中国安全科学学报 ›› 2020, Vol. 30 ›› Issue (4): 108-113.doi: 10.16265/j.cnki.issn1003-3033.2020.04.017

• 公共安全 • 上一篇    下一篇

景区密集人群踩踏事故情景分析

李华 副教授, 李琳倩, 益朋   

  1. 西安建筑科技大学 资源工程学院,陕西 西安 710055
  • 收稿日期:2020-01-09 修回日期:2020-03-20 出版日期:2020-04-28 发布日期:2021-01-27
  • 作者简介:李华(1979—),女,陕西西安人,博士,副教授,硕士生导师,主要从事企业风险评估与安全管理、建筑安全监测与监控、公共安全与应急管理研究。E-mail:lihua@xauat.edu.cn。
  • 基金资助:
    陕西省教育厅专项科研项目(13JK0222);陕西省安全生产监督管理局重点专项(20180572);陕西省消防总队2018年专项(20180635)。

Scenario analysis of stampede accidents in scenic spots

LI Hua, LI Linqian, YI Peng   

  1. School of Resources Engineering, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China
  • Received:2020-01-09 Revised:2020-03-20 Online:2020-04-28 Published:2021-01-27

摘要: 为提高景区密集人群应急管控能力,采用情景分析和动态贝叶斯网络相结合的方法,研究景区密集人群踩踏事故情景演化过程。通过分析景区密集人群踩踏事故,选取情景状态、致灾体、承灾体、驱动要素为关键要素,探索密集人群踩踏事故情景演化的特征与路径;运用动态贝叶斯网络构建密集人群踩踏事故情景网络,从情景状态概率推演情景发展趋势。结果表明:引起事故发生风险概率较高的情景状态依次为人群跌倒、人群僵持、人群流量剧增和人群聚集;预防准备不充分、景区安保管理不足、人流监测设备和人群限流设施不完善等相应驱动要素的介入是造成事故发生的主要原因。

关键词: 景区, 密集人群, 踩踏事故, 情景分析, 动态贝叶斯网络

Abstract: In order to improve emergency management and control ability of huge crowds in scenic spots, a combination of scenario analysis and dynamic Bayesian network was used to study evolution process of stampede accidents. Situation state, disaster-causing body, disaster-bearing body and driving factors were selected as key elements to explore law and path of accident scenario evolution. Then, the dynamic Bayesian network was used to construct a scenario network of huge crowd stampede incidents, and development trend of the scene was deduced by using situation state probability. The results show that scenarios with high risk of accidents are population fall, crowd stalemate, population flow increase and crowd gathering in turn. And the major reasons for them are interventions of corresponding driving factors such as inadequate preparation for prevention, inadequate security management of scenic spots, imperfect human flow monitoring equipment and crowd diversion facilities.

Key words: scenic spots, intensive crowd, stampede accidents, scenario analysis, dynamic Bayesian network

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