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.     

Extended route choice model based on available evacuation route set and its application in crowd evacuation simulation

In this study, we propose an extended route choice model based on an available evacuation route set to simulate the selection of pedestrians in selecting an appropriate route during evacuation in emergency situations. In this model, four parameters (i.e., distance to available route, length of available route, level of congestion in available route, and capacity of available exit) affect the route choice of the pedestrian and the evacuation route set. In this study, the evacuation route set is created and optimized by a modified social force model and a route learning method. Experimental results show that the extended model can effectively reproduce crowd behavior in an emergency situation, which can assist in analyzing emergency evacuation scenarios. Moreover, two important conclusions regarding increasing evacuation efficiency show that the proposed model is in line with real-world situations.     

Event-driven modeling of elevator assisted evacuation in ultra high-rise buildings

In the past few decades, numerous ultra high-rise buildings have been erected in the metropolitan areas of many cities around the world. For the related building designers, building occupants and the governments, fire safety problem is certainly a major concern, especially after the collapse of World Trade Center in the 9/11 event. That disaster makes people reconsider ultra high-rise building evacuation strategies. Of the current strategies, using elevators in ultra high-rise buildings to assist evacuation seems to be promising in improving evacuation efficiency. To quantitatively evaluate elevator assisted evacuation process, an event-driven agent-based modeling approach is proposed in the present paper. This modeling approach could capture not only the movement characteristics of stair-using occupants but also the detailed elevator motion features. The combined effects of occupants’ and elevators’ parameters on the evacuation efficiency have been investigated. Results indicated that the model is helpful to reveal the dynamics of elevator assisted evacuation, and sometime, using elevators to move all occupants to ground safety point may not be an optimal solution.     

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.     

Stochastic network evacuation models

Stochastic network evacuation models are crucial for the safe evacuation of occupants from buildings. In this paper, we compare an analytical closed queueing network and a simulation model for analyzing the evacuation of occupants from a hospital. Evacuation times, arc congestion, and optimization of the evacuation routes and staff assigned to the evacuation network are included.     

Social influence in a virtual tunnel fire – Influence of conflicting information on evacuation behavior

Evacuation from a smoke filled tunnel requires quick decision-making and swift action from the tunnel occupants. Technical installations such as emergency signage aim to guide tunnel occupants to the closest emergency exits. However, conflicting information may come from the behavior of other tunnel occupants. We examined if and how conflicting social information may affect evacuation in terms of delayed and/or inadequate evacuation decisions and behaviors. To this end, forty participants were repeatedly situated in a virtual reality smoke filled tunnel with an emergency exit visible to one side of the participants. Four social influence conditions were realized. In the control condition participants were alone in the tunnel, while in the other three experimental conditions a virtual agent (VA) was present. In the no-conflict condition, the VA moved to the emergency exit. In the active conflict condition, the VA moved in the opposite direction of the emergency exit. In the passive conflict condition, the VA stayed passive. Participants were less likely to move to the emergency exit in the conflict conditions compared to the no-conflict condition. Pre-movement and movement times in the passive conflict condition were significantly delayed compared to all other conditions. Participants moved the longest distances in the passive conflict condition. These results support the hypothesis that social influence affects evacuation behavior, especially passive behavior of others can thwart an evacuation to safety.     

Lessons from the evacuation of the world trade centre, 9/11 2001 for the development of computer-based simulations

This paper reviews the state-of-the-art in evacuation simulations. These interactive computer based tools have been developed to help the owners and designers of large public buildings to assess the risks that occupants might face during emergency egress. The development of the Glasgow Evacuation Simulator is used to illustrate the existing generation of tools. This system uses Monte Carlo techniques to control individual and group movements during an evacuation. The end-user can interactively open and block emergency exits at any point. It is also possible to alter the priorities that individuals associate with particular exit routes. A final benefit is that the tool can derive evacuation simulations directly from existing architects, models; this reduces the cost of simulations and creates a more prominent role for these tools in the iterative development of large-scale public buildings. Empirical studies have been used to validate the GES system as a tool to support evacuation training. The development of these tools has been informed by numerous human factors studies and by recent accident investigations. For example the 2003 fire in the Station nightclub in Rhode Island illustrated the way in which most building occupants retrace their steps to an entrance even when there are alternate fire exits. The second half of the paper uses this introduction to criticise the existing state-of-the-art in evacuation simulations. These criticisms are based on a detailed study of the recent findings from the 9/11 Commission (2004). Ten different lessons are identified. Some relate to the need to better understand the role of building management and security systems in controlling egress from public buildings. Others relate to the human factors involved in coordinating distributed groups of emergency personnel who may be physically exhausted by the demands of an evacuation. Arguably, the most important findings centre on the need to model the ingress and egress of emergency personnel from these structures. The previous focus of nearly all-existing simulation tools has been on the evacuation of building occupants rather than on the safety of first responders. Thanks are due to J. Appleby, P. Cooper, A. Foss, S. Hailey and B. Jenks who were responsible for the design and implementation of the GES application. They also drove the development of the Boyd Orr evacuation scenarios that are used to illustrate the opening sections of this paper.     

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。     

考虑上客点选择的公交紧急疏散时空路网模型

为了在灾难发生后及时将被疏散人员引导到合适上客点,并优化随后的公交疏散路线,建立了一个公交紧急疏散的线性混合整数规划模型。新模型不仅考虑了合理的上客点的选择和相应的建造费用,并且利用时空网络技术细致刻画了公交疏散中被疏散者从疏散集结点到上客点的运行轨迹。与已有的研究多采用非线性整数规划技术相比,新模型的线性化特征可以大大提高了模型的求解效率。在算例分析部分通过改变上客点造价和疏散集结点数量等条件,对不同条件下的疏散方案进行了比较分析,验证了模型的有效性。     

A BIM-based visualization and warning system for fire rescue

Structural fires are common disasters. In Taiwan, about 100 firefighters die during fire rescues each year, primarily because they are unaware of the causes of the fire and unfamiliar with the location’s environment. Meanwhile, evacuees often die in the panic of evacuation. To solve these problems, this research proposes a Building Information Modeling (BIM)-based visualization and warning system for fire rescue. A fire dynamics simulator (FDS) simulates various conditions of structural fires in conjunction with the visualization and integration properties of BIM, and the simulation results for temperature, carbon monoxide, and visibility can be integrated and presented in the BIM model for briefing purposes before rescue operations begin. In addition, this research integrates Internet of Things (IoT) technology, which allows real-time situation monitoring. In the event of a fire, the BIM model will immediately display the situation of the fire scene and control LED escape route pointers according to the actual situation. The primary objective of this system is to provide useful information to firefighters such that they can be aware of the fire’s environment and create an effective rescue plan. Moreover, the automated LED escape route pointer may assist the building’s occupants to escape, provide the firefighters with valuable information, and allow them quickly to discover hazards so that the number of casualties can be minimized.     

Modelling pedestrian merging in stair evacuation in multi-purpose buildings

Despite the total evacuation time of occupants in a multi-storey building not being affected by pedestrian merging on stairs, the calculation of evacuation times in each individual floor depends on the pedestrian merging ratio. In fact, different merging ratios may cause an evacuation to take place from the top to the bottom or vice versa. A simplified simulation model for the calculation of evacuation time at each floor is presented here. This model allows the investigation of the impact of two main variables affecting the evacuation time at each floor, namely 1) different initial numbers of pedestrians at each floor (i.e. occupant load), and 2) different merging ratios at each floor. This means that the model allows the calculation of the evacuation time at each floor considering a building with different occupancy types at different floors (e.g. office, residential, commercial, etc.) and different merging ratios which may be caused by a different configuration of the landing door at each floor. The model is presented in this paper using a case study of a hypothetical building. A detailed discussion on the model assumptions, advantages and limitations is also provided.     

基于出行时间预算的有限理性出行者路网均衡模型

为研究有限理性出行者逐日出行中出发时刻及路径调整的出行行为,引入前景理论,分析出行者依据最大准点到达概率来选择出行时间预算,将此出行时间预算作为到达参考点,进而在给定参考点下选择前景值最大的路径出行,并利用前次流量分配结果调整下次出行时间预算,经过多次出行达到路网流量平衡及准点到达概率最大的稳定状态。基于出行时间预算和前景理论建立了双层模型进行路网逐日均衡配流,用遗传算法求解最佳出行时间预算,用相继平均法计算路径平衡流量。最后基于算例验证模型和算法,并设定不同的出行选择机制分析出行时间预算、路径前景值及准点到达概率三者间的博弈关系。     

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.     

煤矿井下避灾模型研究

为了研究井下矿工面对灾害时的避灾情况,利用基于Agent的建模仿真方法在RePast仿真平台上建立了煤矿井下避灾模型,并根据井下矿工的实际情况抽象出了Agent的种类.通过对井下矿工面对灾害时的行为模式进行分析,提取出了Agent在面对灾害时的行为决策.通过将Agent的行为决策进行量化,在仿真平台上对矿工的避灾路线实现了仿真.仿真结果表明：该模型能够实时动态地显示矿工的避灾情况,灾害发生时,利用该模型生成的避灾路线能够提高Agent避灾成功的比例.     

Exploratory research on reading cognition and escape-route planning using building evacuation plan diagrams

The purpose of evacuation plan diagrams is for readers to comprehend and then plan an evacuation route. However, comprehending such diagrams involves complex issues that have yet to be addressed by research. This study aims to investigate how Taiwanese people interpret evacuation plan diagrams in their buildings. Issues of interest include the amount of time that it takes for a member of the general public to read a diagram and the time that they spend planning their escape route. Correlated and influencing factors are analyzed. The floor plan of an actual department store was used as the diagram for cognitive testing. A method of stimulated measurement was conducted over the Internet. The results of the experiment showed that the time it takes to plan an escape route is about 1.1 to 2 times longer than its reading time. This indicates that there is a significant time difference between diagram interpretation and stimulated planning. It was found that the longer it takes to read a diagram, the longer it takes to plan an escape route. In addition, to understand the difference between interpretations by the general public versus those with an architectural background, an analysis showed that the general public takes two to three times longer than architectural professionals to read a diagram and plan an escape route. Consequently, improvements in reading diagrams could help in the planning of a more efficient escape route. Furthermore, through our analysis, we found that the design of diagram symbols must satisfy conventional use and also that diagrams must avoid the use of metaphorical and abstract symbols. Diagrams that follow our guidelines will generally result in more effective and efficient conveyance of the intended message, thereby assisting in an emergency.     

A Hybrid Route Choice Model for Dynamic Traffic Assignment

Network user equilibrium or user optimum is an ideal state that can hardly be achieved in real traffic. More often than not, every day traffic tends to be in disequilibrium rather than equilibrium, thanks to uncertainties in demand and supply of the network. In this paper we propose a hybrid route choice model for studying non-equilibrium traffic. It combines pre-trip route choice and en-route route choice to solve dynamic traffic assignment (DTA) in large-scale networks. Travelers are divided into two groups, habitual travelers and adaptive travelers. Habitual travelers strictly follow their pre-trip routes which can be generated in the way that major links, such as freeways or major arterial streets, are favored over minor links, while taking into account historical traffic information. Adaptive travelers are responsive to real-time information and willing to explore new routes from time to time. We apply the hybrid route choice model in a synthetic medium-scale network and a large-scale real network to assess its effect on the flow patterns and network performances, and compare them with those obtained from Predictive User Equilibrium (PUE) DTA. The results show that PUE-DTA usually produces considerably less congestion and less frequent queue spillback than the hybrid route choice model. The ratio between habitual and adaptive travelers is crucial in determining realistic flow and queuing patterns. Consistent with previous studies, we found that, in non-PUE DTA, supplying a medium sized group (usually less than 50%) of travelers real-time information is more beneficial to network performance than supplying the majority of travelers with real-time information. Finally, some suggestions are given on how to calibrate the hybrid route choice model in practice to produce realistic results.     

Agent-CA的体育场馆人群疏散模型

根据体育场馆人群疏散的特点与规律,提出一种基于多智能体和元胞自动机相融合的大型体育场馆人群疏散模型（Agent-CA）。将元胞空间中被虚拟人个体占据的元胞视为一个独立的智能体,将元胞及其状态进行封装,扩展为具有自主性的智能体,通过设计各种人群疏散行为策略做为演化规则,实现个体的差异性以体现个人个性、体力、心理等对疏散行为的影响,对体育场馆的人群疏散进行仿真实验。结果表明,Agent-CA综合了多智能体和元胞自动机的优点,充分考虑了个体内在因素,更接近现实大型体育场馆的人群疏散情形,缩短了疏散时间。     

A hybrid hierarchical agent-based simulation approach for buildings indoor layout evaluation based on the post-earthquake evacuation

In the aftermath of severe earthquakes, building occupants evacuation behaviour is a vital indicator of the performance of an indoor building design. However, earthquake evacuation has been systematically neglected in the current building design practice. Arguably, one of the primary reasons for this is that post-earthquake evacuation behaviour is complex and distinct from all other types of evacuation behaviours such as fire. Thus, a comprehensive approach to considering the integration of human evacuation behaviour and a building's indoor layout design, mainly focused on non-structural damage, has been consistently neglected in the literature. In this paper, a hierarchical hybrid Agent-Based Model (ABM) framework integrated with a Cellular Automata (CA) and a 2D Building Information Model (BIM) damage visualisation to consider an approximation of non-structural damage has been developed. The proposed ABM incorporates learning mechanisms and human psychological aspects influencing evacuees' utility during the navigation process. The proposed approach was verified by comparing the results to previous real-life post-earthquake evacuation data and a “model to model” comparison of results from the existing relevant studies. The model prototype was successfully tested to simulate the pedestrian evacuation process from one floor of the new engineering building at The University of Auckland, New Zealand. The proposed simulation approach has been carried out for two different internal layout design alternatives where five population sizes are evacuated through different scenarios. The outputs from this study can be used to improve the design's compatibility of the building's indoor layout with the occupants' post-earthquake evacuation behaviour.     

An immersive virtual reality serious game to enhance earthquake behavioral responses and post-earthquake evacuation preparedness in buildings

Enhancing the earthquake behavioral responses and post-earthquake evacuation preparedness of building occupants is beneficial to increasing their chances of survival and reducing casualties after the mainshock of an earthquake. Traditionally, training approaches such as seminars, posters, videos or drills are applied to enhance preparedness. However, they are not highly engaging and have limited sensory capabilities to mimic life-threatening scenarios for the purpose of training potential participants. Immersive Virtual Reality (IVR) and Serious Games (SG) as innovative digital technologies can be used to create training tools to overcome these limitations. In this study, we propose an IVR SG-based training system to improve earthquake behavioral responses and post-earthquake evacuation preparedness. Auckland City Hospital was chosen as a case study to test our IVR SG training system. A set of training objectives based on best evacuation practice has been identified and embedded into several training scenarios of the IVR SG. Hospital staff (healthcare and administrative professionals) and visitors were recruited as participants to be exposed to these training scenarios. Participants’ preparedness has been measured along two dimensions: 1) Knowledge about best evacuation practice; 2) Self-efficacy in dealing with earthquake emergencies. Assessment results showed that there was a significant knowledge and self-efficacy increase after the training. In addition, participants acknowledged that it was easy, helpful, and engaging to learn best evacuation practice knowledge through the IVR SG training system.     

Simulation-based evaluation of evacuation effectiveness using driving behavior sensitivity analysis

Worldwide, man-made or natural phenomena occasionally occur that create emergency conditions and require the evacuation of areas of different sizes and characteristics. Drivers’ behavior becomes a very important factor for the evacuation operations. This paper provides an analytical study of the effectiveness of evacuation according to drivers’ behavior, using the sensitivity analysis method. Collecting real-time data about this factor is a difficult to impossible task for large scale cases; therefore, traffic simulation is the most appropriate method for analysis. Our goal is to investigate how drivers’ aggressiveness affects the evacuation effectiveness. In this case, we used the AIMSUN traffic simulation model; the parameters of the driver behavior models are chosen through all-at-once sensitivity analysis of the parameters. This model is applied to different demand scenarios for well-defined parameters’ value ranges. This investigation produces estimated ranges of the evacuation duration and the number of evacuated people, both for a baseline “do-nothing” scenario, as well as the outcome of improvement actions. The sensitivity analysis results suggest that evacuation time can be significantly reduced by reversing the most congested links; furthermore, the use of a bus fleet would allow many more people to evacuate the danger zone timely, albeit with a small increase in minimum evacuation time. This methodology could be applicable to other emergency response scenarios, as it obviates the need for real-time data.