How the Ocean Personality Model Affects the Perception of Crowds

Durupınar, Funda; Pelechano, Nuria; Allbeck, Jan M.; Güdükbay, Uğur; Badler, Norman I.

doi:10.1109/mcg.2009.105

Cited by 148 publications

(116 citation statements)

References 3 publications

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“…Other motion control schemes were developed as finite state machines [174], which allowed animators to develop procedural rules for characters [175]. Also, control schemes within origins outside computing have been woven into computing (see Pelechano et al [64] for an excellent overview): physics models are popularly used, as are psychology-like approaches [176,177], cognitive schemes [170,178,179], group traits derived from collective human and animal behavior [180][181][182], machine-learning models that build control functions from trajectory data of real people [183][184][185], and schemes that afford computational efficiency in control across complex solution spaces [186,187]. Of particular relevance is the tradition of using the built environment (urban morphology, road networks, naturalistic paths, and implied movement effort) to impose hierarchies or abstractions that might ease look-up schemes in model databases, balance rendering loads in animation, and scale crowds to large populations [188][189][190][191][192][193][194][195].…”

Section: Animationmentioning

confidence: 99%

“…To some extent, environmental cognition turns the sort of attention that urban studies place on the built environment as a placeholder for people's activity on streetscapes inwards [201], to introspective/egocentric questions [202] of how people use the information that they acquire from streetscape phenomena to engage in tasks and shape their behaviors, and how they might be embodied [203] within that context. Aspects of these emotive-focused theories have appeared in streetscape models in computer graphics where they are used as the basis for belief-desire-intent, perception-action, and personality-lookup schemes to determine motion control [176,204,205], or for endowing characters with synthetic emotions that correspond to their environmental setting [206]. Interestingly, psychologists often make use of virtual reality environments and game engines as proxy settings for their experiments, although these are generally limited to avatar representation of interacting participants for task-performance, rather than representation of the behaviors for characters within those environments [207][208][209].…”

Section: Psychologymentioning

confidence: 99%

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Computational Streetscapes

Torrens

2016

Computation

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Abstract:Streetscapes have presented a long-standing interest in many fields. Recently, there has been a resurgence of attention on streetscape issues, catalyzed in large part by computing. Because of computing, there is more understanding, vistas, data, and analysis of and on streetscape phenomena than ever before. This diversity of lenses trained on streetscapes permits us to address long-standing questions, such as how people use information while mobile, how interactions with people and things occur on streets, how we might safeguard crowds, how we can design services to assist pedestrians, and how we could better support special populations as they traverse cities. Amid each of these avenues of inquiry, computing is facilitating new ways of posing these questions, particularly by expanding the scope of what-if exploration that is possible. With assistance from computing, consideration of streetscapes now reaches across scales, from the neurological interactions that form among place cells in the brain up to informatics that afford real-time views of activity over whole urban spaces. For some streetscape phenomena, computing allows us to build realistic but synthetic facsimiles in computation, which can function as artificial laboratories for testing ideas. In this paper, I review the domain science for studying streetscapes from vantages in physics, urban studies, animation and the visual arts, psychology, biology, and behavioral geography. I also review the computational developments shaping streetscape science, with particular emphasis on modeling and simulation as informed by data acquisition and generation, data models, path-planning heuristics, artificial intelligence for navigation and way-finding, timing, synthetic vision, steering routines, kinematics, and geometrical treatment of collision detection and avoidance. I also discuss the implications that the advances in computing streetscapes might have on emerging developments in cyber-physical systems and new developments in urban computing and mobile computing.

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Section: Animationmentioning

confidence: 99%

Section: Psychologymentioning

confidence: 99%

Computational Streetscapes

Torrens

2016

Computation

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“…Their model is based on Helbing's work and is composed of geometrical information and psychological rules with a force model resembling behaviors of real people. Durupinar et al extend the HiDAC model by specifying agents personalities in order to mimic human behaviors from normal and disaster environments [6]. Guy et al use Eysenck's three-personality model for crowd simulation and show how personality affects the social behavior of crowds, including faster-in-slow effect [7].…”

Section: Related Workmentioning

confidence: 99%

Qualitative Methods of Validating Evacuation Behaviors

Takahashi

2015

Proceedings of the International Conference on Social Modeling and Simulation, Plus Econophysics Colloquium 2014

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Multi-agent simulations (MAS) have been used to study the dynamics of social systems. Disaster-related simulation is one of application fields. The simulation is applied to scenarios that are difficult to perform drills in the real world. The results provide useful data such as the amount of time people take to evacuate buildings and how smoothly rescue responders arrive at target points in the buildings. Making use of the simulation results to plan disaster-prevention measure, we need to verify that the simulation results that are reasonable at scenarios that are not confirmed from real data and observations. In this paper, we discuss the standardization process of MAS-based evacuation simulations by examining qualitative differences perceived in our evacuation simulations. IntroductionDisasters may occur anytime and anywhere in the world. Disaster prevention methods are planned and drills are conducted to check disaster-related social systems involving damage assessment, response measurement, and evacuation guidance because these help save lives during emergencies. These drills are used to estimate the required safe egress time (RSET) and improve prevention plans for emergency situations. Students at schools and occupants of buildings are encouraged to participate in such drills. However, it is difficult to conduct drills that involve a large number of people in real-world environments, such as the scenario that occurred on September 11, 2001, at the World Trade Center (WTC) buildings in New York City [1].We learn how people behaved during disasters from media stories and reports published by those in authority. Their actions involve the following general phenomena: they begin evacuation based on their individual circumstances; they communicate with each other and share information about the emergency; people in T. Takahashi the vicinity of the area where the emergency occurred adjust their actions according to the shared information. These phenomena reflect features of collective behavior in emergency situations. The simulation of these phenomena includes modeling individual emotions, interactions between humans, and characterizing the behavior of groups of people in a crowd. The simulation system that consists of these components provides a solution to a possible emergency. However, nobody can validate the results of the simulations or guarantee how the system would really work during an emergency.We believe that evacuation simulation systems can be used not only to estimate the time taken for evacuation but also to check how smoothly rescue responders reach their targets at emergency sites. In this paper, we discuss qualitative standards of validating simulations result that are hard to be checked with experiments in the real world. Section 21.2 describes related works and background scenarios. Section 21.3 shows our evacuation simulations as an example of disaster-related social systems. Conditions of validating simulations qualitatively are discussed in Sect. 21.4. Section 21.5 provides a descrip...

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“…Another approach is to represent the spatial environment as a continuous space that allows agents to navigate naturally on a continuous plane while considering constraints imposed by the physical geometry of the building. Examples of continuous space representation are the HiDAC model (Durupinar et al 2011), which parameterizes virtual agents based on individual personalities in order to mimic human behaviors in normal and panic situations, and ViCrowd (Musse and Thalmann 2001), which simulates virtual crowds with user-specified or default behavioral rules. The simulation framework uses continuous spatial representation, which allows a wider array of locomotions of the agents and the simulation of high-density crowd scenarios, such as overcrowding and pushing at exits (Aguirre et al 2011a).…”

Section: Egress Simulation Models and Human Behavior Modelingmentioning

confidence: 99%

Computational Framework Incorporating Human Behaviors for Egress Simulations

Chu

Law

2013

J. Comput. Civ. Eng.

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Studies of past emergency events indicate that evacuating occupants often exhibit social behaviors that affect the evacuation process. This paper describes a multiagent-based simulation framework that enables the modeling of social behaviors during evacuation. Each agent is modeled using a three-level representation that allows users to incorporate individual, group, and crowd behavioral rules in the simulation. The authors describe the basic framework and the implementation of several social behaviors, which are based on recent social science studies about human responses in emergency situations. Simulation results from the prototype reveal that social behaviors exhibited by an evacuating crowd can have an effect on the overall egress time and pattern. By representing the virtual agents and the environment specific to an evacuation situation, the research addresses the issues in incorporating human and social behaviors in egress simulations.

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How the Ocean Personality Model Affects the Perception of Crowds

Cited by 148 publications

References 3 publications

Computational Streetscapes

Computational Streetscapes

Qualitative Methods of Validating Evacuation Behaviors

Computational Framework Incorporating Human Behaviors for Egress Simulations

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