoods of local communities must be prioritized, especially since these communities heavily depend on local resources for their livelihoods. The study site demonstrates the economic success of agro-ecotourism, which allows visitors to utilize local resources and is crucial for boosting the local economy, by applying the travel cost method. This study evaluates the economic value of local natural resources, highlighting that visitor expenditure reflects both recreational resource use and local adaptation measures. The findings indicate significant estimated recreational resource values of TWD 15,748.03 by the negative binomial count data model (1 USD = 31.15 TWD). The results suggest that sustainable local agro-ecotourism measures, balancing conservation and to adapting climate changes, can enhance the economy while supporting residents’ livelihoods in Yuanshan Township. Full article

In urban environments, eating and drinking out (EDO) is a widespread activity among residents and visitors, generating a wealth of digital footprints that reflect consumer experiences. These digital traces provide businesses with opportunities to enhance their services and guide entrepreneurs in selecting optimal locations for new establishments. This study investigates the relationship among urban spatial features, pedestrians and digital consumer interactions at EDO venues. It highlights the utility of integrating urban mobility and spatial data to model digital consumer behavior, offering potential urban planning and business strategies. By analyzing Melbourne’s city center, we evaluate how factors, such as pedestrian count by sensors on the streets, residential density, the centralities and geometric properties of streets, and place-specific characteristics, influence consumer reviews and ratings on Google Maps. The study employs a random forest machine learning model to predict review volumes and ratings, categorized into high and low classes. The results indicate that pedestrian counts and residential density are key predictors for both metrics, while centrality measures improve the prediction of visitor scores but negatively impact review volume predictions. The geometric features of streets play varying roles across different prediction tasks. The model achieved a 65% F1-score for review volume classifications and a 62% for visitor score. These findings not only provide actionable understanding for urban planners and business stakeholders but also contribute to a deeper understanding of how spatial dynamics affect digital consumer behavior, paving the way for more sustainable urban development and data-driven decision-making. Full article

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)virus, resulted in significant global health consequences. The interventions introduced to limit the spread of the virus had a significant impact on the course of the pandemic, but they might also involve other pathogens besides SARS-CoV-2. This study investigates the epidemiological variations for major bacterial and common fungal pathogens represented by Candida species infection in Qatar through retrospective hospital records. Retrospective data were collected on all bacterial and Candida infections from Hamad General Hospital between January 2019 and December 2021. Statistical analyses were performed using R (Version 4.1.0). Infection count comparisons were performed using chi-square goodness of fit tests. Differences in patient numbers were tested with one-way ANOVA followed by post hoc Tukey HSD. The number of infections and measures were correlated using Spearman rank correlation. The significance level was set at 0.05, and where applicable, the Holm–Bonferroni method was used to correct for multiple testing. The general trend for bacterial and Candida infections showed a significant decrease (p < 0.05) following the imposed restrictions and increases during restriction lifting periods. The number of bacterial infections correlated with the number of international visitors (ρ = 0.61, p < 0.01), while the number of Candida infections correlated with hospital admissions of COVID-19 patients (ρ = 0.59, p = 0.013). The results provide a general overview of the infection trends in Qatar during the COVID-19 pandemic and illustrate some of the pandemic interventions’ effects on bacterial and Candida infections. However, further research is needed to better understand the underlying trend dynamics. Full article

The early detection of the acute deterioration of escalating illness severity is crucial for effective patient management and can significantly impact patient outcomes. Ambient sensing technology, such as computer vision, may provide real-time information that could impact early recognition and response. This study aimed to develop a computer vision model to quantify the number and type (clinician vs. visitor) of people in an intensive care unit (ICU) room, study the trajectory of their movement, and preliminarily explore its relationship with delirium as a marker of illness severity. To quantify the number of people present, we implemented a counting-by-detection supervised strategy using images from ICU rooms. This was accomplished through developing three methods: single-frame, multi-frame, and tracking-to-count. We then explored how the type of person and distribution in the room corresponded to the presence of delirium. Our designed pipeline was tested with a different set of detection models. We report model performance statistics and preliminary insights into the relationship between the number and type of persons in the ICU room and delirium. We evaluated our method and compared it with other approaches, including density estimation, counting by detection, regression methods, and their adaptability to ICU environments. Full article

Ocean beaches are a major recreational attraction in many coastal cities, requiring accurate visitor counts for infrastructure planning and value estimation. We developed a novel method to assess beach usage on the Gold Coast, Australia, using 507 drone surveys across 24 beaches. The surveys covered 30 km of coastline, accounting for different seasons, times of day, and environmental conditions. Two AI models were employed: one for counting people on land and in water (91–95% accuracy), and another for identifying usage types (85–92% accuracy). Using drone data, we estimated annual beach usage at 34 million people in 2022/23, with 55% on land and 45% in water—approximately double the most recent estimate from lifeguard counts, which are spatially limited and prone to human error. When applying similar restrictions as lifeguard surveys, drone data estimated 15 million visits, aligning closely with lifeguard counts (within 9%). Temporal (time of day, day of the week, season) and spatial (beach location) factors were the strongest predictors of beach usage, with additional patterns explained by weather variables. Our method, combining drones with AI, enhances the coverage, accuracy, and granularity of beach monitoring, offering a scalable, cost-effective solution for long-term usage assessment. Full article

In the context of smart campuses, effective emergency management is crucial for ensuring the safety and well-being of students, staff, and visitors. This paper presents a comprehensive support tool designed to enhance emergency management on smart campuses, integrating a low-cost people-counting system based on cameras and Raspberry Pi devices. It introduces a newly designed architecture and user interfaces that enhance the functionality and user experience of a smart campus disaster management system. Finally, a usability evaluation has been carried out to validate the brand-new user interfaces devoted to emergency management. Full article

Shopping malls have become vibrant public spaces, serving as commercial centers and sociocultural hubs. However, the arrangement of stationary elements such as elevators and escalators significantly impacts the visual quality of the atrium and the overall navigation experience within the complex. This research focuses on analyzing the configuration of elevators and escalators in shopping mall atriums and their influence on visual richness and accessibility. Descriptive-analytical and survey methods are employed, utilizing data from 10 successful malls worldwide. The UCL-Depth map software and space syntax variables are used for analysis. Connectivity, clustering-coefficient, and controllability analyses assess visual richness, while integration, mean-depth, entropy, depth, step-depth, and gate-count analyses evaluate accessibility. The research includes a questionnaire to obtain optimal indices for each space syntax variable, enhancing the accuracy of the findings. The results highlight the significant impact of the positioning of vertical access elements within the atrium on its visual richness and the accessibility of shops. The research identifies an optimal configuration: placing the escalator in the middle of the atrium, deviating 30° from the entrance axis, and separately locating the elevators. This configuration provides the highest level of access to shops and the central atrium from any point, minimizing the number of turns required to reach different locations within the mall. Furthermore, the separate placement of elevators improves the flow of individuals between shops and the atrium, resulting in increased integration. In conclusion, selecting an appropriate configuration of elevators and escalators in shopping mall atriums can greatly enhance wayfinding and improve the visual richness and accessibility of the complex. Architects and designers can utilize these findings to optimize the design of public spaces within shopping centers, promoting social interactions and enhancing the overall visitor experience. Full article

Gardens not only provide people with a place for leisure and relaxation, they also contribute to improving urban ecological environments and promoting social interactions and cohesion. Additionally, from a psychological perspective, gardens play a role in alleviating stress, enhancing happiness, and improving the quality of life. Current research on gardens has primarily employed methods such as questionnaire surveys, environmental psychology analyses, and eye-tracking analyses; however, comprehensive studies on the relationships between multiple factors and levels in garden designs are lacking. Here, we propose a response surface analysis approach based on eye-tracking technology for the design and optimization of gardens. Firstly, the impacts of different garden elements on visitors’ psychology and fixation counts were analyzed using environmental psychology and eye-tracking analyses. Subsequently, the optimal range of each garden feature was determined through single-factor experiments, followed by response surface analysis to obtain the optimal value for each element. The results revealed that changes in garden elements such as the greenery ratio, number of buildings, and water saturation significantly affected visitors’ psychology. The greenery ratio had a greater impact than the number of buildings, which in turn had a greater impact than water saturation. This study is the first to analyze the relationships between multiple garden elements. A strong relationship was found between the greenery ratio and the number of buildings, as well as between the number of buildings and water saturation, while the relationship between the greenery ratio and water saturation was weaker. This approach can not only optimize garden designs but can also be widely applied in fields such as urban planning and public space transformation to enhance visitors’ comfort and satisfaction with the environment and promote sustainable urban development. Full article

This study investigates the relationship between eye-tracking metrics and emotional experiences in the context of cultural landscapes and tourism-related visual stimuli. Fifty-three participants were involved in two experiments: forty-three in the data collection phase and ten in the model validation phase. Eye movements were recorded and the data were analyzed to identify correlations between four eye-tracking metrics—average number of saccades (ANS), total dwell fixation (TDF), fixation count (FC), and average pupil dilation (APD)—and 19 distinct emotional experiences, which were subsequently grouped into three categories: positive, neutral, and negative. The study examined the variations in eye-tracking metrics across architectural, historic, economic, and life landscapes, as well as the three primary phases of a tour: entry, core, and departure. Findings revealed that architectural and historic landscapes demanded higher levels of visual and cognitive engagement, especially during the core phase. Stepwise regression analysis identified four key eye-tracking predictors for emotional experiences, enabling the development of a prediction model. This research underscores the effectiveness of eye-tracking technology in capturing and predicting emotional responses to different landscape types, offering valuable insights for optimizing rural tourism environments and enhancing visitors’ emotional experiences. Full article

This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design’s innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis’ residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy. Full article

This study presents the creation of a virtual reality experience for the Museo Preistorico dei Balzi Rossi e Zona Archeologica (hence Balzi Rossi Museum) commemorating the centenary of Prince Albert I Grimaldi’s archaeological work at the site. The project aims to preserve and convey the site’s heritage through advanced VR technology. Photogrammetry was used for 3D reconstruction of the entire Balzi Rossi coastal cliffs, including the notable “Caviglione” and “Florestano” caves, known for their upper Paleolithic rock engravings. Two subsequent development phases produced the final public VR experience, incorporating Nanite technology for enhanced visual fidelity. This advancement resulted in a more detailed and immersive VR experience, presenting the Balzi Rossi cliffs across different historical periods, including the Würm glaciation. Key to this phase was optimizing the VR experience for performance, focusing on stable frame rates and minimizing motion sickness, and integrating a multi-lingual interface for broader accessibility. Since November 2023, the VR setup at Balzi Rossi Museum has been an educational and interactive feature enabling visitors to virtually explore the site’s history. This study aims to describe a process for optimizing and enabling the creation of VR experiences while maintaining a high polygon count within the context of small teams. Full article