Flow tiles

Chenney, Stephen

doi:10.1145/1028523.1028553

Cited by 172 publications

(91 citation statements)

References 19 publications

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“…A triple of physical processes-particle interaction as flow, forces of attraction and repulsion, and phase change-feature rather ubiquitously in pedestrian models, often as the rules for agent behavior in simulation. For example, the triple is used to produce continuum mechanics for crowd aggregates [84], many-particle interaction within crowd flows [85,86], ebb and flow over vector-fields [87], and the physical or social forces of compression and expansion over repulsive-attractive fields [84,88,89].…”

Section: Physicsmentioning

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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Section: Physicsmentioning

confidence: 99%

Computational Streetscapes

Torrens

2016

Computation

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“…3 For global path planning, almost all cases require some sort of high-level representation of the simulation environment to support interactive simulations. Common techniques are portal graphs, 4,5 roadmaps, 6 potential fields, 7 and NavMeshes. 8 Since the NavMesh concept was first introduced by Snook, various researchers have proposed different ways to construct NavMeshes, which range from triangles to a mix of convex polygons.…”

Section: Background and Related Workmentioning

confidence: 99%

Adaptive grids: an image-based approach to generate navigation meshes

Akaydın

Güdükbay

2013

Opt. Eng.

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Abstract. We propose adaptive grids, an image-based approach for constructing navigation meshes, which are used for path planning. A cellular navigation mesh, called an adaptive grid, is constructed from a top-view range image of a three-dimensional urban model. A navigation graph can then be extracted from this adaptive grid for path planning. We compare our approach with two popular navigation mesh-generation approaches and obtain promising results in terms of path accuracy and memory cost.

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“…In our system, a Cellular Automata (CA) [2] is included as a part of the SDB, and each cell has precomputed the k-best paths of length l to achieve the exit cells. To calculate all the paths (k paths per cell; cell = 1m side square), we are using a variation of the A* algorithm.…”

Section: Fig 2 the Proposed Software Architecturementioning

confidence: 99%

A Distributed Framework for Scalable Large-Scale Crowd Simulation

et al. 2007

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Abstract. Emerging applications in the area of Emergency Response and DisasterManagement are increasingly demanding interactive capabilities to allow for the quick understanding of a critical situation, in particular in urban environments. A key component of these interactive simulations is how to recreate the behavior of a crowd in real-time while supporting individual behaviors. Crowds can often be unpredictable and present mixed behaviors such as panic or aggression, that can very rapidly change based on unexpected new elements introduced into the environment. We present preliminary research specifically oriented towards the simulation of large crowds for emergency response and rescue planning situations. Our approach uses a highly scalable architecture integrated with an efficient rendering architecture and an immersive visualization environment for interaction. In this environment, users can specify complex scenarios, "plug-in" crowd behavior algorithms, and interactively steer the simulation to analyze and evaluate multiple "what if" situations.

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Flow tiles

Cited by 172 publications

References 19 publications

Computational Streetscapes

Computational Streetscapes

Adaptive grids: an image-based approach to generate navigation meshes

A Distributed Framework for Scalable Large-Scale Crowd Simulation

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