Automatic model construction for the behavior of human crowds
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Zhong:2017:ASC,
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author = "Jinghui Zhong and Wentong Cai and Michael Lees and
Linbo Luo",
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title = "Automatic model construction for the behavior of human
crowds",
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journal = "Applied Soft Computing",
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volume = "56",
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pages = "368--378",
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year = "2017",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2017.03.020",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494617301448",
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abstract = "Designing suitable behavioral rules of agents so as to
generate realistic behaviors is a fundamental and
challenging task in many forms of computational
modeling. This paper proposes a novel methodology to
automatically generate a descriptive model, in the form
of behavioral rules, from video data of human crowds.
In the proposed methodology, the problem of modeling
crowd behaviors is formulated as a symbolic regression
problem and the self-learning gene expression
programming is used to solve the problem and
automatically obtain behavioral rules that match data.
To evaluate its effectiveness, we apply the proposed
method to generate a model from a video dataset in
Switzerland and then test the generality of the model
by validating against video data from the United
States. The results demonstrate that, based on the
observed movement of people in one scenario, the
proposed methodology can automatically construct a
general model capable of describing the crowd dynamics
of another scenario in a different context (e.g.,
Switzerland vs. U.S.) as long as that the crowd
behavior patterns are similar.",
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keywords = "genetic algorithms, genetic programming, Agent-based
modeling, Crowd modeling and simulation, Gene
expression programming, Symbolic regression",
- }
Genetic Programming entries for
Jinghui Zhong
Wentong Cai
Michael Lees
Linbo Luo
Citations