A Distributed Framework for Scalable Large-Scale Crowd Simulation

Lozano, Miguel; Morillo, Pedro; Lewis, Daniel W.; Reiners, Dirk; Cruz-Neira, Carolina

doi:10.1007/978-3-540-73335-5_13

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…A review of existing hybrid models for pedestrian simulation can be found in the study by Ijaz, Sohail, and Hashish (2015). More studies recently have focused on developing computational architectures that can cope with largescale crowd modelling, including those by Lozano et al (2007) and by Fernandez et al (2010).…”

Section: Introductionmentioning

confidence: 99%

A Framework for Modelling the Ripple Effect of Crowding At Public Transport Facilities

2017

50th Annual Simulation Symposium (ANSS 2017)

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Many studies can be found successfully applying sophisticated and powerful pedestrian simulation packages for crowd management at public transport stations. However, there are gaps in understanding the ripple effect of crowding and passenger demands at a station to the crowdedness at stations further down a transit line. This paper reports a computational framework that links crowd simulations at transit stations of an urban network. Inherently the framework allows for the simulation of the transferring of mobility characteristics of passengers along a transit line as they travel from one station to another and its impacts on crowdedness at these stations. We also report on a proof of concept demonstrating the application of the framework to a small hypothetical train network. The crowd simulation software used in this study is MassMotion. However, the framework should be adaptable to any crowd simulation package.

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Section: Introductionmentioning

confidence: 99%

A Framework for Modelling the Ripple Effect of Crowding At Public Transport Facilities

2017

50th Annual Simulation Symposium (ANSS 2017)

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“…In recent years, crowd simulations have become an essential tool for many virtual environment applications in education, training, and entertainment [4,12,5]. These applications require both rendering visually plausible images of the virtual world and managing the behavior of high number of autonomous agents.…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Partitioning Methods for Crowd Simulations

Vigueras

Lozano

Ordua

et al. 2008

2008 Eighth International Conference on Hybrid Intelligent Systems

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Simulating the realistic behavior of large crowds of autonomous agents is still a challenge for the computer graphics community. In order to handle large crowds, some scalable architectures have been proposed. Nevertheless, the effective use of distributed systems requires the use of partitioning methods that can properly assign different sets of agents to the existing distributed resources.In this paper, we propose the improvement of the partitioning method for distributed crowd simulations by using irregular shape regions. Concretely, we propose the partition of the virtual world using convex hulls. The performance evaluation results show that the Convex Hull method outperforms the rest of the considered methods in terms of both fitness function values and execution times, regardless of the movement pattern followed by the agents. These results show that the shape of the regions in the partition can improve the performance of the partitioning method, rather than the heuristic method used.

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“…The simulation is then divided up with the set of agents and/or environment being shared equally amongst the machines. Distribution has been applied to agent-based crowd simulations previously (Lozano et al 2007) with promising results. However, distribution introduces its own challenges.…”

Section: Introductionmentioning

confidence: 99%

Cluster based partitioning for agent-based crowd simulations

Wang

Lees

Cai

et al. 2009

Proceedings of the 2009 Winter Simulation Conference (WSC)

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Simulating crowds is a challenging but important problem. There are various methodologies in the literature ranging from macroscopic numerical flow simulations to detailed, microscopic agent simulations. One key issue for all crowd simulations is scalability. Some methods address this issue through abstraction, describing global properties of homogeneous crowds. However, ideally a modeler should be able to simulate large heterogeneous crowds at fine levels of detail. We are attempting to achieve scalability through the application of distributed simulation techniques to agent-based crowd simulation. Distributed simulation, however, introduces its own challenges, in particular how to efficiently partition the load between a number of machines. In this paper we introduce a method of partitioning agents onto machines using an adapted k-means clustering algorithm. We present, validate and use an analysis tool to compare the proposed clustered partitioning approach with a series of existing methods.

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A Distributed Framework for Scalable Large-Scale Crowd Simulation

Cited by 11 publications

References 20 publications

A Framework for Modelling the Ripple Effect of Crowding At Public Transport Facilities

A Framework for Modelling the Ripple Effect of Crowding At Public Transport Facilities

Improving the Performance of Partitioning Methods for Crowd Simulations

Cluster based partitioning for agent-based crowd simulations

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