Digital Twin of Buildings and Occupants for Emergency Evacuation: Framework, Technologies, Applications and Trends
Advanced Engineering Informatics, 2025
Recommended citation: Lin, J.R., Chen, K.Y., Song, S.Y., Cai, Y.H., Pan, P.*, Deng, Y.C. (2025). Digital Twin of Buildings and Occupants for Emergency Evacuation: Framework, Technologies, Applications and Trends. Advanced Engineering Informatics, 66, 103419. doi: 10.1016/j.aei.2025.103419 http://doi.org/10.1016/j.aei.2025.103419
Abstract
Buildings face threats from various emergencies, with emergency evacuation being a key measure for occupant safety. However, enhancing evacuation efficiency necessitates detailed studies of building characteristics and human behaviors. Despite this, a systematic review of digital twin technologies for emergency evacuation is still lacking. Therefore, by collecting and analyzing literature from 2004 to 2025 using PRISMA methodology, this study first proposes a conceptual digital twin framework that integrates buildings, occupants, and their interactions, encompassing the entire loop of sensing, updating, simulation, and decision-making. The current research has made significant progresses in areas such as basic virtual modeling, one-way data mapping, and preliminary bidirectional interaction. However, studies and applications of digital twins remain in the developmental stage, with most at maturity levels L0-L2, while L4-L5 applications are still relatively scarce. It is suggested that the future development of digital twin-based evacuation systems must rely on multidisciplinary collaboration to achieve breakthroughs, including optimizing underlying mechanisms to enhance data and system integration; improving sensing accuracy and developing adaptive algorithms for simulation and prediction; integrating emerging artificial intelligence technologies while addressing data ethics; and enhancing computational efficiency to strengthen system robustness.
The authors are grateful for the financial support received from the National Key Technology R&D Program (No. 2023YFC3805800) and the National Natural Science Foundation of China (No. 52378306, No. 72091512).
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