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3D Indoor Environment Abstraction for Crowd Simulations in Complex Buildings

Author(s):

ORCID
Medium: journal article
Language(s): English
Published in: Buildings, , n. 10, v. 11
Page(s): 445
DOI: 10.3390/buildings11100445
Abstract:

This paper presents an approach for the automatic

ion of built environments needed for pedestrian dynamics from any building configuration. The approach assesses the usability of navigation mesh to perform realistically pedestrian simulation considering the physical structure and pedestrian abilities for it. Several steps are examined including the creation of a navigation mesh, space subdivision, border extraction, height map identification, stairs classification and parametrisation, as well as pedestrian simulation. A social-force model is utilised to simulate the interactions between pedestrians and an environment. To perform quickly different 2D/3D geometrical queries various spatial indexing techniques are used, allowing fast identification of navigable spaces and proximity checks related to avoidance of people and obstacles in built environments. For example, for a moderate size building having eight floors and a net area of 13,000 m², it takes only 104 s to extract the required building information to run a simulation. This approach can be used for any building configuration extracting automatically needed features to run pedestrian simulations. In this way, architects, urban planners, fire safety engineers, transport modellers and many other users without the need to manually interact with a building model can perform immediately crowd simulations.
Copyright: © 2021 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.
  • About this
    data sheet
  • Reference-ID
    10631204
  • Published on:
    01/10/2021
  • Last updated on:
    05/10/2021
 
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