A review of cellular automata models for crowd evacuation

Li, Yang; Chen, Maoyin; Dou, Zhan; Zheng, Xiaoping; Cheng, Yuan; Mébarki, Ahmed

doi:10.1016/j.physa.2019.03.117

Cited by 146 publications

(58 citation statements)

References 102 publications

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“…Nonetheless, it is possible that other methods would yield higher performances, which would be explored through additional experiments. Similarly, experiments can be extended to CA models of other phenomena such as evacuation [40], city growth [7], or traffic dynamics [43]. Finally, our approach determines correct and incorrect simulations based on varying parameters of the model.…”

Section: Discussionmentioning

confidence: 99%

Comparing Implementations of Cellular Automata as Images: A Novel Approach to Verification by Combining Image Processing and Machine Learning

Wozniak

Giabbanelli

2021

Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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Discrete models such as cellular automata may be ported from one platform or language onto another to improve performances, for instance by rewriting legacy Matlab code into C++ or adding optimizations into a Python implementation. Although such transformations can offer benefits such as scalability or maintainability, they also have the risk of introducing bugs. While standard verification techniques can always be applied, this situation presents a unique opportunity since the two implementations can be directly compared based on their simulation runs. Although comparing average results across runs of a same configuration is a common practice, our paper shows that many bugs would not be detected at this aggregate level. We thus propose comparing implementations of cellular automata by analyzing their outputs as images. In this paper, we examine the detection of several implementation errors using five different techniques (supervised/unsupervised image processing, decision trees, random forests, or deep learning) across three different cellular automata models (forest fire, tumor, HIV). We show that in some models, random forests can detect 4 out of 5 erroneous runs, although the accuracy depends both on the model and on the nature of the errors.

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Section: Discussionmentioning

confidence: 99%

Comparing Implementations of Cellular Automata as Images: A Novel Approach to Verification by Combining Image Processing and Machine Learning

Wozniak

Giabbanelli

2021

Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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“…As [23] points out when describing the key role of accidents between agents, the way a discrete model divides the space is a central problem. On the one hand, Dadová and Ferko [24] proposes a more relaxed space division to provide freedom of movement to agents, where cells size is 4 to 5 times larger than the necessary for an agent.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Hybrid agent-based methodology for testing response protocols

López-Ortiz

Sancho-Caparrini

Amor

et al. 2021

Knowledge-Based Systems

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In recent years we have seen multiple incidents with a large number of people injured and killed by one or more armed attackers. Since this type of violence is difficult to predict, detecting threats as early as possible allows to generate early warnings and reduce response time. In this context, any tool to check and compare different action protocols can be a further step in the direction of saving lives. Our proposal combines features from continuous and discrete models to obtain the best of both worlds in order to simulate large and crowded spaces where complex behavior individuals interact. With this proposal we aim to provide a tool for testing different security protocols under several emergency scenarios, where spaces, hazards, and population can be customized. Finally, we use a proof of concept implementation of this model to test specific security protocols under emergency situations for real spaces. Specifically, we test how providing some users of a university college with an app that informs about the type and characteristics of the ongoing hazard, affects in the safety performance.

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“…The Agent-Based Model (ABM), see [24,26], and CA models serve as examples of this group. For the detailed taxonomy of CA models see the overview in [20]. One of the most used CA model is the Floor Field Cellular Automata (FFCA) model, described in [5,18].…”

Section: Introductionmentioning

confidence: 99%

A cellular automaton model for a pedestrian flow problem

Felcman

Kubera

2021

Math. Model. Nat. Phenom.

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The evacuation phenomena in the two dimensional pedestrian flow model are simulated. The intended direction of the escape of pedestrians in panic situations is governed by the Eikonal equation of the pedestrian flow model. A new two-dimensional Cellular Automaton (CA) model is proposed for the simulation of the pedestrian flow. The solution of the Eikonal equation is used to define the probability matrix whose elements express the probability of a pedestrian moving in finite set of directions. The novelty of this paper lies in the construction of the density dependent probability matrix. The relevant evacuation scenarios are numerically solved. Predictions of the evacuation behavior of pedestrians, for various room geometries with multiple exists, are demonstrated. The mathematical model is numerically justified by comparison of CA approach with the Finite Volume Method for the space discretization and Discontinuous Galerkin Method for the implicit time discretization of pedestrian flow model.

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A review of cellular automata models for crowd evacuation

Cited by 146 publications

References 102 publications

Comparing Implementations of Cellular Automata as Images: A Novel Approach to Verification by Combining Image Processing and Machine Learning

Comparing Implementations of Cellular Automata as Images: A Novel Approach to Verification by Combining Image Processing and Machine Learning

Hybrid agent-based methodology for testing response protocols

A cellular automaton model for a pedestrian flow problem

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