Realistic following behaviors for crowd simulation

2013

In the simulation of human crowd behavior including evacuation planning, transportation management, and safety engineering in architecture design, the development of pedestrian model for higher behavior fidelity is an important task. To construct plausible facsimiles of real crowd movements, simulations should exhibit human behaviors for navigation, pedestrian decision-making, and social behaviors such as grouping and crowding. The research field is quite mature in some sense, with a large number of approaches that have been proposed to path finding, collision avoidance, and visually pleasing steering behaviors of virtual humans. However, there is still a clear disparity between the variety of approaches and the quality of crowd behaviors in simulations. Many social science field studies inform us that crowds are typically composed of multiple social groups (James, 1953; Coleman and James, 1961; Aveni, 1977). These observations indicate that one component of the complexity of crowd ...

2012

Spring Simulation Conference (SpringSim 2020), 2020

The nature of the social relationship within a pedestrian group influences the group's structure and behavior and thus its entitativity (i.e. the perception of a group as a unit by other pedestrians). However, existing crowd models ignore the diversity of social relationships and have limitations in reproducing group avoidance behaviors. The proposed model is an adaptation of the social force model that addresses group social relationships. The approach is calibrated by comparing the distances and angles between members of the simulated groups with observations in real crowds. Results show that intra-group distances are a key factor in collision avoidance behavior. Simulation of collision avoidance shows that group members behavior fits better with empirical data than the original model and that individuals avoid splitting groups. By simply tuning the distribution of social relationships in the simulated crowd, the model can be used to reproduce crowd behaviors in several contexts.

2000

We present an investigation of a driven random- walk model which is used to simulate pedestrian motion. We analyse the dependency of the global density and the mean velocity on the boundary density for open and periodic boundary conditions. Global and local fundamental diagrams are presented for a random sequential and parallel update. In addition, an analytical solution for the

Observations conducted by researchers revealed that the group interaction within crowds is a common phenomenon and has great influence on pedestrian behaviour. However, most research currently undertaken by various researchers failed to consider the group dynamics when developing pedestrian flow models. This paper presented a critical review of pedestrian models that incorporates group behaviour. Models reviewed in this paper are mainly created by microscopic modelling approaches such as social force, cellular automata, and agent-based method. The purpose of this literature review is to improve the understanding of group dynamics among pedestrians and highlight the need for considering group dynamics when developing pedestrian simulation models.

International Journal of Advanced Computer Science and Applications, 2016

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.

AIP Conference Proceedings, 2017

CHAPTER 2-MICROSCOPIC PEDESTRIAN SIMULATION MODEL 2.1 2.2(d) The Multi-Agents Pedestrian Model 2.2(e) The Social Force Model 2.3 Advantages and disadvantages of the Microscopic Pedestrian Simulation Models 2.4 Modifications to the Social Force Model 2.4.1 The LKF model by Lakoba, Kaup and Finkelstein 2.4.2 Reproducing the Fundamental Diagram in One-Dimensional System 2.4.2(a) Fundamental Diagram for the Simplest System 2.4.2(b) Modifications by Seyfried et. al. 2.4.3 Modifications by Parisi D.R. et. al CHAPTER 3-DEVELOPMENT OF THE DYNAMIC RESPECT FACTOR AND ITS APPLICATIONS IN VARIOUS SITUATIONS 3.1 vi 3.6 The incorporation of age and gender in a multi-direction crowd 3.6.1 The action of "respect" between pedestrians from different genders and age groups 3.6.2 Background of the Simulation 3.6.3 Simulations involving only elderly and normal pedestrians 3.6.4 Simulations involving only male and female pedestrians 3.6.5 Simulations involving both age and gender factor 3.6.6 Discussions 3.7 Conclusions CHAPTER 4-APPLICATIONS OF THE SOCIAL FORCE MODEL IN

Computer simulation of dense crowds is finding increased use in event planning, congestion prediction, and threat assessment. State-of-the-art particle-based crowd methods assume and aim for collision-free trajectories. That is an idealistic yet not overly realistic expectation, as near-collisions increase in dense and rushed settings compared to typically sparse pedestrian scenarios. We propose Centroidal Particle Dynamics (CPD), a method that explicitly models the compressible personal space area surrounding each entity (~0.8m-1.0m radius) to inform its local pathing and collision avoidance decisions. While personal space has traditionally been modeled as a fixed radius, the reality is that it often changes in response to the surrounding context. For instance, in cases of congestion, entities tend to share more of their personal space than they normally would, simply out of necessity (e.g. passing through a crowded gate or boarding a train). Likewise, entities travelling at higher speeds (e.g. strolling, running) tend to expect a larger area ahead of them to be their personal -unoccupied-space. We illustrate how our proposed agent-based method for local dynamics can reproduce several key emergent dense crowd phenomena at the microscopic level (e.g. emergent lane formation in bidirectional flow and arching near congested gateways) with higher congruence to real trajectory data and with more visually convincing collision avoidance paths than the existing state-of-the-art. We further show how CPD can be efficiently computed on consumer-grade graphics hardware (GPU), achieving interactive frame rates when simulating thousands of crowd entities in the scene, thus making it suitable and ready-for-use in our target applications of entertainment and interactive media projects (i.e. film, gaming, and educational media). Lastly, we discuss crowd motion validation, and how to increase confidence in CPD, potentially making it also suitable for use in safety-critical applications, including urban design, evacuation analysis, and crowd safety planning. This work would not be possible without the dedication, mentorship, honesty, friendship, and trust that Dr. Gabriel Wainer has provided throughout my years at Carleton University's Advanced Realtime Simulation Lab. He had enormous confidence in me at times when I had none; and he inspired me to learn, take risks, improve, and teach. Thank you, professor.

1997

This paper presents a model of crowd behavior to simulate the motion of a generic population in a specific environment. The individual parameters are created by a distributed random behavioral model which is determined by few parameters. This paper explores an approach based on the relationship between the autonomous virtual humans of a crowd and the emergent behavior originated from

Situations characterised by the presence of a high density of pedestrians involved in negative interactions (e.g. flows in opposite directions) often represent a problematic scenario for simulation models, especially those taking a discrete approach to the representation and management of spatial aspects of the environment. While these situations can be relatively infrequent, and even if architects, event organisers and crowd managers actually try to prevent them as much as possible, they simply cannot be neglected and they actually represent interesting situations to be analysed by means of simulation. The paper presents specific extensions to a floor-field Cellular Automata pedestrian model that are specifically aimed at supporting the simulation of high density situations comprising negative interactions among pedestrians without incurring in the traditional limits of discrete approaches. The models are formally described and experimented in experimental and real world situations.