Realistic Crowd Research Articles

Crowd behaviour during high-stress evacuations in an immersive virtual environment.

Understanding the collective dynamics of crowd movements during stressful emergency situations is central to reducing the risk of deadly crowd disasters. Yet, their systematic experimental study remains a challenging open problem due to ethical and methodological constraints. In this paper, we demonstrate the viability of shared three-dimensional virtual environments as an experimental platform for conducting crowd experiments with real people. In particular, we show that crowds of real human subjects moving and interacting in an immersive three-dimensional virtual environment exhibit typical patterns of real crowds as observed in real-life crowded situations. These include the manifestation of social conventions and the emergence of self-organized patterns during egress scenarios. High-stress evacuation experiments conducted in this virtual environment reveal movements characterized by mass herding and dangerous overcrowding as they occur in crowd disasters. We describe the behavioural mechanisms at play under such extreme conditions and identify critical zones where overcrowding may occur. Furthermore, we show that herding spontaneously emerges from a density effect without the need to assume an increase of the individual tendency to imitate peers. Our experiments reveal the promise of immersive virtual environments as an ethical, cost-efficient, yet accurate platform for exploring crowd behaviour in high-risk situations with real human subjects.

Learning behavior patterns from video for agent-based crowd modeling and simulation

This paper proposes a novel data-driven modeling framework to construct agent-based crowd model based on real-world video data. The constructed crowd model can generate crowd behaviors that match those observed in the video and can be used to predict trajectories of pedestrians in the same scenario. In the proposed framework, a dual-layer architecture is proposed to model crowd behaviors. The bottom layer models the microscopic collision avoidance behaviors, while the top layer models the macroscopic crowd behaviors such as the goal selection patterns and the path navigation patterns. An automatic learning algorithm is proposed to learn behavior patterns from video data. The learned behavior patterns are then integrated into the dual-layer architecture to generate realistic crowd behaviors. To validate its effectiveness, the proposed framework is applied to two different real world scenarios. The simulation results demonstrate that the proposed framework can generate crowd behaviors similar to those observed in the videos in terms of crowd density distribution. In addition, the proposed framework can also offer promising performance on predicting the trajectories of pedestrians.

Shadow obstacle model for realistic corner-turning behavior in crowd simulation

This paper describes a novel model known as the shadow obstacle model to generate a realistic corner-turning behavior in crowd simulation. The motivation for this model comes from the observation that people tend to choose a safer route rather than a shorter one when turning a corner. To calculate a safer route, an optimization method is proposed to generate the corner-turning rule that maximizes the viewing range for the agents. By combining psychological and physical forces together, a full crowd simulation framework is established to provide a more realistic crowd simulation. We demonstrate that our model produces a more realistic corner-turning behavior by comparison with real data obtained from the experiments. Finally, we perform parameter analysis to show the believability of our model through a series of experiments.

A panoramic synthetic vision model for improving collision avoidance in crowd simulation

Crowd Simulation is very important in many virtual reality applications, because it improves the sense of immersion of the users by making the population of agents in the environment to move as real crowds do. Recently, models for simulating crowds, in which each agent is equipped with a synthetic vision system, have shown interesting results regarding the natural manner in which the agents navigate inside the environment thanks to their visual perception. In this article, we propose an upgrade to the agent’s visual system with a panoramic view in order to allow an agent to expand its vision beyond the limit of 180o imposed by the common projection provided by rendering APIs. Also, we analyze different parameters, which are used to define the field of view, to investigate the influence they have on the agent’s behavior. The impacts that those changes may cause on the efficiency of the algorithms are also analysed. A visible change on the agent’s behavior is achieved by using the technique, with a slight loss of performance.

An Authoring Framework Oriented to Users for Simulating Realistic Crowds at Crossroads

An Authoring Framework Oriented to Users for Simulating Realistic Crowds at Crossroads

Spatial-size scaling of pedestrian groups under growing density conditions.

We study the dependence on crowd density of the spatial size, configuration, and velocity of pedestrian social groups. We find that, in the investigated density range, the extension of pedestrian groups in the direction orthogonal to that of motion decreases linearly with the pedestrian density around them, both for two- and three-person groups. Furthermore, we observe that at all densities, three-person groups walk slower than two-person groups, and the latter are slower than individual pedestrians, the differences in velocities being weakly affected by density. Finally, we observe that three-person groups walk in a V-shaped formation regardless of density, with a distance between the pedestrians in the front and back again almost independent of density, although the configuration appears to be less stable at higher densities. These findings may facilitate the development of more realistic crowd dynamics models and simulators.

MiSFM: On combination of Mutual Information and Social Force Model towards simulating crowd evacuation

In this paper we propose a novel technique termed miSFM for the simulation of crowd evacuation. miSFM take merits of both Mutual Information (MI) and Social Force Model (SFM). More specifically, MI of interacting agents is adopted to determine the level of order within a crowd during an evacuation. In such a way, SFM can be improved by adapting the forces involved at microscopic level between mutually interacting agents. The key innovation lies in highlighting how the dynamic adjustment of SFM parameters reveals much more realistic crowd movements for the evacuation simulation. Extensive experiments over several alternative and state-of-the-art works demonstrate the advantages of the proposed algorithm.

Crowd simulation using DC model and density information

Realistic crowd simulation is an important issue for the production of virtual worlds for games, crowd management, public space design, education, entertainment or architectural and urban planning. In this paper, crowd simulation is considered from two aspects: intra-group simulation and inter-group simulation. We propose a unified framework for crowd simulation in real-time virtual environment. Based on this framework, for intra-group simulation, we propose a novel density-based information crowd simulation to collision-free. For inter-group simulation, we propose a novel discrete choice (DC) model to realistic simulation of crowds and path planning. Meanwhile, we also propose a variable bounding box method for intra-group/inter-groups intersection problem. The simulation results show that the developed framework allows different group structures to be easily modeled. And the proposed framework could be used for real-time navigation of many moving crowd in complicated virtual environments.

A Data‐Driven Framework for Visual Crowd Analysis

AbstractWe present a novel approach for analyzing the quality of multi‐agent crowd simulation algorithms. Our approach is data‐driven, taking as input a set of user‐defined metrics and reference training data, either synthetic or from video footage of real crowds. Given a simulation, we formulate the crowd analysis problem as an anomaly detection problem and exploit state‐of‐the‐art outlier detection algorithms to address it. To that end, we introduce a new framework for the visual analysis of crowd simulations. Our framework allows us to capture potentially erroneous behaviors on a per‐agent basis either by automatically detecting outliers based on individual evaluation metrics or by accounting for multiple evaluation criteria in a principled fashion using Principle Component Analysis and the notion of Pareto Optimality. We discuss optimizations necessary to allow real‐time performance on large datasets and demonstrate the applicability of our framework through the analysis of simulations created by several widely‐used methods, including a simulation from a commercial game.

Implementing a physics-based model of crowd movement using the Unreal Development Kit

Abstract Crowds of people form in both civilian and military contexts, often in emergency or dangerous situations. Consequently, modelling crowd behaviour and movement has consistently been a subject of research, and such models have been used to study, for example, evacuation scenarios and the effect of crowds on military operations. Recreating realistic crowd behaviour and crowd movement within game engines is of growing interest for both gaming and non-gaming applications. A physically realistic mathematical model of crowd movement that considers the physical pressures the crowd members exert on each other was implemented using computer games technology, specifically the Unreal Engine and the Unreal Development Kit. The model and its implementation were used to simulate two typical evacuation scenarios, orderly and panicked, and the simulations exhibited specific known characteristics of such scenarios. Detailed explanations and examples of the implementation mechanics are provided to facilitate future game technology-based crowd modelling.

Interactive Formation Control in Complex Environments

The degrees of freedom of a crowd is much higher than that provided by a standard user input device. Typically, crowd-control systems require multiple passes to design crowd movements by specifying waypoints, and then defining character trajectories and crowd formation. Such multi-pass control would spoil the responsiveness and excitement of real-time control systems. In this paper, we propose a single-pass algorithm to control a crowd in complex environments. We observe that low-level details in crowd movement are related to interactions between characters and the environment, such as diverging/merging at cross points, or climbing over obstacles. Therefore, we simplify the problem by representing the crowd with a deformable mesh, and allow the user, via multitouch input, to specify high-level movements and formations that are important for context delivery. To help prevent congestion, our system dynamically reassigns characters in the formation by employing a mass transport solver to minimize their overall movement. The solver uses a cost function to evaluate the impact from the environment, including obstacles and areas affecting movement speed. Experimental results show realistic crowd movement created with minimal high-level user inputs. Our algorithm is particularly useful for real-time applications including strategy games and interactive animation creation.

Optimal Acceleration-Velocity-Bounded Trajectory Planning in Dynamic Crowd Simulation

Creating complex and realistic crowd behaviors, such as pedestrian navigation behavior with dynamic obstacles, is a difficult and time consuming task. In this paper, we study one special type of crowd which is composed of urgent individuals, normal individuals, and normal groups. We use three steps to construct the crowd simulation in dynamic environment. The first one is that the urgent individuals move forward along a given path around dynamic obstacles and other crowd members. An optimal acceleration-velocity-bounded trajectory planning method is utilized to model their behaviors, which ensures that the durations of the generated trajectories are minimal and the urgent individuals are collision-free with dynamic obstacles (e.g., dynamic vehicles). In the second step, a pushing model is adopted to simulate the interactions between urgent members and normal ones, which ensures that the computational cost of the optimal trajectory planning is acceptable. The third step is obligated to imitate the interactions among normal members using collision avoidance behavior and flocking behavior. Various simulation results demonstrate that these three steps give realistic crowd phenomenon just like the real world.

The Visual Coupling between Neighbors in Real and Virtual Crowds

Many models of crowd behavior are based on local interactions between pedestrians, but little is known about the actual mechanisms governing these interactions. In Experiments 1 and 2, a participant walked with three human ‘confederates’ or a virtual crowd of 30, while the heading direction or speed of a subset of neighbors was manipulated. In Experiment 3, real crowds of 16 or 20 walked together in a swarming scenario. We find that pedestrians are unidirectionally coupled to neighbors ahead of them, the influence of multiple neighbors is linearly combined, and their weights decrease with distance.

Optimal crowd editing

Simulating realistic crowd behaviors is a challenging problem in computer graphics. Yet, several satisfying simulation models exhibiting natural pedestrians or group emerging behaviors exist. Choosing among these model generally depends on the considered crowd density or the topology of the environment. Conversely, achieving a user-desired kinematic or dynamic pattern at a given instant of the simulation reveals to be much more tedious. In this paper, a novel generic control methodology is proposed to solve this crowd editing issue. Our method relies on an adjoint formulation of the underlying optimization procedure. It is independent to a certain extent of the choice of the simulation model, and is designed to handle several forms of constraints. A variety of examples attesting the benefits of our approach are proposed, along with quantitative performance measures.

Simulating realistic crowd based on agent trajectories

ABSTRACTThis paper presents a model for simulating realistic crowd behaviors at low computation cost. The proposed model is inspired by video data. In our approach, we first classify the crowd into two categories: main and background characters. Whether the agents are main characters or not is influenced by two factors, one is the agent's trajectories and the other one is the change of the environment. In the second stage, we adopt two approaches to simulate the behaviors of main and background characters. Main characters are intelligent agents with the perception, the memory, the planning, and the psychology so that they can make decisions themselves. Background characters are informed of the behavior options for execution by the “smart environment.” Finally, we simulate the road‐crossing scenario in a three‐dimensional virtual environment. The experimental results demonstrate that our approach not only well reflects the characteristics of agent behaviors but also reduces the computation complexity of simulating realistic crowd. Copyright © 2013 John Wiley & Sons, Ltd.

Real-time Collision Avoidance for Pedestrian and Bicyclist Simulation: A Smooth and Predictive Approach

This article introduces a new collision avoidance model enabling the design of efficient realistic virtual pedestrian and cyclist behaviors. It is a force-based model using collision prediction with dynamic time-windows to predict future potential collisions with obstacles and other individuals. It introduces a new type of force called sliding force to allow a smooth avoidance of potential collisions while enabling the pedestrian to continue to progress towards its goal. Unlike most existing models, our forces are not scaled according to the distance to the obstacle but depending on the estimate of the collision time with this obstacle. This inherently integrates obstacles’ velocity. This greatly reduces the compu- tational complexity of the model while ensuring a smooth avoidance. This model is oscillation-free except for concave obstacles. It enables the reproduction of inherent emergent properties of real crowds such as spontaneous organizations of pedestrians into lane lines, etc. This model is computationally efficient and designed for real time simulation of large crowds.

Crowd simulation: applying mobile grids to the social force model

The social force model (SF) is able to reproduce many emergent phenomena observed in real crowds. Unfortunately, in some situations, such as low density environments, SF may produce counterintuitive results where the trajectories simulated look more like particles than to real people. We modify the SF model through the use of a mobile grid to allow the simulated pedestrians to change the direction of their desired velocity at reasonable times, thus avoiding nearby blocked or crowded areas smoothly. Our experiments focus on qualitative behavior, and verify that our model produces the desired trajectories of the pedestrians, achieving softer and more coherent trajectories when compared to the pure SF model solution. Like SF, our model reproduces the “faster-is-slower” and the arching underlying the clogging effects. Finally, we examine the occupation rates of the space when pedestrians were submitted to narrowed corridors and observe the “edge effect.”

More realistic, flexible, and expressive social crowds using transactional analysis

Recent algorithms have been able to simulate “social crowds” that allow agents to interact socially as opposed to only treating other agents as obstacles. Unfortunately, past social crowd algorithms lack realism and flexibility because they do not allow agents to move in and out of different and repeated social interactions, are built around a specific obstacle avoidance algorithm, or are tuned only for a specific social setting and do not allow for artist directed changes. We propose a new, simplified social crowd algorithm that focuses on the evolving social needs of agents and allows each agent to join and leave different encounters as desired. Our algorithm is based on the psychology research area of transactional analysis, does not require a specific obstacle avoidance algorithm, and allows for easy artist direction for determining the precise social environment being simulated. Our algorithm runs in real-time with 3,000 to 4,000 agents without the restrictions of previous research.

Simulating and Evaluating the Local Behavior of Small Pedestrian Groups

Recent advancements in local methods have significantly improved the collision avoidance behavior of virtual characters. However, existing methods fail to take into account that in real life pedestrians tend to walk in small groups, consisting mainly of pairs or triples of individuals. We present a novel approach to simulate the walking behavior of such small groups. Our model describes how group members interact with each other, with other groups and individuals. We highlight the potential of our method through a wide range of test-case scenarios. We evaluate the results from our simulations using a number of quantitative quality metrics, and also provide visual and numerical comparisons with video footages of real crowds.

Crowd and environmental management during mass gatherings

Crowds are a feature of large cities, occurring not only at mass gatherings but also at routine events such as the journey to work. To address extreme crowding, various computer models for crowd movement have been developed in the past decade, and we review these and show how they can be used to identify health and safety issues. State-of-the-art models that simulate the spread of epidemics operate on a population level, but the collection of fine-scale data might enable the development of models for epidemics that operate on a microscopic scale, similar to models for crowd movement. We provide an example of such simulations, showing how an individual-based crowd model can mirror aggregate susceptible-infected-recovered models that have been the main models for epidemics so far.