Etude expérimentale et modélisation des déplacements collectifs de piétons

Qu'elle soit composée de piétons dans une rue commerçante, de supporters quittant un stade, ou de pèlerins à La Mecque, une foule humaine constitue un système dont la dynamique collective est difficile à appréhender. Aujourd'hui, les mécanismes qui sous-tendent la dynamique des foules humaines restent peu connus et sont le plus souvent étudiés de manière qualitative. Ce travail de thèse est une analyse de ces mécanismes. En combinant observations en milieu naturel, expérimentations contrôlées et modélisation mathématique, nous avons mené une étude approfondie du comportement des piétons, de la dynamique globale d'une foule, et du lien qui unit ces deux niveaux d'observation. La réalisation d'expériences impliquant des piétons en interaction nous a permis de caractériser les propriétés du comportement d'évitement, et son rôle dans la dynamique collective. Nos résultats montrent l'existence d'un biais comportemental qui joue un rôle structurant dans le phénomène de formation de files. Nous nous sommes également intéressés aux interactions sociales qui gouvernent le comportement des piétons se déplaçant en groupe. A l'aide d'observations réalisées en milieu urbain, nous avons cherché à comprendre leur rôle dans la configuration de marche des groupes de piétons et leur influence sur l'efficacité du trafic. Enfin, nous proposons une nouvelle approche de modélisation basée sur de simples heuristiques comportementales s'appuyant sur le champ visuel des piétons. Nos travaux permettent d'envisager une meilleure évaluation du trafic piétonnier et ouvrent de nouvelles pistes de recherches pour l'étude d'autres formes de comportements collectifs dans notre société.In a wide variety of social and biological systems, many collective behaviours result from self-organized processes based on local interactions among individuals. Understanding these mechanisms comes down to establishing a link between two distinct levels of observation: the macroscopic patterns displayed at the group level, and the microscopic behaviour of individuals. This work investigates the mechanisms underlying self-organized behaviours in human crowds, such as shoppers in a commercial walkway, supporters leaving a stadium, or pilgrims in Mecca. Using empirical observations in urban environment, controlled laboratory experiments and mathematical modelling, we have studied the behaviour of pedestrians, the nature of their interactions, and the collective patterns of motion. We first conducted laboratory experiments involving interacting pedestrians. From these observations, we extracted a quantitative measurement of the interaction rules. We found the existence of a bias in pedestrian behaviour that is amplified in a collective context and shapes the lane formation phenomenon. Second, we analyzed empirical data collected in natural conditions to study the features of social interactions among people who are walking in groups. We investigated the role of these interactions in group-walking configurations, and we estimated its impact on the traffic efficiency. Finally, we elaborated a new modelling framework for pedestrian behaviour, based on simple behavioural heuristics. Our results suggest applied solutions to evaluate the traffic efficiency in urban environment and open research perspectives for the study of other collective behaviours in social systems

Ranking with social cues: Integrating online review scores and popularity information

Online marketplaces, search engines, and databases employ aggregated social information to rank their content for users. Two ranking heuristics commonly implemented to order the available options are the average review score and item popularity-that is, the number of users who have experienced an item. These rules, although easy to implement, only partly reflect actual user preferences, as people may assign values to both average scores and popularity and trade off between the two. How do people integrate these two pieces of social information when making choices? We present two experiments in which we asked participants to choose 200 times among options drawn directly from two widely used online venues: Amazon and IMDb. The only information presented to participants was the average score and the number of reviews, which served as a proxy for popularity. We found that most people are willing to settle for items with somewhat lower average scores if they are more popular. Yet, our study uncovered substantial diversity of preferences among participants, which indicates a sizable potential for personalizing ranking schemes that rely on social information.Comment: 4 pages, 3 figures, ICWS

Traffic Instabilities in Self-Organized Pedestrian Crowds

In human crowds as well as in many animal societies, local interactions among individuals often give rise to self-organized collective organizations that offer functional benefits to the group. For instance, flows of pedestrians moving in opposite directions spontaneously segregate into lanes of uniform walking directions. This phenomenon is often referred to as a smart collective pattern, as it increases the traffic efficiency with no need of external control. However, the functional benefits of this emergent organization have never been experimentally measured, and the underlying behavioral mechanisms are poorly understood. In this work, we have studied this phenomenon under controlled laboratory conditions. We found that the traffic segregation exhibits structural instabilities characterized by the alternation of organized and disorganized states, where the lifetime of well-organized clusters of pedestrians follow a stretched exponential relaxation process. Further analysis show that the inter-pedestrian variability of comfortable walking speeds is a key variable at the origin of the observed traffic perturbations. We show that the collective benefit of the emerging pattern is maximized when all pedestrians walk at the average speed of the group. In practice, however, local interactions between slow- and fast-walking pedestrians trigger global breakdowns of organization, which reduce the collective and the individual payoff provided by the traffic segregation. This work is a step ahead toward the understanding of traffic self-organization in crowds, which turns out to be modulated by complex behavioral mechanisms that do not always maximize the group's benefits. The quantitative understanding of crowd behaviors opens the way for designing bottom-up management strategies bound to promote the emergence of efficient collective behaviors in crowds.Comment: Article published in PLoS Computational biology. Freely available here: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.100244

LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW

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 behavior 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.Comment: 18 pages; 6 figures; Accepted for publication in PLoS ON

The dynamics of information flow as observed during simulations.

<p>(<b>a</b>) Illustrative example of how one particular piece of information <i>k</i> spreads in the population. The color-coding shows the local information flow, measured as the number of time the information <i>k</i> has been communicated to an individual <i>i</i>. Dark blue zones indicate individuals how have never heard of information <i>k</i>, whereas those who received the information 20 times are colored in dark red. (<b>b</b>) Distribution of the local flow over all pieces of information. The skewed distribution indicates that information spreads unequally in the population. (<b>c</b>) The risk perception of individuals as a function of the average number of information they have received. The grey zone indicates the standard deviation of the average. The visible reverse-U shape indicates that individuals expressing extreme opinions are on average less informed than those having a moderate risk judgment. Results are averaged over 50 simulations with parameters  = 0.1 and  = 0.9, corresponding to the bottom right corner of the maps presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084592#pone-0084592-g004" target="_blank">figure 4</a>.</p

Graphical representation of the risk perception function

<p><b>.</b> The function indicates the perception of risk of an individual owning a total amount of information and indicating the danger and the safety of the situation, respectively. The function parameters are set to  = 0.8, and  = 0.2. In the absence of any information, the risk perception level is 0, whereas large and well-balanced amounts of information for both sides yield a risk level of 0.5. The function always returns a value between 0 and 1.</p

Schematic representation of the model.

<p>Individuals receive pieces of information from the media (1) and from their peers (2). Each piece of information <i>k</i> is given a weight by the individual <i>i</i> and stored in his or her memory (3). The collection of weighted information an individual <i>i</i> owns is finally used to determine the level of risk perception <i>r_i</i> (4). Circled numbers indicate different steps of the elaboration of the model as described in the main text. All model parameters and variables are summarized in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084592#pone-0084592-t001" target="_blank">tables 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084592#pone-0084592-t002" target="_blank">2</a>, respectively.</p

Three representative examples of the search patterns emerging from the model.

<p>The three examples correspond to the same set of parameters as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084592#pone-0084592-g003" target="_blank">Figure 3</a>. (<b>a</b>) With low levels of independent search and social influence , the search volume is constant and low. (<b>b</b>) A spiky search pattern followed by a slow relaxation is visible when  = 0.1 and  = 1. (<b>c</b>) When both variables are high, the search volume stays high during a certain amount of time, until all individuals become inactive almost simultaneously. The search volume corresponds to the number of individuals who engaged in an independent search per unit of time.</p

Description of model parameters and the corresponding values used in the numerical simulations.

<p>Description of model parameters and the corresponding values used in the numerical simulations.</p