Elderly nursing homes in South Korea are vulnerable to evacuation in emergency situations such as fires. There are many elderly residents with reduced self-walking ability, so if a disaster such as a fire occurs, it is very likely to lead to a large number of human injuries. In elderly nursing homes, it is impossible for many elderly people to evacuate voluntarily without the help of staff. Therefore, it is very important to guide the behavior of staff when evacuating. The purpose of this study is to evaluate the effect of evacuation behavior of staff on evacuation time in a fire emergency in an elderly nursing home by adopting an agent-based simulation approach. The effect of staff evacuation behavior on the evacuation time was investigated based on the results of a new agent-based evacuation simulation model. In the simulation model, there are conditions for caregivers to designate and evacuate the elderly, as well as to set the evacuation priority for the elderly. The results of this study show that having the elderly occupants designated by the staff according to their zones and evacuating them together was the most important in reducing the evacuation time. This study contributes to a new agent-based evacuation simulation model by confirming whether the evacuation behavior of employees affects the evacuation time.

The COVID-19 pandemic has fundamentally transformed residential spaces, yet traditional survey-based approaches face limitations in objectively capturing these changes. This study investigates residential design trends in the Post-pandemic era, defined as the period in which pandemic-induced lifestyle changes have become institutionalized in everyday living environments. Residential interior images were collected from Pinterest and Instagram and analyzed using an image-based deep learning approach combined with expert evaluation. A pretrained convolutional neural network (ResNet50) was employed as a visual feature extractor to quantify three spatial attributes—openness and comfort, flexibility and diversity, and nature-friendliness—across four residential space types: balconies, living rooms, entrances, and bedrooms. The model-generated proportional scores were validated by experts and compared between pre-pandemic and post-pandemic periods. The results reveal dual transformation patterns of functional specialization and increased multifunctionality. Balconies evolved into well-being-oriented spaces with enhanced nature-related features, while living rooms emerged as multifunctional hubs with a substantial increase in spatial flexibility. In contrast, entrances exhibited reduced openness, functioning as hygienic buffer zones. These findings indicate a reconfiguration of spatial hierarchy in post-pandemic housing, where auxiliary spaces gain prominence and traditional primary spaces adopt flexible roles. This study demonstrates the value of image-based deep learning for objectively identifying residential design trends and provides practical implications for resilient housing design in the post-pandemic era.

University-affiliated public housing (UAPH) is planned for the purpose of coexistence with the local community as well as housing stability for university students. However, shared spaces such as auxiliary welfare facilities planned to achieve that purpose are not being used by residents because no services or programs are provided. Lifestyle is a useful approach for identifying housing preferences in housing projects for special groups that share similar demographic characteristics. Thus, this study analyzed and clustered the lifestyle of university students, and investigated the differences in preferred auxiliary welfare facilities and residential services by cluster. Data were collected through a self-administered questionnaire survey of 424 university students using an online survey. As a result of the survey, in the factor analysis of university students’ lifestyle types, three categories were identified as individual propensity factors regardless of demographic characteristics except gender. In addition, the characteristics of university students were crucial in examining the preference of auxiliary welfare facilities and residential services of UAPH in terms of community facilities, auxiliary facilities, residential services, and program/operation management. Community facilities showed a high need for academic facilities, a low overall need for residential services, and a high need for program/operational management in terms of cost (housing cost reduction). In this study, there were differences in UAPH opinions according to lifestyle. In particular, many respondents showed a lifestyle that pursued practicality, and accordingly, a preference to utilize practicality, sociality, information, and technology was high. Therefore, for the sustainable operation and management in order to facilitate UAPH construction, the issue of housing cost, including not only rent but also management costs, should be considered most fundamentally, and it is necessary to provide facilities and programs that can provide practical help to the difficulties students face when living alone.

Although metaverse model houses have recently emerged as an interactive alternative to traditional housing marketing tools, empirical research addressing users’ experiences within these environments remains limited. This study aimed to examine how three dimensions of user experience (UX)—operational, sensory, and exploratory—affect user satisfaction and behavioral intentions in metaverse model houses. A total of 83 participants explored a metaverse model house using a tablet PC and completed a questionnaire. Multiple linear regression analysis revealed that exploratory experience significantly influenced user satisfaction, while sensory experience was positively associated with all behavioral intentions, including the intention to revisit, recommend, reside, and purchase. These findings advance our understanding of UX in virtual housing environments and highlight the importance of immersive and exploratory elements in designing effective metaverse model houses. The results offer practical implications for improving digital housing marketing strategies and guiding the future development of metaverse-based architectural platforms.