Scalable behaviors for crowd simulation

Sung, Mankyu; Gleicher, Michael; Chenney, Stephen

doi:10.1111/j.1467-8659.2004.00783.x

Cited by 176 publications

(93 citation statements)

References 18 publications

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“…The microscopic model is more realistic in its nature than the macroscopic model. The microscopic approaches include social force models [1]- [3], cellular automata models [4]- [6] and agent based models [10]- [13]. In this subsection a brief description of each of the modelling techniques is provided and compared with the LEM and ACO based simulation approach.…”

Section: Pedestrian Simulationmentioning

confidence: 99%

GPU Accelerated Nature Inspired Methods for Modelling Large Scale Bi-directional Pedestrian Movement

Dutta

McLeod

Friesen

2014

2014 IEEE International Parallel &Amp; Distributed Processing Symposium Workshops

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Pedestrian movement, although ubiquitous and well-studied, is still not that well understood due to the complicating nature of the embedded social dynamics. Interest among researchers in simulating pedestrian movement and interactions has grown significantly in part due to increased computational and visualization capabilities afforded by high power computing. Different approaches have been adopted to simulate pedestrian movement under various circumstances and interactions. In the present work, bi-directional crowd movement is simulated where an equal numbers of individuals try to reach the opposite sides of an environment. Two movement methods are considered. First a Least Effort Model (LEM) is investigated where agents try to take an optimal path with as minimal changes from their intended path as possible. Following this, a modified form of Ant Colony Optimization (ACO) is proposed, where individuals are guided by a goal of reaching the other side in a least effort mode as well as a pheromone trail left by predecessors. The basic idea is to increase agent interaction, thereby more closely reflecting a real world scenario. The methodology utilizes Graphics Processing Units (GPUs) for general purpose computing using the CUDA platform. Because of the inherent parallel properties associated with pedestrian movement such as proximate interactions of individuals on a 2D grid, GPUs are well suited. The main feature of the implementation undertaken here is that the parallelism is data driven. The data driven implementation leads to a speedup up to 18x compared to its sequential counterpart running on a single threaded CPU. The numbers of pedestrians considered in the model ranged from 2K to 100K representing numbers typical of mass gathering events. A detailed discussion addresses implementation challenges faced and averted. Detailed analysis is also provided on the throughput of pedestrians across the environment.

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Section: Pedestrian Simulationmentioning

confidence: 99%

GPU Accelerated Nature Inspired Methods for Modelling Large Scale Bi-directional Pedestrian Movement

Dutta

McLeod

Friesen

2014

2014 IEEE International Parallel &Amp; Distributed Processing Symposium Workshops

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“…Prompted by the seminal work of Reynolds [9], behavioral animation has been further developed to simulate artificial animals [10,11,12] and it has given impetus to an entire industry of applications for distributed (multiagent) behavioral systems that are capable of synthesizing flocking, schooling, herding, and other behaviors for lower animals, or in the case of human characters, crowd behavior. Numerous crowd interaction models have been developed [13,14,15,16] and work in this area continues.…”

Section: Related Workmentioning

confidence: 99%

Populating Reconstructed Archaeological Sites with Autonomous Virtual Humans

Shao

Terzopoulos

2006

Intelligent Virtual Agents

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Abstract. Significant multidisciplinary efforts combining archaeology and computer science have yielded virtual reconstructions of archaeological sites for visualization. Yet comparatively little attention has been paid to the difficult problem of populating these models, not only to enhance the quality of the visualization, but also to arrive at quantitative computer simulations of the human inhabitants that can help test hypotheses about the possible uses of these sites in ancient times. We introduce an artificial life approach to populating large-scale reconstructions of archaeological sites with virtual humans. Unlike conventional "crowd" models, our comprehensive, detailed models of individual autonomous pedestrians span several modeling levels, including appearance, locomotion, perception, behavior, and cognition. We review our human simulation system and its application to a "modern archaeological" recreation of activity in New York City's original Pennsylvania Station. We also describe an extension of our system and present its novel application to the visualization of possible human activity in a reconstruction of the Great Temple of ancient Petra in Jordan.

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“…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]. There is also a recent trend in supplementing fine-scale detail of gestures and ambulation to characters atop these schemes, and in some instances they are integrated into the control pipeline so that they match with activities such as directional gaze when changing course [196] or expressions that correspond to state cycles [197].…”

Section: Animationmentioning

confidence: 99%

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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Scalable behaviors for crowd simulation

Cited by 176 publications

References 18 publications

GPU Accelerated Nature Inspired Methods for Modelling Large Scale Bi-directional Pedestrian Movement

GPU Accelerated Nature Inspired Methods for Modelling Large Scale Bi-directional Pedestrian Movement

Populating Reconstructed Archaeological Sites with Autonomous Virtual Humans

Computational Streetscapes

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