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Although the hospital is known as a safe place for treating patients, due to various reasons, it is prone to several internal hazards, including fire. This study aimed to identify the factors affecting hospital emergency evacuation during fire. This was a systematic review conducted according to the PRISMA guideline. Thematic Content analysis was utilized to analyze and extract results. We found the studies investigating the factors affecting hospital emergency evacuation during fire through a comprehensive search in various data resources (MEDLINE, Web of Science, Google Scholar, Embase, ProQuest, Scopus, IRANMEDEX, SID, ISC, and Magiran) and other sources from the beginning of January 2000 to the end of December 2019. Thematic Content analysis was also employed to analyze. At first and based on the initial search, 4484 studies were identified, and 48 articles were finally included in the study. Based on the results; five main themes along with 10 sub-themes were identified. The themes included the incident's characteristics, response measures, hospital preparedness, hospital residents, and hospital building, and the sub-themes were emergency evacuation features, fire characteristics, command, operation, patients' and staff's characteristics, planning, logistics, and structure and design hospital. Based on the results of the present study, hospital preparedness as one of the most important factors can reduce the hospital evacuation time. Therefore, hospitals can ensure a timely and more effective response in emergency evacuation during fire by improving their preparedness.

A virtual reality (VR) experiment with 96 participants was carried out to provide recommendations on the design of flashing lights at emergency exit portals for road tunnel emergency evacuation. The experiment was carried out in a Cave Automatic Virtual Environment laboratory. A set of variables was investigated, namely (1) colour of flashing lights, (2) flashing rate, (3) type of light source, (4) number and layout of the lights on the portal. Participants were immersed in a VR road tunnel emergency evacuation scenario and they were then asked to rank different portal designs using a questionnaire based on the Theory of Affordances. Results show that green or white flashing lights perform better than blue lights. A flashing rate of 1 and 4 Hz performed better than a flashing rate of 0.25 Hz. A light emitting diode light source performed better than single and double strobe lights. The three layouts of the lights under consideration performed similarly.

Disasters such as earthquake or tsunami can easily take the lives of thousands of people and millions worth of property in a fleeting moment. A successful emergency evacuation plan is critical in response to disasters. In this paper, we seek to investigate the multi-source, multi-destination evacuation problem. First, we construct a mixed integer linear programming model. Second, based on K shortest paths and user equilibrium, we propose a novel algorithm (hereafter KPUE), whose complexity is polynomial in the numbers of nodes and evacuees. Finally, we demonstrate the effectiveness of algorithm KPUE by a real evacuation network in Shanghai, China. The numerical examples show that the average computation time of the proposed algorithm is 95% less than that of IBM ILOG CPLEX solver and the optimality gap is no more than 5%.

When an emergency occurs in a large ship, it is necessary to provide a scientific and reasonable evacuation strategy for the passengers in the ship, so as to avoid the loss of life and property caused by untimely evacuation. For the traditional Dijkstra’s algorithm, which only selects the optimal path based on the single index of path length, this paper takes into account the influence of path length, congestion and safety, and selects the optimal path in real time according to the dynamically changing external environment, which is more suitable for the application of emergency evacuation in large ship. The results of simulation experiments also prove the effectiveness of the algorithm proposed in this paper.

The efficient and timely management of human evacuation during emergency events is an important area of research where the Internet of Things (IoT) can be of great value. Significant areas of application for optimum evacuation strategies include buildings, sports arenas, cultural venues, such as museums and concert halls, and ships that carry passengers, such as cruise ships. In many cases, the evacuation process is complicated by constraints on space and movement, such as corridors, staircases, and passageways, that can cause congestion and slow the evacuation process. In such circumstances, the Internet of Things (IoT) can be used to sense the presence of evacuees in different locations, to sense hazards and congestion, to assist in making decisions based on sensing to guide the evacuees dynamically in the most effective direction to limit or eliminate congestion and maximize safety, and notify to the passengers the directions they should take or whether they should stop and wait, through signaling with active IoT devices that can include voice and visual indications and signposts. This paper uses an analytical queueing network approach to analyze an emergency evacuation system, and suggests the use of the Pull Policy, which employs the IoT to direct evacuees in a manner that reduces downstream congestion by signalling them to move forward when the preceding evacuees exit the system. The IoT-based Pull Policy is analyzed using a realistic representation of evacuation from an existing commercial cruise ship, with a queueing network model that also allows for a computationally very efficient comparison of different routing rules with wide-ranging variations in speed parameters of each of the individual evacuees.Numerical examples are used to demonstrate its value for the timely evacuation of passengers within the confined space of a cruise ship.

During a fire evacuation, long lateral evacuation distances, large crowds waiting for evacuation at the same level, and easily panicked populations are common. This research aimed to look into the large-scale evacuation behavior of urban underground complexes with limited evacuation and egress during a fire. A simplified model for large-scale group evacuation of urban subsurface complexes was constructed using system dynamics theory. The Vensim software was used for quantitative simulation. The model could represent the typical phenomenon of group evacuation behaviors, such as quick or slow, under seven operating situations with total initial numbers of 350, 400, 450, 500, 1000, 2000, and 4000. The results of an analysis of critical affecting factors show “total initial number” and “panic state” during a large-scale group evacuation: a large beginning population will result in a rapid reduction in system evacuation capability, delaying the completion of the evacuation process significantly; meanwhile, if the level of panic is deficient, the system’s evacuation efficiency will remain low for an extended period, making it difficult to evacuate trapped persons promptly. According to the findings, the developed system dynamics model, which combines the advantages of a continuous model with the advantages of a discrete model, is very accurate. At the same time, we should emphasize the importance of the evacuation guide and reinforce the fire education and behavior drills for the building’s workers. This research presents a simplified model for the evacuation of large groups of people from metropolitan underground complexes. Furthermore, the findings may give theoretical support for the development of rules and safety management practices.

Humanitarian logistics is an integral part of disaster relief operations, which involves the phases of preparedness, disaster operations, and post-disaster operations. Integrating the planning and execution between phases minimizes the gaps in providing relief to the affected population. This paper presents a two-stage multi-objective mathematical model for integrated decision-making during the preparation and response phases. The proposed model is developed to jointly optimize the location of emergency shelters (and/or depots) and coordinate the movement of relief vehicles between the disaster site and emergency shelters. Focusing on the optimal distribution of relief supplies to the emergency shelters, the proposed model aims to minimize the operational, distributive injustice, and dissatisfaction costs. To address the computational complexity of the introduced model, two multi-objective meta-heuristics, namely multi-objective vibration damping optimization and non-dominated sorting genetic algorithm (NSGA-II), are used. A comprehensive sensitivity analysis is conducted to study the impacts of variations in key parameters on model output under different scenarios. Our results suggests that the employed solution algorithms outperform the traditional optimization methods in achieving the Pareto-Front solutions.

This study focuses on the northern scenic area of Changbai Mountain, aiming to evaluate the emergency evacuation capacity of the region in the context of geological disasters and to formulate corresponding improvement strategies. Due to the relatively small area of this region, difficulties in data acquisition, and insufficient precision, traditional models for evaluating emergency evacuation capacity are typically applied to urban built environments, with relatively few studies addressing scenic areas. To tackle these issues, this research employs the Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN), which successfully resolves the problem of blurriness in remote sensing images and significantly enhances image clarity. Coupled with the Graph Convolutional Network (GCN) model, the study calculates the emergency evacuation time for each raster point, providing a comprehensive assessment of the region’s evacuation capacity. Based on the evaluation results, the study proposes targeted improvement measures for areas identified as having poor emergency evacuation capacity, taking into account the existing infrastructure of the scenic area. By constructing an indicator system encompassing effectiveness, accessibility, and safety, the feasibility of each proposed enhancement strategy is assessed scientifically and rationally. Through these integrated tools and methodologies, this research significantly improves the accuracy of data processing, evaluation, and decision support, showcasing a comprehensive approach to scenic area research that provides critical support for geological disaster management, emergency planning, and the overall safety of the Changbai Mountain scenic area.

Emergency evacuations in built environments pose significant challenges for individuals with disabilities, yet traditional simulation models often fail to account for heterogeneous mobility needs. While considerable advances have been made in pedestrian dynamic modeling, a critical gap persists in the realistic incorporation of disability-specific movement limitations and environmental barriers. This paper presents an inclusive evacuation simulation framework based on an extended social force model, explicitly integrating wheelchair users and visually impaired individuals. The model modifies agent parameters such as desired speed, relaxation time, body size, and barrier navigation capability to reflect empirical observations. Key enhancements include a probabilistic falling mechanism under high crowd pressure and dynamic interaction with environmental obstacles. A single-room evacuation scenario involving 50 agents, including 20% disabled individuals, was simulated using this framework. Results demonstrated that the presence of disabled individuals increased total evacuation time by approximately 50% compared to an all-able-bodied crowd, led to a 40% reduction in peak evacuation throughput after crowd falls, and caused arching, clogging, and faster-is-slower effects to intensify. Two fall incidents occurred within the first 4 s, resulting in partial door blockage and additional delays. Heatmaps revealed localized congestion zones induced by mobility impairments, and kinetic energy analysis illustrated significant dissipation due to frictional interactions at the exit. The findings underscore the necessity of inclusive modeling to identify critical vulnerabilities in evacuation plans and highlight the importance of design interventions such as wider doorways, alternative accessible exits, and controlled evacuation flow for heterogeneous crowds. This work offers a robust foundation for performance-based inclusive design and supports future extensions into multi-level structures, dynamic assistance modeling, and optimization-based evacuation planning.

Emergency evacuation has received more attention as an effective tool of flash flood disaster prevention that calls for systematic thinking rooted in natural and social sciences. Although personal factors influencing emergency evacuation decisions (EED) after receiving a flood warning have been widely discussed, few studies have referred this issue to the flash flood characteristics. This study explored the personal factors influencing EED under different flash flood characteristics (i.e., the frequency, occurrence time, and severity of flash floods) through field survey data. Three typical flash flood characteristics in three towns were selected as case studies. An ordinary logistical model and path analysis were used to analyze the independent influence and influence process of the personal factors on evacuation intention under the three flash flood characteristics. The results showed that personalized risk perception and warning type consistently influenced evacuation intention regardless of the flash flood characteristics, while the independent influence of flood experience and reliance on hazard information on evacuation intention was varied with the flash flood characteristics. Perceived exposure influenced evacuation intention through the mediations of flood experience when there were high-frequency, recent, and loss-causing flash floods, and of risk perception when there were low-frequency, distant, and few-loss-causing flash floods. The effect of warning type on evacuation intention was varied with the flash flood characteristics if the warning type changed from the suggestive rainstorm red warning to mandatory ready-to-evacuate warning. However, if the warning type changed from the ready-to-evacuate to immediate-evacuation warning, there was no significant difference in this effect regardless of the flash flood characteristics. Therefore, it is necessary to implement distinctive emergency management according to specific flash flood characteristics.