Evacuation Scenarios Research Articles

UAVs as a Tool for Optimizing Boat-Supported Flood Evacuation Operations

The frequency and intensity of flood events are increasing year by year as a result of climate change. This poses significant threats to human settlements and adversely affects biodiversity, agriculture, and infrastructure. One of the most prominent and traditional flood evacuation approaches is through the use of boats. Nonetheless, serious challenges exist with respect to determining the optimal deployment locations, routes, and timing. Given research advances in the Unmanned Aerial Vehicles (UAVs) sector—and their ability to offer real-time data and aerial monitoring services—we argue that their applications could help enhance boat-supported flood evacuation operations. In this opinion piece, we explore new opportunities for disaster management and underscore the advantages of integrating UAVs into flood evacuation methodologies, including areas of rapid field assessment, optimal route planning, and improved coordination between rescue boats. Notwithstanding the potential of UAVs, we emphasize several gaps to be explored in terms of large-scale data management/processing, regulatory limitations, and technological know-how. Furthermore, we provide recommendations for bolstering boat deployment protocols, disaster preparedness training programs, policy frameworks, and emergency response systems, which could maximize their efficacy in flood evacuation scenarios.

A study of staff pre‐evacuation behaviors in a Malaysian hotel

AbstractSimulating fire and evacuation scenarios is crucial for engineers to assess building safety during fire incidents. Accurate simulations require data on occupants' behaviors, particularly during the pre‐evacuation phase as these decisions significantly impact evacuation duration. Gathering comprehensive data from diverse regions while considering cultural and regional variations is necessary to understand how occupants' behavior is influenced. Thus, this study focuses on examining the behavior of Malaysian hotel staff during unannounced fire drill to gain insights into factors affecting their behavior during pre‐evacuation stage, such as fire experience, fire alarm, drill participation, fire training, and awareness. The study categorizes the actions performed by the hotel staff into sequences and analyses them based on influencing factors. The findings indicate that instead of immediately evacuating in response to emergency notification, the hotel staff engage in various actions. Most staff members initially investigate or ignore the emergency, resulting in longer pre‐evacuation times. Moreover, the results suggest that previous drill participation and high awareness levels contribute to shorter pre‐evacuation times. Conversely, previous fire experience, fire training, and fire alarm familiarity have no effect on pre‐evacuation time.

Voxel Size Performance in 3D Indoor Model Creation for 3D Evacuation Simulations

Abstract. 3D voxel models facilitate simulating 3D pedestrian motions within indoor environments with diverse furniture, equipment, and complex room layouts featuring stairs and ramps. Reasonable voxel sizes enable the effective identifications of indoor spaces, navigable surfaces, and vertical links from voxel models for the simulations. However, the performance of different voxel sizes on these identifications has yet to be thoroughly investigated. The choice of appropriate voxel sizes for various 3D evacuation scenarios has to be clarified to avoid unreliable simulation outcomes. Therefore, this paper aims to assess the performance of different voxel sizes on the identifications of the spaces, surfaces, and links. Firstly, we introduce three key metrics for the assessment: accuracy, robustness, and efficiency. Then, three IFC models are selected based on predefined criteria for the tests. Experimental procedures are elaborated to include data preparation, collection, and analysis. The primary findings of this investigation are: 1) with increasing voxel sizes, accuracy decreases, robustness changes, and efficiency increases; 2) larger indoor spaces exhibit smaller changes in accuracy across different voxel sizes, while smaller indoor spaces show more significant changes in accuracy. 3) when the voxel size is tiny, it becomes challenging to complete identifications. Conversely, with relatively larger voxel sizes, some indoor spaces and vertical links cannot be identified. 4) Indoor spaces with complex and non-orthogonal geometries require more identification time. This work is expected to advance the creation of 3D indoor model to facilitate 3D evacuation simulations.

Study on pedestrian evacuation simulation model considering group behavior

As a crowded place on the university campus, the cafeteria inevitably has many safety hazards, such as fire accidents caused by open flames and large appliances, stampede accidents caused by overcrowding during peak dining hours, etc. Therefore, studying pedestrian evacuation in university campus cafeterias is particularly necessary. Pedestrians on campus mostly travel in groups. Previous studies mainly used continuous models to discuss pedestrian group evacuation behavior. In this study, based on the cellular automaton pedestrian evacuation simulation model, the floor field calculation method was improved. A cellular automaton pedestrian evacuation simulation model considering group behavior was established and applied to the evacuation scenario of students in a university campus cafeteria. The study found that under the different group configurations, the pedestrian ratios, and the pedestrian densities, the pedestrian evacuation efficiency had significant differences. The results showed that when the different group configurations existed in the scenario, the higher the proportion of the pedestrians with three-person front-to-back group configurations, the higher the evacuation efficiency. When only one type of the group configuration existed in the scenario, at low pedestrian density, the evacuation efficiency of the individual pedestrian groups was higher compared to the other six group configurations. While at high pedestrian density, the evacuation efficiency of the three-person front-to-back group configurations was higher. These findings provided important references for pedestrian evacuation in university campus cafeterias and provided insights for the simulation research of group pedestrian evacuation models, contributing to enhancing campus safety management and ensuring the safety of teachers, students, and staff.

Agent-based modelling of evacuation scenarios for a landslide-generated tsunami in Milford Sound, New Zealand

Agent-based modelling is a useful tool for evacuation planning as it can increase understanding of the factors affecting potential evacuation outcomes. In New Zealand, Milford Sound has been shown to have a high risk from landslide-generated tsunami, with an estimated 1-in-1000-year wave runup of ∼17 m arriving on shore within 2–7 min. With an annual average of >1500 people visiting a day, there is potential for widespread loss of life. However, the number of people present varies substantially with time of day and season, yet how this affects the ability to evacuate remains unknown. This research developed an agent-based model to understand how many people can be safely evacuated in Milford Sound and explored how the number of people initially exposed affected the evacuation outcome alongside the effect of potential changes to evacuation messaging. Assuming a 17 m wave, the results suggest that currently no one can safely evacuate before the shortest wave arrival time regardless of the number of people present. Altering evacuation messaging results in minimal gains, with only ∼5 % of the exposed population reaching safety in time. This work demonstrates the importance of evacuation modelling for understanding risk in isolated tourism destinations where the population exposure can fluctuate dramatically across multiple timescales. Accounting for changing population exposure is essential to understand whether evacuation is a suitable risk treatment and can provide valuable information for determining safe levels of population exposure in locations with high hazard but limited evacuation options.

Revolutionizing crowd safety: A breakthrough hybrid whale-bat chaotic algorithm (WOABCM) for optimized evacuation simulations

The Whale-Bat Chaotic Algorithm (WOABCM) is a revolutionary tool for optimizing pathfinding navigation in crowd evacuation scenarios. Conventional evacuation plans rely on static routes, causing traffic jams and decreased safety. WOABCM uses the erratic behavior of whales and bats to optimize escape routes in real time, making it particularly useful in large crowd evacuations. The algorithm can adapt to different scenarios, ensuring its continued efficacy across real-world conditions. Iteratively iterating through the evacuation environment, WOABCM iteratively finds the most effective ways for agents to reach exits. In this experiment, the results show notable improvements in evacuation times, with WOABCM determining and directing agents through the shortest and least crowded paths in a room setting and assigning agents to exits in more complicated scenarios. The algorithm's flexibility also allows it to swiftly recompute pathways for agents, avoiding obstructions or congestion points. This groundbreaking approach to crowd evacuation simulation demonstrates how chaos theory-inspired algorithms can be used to solve practical problems

Spatial Analysis with Detailed Indoor Building Models for Emergency Services

This paper presents a systematic approach to perform spatial analysis with detailed indoor building models for emergency service decision supports. To achieve a more realistic spatial application, this research integrates three-dimensional (3D) indoor building models and their attributes to simulate an emergency evacuation scenario. Indoor building models of a complicated train station with different levels of detail are generated from two-dimensional (2D) floor plans and Building Information Model (BIM) datasets. In addition to the 3D building models, spatial and non-spatial attributes are also associated with the created building models and the objects within them. The ant colony optimization (ACO) algorithm is modified to analyze the indoor building models for emergency service decision support applications. The detailed indoor models and the proposed spatial analysis algorithms are tested in simulated emergency evacuation scenarios to select the best routes during emergency services. The experimental results demonstrate that the proposed system is helpful for selecting the optimal route with the least cost at varying time stamps. Together with the developed spatial analysis framework, they have a great potential for effective decision support during emergency situations.

Experimental study on mechanical transfer regularity and step distance of individuals under different collision impulse

In the process of emergency evacuation, the fall of an individual may lead to the instability of surrounding pedestrians. The evacuation routes would be obstructed by fallen pedestrians, thereby diminishing the evacuation efficiency and increasing the overall crowd risk. The purpose of this study is to explore the balance-recovery process of pedestrians under collision. The study conducted a series of experiments to obtain the total step distance and number of steps taken by individuals during the balance-recovery process under different impulses. The impulse applied by the unstable individual to the obstacle ahead for balance recovery was also measured. The gait characteristics and mechanical principles of the individuals in the balance-recovery process after collision force were analyzed. The results indicate that individuals can recover balance within a 90 cm step distance if the impulse received is in the range of 20–160 N s. The relationship between the step distance of an individual and the impulse received can be expressed as a quadratic function. When stepping is prohibited, the impulse applied by the individual to the obstacle ahead is approximately 1.85 times the impulse he received. However, if stepping is allowed, the dispersion of the impulse applied to the obstacle gradually decreases with increasing step space. Additionally, logistic regression models were constructed to calculate individual instability probabilities for different conditions based on the experimental data. These results can serve as a foundation method for evaluating the individual and group instability risk during emergency evacuation scenarios.

Quantitative evaluation method for post-earthquake functionality loss of pedestrian flow pathways within a complex building using evacuation and rescue simulation

The complex building is a single building, characterized by its large size and complicated structural systems, such as high-rise-buildings, long-span-podium, and large underground space, as well as diverse functions. The pedestrian flow pathways within a complex building play a crucial role in urgent evacuation and rescue following seismic events. This study proposed a quantitative method for evaluating the functionality loss of pedestrian flow pathways within the complex building following seismic events. The method performed the component fragility analysis and constructed the speed reduction models that considered the variations of occupants' motions under different damage states of various nonstructural components. To evaluate the functionality loss of pedestrian flow pathways, the evacuation and rescue scenarios were established by analyzing the maximum speeds and behavioral delays of occupants at different spatial locations within the post-earthquake complex building. Based on the simulation, a quantitative index was constructed. Finally, a process accounting for uncertainties such as component damage was presented by generating the engineering demand parameters. A case study was employed to demonstrate the functionality loss of pedestrian flow pathways on each floor and in different areas within the post-earthquake complex building, which has reference significance for improving the post-earthquake complex building 's evacuation capacity as soon as possible to deal with secondary risk.

The Collection and Compilation of Small Group Data for Scenario Setting of Simulations and Experiments

The influence of small groups in evacuation cannot be ignored. However, the current simulations and experimental studies have oversimplified the settings of small groups and evacuation scenarios. A significant disparity exists between the findings of existing studies and real-world scenarios. This paper compiled data on the number and size of small groups and the location of small group members in built environments. Subsequently, a scenario with intricate functions and shapes was established, and finally, these data was employed in agent-based simulations. The data encompassed 50 small groups comprising a total of 111 members. These groups, ranging from 2 to 4 members each, exhibited spatial separations between members spanning from 1 m to 23 m. Simulation outcomes indicated a detrimental effect of small groups on overall evacuation. A significant and positive correlation was observed between the distance separating small group members and the escalation in evacuation time, total jam time, and evacuation distance. The data provides a foundation for configuring initial scenarios in small group evacuation experiments and simulations. The simulation results can provide a basis for hospital safety evacuation management.

СТРУКТУРНАЯ СХЕМА СИСТЕМЫ ИНТЕЛЛЕКТУАЛЬНОГО УПРАВЛЕНИЯ ЭВАКУАЦИЕЙ

In an emergency, such as a fire or a terrorist attack, an important aspect is the safe evacuation of people. The unpredictability of the situation makes the task of emergency evacuation one of the main problems. The development of artificial intelligence technologies opens up possible promising ways of developing evacuation management systems. An intelligent evacuation management system can help track the movement and coordinates of people and related critical factors during the evacuation process (for example, slowing down traffic along the paths, blocking the paths, etc.). The study is devoted to the construction of a decision support system for the evacuation of people from buildings based on the results of modeling the development of a fire, intelligent forecasting of the evacuation time in the conditions of the current situation during a fire. The system allows dynamically forming optimal evacuation routes in a changing situation. The proposed results allow us to further move on to the development of an automated intelligent evacuation management system, which will ensure increased safety for evacuees, regardless of evacuation scenarios and the current situation in the building.

Optimizing phased-evacuation strategy for high-rise buildings in fire

Evacuation in high-rise buildings is a critical safety issue due to multiple floors and high occupant density. Phased evacuation strategies can mitigate this challenge. This research focuses on developing optimal phased evacuation strategies to enhance occupant safety during high-rise building fires. The study demonstrates these strategies in a 25-story building, housing 2400 individuals, across various scenarios. To simulate fire and smoke development, a two-zone model is employed to analyze behavior within the building. Pedestrian evacuation scenarios are simulated using Pathfinder and a mathematical model, providing insights into individual movements and congestion during evacuation to analyze different phased evacuation strategies. Findings underscore the importance of smoke flow and pedestrian motion in optimizing phased evacuation strategies. The modeling results show that implementing the optimal strategy can lead to a 65 % reduction in staircase congestion time. This study provides a quantitative analysis of high-rise building fires, considering dynamic fire and evacuation evolution. It emphasizes the importance of phased evacuation strategies and advocates for their real-life application. This research enhances the understanding of high-rise building evacuations to improve safety measures and provides a robust framework for optimizing evacuation strategies by integrating fire modeling. This study demonstrates the effectiveness of phased evacuation and paves the way for future applications to ensure occupant safety in fire emergencies.

Evacuation scenario optimization in buildings with human anthropometric characteristics

Emergency evacuations are critical situations that threaten people's lives and safety, necessitating robust planning and prevention strategies. Anthropometric data, which are numerical expressions of human body measurements, play a vital role in architectural design by helping determine safe evacuation routes. This data is crucial for understanding the physical characteristics and mobility of building occupants, allowing for optimized evacuation routes that accommodate the needs of various groups, such as the elderly, children, and individuals with physical disabilities. The Social Forces Model (SFM) is an important tool used to understand the behavior and movement of crowds, modeling these aspects mathematically by considering social interactions and environmental factors. This study develops a system to enhance emergency evacuation scenarios in buildings using anthropometric data and SFM. This system analyzes the evacuation process and identifies optimal routes, contributing significantly to ensuring safety and minimizing damage during disasters. The research shows that using anthropometric data and SFM in architectural design is essential for effective emergency evacuation planning and management. Simulations that incorporate the diverse physical characteristics of people demonstrate notable improvements in evacuation times compared to those using fixed values, with up to 10.20 % improvement in single-exit scenarios and up to 13.45 % in two-exit scenarios. Furthermore, evacuation routes are optimized using SFM, and histograms are employed to identify the most preferred routes during panic situations. This data-driven approach informs the design of building and floor layouts, as well as the materials and decorations used, ensuring that evacuation routes remain clear and accessible. Removing obstacles and facilitating easy passage during emergencies significantly enhances the effectiveness of evacuations. Overall, the integration of anthropometric data and SFM in building design is shown to create faster and safer evacuation processes, ultimately improving emergency response and safety.

Flood risk zoning of cascade reservoir dam break based on a 1D-2D coupled hydrodynamic model: A case study on the Jinsha-Yalong River

Large-scale earthquakes and huge landslide surges may cause the failure of cascade reservoirs, resulting in massive downstream flooding. The flood risk zoning of cascade reservoir dam breaks is crucial for local governments to formulate emergency plans and for individuals to garner their awareness of the flood risks. This paper presents a flood risk zoning approach based on a one-dimensional and two-dimensional coupled hydrodynamic model. The flooding area simulated by the coupled hydrodynamic model was divided into a Deadly Zone (D-zone) and an Escape Zone based on whether there is sufficient time for the public to evacuate the danger area under a specific evacuation generation time (EGT). A case study was conducted on the Jinsha-Yalong River (JYR), a confluence river located in Panzhihua City, to map the risk zones. This study considered a dam-break flood in Yalong River encountering two river flood scenarios in Jinsha River (scenario A: the annual average flow; scenario B: a 1000-year return period flood). Two evacuation scenarios (EGT of 30 and 60 min) were also considered; thereafter, four combined evacuation scenarios (A-EGT30, A-EGT60, B-EGT30, and B-EGT60) were simulated. The coupled hydrodynamic model is demonstrated to be effective through the analysis of water balance and hydrographs of special section discharge. The results show that: (1) in comparison to scenario A, scenario B reached its maximum flood extent approximately 5 min earlier, signifying that flood spread speed increases with the increase in flood magnitude; (2) the D-zone area was 127.7 % larger in scenario A-EGT60 compared to A-EGT30 and 120.8 % larger in scenario B-EGT60 compared to B-EGT30. These findings underscore the importance of flood scenarios and EGT on flood risk zones. The proposed risk zoning method can help government agencies in the JYR and major river regions worldwide with flood management and emergency strategy development.

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.

Fire safety research of rubber floor covering in a China ice sports center

AbstractThis article examines the fire safety of rubber flooring used in an ice sports center in China. The fire characteristics and parameters of the rubber floor were determined through material fire tests. Subsequently, a fire safety analysis was conducted using the fire intrinsic safety method. Additionally, numerical simulation was employed to investigate fire and evacuation scenarios, particularly when the safety grade of the floor decoration materials falls below standard requirements. The results indicate that, under specific safety conditions, the ice sports center can utilize B1 grade rubber flooring in the required area while ensuring compliance with fire safety standards.

Density control in pedestrian evacuation with incorrect feedback information: Data correction

Improving evacuation efficiency is a central concern in evacuation simulation research. Incorrect feedback information can affect the effectiveness of evacuation control in partially observable evacuation. In this paper, we introduce a framework for evacuation guidance control, emphasizing data prediction and correction to mitigate the impact of abnormal observed data. The framework is built upon force-driven Cellular Automaton (CA) models and employs a data correction module to rectify abnormal information. Guided by this framework, we implement the data correction module’s functionality using Back Propagation (BP) neural networks. We utilize historical simulation data to train the BP network, obtaining a model for correcting abnormal data. Additionally, we integrate the data correction model with density control algorithms to facilitate pedestrian flow management in abnormal evacuation scenarios. Subsequently, we conduct two comparative simulation experiments to verify the algorithm’s effectiveness. One experiment utilizes an abnormal data replacement method, while the other employs a data correction method. The results show that the method of abnormal data replacement is simple, but it cannot improve the control efficiency in abnormal evacuation scenarios. The data correction method proposed in this paper can effectively improve evacuation efficiency and alleviate the congestion at exits caused by abnormal data, which reduces evacuation efficiency. The results are expected to provide insights into improving evacuation systems’ efficiency and personnel safety.

Analysis of the effects of urban micro-scale vulnerabilities on tsunami evacuation using an agent-based model – case study in the city of Iquique, Chile

Abstract. The occurrence of mega-tsunamis over the last couple of decades has greatly increased the efforts of the research community and practitioners to work hand in hand to reduce risks from these highly destructive threats. Protecting the population through evacuation is the best alternative for avoiding loss of life in the wake of the occurrence of a tsunamigenic earthquake. Therefore, guaranteeing the proper state of evacuation routes is very important to ensuring appropriate movement to the safe zones. This study carries out a detailed analysis of possible evacuation scenarios, considering the actual state of the escape routes of Iquique, a Chilean city prone to tsunamis, with a substantial number of urban micro-scale vulnerabilities, i.e., elements that obstruct or complicate pedestrian flow. The quantification of the delay in evacuation processes resulting from the presence of urban micro-vulnerabilities is carried out through micro-scale agent-based model (ABM) simulations. In addition, these results are integrated with high-resolution tsunami inundation simulations, allowing for an estimation of the potential number of people that the tsunami may reach under different scenarios by emulating the dynamics and behavior of the population and the decision-making regarding the starting time of the evacuation.

Identification of primary input parameters affecting evacuation in ventilated main control room through CFAST simulations and application of a machine learning algorithm to replace CFAST model

Accurately predicting evacuation time in a ventilated main control room (MCR) during fire emergencies is crucial for ensuring the safety of personnel at nuclear power plants. This study proposes to use neural networks alongside consolidated fire and smoke transport (CFAST) simulations to serve as a surrogate model for physics-based simulation tools. Our neural networks can promptly predict the evacuation time in MCRs, proving to be a valuable asset in fire emergencies and eliminating the need for time-consuming rollouts of the CFAST simulations. The CFAST model simulates fire and evacuation scenarios in a ventilated MCR with variations in input parameters such as door conditions, ventilation flow rate, leakage area, and fire propagation time. Target output parameters, such as hot gas layer temperature (HGLT), heat flux (HF), and optical density (OD), are used alongside standardized evacuation variables to train a machine learning model for predicting evacuation time. The findings suggest that high ventilation flow rates help to dilute smoke and discharge hot gas, leading to lower target output parameters and quicker evacuation. Standardized evacuation variables exceed the required abandonment criteria for all door conditions, indicating the importance of proper evacuation procedures. The results show that neural networks can generate evacuation times close to those obtained from CFAST simulations.

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.