The Crisis Management Manual is essential for safe and rapid evacuation in the event of an accident. However, the disaster response measures in the current ship evacuation manual are not systematic and are simple and impractical. In particular, the current instructions when the ship is heeling call for evacuation to the highest deck, in the opposite direction. This advice is dangerous, however, because it is isolated to a specific space, due to the walking limit angle according to the angle of heel during evacuation. This study focuses on the MV Sewol ferry accident on 16 April 2014, to evaluate the risk of isolation at sea when evacuating to the highest deck opposite in the direction of heeling when the ship was sinking. According to the initial angle of heel and various angular velocities, hazard maps were created for each safety factor to predict the risks for each situation, by performing a comprehensive evaluation of the safe and dangerous situations when evacuating to the highest deck. The problems and limitations of the current evacuation manuals and systems were identified, and the necessity of a new evacuation solution was presented.

Subway systems are highly vulnerable to disasters because of their confined underground structures and limited evacuation routes, making accurate evacuation analysis essential for reducing casualties. Most existing studies overlook panic behavior, leading to unrealistic assessments of evacuation efficiency. This study develops a modeling framework that integrates panic behavior into agent-based subway evacuation simulations. The framework incorporates three behavioral factors: reaction delays to emergency cues, hesitation at decision points, and irrational route choices. Simulation experiments were conducted under different occupancy conditions, and results from panic-integrated and non-panic scenarios were compared. Findings show that the inclusion of panic significantly prolongs evacuation time, with delays of up to 30% in full-scale scenarios due to congestion and route errors. These outcomes demonstrate that panic behavior exerts a decisive influence on evacuation dynamics and should not be neglected in simulation studies. Incorporating panic into evacuation modeling provides a more realistic basis for designing safer subway systems and developing effective emergency response strategies.

The Ministry of Land, Infrastructure, and Transport conducted a demonstration project targeting pilot areas to commercialize drone delivery services in urban areas and to present a standard model. In this study, flight data on drone delivery routes in Ulju and drone hovering in Yeosu were collected and analyzed for flight safety. Since there are no domestic or international regulations on the stability of drone flight, we were given the task of analyzing whether drone path flight should be maintained within a 10 m error range from the planned path line by the Korea Transportation Safety Authority and whether hovering works while satisfying the left and right radius errors and altitude errors within 3 m. Accordingly, the drone flight path data analyzed in Ulju met the criteria of up to 1.07%, and the hovering data analyzed in Yeosu met the criteria of less than 3% for the entire section data. Therefore, the drone flight stability evaluation analyzed in this paper is considered to have been passed. Based on the results of this study, is the data are expected to serve as a cornerstone for establishing guidelines for drone delivery flight data analysis regulations.

Modern subway systems have increased in size and complexity, and this growth presents significant challenges for planners of emergency evacuations. In this study, the effectiveness of the simplified and advanced evacuation analysis methods recommended by the International Maritime Organization (IMO) are evaluated for Guryong Station in Seoul, South Korea. The simplified evacuation analysis method facilitates rapid assessments by using general parameters, while the advanced evacuation analysis entails performing detailed simulations of human behavior and physical interactions. Our findings indicate that the results of the simplified evacuation analysis method are reasonably close to those of the more time-consuming advanced evacuation analysis method, thereby demonstrating the practical applicability of the former method for conducting initial evacuation safety assessments. Specifically, both the simplified and advanced methods showed a 20% reduction in Total Evacuation Time when tunnel evacuation routes were utilized. This finding demonstrates that the simplified method can produce results comparable to the advanced method, making it a reliable tool for initial assessments and for evaluating alternative strategies to reduce evacuation time. By demonstrating that the simplified evacuation analysis method can yield reliable results, we provide valuable insights for developing smart, resilient cities with efficient emergency-response capabilities.

The COVID-19 pandemic highlighted the need for a scientific framework that enables quantitative assessment and control of airborne infection risks in indoor environments. This study identifies limitations in the traditional Wells–Riley model—specifically its assumptions of perfect mixing and steady-state conditions—and addresses these shortcomings by adopting the REHVA (Federation of European Heating, Ventilation and Air Conditioning Associations) infection risk assessment model. We propose a five-tier risk classification system (Monitor, Caution, Alert, High Risk, Critical) based on two key metrics: the probability of infection (Pₙ) and the event reproduction number (R_event). Unlike the classical model, our approach integrates airborne virus removal mechanisms—such as natural decay, gravitational settling, and filtration—with occupant dynamics to reflect realistic contagion scenarios. Simulations were conducted across 10 representative indoor settings—such as classrooms, hospital waiting rooms, public transit, and restaurants—considering ventilation rates and activity-specific viral emission patterns. The results quantify how environmental variables (ventilation, occupancy, time) impact each setting’s infection risk level. Our findings indicate that static mitigation measures such as mask-wearing or physical distancing are insufficient without dynamic, model-based risk evaluation. We emphasize the importance of incorporating real-time crowd density, occupancy duration, and movement trajectories into risk scoring. To support this, we propose integrating computer vision (CCTV-based crowd detection) and entry/exit counting sensors within a live airborne risk assessment framework. This integrated system would enable proactive, science-driven epidemic control strategies, supporting real-time adaptive interventions in indoor spaces. The proposed platform could serve as a practical tool for early warning and management during future airborne disease outbreaks.