Evacuation Path Research Articles

A Passenger Ship Emergency Evacuation Line Efficiency Evaluation Operator Considering Safety Capacity and its Application

Introduction: Aiming at evaluating the efficiency of passenger ship emergency evacuation routes, this study proposes an evaluation operator for passenger ship emergency evacuation routes, and then conducts simulation research on the passenger ship evacuation process. Method: The main innovations of this study are as follows. Firstly, this study proposes an evaluation operator for passenger ship emergency evacuation lines based on consideration of the passage capacity, ship heeling effect, traffic flow obstruction, personnel evacuation mood, accident location, and other factors. Secondly, this study discretizes the emergency evacuation path of passenger ships into a three-dimensional topological network structure and uses simulation data to calculate the traffic capacity between nodes. By comparing the calculated value with the simulated value, a risk assessment method for emergency evacuation is given. Thirdly, this study takes the three-story ro-ro passenger ship in the European "SAFEGUARD" project as an example and gives three passenger ship evacuation simulation processes under different passenger flow densities. Result: The results of the calculation example show that the risk-prone areas are mainly concentrated in the passenger ship stairs, and the risk value increases with the passenger flow. Conclusion: The calculation results verify the effectiveness of the emergency evacuation line efficiency evaluation operator proposed in this study.

Urban Emergency Evacuation Path Optimization Based on Uncertain Environments to Enhance Response for Symmetric and Asymmetric Evacuation Problems

Background: Serious secondary disasters caused by extreme natural weather conditions occur frequently, making it essential to establish a scientific and efficient modern emergency management system to maximize life-saving efforts. Methods: This study focuses on the uncertain environment of urban road networks and employs fuzzy theory to construct a 0–1 integer programming model for emergency evacuation paths that minimizes the average expected travel time. Results: We enhanced the neighborhood search strategy of the traditional ACO_time by incorporating the 2-opt and 3-opt perturbation mechanisms from the SA algorithm. Additionally, we utilized improved ant-volume and ant-perimeter models, along with their combinations, in the pheromone-updating mechanism of the basic ACO. The heuristic principles of the A* algorithm were integrated, introducing the joint influence of path and time into the heuristic function of the ACO algorithm. Conclusions: The IACO3 algorithm was tested on the Sioux Falls network and the Berlin Heisenheimer Center network. The computation time of the improved IACO3 algorithm was reduced by up to 20% compared to the original IACO3 algorithm in relation to the SA algorithm, with only a 4–5% increase in computation time compared to the ACO_time algorithm, which translates to an increase of merely 4–5 s. This demonstrates the superior solution efficiency of the IACO3 algorithm.

Enhancing Fire Safety Knowledge among Underwater Road Tunnel Users: A Survey in China

In recent years, the number of underwater road tunnels in Chinese cities has increased. However, the current situation of personal fire safety literacy as it pertains to these tunnels remains unclear. To address this gap, a questionnaire survey was conducted to investigate people’s awareness of escape slides, evacuation signs, and the correct evacuation paths for fire escape. A total of 1049 respondents in Changsha, China, were surveyed, with 791 valid questionnaires collected and analyzed. The findings revealed that a significant proportion of respondents (81.80%) were unaware of the presence of escape slides in underwater road tunnels, while 87.86% could not recognize them and 93.05% could not use them. Only 42.04% of respondents could identify evacuation signs in underwater road tunnels. In the event of a fire, just half of the respondents could select the appropriate escape or evacuation path. Additionally, demographic differences among respondents also influenced their level of fire safety literacy. Based on these findings, it is recommended that the government and relevant organizations should enhance the dissemination of knowledge regarding escape slides and evacuation signs in underwater road tunnels.

Fire emergency management of large shopping malls: IoT-based evacuee tracking and dynamic path optimization

As urbanization accelerates and buildings become more complex, fire emergency evacuation has become increasingly challenging. Traditional evacuation plans often struggle with slow response times and suboptimal path planning in real-time dynamic and complex fire scenarios. To address these issues, this study proposes the IoT-based DWM-Evac model for fire emergency evacuation path planning. The model leverages IoT technology by using various sensors placed inside buildings to monitor fire incidents and spread in real-time, collecting critical data such as temperature, smoke concentration, and flame location. It integrates Dynamic Graph Neural Networks (DGNN), Whale Optimization Algorithm (WOA), and Markov Decision Process (MDP) to enhance path efficiency and safety. Experimental results indicate that the DWM-Evac model achieves an average evacuation time of 315 s in a virtual mall environment, 25 s shorter than traditional plans, with an average path length of 255 meters and a path safety score of 0.92, higher than the traditional plan’s 0.88. The application of IoT in fire emergency management not only improves response speed but also optimizes path planning, significantly enhancing personnel safety.

Optimization of personnel evacuation routes for sudden nuclear accidents based on hybrid genetic-grey wolf optimizer algorithm

To address the emergency situation resulting from a sudden nuclear accident, it is imperative to develop an optimal emergency response plan that ensures the prompt and safe evacuation of affected individuals from the hazardous area.The paper proposed the hybrid genetic-grey wolf optimizer algorithm (GA-GWO).This algorithm integrated radiation environment and road network to plan evacuation paths that minimizes the radiation dose. This algorithm enhanced the convergence and search capabilities of the genetic algorithm (GA) by incorporating wolf pack characteristics and internal competitive behavior. This improvement allowed for more efficient exploration of potential evacuation routes. The feasibility of GA-GWO was validated through a visualization simulation utilizing Web Geographic Information Systems (WebGIS). The findings indicated that GA-GWO generated evacuation paths that minimize radiation dose, providing safe routes for the affected population. This approach demonstrates significant potential for application in emergency response planning for sudden nuclear accidents, ensuring both prompt and safe evacuations.

Approximating the probabilistic p-Center problem under pressure

The Probabilistic p-Center problem under Pressure (Min PpCP) is a variant of the usual Minp-Center problem we recently introduced in the context of wildfire management. The problem is to locate p shelters minimizing the maximum distance people will have to cover in case of fire in order to reach the closest accessible shelter. The landscape is divided into zones and is modeled as an edge-weighted graph with vertices corresponding to zones and edges corresponding to direct connections between two adjacent zones. The risk associated with fire outbreaks is modeled using a finite set of fire scenarios. Each scenario corresponds to a fire outbreak on a single zone (i.e., on a vertex) with the main consequence of modifying evacuation paths in two ways. First, an evacuation path cannot pass through the vertex on fire. Second, the fact that someone close to the fire may not take rational decisions when selecting a direction to escape is modeled using new kinds of evacuation paths. In this paper, we characterize the set of feasible solutions of Min PpCP-instance. Then, we propose some approximation results for Min PpCP. These results require approximation results for two variants of the (deterministic) Minp-Center problem called Min MACp-Center and Min Partialp-Center.

Experiment and path optimization on Large-Space Multi-Area crowd evacuation

Abstract In large spaces where there are multiple evacuation zones, the flow patterns during evacuation are intricate, leading to congestion in emergency scenarios, and affecting evacuation efficiency. To address this, we proposed an optimization method for crowd evacuation paths in large-space multi-area environment settings under fire scenarios by utilizing multi-agent simulation. Using Pathfinder software, disorganized evacuation simulations are conducted within expansive multi-zonal settings to pinpoint areas with inadequate evacuation capabilities. Subsequent evacuation experiments are performed for verification, facilitating adjustments to agent-based model parameters for evacuees in fire scenarios, and carrying out organized evacuation simulations. Through meticulous simulation analysis, we aim to optimize the most efficient evacuation paths under organized responses in large-space multi-area settings. Results indicate that in emergency conditions due to fire, the evacuation speeds reach 1.8 m/s, and significant reductions in total evacuation time are achieved through simulation optimization, with the time required for the slowest individuals to evacuate decreasing from 240.32 s to 209.32 s. Furthermore, the flow rate per second at congested exits diminishes from 2.01 persons/s to 1.39 persons/s, highlighting the efficacy of the proposed path optimization method.

INFED: Enhancing fire evacuation dynamics through 3D congestion-aware indoor navigation framework

This paper introduces Indoor Navigation Framework for Fire Evacuation Dynamics (INFED), a novel indoor navigation framework that combines dynamic fire constraints and path congestion management. INFED considers the three-dimensional (3D) attributes of both the agents (speed, volume, location, count) and the environment (3D volume, congestion, corridor height, and corridor length) to estimate navigation routes that avoid fire-affected evacuation paths. It achieves this by integrating various proposed algorithms as modules: Environment Establisher, Fired/Safe Node Identifier, Pre-processor, Weighted Graph Generator, and Path Generator. The 3D features of the agent and environment are used to effectively estimate the capacity of the corridors in an indoor environment for the estimation of path congestion. The path congestion so computed is used during evacuation to identify the safest and congestion-free path. We discuss the performance of INFED by implementing it on various realistic scenarios in a commercial floor setup. We found that the incorporation of safety constraints results in longer evacuation routes, ranging from a 6% increase under mild fire and congestion conditions to a 40% increase under severe fire and congestion conditions. In the event of a worst-case scenario where fire-free paths are scarce, INFED utilizes congestion to reduce agent speed along the recommended evacuation route. This mechanism is activated when congestion surpasses a threshold of 0.3. The system can be used by stakeholders to test various evacuation hypotheses, which can lead to better preparedness and rescue operations, ultimately saving lives in the event of a fire.

The Impact of Pedestrian Lane Formation by Obstacles on Fire Evacuation Efficiency in the Presence of Unfair Competition

After a fire breaks out, pedestrians simultaneously move towards the exit and quickly form a crowded area near the exit. With the intensification of pedestrians’ tendencies towards unfair competition, there is an increase in pushing and collisions within the crowd. The possibility of stampedes within the crowd also gradually increases. Analyzing the causes and psychological tendencies behind pedestrian pushing and collisions has a positive effect on reducing crowd instability and improving evacuation efficiency. This research proposes a modified social force model considering the unfair competition tendency of pedestrians. The model considers factors such as the gap between pedestrians’ actual and maximum achievable speed, effective radius, and their distance from the exit. In order to overcome the shortage of “deadlock” in the classical social force model in a high-density environment, this research introduces the feature of variable pedestrian effective radius. The effective radius of pedestrians dynamically changes according to the density of the surrounding crowd and queuing time. Through validation, the evacuation efficiency of this model aligns well with the actual situation and effectively reflects pedestrians’ pushing and squeezing behaviors in high-density environments. This research also analyzes how to strategically arrange obstacles to mitigate the exacerbating effect of unfair pedestrian competition on exit congestion. Five experiments were conducted to analyze how the relative position of obstacles and exits, the number of evacuation paths, and the size of the obstacle-free area before the exit affect evacuation efficiency in the presence of unfair pedestrian competition. The results show that evacuation efficiency can be improved when obstacles play a role in guiding or reducing the interaction of pedestrians in different queues. However, when obstacles hinder pedestrians, the evacuation efficiency is reduced to a certain extent.

Application of multi-objective artificial gorilla troops optimization algorithm in intelligent evacuation path planning for building fires

In the event of a sudden incident in large buildings, optimizing personnel evacuation paths has a direct and significant impact on overall evacuation efficiency. To address personnel evacuation scenarios caused by building fires in complex environments, this study presents a path planning approach based on the Multi-Objective Gorilla Optimization (MIGTO) algorithm. The objective function comprises path risk, congestion cost, and path length. However, traditional GTO algorithms face limitations in addressing such problems, including slow convergence, premature convergence, and susceptibility to local optima. To address the aforementioned challenges, this study introduces variable updates to the spiral position during the algorithm development phase to improve search efficiency. The nonlinear parameters are adjusted to achieve a harmonious balance between global and local search capabilities. In addition, the alert perturbation mechanism is implemented before each optimization iteration, which improves the algorithm's ability to overcome local optima. Furthermore, a compound chaotic mapping is introduced to increase the diversity of the population. Experimental results demonstrate the outstanding performance of the MIGTO algorithm in various grid map path planning scenarios. Whether faced with complex obstacles, multiple exits, or multiple fire sources, the path inferiority indices of MIGTO in path planning are significantly superior to other algorithms. The MIGTO algorithm is not only suitable for dynamic planning of fire evacuation paths, but can also be used in the Pathfinder evacuation simulation software, where it outperforms the software's built-in algorithms.

Research on Application of Improved Quantum Optimization Algorithm in Path Planning

For the building emergency evacuation path planning problem, existing algorithms suffer from low convergence efficiency and the problem of getting trapped in local optima. The Bloch Spherical Quantum Genetic Algorithm (BQGA) based on the least-squares principle for single-robot path planning and Bloch Spherical Quantum Bee Colony Algorithm (QABC) for multi-robots path planning are studied. Firstly, the characteristics of three-dimensional path planning are analyzed, and a linear decreasing inertia weighting approach is used to balance the global search ability of chromosomes and accelerate the search performance of the algorithm. Then, the application algorithm can generate a clear motion trajectory in the raster map. Thirdly, the least squares approach is used to fit the results, thus obtaining a progressive path. Finally, multi-robots path planning approaches based on QABC are discussed, respectively. The experimental results show that BQGA and QABC do not need to have a priori knowledge of the map, and they have strong reliability and practicality and can effectively avoid local optimum. In terms of convergence speed, BQGA improved by 3.39% and 2.41%, respectively, while QABC improved by 13.31% and 17.87%, respectively. They are more effective in sparse paths.

Integrated assessment of bioaerosol dispersion patterns and infection risk in a typical urban environment: Implications for urban biosecurity management

Exposure to bioaerosols in high-density urban environments will pose a severe threat to human life and health and present significant challenges to the sustainability and resilience of cities. In this study, the aerodynamic dispersion patterns of bioaerosols at two release locations (open and dense areas) under both thermal conditions in Zhongguancun, Beijing, are investigated. By coupling a dose-response model and an improved cellular automaton, the infection risk within exposed populations was assessed, and emergency evacuation strategies for distinct populations in high-risk areas were devised. This study reveals that bioaerosol distribution is notably influenced by factors such as thermal conditions, release locations and pedestrian height. Bioaerosol concentration above the release source decreases with increasing pedestrian height at two release locations. Under the same exposure time, the infection probability of different groups in this area declines with pedestrian height increases, with adult males having the highest and elderly females having the lowest probability. Thermal conditions and building layout near the release source were second only to exposure time in influencing infection probability and evacuation path. Proximity to the release source indicates a high infection probability but a short evacuation distance to safe areas, while downstream areas exhibit lower infection probability but require longer evacuation distances. The layout of buildings near the release source has the most significant effect on evacuation time. Evacuation for high-risk populations should be prioritized upstream or either side of the mainstream. This study aims to mitigate potential biological threats, address challenges in enhancing urban biosecurity management, and enable sustainable urban development.

Emergency Evacuation Path Planning Method for the Large Ships

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.

A double‐layer crowd evacuation simulation method based on deep reinforcement learning

AbstractExisting crowd evacuation simulation methods commonly face challenges of low efficiency in path planning and insufficient realism in pedestrian movement during the evacuation process. In this study, we propose a novel crowd evacuation path planning approach based on the learning curve–deep deterministic policy gradient (LC‐DDPG) algorithm. The algorithm incorporates dynamic experience pool and a priority experience sampling strategy, enhancing convergence speed and achieving higher average rewards, thus efficiently enabling global path planning. Building upon this foundation, we introduce a double‐layer method for crowd evacuation using deep reinforcement learning. Specifically, within each group, individuals are categorized into leaders and followers. At the top layer, we employ the LC‐DDPG algorithm to perform global path planning for the leaders. Simultaneously, at the bottom layer, an enhanced social force model guides the followers to avoid obstacles and follow the leaders during evacuation. We implemented a crowd evacuation simulation platform. Experimental results show that our proposed method has high path planning efficiency and can generate more realistic pedestrian trajectories in different scenarios and crowd sizes.

Shortest or locally quickest? A prediction-based approach for evacuation choice simulation between multiple staircases

Staircase choice is one of the most critical factors leading to the difference in pedestrian flow and evacuation routes in buildings with multiple staircases. Neither the shortest path to the building exit nor the locally quickest path to the nearest staircase can represent the natural mode of evacuation path choices for an authentic evacuation simulation. Thus, a prediction-based approach is established to predict and simulate evacuation choices, which helps to address three key issues: (1) extracting evacuation data through a controlled experiment; (2) establishing a Logit model for staircase choice prediction based on experimental data; (3) developing a prediction-based cellular automaton model. The proposed approach has achieved the coupling between choice prediction and evacuation simulation. A comparison with Pathfinder software is conducted to reveal the superiority of the prediction-based CA model for simulating staircase choice.

An improved social force model for improving pedestrian avoidance by reducing search size

In order to solve the slow simulation speed of the social force model in complex indoor scenes, an improved social force model is proposed to reduce the iteration scale of obstacles. The proposed model takes into account the factor of a person’s field of view (FOV) in indoor scenes, and the acting force between pedestrians and obstacles in the social force model is improved to find the optimal number of obstacle iterations without affecting the pedestrian movement law in the traditional model, so as to accelerate the simulation speed. The self-built simulation platform and real evacuation experiments are used to compare the performance indicators of traditional and optimized models and to analyze the relationship between scene complexity and machine computing efficiency. The results show that the optimized model takes less time to predict the evacuation path than traditional models, and as the number of initial pedestrians and obstacles increases, the proportion of machine computation time between the two models will gradually increase. Second, the CPU computation time of the optimized model increases gently, indicating better stability. Therefore, reducing the obstacle iteration scale based on the pedestrian’s FOV factor provides an effective method to solve the slow simulation speed of traditional models.

Design of emergency evacuation route for large buildings based on cloud computing and GIS big data

In order to ensure the safety of life and property in large buildings, the design of emergency evacuation routes for large buildings based on cloud computing and GIS big data is studied. Combining cloud computing and GIS big data, a command model for emergency evacuation of large buildings is built. Emergency evacuation functions are realized through the access layer, business logic layer, cloud computing layer and data layer. GIS big data of large buildings is stored in the model data layer. GIS geographic data is clustered through the MapReduce based parallel K-means clustering algorithm in the cloud computing layer. After clustering, the emergency evacuation road network of large buildings is constructed through GIS in the business logic layer. On the road network, the emergency evacuation route selection method combining Dikstra algorithm and ant colony algorithm is used to realize the optimal route selection of emergency evacuation of large buildings. Experiments show that this method can effectively select the best evacuation path in large buildings, and the evacuation speed of the selected path is fast, which can ensure the safety of people in buildings.

A Semantic Approach to Dynamic Path Planning for Fire Evacuation through BIM and IoT Data Integration

Fire evacuation path planning involves multiple data sources. In order to develop a dynamic planning, a comprehensive knowledge of the environment involving building information and fire development is required. This article presents a semantic approach that integrates Building Information Modeling (BIM) and Internet of Things (IoT) information to provide a data foundation for dynamic path planning. First, a fire evacuation (FE) ontology is introduced to fuse both knowledge and information relevant to dynamic path planning. Next, a dynamic knowledge graph that evolves according to the development of fire situation is instantiated based on the relevant FE ontology. Finally, to validate the feasibility of the semantic approach based on the ontology and knowledge graph, an example of application is conducted using a specific building as an example. This study provides a data foundation for more intelligent and precise decision-making in fire evacuation scenarios and offers a new approach for safety design and management in the field of construction.

An Integrated Solution for Nuclear Power Plant On-Site Optimal Evacuation Path Planning Based on Atmospheric Dispersion and Dose Model

Safety in nuclear energy utilization is crucial. In the event of a radioactive release incident, coupled with meteorological uncertainties, a radioactive plume can impact personnel evacuation. This paper presents an integrated solution for radionuclide release accident assessment and emergency evacuation decision making. The solution consists of three processes: atmospheric dispersion calculation, dose calculation, and path planning. The individual processes are connected through data exchange, thus allowing users to choose specific models based on experience. The proposed scheme combination is the Gaussian plume model, the dose conversion factor method, and an improved Dijkstra’s path planning algorithm. This algorithm, combined with dispersion and dose results, weighs nodes using the moving expected dose, facilitating the path with minimum dose risk. A program for Atmospheric Diffusion and Dose Calculation (ADDC) is developed based on the recommended scheme. Advantages include ease of use, minimal data requirements, data accessibility, and efficient evacuation. Dose estimates and optimal evacuation routes can be obtained quickly and at very low cost in response to rapidly changing environmental conditions. In a case study at a Chinese planned nuclear plant, we consider a spent fuel pool water loss scenario, assessing dose risks from 2020 to 2022 meteorological statistics. In dose calculation, results reveal that during an SFP drying accident, the radiation dose in the core area (100 m away) can reach 30–150 mSv within 2 h, and at 500 m away, it can reach 5–15 mSv. The dose in all downwind directions can drop below 250 mSv within 60 m. In path planning, results reveal the program is capable of accurately and efficiently calculating the minimum dose evacuation route. The program’s path reduces the effective dose by up to 67.3% compared to the shortest route, enhancino safety, and guiding post-accident decision making and planning.

Integration of BIM data and real-time game engine applications: Case studies in construction safety management

Building Information Modeling (BIM) has unique features that improve safety management in construction by visually identifying potential risks. Integrating BIM with a real-time game engine is a cutting-edge idea for more effective safety management. This study aims to conduct two case studies by integrating BIM data with game engines from two aspects: 1) Construction Safety Training and 2) Pre-construction Safety Management. A framework that covers techniques for extraction of safety ideas, managing the game engine, and character modeling tools and resources is used to carry out the case studies. In the first case study, a construction site was created by Revit, and a real-life scaffolding failure accident was simulated by Unity to warn workers to prevent similar future events. The second case study was conducted on the procedure of evacuation modeling in an emergency, integrating a BIM model and Unity following distinct pathways. This evacuation modeling can be used as a training platform for the occupants to acquaint themselves with the inside facility, show directions of the shortest evacuation path from specific points, and provide necessary information on emergency equipment. Finally, the study explains how the integration of the BIM model and game engine applications can be applied for effective, straightforward, and helpful safety management with the most efficient BIM data transition.