CellEVAC: An adaptive guidance system for crowd evacuation through behavioral optimization
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- @Article{LOPEZ-CARMONA:2021:SS,
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author = "Miguel A. Lopez-Carmona and Alvaro {Paricio Garcia}",
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title = "{CellEVAC:} An adaptive guidance system for crowd
evacuation through behavioral optimization",
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journal = "Safety Science",
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volume = "139",
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pages = "105215",
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year = "2021",
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ISSN = "0925-7535",
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DOI = "doi:10.1016/j.ssci.2021.105215",
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URL = "https://www.sciencedirect.com/science/article/pii/S0925753521000606",
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keywords = "genetic algorithms, genetic programming, Crowd
evacuation, Behavioral optimization, Exit-choice
decisions, Simulation-optimization modeling, Cell-based
evacuation, Evacuation safety",
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abstract = "A critical aspect of crowds' evacuation processes is
the dynamism of individual decision making. Identifying
optimal strategies at an individual level may improve
both evacuation time and safety, which is essential for
developing efficient evacuation systems. Here, we
investigate how to favor a coordinated group dynamic
through optimal exit-choice instructions using
behavioral strategy optimization. We propose and
evaluate an adaptive guidance system (Cell-based Crowd
Evacuation, CellEVAC) that dynamically allocates colors
to cells in a cell-based pedestrian positioning
infrastructure, to provide efficient exit-choice
indications. The operational module of CellEVAC
implements an optimized discrete-choice model that
integrates the influential factors that would make
evacuees adapt their exit choice. To optimize the
model, we used a simulation-optimization modeling
framework that integrates microscopic pedestrian
simulation based on the classical Social Force Model.
In the majority of studies, the objective has been to
optimize evacuation time. In contrast, we paid
particular attention to safety by using Pedestrian
Fundamental Diagrams that model the dynamics of the
exit gates. CellEVAC has been tested in a simulated
real scenario (Madrid Arena) under different external
pedestrian flow patterns that simulate complex
pedestrian interactions. Results showed that CellEVAC
outperforms evacuation processes in which the system is
not used, with an exponential improvement as
interactions become complex. We compared our system
with an existing approach based on Cartesian Genetic
Programming. Our system exhibited a better overall
performance in terms of safety, evacuation time, and
the number of revisions of exit-choice decisions.
Further analyses also revealed that Cartesian Genetic
Programming generates less natural pedestrian reactions
and movements than CellEVAC. The fact that the decision
logic module is built upon a behavioral model seems to
favor a more natural and effective response. We also
found that our proposal has a positive influence on
evacuations even for a low compliance rate
(40percent)",
- }
Genetic Programming entries for
Miguel A Lopez-Carmona
Alvaro Paricio Garcia
Citations