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With public safety receiving widespread attention from society, the question of how to effectively evacuate crowds has become a key issue. Leaders can provide pedestrians with clear and accurate route information and play an important role in daily crowd management and emergency safety evacuation. In this study, an evacuation model with static guidance considering the leader's influence and the pedestrians' decision-making behavior is proposed. The model is validated using experimental data, including evacuation behavior, evacuation time, and the percentage of the cumulative number of evacuees over time, and the simulation results match the experimental results well. Then, the model is applied to investigate the effect of different locations, numbers of static leaders, and different pedestrian distributions on evacuation efficiency in a room with unavailable exits. The results show that a leader located in the center of each potential exit can improve the overall evacuation efficiency, and the farther the guided pedestrian was from the correct exit, the better the overall evacuation performance of pedestrians. The distance parameter of multiple leaders is defined, and an optimal number of leaders exists in each specific scenario due to the overlap of leaders' influencing areas. Furthermore, whether the pedestrians are uniformly or non-uniformly distributed, the evacuation time is shorter when the guided pedestrians are located farther from the correct exit. These findings can provide suggestions for crowd management and the arrangement of leaders in emergency evacuations. [ABSTRACT FROM AUTHOR]

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

To decrease liquid ammonia leakage-induced casualties of ammonia chemical enterprises, a simulation comparison method of liquid ammonia toxicity leakage and crowd evacuation was proposed, a spatial model was constructed with Pyrosim software, using 8 liquid ammonia tanks as a layout example. The ammonia diffusion path under eight leakage scenes was analysed with FDS software by setting eight ammonia leakage points. The personnel evacuation model was built with the Pathfinder software, obtaining the crowd evacuation time under the eight scenes. Finally, the optimal site for liquid ammonia tanks in this case was determined through a comparison of ammonia diffusion path and evacuation time. Results show that among the eight candidate sites for ammonia tanks, the Leakage Scene 4 shows the longest time (78 s) for diffusion to the two evacuation exits, which is longer than the crowd emergency evacuation time (73 s) in the scene. Therefore, the Leakage Scene 4 can be used for the setting of liquid ammonia tanks. The obtained conclusions provide a safe suggestion for the scientific planning of the liquid ammonia storage sites of chemical enterprises. [ABSTRACT FROM AUTHOR]

Most existing crowd evacuation methods focus on internal factors and do not consider the influence of the external environment factors, producing unrealistic global behavior occurs when individuals are moving through the crowded space. As an essential part of a building, safety indication signs (SISs) are a form of environmental information and perceptual access that play an important role in promoting wayfinding by virtue of their guiding role in movement direction and route selection by providing guidance, warning and mandatory message to people. In this paper, we propose an innovative crowd simulation method guided by SISs with reinforcement learning strategy for use in emergencies. To illustratethe guiding function of the SISs, we establish a guidance field for each SIS and add the attractive force to the guidance field. Besides, we formulate the multi-agent (crowd) navigation problem as an action-selection problem and design a novel reinforcement learning strategy for driving individuals to accomplish collision-free movement more efficiently. Particularly, we define a state transition strategy between the SIS area and the non-SIS area to achieve continuity of guidance. We use extensive simulations to highlight the potential of our method in different scenarios and evaluate the results in terms of evacuation efficiency and the reasonableness of SIS placement. In practice, our system runs at interactive rates and can solve complex planning problems involving one or more groups. [ABSTRACT FROM AUTHOR]

A kinetic modeling of crowd evacuation with leaders and followers is considered in this paper, in which the followers may not know the full information about the walking environment and the evacuation strategy, but they follow the leaders and learn the walking strategy to get out of the walking venue. Based on the kinetic theory of active particles, the learning dynamics are considered by introducing an activity variable u , which represents the learning level of the followers and measures how much knowledge a follower has learned about the walking strategy, the walking environment, or the geometry of the walking venue. Several fundamental factors are considered in this leader–follower learning system of crowd evacuation, including: (1) the rational motion of all pedestrians, i.e. the trend to the exit or to a preferred direction, the ability to avoid collisions with walls or obstacles, and the tendency to search for less crowded direction with minimal density gradient, (2) the irrational motion of followers to follow other pedestrians induced by panic, (3) the learning dynamics of the followers who learn the walking strategy during interaction with others, and, (4) the transition from a follower to a leader when one's activity reaches the highest level of learning. A numerical comparison of a metro platform evacuation with and without learning shows a reasonably good predictive ability of the model that the learning effect plays a significant role in the evacuation dynamics. [ABSTRACT FROM AUTHOR]

Emergency signs play an important role in evacuation guidance, and some works show that the behavior of the crowd in the process of evacuation is easily affected by emotion and personality. However, existing crowd simulation methods rarely involve the interaction between emotion and emergency signs. Thus, a crowd evacuation simulation method considering emotion and emergency signs is proposed to simulate the crowd evacuation behavior under the guidance with signs, and it is verified by simulation experiments through Unity3D. The method provides a novel research perspective for guided crowd evacuation simulation, and it can make simulation results more real to the actual scenes. [ABSTRACT FROM AUTHOR]

• The fire risk model is established. • The Dynamic Avoid Smoking A-Star (DASA-Star) algorithm is constructed. • The evacuation path planning of subway station fire environment is simulated. • The algorithm simulation verifies the effectiveness of DASA-Star algorithm. Considerable casualties can easily be caused once a fire occurs in a metro station, and a safe and effective evacuation path in time should be provided, taking on critical significance in the rapid evacuation of the crowd. In existing research, crowd evacuation paths have been generally planned without considering the fire environment and the real-time effect of fire products on the evacuation paths, such that the planned paths do not fit the realistic environment of crowd evacuation. An algorithm is proposed in this study to dynamically plan evacuation paths in fire scenarios in metro stations. First, the equivalent length of the path under the effect of fire on evacuation speed is determined in accordance with the effects of ambient temperature, visibility, and CO concentration, and then the fire risk model is built. Second, the fire risk model is incorporated into the A-Star algorithm, and the evaluation function of the A-Star algorithm is optimized, such that the determined path is capable of avoiding areas with higher fire risk. Subsequently, a dynamic update mechanism considering time factor is introduced to update the search environment information data of the algorithm in real time for dynamic path planning, with the aim of coping with the dynamically changing fire environment. Lastly, the A-Star algorithm is optimized, and the Dynamic Avoid-Smoke A-Star (DASA-Star) algorithm is built. As indicated by the simulation results, the DASA-Star algorithm is capable of making a trade-off between the fire risk and geometric length of the path in an underground station fire scenario and achieving dynamic planning of evacuation paths based on FDS fire simulation results. Accordingly, the algorithm conforms to the real-time requirements of path planning under fire conditions, and it is capable of more effectively planning the optimal evacuation path under the effect of fire. [ABSTRACT FROM AUTHOR]

To avoid crowd evacuation simulations depending on 2D environments and real data, we propose a framework for crowd evacuation modeling and simulation by applying deep reinforcement learning (DRL) and 3D physical environments (3DPEs). In 3DPEs, we construct simulation scenarios from the aspects of geometry, semantics and physics, which include the environment, the agents and their interactions, and provide training samples for DRL. In DRL, we design a double branch feature extraction combined actor and critic network as the DRL policy and value function and use a clipped surrogate objective with polynomial decay to update the policy. With a unified configuration, we conduct evacuation simulations. In scenarios with one exit, we reproduce and verify the bottleneck effect of congested crowds and explore the impact of exit width and agent characteristics (number, mass and height) on evacuation. In scenarios with two exits and a uniform (nonuniform) distribution of agents, we explore the impact of exit characteristics (width and relative position) and agent characteristics (height, initial location and distribution) on agent exit selection and evacuation. Overall, interactive 3DPEs and unified DRL enable agents to adapt to different evacuation scenarios to simulate crowd evacuation and explore the laws of crowd evacuation. [ABSTRACT FROM AUTHOR]

In order to cope with limited land availability and the increasing urban population, more high-rise buildings are being built throughout cities, but this has also led to new challenges in the emergency evacuation of people. Therefore, we conducted an evacuation experiment of a group of people in the stairwells of high-rise buildings to explore the movement law of groups in stairwells. The experiment had 18 scenes, including the evacuation movement of an independent group and the crowd evacuation movement of different groups. Analyzing the independent groups' movement showed that regardless of the type of group, the average speed of the crowd was more than 1.2 m/s, which was longer than that of previous studies. The more constraints in the evacuation process, the smaller the speed difference between males and females. Group cohesion was mostly concentrated at about 1 s. Moreover, group velocity and cohesion were found to have no significant correlation in stairwell movement. Analyzing the crowd evacuation time of eight different scenes showed that it took longer for crowds bearing weight to evacuate than those not bearing weight. According to the spatiotemporal evolution of crowd evacuation in stairwells, the crowd moves intermittently in the evacuation process, especially in the early and middle stages of evacuation, and the influence of group factors on this process was not obvious. Regardless of the kind of scene or stairwell, the number of pedestrians generally first increased and then decreased to a significantly low value, and the number of pedestrians reached about 30 in the scenario of people without any load. Additionally, the speed of the pedestrians entering the stairwell in the back of the group was not faster than that of the pedestrians in the front, which showed that pedestrians rarely engaged in overtaking behavior. The research results of this paper not only enrich evacuation research but also provide support for the design of high-rise stairs. [ABSTRACT FROM AUTHOR]

Human crowds have become hotspot research, particularly in crowd analysis to ensure human safety. Adaptations of machine learning (ML) approaches, especially deep learning, play a vital role in the applications of evacuation, detection, and prediction pertaining to crowd analysis. Further development in the analysis of crowd is needed to understand human behaviors due to the fast growth of crowd in urban megacities. This article presents a comprehensive review of crowd analysis ML-based systems, where it is categorized with respect to its purposes, viz. crowd evacuation that provides efficient evacuation routes, abnormality detection that could detect the occurrence of any irregular movement or behavior, and crowd prediction that could foresee the occurrence of any possible disasters or predict pedestrian trajectory. Moreover, this article reviews the applied techniques of machine learning with a brief discussion on the used software and simulation platforms. This work also classifies crowd evacuation into data-driven methods and goal-driven learning methods that have attracted significant attention due to their potential to adopt virtual agents with learning capabilities. This review finds that convolutional neural networks and recurrent neural networks have shown superiority in abnormality detection and prediction, whereas deep reinforcement learning has shown potential performance in the development of human level capacities of reasoning. These three methods contribute to the modeling and understanding of pedestrian behavior and will enhance further development in crowd analysis to ensure human safety. [ABSTRACT FROM AUTHOR]

This work deals with the optimal design for the location of the exit doors at meeting places (such as sports centers, public squares, street markets, transport stations, etc.) to guarantee a safer emergency evacuation in events of a sporting, social, entertainment or religious type. This problem is stated as an optimal control problem of nonlinear partial differential equations, where the state system is a reformulation of the Hughes model (coupling the eikonal equation for a density-weighted walking velocity of pedestrians and the continuity equation for conservation of the pedestrian density), the control is the location of the exit doors at the domain boundary (subject to several geometric constraints), and the cost function is related to the evacuation rate. We provide a full numerical algorithm for solving the problem (a finite element technique for the discretization and a gradient-free procedure for the optimization), and show several numerical results for a realistic case. [ABSTRACT FROM AUTHOR]

• A FFCA model considering the stampede accidents on inclined staircases is proposed. • Stampede phenomena are based on crowd forces, slopes, and derived probabilities. • The arching, clogging and pedestrian domino effect were observed and analyzed. • The effects of unbalance and stampede of pedestrians on evacuation was revealed. • Certain supplements are made to the research in existing stampede incidents. An extended floor field cellular automata (FFCA) model considering the stampede accidents on inclined staircases is proposed to study shoving behavior and pedestrian dynamics. In this model, two stampede evolution pathways are investigated: Pedestrians falling after losing balance, and falling directly due to being crowded. The results show that this model could trigger some characteristics of real irrational evacuation processes, such as: (1) the mutual crowding and shoving among pedestrians; (2) the unbalance phenomenon on inclined staircases; (3) the effect of pedestrians falling like dominoes, which is consistent with the findings of most stampede investigations to some extent. The proposed model considers the impact of fallen pedestrians on the movement of ordinary pedestrians, which shows a reduction in the overall evacuation efficiency. Moreover, the steeper the slope, the greater the risk and severity of injuries during the crowded evacuation in this scenario. Additionally, falling phenomena of pedestrians show a certain lag related to the state of unbalance. Unbalanced pedestrians tend to appear from the rear to the front successively, and fallings often occur some time later the onset of unbalance, progressing from front to rear. This pattern reflects the "domino effect" among pedestrians. Lastly, unbalanced pedestrians constitute a significant portion of the total injured pedestrians. Considering the time delay of fallings after being unbalanced, the importance of early emergency response and intervention during crowded evacuations is emphasized. It is expected to provide some theoretical support for safety management. [ABSTRACT FROM AUTHOR]

The evacuation of human crowds consisting of several groups in complex venues is studied, in this paper, by the kinetic theory of active particles. We consider a complex venue that consists of several chambers which are separated by interior walls and doors, for which the geometrical effects may come from the walls both on the boundary and inside the domain, the exits, and the doors connecting the chambers. The geometrical properties are incorporated in the geometrical preferred direction, which is specified such that an individual can avoid the wall and approach the exit through the evacuation vector. The notion of group is corresponding to the concept of functional subsystem in the mathematical theory of active particles. In the present framework of several groups, the rules of interactions can be specified for both the interactions among the same group and the interactions between different groups. Furthermore, the motility is assigned for each group, which serves as the average activity of a group. Some interesting features and phenomena are shown in the numerical tests of the evacuation of a crowd with one, two or three groups. [ABSTRACT FROM AUTHOR]

During an earthquake, interior nonstructural components of a building will be damaged. The damaged objects will obstruct pedestrians' evacuation routes and increase casualties. But this issue has received scant attention in evacuation simulation research. This paper focuses on this issue and proposes an indoor seismic evacuation model to simulate crowd evacuation in a dynamic environment. A physical model of nonstructural components is presented to simulate the dynamics of the indoor scenario. The flow field algorithm is constructed to guide pedestrian's avoidance behaviors globally to reflect the impact of environmental changes on indoor crowd path selection, and a modified social force model is built to simulate the joint influence of seismic forces and the environment on pedestrian motion states. The results of the experiments shows that the proposed model can generate realistic evacuation scene and rational evacuation routes in the earthquake. [ABSTRACT FROM AUTHOR]

SUMMARY Many models have been developed in recent years to describe pedestrian group behavior. Most prior research on group behavior focuses mainly on small groups and does not provide a general approach for grouping pedestrians with respect to different group sizes. They also analyze pedestrian group dynamics in normal circumstances. However, people's reactions who are aware of an emergency are not the same as how they respond to normal or usual situations. The central focus of this paper is to study pedestrian group behavior during evacuation. To accomplish these objectives, a new model is proposed with some extensions of a social force model in its emergency context. The new extensions are intended to simulate the single leader‐centered and group‐centered crowd behavior in emergencies. Besides small pedestrian groups, larger ones are also considered, and extensions are added in this direction. The model is calibrated using the results of an existing experimental study. Then, the model is verified by testing for the occurrence of certain emergent patterns. The results indicate that the proposed model can capture these trends. Additionally, the model is validated by comparing the simulation results with experimental results available in the literature. Also, several different evacuation scenarios are used to evaluate the proposed model. The simulation results show that leader‐centered behavior performs better than group‐centered behavior with respect to the evacuation time for small groups. The number of leaders also affects the evacuation time. Moreover, an increase in the number of leaders positively influences the effect of the width of the door on evacuation time, but the size of this effect depends on the number of leaders. Finally, group‐centered behavior results in less evacuation time than leader‐centered behavior in the presence of multiple exits, and leader‐centered (also called single leader‐centered in this study) behavior causes a more unbalanced use of the exits than group‐centered behavior. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Previous studies indicate that congestion is more likely to occur in the exit restriction area. Besides the background music in public places may have a potential impact on pedestrian movement. However, the influence mechanisms of low-boundary exit and background music on pedestrian evacuation are still unclear. Therefore, in this paper, the evacuation characteristics of pedestrians at a low-boundary exit were investigated with a series of controlled experiments. Different commands (listening to music while walking and following the tempo of the music) and different types of music (music and metronome) were designed in the experiment to analyze the influence of sound signal on the evacuation process. The result indicates that a larger width of low-boundary exit leads to a shorter evacuation time. The flow of low-boundary exit is higher than that of high-boundary exit. In addition, it is found that sound signal has a negative effect on evacuation efficiency, and the influence level is varied with different commands and music types. Specifically, the flow of following the beat of music is lower than that of listening to music, while the flow of walking with the beat of metronome is higher than that of walking with the beat of music. This study can contribute to understanding the evacuation dynamics of pedestrians when passing through a low-boundary exit. Furthermore, the performance of sound signal can be useful to develop management strategies for pedestrians under the emergency evacuation. [ABSTRACT FROM AUTHOR]

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

Understanding human response to crowd emergencies is extremely complex, and it plays a significant role in engineering construction designs and crowd safety. Individual choices, reasoning, and behaviours cannot be fully described by equations or rule-based methods. Accordingly, this research proposes a neuro-symbolic approach for modelling agents with human-level capabilities of reasoning and performance in an emergency evacuation. The proposed neuro-symbolic approach combines deep reinforcement learning with evaluative fuzzy logic to address the challenges of large amounts of required data, time, and trials-and-errors for policy optimization and to handle the assumption of reward function that may not be practical in real scenarios. This neuro-symbolic model has the potential to deal with the complexity of the environment and decision-making process via deep reinforcement learning and enhances the cognitive and visual intelligence via an evaluative fuzzy function, which continuously evaluates agent actions during the training process to boost pedestrian active response to their surroundings, with full awareness of time, thereby, the human-level capacity of reasoning. Moreover, this proposed model optimizes the computational demands of deep reinforcement learning and enables faster learning of new situations. The findings indicate that the proposed model can produce behavioural patterns that align with real observations of crowd evacuation, such as laminar flow, stop-and-go flow, and crowd turbulence. On top of that, a new evacuation behaviour is observed, as some pedestrians avoid congestion at the exit until the density reduces which reflects a level of human reasoning. The proposed model illustrates a higher accuracy and much faster converge than the pure proximal policy optimization model with substantially minimal training timesteps of as little percentage as 2 to 8. Meanwhile, the reliability study records an increase of the mean and standard deviation of evacuation time from 39.7 s, 1.06 to 155.09 s, 7.39 as crowd size increases from 15 to 200 pedestrians, which implies a rise of uncertainty. Therefore, we perceive that this work can provide crowd authorities and construction engineers with insights into complex behaviour and critical conditions to make better evacuation plans and sustainable designs to ensure crowd safety. It also provides a promising alternative to the evident lack of data on critical crowd conditions. [ABSTRACT FROM AUTHOR]

This paper proposes an extended floor field cellular automaton (FFCA) model for evacuation simulations, which integrates the natural step length into pedestrian movement. The model enables pedestrians to occupy multiple grids and extends the interaction area to a disk with a radius determined by the natural step length. These modifications facilitate pedestrian movement in various directions, limited only by the chosen numerical resolution. The model is calibrated to reproduce known density-velocity relations by simulating different evacuation scenarios. The flow rate through the bottleneck scenario matches the experimental results very well. Interestingly, the evacuation time, crowd shape near the exit and average conflict frequency are found to be independent of the discretisation degree, which is different from previous work. Compared with the traditional FFCA model, the proposed model yields larger and more reasonable velocity variation and evacuation path. The main parameters have a similar influence on the evacuation time, indicating that the proposed model retains the robustness of the traditional FFCA model. [ABSTRACT FROM AUTHOR]

Understanding how crowds behave in an emergency has long been held to be necessary to emergency response and management. Competition due to mass panic is often assumed to be a natural response during emergencies, which is in contrast with the current psychological research that evacuees rarely demonstrate competitive behaviors, but do exhibit cooperative behaviors. In this work cooperative behaviors and evacuation efficiency has been examined in detail by using a cellular automata evacuation model from a game-theoretical perspective. Simulation results show that the self-regarding evacuees are able to be cooperative to each other when faced with an emergency. It is found that during an emergency evacuation high levels of escape aspiration promote cooperation among evacuees while in non-emergency situations they inhibit cooperative behaviors, and heterogeneous learning rates for different strategies can lead to various levels of cooperation. This work provides a new insight into crowd behaviors in an emergency evacuation. [ABSTRACT FROM AUTHOR]

An extended floor field cellular automata model considering crowding and trampling damage mechanism under the internal crushing is proposed to study pedestrian dynamics with crowded pushing and shoving behavior under irrational conditions. In this model, two damage modes of pedestrian evacuation: mechanical asphyxiation due to crushing and fall due to crowding, are investigated by constructing injury gradients and fall probabilities. The results show that the model could reproduce some real features of irrational evacuation to some extent, such as (1) the mutual pushing and crowding of pedestrians; (2) the trampling and crushing damage phenomena that often occur in real trampling accidents; and (3) the accident location is located near the exit, which is consistent with the findings of most trampling accident investigations. In addition, the crowding resultant force acting on someonce within the crowd displays that the magnitude of crowding force gradually decreasing with the distance away from the exit, and the peak crowding force appears at the arch crowd in the region near the exit, which is very easy to cause pedestrian crushing damage. When the initial number of pedestrians is small, almost no crushing damage occurs, and the damage mechanism in the scene is mainly crowded fall; with the increase of the initial number of pedestrians, the number of pedestrians damaged by crushing will increase exponentially and become the main factor of pedestrian casualties. Crowding and trampling damage occurs obviously near the exits, but as the initial number of pedestrians increases, the number of crushing damage grows rapidly away from the exits and gradually exceeds that of relatively near the exits. Therefore, the safety management near the exit should be strengthened to ensure the personal safety of pedestrians in front of the exit. • A floor-field model considering crowding and trampling damages is proposed. • Damage phenomena are based on crowd forces, density, and derived probabilities. • The arching, clogging and pedestrian damage phenomena were observed and analyzed. • The effects of mechanical asphyxiation and trampling on evacuation was revealed. • Certain supplements are made to the research in existing stampede incidents. [ABSTRACT FROM AUTHOR]

Airports, shopping malls, stadiums, and large venues in general, can become congested and chaotic at peak times or in emergency situations. Linear Model Predictive Control (MPC) is an effective technology in generating dynamic speed or distance instructions for regulating pedestrian flows, and constitutes a promising interventional technique to improve safety and evacuation time during emergency egress operations. We compare linear and nonlinear MPC controllers and study the influence of using continuous vs. discrete control actions. We aim to evaluate the efficacy of simple instructions that pedestrians can easily follow during evacuations. Linear and Nonlinear AutoRegressive eXogenous models (ARX and NLARX) for prediction are identified from input–output data from strategically designed microscopic evacuation simulations. A microscopic simulation framework is used to design and validate different MPC controllers tuned and refined using the identified models. We evaluate the prediction models' performance and study how the controlled variable type, density, or crowd-pressure, influences the controllers' performance. As a relevant contribution, we show that MPC control with discrete instructions is ideally suited to design and deploy practical pedestrian flow control systems. We found that an adequate size of the set of speed instructions is critical to obtain a good balance between controllability and performance, and that density output control is preferred over crowd-pressure. • Linear and nonlinear MPC controllers for regulating pedestrian flows. • Linear and nonlinear prediction of crowd dynamics using ARX models. • Discrete instructions as control actions for regulating pedestrian flows using MPC control. • Comparing the performance of MPC controllers when regulating pedestrian density or crowd-pressure. [ABSTRACT FROM AUTHOR]

The current evacuation model does not consider the impact of emotion and personality on crowd evacuation. Thus, there is large difference between evacuation results and the real-life behavior of the crowd. In order to generate more realistic crowd evacuation results, we present a method of emotion contagion for crowd evacuation. First, we combine OCEAN (Openness, Extroversion, Agreeableness, Neuroticism, Conscientiousness) model and SIS (Susceptible Infected Susceptible) model to construct the P-SIS (Personalized SIS) emotional contagion model. The P-SIS model shows the diversity of individuals in crowd effectively. Second, we couple the P-SIS model with the social force model to simulate emotional contagion on crowd evacuation. Finally, the photo-realistic rendering method is employed to obtain the animation of crowd evacuation. Experimental results show that our method can simulate crowd evacuation realistically and has guiding significance for crowd evacuation in the emergency circumstances. [ABSTRACT FROM AUTHOR]

In the study of pedestrian movements, a consideration of group behaviors is important because of their potential impacts on pedestrian flow dynamics. In this paper, we investigate the group behaviors during emergency evacuation, which is a critical case for emergency crowd management but has not been fully explored and understood. It has been well recognized that in evacuation situations, some people within a crowd, especially those who are with families and friends, often move in small groups and act in particular patterns distinct from individuals. As a result, the crowd is a mixture of individuals and groups rather than a pure collection of individuals. To capture and evaluate the influence of group behaviors on crowd evacuation, we propose an extended floor field cellular automaton (CA) model that takes into account such phenomena. Our model is formulated by leveraging the leader-follower behavior rule that is evident in pedestrian group behaviors. To calibrate and validate the proposed model, a few field experiments of crowd evacuation were conducted in a university building. Through a representative case study, it is demonstrated that the proposed extended floor field CA model can replicate the well-known phenomena in crowd evacuation such as collective arch-like clogging at the exit as well as other commonly observed group behaviors in evacuation. Moreover, it is found that the total crowd evacuation time significantly increases with the presence of pedestrian groups in the crowd. The results also show that such negative effects of group behaviors in crowd evacuation intensify when the density of the crowd is higher. Subsequently, sensitivity analyses are performed to further explore how pedestrian group behaviors are influenced by model parameters that reflect the pedestrian flow dynamics in evacuation scenarios. With its capability of realistically replicating the field pedestrian evacuation, the proposed model can serve as a valuable tool for predicting crowd evacuation time and designing guidelines for pedestrian evacuation in emergency situations, in particular when group behaviors are salient. [ABSTRACT FROM AUTHOR]

This study proposes an evacuation guidance model for heterogeneous populations to investigate the effects of guidance strategies on the evacuation process in large-scale pedestrian facilities with multiple exits. The guidance model integrates an exit choice model and a microscopic pedestrian simulation model with discrete space representation. The exit choice model, considering the travel distance to each exit, the level of congestion and the movement speed of pedestrians, generates the allocation of exits to pedestrians. The microscopic simulation model depicts the pedestrian movements during the evacuation under guidance. Numerical simulations are conducted to evaluate the performance of the guidance strategy. Simulation results indicate that by balancing the time interval for sending evacuation instruction and the frequency of the target exit changes, better evacuation performance can be achieved. Comparison between two cases with and without instruction reveals that the performance of the guidance strategy is closely related to the pedestrian density level and the heterogeneous speeds of pedestrians. The evacuation performance is also affected by the compliance behavior and non-compliance behavior of pedestrians. The findings can provide theoretical suggestions for the effective management of crowd movement at large events. • An evacuation guidance model is proposed to investigate the performance of guidance strategy. • The time interval for instruction and inertia to maintaining recommendation are considered. • The impact of guidance strategy on evacuation dynamics is studied. • The performance of guided evacuation is closely related to density and heterogeneity. [ABSTRACT FROM AUTHOR]

Casualties during crowd evacuation in many unexpected events are closely related to panic behaviors. In this paper the evacuation crowd system is abstracted into a dynamic complex network composed of three types of people, namely calm people, panic people, and herding people, as well as their interactions. The mathematical model of these interactions is established based on complex network theory. Further, the evolution of herding people to panic people is interpreted by a specific concept of “herding–panic threshold,” as well as its utility threshold model, in which the own characteristics of herding people and the external pressure from the surrounding crowd are comprehensively considered. To verify the rationality of some assumption in the utility threshold model, a questionnaire survey about the evacuation psychological behaviors is carried out. Through theoretical analysis and simulation, the influence of some key parameters on the herding–panic behavior is discussed in detail, via analyzing the change of panic rate. Some safety measures to control herding–panic in evacuation are also suggested. [ABSTRACT FROM AUTHOR]

The growing need for more and more complicated underground urban systems has resulted to the development of a challenging environment for the setting up of effective evacuation procedure. The special circumstances and complexity, which characterize the underground spaces, as well as the severity of the consequences if an accident occurs, stimulated the research for different evacuation approaches in urban environments, in order to improve the efficiency of emergency evacuation and prevent crowd casualties in potential accidents, especially in the case of fire. The paper presents the most well respected standards, guidelines and regulations enforced worldwide, aiming to preserve the safeguarding of life and property against fire, explosion, and related hazards associated with developed subterranean spaces. Moreover, the evolution of underground evacuation approaches, from prescriptive methods to contemporary performance methods is displayed, followed by the examination of the approaches in crowd evacuation practices. Finally, the paper presents and analyses the factors being taken into account for planning the evacuation process, as illustrated through cases where modeling is conducted with special software codes. [ABSTRACT FROM AUTHOR]

First responders and security personnel face many challenges to safely evacuate crowded environments. Conducting frequent physical and table-top exercises are neither feasible nor economical. This is where modeling and simulation comes into play in providing a risk-free and economical method to practice various evacuation strategies, train first responders, and provide accurate decision-support and emergency guidance. With the aid of formal methods, here we present a suite of various egress strategies build on top of an open-source modeling and simulation environment. We use Cellular Discrete-Event System Specification (Cell-DEVS) formalism to explore emergency evacuation scenarios by building 12 egress models representing aspects of human behaviors under emergencies and the activities of authorities in guiding the crowd. The proposed framework explores random and controlled human movement, as well as implementing psychological conditions such as herd following and panicked states. We provide results analysis to compare evacuation speed under various egress methods, allowing for decision making when using the suite for training purposes. The outcome of this work is available on a public repository to serve the DEVS community, researchers, and public safety authorities interested in emergency evacuation simulations. [ABSTRACT FROM AUTHOR]

In the emergency under smoke or gas, the visual field of pedestrians is limited, unreasonable evacuation guidance methods will affect the efficiency of evacuation, and even cause evacuation failure in serious cases. Therefore, the optimized evacuation guidance strategy is investigated in this study. First, a distance factor is introduced to modify the follow-up behavior of pedestrians, which solves the problem of leaders being blocked by pedestrians. Then, a reward grid-based leader initial position optimization method is proposed to ensure the safe guidance of pedestrians. By considering the density and distance factors comprehensively, the leader's position is theoretically available by calculation. Finally, a leader exit selection strategy based on evacuation urgency degree is proposed. The leaders select exits in order of evacuation urgency degree, thus ensuring a more balanced exit utilization. The simulation results show that the proposed model can improve the safety of evacuation, and reduce the evacuation time. • The follow-up behavior of pedestrians is modified by introducing a distance factor. • A reward grid-based leader location-allocation method is proposed to optimize the initial location of leaders. • An exit selection strategy for leaders based on evacuation urgency degree is proposed. [ABSTRACT FROM AUTHOR]

Behavioral modification using active instructions is a promising interventional method to optimize crowd evacuations. However, existing research efforts have been more focused on eliciting general principles of optimal behavior than providing explicit mechanisms to dynamically induce the desired behaviors, which could be claimed as a significant knowledge gap in crowd evacuation optimization. In particular, we propose using dynamic distance-keeping instructions to regulate pedestrian flows and improve safety and evacuation time. We investigate the viability of using Model Predictive Control (MPC) techniques to develop a behavioral controller that obtains the optimal distance-keeping instructions to modulate the pedestrian density at bottlenecks. System Identification is proposed as a general methodology to model crowd dynamics and build prediction models. Thus, for a testbed evacuation scenario and input–output data generated from designed microscopic simulations, we estimate a linear AutoRegressive eXogenous model (ARX), which is used as the prediction model in the MPC controller. A microscopic simulation framework is used to validate the proposal that embeds the designed MPC controller, tuned and refined in closed-loop using the ARX model as the Plant model. As a significant contribution, the proposed combination of MPC control and System Identification to model crowd dynamics appears ideally suited to develop realistic and practical control systems for controlling crowd motion. The flexibility of MPC control technology to impose constraints on control variables and include different disturbance models in the prediction model has confirmed its suitability in the design of behavioral controllers in crowd evacuations. We found that an adequate selection of output disturbance models in the predictor is critical in the type of responses given by the controller. Interestingly, it is expected that this proposal can be extended to different evacuation scenarios, control variables, control systems, and multiple-input multiple-output control structures. • System Identification for modeling Crowd Dynamics. • Model Predictive Control for behavioral modification. • Distance-keeping instructions to optimally control pedestrian flows. • Output disturbance models, a trustworthy ally of MPC control of pedestrians crowds. [ABSTRACT FROM AUTHOR]

As one of the most common self-organizing phenomena in social systems, queueing has been attracting a lot of interest. Understanding the local interaction mechanism in queueing behaviors is a critical issue in the field of crowd dynamics. However, most studies ignore the heuristic decision during queueing processes. Even less is known about the effect of queueing behaviors on the collective movement. Therefore, an extended social force queueing model is proposed, which is based on vision and morality. Guided by visual and moral information, the desired speed and direction are heuristically adjusted in the self-driven force. Numerical simulations indicate that our model constructs a more realistic and detailed framework for the interpretation of pedestrian queueing. This model successfully predicts the emergence of the "detours" phenomenon, which is beneficial for reducing crowd pressure. Furthermore, our model may provide guidance for crowd management in safety science and navigation in autonomous robots. • A queueing behavior-based social force model for crowd evacuation is proposed. • Vision and morality information are incorporated to achieve a queueing motion. • Self-organized phenomenon appeared is in agreement with empirical observations. • The queuing behavior apparently reduces the local pressure that indicates risk. [ABSTRACT FROM AUTHOR]

The challenge of controlling crowd movement at large events expands not only to the realm of emergency evacuations but also to improving non-critical conditions related to operational efficiency and comfort. In both cases, it becomes necessary to develop adaptive crowd motion control systems. In particular, adaptive cell-based crowd evacuation systems dynamically generate exit-choice recommendations favoring a coordinated group dynamic that improves safety and evacuation time. We investigate the viability of using this mechanism to develop a "leader-follower" evacuation system in which a trained evacuation staff guides evacuees safely to the exit gates. To validate the proposal, we use a simulation–optimization framework integrating microscopic simulation. Evacuees' behavior has been modeled using a three-layered architecture that includes eligibility, exit-choice changing, and exit-choice models, calibrated with hypothetical-choice experiments. As a significant contribution of this work, the proposed behavior models capture the influence of leaders on evacuees, which is translated into exit-choice decisions and the adaptation of speed. This influence can be easily modulated to evaluate the evacuation efficiency under different evacuation scenarios and evacuees' behavior profiles. When measuring the efficiency of the evacuation processes, particular attention has been paid to safety by using pedestrian Macroscopic Fundamental Diagrams (p-MFD), which model the crowd movement dynamics from a macroscopic perspective. The spatiotemporal view of the evacuation performance in the form of crowd-pressure vs. density values allowed us to evaluate and compare safety in different evacuation scenarios reasonably and consistently. Experimental results confirm the viability of using adaptive cell-based crowd evacuation systems as a guidance tool to be used by evacuation staff to guide evacuees. Interestingly, we found that evacuation staff motion speed plays a crucial role in balancing egress time and safety. Thus, it is expected that by instructing evacuation staff to move at a predefined speed, we can reach the desired balance between evacuation time, accident probability, and comfort. • CellEVAC, a trustworthy ally for leader-follower crowd evacuations. • Three-layered behavior model for modeling leader-follower imitation behaviors. • Behavioral model optimization to implement evacuation systems. • pMFDs based on crowd pressure to measure safety. • Imitation behavior, a trustworthy ally of CellEVAC in leader-follower evacuations. [ABSTRACT FROM AUTHOR]

The research of crowd evacuation plays the predominant role of guaranteeing the controllability of the stampede disaster and decreasing accidents casualties. Herd behavior is one of the characteristic crowd evacuation behaviors, which has the definite influence on evacuees' decision-making and evacuation efficiency. In this paper, the crowd evacuation observation experiments were conducted to investigate herd behavior by considered 16 working conditions, including change the number of participants, alter urgency levels and set barrier. The participant allotment and crowd movement time on different sides and exits are obtained. The judgment area and crowd density are proposed to understand the herd behavior and choices of decision maker. The critical states of herd behavior in direction and exit choices are confirmed according to the video observation and data analysis. The results indicate that herd behavior moderately leads to the different distribution and movement time of the experimental crowd. Decision maker does not present herd behavior when crowd density is relatively high or low. When crowd density in direction choice judgment area is within the range of [1/12, 1/6] (person/m2), and the crowd density in exit choice judgment area is within the range of [1/27, 2/27] (person/m2), decision maker basically triggers herd behavior. This study provides insight into the critical state of herd behavior which deepens the understanding of crowd evacuation and contributes to the reference of the crowd accident management. • Investigating herd behavior by conducting the crowd evacuation experiments. • Putting forward the critical state of the crowd herd behavior in decision-making. • Determining judgment area and crowd density to estimate herding choice. • Implementing participant allotment and movement time of experimental participants. [ABSTRACT FROM AUTHOR]

In the study of pedestrian evacuation dynamics, pedestrian aggressiveness is important to consider as it potentially impacts evacuation efficiency. A representative example is the 'faster-is-slower' phenomenon. In reality, a crowd is a mixture of patient and impatient pedestrians, rather than a collection of like individuals. The impact of such heterogeneity has been reported in previous studies; however, these studies are all based on handcrafted solutions. In this study, we propose a heuristic approach for incorporating a genetic algorithm into the floor field cellular automata model to investigate the optimal initial pedestrian evacuation condition. Pedestrian aggressiveness is represented by two different approaches: the revised friction function and the persisting probability. To verify our genetic algorithm approach, we compared the results with the theoretical analysis and simulation results based on handcrafted solutions. The first contribution of this study is that the initial condition, a mixture of patient and impatient pedestrians, affects evacuation efficiency. Another contribution of this study is the heuristic approach incorporating a genetic algorithm. Our approach exerts the computational efficiency advantage of the floor field cellular automata model. • Pedestrian experiment is conducted to show the potential impact of initial condition. • Pedestrian aggressiveness is represented by two different approaches. • A heuristic approach incorporating a genetic algorithm to is proposed. • Theoretical analysis is conducted as verification. • Alternate egress of patient and impatient pedestrians improves evacuation efficiency. [ABSTRACT FROM AUTHOR]

In recent years, crowded stampede incidents have occurred frequently, resulting in more and more serious losses. The common cause of such incidents is that when large-scale populations gather in a limited area, the population is highly unstable. In emergency situations, only when the crowd reaches the safe exit as soon as possible within a limited evacuation time to complete evacuation can the loss and casualties be effectively reduced. Therefore, the safety evacuation management of people in public places in emergencies has become a hot topic in the field of public security. Based on the analysis of the factors affecting the crowd path selection, this paper proposes an improved path-planning algorithm based on BEME (Balanced Evacuation for Multiple Exits). And pedestrian evacuation simulation is carried out in multi-exit symmetrical facilities. First, this paper optimizes the update method of the GSDL list in the BEME algorithm as the basis for evacuating pedestrians to choose an exit. Second, the collision between pedestrians is solved by defining the movement rule and collision avoidance strategy. Finally, the algorithm is compared with BEME and traditional path-planning algorithms. The results show that the algorithm can further shorten the global evacuation distance of the symmetrical evacuation scene, effectively balance the number of pedestrians at each exit and reduce the evacuation time. In addition, this improved algorithm uses a collision avoidance strategy to solve the collision and congestion problems in path planning, which helps to maximize evacuation efficiency. Whether the setting of the scene or the setting of the exit, all studies are based on symmetric implementation. This is more in line with the crowd evacuation in the real scene, making the experimental results more meaningful. [ABSTRACT FROM AUTHOR]

• CellEVAC: adaptive cell-based colored indications to guide evacuees. • Behavioral model optimization to implement evacuation systems. • AnyLogic and Matlab simulation–optimization framework, a cutting-edge solution. • Pedestrian Fundamental diagrams to the rescue of safe evacuations. • Imitation behavior, a trustworthy ally of CellEVAC. A critical aspect of crowds' evacuation processes is the dynamism of individual decision making. Identifying optimal strategies at an individual level may improve both evacuation time and safety, which is essential for developing efficient evacuation systems. Here, we investigate how to favor a coordinated group dynamic through optimal exit-choice instructions using behavioral strategy optimization. We propose and evaluate an adaptive guidance system (Cell-based Crowd Evacuation, CellEVAC) that dynamically allocates colors to cells in a cell-based pedestrian positioning infrastructure, to provide efficient exit-choice indications. The operational module of CellEVAC implements an optimized discrete-choice model that integrates the influential factors that would make evacuees adapt their exit choice. To optimize the model, we used a simulation–optimization modeling framework that integrates microscopic pedestrian simulation based on the classical Social Force Model. In the majority of studies, the objective has been to optimize evacuation time. In contrast, we paid particular attention to safety by using Pedestrian Fundamental Diagrams that model the dynamics of the exit gates. CellEVAC has been tested in a simulated real scenario (Madrid Arena) under different external pedestrian flow patterns that simulate complex pedestrian interactions. Results showed that CellEVAC outperforms evacuation processes in which the system is not used, with an exponential improvement as interactions become complex. We compared our system with an existing approach based on Cartesian Genetic Programming. Our system exhibited a better overall performance in terms of safety, evacuation time, and the number of revisions of exit-choice decisions. Further analyses also revealed that Cartesian Genetic Programming generates less natural pedestrian reactions and movements than CellEVAC. The fact that the decision logic module is built upon a behavioral model seems to favor a more natural and effective response. We also found that our proposal has a positive influence on evacuations even for a low compliance rate (40%). [ABSTRACT FROM AUTHOR]

• A novel model to quantify the stress level of evacuees in a fire evacuation. • The impact of stress variation on evacuees under fire pressure is investigated. • Egress dynamics of evacuees under a compartment room fire scenario are characterised. • Results highlight the importance to consider stress in crowd safety assessment. When evacuees directly confront a life-endangering situation in a fire accident, the raised stress created by the physical threat of fire tends to affect evacuees' escape behaviours (i.e., shifting from a calm exit mannerism to a stressful escape behaviour). This behavioural phenomenon inevitably affects crowd movement patterns and subsequently, the overall evacuation characteristics. However, limited works address the impact of stress variation on evacuees under the pressure of a fire emergency. In this paper, we propose an evacuation model to numerically determine the stress level of evacuees according to their specific surrounding environment. Hence, it enables evacuees to perceive the fire risk and acts in response to the fire hazard. Rather than solely placing emphasis on representing the physical movement of evacuees only, as are the common approach used in previous works, this study focuses on the influence of stress factor on the crowd evacuation performance. Our proposed model contributes to a better understanding of the crowd evacuation dynamics by investigating the effect of stress variation on evacuees, which provides an additional aspect of insight to existing evacuation frameworks. [ABSTRACT FROM AUTHOR]

Evacuation simulations in virtual indoor fire scenes hold great significance for public safety. However, existing evacuation simulation methods are inefficient and provide poor visualized when applied to virtual reality (VR) simulations. Additionally, the influences of the interaction of evacuation processes on the choice of multiple exits have not been fully considered. In the paper, we propose a VR simulation method for crowd evacuation in a multiexit indoor fire environment. An indoor 3D scene model and character model, for studying the environmental factors that affect the multiexit selection of personnel during the fire process, are combined with environmental factors to enhance the evacuation route planning algorithm to improve the efficiency of the VR simulation of evacuation in the scene. In addition, a prototype system that supports multiple experience modes is proposed, and case experiment analyses are performed. The results show that the method described in this paper can effectively support the real-time simulation of indoor fire evacuations in virtual scenes, providing both reliable simulation results and good visualization effects. [ABSTRACT FROM AUTHOR]

Cell-based crowd evacuation systems provide adaptive or static exit-choice indications that favor a coordinated group dynamic, improving evacuation time and safety. While a great effort has been made to modeling its control logic by assuming an ideal communication and positioning infrastructure, the architectural dimension and the influence of pedestrian positioning uncertainty have been largely overlooked. In our previous research, a cell-based crowd evacuation system (CellEVAC) was proposed that dynamically allocates exit gates to pedestrians in a cell-based pedestrian positioning infrastructure. This system provides optimal exit-choice indications through color-based indications and a control logic module built upon an optimized discrete-choice model. Here, we investigate how location-aware technologies and wearable devices can be used for a realistic deployment of CellEVAC. We consider a simulated real evacuation scenario (Madrid Arena) and propose a system architecture for CellEVAC that includes: a controller node, a radio-controlled light-emitting diode (LED) wristband subsystem, and a cell-node network equipped with active Radio Frequency Identification (RFID) devices. These subsystems coordinate to provide control, display, and positioning capabilities. We quantitatively study the sensitivity of evacuation time and safety to uncertainty in the positioning system. Results showed that CellEVAC was operational within a limited range of positioning uncertainty. Further analyses revealed that reprogramming the control logic module through a simulation optimization process, simulating the positioning system's expected uncertainty level, improved the CellEVAC performance in scenarios with poor positioning systems. [ABSTRACT FROM AUTHOR]