Crowd Behavior Simulation Research Articles

Review of Virtual Simulation of Crowd Motion for Urban Emergency Management

With increasing urbanization in China, there are more and more urban crowds, bringing new challenges to public safety management. The analysis and modeling of crowd behavior are important aspects of emergency management in smart cities. This paper reviews and summarizes existing research into crowd behavior simulation and analyzes existing simulation data collection methods. Crowd simulation technology is discussed from three aspects: crowd modeling, motion navigation, and emotion-driven crowd animation. The indicators of the simulation models are analyzed from the perspective of model evaluation. Finally, combined with the status quo, the existing research limitations are analyzed, and the direction of further research into crowd simulation behavior is discussed.

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.

Crowd Behavior Simulation With Emotional Contagion in Unexpected Multihazard Situations

Numerous research efforts have been conducted to simulate the crowd movements, while relatively few of them are specifically focused on multihazard situations. In this paper, we propose a novel crowd simulation method by modeling the generation and contagion of panic emotion under multihazard circumstances. In order to depict the effect from hazards and other agents to crowd movement, we first classify hazards into different types (transient and persistent, concurrent and nonconcurrent, and static and dynamic) based on their inherent characteristics. Second, we introduce the concept of perilous field for each hazard and further transform the critical level of the field to its invoked-panic emotion. After that, we propose an emotional contagion model to simulate the evolving process of panic emotion caused by multiple hazards. Finally, we introduce an emotional reciprocal velocity obstacles (RVOs) model to simulate the crowd behaviors by augmenting the traditional RVO model with emotional contagion, which for the first time combines the emotional impact and local avoidance together. Our experimental results demonstrate that the overall approach is robust, can better generate realistic crowds and the panic emotion dynamics in a crowd. Furthermore, it is recommended that our method can be applied to various complex multihazard environments.

Simulation of Evacuating Crowd Based on Deep Learning and Social Force Model

It is always difficult to evacuate crowds in public places like subway stations. The traditional crowd behavior simulation models often ignore two important issues in crowd evacuation: pedestrian tracking and individual differences. To solve the problem, this paper combines social force model (SFM) with deep learning into a novel pedestrian detection method. Firstly, several deep learning algorithms for pedestrian detection were compared, and the best ones for sparse and dense crowds were determined. Next, the pedestrian positions in a real video were acquired by the selected algorithms, and converted into actual coordinates in the scene. Then, the evacuation process was simulated with our method and the SFM based on these coordinates. The results show that our model output closer-to-reality results than the SFM. The research findings shed important new light on evacuation in crowded areas.

Emotion-based diversity crowd behavior simulation in public emergency

Computer simulations of crowd behaviors in public emergency have become a hot research field, as they are able to provide rich valuable data for public safety analysis and disaster-preparedness measures. In most of the existing crowd escape simulation systems, human behaviors are often limited to taking flight or running away. However, due to the personality and the current emotion of each individual, they may behave in various ways, such as Samaritan or giving up on life et al. In order to simulate such like diversity crowd behavior, this paper presents an emotion-based diversity behavior model, by adopting the OCEAN personality model and the OCC emotion model, while being enriched with the incorporation of CA-SIRS emotion contagion model. We also consider the effects of Yerkes–Dodson Law on individuals’ behaviors, which allows individuals to make authentic behaviors under stressful circumstances. The proposed model can provide more diverse individual behaviors compared to the existing emotion-based escape simulation models. The psychology-based individual modeling enables our model to be applied in various scenarios with slightly parameter adjustments.

Crowd behavior simulation based on shadow obstacle and ORCA models

Crowd behavior simulation has become one of the key support technologies for evacuation drills and safety monitoring. However, complex psychology, personality traits and interactions among individuals increase the challenges of creating realistic crowd behavioral simulations. In this paper, we propose a crowd behavior simulation method to simulate crowd behaviors by combining the shadow obstacle model (SOM) and optimal reciprocal collision avoidance (ORCA) model. First, the SOM is converted to the expected speed, as well as the half plane of ORCA. Then, a manual generation method of SOM is proposed to simulate the crowd behaviors in corners or beside a non-closed wall. Finally, we combine behavior simulation and physical simulation techniques, and propose a velocity-based crowd simulation framework. To test the effectiveness of our proposed method, several simulation methods are implemented in a Unity3D engine-based crowd simulation in subway station environment, and the comparison results demonstrate that our proposed method outperforms force-based simulation methods.

3D visual simulation of individual and crowd behavior in earthquake evacuation

Simulation of behaviors in emergencies is an interesting subject that helps to understand evacuation processes and to give out warnings for contingency plans. Individual and crowd behaviors in the earthquake are different from those under normal circumstances. Panic will spread in the crowd and cause chaos. Without considering emotion, most existing behavioral simulation methods analyze the movement of people from the point of view of mechanics. After summarizing existing studies, a new simulation method is discussed in this paper. First, 3D virtual scenes are constructed with the proposed platform. Second, an individual cognitive architecture, which integrates perception, motivation, behavior, emotion, and personality, is proposed. Typical behaviors are analyzed and individual evacuation animations are realized with data captured by motion capture devices. Quantitative descriptions are presented to describe emotional changes in individual evacuation. Facial expression animation is used to represent individuals’ emotions. Finally, a crowd behavior model is designed on the basis of a social force model. Experiments are carried out to validate the proposed method. Results showed that individuals’ behavior, emotional changes, and crowd aggregation can be well simulated. Users can learn evacuation processes from many angles. The method can be an intuitional approach to safety education and crowd management.

An Application of Graphics Processing Units to Geosimulation of Collective Crowd Behaviour

The goal of the paper is to assess the ways for computational performance and efficiency improvement of collective crowd behaviour simulation by using parallel computing methods implemented on graphics processing unit (GPU). To perform an experimental evaluation of benefits of parallel computing, a new GPU-based simulator prototype is proposed and the runtime performance is analysed. Based on practical examples of pedestrian dynamics geosimulation, the obtained performance measurements are compared to several other available multiagent simulation tools to determine the efficiency of the proposed simulator, as well as to provide generic guidelines for the efficiency improvements of the parallel simulation of collective crowd behaviour.

意思決定プロセスモデルによる群集流動シミュレーション

意思決定プロセスモデルによる群集流動シミュレーション

Toward a Hybrid Approach for Crowd Simulation

We address the problem of simulating pedestrian crowd behaviors in real time. To date, two approaches can be used in modeling and simulation of crowd behaviors, i.e. macroscopic and microscopic models. Microscopic simulation techniques can capture the accuracy simulation of individualistic pedestrian behavior while macroscopic simulations maximize the efficiency of simulation; neither of them assures the two goals at the same time. In order to achieve the strengths of the two classes of crowd modeling, we propose a hybrid architecture for defining the complex behaviors of crowd at two levels, the individual behaviors, and the aggregate motion of pedestrian flow. It consists of combining a microscopic and a macroscopic model in an unified framework, we simulate individual pedestrian behaviors in regions of low density by using a microscopic model, and we use a faster continuum model of pedestrian flow in the remainder regions of the simulation environment. We demonstrate the flexibility and scalability of our interactive hybrid simulation technique in a large environment. This technique demonstrates the applicability of hybrid techniques to the efficient simulation of large-scale flows with complex dynamics.

Simulating and Visualizing Real-Time Crowds on GPU Clusters

We present a set of algorithms for simulating and visualizing real-time crowds in GPU (Graphics Processing Units) clusters. First we will present crowd simulation and rendering techniques that take advantage of single GPU machines, then using as an example a wandering crowd behavior simulation algorithm, we explain how this kind of algorithms can be extended for their use in GPU cluster environments. We also present a visualization architecture that renders the simulation results using detailed 3D virtual characters. This architecture is adaptable in order to support the Barcelona Supercomputing Center (BSC) infrastructure. Results show that our algorithms are scalable in different hardware platforms including embedded systems, desktop GPUs and GPU clusters, in particular, the BSC's Minotauro cluster.

Parallel simulation and optimization of CUDA-based real-time huge crowd behavior

That the individuals search relevant objects from the environment may cause high time complexity during the crowd simulation.If the crowd should be simulated in real-time,the time complexity of the model needs reducing and the computing capability of the simulation platform needs enhancing.In this paper,the Biods model was studied as a typical case and a solution of how to parallelize and optimize the real-time huge crowd simulation based on Compute Unified Device Architecture(CUDA) was presented.Each individual was correspondent to a logical Graphic Processing Unit(GPU) thread.By discretizing simulation environment,the efficiency of searching the relevant individuals was improved.The individual information was organized into an array with the spatial locality by parallel radix sort in order to improve the utilization of the GPU memory bandwidth.The experiment verifies the solution presented here has improved number of simulation individuals up to about 7.3 times as CPU solution.

Spatial activity-based modeling for pedestrian crowd simulation

This paper proposes a spatial activity-based modeling approach and applies it to pedestrian crowd behavior simulations. Compared to conventional pedestrian crowd models where pedestrians make movement decisions in a time-based manner, the spatial activity-based pedestrian crowd model allows a pedestrian to make decisions only when needed, that is, when there is significant change in the pedestrian’s spatial position, as defined by a threshold called ‘space resolution’. To demonstrate the spatial activity-based modeling, we develop an activity-based model for pedestrian crowd simulation in a two-dimensional environment and compare it with a conventional time-based simulation model. Experiment results show that the spatial activity-based model can lead to more efficient simulations when simulating heterogeneous pedestrian crowds.

A model for simulation of crowd behaviour in the evacuation from a smoke-filled compartment

The modelling of crowd evacuation from a building has been studied over the past decades. In this study, a numerical model based on cellular automaton is proposed to simulate the human behaviour termed “flow with the stream” in emergency evacuation from a large smoke-filled compartment. In the model, the smoke effect in the context of visibility is considered since visibility range can affect the human behaviour significantly. To simulate the reality that the smoke concentration in a fire compartment is not constant, the proposed model is developed to deal with the scenario in which the visibility range varies in the course of time. An empirical formula is incorporated into the proposed model to estimate the visibility range. The results of numerical tests show that the proposed model can also be used to investigate the effect of the number of guiders through case study.

Simulation of autonomous crowd behaviour on Xbox 360

A crowd simulator which creates autonomous characters' behaviour in crowds consists many components such as pathfinding, collision avoidance, character creation, behaviour system, and level of details. The majority of these involve different level of

Advances in Games Technology: Software, Models, and Intelligence

Games technology has undergone tremendous development. In this article, the authors report the rapid advancement that has been observed in the way games software is being developed, as well as in the development of games content using game engines. One area that has gained special attention is modeling the game environment such as terrain and buildings. This article presents the continuous level of detail terrain modeling techniques that can help generate and render realistic terrain in real time. Deployment of characters in the environment is increasingly common. This requires strategies to map scalable behavior characteristics for characters as well. The authors present two important aspects of crowd simulation: the realism of the crowd behavior and the computational overhead involved. A good simulation of crowd behavior requires delicate balance between these aspects. The focus in this article is on human behavior representation for crowd simulation. To enhance the player experience, the authors present the concept of player adaptive entertainment computing, which provides a personalized experience for each individual when interacting with the game. The current state of game development involves using very small percentage (typically 4% to 12%) of CPU time for game artificial intelligence (AI). Future game AI requires developing computational strategies that have little involvement of CPU for online play, while using CPU’s idle capacity when the game is not being played, thereby emphasizing the construction of complex game AI models offline. A framework of such nonconventional game AI models is introduced.

Modeling group structures in pedestrian crowd simulation

Grouping is a common phenomenon in pedestrian crowds and plays important roles in affecting crowd behavior. Group modeling is still an open challenging problem and has not been incorporated by existing crowd simulation models. Motivated by the need of group modeling for crowd behavior simulation, this paper presents a unified and well-defined framework for modeling the structure aspect of different groups in pedestrian crowds. Both intra-group structure and inter-group relationships are considered and their effects on the crowd behavior are modeled. Based on this framework, an agent-based crowd simulation system is developed and crowd behavior simulations using two different group structures are presented. The simulation results show that the developed framework allows different group structures to be easily modeled. Besides, different group sizes, intra-group structures and inter-group relationships can have significant impacts on crowd behaviors.

SIMULATION OF REFUGE FROM INUNDATED UNDERGROUND SPACE BY PARTICLE-SYSTEM MODEL OF CROWD BEHAVIOR

A flooding to an urban underground space may cause the serious damage on a life of human. The effective simulation that handles directly individual action of human being has not been applied to the refuge from inundated urban underground space, up to now. In this study, the distinct-element-method type humanaction model is built and improved for the simulation of crowd behavior in the flow field, which is calculated by the shallow-water-flow analysis. In the human action model, an individual recognizes the situation of circumference, and actively moves based on self decision making. Interaction zone of individuals is set with assuming that individuals decide their moving direction based on the visual information of their circumference. The driving force of active motion of individuals is set by taking previous observation of moving speed of crowd into account.

Crowd Behavior Observation for Military Simulation

Observations are frequently used to examine naturally occurring behavior. The researchers used this tool to determine what types of behaviors crowds exhibited and the degree of aggression expressed by the crowds. Footage of the 1999 WTO protest in Seattle was examined and coded for 55 behaviors. Of the observed behaviors standing on an elevated platform, chanting, yelling and shouting, and raising flags were the most frequent behaviors. Overall, most of the action was moderately aggressive. Highly aggressive behaviors such as fighting and attacking police officers composed only 1% of the observed behaviors. A similar observation was conducted of an anti-war protest in New York. This observation found comparable results of prevalent nonviolent behaviors. The findings from these studies along with those obtained from interviews of returning military officers will be used to develop a model for simulation of crowd behaviors.