Human centric VR system development supporting fire emergency evacuation: A novel knowledge-data dual driven approach

IF 9.4 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Expert Systems with Applications Pub Date : 2025-05-10 Epub Date: 2025-02-13 DOI:10.1016/j.eswa.2025.126895
Jiaxin Ling , Xiaojun Li , Yi Shen , Chao Chen , Zhiguo Yan , Hehua Zhu , Haijiang Li
{"title":"Human centric VR system development supporting fire emergency evacuation: A novel knowledge-data dual driven approach","authors":"Jiaxin Ling ,&nbsp;Xiaojun Li ,&nbsp;Yi Shen ,&nbsp;Chao Chen ,&nbsp;Zhiguo Yan ,&nbsp;Hehua Zhu ,&nbsp;Haijiang Li","doi":"10.1016/j.eswa.2025.126895","DOIUrl":null,"url":null,"abstract":"<div><div>Catastrophic fire accidents happened inside the tunnel have made it evident that human factors, especially misconduct, should be taken into account when it comes to fire emergency evacuation. However, conventional approaches separate fire safety education from evacuation training, failing to account for individual capabilities and behavioral dynamics, resulting in less intuitive and ineffective preparedness. A human-centric and more adaptive training for tunnel fire evacuation which takes both knowledge learning and behavior training into account is in urgent need. Motivated by such need, this study proposes a knowledge-data dual driven (KD3) framework, to seamlessly combine tunnel fire knowledge transfer and evacuation training into a unified system. A Virtual Reality (VR) system is developed based on KD3, which is composed of interactive fire-knowledge transfer module and immersive fire training module. To verify the applicability and effectiveness of the established system, the interactive fire-knowledge transfer module was open to public for different tunnel users to learn, and a total of 50 participants were recruited to conduct VR training. Results verify the rationale of the developed system, as well as the proposed KD3 framework, demonstrating that the integration of knowledge learning and VR training significantly improves individuals’ evacuation decision-making and escape behavior during tunnel fires. These findings contribute to a paradigm shift in fire evacuation training by bridging the gap between theoretical learning and practical application. The study provides critical insights into human-centric emergency preparedness and offers practical guidance for future adaptive training systems in emergency.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"273 ","pages":"Article 126895"},"PeriodicalIF":9.4000,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957417425005172","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/13 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Catastrophic fire accidents happened inside the tunnel have made it evident that human factors, especially misconduct, should be taken into account when it comes to fire emergency evacuation. However, conventional approaches separate fire safety education from evacuation training, failing to account for individual capabilities and behavioral dynamics, resulting in less intuitive and ineffective preparedness. A human-centric and more adaptive training for tunnel fire evacuation which takes both knowledge learning and behavior training into account is in urgent need. Motivated by such need, this study proposes a knowledge-data dual driven (KD3) framework, to seamlessly combine tunnel fire knowledge transfer and evacuation training into a unified system. A Virtual Reality (VR) system is developed based on KD3, which is composed of interactive fire-knowledge transfer module and immersive fire training module. To verify the applicability and effectiveness of the established system, the interactive fire-knowledge transfer module was open to public for different tunnel users to learn, and a total of 50 participants were recruited to conduct VR training. Results verify the rationale of the developed system, as well as the proposed KD3 framework, demonstrating that the integration of knowledge learning and VR training significantly improves individuals’ evacuation decision-making and escape behavior during tunnel fires. These findings contribute to a paradigm shift in fire evacuation training by bridging the gap between theoretical learning and practical application. The study provides critical insights into human-centric emergency preparedness and offers practical guidance for future adaptive training systems in emergency.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
支持火灾紧急疏散的以人为中心的虚拟现实系统开发:知识-数据双驱动的新方法
隧道内发生的特大火灾事故表明,在进行火灾应急疏散时,应充分考虑人为因素,尤其是不当行为的影响。然而,传统方法将消防安全教育与疏散培训分开,未能考虑到个人能力和行为动态,导致缺乏直觉和无效的准备。一种集知识学习和行为训练于一体的以人为本的适应性强的隧道火灾疏散训练方法是迫切需要的。基于这一需求,本研究提出了一种知识-数据双驱动(KD3)框架,将隧道火灾知识传递与疏散训练无缝结合为一个统一的系统。基于KD3开发了虚拟现实(VR)系统,该系统由交互式消防知识传递模块和沉浸式消防训练模块组成。为验证所建立系统的适用性和有效性,互动式消防知识传递模块向公众开放,供不同的隧道使用者学习,并招募了50名参与者进行VR培训。结果验证了所开发系统的基本原理,以及所提出的KD3框架,表明知识学习和VR训练的结合显著提高了隧道火灾中个体的疏散决策和逃生行为。这些发现有助于通过弥合理论学习和实际应用之间的差距,在火灾疏散培训的范式转变。该研究为以人为中心的应急准备提供了重要见解,并为未来的应急适应性训练系统提供了实用指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
期刊最新文献
PDGAGRN: Graph diffusion pretraining and dynamic graph learning for gene regulatory network inference from single-cell RNA-sequencing data LDGC3: Learnable deep graph contrastive clustering with triple cluster-structure awareness MOSS‑GAN: a GAN‑enhanced Mamba model with spatial‑spectral co‑optimization for nearshore green tide detection in UAV hyperspectral imagery Visual tracking method with hybrid spatio-temporal backbone network and dual-memory mechanism Developing a totally unimodular linear program for optimal conformance checking: When and why it complements A*
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1