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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F24%3APU151380" target="_blank" >RIV/00216305:26110/24:PU151380 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S266616592400142X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S266616592400142X?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.dibe.2024.100461" target="_blank" >10.1016/j.dibe.2024.100461</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/CK02000118" target="_blank" >CK02000118: Dopravní digitální dvojče Evropská</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Developments in the Built Environment

ISSN

2666-1659

e-ISSN

—

Svazek periodika

18

Číslo periodika v rámci svazku

100461

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

„“-„“

Kód UT WoS článku

001247106000001

EID výsledku v databázi Scopus

2-s2.0-85193863102