Search Results (43)

This study systematically reviews the application of virtual reality (VR) in building evacuation scenarios in disaster contexts, highlighting its transformative potential to enhance preparedness, evacuation strategies, and safety training. Disasters such as fires, earthquakes, and multi-hazard emergencies pose significant challenges in densely populated urban environments, requiring innovative solutions beyond traditional methods. Analyzing 48 peer-reviewed studies (2014–2024) following PRISMA guidelines, this review focuses on VR applications in public buildings, transportation hubs, and high-risk workplaces, with VR simulations emerging as the predominant methodology. Key findings demonstrate VR’s ability to simulate realistic scenarios, improve spatial navigation, and optimize crowd dynamics and mobility accessibility. VR enhances evacuation efficiency and safety compliance by enabling adaptive training for diverse populations, including students, professionals, and vulnerable groups. In public and high-risk environments, VR addresses challenges such as visibility limitations, structural complexity, and the need for customized evacuation protocols. However, gaps remain in exploring multi-hazard environments and mixed-use spaces and ensuring scalability. Future research should integrate VR with artificial intelligence and machine learning for predictive and adaptive evacuation models. Expanding VR applications to underrepresented groups, including individuals with disabilities and the elderly, and collaborating with policymakers and urban planners are vital for translating research into practice. Overall, VR provides a scalable, adaptable, and inclusive solution for building evacuation preparedness, offering actionable insights to enhance resilience and safety in diverse architectural and disaster contexts. Its ability to transform evacuation strategies positions VR as a pivotal tool in advancing disaster management. Full article

Museums play a vital role in preserving cultural heritage and for this reason, they require strict indoor environmental controls. Balancing indoor environmental quality with reduced energy consumption poses significant challenges. Over the course of a year (2023), indoor microclimate conditions, atmospheric pollutant concentrations (O₃, TVOC, CO, CO₂, particulate matter), and energy use were monitored at the Archaeological Museum of Kavala. Maximum daily fluctuations in relative humidity were 15% in summertime, while air temperature variations reached 2.0 °C, highlighting unstable microclimatic conditions. Particulate matter was the primary threat to the preservation of artworks, followed by indoor O₃ and NO₂, whose concentrations exceeded recommended limits for cultural conservation. In 2023, the Energy Use Intensity (EUI) was 86.1 kWh m⁻², a value that is significantly correlated with the number of visitors and the outdoor air temperature. Every person visiting the museum was assigned an average of 7.7 kWh of energy. During the hottest days and when the museum was crowded, the maximum amount of energy was consumed. Over the past decade (2013–2023), the lowest EUI was recorded during the COVID-19 pandemic at 53 kWh m⁻². Energy consumption is linked to indoor environmental quality; thus, both must be continuously monitored. Full article

Spontaneous recreational activities in public spaces are a vital source of public vitality. Given the similarity between the walking patterns of recreational crowds in public spaces and the movement of electrons on a two-dimensional circuit surface, this study combines big data from various sources to create an “electrical conductivity surface” that attracts and aggregates recreational crowds. Using current flow simulation, we generate the path selection preferences of people as they move across public spaces. The results reveal an uneven distribution of public spaces in Nanjing’s main urban area, with high-vitality areas mostly concentrated in the urban center. The core demand for enhancing public vitality lies is improving connectivity between multiple spaces. Based on this, the public space plan for Nanjing’s main urban area emphasizes overall connectivity by aligning with the natural landscape, thus linking the city’s green and gray infrastructure. In this study, we have assessed current public space services and their development potential from a number of different angles, developing a digital approach for optimizing the urban layout. We aim to provide a human-centric, bottom-up perspective to complement the top-down city planning and management approach. This will enable urban planners to make informed decisions for creating and managing more vibrant cities. Full article

Emergency department (ED) overcrowding and limited staff availability pose ongoing challenges to healthcare efficiency. Recent advancements in automated health technologies, such as the health pod, aim to alleviate these pressures by automating vital sign measurements for low-risk patients. Over three months, the CAPSULA Health Pod was implemented and used in a paired setting with normal triage procedures in an urban hospital ED; it demonstrated improvements in triage efficiency and patient satisfaction, aligning with evidence that supports automation as a solution in high-demand healthcare settings. With 1342 assessments across 404 patients, despite some challenges with elderly patient engagement, CAPSULA achieved excellent measurement accuracy and relevant efficiency for the first assessment of patients in crowded situations and for reassessment. The findings indicate CAPSULA’s potential to reduce patient wait times, improve workflow efficiency, and support resource-limited EDs. Although the main limitation remains IT integration, the system demonstrates scalability and potential for broader adoption. Full article

Town development, a crucial stage of urbanization, has been increasingly prioritized in recent sustainable socio-economic growth strategies. Vitality, especially the one measured by aggregate crowd behaviors, is widely recognized as a crucial development element. Conducting comprehensive assessments of the drivers of town vitality, particularly crowd vitality, is thus essential for addressing challenges and monitoring progress. This study examines representative towns in China and employs multiple datasets along with XGBoost-SHAP to investigate the mechanisms of development environment factors on aggregate crowd vitality. Key findings highlight the study’s novelty and broader implications: (1) The degree of industrial agglomeration is the most significant factor impacting the dependent measures, providing new data-driven insights into the role of economic clustering in town development. (2) Other indicators, such as the minimum distance to the town center, the enclosure, and car and pedestrian friendliness, can effectively predict town vitality, offering practical considerations for town planning. (3) Industrial innovation and diversification, rational planning of living circles, and enhancement of town conditions emerge as three crucial strategies for promoting urbanization. This study enhances empirical insights with strategies for addressing urbanization challenges, emphasizing how crowd data can be used to inform urbanization policies and planning practices, aiding urban planners in building more sustainable systems. Full article

Jane Jacob’s concepts of urban vitality and diversity have become prevailing urban planning philosophies in most countries for making cities more livable. Recent changes in demographics and the impacts of COVID-19 have exacerbated the economic and social challenges that cities commonly face, particularly spatiotemporal heterogeneities. Being able to understand these heterogeneities in scalable approaches is fundamental to tackling these challenges in cities. Therefore, this article aims to provide a new form of scalable estimation of urban vitality by using the de facto population. Instead of merely adopting static statistical information such as morphological characteristics in areas, we leverage dynamic factors such as internal mobility flows and energy use intensity as proxies for the spatiotemporal dynamics of indoor and outdoor behaviors of crowds. In this way, we combine dynamic attributes and static features to describe the patterns of urban vitality, which are directly related to spatiotemporal dynamics in urban places. We utilize GNSS-based mobile data and building energy usage intensity as dynamic proxies along with static data such as land use mix and age distribution. To better capture spatial heterogeneity, we use a Multiscale Geographically Weighted Regression (MGWR) model to identify the relationships between the de facto population and the dynamic and static factors. Drawing from the factors determining urban vitality, this article provides policy implications for alleviating spatiotemporal urban imbalances. These data-driven implications can fill the technical knowledge gaps in establishing planning strategies for achieving urban sustainability while enhancing overall subjective livability. Full article

Although it is well established that thermal environments significantly influence travel behavior, the synergistic effects of points of interest (POI) and thermal environments on behavior remain unclear. This study developed a vision-based outdoor evaluation model aimed at uncovering the driving factors behind human behavior in outdoor spaces. First, Yolo v5 and questionnaires were employed to obtain crowd activity intensity and preference levels. Subsequently, target detection and clustering algorithms were used to derive variables such as POI attractiveness and POI distance, while a validated environmental simulator was utilized to simulate outdoor thermal comfort distributions across different times. Finally, multiple classification models were compared to establish the mapping relationships between POI, thermal environment variables, and crowd preferences, with SHAP analysis used to examine the contribution of each variable. The results indicate that XGBoost achieved the best predictive performance (accuracy = 0.95), with shadow proportion (|SHAP| = 0.24) and POI distance (|SHAP| = 0.12) identified as the most significant factors influencing crowd preferences. By extrapolation, this classification model can provide valuable insights for optimizing community environments and enhancing vitality in areas with similar climatic and cultural contexts. Full article

The revitalization of historic districts is crucial for the sustainable development of cities, with street corner spaces being a vital component of the public space in these districts. However, street corner spaces have been largely overlooked in previous research on crowd dynamics within historic districts. This study investigates the key factors influencing crowd dynamics in street corner spaces within historic districts. First, a hierarchical model of vitality-influencing factors was developed based on prior research. Potential factors influencing the vitality of street corners were quantified using multi-source data collection methods, including deep learning algorithms, and crowd vitality within these spaces was assessed through multidimensional measurements. The impact of each element on crowd vitality was then analyzed through a multivariate linear regression model. The findings revealed that eight factors—corner building historicity, first-floor functional communality, transparency, openness, density of functional facilities, greenness, functional variety of buildings, and walkability—significantly influence the vitality of corner spaces, collectively explaining 77.5% of the vitality of these spaces. These conclusions offer new perspectives and scientific evidence for the revitalization and conservation of historic districts. Full article

Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention technologies becomes urgent. The paper presents a novel deep learning model which is developed to detect and alleviate bird strike issues in airport conditions boosting aircraft safety. Based on an extensive database of bird images having different species and flight patterns, the research adopts sophisticated image augmentation techniques which generate multiple scenarios of aircraft operation ensuring that the model is robust under different conditions. The methodology evolved around the building of a spatiotemporal convolutional neural network which employs spatial attention structures together with dynamic temporal processing to precisely recognize flying birds. One of the most important features of this research is the architecture of its dual-focus model which consists of two components, the attention-based temporal analysis network and the convolutional neural network with spatial awareness. The model’s architecture can identify specific features nested in a crowded and shifting backdrop, thereby lowering false positives and improving detection accuracy. The mechanisms of attention of this model itself enhance the model’s focus by identifying vital features of bird flight patterns that are crucial. The results are that the proposed model achieves better performance in terms of accuracy and real time responses than the existing bird detection systems. The ablation study demonstrates the indispensable roles of each component, confirming their synergistic effect on improving detection performance. The research substantiates the model’s applicability as a part of airport bird strike surveillance system, providing an alternative to the prevention strategy. This work benefits from the unique deep learning feature application, which leads to a large-scale and reliable tool for dealing with the bird strike problem. Full article

Green stormwater infrastructure (GSI) is a key approach to greening and cooling high-density blocks. Previous studies have focused on the impact of a single GSI on thermal comfort on sunny days, ignoring rainwater’s role and GSI combinations. Therefore, based on measured data of a real urban area in Nanjing, China, this study utilized 45 single-GSI and combination simulation scenarios, as well as three local climate zone (LCZ) baseline scenarios to compare and analyze three high-density blocks within the city. Among the 32 simulations specifically conducted in LCZ1 and LCZ2, 2 of them were dedicated to baseline scenario simulations, whereas the remaining 30 simulations were evenly distributed across LCZ1 and LCZ2, with 15 simulations allocated to each zone. The physiological equivalent temperature (PET) was calculated using the ENVI-met specification to evaluate outdoor thermal comfort. The objective of this research was to determine the optimal GSI combinations for different LCZs, their impact on pedestrian thermal comfort, GSI response to rainwater, and the effect of GSI on pedestrian recreation areas. Results showed that GSI combinations are crucial for improving thermal comfort in compact high-rise and mid-rise areas, while a single GSI suffices in low-rise areas. In extreme heat, rainfall is vital for GSI’s effectiveness, and complex GSI can extend the thermal comfort improvement time following rainfall by more than 1 h. Adding shading and trees to GSI combinations maximizes thermal comfort in potential crowd activity areas, achieving up to 54.23% improvement. Future GSI construction in high-density blocks should focus on different combinations of GSI based on different LCZs, offering insights for GSI planning in Southeast Asia. Full article

Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China’s coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China’s and even the world’s coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as “humans, machines, the environment, and management” in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified. Full article

Predicting and assessing the vitality of public urban spaces is crucial for effective urban design, aiming to prevent issues such as “ghost streets” and minimize resource wastage. However, existing assessment methods often lack temporal dynamics or heavily rely on historical big data, limiting their ability to accurately predict outcomes for unbuilt projects. To address these challenges, this study integrates previous methodologies with observations of crowd characteristics in public spaces. It introduces the crowd-frequency hypothesis and develops an algorithm to establish a time-dimensional urban vitality dynamic prediction model. Through a case study of the Rundle Mall neighborhood in Adelaide, Australia, the effectiveness of the prediction model was validated using on-site observation sampling and comparative verification. The prediction model framework allows for the determination of urban vitality within specific time ranges by directly inputting basic information, providing valuable support to urban planners and government officials during the design and decision-making processes. It offers a cost-effective approach to achieve sustainable urban vitality construction. Furthermore, machine learning techniques, specifically the decision tree model, were applied to case data to develop a set of preliminary algorithm tools, which enable output of reference urban vitality levels (high-medium-low). Full article

The unprecedented COVID-19 health crisis severely disrupted global sports in 2020, prompting lengthy suspensions followed by resumed competitions under abnormal behind-closed-doors conditions without fans. These disruptions necessitated tactical adaptations by coaches and teams, attempting to still achieve successful outcomes. This study investigates the pandemic’s impacts on performance metrics and indicators within Spanish professional soccer. Utilizing systematic notational analysis, 760 match cases from the 2019–2020 La Liga season were examined, comprising 27 matchdays from the pre-COVID context and 11 after resumption. Multivariate tests identified significant pre/post differences and interactions for various technical indicators including shots, cards, corners, and offside calls. The pandemic was associated with a reduction from 12 to just 5 identifiable playing styles, suggestive of increased conservatism featuring more passive play, limited attacking depth, and horizontal ball movement. Such tactical changes appear provoked by condensed fixture scheduling post-lockdown, the lack of supportive crowds, and compromised player fitness/recovery. By quantifying these COVID-precipitated changes, the analysis provides tangible evidence for coaches to make informed adjustments in training and preparation for functioning effectively in disrupted environments. The findings emphasize that versatility and flexibility will be vital to optimize performance during times of unprecedented uncertainty. Full article

Assessing the vitality of public open spaces is critical in urban planning and provides insights for optimizing residents’ lives. However, prior research has fragmented study scopes and lacks fine-grained behavioral data segmentation capabilities and diverse vitality dimension assessments. We utilized computer vision technology to collect fine-grained behavioral data and proposed an automated spatial vitality monitoring framework based on discrete trajectory feature points. The framework supported the transformation of trajectory data into four multidimensional vitality indicators: crowd heat, resident behavior ratio, movement speed, and spatial participation. Subsequently, we designed manual validation mechanisms to demonstrate the monitoring framework’s efficacy and utilized the results to explore the changes in vitality, and the influencing factors, in a small public space. Discrete trajectory feature points effectively addressed the literature’s fragmented study scope and limited sample size issues. Spatial boundaries had a significantly positive impact on spatial vitality, confirming the “boundary effect” theory. The peak spatial vitality periods were from 08:30 to 09:30 and from 17:30 to 18:30. A higher enclosure degree and better rest facilities positively impacted spatial vitality, while a lower enclosure degree did not consistently suppress spatial vitality in all situations. Overall, spatial features and spatial vitality have a complex nonlinear relationship. Full article

Crowd flow prediction plays a vital role in modern city management and public safety prewarning. However, the existing approaches related to this topic mostly focus on single sites or road segments, and indoor regional crowd flow prediction has yet to receive sufficient academic attention. Therefore, this paper proposes a novel prediction model, named the spatial–temporal attention-based crowd flow prediction network (STA-CFPNet), to forecast the indoor regional crowd flow volume. The model has four branches of temporal closeness, periodicity, tendency and external factors. Each branch of this model takes a convolutional neural network (CNN) as its principal component, which computes spatial correlations from near to distant areas by stacking multiple CNN layers. By incorporating the output of the four branches into the model’s fusion layer, it is possible to utilize ensemble learning to mine the temporal dependence implicit within the data. In order to improve both the convergence speed and prediction performance of the model, a building block based on spatial–temporal attention mechanisms was designed. Furthermore, a fully convolutional structure was applied to the external factors branch to provide globally shared external factors contexts for the research area. The empirical study demonstrates that STA-CFPNet outperforms other well-known crowd flow prediction methods in processing the experimental datasets. Full article