Detail publikačního výsledku

Real-time RSET prediction across three types of geometries and simulation training dataset: A comparative study of machine learning models

UHLÍK, O.; OKŘINOVÁ, P.; TOKAREVSKIKH, A.; APELTAUER, T.; APELTAUER, J.

Originální název

Real-time RSET prediction across three types of geometries and simulation training dataset: A comparative study of machine learning models

Anglický název

Real-time RSET prediction across three types of geometries and simulation training dataset: A comparative study of machine learning models

Druh

Článek WoS

Originální abstrakt

Agent-based evacuation models provide useful data of the evacuation process, but they are not primarily designed for use during an emergency. The paper aims to test predicting RSET using a surrogate ML model trained on a simulation dataset with 60 samples. A total of 9 machine learning algorithms were tested on 3 simple geometries: bottleneck, stairway and walkway. A set of 7 spatial features was used to train the surrogate models. The results showed a relatively good ability of Artificial Neural Network to learn in scenarios involving bottlenecks and stairways, with an R2: 0.99 on the testing dataset. In the walkway scenario, all models experienced a significant drop in performance, with Gradient Boost performing the best (R2: 0.92). The paper demonstrated ability to generalize effectively in bottleneck-type tasks with training on a relatively small dataset containing spatial parameters obtainable in real-time from camera systems.

Anglický abstrakt

Agent-based evacuation models provide useful data of the evacuation process, but they are not primarily designed for use during an emergency. The paper aims to test predicting RSET using a surrogate ML model trained on a simulation dataset with 60 samples. A total of 9 machine learning algorithms were tested on 3 simple geometries: bottleneck, stairway and walkway. A set of 7 spatial features was used to train the surrogate models. The results showed a relatively good ability of Artificial Neural Network to learn in scenarios involving bottlenecks and stairways, with an R2: 0.99 on the testing dataset. In the walkway scenario, all models experienced a significant drop in performance, with Gradient Boost performing the best (R2: 0.92). The paper demonstrated ability to generalize effectively in bottleneck-type tasks with training on a relatively small dataset containing spatial parameters obtainable in real-time from camera systems.

Klíčová slova

Evacuation modeling, Machine learning, Required safe egress time, Agent-based models

Klíčová slova v angličtině

Evacuation modeling, Machine learning, Required safe egress time, Agent-based models

Autoři

UHLÍK, O.; OKŘINOVÁ, P.; TOKAREVSKIKH, A.; APELTAUER, T.; APELTAUER, J.

Rok RIV

2025

Vydáno

24.05.2024

Nakladatel

Elsevier

Místo

Netherlands

ISSN

2666-1659

Periodikum

Developments in the Built Environment

Svazek

18

Číslo

100461

Stát

Spojené království Velké Británie a Severního Irska

Strany počet

13

URL

https://www.sciencedirect.com/science/article/pii/S266616592400142X?via%3Dihub

Plný text v Digitální knihovně

http://hdl.handle.net/11012/249353

BibTex

@article{BUT188671,
  author="Ondřej {Uhlík} and Petra {Okřinová} and Artem {Tokarevskikh} and Tomáš {Apeltauer} and Jiří {Apeltauer}",
  title="Real-time RSET prediction across three types of geometries and simulation training dataset: A comparative study of machine learning models",
  journal="Developments in the Built Environment",
  year="2024",
  volume="18",
  number="100461",
  pages="13",
  doi="10.1016/j.dibe.2024.100461",
  url="https://www.sciencedirect.com/science/article/pii/S266616592400142X?via%3Dihub"
}

Dokumenty

1-s2.0-S266616592400142X-main