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I investigate collective behaviour using a wide range of theoretical and experimental approaches. Individual-based (Lagrangian) computer modelling is used extensively to reveal how individual movement and interactions result in group characteristics. This technique is used to gain insight into the structured patterns of movement within human crowds and the development of trail networks by ants. These models reveal the importance of interactions among individuals to density-dependent group behaviour. A simulation of animal groups in three-dimensional space reveals the existence of several robust collective patterns. Simulated groups show similar group-level behaviour and internal structure to natural groups. The model also reveals how differences among individuals influence group structure, and how individuals employing simple, local rules of thumb, can accurately change their relative position within a group (for example, to move to the centre, or to the periphery) without necessitating information regarding their current position within the group. New techniques in computer vision are introduced that can facilitate the automatic analysis of collective motion. This software can simultaneously track and analyse the movement of a large number (hundreds) of organisms. Computer vision is used to reveal the spatio-temporal patterns of activity in ant colonies for the first time. I also show how it can record detailed aspects of individual behaviour, including the movement of, and production of honeydew and offspring by, aphids. This technique is used in a detailed analysis of ant exploratory behaviour, revealing temporal and spatial information about the movement patterns of individual ants, and the relationship between individual behaviour and collective exploration. Simultaneous digital tracking of organisms is a powerful technique that in the future is likely to provide insight into the behaviour of many animal groups.

There are various kinds of creature living in the world and each kind of creatures has its own unique life habits and behavior patterns. For these reasons between the creature as well as the biology and the environment can have many interactions with each other, and we may observe these interactions easily in the frequently daily life. However the humanity may be the quantity hugest, and also have the most complex behavior at the fine race group in all kind of creatures on earth. So how could we penetrate into by observed and analysis to obtain the information which translates to the computer simulation realistically is a topic of the very hardship with challenge to presents the human behavior. Virtually all previous work has been agent-based, meaning that motion is computed separately for each individual. Such models can capture each person¡¦s unique situation. So the agent-based modeling will inevitably result in a large number of calculation and make poor efficiency. In addition, there are leader-follower system which consists of a leader and multiple followers. This leader will lead the entire group to the destination, and the followers will follow the motion of their leader closely. In our research apply the physical properties of electric charge to the simulation of pedestrians by using the basic concepts of electromagnetism. The simulation method contains the agent-based modeling and the leader-follower system at the same time. The agent-based modeling simulates pedestrian of the individual motion. The goal of the leader-follower system is to simulate the real world behavior that people in a community often move by following a specific object.

Thesis--Friedrich-Wilhelms-Universität zu Berlin.

Injustice, be it procedural, retributive, or restorative, can propel the crowd into different forms of collective action. This dissertation explores the motivations and responses that crowds undertake in response to perceived injustices using a sequential exploratory research design. The dissertation uses multicase research to investigate the injustices against which crowds (broadly defined) are fighting and provides nuances into each type of injustice. Focusing on these cases, the research adapts and applies Hirschman's Exit, Voice and Loyalty theory to provide a typological model connecting injustices to crowd response types. Second, a series of linear, logistic and multinominal regression models using newly collected nationally representative data show the associations between perceived injustices and support for responses of various types. The research contributes a theoretical model for understanding responses to injustices which advances the discussion about the public's role in fighting injustice within society. / Doctor of Philosophy / People can react in all manner to injustices. They might march in the streets demanding justice, inform police of the identities of wrongdoers, or bury a sexual offender alive. This dissertation connects these seemingly disjointed occurrences and investigates the diverse perceived injustices (retributive, procedural and restorative) behind such actions. It examines multiple case studies of initiatives by the crowd from around the global to illustrate the nuances in the perceived injustices. The research also adapts the Hirschman's Exit, Voice and Loyalty framework about failing companies and States to connect what appears to be a random assortment of actions. Using the findings from the multiple case studies, the dissertation tests using a series of statistical models whether perceived injustices among the general US population lead to increased support for certain ways in which the crowd can fight for justice.

Thesis (Ph. D.)--University of Washington, 2008. / Vita. Includes bibliographical references (p. 89-100).

As the human population grows, so too does the need to understand human behavior. One particularly important aspect of human behavior is how it changes within conglomerations of people, i.e. crowds. In this thesis, a method for modeling crowd behavior is proposed. This method draws inspiration from the concept of behavioral intention and the related forces of attitudes, influences, and social norms. These topics are first defined and detailed, followed by a survey of related research. Next, the model is presented and adapted to three common crowd dynamics, each stressing a different component of behavioral intention. Observations are made about these models, and extensions to the models and directions for future research are considered.

Computer vision algorithms have played a pivotal role in commercial video surveillance systems for a number of years. However, a common weakness among these systems is their inability to handle crowded scenes. In this thesis, we have developed algorithms that overcome some of the challenges encountered in videos of crowded environments such as sporting events, religious festivals, parades, concerts, train stations, airports, and malls. We adopt a top-down approach by first performing a global-level analysis that locates dynamically distinct crowd regions within the video. This knowledge is then employed in the detection of abnormal behaviors and tracking of individual targets within crowds. In addition, the thesis explores the utility of contextual information necessary for persistent tracking and re-acquisition of objects in crowded scenes. For the global-level analysis, a framework based on Lagrangian Particle Dynamics is proposed to segment the scene into dynamically distinct crowd regions or groupings. For this purpose, the spatial extent of the video is treated as a phase space of a time-dependent dynamical system in which transport from one region of the phase space to another is controlled by the optical flow. Next, a grid of particles is advected forward in time through the phase space using a numerical integration to generate a "flow map". The flow map relates the initial positions of particles to their final positions. The spatial gradients of the flow map are used to compute a Cauchy Green Deformation tensor that quantifies the amount by which the neighboring particles diverge over the length of the integration. The maximum eigenvalue of the tensor is used to construct a forward Finite Time Lyapunov Exponent (FTLE) field that reveals the Attracting Lagrangian Coherent Structures (LCS). The same process is repeated by advecting the particles backward in time to obtain a backward FTLE field that reveals the repelling LCS. The attracting and repelling LCS are the time dependent invariant manifolds of the phase space and correspond to the boundaries between dynamically distinct crowd flows. The forward and backward FTLE fields are combined to obtain one scalar field that is segmented using a watershed segmentation algorithm to obtain the labeling of distinct crowd-flow segments. Next, abnormal behaviors within the crowd are localized by detecting changes in the number of crowd-flow segments over time. Next, the global-level knowledge of the scene generated by the crowd-flow segmentation is used as an auxiliary source of information for tracking an individual target within a crowd. This is achieved by developing a scene structure-based force model. This force model captures the notion that an individual, when moving in a particular scene, is subjected to global and local forces that are functions of the layout of that scene and the locomotive behavior of other individuals in his or her vicinity. The key ingredients of the force model are three floor fields that are inspired by research in the field of evacuation dynamics; namely, Static Floor Field (SFF), Dynamic Floor Field (DFF), and Boundary Floor Field (BFF). These fields determine the probability of moving from one location to the next by converting the long-range forces into local forces. The SFF specifies regions of the scene that are attractive in nature, such as an exit location. The DFF, which is based on the idea of active walker models, corresponds to the virtual traces created by the movements of nearby individuals in the scene. The BFF specifies influences exhibited by the barriers within the scene, such as walls and no-entry areas. By combining influence from all three fields with the available appearance information, we are able to track individuals in high-density crowds. The results are reported on real-world sequences of marathons and railway stations that contain thousands of people. A comparative analysis with respect to an appearance-based mean shift tracker is also conducted by generating the ground truth. The result of this analysis demonstrates the benefit of using floor fields in crowded scenes. The occurrence of occlusion is very frequent in crowded scenes due to a high number of interacting objects. To overcome this challenge, we propose an algorithm that has been developed to augment a generic tracking algorithm to perform persistent tracking in crowded environments. The algorithm exploits the contextual knowledge, which is divided into two categories consisting of motion context (MC) and appearance context (AC). The MC is a collection of trajectories that are representative of the motion of the occluded or unobserved object. These trajectories belong to other moving individuals in a given environment. The MC is constructed using a clustering scheme based on the Lyapunov Characteristic Exponent (LCE), which measures the mean exponential rate of convergence or divergence of the nearby trajectories in a given state space. Next, the MC is used to predict the location of the occluded or unobserved object in a regression framework. It is important to note that the LCE is used for measuring divergence between a pair of particles while the FTLE field is obtained by computing the LCE for a grid of particles. The appearance context (AC) of a target object consists of its own appearance history and appearance information of the other objects that are occluded. The intent is to make the appearance descriptor of the target object more discriminative with respect to other unobserved objects, thereby reducing the possible confusion between the unobserved objects upon re-acquisition. This is achieved by learning the distribution of the intra-class variation of each occluded object using all of its previous observations. In addition, a distribution of inter-class variation for each target-unobservable object pair is constructed. Finally, the re-acquisition decision is made using both the MC and the AC.

This thesis is concerned with the user experience of crowds, incorporating issues of comfort, satisfaction, safety and performance within a given crowd situation. Factors that influence the organisation and monitoring of crowd events will be considered. A comprehensive review of the literature revealed that crowd safety, pedestrian flow modeling, public order policing and hooliganism prevention, has received the greatest attention with previous research on crowds. Whereas crowd performance, comfort and satisfaction has received less attention, particularly within spectator events (sporting and music for example). Original research undertaken for this doctoral thesis involved a series of studies: user focus groups, stakeholder interviews, and observational research within event security and organisation. Following on from these investigations, the findings have been integrated with a tool to assist crowd organisers and deliverers during the planning of crowd events, and accompanying user feedback interviews following use of the tool. The overarching aim of the research within this thesis was to explore the complex issues that contribute to the user experience of being in a crowd, and how this might be improved. The crowd user focus groups revealed differences in factors affecting crowd satisfaction, varying according to age and user expectations. Greater differences existed between crowd users, than across crowd situations, highlighting the importance of identifying expected crowd members when planning individual events. Additionally, venue design, organisation, safety and security concerns were found to highly affect crowd satisfaction, irrespective of group differences or crowd situations, showing the importance of these issues when considering crowd satisfaction for all crowd events, for any crowd members. Stakeholder interviews examining crowds from another perspective suggested that overall safety was a high priority due to legal obligations, in order to protect venue reputation. Whereas, comfort and satisfaction received less attention within the organisation of crowd events due to budget considerations, and a lack of concern as to the importance of such issues. Moreover, communication and management systems were sometimes inadequate to ensure compliance with internal procedures. In addition a lack of usable guidance was seen to be available to those responsible for organising crowd situations. Eleven themes were summarised from the data, placed in order of frequency of references to the issues: health and safety, public order, communication, physical environment, public relations, crowd movement, event capacity, facilities, satisfaction, comfort, and crowd characteristics. Results were in line with the weighting of the issues within the literature, with health and safety receiving the most attention, and comfort and satisfaction less attention. These results were used to form the basis of observational checklists for event observations across various crowd situations. Event observations took two forms: observing the role of public and private security, and observing crowd events from the user perspective. Observations within public and private security identified seven general themes: communication, anticipating crowd reaction, information, storage, training, role confusion, financial considerations and professionalism. Findings questioned the clarity of the differing roles of public and private security, and understanding of these differences. Also the increasing use of private over public security within crowd event security, and the differing levels of training and experience within public and private security were identified. Event observations identified fifteen common themes drawn from the data analysis: communication, public order, comfort, facilities, queuing systems, transportation, crowd movement, design, satisfaction, health and safety, public relations, event capacity, time constraints, encumbrances, and cultural differences. Key issues included the layout of the event venue together with the movement and monitoring of crowd users, as well as the availability of facilities in order to reduce competition between crowd users, together with possible links to maintaining public order and reducing anti-social behaviour during crowd events. Findings from the focus groups, interviews, and observations were then combined (to enhance the robustness of the findings), and developed into the Crowd Satisfaction Assessment Tool (CSAT) prototype, a practical tool for event organisers to use during the planning of crowd events. In order to assess proof of concept of the CSAT, potential users (event organisers) were recruited to use the CSAT during the planning of an event they were involved in organising. Semi-structured feedback interviews were then undertaken, to gain insight into the content, usefulness, and usability of the CSAT. Separately human factors researchers were recruited to review the CSAT, providing feedback on the layout and usability of the tool. Feedback interviews suggested the CSAT was a useful concept, aiding communication, and providing organisers with a systematic and methodical structure for planning ahead, prioritising ideas, and highlighting areas of concern. The CSAT was described as being clear and easy to follow, with clear aims, and clear instructions for completion, and was felt to aid communication between the various stakeholders involved in the organisation and management of an event, allowing information to be recorded, stored and shared between stakeholders, with the aim of preventing the loss of crucial information. The thesis concludes with a summary model of the factors that influence crowd satisfaction within crowd events of various descriptions. Key elements of this are the anticipation, facilities, and planning considered before an event, influences and monitoring during an event and reflection after an event. The relevance and impact of this research is to assist the planning of crowd events, with the overall aim of improving participant satisfaction during crowd events. From a business perspective the issue is important with competition between events, the desire to encourage return to events, and to increase profit for organisers. From an ergonomics perspective, there is the imperative of improving the performance of crowd organisers and the experience of crowd users.

<p>Using the wisdom of crowds---combining many individual forecasts to obtain an aggregate estimate---can be an effective technique for improving forecast accuracy. When individual forecasts are drawn from independent and identical information sources, a simple average provides the optimal crowd forecast. However, correlated forecast errors greatly limit the ability of the wisdom of crowds to recover the truth. In practice, this dependence often emerges because information is shared: forecasters may to a large extent draw on the same data when formulating their responses. </p><p>To address this problem, I propose an elicitation procedure in which each respondent is asked to provide both their own best forecast and a guess of the average forecast that will be given by all other respondents. I study optimal responses in a stylized information setting and develop an aggregation method, called pivoting, which separates individual forecasts into shared and private information and then recombines these results in the optimal manner. I develop a tailored pivoting procedure for each of three information models, and introduce a simple and robust variant that outperforms the simple average across a variety of settings.</p><p>In three experiments, I investigate the method and the accuracy of the crowd forecasts. In the first study, I vary the shared and private information in a controlled environment, while the latter two studies examine forecasts in real-world contexts. Overall, the data suggest that a simple minimal pivoting procedure provides an effective aggregation technique that can significantly outperform the crowd average.</p> / Dissertation