scholarly journals Local interactions underlying collective motion in human crowds

2018 ◽  
Vol 285 (1878) ◽  
pp. 20180611 ◽  
Author(s):  
Kevin W. Rio ◽  
Gregory C. Dachner ◽  
William H. Warren
Keyword(s):  
Velocity Vector ◽  
Collective Motion ◽  
Weighted Average ◽  
Lateral Position ◽  
Self Organization ◽  
Local Interactions ◽  
Heading Direction ◽  
Symmetric Region ◽  
Crowd Motion ◽  
Global Patterns

It is commonly believed that global patterns of motion in flocks, schools and crowds emerge from local interactions between individuals, through a process of self-organization. The key to explaining such collective behaviour thus lies in deciphering these local interactions. We take an experiment-driven approach to modelling collective motion in human crowds. Previously, we observed that a pedestrian aligns their velocity vector (speed and heading direction) with that of a neighbour. Here we investigate the neighbourhood of interaction in a crowd: which neighbours influence a pedestrian's behaviour, how this depends on neighbour position, and how the influences of multiple neighbours are combined. In three experiments, a participant walked in a virtual crowd whose speed and heading were manipulated. We find that neighbour influence is linearly combined and decreases with distance, but not with lateral position (eccentricity). We model the neighbourhood as (i) a circularly symmetric region with (ii) a weighted average of neighbours, (iii) a uni-directional influence, and (iv) weights that decay exponentially to zero by 5 m. The model reproduces the experimental data and predicts individual trajectories in observational data on a human ‘swarm’. The results yield the first bottom-up model of collective crowd motion.

scholarly journals Collective Motion in Human Crowds

2018 ◽  
Vol 27 (4) ◽  
pp. 232-240 ◽  
Author(s):  
William H. Warren
Keyword(s):  
Collective Behavior ◽  
Collective Motion ◽  
Local Level ◽  
Self Organization ◽  
Coherent Motion ◽  
Global Level ◽  
Crowd Behavior ◽  
Local Interactions ◽  
Fish Schools ◽  
Emergency Situations

The balletic motion of bird flocks, fish schools, and human crowds is believed to emerge from local interactions between individuals in a process of self-organization. The key to explaining such collective behavior thus lies in understanding these local interactions. After decades of theoretical modeling, experiments using virtual crowds and analysis of real crowd data are enabling us to decipher the “rules of engagement” governing these interactions. On the basis of such results, my students and I built a dynamical model of how a pedestrian aligns his or her motion with that of a neighbor and how these binary interactions are combined within a neighborhood of interaction. Computer simulations of the model generate coherent motion at the global level and reproduce individual trajectories at the local level. This approach has yielded the first experiment-driven, bottom-up model of collective motion, providing a basis for understanding more complex patterns of crowd behavior in both everyday and emergency situations.

GLOBAL PATTERNS FROM LOCAL INTERACTIONS: A DYNAMICAL SYSTEMS APPROACH

2004 ◽  
Vol 14 (08) ◽  
pp. 2555-2578 ◽  
Author(s):  
RICHARD J. WIEDERIEN ◽  
FIRDAUS E. UDWADIA
Keyword(s):  
Traveling Waves ◽  
Systems Approach ◽  
Initial Conditions ◽  
Square Lattice ◽  
Two Dimensional ◽  
Local Interactions ◽  
Dimensional Lattice ◽  
Cumulative Score ◽  
Global Patterns ◽  
Steady State Solutions

In this paper the global patterns that result from local interactions between players on a two-dimensional lattice are studied. The assumptions on interaction between players are based on the Prisoner's Dilemma game that has been used extensively in game theory and in the study of biological systems. Each player is located on a square lattice, and is assumed to cooperate or defect, based on mimicking the neighbor with the highest cumulative score from the preceding round of play. The edges of the lattice are glued to form a torus. Computer simulations are conducted for different sized lattices, different payoff values, and different initial conditions. Though the paper is primarily concerned with player behavior without self-interaction, some results with self-interaction are also included. The influence of "ideal" cooperators on the evolution of the system dynamics is also studied. Three generic regimes of behavior are identified. Complex global patterns with complicated dynamics and sometimes unpredictable results occur. Steady-state solutions, simple and complex periodic solutions, and traveling waves are observed depending on the initial conditions and the payoff values.

scholarly journals Single-particle states vs. collective modes: friends or enemies ?

2018 ◽  
Vol 178 ◽  
pp. 02003 ◽  
Author(s):  
T. Otsuka ◽  
Y. Tsunoda ◽  
T. Togashi ◽  
N. Shimizu ◽  
T. Abe
Keyword(s):  
Single Particle ◽  
Collective Motion ◽  
Collective Mode ◽  
Self Organization ◽  
Collective Modes ◽  
Particle State ◽  
Nuclear Forces ◽  
Atomic Nuclei ◽  
Many Body Systems ◽  
Underlying Mechanisms

The quantum self-organization is introduced as one of the major underlying mechanisms of the quantum many-body systems. In the case of atomic nuclei as an example, two types of the motion of nucleons, single-particle states and collective modes, dominate the structure of the nucleus. The collective mode arises as the balance between the effect of the mode-driving force (e.g., quadrupole force for the ellipsoidal deformation) and the resistance power against it. The single-particle energies are one of the sources to produce such resistance power: a coherent collective motion is more hindered by larger spacings between relevant single particle states. Thus, the single-particle state and the collective mode are “enemies” against each other. However, the nuclear forces are rich enough so as to enhance relevant collective mode by reducing the resistance power by changing single-particle energies for each eigenstate through monopole interactions. This will be verified with the concrete example taken from Zr isotopes. Thus, the quantum self-organization occurs: single-particle energies can be self-organized by (i) two quantum liquids, e.g., protons and neutrons, (ii) monopole interaction (to control resistance). In other words, atomic nuclei are not necessarily like simple rigid vases containing almost free nucleons, in contrast to the naïve Fermi liquid picture. Type II shell evolution is considered to be a simple visible case involving excitations across a (sub)magic gap. The quantum self-organization becomes more important in heavier nuclei where the number of active orbits and the number of active nucleons are larger.

Self-Organization and Peirce’s Notion of Communication and Semiosis

2011 ◽  
Vol 1 (2) ◽  
pp. 53-61
Author(s):  
João Queiroz ◽  
Angelo Loula
Keyword(s):  
Complex System ◽  
Artificial Life ◽  
Self Organization ◽  
Central Control ◽  
Local Interactions ◽  
Global Pattern ◽  
Self Organized ◽  
Experimental Protocols ◽  
Systemic Process ◽  
Self Organizing

Semiosis can be described as an emergent self-organizing process in a complex system of distributed sign users interacting locally and mutually affecting each other. Contextually grounded, semiosis is characterized as a pattern that emerges through the cooperation between agents in a communication act, which concerns an utterer, a sign, and an interpreter. Some implications of this approach are explored in the context of Artificial Life experimental protocols. To model communication as a self-organized process, the authors create a scenario to investigate a potentially self-organizing dynamic of communication, via local interactions. According to the results, a systemic process (symbol-based communication) emerges as a global pattern (a common repertoire of signs) from local interactions, without any external or central control.

scholarly journals Emergence of self-organized multivortex states in flocks of active rollers

2020 ◽  
Vol 117 (18) ◽  
pp. 9706-9711 ◽  
Author(s):  
Koohee Han ◽  
Gašper Kokot ◽  
Oleh Tovkach ◽  
Andreas Glatz ◽  
Igor S. Aranson ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Collective Behavior ◽  
Rotational Motion ◽  
Collective Motion ◽  
Self Organization ◽  
Design Strategies ◽  
Vortex Phase ◽  
Active Elements ◽  
Self Organized ◽  
Dynamic Materials

Active matter, both synthetic and biological, demonstrates complex spatiotemporal self-organization and the emergence of collective behavior. A coherent rotational motion, the vortex phase, is of great interest because of its ability to orchestrate well-organized motion of self-propelled particles over large distances. However, its generation without geometrical confinement has been a challenge. Here, we show by experiments and computational modeling that concentrated magnetic rollers self-organize into multivortex states in an unconfined environment. We find that the neighboring vortices more likely occur with the opposite sense of rotation. Our studies provide insights into the mechanism for the emergence of coherent collective motion on the macroscale from the coupling between microscale rotation and translation of individual active elements. These results may stimulate design strategies for self-assembled dynamic materials and microrobotics.

scholarly journals Cellular-automata-based ecological and ecohydraulics modelling

2009 ◽  
Vol 11 (3-4) ◽  
pp. 252-265 ◽  
Author(s):  
Qiuwen Chen ◽  
Fei Ye ◽  
Weifeng Li
Keyword(s):  
Cellular Automata ◽  
Spatial Heterogeneity ◽  
Riparian Vegetation ◽  
Regulated River ◽  
Spatially Explicit ◽  
Local Interactions ◽  
Rule Based ◽  
Lumped Models ◽  
Global Patterns ◽  
Modelling Application

Spatially lumped models may fail to take into account the effects of spatial heterogeneity and local interactions. These properties sometimes are crucial to the dynamics and evolutions of ecosystems. This paper started from the fundamental aspects of CA and focused on the development and application of the approach to ecological and ecohydraulics modelling. Application cases include modelling of prey–predator dynamics by stochastic CA and simulation of riparian vegetation successions in a regulated river by rule-based CA. The results indicated that spatially explicit paradigms such as cellular automata (CA) have a strong capability to bridge the local processes and global patterns.

scholarly journals Swarming, swirling and stasis in sequestered bristle-bots

2013 ◽  
Vol 469 (2151) ◽  
pp. 20120637 ◽  
Author(s):  
L. Giomi ◽  
N. Hawley-Weld ◽  
L. Mahadevan
Keyword(s):  
Cell Phone ◽  
Autonomous Agents ◽  
Collective Motion ◽  
Relative Magnitude ◽  
Single Parameter ◽  
Local Interactions ◽  
Substrate Topography ◽  
Space And Time ◽  
Behavioural Repertoire

The collective ability of organisms to move coherently in space and time is ubiquitous in any group of autonomous agents that can move and sense each other and the environment. Here, we investigate the origin of collective motion and its loss using macroscopic self-propelled bristle-bots, simple automata made from a toothbrush and powered by an onboard cell phone vibrator-motor, that can sense each other through shape-dependent local interactions, and can also sense the environment non-locally via the effects of confinement and substrate topography. We show that when bristle-bots are confined to a limited arena with a soft boundary, increasing the density drives a transition from a disordered and uncoordinated motion to organized collective motion either as a swirling cluster or a collective dynamical stasis. This transition is regulated by a single parameter, the relative magnitude of spinning and walking in a single automaton. We explain this using quantitative experiments and simulations that emphasize the role of the agent shape, environment and confinement via boundaries. Our study shows how the behavioural repertoire of these physically interacting automatons controlled by one parameter translates into the mechanical intelligence of swarms.

scholarly journals Deep attention networks reveal the rules of collective motion in zebrafish

2018 ◽  
Author(s):  
Francisco J.H. Heras ◽  
Francisco Romero-Ferrero ◽  
Robert C. Hinz ◽  
Gonzalo G. de Polavieja
Keyword(s):  
Danio Rerio ◽  
Collective Behavior ◽  
Collective Motion ◽  
Weighted Average ◽  
Data Driven ◽  
List Type ◽  
Attention Networks ◽  
Interaction Information ◽  
Large Groups ◽  
Simple Models

AbstractA variety of simple models has been proposed to understand the collective motion of animals. These models can be insightful but lack important elements necessary to predict the motion of each individual in the collective. Adding more detail increases predictability but can make models too complex to be insightful. Here we report that deep attention networks can obtain in a data-driven way a model of collective behavior that is simultaneously predictive and insightful thanks to an organization in modules. The model obtains that interactions between two zebrafish, Danio rerio, in a large groups of 60-100, can be approximately be described as repulsive, attractive or as alignment, but only when moving slowly. At high velocities, interactions correspond only to alignment or alignment mixed with repulsion at close distances. The model also shows that each zebrafish decides where to move by aggregating information from the group as a weighted average over neighbours. Weights are higher for neighbours that are close, in a collision path or moving faster in frontal and lateral locations. These weights effectively select 5 relevant neighbours on average, but this number is dynamical, changing between a single neighbour to up to 12, often in less than a second. Our results suggest that each animal in a group decides by dynamically selecting information from the group.HighlightsAt 30 days postfertilization, zebrafish, Danio rerio, can move in very cohesive and predictable large groupsDeep attention networks obtain a predictive and understadable model of collective motionWhen moving slowly, interations between pairs of zebrafish have clear components of repulsion, attraction and alignmentWhen moving fast, interactions correspond to alignment and a mixture of alignment and repulsion at close distancesZebrafish turn left or right depending on a weighted average of interaction information with other fish, with weights higher for close fish, those in a collision path or those moving fast in front or to the sidesAggregation is dynamical, oscillating between 1 and 12 neighbouring fish, with 5 on average

scholarly journals Collision Avoidance Mechanism for Symmetric Circular Formations of Unitary Mass Autonomous Vehicles at Constant Speed

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Vander L. S. Freitas ◽  
Elbert E. N. Macau
Keyword(s):  
Data Collection ◽  
Collision Avoidance ◽  
Mobile Agents ◽  
Autonomous Vehicles ◽  
Coupled Oscillators ◽  
Collective Motion ◽  
Interconnection Network ◽  
Constant Speed ◽  
Local Interactions ◽  
Use Phase

Collective motion is a promising field that studies how local interactions lead groups of individuals to global behaviors. Biologists try to understand how those subjects interplay in nature, and engineers are concerned with the application of interaction strategies to mobile vehicles, satellites, robots, etc. There are several models in literature that employ strategies observed in groups of beings in nature. The aim is not to literally mimic them but to extract suitable strategies for the chosen application. These models, constituted of multiple mobile agents, can be used in tasks such as data collection, surveillance and monitoring. One approach is to use phase-coupled oscillators to design the mobile agents, in which each member is an oscillator and they are coupled according to an interconnection network. This design usually does not keep track and handle the possible collisions within the group, and real applications obviously must manage these situations to prevent the equipment from crashing. This paper introduces a collision avoidance mechanism to a model of particles with phase-coupled oscillators dynamics for symmetric circular formations.

The Birds of Rome

2020 ◽  
pp. 19-26
Author(s):  
Helmut Satz
Keyword(s):  
Computer Analysis ◽  
High Speed ◽  
Nearest Neighbors ◽  
Local Interactions ◽  
Precision Data ◽  
Global Patterns ◽  
University Of Rome ◽  
The University

With the help of high speed stereographic cameras and a computer analysis of the results, a team from the University of Rome provided first precision data of large starling flocks. They determined shape and correlation of the flocks as well as the local internal coordination between birds. The overall global patterns were thus shown to be the result of local interactions between six to seven nearest neighbors.

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