Simon Garnier

The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics

Human crowd motion is mainly driven by self-organized processes based on local interactions among pedestrians. While most studies of crowd behaviour consider only interactions among isolated individuals, it turns out that up to 70% of people in a crowd are actually moving in groups, such as friends, couples, or families walking together. These groups constitute medium-scale aggregated structures and their impact on crowd dynamics is still largely unknown. In this work, we analyze the motion of approximately 1500 pedestrian groups under natural condition, and show that social interactions among group members generate typical group walking patterns that influence crowd dynamics. At low density, group members tend to walk side by side, forming a line perpendicular to the walking direction. As the density increases, however, the linear walking formation is bent forward, turning it into a V-like pattern. These spatial patterns can be well described by a model based on social communication between group members. We show that the V-like walking pattern facilitates social interactions within the group, but reduces the flow because of its “non-aerodynamic” shape. Therefore, when crowd density increases, the group organization results from a trade-off between walking faster and facilitating social exchange. These insights demonstrate that crowd dynamics is not only determined by physical constraints induced by other pedestrians and the environment, but also significantly by communicative, social interactions among individuals.

The biological principles of swarm intelligence

AbstractnThe roots of swarm intelligence are deeply embedded in the biological study of self-organized behaviors in social insects. From the routing of traffic in telecommunication networks to the design of control algorithms for groups of autonomous robots, the collective behaviors of these animals have inspired many of the foundational works in this emerging research field. For the first issue of this journal dedicated to swarm intelligence, we review the main biological principles that underlie the organization of insects’ colonies. We begin with some reminders about the decentralized nature of such systems and we describe the underlying mechanisms of complex collective behaviors of social insects, from the concept of stigmergy to the theory of self-organization in biological systems. We emphasize in particular the role of interactions and the importance of bifurcations that appear in the collective output of the colony when some of the system’s parameters change. We then propose to categorize the collective behaviors displayed by insect colonies according to four functions that emerge at the level of the colony and that organize its global behavior. Finally, we address the role of modulations of individual behaviors by disturbances (either environmental or internal to the colony) in the overall flexibility of insect colonies. We conclude that future studies about self-organized biological behaviors should investigate such modulations to better understand how insect colonies adapt to uncertain worlds.n

Experimental study of the behavioural mechanisms underlying self-organization in human crowds

In animal societies as well as in human crowds, many observed collective behaviours result from self-organized processes based on local interactions among individuals. However, models of crowd dynamics are still lacking a systematic individual-level experimental verification, and the local mechanisms underlying the formation of collective patterns are not yet known in detail. We have conducted a set of well-controlled experiments with pedestrians performing simple avoidance tasks in order to determine the laws ruling their behaviour during interactions. The analysis of the large trajectory dataset was used to compute a behavioural map that describes the average change of the direction and speed of a pedestrian for various interaction distances and angles. The experimental results reveal features of the decision process when pedestrians choose the side on which they evade, and show a side preference that is amplified by mutual interactions. The predictions of a binary interaction model based on the above findings were then compared with bidirectional flows of people recorded in a crowded street. Simulations generate two asymmetric lanes with opposite directions of motion, in quantitative agreement with our empirical observations. The knowledge of pedestrian behavioural laws is an important step ahead in the understanding of the underlying dynamics of crowd behaviour and allows for reliable predictions of collective pedestrian movements under natural conditions.

Collective information processing and pattern formation in swarms, flocks, and crowds.

The spontaneous organization of collective activities in animal groups and societies has attracted a considerable amount of attention over the last decade. This kind of coordination often permits group-living species to achieve collective tasks that are far beyond single individuals capabilities. In particular, a key benefit lies in the integration of partial knowledge of the environment at the collective level. In this contribution, we discuss various self-organization phenomena in animal swarms and human crowds from the point of view of information exchange among individuals. In particular, we provide a general description of collective dynamics across species and introduce a classification of these dynamics not only with respect to the way information is transferred among individuals but also with regard to the knowledge processing at the collective level. Finally, we highlight the fact that the individuals ability to learn from past experiences can have a feedback effect on the collective dynamics, as experienced with the development of behavioral conventions in pedestrian crowds.

Alice in Pheromone Land: An Experimental Setup for the Study of Ant-like Robots

The pheromone trail laying and trail following behaviors of ants have proved to be an efficient mechanism to optimize path selection in natural as well as in artificial networks. Despite this efficiency, this mechanism is under-used in collective robotics because of the chemical nature of pheromones. In this paper we present a new experimental setup which allows to investigate with real robots the properties of a robotics systems using such behaviors. To validate our setup, we present the results of an experiment in which a group of 5 robots has to select between two identical alternatives a path linking two different areas. Moreover, a set of computer simulations provides a more complete exploration of the properties of this system. At last, experimental and simulation results lead us to interesting prediction that will be testable in our setup.

Self-Organized Aggregation Triggers Collective Decision Making in a Group of Cockroach-Like Robots

Self-amplification processes are at the origin of several collective decision phenomena in insect societies. Understanding these processes requires linking individual behavioral rules of insects to a choice dynamics at the colony level. In a homogeneous environment, the German cockroach Blattella germanica displays self-amplified aggregation behavior. In a heterogeneous environment where several shelters are present, groups of cockroaches collectively select one of them. In this article, we demonstrate that the restriction of the self-amplified aggregation behavior to distinct zones in the environment can explain the emergence of a collective decision at the level of the group. This hypothesis is tested with robotics experiments and dedicated computer simulations. We show that the collective decision is influenced by the available spaces to explore and to aggregate in, by the size of the population involved in the aggregation process and by the probability of encounter zones while the robots explore the environment. We finally discuss these results from both a biological and a robotics point of view.

The embodiment of cockroach aggregation behavior in a group of micro-robots

We report the faithful reproduction of the self-organized aggregation behavior of the German cockroach Blattella germanica with a group of robots. We describe the implementation of the biological model provided by Jeanson et al. in Alice robots, and we compare the behaviors of the cockroaches and the robots using the same experimental and analytical methodology. We show that the aggregation behavior of the German cockroach was successfully transferred to the Alice robot despite strong differences between robots and animals at the perceptual, actuatorial, and computational levels. This article highlights some of the major constraints one may encounter during such a work and proposes general principles to ensure that the behavioral model is accurately transferred to the artificial agents.

Aggregation behaviour as a source of collective decision in a group of cockroach-like-robots

In group-living animals, aggregation favours interactions and information exchanges between individuals, and thus allows the emergence of complex collective behaviors. In previous works, a model of a self-enhanced aggregation was deduced from experiments with the cockroach Blattella germanica. In the present work, this model was implemented in micro-robots Alice and successfully reproduced the agregation dynamics observed in a group of cockroaches. We showed that this aggregation process, based on a small set of simple behavioral rules of interaction, can be used by the group of robots to select collectively an aggregation site among two identical or different shelters. Moreover, we showed that the aggregation mechanism allows the robots as a group to “estimate” the size of each shelter during the collective decision-making process, a capacity which is not explicitly coded at the individual level.

Path selection and foraging efficiency in Argentine ant transport networks

We experimentally investigated both individual and collective behavior of the Argentine ant Linepithema humile as they crossed symmetrical and asymmetrical bifurcations in gallery networks. Ants preferentially followed the branch that deviated the least from their current direction and their probability to perform a U-turn after a bifurcation increased with the turning angle at the bifurcation. At the collective level, colonies were better able to find the shortest path that linked the nest to a food source in a polarized network where bifurcations were symmetrical from one direction and asymmetrical from the other than in a network where all bifurcations were symmetrical. We constructed a model of individual behavior and showed that an individual’s preference for the least deviating path will be amplified via the ants’ mass recruitment mechanism thus explaining the difference found between polarized and non-polarized networks. The foraging efficiency measured in the simulations was three times higher in polarized than in non-polarized networks after only 15xa0min. We conclude that measures of transport network efficiency must incorporate both the structural properties of the network and the behavior of the network users.

Collective decision-making by a group of cockroach-like robots

In group-living animals, aggregation favours interactions as well as information exchanges between individuals, and allows thus the emergence of complex collective behaviors. In previous works, a model of a self-enhanced aggregation was deduced from experiments with the cockroach Blattella germanica. In this work, this model was implemented in micro-robots Alice and successfully reproduced the aggregation dynamics observed in a group of cockroaches. We showed that this aggregation process, based on a small set of simple behavioral rules and interactions among individuals, can be used by the group of robots to select collectively an aggregation site among two identical or different shelters. Moreover, we showed that the aggregation mechanism allows the robots as a group to estimate the size of each shelter during the collective decision-making process, a capacity which is not explicitly coded at the individual level but that simply emerges from the aggregation behaviour.