Realistic following behaviors for crowd simulation

Lemercier, Samuel; Jelić, Asja; Kulpa, Richard; Hua, Jiale; Fehrenbach, Jérôme; Degond, Pierre; Appert-Rolland, Cécile; Donikian, Stéphane; Pettré, Julien

doi:10.1111/j.1467-8659.2012.03028.x

Cited by 109 publications

(98 citation statements)

References 26 publications

(34 reference statements)

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“…Qualitative methods for crowd evaluation have been proposed and include visual comparison [14], [15] and perceptual experiments [16], [17], [18]. Quantitative methods fall into two main categories: model-based [19], [20] and data-driven [21], [22], [23], [24].…”

Section: Related Workmentioning

confidence: 99%

Trending Paths: A New Semantic-Level Metric for Comparing Simulated and Real Crowd Data

Wang

Ondřej

O’Sullivan

2017

IEEE Trans. Visual. Comput. Graphics

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Abstract-We propose a new semantic-level crowd evaluation metric in this paper. Crowd simulation has been an active and important area for several decades. However, only recently has there been an increased focus on evaluating the fidelity of the results with respect to real-world situations. The focus to date has been on analyzing the properties of low-level features such as pedestrian trajectories, or global features such as crowd densities. We propose the first approach based on finding semantic information represented by latent Path Patterns in both real and simulated data in order to analyze and compare them. Unsupervised clustering by non-parametric Bayesian inference is used to learn the patterns, which themselves provide a rich visualization of the crowd behavior. To this end, we present a new Stochastic Variational Dual Hierarchical Dirichlet Process (SV-DHDP) model. The fidelity of the patterns is computed with respect to a reference, thus allowing the outputs of different algorithms to be compared with each other and/or with real data accordingly. Detailed evaluations and comparisons with existing metrics show that our method is a good alternative for comparing crowd data at a different level and also works with more types of data, holds fewer assumptions and is more robust to noise.

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Section: Related Workmentioning

confidence: 99%

Trending Paths: A New Semantic-Level Metric for Comparing Simulated and Real Crowd Data

Wang

Ondřej

O’Sullivan

2017

IEEE Trans. Visual. Comput. Graphics

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“…This is not the case for pedestrians. Indeed, while the distance between pedestrians is evaluated almost instantaneously, evaluating the speed difference takes more time -not negligeable compared to the typical time scales of pedestrian motion [7,16,17], and this should be taken into account in the modeling.…”

Section: Reaction Time In Pedestrian Trafficmentioning

confidence: 99%

Transport in Physical Space: The Example of Pedestrians, Cars, and Molecular Motors

Appert-Rolland

Klein

Ebbinghaus

et al. 2017

Proceedings of the Asia-Pacific Econophysics Conference 2016 — Big Data Analysis and Modeling Toward Super Smart Society — (APE

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Transport systems in physical space exhibit various phenomena which may have some counterparts in socio-or econo-systems. We review here several of them. In highway vehicular traffic, the introduction of a reaction time leads to metastability and hysteresis. Pattern formation occurs in pedestrian flows. At a microscopic scale, we can learn from molecular pedestrians that transporting an object by opposite teams can be more efficient in a crowded environment and allow for an easy control of the system. Besides, we will show that the interplay between transport and the dynamics of the underlying network can sometimes lead to positive effects in terms of efficiency of transport.

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“…Generally, steering behaviors couple small motion (really angular and linear acceleration) decisions relative to known or estimated positional and rotation data that characters or agents in streetscape models compute from information that they poll within the model environment; those computational "decisions" on acquired information are then composited into larger model maneuvers that mimic real-life behavior, such as avoiding, fleeing, evading, pursuing, following, milling, etc. [402]. Model steering behaviors can be brokered by individual entities or negotiated among groups [181,229,268,354,363,381,402,403].…”

Section: Steering To Avoid and Avail Of Interactionmentioning

confidence: 99%

“…[402]. Model steering behaviors can be brokered by individual entities or negotiated among groups [181,229,268,354,363,381,402,403].…”

Section: Steering To Avoid and Avail Of Interactionmentioning

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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Realistic following behaviors for crowd simulation

Cited by 109 publications

References 26 publications

Trending Paths: A New Semantic-Level Metric for Comparing Simulated and Real Crowd Data

Trending Paths: A New Semantic-Level Metric for Comparing Simulated and Real Crowd Data

Transport in Physical Space: The Example of Pedestrians, Cars, and Molecular Motors

Computational Streetscapes

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