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 , Xiaojun Li , Yi Shen , Chao Chen , Zhiguo Yan , Hehua Zhu , 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":7.5000,"publicationDate":"2025-02-13","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":"","PubModel":"","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.
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.