Emergency Evacuation Scenarios Research Articles

Optimizing crowd evacuation: evaluation of strategies for safety and efficiency

Predicting and controlling crowd dynamics in emergencies is one of the main objectives of simulated emergency exercises. However, during emergency exercises, there is often a lack of sense of danger by the actors involved and concerns about exposing real people to potentially dangerous environments. These problems impose limitations in running an emergency drill, harming the collection of valuable information for posterior analysis and decision-making. This work aims to mitigate these problems by using Agent Based Modelling (ABM) simulator to deepen the comprehension of human actions when exposed to a sudden variation in extensive crowded environmental conditions and how evacuation strategies affect evacuation performance. To assess the impact of the evacuation strategy employed, we propose a modified informed leader-flowing approach and compare it with common evacuation strategies in a simulated environment, replicating stadium benches with narrow corridors leading to different exit points. The objective is to determine the impact of each set of configurations and evacuation strategies and compare them against other established ones. Our experiments determined that agents following the crowd generally lead to a higher number of victims due to the rise of herding phenomena near the exits, which was significantly reduced when agents were guided towards the exit via knowing the exit beforehand or following leader agent with real-time information regarding exit location and exit current state, proving that relevant and controlled information in combination with Follow Leader strategies can be crucial in an emergency evacuation scenario with limited evacuation exit capabi and distribution.

Enhancing Health, Safety, Security & Environment (HSSE) Training through Virtual Reality: An Offshore Emergency Evacuation Exercise

The offshore oil and gas industry poses unique safety challenges, particularly during emergencies such as fires, where quick and effective evacuation is critical. This paper explores the use of virtual reality (VR) technology in Health, Safety, Security, and Environment (HSSE) training, specifically through a VR exercise developed for the emergency evacuation scenario on an offshore platform at Universiti Teknologi PETRONAS (UTP). The exercise focuses on training students to safely evacuate the platform during a fire emergency, navigating through realistic escape routes while avoiding hazards. The study evaluates the effectiveness of VR in enhancing students’ situational awareness, response times, and overall preparedness for real-world emergency situations. The results demonstrate that VR-based training significantly improves understanding of evacuation procedures and decision-making under stress, offering a valuable tool for HSE education in high-risk industries.

Designing for Safety: A Review of Human-Centered Approaches in Evacuation App Development

This review focuses on the development of evacuation apps used in emergencies with the principal role of User-Centered Design (UCD). With intensifying dependence on technological growth, precisely emergency evacuation scenarios, these apps are helpful for their usage and design. This study sheds light on the importance of user experience by designing human-centric elements and integrating them into user experience. It is ensured explicitly that their accessibility and effectiveness during high-stress situations do not impact the app's functionality. Existing apps lack interface useability regarding human-centric approaches, present inconsistent information, and lack disability and demographic integration, causing a bad user experience. Incorporation of UCD principles of usability, user needs, and iterative design, the design of more inclusive and responsive apps meets these critical challenges identified in the review. The recommendations, thus provided, help improve functionality as well as usability. The objective of this paper is to support the development of user-friendly apps facilitating users during emergencies following an in-depth literature review of such apps being used in such scenarios.

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.

Exploring the Complexity of Pedestrian Dynamics: Impact of Societal Behaviors and Personal Attributes in Urban Environments

Pedestrian dynamics in urban environments are influenced by a complex interplay of physical, environmental, social, cultural, psychological, and health-related factors. This study investigates pedestrian dynamics, focusing on the impact of social and cultural factors such as dress codes and gender at the operational decision-making level, and explores how these factors, along with societal norms, values, and practices, shape pedestrian behavior in various facilities. We focus on pedestrian facilities in real-world and experimental contexts in Iran, examining the roles of gender and specific dress codes, including Islamic clothing such as the hijab. Data were gathered from sidewalks and signalized and unsignalized intersections, and during evacuation drills in Iran, using video recordings to capture pedestrian movements and behaviors. We developed and calibrated the culturally informed social force model (CISFM), an extension of the traditional social force model (SFM), to incorporate cultural and social parameters. Our key findings reveal notable disparities in pedestrian behavior based on gender and clothing type, particularly the traditional Iranian Chador and Manteau. Women generally exhibited slower walking speeds, while clothing type significantly influenced group dynamics and awareness of surroundings. The calibrated CISFM showed improved predictive capabilities in various urban settings, except in emergency evacuation scenarios. This research contributes to a more nuanced understanding of pedestrian dynamics, highlighting the need for pedestrian models to integrate cultural and social factors for more accurate and representative simulations, especially in multicultural urban environments.

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.

Research on emergency evacuation behavior rules of pedestrians under fire and explosion accidents in chemical parks considering social relationships

Chemical parks present a high risk of sudden accidents such as fires and explosions, given the significant number of hazardous sources present. This is why emergency evacuation after accidents is so essential. The behavior of crowds during evacuation is complex, and social relationships among pedestrians can have differentiated effects on the evacuation process. However, most of the current crowd evacuation research has focused on life scenarios and natural disaster contexts. Less attention has been paid to the emergency evacuation of crowds affected by social relations in the context of chemical accident disasters. A comprehensive evacuation model that considers the dynamic evolution of crowd transfer both within and outside the factory area is proposed in this paper. Using the Anylogic software based on the social force model, this study constructs emergency evacuation scenarios for crowd in the context of fire and explosion accidents, and toxic gas leakage incidents. Specifically, this study analyzes the impact of three factors, namely inter-personal distance, group size, and proportion of different group sizes in the total population, on the evacuation efficiency. It has been found that when large groups of pedestrians are split into smaller ones for evacuation, the evacuation efficiency can be effectively improved. The study includes calculations of the lethal range of a toxic gas and simulated the process of relocating large crowds of people to nearby shelters. In addition, a strategy for deploying pedestrian groups based on social relationships was developed for the context of mass evacuation. These measures have the potential to optimize the evacuation efficiency of pedestrian groups in chemical parks, including expanding and modifying shelters, diverting dense crowds from residential plots, using interpersonal spacing of 0.1 m, and splitting large groups of pedestrians into smaller ones. The research results can provide a reference for decision making for large-scale group evacuation in chemical parks, and improve the emergency evacuation capability and local risk response capability in chemical parks.

A Prescriptive Simulation Framework with Realistic Behavioural Modelling for Emergency Evacuations

Emergency and crisis simulations play a pivotal role in equipping authorities worldwide with the necessary tools to minimize the impact of catastrophic events. Various studies have explored the integration of intelligence into Multi-Agent Systems (MAS) for crisis simulation. This involves incorporating psychological behaviours from the social sciences and utilizing data-driven machine learning models with predictive capabilities. A recent advancement in behavioural modelling is the Conscious Movement Model (CMM), designed to modulate an agent’s movement patterns dynamically as the situation unfolds. Complementing this, the model incorporates a Conscious Movement Memory-Attention (CMMA) mechanism, enabling learnability through training on pedestrian trajectories extracted from video data. The CMMA facilitates mapping a pedestrian’s attention to their surroundings and understanding how their past decisions influence their subsequent actions. This study proposes an efficient framework that integrates the trained CMM into a simulation model specifically tailored for emergency evacuations, ensuring realistic outcomes. The resulting simulation framework automates strategy management and planning for diverse emergency evacuation scenarios. A single-objective method is presented for generating prescriptive analytics, offering effective strategy options based on predefined operational rules. To validate the framework’s efficacy, a case study of a theatre evacuation is conducted. In essence, this research establishes a robust simulation framework for crisis management, with a particular emphasis on modelling pedestrians during emergency evacuations. The framework generates prescriptive analytics to aid authorities in executing rescue and evacuation operations effectively.

Novel learning framework for optimal multi-object video trajectory tracking

BackgroundWith the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emergency evacuation scenarios. Correctly and effectively evacuating crowds in virtual emergency scenarios are becoming increasingly urgent. One good solution is to extract pedestrian trajectories from videos of emergency situations using a multi-target tracking algorithm and use them to define evacuation procedures. MethodsTo implement this solution, a trajectory extraction and optimization framework based on multi-target tracking is developed in this study. First, a multi-target tracking algorithm is used to extract and preprocess the trajectory data of the crowd in a video. Then, the trajectory is optimized by combining the trajectory point extraction algorithm and Savitzky–Golay smoothing filtering method. Finally, related experiments are conducted, and the results show that the proposed approach can effectively and accurately extract the trajectories of multiple target objects in real time. ResultsIn addition, the proposed approach retains the real characteristics of the trajectories as much as possible while improving the trajectory smoothing index, which can provide data support for the analysis of pedestrian trajectory data and formulation of personnel evacuation schemes in emergency scenarios. ConclusionsFurther comparisons with methods used in related studies confirm the feasibility and superiority of the proposed framework.

Analyzing emergency evacuation scenarios in Ghana based on different groups profiles in a classroom

It depends on both the pedestrians and the building whether they can be evacuated from a building safely and quickly in any situation. Understanding pedestrian behavior is crucial when designing buildings for safe evacuation in emergencies like earthquakes and fires. Since this topic has yet to be fully explored in Ghana, we conducted experiments in a classroom to capture the characteristics of pedestrian movements during an emergency. In this experimental study, students were asked to pretend to evacuate during an emergency. The simulation and comparison of the evacuation of students from a classroom were conducted using a multi-grid model in our study. The study examines several factors that influence evacuation characteristics. These factors include pedestrian density, speed, time, door width, body mass index, and classroom capacity. The paper describes in detail all study phases, including planning, preliminary work, observational studies, empirical data collection, and statistical analysis. It thoroughly examines pre-movement time, group density, and velocities for groups and individuals moving through doorways, traveling distances, and walking in educational settings. A person’s state of mind and psychological abilities are also taken into account, as well as their level of emotional involvement in the movement. The obtained results are also discussed for evacuation planning in educational institutions under different conditions.

Predictive Agent-Based Crowd Model Design Using Decentralized Control Systems

As a complex system, crowd dynamics emerge bottom-up from the local interactions between pedestrians as component subsystems. This article proposes a predictive agent-based crowd simulation model to analyze the outcomes of emergency evacuation scenarios taking into account collisions between pedestrians, smoke, fire sprinklers, and exit indicators. The crowd model is based on a decentralized control system structure, where each pedestrian agent is governed through a deliberative-reactive control architecture. The simulation model for evacuation includes a routing-based control system for dynamic-guided evacuation. A design case illustrates the modeling process. Results show that the crowd simulation model based on agent autonomy and local interactions is able to generate higher level crowd dynamics through emergence.

Evacuation Simulation in a Cultural Asset Fire: Impact of Fire Emergency Evacuation Facilities for People with Disabilities on Evacuation Time

More cautious fire safety evacuation assessment and escape route assistance are required when people with disabilities encounter a fire hazard in a historical museum. This study uses the old Chiayi City Hall, which is mainly used for an exhibition space, as the example. The core of this study is the problem of fire evacuation that emerged after the historic building opened and was reused as a museum; it uses Pathfinder evacuation simulation software, discussing the difference between traditional and segregated evacuation and assuming the original evacuation, elevator emergency evacuation, and external ramp evaluation. There are three evacuation scenarios, and the number of accommodated people is set to 730, pursuant to the applicable law. Comparing the conditions of people with disabilities and ordinary members of the public during evacuation through the foregoing three scenarios, the overall evacuation time and survival rate of the original evacuation scenario are 440 s and 49.8%, respectively; and the overall evacuation time and survival rate of the barrier-free elevator emergency evacuation scenario are 332 s and 65.4%, respectively; the overall evacuation time and survival rate of the external ramp evaluation scenario are 320 s and 65.6%, respectively. The computer data analysis shows that the use of the external ramp evaluation gives people with disabilities the best evacuation time and survival rate because the architectural form of cultural heritage buildings is more fragile, specific, and fast-burning than that of ordinary buildings. As the global awareness of cultural asset preservation and revitalization is increasing, the evacuation of people with disabilities in the building in the case of fire is very important. The results of this study can be used as an emergency evacuation design recommendation for people with disabilities in the cultural assets through evacuation simulation analysis.

Evaluation of Metro Station Design in Terms of Normal Operation and Emergency Evacuation Scenarios with 3D Simulation Software

Yaya simülasyonu teknolojilerinin mimarlık ve şehir planlama disiplinlerinde kullanımı yeni bir kavram olmamakla birlikte; makine öğrenmesi tabanlı, insan davranışlarına benzer sonuçlar veren, ajan bazlı, gerçekçiliği ve geçerliliği bilimsel olarak ispatlanmış üç boyutlu simülasyon yazılımları son yıllarda ortaya çıkmışlardır. Bu yazılımlar, özellikle kalabalık ve karmaşık mekanlarda ortaya çıkabilecek sirkülasyon ve acil durum tahliye problemlerinin henüz tasarım aşamasındayken görülmesine olanak sağlamakta; bu sayede yapı inşa edilmeden önce projede gerekli revizyonların gerçekleştirilmesine imkan vermektedir. Ulusal ve uluslararası standartlar doğrultusunda gerçekleştirilen bu çalışmanın amacı, güncel simülasyon teknolojilerinin yapılara ilişkin normal işletme ve acil durum tahliye senaryolarının değerlendirilmesinde kullanımını örnek olarak seçilen gerçek bir metro istasyonu projesi üzerinden incelemektir. Seçilen raylı sistem hattındaki istasyona özgü yolcu yüklerinin bulunması amacıyla klasik elle hesaplama yöntemleri kullanılmış, ortaya çıkan değerler Massmotion yazılımına aktarılarak mekânsal kapasite analizleri gerçekleştirilmiş ve istasyonun tüm katlarına ilişkin hizmet seviyeleri bulunmuştur. Sonuç olarak metro istasyonunun normal işletme ve acil durum tahliye senaryolarının gereksinimlerini karşılayıp karşılamayacağı test edilmiş, ortaya çıkan sonuçlar çalışma kapsamında verilmiştir.

Influence of committed volunteers on helping behavior in emergency evacuations

We study how the presence of committed volunteers influences the collective helping behavior in emergency evacuation scenarios. In this study, committed volunteers do not change their decision to help injured persons, implying that other evacuees may adapt their helping behavior through strategic interactions. An evolutionary game theoretic model is developed which is then coupled to a pedestrian movement model to examine the collective helping behavior in evacuations. By systematically controlling the number of committed volunteers and payoff parameters, we have characterized and summarized various collective helping behaviors in phase diagrams. From our numerical simulations, we observe that the existence of committed volunteers can promote cooperation but adding additional committed volunteers is effective only above a minimum number of committed volunteers. This study also highlights that the evolution of collective helping behavior is strongly affected by the evacuation process.

A Quantitative Approach of Subway Station Passengers’ Heterogeneity of Decision Preference Considering Personality Traits during Emergency Evacuation

Subway station emergencies are gradually increasing in China. The aim of this research is to study the effects of “Dist”, “Pedestrian flow” and “Crowd density” on the heterogeneity of passengers’ decision-making preference and explore the relationship between heterogeneity and personality. Firstly, a questionnaire of 20 emergency evacuation scenarios, that includes the Eysenck Personality Questionnaire, is designed. Secondly, the heterogeneity of passengers’ decision preference is quantified by the random parameter logit model. Finally, personality traits and influencing factors are used as abscissa and ordinate respectively, to study the relationship between personality traits and preference heterogeneity. The results show that the coefficients of “Dist”, “Pedestrian flow” and “Crowd density” are −0.101, 0.236 and −0.442 respectively, which are statistically significant. The proportion of extroverted passengers of the exit is 9% higher than that of introverted passengers when “Pedestrian flow” of the exit is greater than the average value, while the proportion of introverted passengers is 7% higher than that of extroverted passengers when “Crowd density” is smaller than the average value. The conclusion is that the three influence factors are random variables, and “Dist” shows the lowest level of heterogeneity. Extroverted passengers are more likely to follow a large crowd for evacuation, but introverted passengers are more likely to avoid crowded exits.

Are friends electric? The benefits and risks of human-robot relationships.

SummarySocial robots that can interact and communicate with people are growing in popularity for use at home and in customer-service, education, and healthcare settings. Although growing evidence suggests that co-operative and emotionally aligned social robots could benefit users across the lifespan, controversy continues about the ethical implications of these devices and their potential harms. In this perspective, we explore this balance between benefit and risk through the lens of human-robot relationships. We review the definitions and purposes of social robots, explore their philosophical and psychological status, and relate research on human-human and human-animal relationships to the emerging literature on human-robot relationships. Advocating a relational rather than essentialist view, we consider the balance of benefits and harms that can arise from different types of relationship with social robots and conclude by considering the role of researchers in understanding the ethical and societal impacts of social robotics.

EXPERIMENTAL STUDY TO COMPARE FACTORS INFLUENCING EXIT CHOICE BEHAVIOUR IN EMERGENCY EVACUATION SITUATIONS USING VIRTUAL REALITY TECHNIQUES

Abstract. Successful emergency evacuation of a large crowd depends on understanding human behaviour and its interaction with environmental stimuli in that situation. A careful study of human behaviour in these stressful and often time-bound situations can enable building designers to account for these effects to develop the most efficient evacuation strategies. One of the major roadblocks of the field has been the lack of reliable data collection techniques. Traditionally, most of the data analysed for these studies is either collected from historical events or through stated preference (SP) surveys given the challenges of conducting high-risk emergency evacuation experiments. The project is aimed at conducting emergency evacuation scenarios in a virtual reality (VR) environment. Eighty-four participants participated in multiple cases as a part of three VR scenarios to test various factors affecting their decision-making process. Participants were immersed in VR scenarios and subjected to a series of choices. Incorporation of VR technology enabled the experiment to record participants' stated preference with a much greater degree of certainty and realism as opposed to traditional pen and paper methods. The study devised a discrete choice model and calibrated it using the data obtained from the VR-based survey. When testing multiple competing factors in the VR scenarios and comparing the results with previous studies, in one VR scenario, the direction of exit signs was found more influential than crowding. In another scenario, familiarity with an exit appeared to be more influential than herding behaviour and exit distance. Overall, the VR technology is demonstrated to provide an advantage as a means to collect data and has come out as a promising tool to be incorporated in future emergency exit choice studies.

Incorporating Intelligence for Overtaking Moving Threatening Obstacles

Crowd management and fire safety studies indicate that the correct prediction of the threat caused by fire is crucial behavior which could lead to survival. Incorporating intelligence into exit choice models for accomplishing evacuation simulations involving such behavior is essential. Escaping from moving source of panic such as fire is of tremendous frightening event while evacuation situation. Predicting the dynamic of fire spreading and the exit clogging are intelligent aspects which help the individuals follow the correct behaviors for their evacuation. This article proposes an intelligent approach to accomplishing typical evacuations. The agents are provided with the ability to find optimal routes that enable them overcome spreading fire. Fire and safe floor fields are proposed to provide the agents with the capability of determining intermediate points to compose optimal routes toward the effective chosen exit. The instinct human behavior of being far from the fire to protect himself from sudden unexpected attack is introduced as essential factor risen in emergency situation. Simulations are conducted in order to examine the simulated evacuees’ behavior regarding overtaking the fire and to test the efficiency of making smart and effective decisions during emergency evacuation scenarios

Assessing perceived driving difficulties under emergency evacuation for vulnerable population groups

The devastating impacts of natural hazards, including loss of lives and properties, underline the importance of efficient hazard preparedness, especially in the areas with frequent hazard occurrence. Several studies indicated that driving during emergency evacuation is quite challenging due to dense traffic flow, inclement weather conditions, and unexpected maneuvers of other evacuees. However, limited research has been directed towards assessing the perceived driving difficulties of individuals, including vulnerable population, under emergency evacuation. This study deploys a driving simulator in order to emulate realistic emergency evacuation scenarios and to quantify the perceived driving difficulties of individuals under emergency evacuation. Based on the data, collected using a driving simulator, a number of statistical models are proposed to determine a set of performance indicators, including the mental demand, physical demand, temporal demand, performance, effort, and frustration, experienced by individuals as a result of emergency evacuation. The statistical models also capture a variety of different driver characteristics, traffic characteristics, driving conditions, and evacuation route characteristics. The analysis results suggest that the considered performance indicators are significantly influenced with a number of factors, including age, gender, education, race, presence of chronic diseases, and self-reported driving ability. The insights from the conducted research can be applied at the hazard preparedness stage to mitigate the perceived driving difficulties of individuals under emergency evacuation and ensure their safety.