|本期目录/Table of Contents|

[1]卢颖,路越茗,余易凡,等.踩踏事故预防监控领域科学知识图谱研究*[J].中国安全生产科学技术,2021,17(7):172-177.[doi:10.11731/j.issn.1673-193x.2021.07.028]
　LU Ying,LU Yueming,YU Yifan,et al.Research on scientific knowledge map in field of prevention and monitoring on stampede accidents[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(7):172-177.[doi:10.11731/j.issn.1673-193x.2021.07.028]
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踩踏事故预防监控领域科学知识图谱研究*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
17
期数:
2021年7期
页码:
172-177
栏目:
职业安全卫生管理与技术
出版日期:
2021-07-31

文章信息/Info

Title:
Research on scientific knowledge map in field of prevention and monitoring on stampede accidents
文章编号:
1673-193X(2021)-07-0172-06
作者:
卢颖; 路越茗; 余易凡; 姜学鹏
（1.武汉科技大学 资源与环境工程学院，湖北 武汉 430081；
2.湖北省工业安全工程技术研究中心，湖北 武汉 430081)
Author(s):
LU Ying1; 2;  LU Yueming1;  YU Yifan1;  JIANG Xuepeng1; 2
(1.School of Resources and Environmental Engineering,Wuhan University of Science and Technology,Wuhan Hubei 430081,China;
2.Hubei Industrial Safety Engineering Technology Research Center,Wuhan Hubei 430081,China)
关键词:
踩踏事故; 事故预防; 事故监控; 知识图谱; CiteSpace
Keywords:
stampede accident;  accident prevention;  accident monitoring;  knowledge map;  CiteSpace
分类号:
X956
DOI:
10.11731/j.issn.1673-193x.2021.07.028
文献标志码:
A
摘要:
为解决踩踏事故预防监控领域发展现状、发展趋势及发展脉络不清晰问题，运用可视化软件CiteSpace，对Web of Science核心合集及中国知网收集到的365篇相关文献开展知识图谱研究。结果表明：中国在踩踏事故预防监控领域发文量最多，占40.6%，其次为美国，占15.4%，29个国家间仅有25条合作连线；国内外研究热点集中于卷积神经网络等计算机视觉技术在踩踏事故预防监控领域应用；针对“踩踏事故预防监控”领域涵盖学术外延范围，国内外存在一定差别；梳理获得踩踏事故预防领域3条主要发展路径；以深度学习为主的技术研究是当前及未来一段时间研究热点。
Abstract:
In order to solve the problem of unclear development status,development trend and development context in the field of the prevention and monitoring of stampede accidents at home and abroad,the visual software CiteSpace was used to study the knowledge map of 365 related literatures collected from the core collection of Web of Science and CNKI.The results showed that China had the largest number of papers in this field,accounting for 40.6%,followed by the United States with 15.4%,and there were only 25 cooperative connections among 29 countries.Six of the top 20 hot research key words at home and abroad were the same,focusing on the application research of computer vision technology such as convolutional neural network in the field of the prevention and monitoring of stampede accidents.There were some differences in the academic extension scope covered by the field of “prevention and monitoring of stampede accidents” at home and abroad,and the further consensus was needed.Three main development paths of research in this field were obtained.The technical research focusing on deep learning was not only the current research hotspot,but also the research hotspot in the future.

参考文献/References:

［1］王君玲，盛玲玉.中国煤矿企业应急管理研究回溯与前瞻-基于CiteSpace知识图谱的文献计量分析 ［J］.中国安全生产科学技术，2018，14（9）：175-179. WANG Junling，SHENG Lingyu.Retrospect and prospect of research on emergency management of coal mining enterprises in China-bibliometric analysis based on citespace knowledge map［J］.Journal of Safety Science and Technology，2018，14（9）：175-179.
［2］李杰，李平，谢启苗，等.安全疏散研究的科学知识图谱 ［J］.中国安全科学学报，2018，28（1）：1-7. LI Jie，LI Ping，XIE Qimiao，et al.Scientific knowledge map of safety evacuation research ［J］.China Safety Science Journal，2018，28(1)：1-7.
［3］ZOU X，YUE W L，VU H L.Visualization and analysis of mapping knowledge domain of road safety studies ［J］.Accident Analysis & Prevention，2018，118：131-145.
［4］曹旭，刘艺.国内踩踏事故研究的科学知识图谱分析 ［J］.山西能源学院学报，2020，33（1）：71-74. CAO Xu，LIU Yi.Analysis of scientific knowledge map of stampede accident research in China ［J］.Journal of Shanxi Institute of Energy，2020,33(1):71-74.
［5］ALMEIDA M M D，SCHREEB J V.Human stampedes:an updated review of current literature ［J］.Prehospital and Disaster Medicine，2018，34（1）：82-88.
［6］王启全.地铁拥挤踩踏事故脆弱性区域智能化预警研究 ［J］.中国安全科学学报，2018，28（11）：162-167. WANG Qiquan.Study on intelligent early warning of vulnerability area of subway congestion and treading accident ［J］.China Safety Science Journal，2018，28（11）：162-167.
［7］SINGH U，DETERME J F，DONCKER P D，et al.Crowd forecasting based on wifi sensors and lstm neural networks ［J］.IEEE Transactions on Instrumentation and Measurement，2020，69（9）：6121-6131.
［8］HELBING D，MUKERJI P.Crowd disasters as systemic failures:analysis of the love parade disaster ［J］.EPJ Data Science，2012，1（1）：1-40.
［9］FREDERICK M B，HSU E B.Ram janki temple:understanding human stampedes ［J］.The Lancet，2011，377（9760）：106-107.
［10］PIOTR D，CHRISTIAN W，BERNT S，et al.Pedestrian detection:an evaluation of the state of the art ［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence，2012，34（4）：743-761.
［11］FU M，XU P，LI X D，et al.Fast crowd density estimation with convolutional neural networks ［J］.Engineering Applications of Artificial Intelligence，2015，43：81-88.
［12］PANDEY A，PANDEY M，SINGH N，et al.Kumbh mela:a case study for dense crowd counting and modeling ［J］.Multimedia Tools and Applications，2020，79：25-26.
［13］BURKLE F M,HSU E B.Cambodian bonom touk stampede highlights preventable tragedy ［J］.Prehospital and Disaster Medicine，2012，27（5）：481-482.
［14］ILLIYAS F T，MANI S K，PRADEEPKUMAR A P，et al.Human stampedes during religious festivals:a comparative review of mass gathering emergencies in India ［J］.International Journal of Disaster Risk Reduction，2013，5：10-18.
［15］DAVIES A C，YIN J H，VELASTIN S A，et a1.Crowd monitoring using image processing［J］.IEEE Electronics and Communication Engineering Journal，1995，7(1)：37-47.
［16］MARANA A N，VELASTIN S A，COSTA L F，et al.Estimation of crowd density using image processing ［J］.IEEE Colloquium on Image Processing for Security Applications，1997，11(3)：1-11.
［17］HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks ［J］.Science，2006，313（5786）：504-507.
［18］YI J，PAN S，CHEN Q.Simulation of pedestrian evacuation in stampedes based on a cellular automaton model ［J］.Simulation Modelling Practice and Theory，2020，104：102147.
［19］KHAMIS N，SELAMAT H，ISMAIL F S，et al.Optimized exit door locations for a safer emergency evacuation using crowd evacuation model and artificial bee colony optimization ［J］.Chaos Solitons & Fractals，2020，131：109505.
［20］CHOW T W S，YAM J Y F，CHO S Y.Fast training algorithm for feed forward neural networks:application to crowd estimation at underground stations ［J］.Artificial Intelligence in Engineering，1999，13（3）：301-307.

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备注/Memo

备注/Memo:
收稿日期： 2021-03-24
* 基金项目： 国家自然科学基金项目(51874213)；湖北省自然科学基金青年项目(2018CFB186)；湖北省应急管理厅安全生产专项(KJZX201907011)
作者简介： 卢颖，博士，讲师，主要研究方向为城市公共安全风险理论与控制技术、消防安全管理。
更新日期/Last Update: 2021-08-05