Optimization of Rescue Space Allocation Considering Panic Emotions and Zonal Guidance

WANG Yi; XIAO Haoyu; ZHU Jiabo

doi:10.3724/j.gyjzG25080705

Volume 55 Issue 12

Dec. 2025

Turn off MathJax

Article Contents

Article Navigation > INDUSTRIAL CONSTRUCTION > 2025 > 55(12): 107-114

WANG Yi, XIAO Haoyu, ZHU Jiabo. Optimization of Rescue Space Allocation Considering Panic Emotions and Zonal Guidance[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(12): 107-114. doi: 10.3724/j.gyjzG25080705

Citation:

WANG Yi, XIAO Haoyu, ZHU Jiabo. Optimization of Rescue Space Allocation Considering Panic Emotions and Zonal Guidance[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(12): 107-114. doi: 10.3724/j.gyjzG25080705

WANG Yi, XIAO Haoyu, ZHU Jiabo. Optimization of Rescue Space Allocation Considering Panic Emotions and Zonal Guidance[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(12): 107-114. doi: 10.3724/j.gyjzG25080705

Citation:

PDF( 1759 KB)

Optimization of Rescue Space Allocation Considering Panic Emotions and Zonal Guidance

doi: 10.3724/j.gyjzG25080705

1. College of Architecture and Art，Hefei University of Technology，Hefei 230001，China;
2. Anhui Provincial Key Laboratory of Huizhou Architecture，Hefei 230000，China;
3. College of Civil Engineering，Hefei University of Technology，Hefei 230001，China

Received Date: 2025-08-07
Available Online: 2026-01-06
Publish Date: 2025-12-20

Abstract

Abstract

This study aims to explore how optimizing the spatial deployment of guidance personnel during emergency evacuations can improve evacuation efficiency and curb the spread of panic. An improved cellular automaton （CA） model， combined with a genetic algorithm （GA）， was employed for the analysis. An evacuation simulation model was constructed that incorporates both panic propagation and the guiding effect of rescue personnel. The GA module was further implemented with a designed objective function to minimize the total evacuation time. The model was applied to both simplified scenarios and a real shopping mall fire scenario to simulate evacuation processes under different numbers and spatial distributions of guidance personnel. The effects of response time and exit signage clarity on evacuation performance were also analyzed. The results indicate that a rational spatial allocation of guidance personnel， rapid response， and clear signage can significantly enhance evacuation efficiency， effectively mitigate panic propagation and congestion near exits， and provide practical recommendations for optimizing resource allocation in real-world evacuation management.
- panic emotion,
- rescue space allocation,
- genetic algorithm,
- simulation modeling,
- rescue personnel

FullText(HTML)

References(30)

References

[1]	YU R，MAO Q，LYU J，et al. An extended model for crowd evacuation considering rescue behavior[J]. Physica A:Statistical Mechanics and its Applications，2022，605，127989.
[2]	VAN DER WAL C N，ROBINSON M A，DE BRUIN W B，et al. Evacuation behaviors and emergency communications:an analysis of real-world incident videos[J]. Safety Science，2021，136，105121.
[3]	BERNARDINI G，LOVREGLIO R，QUAGLIARINI E. Proposing behavior-oriented strategies for earthquake emergency evacuation:a behavioral data analysis from New Zealand，Italy and Japan[J]. Safety Science，2019，116:295-309.
[4]	NISHINARI K，KIRCHNER A，NAMAZI A，et al. Extended floor field CA model for evacuation dynamics[J]. IEICE Transactions on Information and Systems，2004，87（3）:726-732.
[5]	HELBING D，FARKAS I，VICSEK T. Simulating dynamical features of escape panic[J]. Nature，2000，407（6803）:487-490.
[6]	SHIWAKOTI N，SARVI M，ROSE G，et al. Animal dynamics based approach for modeling pedestrian crowd egress under panic conditions[J]. Procedia-Social and Behavioral Sciences，2011，17:438-461.
[7]	FU L，SONG W，LYU W，et al. Simulation of emotional contagion using modified SIR model:a cellular automaton approach[J]. Physica A:Statistical Mechanics and its Applications，2014，405:380-391.
[8]	ZHENG L，PENG X，WANG L，et al. Simulation of pedestrian evacuation considering emergency spread and pedestrian panic[J]. Physica A:Statistical Mechanics and its Applications，2019，522:167-181.
[9]	包子阳，余继周，杨杉. 智能优化算法及其MATLAB实例[M]. 3版. 北京:电子工业出版社，2021.
[10]	REDDY G T，REDDY M P，LAKSHMANNA K. Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis[J]. Evolutionary Intelligence，2020，13（2）:185-196.
[11]	SOHAIL A. Genetic algorithms in the fields of artificial intelligence and data sciences[J]. Annals of Data Science，2023，10（4）:1007-1018.
[12]	POURRAHMANI E，DELAVAR M R，MOSTAFAVI M A. Optimization of an evacuation plan with uncertain demands using fuzzy credibility theory and genetic algorithm[J]. International Journal of Disaster Risk Reduction，2016，14:357-372.
[13]	DONG K，YANG D，SHENG J，et al. Dynamic planning method of evacuation route in dam-break flood scenario based on the ACO-GA hybrid algorithm[J]. International Journal of Disaster Risk Reduction，2024，100，104219.
[14]	VON SCHANTZ A，EHTAMO H. Minimizing the evacuation time of a crowd from a complex building using rescue guides[J]. Physica A:Statistical Mechanics and its Applications，2022，594，127011.
[15]	ZHANG C Y，ZHAO R，WANG N，et al. A novel historical data-based method for predicting firefighters demand in urban fires[J]. Fire Safety Journal，2024，147，104200.
[16]	KURDI H，ALMULIFI A，AL-MEGREN S，et al. A balanced evacuation algorithm for facilities with multiple exits[J]. European Journal of Operational Research，2021，289（1）:285-296.
[17]	SRINIKETH K，LE A V，MOHAN R E，et al. Robot-aided human evacuation optimal path planning for fire drill in buildings[J]. Journal of Building Engineering，2023，72，106512.
[18]	GUO K，ZHANG L. Simulation-based passenger evacuation optimization in metro stations considering multi-objectives[J]. Automation in Construction，2022，133，104010.
[19]	GUO K，ZHANG L. Adaptive multi-objective optimization for emergency evacuation at metro stations[J]. Reliability Engineering and System Safety，2022，219，108210.
[20]	QHTANIA A S AL，SHAMMARIA A S AL，KURDIA H A. A fast genetic algorithm-based evacuation plan generator[J]. Procedia Computer Science，2017，113:330-337.
[21]	LI Y，CAI W，KANA A A，et al. Design of level of service on facilities for crowd evacuation using genetic algorithm optimization[J]. Safety Science，2019，120:237-247.
[22]	KIRCHNER A，KLÜPFEL H，NISHINARI K，et al. Simulation of competitive egress behavior:Comparison with aircraft evacuation data[J]. Physica A:Statistical Mechanics and its Applications，2003，324（3/4）:689-697.
[23]	王益，朱佳波，胡义. 考虑恐慌行人和多救援人员分区引导的疏散研究[J]. 安全与环境学报，2024，24（5）:1955-1964.
[24]	SEIKE M，KAWABATA N，HASEGAWA M. Experiments of evacuation speed in smoke-filled tunnel[J]. Tunnelling and Underground Space Technology，2016，53:61-67.
[25]	CHEN A，HE J，LIANG M，et al. Crowd response considering herd effect and exit familiarity under emergent occasions:a case study of an evacuation drill experiment[J]. Physica A:Statistical Mechanics and its Applications，2020，556，124654.
[26]	杨晶. 大型商场的行人疏散仿真方法研究[D]. 西安:西安建筑科技大学，2020.
[27]	LI X J，CHEN W B，CHEN R X，et al. Evacuation-path-selection model of real-time fire diffusion in urban underground complexes[J]. Computers& Industrial Engineering，2023，177，109014.
[28]	GAO J，HE J，GONG J. A simplified method to provide evacuation guidance in a multi-exit building under emergency[J]. Physica A:Statistical Mechanics and its Applications，2020，545，123554.
[29]	YOUNES M W，BALAMURUGAN B，ALSHAMRANI S S. An adaptive genetic algorithm for solving multi-objective engineering design problems[J]. Mathematics，2022，10（3），350.
[30]	ELBELTAGI E，HEGAZY T，GRIERSON D. Comparison among five evolutionary-based optimization algorithms[J]. Advanced Engineering Informatics，2020，44，101046.