Physics-based model and data dual-driven approaches for predictive evacuation
Physics-based models or data-driven methodologies can help acquire the evacuation process for optimizing evacuation and rescue plans. However, neither of these methodologies can predict the process rapidly and precisely. Physics-based models rely on physical rules of human behavior but are computati...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Developments in the Built Environment |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666165923001515 |
| _version_ | 1797387811888824320 |
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| author | Yuxin Zhang Zhiguo Yan Hehua Zhu Pingbo Tang |
| author_facet | Yuxin Zhang Zhiguo Yan Hehua Zhu Pingbo Tang |
| author_sort | Yuxin Zhang |
| collection | DOAJ |
| description | Physics-based models or data-driven methodologies can help acquire the evacuation process for optimizing evacuation and rescue plans. However, neither of these methodologies can predict the process rapidly and precisely. Physics-based models rely on physical rules of human behavior but are computationally expensive. Data-driven approaches need a significant amount of diverse data but lack an understanding of underlying human behavior in spinning emergencies. This short communication aims to initiate systematic discussions about a physics-based model and data dual-driven approach, combining both approaches’ strengths. This combined approach puts forward iterative updating loops, using physics-based models to identify evacuation stages, capture operational mechanisms, and act as rational boundaries while using data-driven methods to process each stage’s results rapidly. The authors synthesize a roadmap highlighting bottlenecks and potential research directions for achieving such a combined approach, calling for attention and collaboration in predictive evacuation for disaster response. |
| first_indexed | 2024-03-08T22:31:31Z |
| format | Article |
| id | doaj.art-95b0a0b6fcff45ba93b6d1177166622b |
| institution | Directory Open Access Journal |
| issn | 2666-1659 |
| language | English |
| last_indexed | 2024-03-08T22:31:31Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Developments in the Built Environment |
| spelling | doaj.art-95b0a0b6fcff45ba93b6d1177166622b2023-12-18T04:25:05ZengElsevierDevelopments in the Built Environment2666-16592023-12-0116100269Physics-based model and data dual-driven approaches for predictive evacuationYuxin Zhang0Zhiguo Yan1Hehua Zhu2Pingbo Tang3State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China; Department of Geotechnical Engineering, Tongji University, Shanghai, ChinaState Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China; Department of Geotechnical Engineering, Tongji University, Shanghai, ChinaState Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China; Department of Geotechnical Engineering, Tongji University, Shanghai, ChinaDepartment of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, United States; Corresponding author.Physics-based models or data-driven methodologies can help acquire the evacuation process for optimizing evacuation and rescue plans. However, neither of these methodologies can predict the process rapidly and precisely. Physics-based models rely on physical rules of human behavior but are computationally expensive. Data-driven approaches need a significant amount of diverse data but lack an understanding of underlying human behavior in spinning emergencies. This short communication aims to initiate systematic discussions about a physics-based model and data dual-driven approach, combining both approaches’ strengths. This combined approach puts forward iterative updating loops, using physics-based models to identify evacuation stages, capture operational mechanisms, and act as rational boundaries while using data-driven methods to process each stage’s results rapidly. The authors synthesize a roadmap highlighting bottlenecks and potential research directions for achieving such a combined approach, calling for attention and collaboration in predictive evacuation for disaster response.http://www.sciencedirect.com/science/article/pii/S2666165923001515Physics-based modelData-driven evacuation approachEvacuation predictionFireDual-driven framework |
| spellingShingle | Yuxin Zhang Zhiguo Yan Hehua Zhu Pingbo Tang Physics-based model and data dual-driven approaches for predictive evacuation Developments in the Built Environment Physics-based model Data-driven evacuation approach Evacuation prediction Fire Dual-driven framework |
| title | Physics-based model and data dual-driven approaches for predictive evacuation |
| title_full | Physics-based model and data dual-driven approaches for predictive evacuation |
| title_fullStr | Physics-based model and data dual-driven approaches for predictive evacuation |
| title_full_unstemmed | Physics-based model and data dual-driven approaches for predictive evacuation |
| title_short | Physics-based model and data dual-driven approaches for predictive evacuation |
| title_sort | physics based model and data dual driven approaches for predictive evacuation |
| topic | Physics-based model Data-driven evacuation approach Evacuation prediction Fire Dual-driven framework |
| url | http://www.sciencedirect.com/science/article/pii/S2666165923001515 |
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