智能指示标志在提高人群疏散效率中的应用研究

doi:10.16182/j.issn1004731x.joss.23-0631

系统仿真学报 ›› 2024, Vol. 36 ›› Issue (10): 2383-2395.doi: 10.16182/j.issn1004731x.joss.23-0631

• 论文 • 上一篇

智能指示标志在提高人群疏散效率中的应用研究

牛恒¹, 韩延彬¹, 李梁², 陈威霖¹, 牛四杰¹, 侯清涛³

^1.济南大学信息科学与工程学院, 山东济南 250000
^2.山东师范大学信息科学与工程学院, 山东济南 250000
^3.浪潮软件股份有限公司, 山东泰安 271000

收稿日期:2023-05-26 修回日期:2023-08-01 出版日期:2024-10-15 发布日期:2024-10-18
通讯作者: 韩延彬
第一作者简介:牛恒(1999-)，男，硕士生，研究方向为人群疏散仿真。
基金资助:
山东省自然科学基金面上项目(ZR2022MG033)

Research on Application of Intelligent Indication Signs in Improving Efficiency of Crowd Evacuation

Niu Heng¹, Han Yanbin¹, Li Liang², Chen Weilin¹, Niu Sijie¹, Hou Qingtao³

^1.School of Information Science and Engineering, Ji'nan University, Ji'nan 250000, China
^2.School of Information Science and Engineering, Shandong Normal University, Ji'nan 250000, China
^3.Inspur Software Co. , Ltd, Taian 271000, China

Received:2023-05-26 Revised:2023-08-01 Online:2024-10-15 Published:2024-10-18
Contact: Han Yanbin

摘要/Abstract

摘要：

为了解决密闭空间内密集人群应急疏散效率低下的问题，构建了基于智能引导标志的人群疏散引导模型，提出了基于均衡拥堵策略的指示标志布局优化方法和基于社交网络搜索的指示标志引导权重优化方法，用以提高疏散效率。仿真实验证明：优化后的智能指示标志布局可以有效缓解疏散过程中的局部拥堵，减少行人由拥堵引起的时间消耗；优化后的智能指示标志引导权重可进一步均衡出口利用率，有利于充分利用环境资源，进一步提升人群疏散效率。

关键词: 智能指示标志, 疏散引导, 交互速度障碍, 社交网络搜索

Abstract:

Aiming at the low emergency evacuation efficiency of dense crowds in confined spaces, a crowd evacuation guidance model based on intelligent signs is constructed in this paper. A balanced congestion strategy to optimize the signs layout and a social network search-based sign guidance weights optimization method are proposed to improve evacuation efficiency. Simulation experiments show that the optimized intelligent sign layout can effectively alleviate the local congestion during evacuation and reduce the time consumption of pedestrians caused by congestion. The optimized intelligent sign guidance weights can further balance the utilization rate of exits, which is conducive to making full use of the environmental resources to enhance the efficiency of crowd evacuation further.

Key words: intelligent indication signs, evacuation guidance, reciprocal velocity obstacles, social network search

中图分类号:

TP391.9

牛恒,韩延彬,李梁等 . 智能指示标志在提高人群疏散效率中的应用研究[J]. 系统仿真学报, 2024, 36(10): 2383-2395.

Niu Heng,Han Yanbin,Li Liang,et al . Research on Application of Intelligent Indication Signs in Improving Efficiency of Crowd Evacuation[J]. Journal of System Simulation, 2024, 36(10): 2383-2395.

图/表 16

图1

图2

图3

图4

图5

图6

表1

仿真实验参数

参数	数值
行人速度ν/(m/s)	3
拥塞衡量度 $β$	0.5
行人有限理性强度 $σ$	0
权重上限 $B U$	1.8
权重下限 $B L$	0.3
种群数量m	10
最大迭代轮数k	100
行人直径P/m	0.48

表1

图7

图8

图9

图10

表2

不同人数的SNS结果

疏散人数	权重	疏散时间/s
500	$x 0 = 1.6, x 1 = 0.9, x 2 = 1.2, x 3 = 1.2, x 4 = 1.7, x 5 = 1.3, x 6 = 1.3, x 7 = 0.7, x 8 = 0.8, x 9 = 1.7$	287
1 000	$x 0 = 0.4, x 1 = 1.7, x 2 = 1.0, x 3 = 1.2, x 4 = 1.3, x 5 = 1.8, x 6 = 0.2, x 7 = 0.6, x 8 = 1.8, x 9 = 0.8$	417
2 000	$x 0 = 0.7, x 1 = 1.8, x 2 = 0.3, x 3 = 1.3, x 4 = 1.8, x 5 = 1.8, x 6 = 1.3, x 7 = 0.2, x 8 = 0.4, x 9 = 0.8$	813
3 000	$x 0 = 0.7, x 1 = 1.7, x 2 = 1.8, x 3 = 1.8, x 4 = 1.7, x 5 = 1.8, x 6 = 1.0, x 7 = 1.6, x 8 = 1.8, x 9 = 1.4$	1 264

表2

图11

图12

图13

图14

参考文献 22

1	Haghani M. Empirical Methods in Pedestrian, Crowd and Evacuation Dynamics: Part I. Experimental Methods and Emerging Topics[J]. Safety Science, 2020, 129: 104743.
2	Zhou Min, Dong Hairong, Ioannou P A, et al. Guided Crowd Evacuation: Approaches and Challenges[J]. IEEE/CAA Journal of Automatica Sinica, 2019, 6(5): 1081-1094.
3	宋英华, 张哲谦, 霍非舟, 等. 考虑指示标志的视野受限情况下人员疏散模型[J]. 系统仿真学报, 2022, 34(11): 2416-2424.
	Song Yinghua, Zhang Zheqian, Huo Feizhou, et al. Evacuation Model Considering the Restricted View of the Sign[J]. Journal of System Simulation, 2022, 34(11): 2416-2424.
4	孔云科. 基于物联网的智能疏散指示标志应用探讨[J]. 消防科学与技术, 2017, 36(8): 1158-1160.
	Kong Yunke. Application of Internet of Things in Intelligent Evacuation Indication Sign[J]. Fire Science and Technology, 2017, 36(8): 1158-1160.
5	Zhu Yu, Chen Tao, Ding Ning, et al. Follow the Evacuation Signs or Surrounding People During Building Evacuation, an Experimental Study[J]. Physica A: Statistical Mechanics and Its Applications, 2020, 560: 125156.
6	周慧, 代张音, 范芮雯, 等. 疏散指示标志安装高度对应急疏散效率的影响[J]. 科学技术与工程, 2022, 22(11): 4660-4667.
	Zhou Hui, Dai Zhangyin, Fan Ruiwen, et al. Influence of Evacuation Indicator Installation Height on Emergency Evacuation Efficiency[J]. Science Technology and Engineering, 2022, 22(11): 4660-4667.
7	Ma Peijie, Jiang Yanqun, Zhu Junfang, et al. The Effect of Escape Signs on the Pedestrians Evacuation[J]. Physica A: Statistical Mechanics and Its Applications, 2019, 534: 121133.
8	Li Minghua, Xu Chengyong, Xu Yan, et al. Dynamic Sign Guidance Optimization for Crowd Evacuation Considering Flow Equilibrium[J]. Journal of Advanced Transportation, 2022, 2022: 2555350.
9	Zhao Hantao, Schwabe Amray, Schläfli Fabian, et al. Fire Evacuation Supported by Centralized and Decentralized Visual Guidance Systems[J]. Safety Science, 2022, 145: 105451.
10	赵薇. 智能疏散指示标志诱导系统优化设计与人流控制研究[J]. 中国安全科学学报, 2016, 26(5): 169-174.
	Zhao Wei. Research on Optimizing Intelligent Evacuation Direction Signage Design and Controlling Flow of Evacuees[J]. China Safety Science Journal, 2016, 26(5): 169-174.
11	Zhang Zhe, Jia Limin, Qin Yong. Optimal Number and Location Planning of Evacuation Signage in Public Space[J]. Safety Science, 2017, 91: 132-147.
12	Fu Meiqing, Liu Rui. BIM-based Automated Determination of Exit Sign Direction for Intelligent Building Sign Systems[J]. Automation in Construction, 2020, 120: 103353.
13	Galea E R, Xie Hui, Deere S, et al. Evaluating the Effectiveness of an Improved Active Dynamic Signage System Using Full Scale Evacuation Trials[J]. Fire Safety Journal, 2017, 91: 908-917.
14	石为人, 王楷. 基于Floyd算法的移动机器人最短路径规划研究[J]. 仪器仪表学报, 2009, 30(10): 2088-2092.
	Shi Weiren, Wang Kai. Floyd Algorithm for the Shortest Path Planning of Mobile Robot[J]. Chinese Journal of Scientific Instrument, 2009, 30(10): 2088-2092.
15	Wang Xiang, Mohcine Chraibi, Chen Juan, et al. Modeling Boundedly Rational Route Choice in Crowd Evacuation Processes[J]. Safety Science, 2022, 147: 105590.
16	赵利强, 郭梦倩, 唐水雄, 等. 基于有限理性约束的自适应人群疏散仿真模型[J]. 系统仿真学报, 2022, 34(10): 2162-2170.
	Zhao Liqiang, Guo Mengqian, Tang Shuixiong, et al. Adaptive Crowd Evacuation Simulation Model Based on Bounded Rationality Constraints[J]. Journal of System Simulation, 2022, 34(10): 2162-2170.
17	刘延东, 黄高翔, 陈文. 基于增强心理行为异质性的改进社会力模型[J]. 系统仿真学报, 2023, 35(5): 1120-1130.
	Liu Yandong, Huang Gaoxiang, Chen Wen. Improved Social Force Model Based on Enhancing Psych Behavioral Heterogeneity[J]. Journal of System Simulation, 2023, 35(5): 1120-1130.
18	李巧茹, 燕锦秀, 田晓勇, 等. 基于演化博弈理论的疏散动力学研究[J]. 系统仿真学报, 2023, 35(2): 300-307.
	Li Qiaoru, Yan Jinxiu, Tian Xiaoyong, et al. Evacuation Dynamics Research Based on Evolutionary Game Theory[J]. Journal of System Simulation, 2023, 35(2): 300-307.
19	Jur van den Berg, Lin Ming, Manocha D. Reciprocal Velocity Obstacles for Real-time Multi-agent Navigation[C]//2008 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2008: 1928-1935.
20	Li Liang, Liu Hong, Han Yanbin. Arch Formation-based Congestion Alleviation for Crowd Evacuation[J]. Transportation Research Part C: Emerging Technologies, 2019, 100: 88-106.
21	Han Yanbin, Liu Hong, Li Liang. An Evacuation Guidance Model for Pedestrians with Limited Vision[J]. Transportmetrica A Transport Science, 2023, 19(1): 1977867.
22	Talatahari Siamak, Bayzidi Hadi, Saraee Meysam. Social Network Search for Global Optimization[J]. IEEE Access, 2021, 9: 92815-92863.

[1]	李锦文, 王鹏, 潘优美, 惠新遥. 无人自主系统能力边界参数自适应判别方法[J]. 系统仿真学报, 2024, 36(10): 2359-2370.
[2]	余洋, 夏雨星, 陆文韬, 刘霡, 高世轩, 陈东阳. 基于荷-储碳流模型的电力系统双层优化调度[J]. 系统仿真学报, 2024, 36(10): 2288-2299.
[3]	马宗方, 张琳旋, 宋琳, 王嘉. 基于改进动态窗口的车库AGV路径规划及仿真[J]. 系统仿真学报, 2024, 36(10): 2265-2276.
[4]	陈世康, 王智民, 梁标, 顾焜仁, 宋珂满, 高远. 面向体系仿真的蓝军舰艇编队防空作战指控建模方法[J]. 系统仿真学报, 2024, 36(10): 2246-2256.
[5]	杨哲, 崔颖函, 郭灵犀, 李嘉鑫, 吴旭生. 融合数据仿真与深度学习算法的飞行器残骸搜寻技术[J]. 系统仿真学报, 2024, 36(10): 2238-2245.
[6]	杨妹, 王鹏. 一种基于插件的虚幻引擎HLA分布式仿真适配器[J]. 系统仿真学报, 2024, 36(10): 2231-2237.
[7]	周芳, 范波, 刘小毅, 丁一珊, 张宁馨, 邵亚超, 翟小玉. 一种基于改进行为树的智能对手行为模拟技术[J]. 系统仿真学报, 2024, 36(9): 1995-2003.
[8]	徐丽霞, 钟季龙, 伍劭实, 丁一珊, 翟小玉, 陈世钊, 王鹥喆, 温雪, 曾隽芳, 侯新文. 基于云模型和最大均值差异的指标迁移学习[J]. 系统仿真学报, 2024, 36(9): 2004-2015.
[9]	姚万业, 庞泽伟, 孙沛杰, 王祝. 基于窗口化匹配估计的ORB-SLAM算法研究[J]. 系统仿真学报, 2024, 36(9): 2032-2042.
[10]	朱前坤, 李继武, 杜永峰. 老旧教学楼火灾场景人员疏散仿真研究[J]. 系统仿真学报, 2024, 36(9): 2043-2053.
[11]	赵佰亭, 施建国, 贾晓芬. 井筒提升机数字孪生系统研究[J]. 系统仿真学报, 2024, 36(9): 2054-2064.
[12]	李超, 李贾宝, 丁才昌, 叶志伟, 左方威. 基于DRL的边缘监控任务卸载与资源分配算法[J]. 系统仿真学报, 2024, 36(9): 2113-2126.
[13]	李淑庆, 宋易宵, 钟国剑. 考虑近邻度值之和的城市轨道网络抗毁性研究[J]. 系统仿真学报, 2024, 36(9): 2127-2136.
[14]	王越龙, 王松艳, 晁涛. 基于多步信息辅助的Q-learning路径规划算法[J]. 系统仿真学报, 2024, 36(9): 2137-2148.
[15]	徐健, 刘高峰, 赵一剑, 郑自立, 闫焕营. 装配机器人的数字孪生虚实同步及抓取方法[J]. 系统仿真学报, 2024, 36(9): 2181-2192.

智能指示标志在提高人群疏散效率中的应用研究

Research on Application of Intelligent Indication Signs in Improving Efficiency of Crowd Evacuation

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 16

参考文献 22

相关文章 15

编辑推荐

Metrics

本文评价