Crowd evacuation simulation using active route choice model based on human characteristics

Modern buildings have become larger in scale and function, and the complexity has also increased considerably. For these reasons, there are more difficulties in evacuation and rescue when an emergency occurs, so effective evacuation methods and risk should be predicted and applied to building design, safety training, and education. We have developed an active route choice model based on human body organs and characteristics that detects risks and route conditions, communicates with neighboring occupants, determines the bottleneck point, and selects evacuation routes according to each occupant's personal characteristics. In this study, we introduce the implementation process and characteristics of the active route choice model, and by applying the model to the occupants, we compared the evacuation times according to the route condition, number of occupants, and corridor width in a virtual environment. We believe that realistic and valid results can be obtained by applying the active route choice model in crowd evacuation simulation.     

Investigating the influence of route turning angle on compliance behaviors and evacuation performance in a virtual-reality-based experiment

Route turning is one of the most essential and ubiquitous physical features in the complex building environment. Under the influence of route turning, evacuees’ approaching perspective to an emergency sign could vary, affecting their information perception and behavioral compliance during the evacuation. Although conventional simulation methods assess the effectiveness of the emergency sign in the visible region, they fail to consider evacuees’ wayfinding behaviors and interaction with the emergency sign. It remains unclear whether the route turning angle affects evacuees’ compliance for detecting and responding to the emergency sign. To investigate such an influence, a virtual-reality-based method for assessing human evacuation behaviors in building fire evacuations was proposed. In this study, two evacuation routes with different turning angles in a shopping mall were created and implemented in a virtual-reality environment, and 67 subjects participated in the immersive virtual-reality-based experiment. All participants took the two routes to find the nearest exit for evacuation in a fire event, aiming to evaluate the effect of the route turning angle on the evacuation process. The participants were asked to complete a questionnaire at the end of the experiment. Next, statistical analyses were conducted on evacuation results, information perception, and evacuation performance of the participants. The results indicated the route turning angle significantly affected participants' behavioral compliance with emergency signs. The results also suggested the route turning angle was influential on participants’ information perception and evacuation performance. Besides, a significant effect on rotation change, wayfinding pause, and speed deviation were observed. This study validates the effectiveness of investigating evacuees’ interaction with emergency signs using virtual-reality technology and has potential implications for complex building path planning and evacuation simulation modeling.     

Two-phase evacuation route planning approach using combined path networks for buildings and roads

This paper addresses the problem of modeling evacuation routes from a building and out of an affected area. The evacuation route involves pathways such as corridors, and stairs in buildings and road networks and sidewalks outside the building. To illustrate such an approach, we consider the problem of finding evacuation paths from an urban building and out of a predetermined neighborhood of the building on foot. A case study for a college campus building and small set of road around it is provided. There are a pre-defined set of exit points out of the target building and out of the road network serving the building. A two-step approach with an uncapacitated network model for route finding and a capacitated scheduling algorithm for evacuation time computation is proposed. A recent efficient heuristic algorithm is selected as a reference for comparative analysis. The process of creating a combined building and road path network data is discussed. The key results are the competitive evacuation time provided by the proposed uncapacitated route planning model, simple pedestrian flow capacity formulas for corridors and roads from readily available geometric data, and the illustration of the creation and use of combined building and road path network.     

Modeling an ontology on accessible evacuation routes for emergencies

Providing alert communication in emergency situations is vital to reduce the number of victims. However, this is a challenging goal for researchers and professionals due to the diverse pool of prospective users, e.g. people with disabilities as well as other vulnerable groups. Moreover, in the event of an emergency situation, many people could become vulnerable because of exceptional circumstances such as stress, an unknown environment or even visual impairment (e.g. fire causing smoke). Within this scope, a crucial activity is to notify affected people about safe places and available evacuation routes. In order to address this need, we propose to extend an ontology, called SEMA4A (Simple EMergency Alert 4 [for] All), developed in a previous work for managing knowledge about accessibility guidelines, emergency situations and communication technologies. In this paper, we introduce a semi-automatic technique for knowledge acquisition and modeling on accessible evacuation routes. We introduce a use case to show applications of the ontology and conclude with an evaluation involving several experts in evacuation procedures.     

An emergency aircraft evacuation simulation considering passenger emotions

The evacuation behavior of passengers was formulated as an autonomous agent and multi-agent model (AAMAS) evolving over a two-dimensional grid cell that represents aircraft cabins and passengers. In this model, the autonomous agents are initially placed in seat squares and move toward an emergency exit after an aircraft accident occurs. The autonomous agent mimics the behavior of passengers in the cabin, who must not only view their surroundings to collect the useful information but also select a route to an emergency exit. As the situation evolves, the agents feel the mental stress or strong fear or anxiety; thereby reacting unfavorably in the situation that they panic at. This abnormal evacuation behavior of panic agents generates time delays in the evacuation flow towards the exits. Therefore, such panic and its effect on evacuation behavior should be considered as an important factor in evacuation simulations. In this paper, it is supposed that the level of panic depends on three factors: remaining time, frequency of waiting and the difficulty of finding an exit. The dependencies of these factors on the time needed to complete an evacuation and the number of the panic agents in the aircraft were determined by the simulation. Considering the simulation results and situations of the actual aircraft accident “Garuda Indonesia Airways Accident”, it was possible to develop aircraft evacuation scenarios that considered passenger emotions.     

高铁车厢内部紧急情况下人员疏散仿真研究

在引入元胞自动机理论的基础上,模拟分析了紧急情况下人员生理及心理的不同对疏散过程的影响,通过人员动态调整自身行为,并引入人员竞争力,对人员疏散过程建立模型。在疏散过程中,根据存在着一定程度的外部干扰因素,提出疏散干扰度概念。计算机仿真的结果表明,考虑人员竞争力,以及出口疏散干扰的问题,可以更加真实地模拟人员紧急疏散过程和疏散状况。     

Estimation of the evacuation time in an emergency situation in hospitals

In this paper, a prediction model is presented that estimates the evacuation time in an emergency situation for hospitals. The model is generic enough to be used in various hospital settings. This model can provide incident managers with estimates of the evacuation times of different types of patients and can offer support to the managers with their resource allocation decisions in emergency situations. The major advantage of the prediction model is that the computation time is very short and the model does not need a lengthy and costly design. The model was applied for several different evacuation scenarios and the results were compared with those of a simulation model which had already been designed for use by the hospital. The comparison shows that the prediction model can provide estimates of the evacuation time that are similar to the results found by using costly and time-consuming simulation models.     

A social force evacuation model driven by video data

This paper proposes a video data-driven social force model for simulating crowd evacuation. The initialization of pedestrian position, path navigation, and goal selection in the improved social force model was guided by real video data. To initialize pedestrian position and determine path navigation, the distribution of the pedestrians is set according to the real video. We also extracted the trajectories of pedestrian movement from the videos, and these trajectories were stored into a path set to guide the evacuation of pedestrians. Moreover, a fitness function was defined to model the behavior of a pedestrian goal selection. The fitness function could process the evacuation parameters, which were extracted from the video, and consider the degree and distance of exit congestion. Furthermore, we quantified the relationship values among pedestrians, and a new force called “group force” was added to the primary social force model. Pedestrians with close relationship gathered into one group and walked together. To validate the effectiveness of the proposed method, the video data-driven model was applied to simulate campus halls and roads. Simulation results show that the proposed approach is consistent with real-world situations and can assist in analyzing emergency evacuation scenarios.     

An efficient partially dedicated strategy for evacuation of a heterogeneous population

When a heterogeneous population evacuates from a high-rise building, residents who occupy a large area and move at a slow pace, such as people in wheelchairs, tend to block pathways and significantly affect the evacuation of other occupants. In this study, we propose a partially dedicated evacuation strategy, in which an evacuation path is dedicated to a high-speed subpopulation of people without disabilities while another route is dedicated to the remaining heterogeneous population to minimize the blocking effect. The key factor in this strategy is determining the exact proportion of people without disabilities at each floor to each route. We use a time-expanded network flow model and a simulation-based optimization approach to solve the problem systematically. Simulation experiments show that the proposed evacuation strategy can reduce the average evacuation time of the entire population by 10%. The proposed partially dedicated evacuation strategy can be applied to other problems.     

ViCTS: A novel network partition algorithm for scalable agent-based modeling of mass evacuation

Emergency evacuation is a critical response to deadly disasters such as hurricanes, floods, and earthquakes, etc. However, mass emergency evacuation itself is a complex process that sometimes could lead to chaotic situations and unintended consequences. In many emergency scenarios, mass evacuation is necessary to cope with severe public threats within tight spatiotemporal ranges. To better understand complex phenomena like mass evacuation, and study possible consequences, agent-based models (ABMs) have been widely developed in previous work. Existing models simulate individual behaviors, posing computational challenges when applied to large geographic areas and sophisticated behaviors. A key strategy for resolving such computational challenges is to partition transportation networks into smaller regions and resolve corresponding computational costs by taking advantage of advanced cyberinfrastructure and cyberGIS. In this study, a novel network partition algorithm is developed to improve the scalability of agent-based modeling of mass evacuation based on a cutting-edge cyberGIS-enabled computational framework that exploits the spatial movement patterns of emergency evacuation. Specifically, the algorithm is termed as Voronoi Clustering based on Target-Shift, or ViCTS. It is enlightened by network Voronoi diagrams and designed to resolve computational scalability challenges caused by the unique characteristics of evacuation traffic. We conducted a set of computational experiments with real street network data in various evacuation scenarios to test the effectiveness and efficiency of the algorithm. Computational experiments show that ViCTS outperforms a widely used network partition algorithm for microscopic traffic simulation in terms of achieving optimal computational performance by balancing computational loads and reducing communications across high-performance parallel computing resources.     

Efficient computation of evacuation routes on a three-dimensional geometric network

We consider a real-time emergency evacuation problem that seeks to compute a set of rapid evacuation routes in a building. Given a three-dimensional geometric structure of the evacuation network, an emergency evacuation route is a sequence of movements of people away from the threat or actual occurrence of a hazard (such as a fire, a hidden bomb) to a safe exit in the network. In such a network each room/crossing/exit in the building is designated as a node and the corridors/staircases/links between the rooms are edges. The evacuation times assigned to the edges are normally distributed random variables. This stochastic routing problem subject to deadline constraints is NP-hard. We provide a new pseudo-polynomial-time dynamic programming algorithm to solve this problem. Based on this algorithm, we construct two types of approximation algorithm, namely a fully polynomial-time approximation scheme providing “almost-optimal” solutions and a fully polynomial-time approximately feasible scheme yielding a best “almost feasible” solution. We present a case study and results of computational experiments to illustrate the working and efficacy of the proposed solution methods, respectively.     

面向大型场馆疏散的改进多蚁群算法

针对大型场馆应急疏散的路径优化问题,提出了一种基于遗传算法交叉变异算子的多蚁群算法.该算法通过引入多蚁群信息素组的概念,将遗传算法交叉和变异的思想应用到信息素更新模型中,解决了传统蚁群算法易陷入局部最优的问题.最后,将此模型应用在武汉体育馆及其周边路网集成环境中.实验结果表明,该算法能够为大型场馆中大规模人群提供一个有效可行的疏散方案.     

Modeling cooperative and competitive behaviors in emergency evacuation: A game-theoretical approach

A game-theoretical model to study evacuees’ cooperative and competitive behaviors during an emergency evacuation is proposed. The model integrated with evacuation dynamics model determines the density of cooperative and competitive evacuees and their related evacuation times. Computer simulation results show that (1) as urgency of evacuation increases, cooperation among evacuees’ decreases; (2) in an emergency situation, individual hyper-rationality among evacuees diminishes evacuation efficiency; (3) the imitation effect enhances cooperation among evacuees, yet reduces evacuation efficiency. This study provides a methodological pattern to research crowd behaviors in emergency evacuation.     

An efficient dynamic route optimization for urban flooding evacuation based on Cellular Automata

Flooding has been one of the major issues that have seriously hampered the developments of the urban systems, as well as threatened the urban water environments. To mitigate the impacts of floods, it is highly important to identify effective evacuation plans with the aid of computer-based methods. However, effectively identifying the dynamic evacuation route based on the evolution of flooding status is difficult and challenging. To this end, this study develops a Cellular Automata-based Dynamic Route Optimization (CADRO) algorithm to identify the dynamic flood evacuation route (FER), where the hydrodynamics, topography and human response time are incorporated. The proposed CADRO is applied in a suburb of Yangzhou City, China, and results demonstrate the people trapped rate, the mean and maximum length of FERs are shortened by nearly 32.70%, 34.04% and 7.90%, respectively compared with the traditional A* algorithm used for evacuation route optimization. One important feature of the proposed CADRO is that it can identify the dynamical variation of terrain connectivity within the flooding process, thereby offering the optimal FERs. In addition, the impacts of the human response time to the FERs are investigated in this study. It is anticipated that the proposed CADRO can be greatly beneficial to the urban flooding risk management.     

基于改进蚁群算法的应急救援路线选择

应急救援路线的选择关系到应急救援的成败,合理有效地选择应急救援路线对挽救生命和财产具有重要意义,其属于组合优化问题。针对蚁群算法求解速度慢、算法稳定性差、易出现早熟或停滞等缺陷和应急救援路线选择的特点,主要研究了改进蚁群算法在应急救援路线选择中的应用并根据实际应用提出了应急救援路线选择的蚁群算法的数学模型,为城市应急救援路线选择提供了有效的解决方案。通过实验证明该模型可以应用到解决应急救援路线选择问题方面,具有快速、高效的特点。     

Optimized evacuation route based on crowd simulation

Computational Visual Media - An evacuation plan helps people move away from an area or a building. To assist rapid evacuation, we present an algorithm to compute the optimal route for each local...     

人群应急疏散可视仿真研究进展和问题

人群应急疏散可视仿真是用智能体来模拟具有自主感知、情绪和行为能力的人群个体,并采用3维可视的方式来直观呈现人群应急疏散情景,可以为制定人群应急预案提供形象直观的分析方法。本文从人群仿真数据的来源、人群导航模型的构建、人群行为模型、人群情绪感染、人群渲染5个方面概述目前研究的进展,然后从仿真模型的可验证性、人群疏散导航模型的构建、人与环境的物理模型、动物逃生实验与仿真、疏散中的社会行为表现以及人群情绪的可视计算6个角度讨论需要进一步研究的问题。针对需要深入研究的问题,指出借助于紧急事件的视频监控分析和虚拟人群情景的用户调查,有助于完善人群仿真模型。结合物理模型,可以更准确地描述人群应急疏散场景。开展动物逃生实验分析,有助于完善人群运动导航算法。建立人群社会行为模型,可以更详细描述疏散中人群行为的多样性。构建基于多通道感知的人群情绪感染计算方法,可以详尽描述情绪感染的过程。人群应急疏散行为的可视仿真研究在城市的安全管理方面具有重要的应用前景,但其研究仍存在很多亟待解决的问题,综合地运用多学科知识,完善实验手段是进一步推动研究的关键所在。     

Modeling human behavior during emergency evacuation using intelligent agents: A multi-agent simulation approach

It is costly and takes a lot of time for disaster employees to execute several evacuation drills for a building. One cannot glean information to advance the plan and blueprint of forthcoming buildings without executing many drills. We have developed a multi-agent system simulation application to aid in running several evacuation drills and theoretical situations. This paper combines the genetic algorithm (GA) with neural networks (NNs) and fuzzy logic (FL) to explore how intelligent agents can learn and adapt their behavior during an evacuation. The adaptive behavior focuses on the specific agents changing their behavior in the environment. The shared behavior of the agent places an emphasis on the crowd-modeling and emergency behavior in the multi-agent system. This paper provides a fuzzy individual model being developed for realistic modeling of human emotional behavior under normal and emergency conditions. It explores the impact of perception and emotions on the human behavior. We have established a novel intelligent agent with characteristics such as independence, collective ability, cooperativeness, and learning, which describes its final behavior. The contributions of this paper lie in our approach of utilizing a GA, NNs, and FL to model learning and adaptive behavior of agents in a multi-agent system. The planned application will help in executing numerous evacuation drills for what-if scenarios for social and cultural issues such as evacuation by integrating agent characteristics. This paper also compares our proposed multi-agent system with existing commercial evacuation tools as well as real-time evacuation drills for accuracy, building traffic characteristics, and the cumulative number of people exiting during evacuation. Our results show that the inclusion of GA, NNs, and fuzzy attributes made the evacuation time of the agents closer to the real-time evacuation drills.     

大型公共场所突发事件下人员疏散仿真研究

随着突发事件的频繁发生,人员疏散任务日益紧迫.针对突发事件下恐慌心理对人员疏散的影响,采用元胞自动机理论对人员疏散进行建模,提出逃生收益的概念,引入动态参数来描述逃生人员的逃生目的性及其周围的逃生环境,并分析了人员恐慌心理对逃生行人流产生的影响及由此形成的一些基本规律,如人群拱形分布、拥塞、间歇性出口人流等.仿真结果表明,运用逃生收益规则对人员紧急疏散的模拟很好地再现了人员紧急疏散过程及疏散状况,其模型更能体现真实的疏散情况.     

Behavioral,data-driven,agent-based evacuation simulation for building safety design using machine learning and discrete choice models

To improve occupant safety during building emergencies, evacuation simulations have been widely used for building safety design. Since occupant behavior is a determining factor for the outcome of building emergencies, accurately capturing how occupants make decisions and integrating occupants’ decision-making processes in evacuation simulations is important. In this study, based on the results of fire evacuation experiments in a virtual metro station, how different social (crowd flow) and environmental (visual access and vertical movement) factors would affect individuals’ wayfinding behavior was predicted using machine learning and discrete choice models. The trained models were further employed in agent-based evacuation simulations to examine crowd evacuation performance under different building design scenarios. Both the machine learning and discrete choice models could accurately predict individuals’ directional choices during emergency evacuations. Different building attributes could collectively influence occupant behavior, leading to distinct exit choices and evacuation times. While both the trained machine learning and discrete choice models generated similar results, the discrete choice model had better interpretability. Moreover, by comparing the trained models in this study with a model developed in a prior study, it was found that agents had significantly distinct responses to different building designs. Critical factors (e.g., type and size of buildings, occupants’ familiarity with the building) for the applicability of evacuation models were identified. Furthermore, recommendations were provided for future research that aims at employing evacuation simulations for building design evaluation and optimization.