Crowd Simulation Models Research Articles

A crowd simulation model based on emotional cognition and contagion for emergency evacuation

Emotion is a crucial factor which influences evacuation effects. However, the studies and quantitative analysis of evacuation emotions, including the emotion generated by external factors and internal personality or cognition levels, emotional contagion evolution, and the regulation mechanism of pedestrians to negative emotion, are still rare. In this paper, an evacuation model based on emotional cognition and contagion (EMECC) is presented. Firstly, individual’s emotion is generated and quantified based on Lazarus’s cognitive theory. Secondly, the emotional contagion between individuals is simulated by SIS (Susceptible Infected Susceptible) infectious disease model. Combining with cellular automata model, an emotion-driven moving rule is proposed to guide pedestrians move towards the directions with more positive individuals so that positive emotions can be spread effectively. Various experiments on model parameters, obstacles, and emotional contagion process are implemented to verify the effectiveness of the EMECC model. The simulation and experimental results show that emotional regulation mechanism can improve pedestrian’s decision-making ability and contagion of positive emotion can accelerate evacuation process. The EMECC model can simulate emotional changes dynamically and guide pedestrians efficiently and reasonably in emergency evacuation.

Simulating real-life scenarios to better understand the spread of diseases under different contexts

Current statistical models to simulate pandemics miss the most relevant information about the close atomic interactions between individuals which is the key aspect of virus spread. Thus, they lack a proper visualization of such interactions and their impact on virus spread. In the field of computer graphics, and more specifically in computer animation, there have been many crowd simulation models to populate virtual environments. However, the focus has typically been to simulate reasonable paths between random or semi-random locations in a map, without any possibility of analyzing specific individual behavior. We propose a crowd simulation framework to accurately simulate the interactions in a city environment at the individual level, with the purpose of recording and analyzing the spread of human diseases. By simulating the whereabouts of agents throughout the day by mimicking the actual activities of a population in their daily routines, we can accurately predict the location and duration of interactions between individuals, thus having a model that can reproduce the spread of the virus due to human-to-human contact. Our results show the potential of our framework to closely simulate the virus spread based on real agent-to-agent contacts. We believe that this could become a powerful tool for policymakers to make informed decisions in future pandemics and to better communicate the impact of such decisions to the general public.

People flow management in a healthcare facility through crowd simulation and agent-based modeling methods

The study investigates the optimization of user flow and space management in a hospital located in Turin by simulating activities and patient flows of the blood drawing center. The simulation aims to verify the maximum number of people allowed to occupy the spaces simultaneously, manage user flows, and verify compliance with COVID-19 pandemic restrictions. Pedestrian Dynamics, supported by Building Information Modeling (BIM) methods, and NetLogo are used to simulate and optimize user flow and space management relying on crowd simulation and Agent-Based Modeling. Patients’ movements and hospital activities are modeled to identify bottlenecks, crowded situations, and other issues. The methodology also allows the comparison of the two selected tools via the two simulations that are set with equal parameters. The results of the simulations enabled the analysis of current conditions and the testing of scenarios and hypotheses without inconveniencing users and interrupting hospital activities. Some improvements in space and people flow management could be proposed, e.g., distributing patients’ entries over the center opening times via an online booking system and modifying room functions. The proposed improvements support the facility managers to avoid, or at least minimize, crowded situations during which is not possible for patients to comply with COVID-19 pandemic restrictions, while minimizing layout changes and consequently the costs and efforts of implementing the changes. The proposed methodology will enable better management of future emergencies in healthcare facilities. The tool comparison will support future studies on simulation tool selection according to simulation and stakeholder needs.

Crowd model calibration at strategic, tactical, and operational levels: Full-spectrum sensitivity analyses show bottleneck parameters are most critical, followed by exit-choice-changing parameters

ABSTRACT Crowd motion simulation requires specification of a range of parameters, each reflecting certain aspects of agent behavior. But what parameters matter the most? Are they all equally important? The question is important given that available data and resources for parameter calibration are limited, and priorities often need to be made. Here, for the first time, a full-spectrum sensitivity analysis of crowd model parameters is reported. It is shown that estimates of simulated evacuation time are, by far, most dependent on the value of locomotion/operational parameters, especially those that determine discharge rate at bottlenecks. The next most critical set of parameters are those that influence change of direction choices. If a crowd simulation model fails to reproduce bottleneck flows accurately, efforts to refine other modeling layers will be in vain. Similarly, if the model fails to represent exit choice adaptation/changing accurately, efforts to refine the exit choice model will be fruitless.

Emotional Contagion-Aware Deep Reinforcement Learning for Antagonistic Crowd Simulation

The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). Firstly, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations.

Predictive Agent-Based Crowd Model Design Using Decentralized Control Systems

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

Modelling and simulating serpentine group behaviour in crowds using modified social force model

Crowd dynamics has emerged as an important topic of investigation in recent years. Several studies have been conducted to better understand the underlying factors that can influence crowd dynamics and lead to risky situations. Particularly, field studies are essential for realistically capturing the dynamics. This paper attempts to uncover one intriguing behaviour observed in the world’s largest mass religious gathering Kumbh Mela-2016, held in Ujjain, India. The authors define this behaviour, where groups attempt to overtake the others in the crowd, form flexible chains, and not allow non-group members to penetrate through them, as serpentine group behaviour. This behaviour has been modelled and simulated using a modified social force model which has been developed in this work. This can aid in the development of a realistic crowd simulation model that can handle such unique crowd behaviours and help manage large crowds and ensure safe movement.

BIM-based determination of indoor navigation sign layout using hybrid simulation and optimization

Indoor navigation is difficult in large and complex navigation decision points. Helping occupants navigate more easily in these spaces is one of the important means to improve satisfaction. The navigation sign is an effective and one of the most common ways to achieve this goal. This study aims to propose a novel building information modeling based framework for designing navigation sign layout that better spatially matches the navigation cue demanded by occupants and the navigation cue supplied by signs. The demand for and supply of navigation cues are obtained from the crowd simulation and mathematical modeling, respectively. A programming model is applied to maximize the overlap between them by tuning the location of signs, the orientation of signs, and the navigation texts displayed on signs. To validate the effectiveness of the framework, the entrance lobby of a large hospital is used for the case study. An occupant navigation simulation model and a virtual reality based experiment are used for evaluating the sign layouts before and after implementing the proposed framework. The framework is supported to be effective in reducing the difficulty in navigation and navigation time.

HDRLM3D: A Deep Reinforcement Learning-Based Model with Human-like Perceptron and Policy for Crowd Evacuation in 3D Environments

At present, a common drawback of crowd simulation models is that they are mainly simulated in (abstract) 2D environments, which limits the simulation of crowd behaviors observed in real 3D environments. Therefore, we propose a deep reinforcement learning-based model with human-like perceptron and policy for crowd evacuation in 3D environments (HDRLM3D). In HDRLM3D, we propose a vision-like ray perceptron (VLRP) and combine it with a redesigned global (or local) perceptron (GOLP) to form a human-like perception model. We propose a double-branch feature extraction and decision network (DBFED-Net) as the policy, which can extract features and make behavioral decisions. Moreover, we validate our method’s ability to reproduce typical phenomena and behaviors through experiments in two different scenarios. In scenario I, we reproduce the bottleneck effect of crowds and verify the effectiveness and advantages of HDRLM3D by comparing it with real crowd experiments and classical methods in terms of density maps, fundamental diagrams, and evacuation times. In scenario II, we reproduce agents’ navigation and obstacle avoidance behaviors and demonstrate the advantages of HDRLM3D for crowd simulation in unknown 3D environments by comparing it with other deep reinforcement learning-based models in terms of trajectories and numbers of collisions.

ACSEE: Antagonistic Crowd Simulation Model With Emotional Contagion and Evolutionary Game Theory

Antagonistic crowd behaviors are often observed in cases of serious conflict. Antagonistic emotions, which is the typical psychological state of agents in different roles (i.e. cops, activists, and civilians) in crowd violent scenes, and the way they spread through contagion in a crowd are important causes of crowd antagonistic behaviors. Moreover, games, which refers to the interaction between opposing groups adopting different strategies to obtain higher benefits and less casualties, determine the level of crowd violence. We present an antagonistic crowd simulation model, ACSEE, which is integrated with antagonistic emotional contagion and evolutionary game theories. Our approach models the antagonistic emotions between agents in different roles using two components: mental emotion and external emotion. We combine enhanced susceptible-infectious-susceptible (SIS) and game approaches to evaluate the role of antagonistic emotional contagion in crowd violence. Our evolutionary game theoretic approach incorporates antagonistic emotional contagion through deterrent force, which is modelled by a mixture of emotional forces and physical forces defeating the opponents. Antagonistic emotional contagion and evolutionary game theories influence each other to determine antagonistic crowd behaviors. We evaluate our approach on real-world scenarios consisting of different kinds of agents. We also compare the simulated crowd behaviors with real-world crowd videos and use our approach to predict the trends of crowd movements in violence incidents. We investigate the impact of various factors (number of agents, emotion, strategy, etc.) on the outcome of crowd violence. We present results from user studies suggesting that our model can simulate antagonistic crowd behaviors similar to those seen in real-world scenarios.

A Perceptually-Validated Metric for Crowd Trajectory Quality Evaluation

Simulating crowds requires controlling a very large number of trajectories and is usually performed using crowd motion algorithms for which appropriate parameter values need to be found. The study of the relation between parametric values for simulation techniques and the quality of the resulting trajectories has been studied either through perceptual experiments or by comparison with real crowd trajectories. In this paper, we integrate both strategies. A quality metric, QF, is proposed to abstract from reference data while capturing the most salient features that affect the perception of trajectory realism. QF weights and combines cost functions that are based on several individual, local and global properties of trajectories. These trajectory features are selected from the literature and from interviews with experts. To validate the capacity of QF to capture perceived trajectory quality, we conduct an online experiment that demonstrates the high agreement between the automatic quality score and non-expert users. To further demonstrate the usefulness of QF, we use it in a data-free parameter tuning application able to tune any parametric microscopic crowd simulation model that outputs independent trajectories for characters. The learnt parameters for the tuned crowd motion model maintain the influence of the reference data which was used to weight the terms of QF.

Modeling the evolution of COVID-19 via compartmental and particle-based approaches: Application to the Cyprus case.

We present two different approaches for modeling the spread of the COVID-19 pandemic. Both approaches are based on the population classes susceptible, exposed, infectious, quarantined, and recovered and allow for an arbitrary number of subgroups with different infection rates and different levels of testing. The first model is derived from a set of ordinary differential equations that incorporates the rates at which population transitions take place among classes. The other is a particle model, which is a specific case of crowd simulation model, in which the disease is transmitted through particle collisions and infection rates are varied by adjusting the particle velocities. The parameters of these two models are tuned using information on COVID-19 from the literature and country-specific data, including the effect of restrictions as they were imposed and lifted. We demonstrate the applicability of both models using data from Cyprus, for which we find that both models yield very similar results, giving confidence in the predictions.

The existence of cautious pedestrians might facilitate evacuation dynamics

How to accurately simulate the real collective behavior of pedestrian evacuation remains an important issue in research of complex systems. The existing crowd simulation models in general assumed that pedestrians move directly towards the exit. In real life, however, there exist “cautious” pedestrians who fear trampling and crowding during evacuation, so we use an improved social force model to investigate how their presence influences evacuation dynamics. Simulation results reveal that the total evacuation time does not increase monotonically with the increment of the number of cautious pedestrians. Specifically, the presence of a moderate number of cautious pedestrians with rational psychological tolerance might substantially facilitate evacuation efficiency, compared with a conventional evacuation scenario. In addition, lower psychological tolerance promotes emergency evacuation, while increasing the tolerance threshold is conducive to slow evacuation. We hope that our research will provide some insights into the reasonable optimal strategies for emergency evacuation.

A speed-based model for crowd simulation considering walking preferences

To investigate the influence of the walking preferences on pedestrian movements, a modified collision-free speed model is proposed by considering the expectations of comfortable walking for a pedestrian. In the model, the walking directions of pedestrians are determined by taking human perception of comfort and preference for walking straight to their intended destinations into account. The restriction of walls in heading direction on pedestrian's walking is introduced to avoid potential collisions among pedestrians and obstacles. Model validation with respect to experimental data shows that our model performs better than the original model with regards to the trajectory's distribution and the velocity profile, and can effectively alleviate backward movements. Furthermore, the speed-density relation in corridor inferred from the new model fits the experimental data well and the model performs more accurately in simulating the flow-width relation in bottleneck scenario than the original collision-free speed model.

A review on crowd simulation and modeling

Crowd simulation has emerged in the last decade as a widely used method of visual effects, computer games, and urban planning, etc. The improvement of hardware performance and the urgent need of special effect lead to an unprecedented wave of crowd simulation studies. This paper reports a review of crowd simulation models from traditional methods to recent methods (e.g. group simulation, emotion contagion). Traditional models can simulate general crowd dynamics which have the advantages of both microscopic and macroscopic models. The recent studies of crowd simulation from group simulation to social psychology crowds are possible to simulate realistic crowds. The purpose of this review is to introduce commonly used crowd simulation methods for newcomers to this field by making a systematic literature review, discussions and analysis of different models. The results reveal the traditional models can simulate most of the normal crowds, but lack expressiveness for special groups which needs to be solved urgently in the current applications, particularly on visual effects and urban planning. Group simulation and emotion contagion could improve the simulation realism, but it also needs to be improved in computation cost and model optimization. Also, future research directions are suggested aiming to develop new applications focused on more realistic, natural and efficient crowd simulation.

Crowd Modeling and Simulation for Safer Building Design

Crowd modeling and simulation are very important in the investigation and study of the dynamics of a crowd. They can be used not only to understand the behavior of a crowd in different environments, but also in risk assessment of spaces and in designing spaces that are safer for crowds, especially during emergency evacuations. This paper provides an overview of the use of the crowd simulation model for three main purposes; (1) as a modeling tool to simulate behavior of a crowd in different environments, (2) as a risk assessment tool to assess the risk posed in the environment, and (3) as an optimization tool to optimize the design of a building or space so as to ensure safer crowd movement and evacuation. Result shows that a simulation using the magnetic force model with a pathfinding feature provides a realistic crowd simulation and the use of ABC optimization can reduce evacuation time and improve evacuation comfort. This paper is expected to provide readers with a clearer idea on how crowd models are used in ensuring safer building planning and design.

A Turing test for crowds.

The accuracy and believability of crowd simulations underpins computational studies of human collective behaviour, with implications for urban design, policing, security and many other areas. Accuracy concerns the closeness of the fit between a simulation and observed data, and believability concerns the human perception of plausibility. In this paper, we address both issues via a so-called ‘Turing test’ for crowds, using movies generated from both accurate simulations and observations of real crowds. The fundamental question we ask is ‘Can human observers distinguish between real and simulated crowds?’ In two studies with student volunteers (n = 384 and n = 156), we find that non-specialist individuals are able to reliably distinguish between real and simulated crowds when they are presented side-by-side, but they are unable to accurately classify them. Classification performance improves slightly when crowds are presented individually, but not enough to out-perform random guessing. We find that untrained individuals have an idealized view of human crowd behaviour which is inconsistent with observations of real crowds. Our results suggest a possible framework for establishing a minimal set of collective behaviours that should be integrated into the next generation of crowd simulation models.

Emotion-Based Crowd Simulation Model Based on Physical Strength Consumption for Emergency Scenarios

Increasing attention is being given to the modeling and simulation of traffic flow and crowd movement, two phenomena that both deal with interactions between pedestrians and cars in many situations. In particular, crowd simulation is important for understanding mobility and transportation patterns. In this paper, we propose an emotion-based crowd simulation model integrating physical strength consumption. Inspired by the theory of “the devoted actor,” the movements of each individual in our model are determined by modeling the influence of physical strength consumption and the emotion of panic. In particular, human physical strength consumption is computed using a physics-based numerical method. Inspired by the James-Lange theory, panic levels are estimated by means of an enhanced emotional contagion model that leverages the inherent relationship between physical strength consumption and panic. To the best of our knowledge, our model is the first method integrating physical strength consumption into an emotion-based crowd simulation model by exploiting the relationship between physical strength consumption and emotion. We highlight the performance on different scenarios and compare the resulting behaviors with real-world video sequences. Our approach can reliably predict changes in physical strength consumption and panic levels of individuals in an emergency situation.

Crowd Modelling Validation for Modified Social Force Model

Crowd modeling is mainly used to observe and analyze the movement pattern of the crowd, including their behavior with the influence of building geometry. It also has been used widely in many application areas such as for transportation services, urban planning and event planning. Representation of crowd dynamics using a simulation tool is useful in various crowd studies, where experiments with humans are too dangerous and not practical to be implemented. As to ensure the validity and accuracy of the developed simulation model, it has to be validated with the real data, in which most of recent crowd modeling works are lacking. Therefore, in this paper, we propose three types of approaches to validate our proposed crowd simulation model, the Magnetic Social Force Model, which are the component testing, qualitative validation and quantitative validation. Real data of crowd movement at concourse area of a train station in Kuala Lumpur has been used for the validation purpose in this work. By comparing the simulation analysis with the real data, results for component testing shows that our proposed crowd model has successfully produced crowd trajectories that are similar to the real crowd data with an accuracy of 90%. Meanwhile, for the qualitative validation, the proposed model is able to produce collective types of self-organized crowd behaviours such as lane formation, counter flow formation and corner hugging formation. Furthermore, the model has also been validated using the fundamental diagram.

Follower behavior under stress in immersive VR

Understanding human decision making is a key requirement to improve crowd simulation models so that they can better mimic real human behavior. It is often difficult to study human decision making during dangerous situations because of the complexity of the scenarios and situations to be simulated. Immersive virtual reality offers the possibility to carry out such experiments without exposing participants to real danger. In the real world, it has often been observed that people tend to follow others in certain situations (e.g., unfamiliar environments or stressful situations). In this paper, we study human following behavior when it comes to exit choice during an evacuation of a train station. We have carried out immersive VR experiments under different levels of stress (alarm only or alarm plus fire), and we have observed how humans consistently tend to follow the crowd regardless of the levels of stress. Our results show that decision making is strongly influenced by the behavior of the virtual crowd: the more virtual people running, the more likely are participants to simply follow others. The results of this work could improve behavior simulation models during crowd evacuation, and thus build more plausible scenarios for training firefighters.