A hybrid multi-scale approach for simulation of pedestrian dynamics

Kneidl, Angelika; Hartmann, Dirk; Borrmann, André

doi:10.1016/j.trc.2013.03.005

Cited by 65 publications

(32 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…From the data shown in Table II we can extract two main conclusions: the prevalence of the microscopic models respect to the macroscopic ones and the concentration of works in two bibliographic areas: on the one hand research publications related [Reynolds 1987;1999;Ondrej et al 2010;Martinez-Gil et al 2014 et al 2008a;Pettré et al 2009;Karamouzas et al 2009;Guy et al 2009;van den Berg et al 2011;Olivier et al 2012;He et al 2016] Path finding and route choice Mechanics Social force [Freimuth and Lam 1992;Helbing et al 1997c;Helbing et al 1997a;Gilman et al 2005 [Chenney 2004;Treuille et al 2006;Narain et al 2009;Golas et al 2014b] and virtual Agency Agent-based [Kuffner 1999] environments Datadriven Data-driven [Lerner et al 2007;Lee et al 2007;Ju et al 2010;] CA CA [Kneidl et al 2013 Yilmaz et al 2009;Pluchino et al 2014] to Computer Graphics and, more specifically, Computer Animation (Group A); on the other hand, publications related to Transportation Research. This indicates that fundamental studies have made way to research in fields where the models are applied.…”

Section: Summary Of Modeling Categoriesmentioning

confidence: 99%

Modeling, Evaluation, and Scale on Artificial Pedestrians

et al. 2017

View full text Add to dashboard Cite

Modeling pedestrian dynamics and their implementation in a computer are challenging and important issues in the knowledge areas of transportation and computer simulation. The aim of this paper is to provide a bibliographic outlook so that the reader could have a quick access to the most relevant works related with this problem. We have used three main axes to organise the paper contents: pedestrian models, validation techniques and multiscale approaches. The backbone of the paper is the classification of existing pedestrian models; we have organised the works in the literature under five categories, according to the techniques used for implementing the operational level in each pedestrian model. Then, the main existing validation methods, oriented to evaluate the behavioural quality of the simulation systems, are reviewed. Furthermore, we review the key issues that arise when facing multiscale pedestrian modeling, where we firstly focus on the behavioural scale (combinations of micro and macro pedestrian models) and secondly, on the scale size (from individuals to crowds). The paper begins introducing the main characteristics of walking dynamics and its analysis tools and concludes with a discussion about the contributions that different knowledge fields can do in a near future to this exciting area.

show abstract

Section: Summary Of Modeling Categoriesmentioning

confidence: 99%

Modeling, Evaluation, and Scale on Artificial Pedestrians

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Examples of microscopic models are cellular automata models (e.g., [10,[25][26][27][28][29][30][31]), lattice gas models (e.g., [32]), social force models (e.g., [4,11,[33][34][35]), motion planning with velocity obstacles (e.g., [36,37]), agent-based models (e.g., [38][39][40]), game theoretic models (e.g., [41][42][43]), approaches based on experiments with animals (e.g., [44][45][46][47]), and hybrid models (e.g., [48]). Cellular automata models and lattice gas models partition the space into grids or hexagons.…”

Section: Evacuation Models and Crowd Dynamicsmentioning

confidence: 99%

“…Davidich et al [10] found that standing pedestrians affect crowd dynamics strongly and should be considered when designing critical infrastructures such as railway stations. Kneidl et al [48] developed a hybrid model which combines a dynamic navigation field with a navigation graph. Space and time are discretized using cellular automata, which act as the underlying grid for constructing the navigation field.…”

Section: Evacuation Models and Crowd Dynamicsmentioning

confidence: 99%

“…Such a dynamic navigation field allows evacuees (a) to follow a direction which is subject to minimization of estimated travel times, and (b) to steer around congestions even when the congestions are not visible from their actual position. However, Kneidl et al [48] aimed to provide a realistic simulation of crowd dynamics rather than optimizing evacuation paths, so they also added a navigation graph constructed on top of the scenarios' geometry which acts as a visibility graph to help distinguish between visible and invisible areas.…”

Section: Evacuation Models and Crowd Dynamicsmentioning

confidence: 99%

See 1 more Smart Citation

Integrating Decentralized Indoor Evacuation with Information Depositories in the Field

Zhao

Winter

Tomko

2017

IJGI

View full text Add to dashboard Cite

Abstract:The lonelier evacuees find themselves, the riskier become their wayfinding decisions. This research supports single evacuees in a dynamically changing environment with risk-aware guidance. It deploys the concept of decentralized evacuation, where evacuees are guided by smartphones acquiring environmental knowledge and risk information via exploration and knowledge sharing by peer-to-peer communication. Peer-to-peer communication, however, relies on the chance that people come into communication range with each other. This chance can be low. To bridge between people being not at the same time at the same places, this paper suggests information depositories at strategic locations to improve information sharing. Information depositories collect the knowledge acquired by the smartphones of evacuees passing by, maintain this information, and convey it to other passing-by evacuees. Multi-agent simulation implementing these depositories in an indoor environment shows that integrating depositories improves evacuation performance: It enhances the risk awareness and consequently increases the chance that people survive and reduces their evacuation time. For evacuating dynamic events, deploying depositories at staircases has been shown more effective than deploying them in corridors.

show abstract

“…Traditionally, modeling has focused on (directly) providing navigation information to characters in streetscape models via navigation graphs that they traverse, for example [193,297,328,[352][353][354][355][356]. Navigation graphs, as they are used in agent-based models, have much in common with the path-planning schemes that we discussed earlier, but may also add aspects of the decision-making process (time-to-task, landmark salience, visibility, activity schedule) as actionable information on graph vertices or edges.…”

Section: Navigation and Way-findingmentioning

confidence: 99%

Computational Streetscapes

Torrens

2016

Computation

View full text Add to dashboard Cite

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.

show abstract

A hybrid multi-scale approach for simulation of pedestrian dynamics

Cited by 65 publications

References 39 publications

Modeling, Evaluation, and Scale on Artificial Pedestrians

Modeling, Evaluation, and Scale on Artificial Pedestrians

Integrating Decentralized Indoor Evacuation with Information Depositories in the Field

Computational Streetscapes

Contact Info

Product

Resources

About