Crowd Evacuation Research Articles

Simulation of Crowd Evacuation under Attack Considering Emotion Spreading

Abstract In recent years, attacks against crowded places such as campuses and theaters have frequently and negatively impacted social security and stability. In such an event, the crowd will be subjected to higher psychological stress, and their emotion will spread to others rapidly. This paper establishes the Attack-Evacuation Evacuation Simulation Model (AEES-SFM) based on the social force model to consider emotion spreading under attack. In this model, (1)Attack-Escape Derive Force was considered for the interaction between attackers and evacuees and (2) emotion spreading among the evacuees was considered to modify the value of psychological forces. To validate the simulation, several experiments were carried out in a university in China. Comparing the simulation and experimental results, it is found that the simulation results are similar to the experimental results when considering emotion spreading. Therefore, the AEES-SFM proved to be effective. By comparing the results of the evacuation simulation without emotion spread, the emotion spread model reduces the evacuation time and the number of casualties by about 30%, which is closer to the real experimental results. And the results are still applicable in the case of a 40-person evacuation. This paper provides theoretical support and practical guidance for campus response to violent attacks.

Heterogeneous emotional contagion of the cyber–physical society

When emergencies occur, panic spreads quickly across cyberspace and physical space. Despite widespread attention to emotional contagion in cyber–physical societies (CPS), existing studies often overlook individual relationship heterogeneity, which results in imprecise models. To address this issue, we propose a heterogeneous emotional contagion method for CPS. First, we introduce the Strong–Weak Emotional Contagion Model (SW-ECM) to simulate the heterogeneous emotional contagion process in CPS. Second, we formulate the mean-field equations for the SW-ECM to accurately capture the dynamic evolution of heterogeneous emotional contagion in the CPS. Finally, we construct a small-world network based on strong–weak relationships to validate the effectiveness of our method. The experimental results show that our method can effectively simulate the heterogeneous emotional contagion and capture changes in relationships between individuals, providing valuable guidance for crowd evacuations prone to emotional contagion.

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

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

Experiment and path optimization on Large-Space Multi-Area crowd evacuation

Abstract In large spaces where there are multiple evacuation zones, the flow patterns during evacuation are intricate, leading to congestion in emergency scenarios, and affecting evacuation efficiency. To address this, we proposed an optimization method for crowd evacuation paths in large-space multi-area environment settings under fire scenarios by utilizing multi-agent simulation. Using Pathfinder software, disorganized evacuation simulations are conducted within expansive multi-zonal settings to pinpoint areas with inadequate evacuation capabilities. Subsequent evacuation experiments are performed for verification, facilitating adjustments to agent-based model parameters for evacuees in fire scenarios, and carrying out organized evacuation simulations. Through meticulous simulation analysis, we aim to optimize the most efficient evacuation paths under organized responses in large-space multi-area settings. Results indicate that in emergency conditions due to fire, the evacuation speeds reach 1.8 m/s, and significant reductions in total evacuation time are achieved through simulation optimization, with the time required for the slowest individuals to evacuate decreasing from 240.32 s to 209.32 s. Furthermore, the flow rate per second at congested exits diminishes from 2.01 persons/s to 1.39 persons/s, highlighting the efficacy of the proposed path optimization method.

An investigation into the influence of context effects on crowd exit selection under gender difference in indoor evacuation.

Exit selection is crucial in indoor emergency evacuation. Domestic and foreign scholars have found that exit choice behavior is influenced by three factors: environmental factors, social interactions, and individual internal factors. Previous studies have shown that in addition to a single environmental factor affecting exit decisions, the influence of other available exit options in the context can ultimately lead to a reversal of exit decisions -The context effect. However, the impact of context effects on exit decisions in emergency situations has not been thoroughly explored. Therefore, this article identifies three basic independent variables: context effects, crowd flows, and gender differences, to study the exit decisions of different gender groups facing different crowd flows, as well as how context effects affect existing exit decisions. In this paper, we used virtual reality technology to construct an indoor fire scene and designed a total of 15 virtual experiments with different crowd distribution or context effects. 131 participants were divided into two groups, male and female, and their exit decisions were observed under different crowd flows and contextual effects. The research results show that: 1) Both men and women have an innate preference to avoid crowded exits, and the proportion of following crowd evacuation significantly decreases when there are crowded crowds in the scene; 2) The exit decisions of female participants are more influenced by the crowd, while men tend to be more influenced by context effects when evacuating independently; 3) The context effects on exit decisions in emergency situations is statistically significant, and this performance is more significant in the male population. Further analysis reveals that similarity effects have a more significant impact on exit decisions than attraction effects. These findings provide deeper insights into the exit choice behavior of the population and may contribute to the design of safe exits in indoor buildings. In addition, this article emphasizes the importance of context effects and provides a foundation for future research.

Knowledge Management Model for Urban Flood Emergency Response Based on Multimodal Knowledge Graphs

Recently, frequent flood disasters in China have seriously threatened economic development and public safety. This paper addresses the need for a dynamic urban flood emergency knowledge management system in emergency management departments and the lack of systematic knowledge among emergency managers regarding urban flood control. A multimodal knowledge graph-based urban flood emergency knowledge management model was constructed to enhance the decision-making capabilities of emergency management departments, improve the efficiency of public emergency evacuation, and reduce losses from urban flood disasters by analyzing the shortcomings of the existing emergency management system. An intelligent and dynamic flood emergency knowledge management model was built. This paper integrates multimodal knowledge graph technology to establish a multimodal emergency knowledge management framework for urban flood control. It develops and simulates the proposed model’s application scenarios for urban flood emergency evacuation using the Flocking algorithm on the NetLogo platform. Through simulation experiments, the practicality and effectiveness of the model in real flood disaster situations were examined, particularly in simulating crowd evacuation behavior. The study found that the model significantly improves the accuracy of information and decision-making speed during emergency responses and supports emergency management departments in conducting targeted and personalized emergency decisions. This research provides a scientific basis for emergency management departments to optimize their emergency response strategies to flood disasters and serves as a reference and example for the application of multimodal knowledge graph technology in emergency management.

Crowd navigation for dynamic hazard avoidance in evacuation using emotional reciprocal velocity obstacles

Crowd evacuation can be a challenging task, especially in emergency situations involving dynamically moving hazards. Effective obstacle avoidance is crucial for successful crowd evacuation, particularly in scenarios involving dynamic hazards such as natural or man-made disasters. In this paper, we propose a novel application of the emotional reciprocal velocity obstacles (ERVO) method for obstacle avoidance in dynamic hazard scenarios. ERVO is an established method that incorporates agent emotions and obstacle avoidance to produce more efficient and effective crowd navigation. Our approach improves on previous research by using ERVO to model the perceptive danger posed by dynamic hazards in real-time, which is crucial for rapid response in emergency situations. We conducted experiments to evaluate our approach and compared our results with other velocity obstacle methods. Our findings demonstrate that our approach is able to improve agent coordination, reduce congestion, and produce superior avoidance behavior. Our study shows that incorporating emotional reciprocity into obstacle avoidance can enhance crowd behavior in dynamic hazard scenarios.

NegEmotion: Explore the Double-Edged Sword Effect of Negative Emotion on Crowd Evacuation

NegEmotion: Explore the Double-Edged Sword Effect of Negative Emotion on Crowd Evacuation

Seismic fragility analysis of human perception in high‐rise buildings subjected to far‐field long‐period earthquake ground motions: A case study for Shanghai tower

AbstractHuman perception in high‐rise buildings during far‐field long‐period earthquake ground motions often induces psychological discomfort, including fear and anxiety. A comprehensive understanding on human perception of seismic events is vital for city planning and emergency management, facilitating effective evacuation plans and minimizing stampede risks. This study addresses a gap by employing the seismic fragility analysis method to delineate how human perception in high‐rise buildings is affected during far‐field long‐period ground motions. The methodology involves several key steps. First, the far‐field long‐period earthquake ground motion was identified by detecting the later‐arriving surface waves, and a series of records were selected from the next generation attenuation (NGA) West‐2 ground motion database of the Pacific Earthquake Engineering Research center. Then, a high‐rise building in Shanghai, China was modeled using ETABS 20. Through the linear‐elastic time history analysis, the structural seismic response at each floor was computed. Furthermore, human perception thresholds to vibration were introduced to assess the degree of human perception at each floor, illustrating the difference of human perception to seismic tremor at different floors. Finally, a novel earthquake intensity measure (IM), namely average response spectrum intensity (ARSI) within the vibration period ranging from 0.1 s to 10 s was introduced to generate seismic fragility curves for human perception. According to the investigation, it was found that the corner frequency and the associated energy ratio are useful indicators to identify the far‐field long‐period earthquake ground motions. The ARSI is an effective parameter to assess human perception to seismic tremor compared to the spectral acceleration at a given fundamental period. The generated seismic fragility analysis can provide a complete and thorough understanding on the probability of people that should be evacuated under different earthquake intensity levels. The probability of human perception at different floors varies along the building height, demonstrating the difficulty to make crowd evacuation plan in practice. This insight is vital for understanding and mitigating seismic concerns in high‐rise buildings, particularly in low‐to‐moderate seismicity regions.

A Novel Movable Mannequin Platform for Evaluating and Optimising mmWave Radar Sensor for Indoor Crowd Evacuation Monitoring Applications

Developing mmWave radar sensors for indoor crowd motion sensing and tracking faces a critical challenge: the scarcity of large-scale, high-quality training data. Traditional human experiments encounter logistical complexities, ethical considerations, and safety issues. Replicating precise human movements across trials introduces noise and inconsistency into the data. To address this, this study proposes a novel solution: a movable platform equipped with a life-size mannequin to generate realistic and diverse data points for mmWave radar training and testing. Unlike human subjects, the platform allows precise control over movements, optimising sensor placement relative to the target object. Preliminary optimisation results reveal that sensor height impacts tracking performance, with an optimal sensor placement above the test subject yields the best results. The results also reveal that the 3D data format outperforms 2D data in accuracy despite having fewer frames. Additionally, analysing height distribution using 3D data highlights the importance of the sensor angle—15° downwards from the horizontal plane.

Order–disorder phase transitions in front of the exit during human crowd evacuations

Human crowds often exhibit collective behaviors through self-organization, such as orderly queueing and disordered congestion at exits. Understanding the dynamic mechanisms behind these behaviors is crucial for pedestrian traffic management and the handling of mass crowds. Although current models and experiments have provided profound insights into ordered and disordered pedestrian groups separately, the transition mechanism from order to disorder within pedestrian crowds in front of exits remains unclear. In this study, a pedestrian queueing evacuation experiment was presented to demonstrate the phase transition process of pedestrian movement states as urgency levels in the environment change. We discovered the migration pattern of the phase transition point under evacuation control and identified different propagation modes of pedestrian velocity waves. Furthermore, a pedestrian motion model based on experimental data and observed phenomena was established. This model not only replicates the observed phenomena and regularities from the experiments but also reveals the quantitative phase transition mechanism of pedestrian movement under the influence of a single factor (i.e., urgency level or queueing ratio). Our findings hold significant implications for various domains, including pedestrian management, traffic control, and pedestrian dynamics.

Rheology of a crowd: from faster-is-slower to shear thickening

The evacuation speed of pedestrians through bottlenecks usually leads to the faster-is-slower (FIS) phenomenon, which relies on frictional clogging and clusters’ size. However, less attention is given in literature to the rheology of pedestrian crowds and the forces ruling their dynamics. Here, we establish a link between the faster-is-slower phenomenon (FIS) in pedestrian crowds and the shear-thickening rheological behavior of (non)Brownian suspensions. We analyze pedestrian room egress through particle-based simulations using the Social Force Model, revealing an S-shaped shear rate dependency characteristic of discontinuous shear thickening (DST). At the same time, the crowd flow viscosity near the exit exhibits a steep increase coinciding with FIS occurrence, establishing a correlation between FIS and shear thickening. Our results prove that crowd evacuations are governed by two distinct critical jamming densities, one frictional and another frictionless, and that contact forces alone cannot lead to FIS, but social force interactions are necessary to avoid contacts at low desired speeds. Our results point at the suitability of (non)Brownian suspensions as models that provide an original rheological perspective to pedestrian dynamics.

An evacuation model considering pedestrian fall behavior in an inclined passenger ship

Passenger ships are likely to be inclined during and after accidents, which is a vital factor that affects pedestrian behaviors and crowd dynamics in the evacuation process. On a significantly inclined ship, pedestrian fall behavior becomes more complex, increasing the risk of stampedes. Nevertheless, this type of evacuation has rarely been investigated. In this study, an extended cellular automata (CA) model that meets the inherent characteristics of passenger ship evacuations is constructed. The inclination force and self-adjusting force are incorporated into the model to evaluate the balance mechanism of pedestrians, while a method to calculate the probability of pedestrians falling down is established by considering the ship heeling angle and pedestrian heterogeneity. Further, the state of the pedestrians after falling to the ground is analyzed. A hazard field is introduced to describe the interaction between normal pedestrians and fallen pedestrians. The simulation results show that deaths occur when the ship heeling angle is greater than 20° and the crowd is evacuated successfully and fastest at the ship heeling angle of 5°, even better than on the flat ground. The greater the ship heeling angle, the more likely pedestrians are to fall over, and the earlier the first death occurs, thereby resulting in longer evacuation time and increased fatalities. The heterogeneity of passengers has considerable effects on the effectiveness of the ship evacuation, with a greater proportion of strong-resistance pedestrians reducing the crowd death and fall risks, even though the influence of crowd constitution on crowd evacuation time is insignificant. The death toll and evacuation time increased with the initial number of pedestrians aboard the ship. This study is beneficial in terms of understanding the evolution mechanism of evacuation dynamics and developing effective strategies for crowd emergency management on large passenger vessels.

A double‐layer crowd evacuation simulation method based on deep reinforcement learning

AbstractExisting crowd evacuation simulation methods commonly face challenges of low efficiency in path planning and insufficient realism in pedestrian movement during the evacuation process. In this study, we propose a novel crowd evacuation path planning approach based on the learning curve–deep deterministic policy gradient (LC‐DDPG) algorithm. The algorithm incorporates dynamic experience pool and a priority experience sampling strategy, enhancing convergence speed and achieving higher average rewards, thus efficiently enabling global path planning. Building upon this foundation, we introduce a double‐layer method for crowd evacuation using deep reinforcement learning. Specifically, within each group, individuals are categorized into leaders and followers. At the top layer, we employ the LC‐DDPG algorithm to perform global path planning for the leaders. Simultaneously, at the bottom layer, an enhanced social force model guides the followers to avoid obstacles and follow the leaders during evacuation. We implemented a crowd evacuation simulation platform. Experimental results show that our proposed method has high path planning efficiency and can generate more realistic pedestrian trajectories in different scenarios and crowd sizes.

Crowd evacuation simulation based on hierarchical agent model and physics‐based character control

AbstractCrowd evacuation has gained increasing attention in recent years. The agent‐based method has shown a superior capability to simulate complex behaviors during crowd evacuation simulation. For agent modeling, most existing methods only consider the decision process but ignore the detailed physical motion. In this article, we propose a hierarchical framework for crowd evacuation simulation, which combines the agent decision model with the agent motion model. In the decision model, we integrate emotional contagion and scene information to determine global path planning and local collision avoidance. In the motion model, we introduce a physics‐based character control method and control agent motion using deep reinforcement learning. Based on the decision strategy, the decision model can use a signal to control the agent motion in the motion model. Compared with existing methods, our framework can simulate physical interactions between agents and the environment. The results of the crowd evacuation simulation demonstrate that our framework can simulate crowd evacuation with physical fidelity.

Selection of urban flood shelter locations based on risk assessment

Within the framework of global climate change, there is a recurring occurrence of floods and waterlogging disasters, which pose significant risks to human lives and overall safety. The phenomenon of urbanization has led to an increased vulnerability of cities to flood disasters. Evacuation serves as a viable strategy for adapting to climate change, managing intense precipitation events, and mitigating the impact of flood disasters. Hence, the meticulous choice of shelters has emerged as a crucial element in enhancing the effectiveness of evacuations. Nevertheless, the process of choosing shelters sometimes neglects to consider the potential for flooding and has not undergone thorough testing. Hence, the objective of this research is to systematically choose shelters within the central metropolitan region, incorporate flood risk considerations into the shelter site model, and assist cities in mitigating the consequences of floods resulting from climate change. To begin with, employ a two-dimensional shallow water model in order to replicate instances of intense precipitation and produce data on flood inundation. Subsequently, the simulation results are utilized to assess the flood risk using the entropy weight approach. This risk is then incorporated into the multi-objective optimization model to determine the optimal shelter placement. Ultimately, the ABM model was employed to create a simulation of crowd evacuation in urban areas, confirming the efficacy of shelter selection and thoroughly examining the influence of various shelters on the efficiency of community emergency evacuation. The study's findings indicate that a total of 26 shelters were chosen within the study area using a multi-objective methodology, ensuring the inclusion of all affected populations. The utilization of multi-objective models in the selection of shelter places has been shown to enhance evacuation efficiency, as confirmed by the ABM model. The simulation findings indicate a decline in the population's death rate from 1.94% to 0.98%, an increase in the evacuation rate from 97.4% to 98.4%, and a reduction in the number of casualties by 133 individuals.

Stochastic Optimal Intervention for Robot-Assisted Crowd Evacuation

Stochastic Optimal Intervention for Robot-Assisted Crowd Evacuation

A path planning method based on deep reinforcement learning for crowd evacuation

A path planning method based on deep reinforcement learning for crowd evacuation

Crowd Evacuation Management with Optimal Time‐Multiplexed Exit‐Choice Recommendations through Simulation Optimization

AbstractExit‐choice behavior significantly influences the performance of egress operations in crowded spaces. Thus, strategically issuing active exit‐choice recommendations that provide clear and concise guidance to pedestrians can help improve evacuation safety and efficiency. This study investigates how to optimally regulate pedestrian density at the exits by generating time‐multiplexed exit‐choice recommendations. The study develops a simulated crowd evacuation scenario with multiple exits, where pedestrians receive periodic sequences of exit‐choice instructions. The optimal period for each instruction is obtained through simulation‐optimization processes with three minimization objectives: evacuation time, cumulative density, and cumulative density variance. First, the effect of collective behaviors on evacuation performance is studied, which justifies the need for active mechanisms to improve exit‐choice decision‐making coordination. Then, several optimal plans for the different objectives are obtained and evaluated regarding evacuation time and cumulative density performance. Finally, the influence of adoption rate and collective commitment to instructions are evaluated. This study suggests that static plans, defined as time‐multiplexed exit‐choice instructions obtained through simulation‐optimization, reasonably represent a first step in the real deployment of intelligent crowd management systems.

Optimization Model of Emergency Evacuation Network Based on Genetic Algorithm

Aggregation and dispersion are the normal states. In economic production and social life, the evacuation of people and materials deserves extensive attention. From the perspective of mathematics and economics, arranging a thoughtful evacuation is essential for the best balance between personal satisfaction and cost. In this paper, evacuation is deeply studied by a genetic algorithm; the balance, cost, and willingness (satisfaction) priority models, which consider both satisfaction and cost, are established. In this paper, based on the field investigation and questionnaire survey conducted by Oriental Sports Center, overall satisfaction is inferred by the satisfaction of each person in the tested sample, and the time cost from the evacuation point to different reception points is inferred through actual investigation. Based on these data, a balance model is established, and the calculation is performed. The ratio of the total willingness value of the evacuated individuals to the total cost is taken as the function of the optimization objective. When the optimization objective function is complex, the genetic algorithm is used to search for the optimal solution by simulating the process of natural evolution. At the same time, the cost priority and willingness priority models are established to compare the advantages and disadvantages of different evacuation schemes and their applicable situations. The evacuation of Oriental Sports Center is studied using a genetic algorithm. The objective function value of equilibrium model optimization is 0.2155 (optimal solution), significantly different from willingness priority model optimization (0.0300). After calculating the optimization objective function value and cost value under the conditions of cost priority and willingness priority, a “People-oriented and cost-balanced” suggestion is put forward for a large crowd evacuation scheme.