摘要
为动态探究影响人群聚集风险的主要因素及定量评估人群聚集风险,依据贝叶斯估计理论改进静态贝叶斯网络模型,建立动态贝叶斯网络模型。用该模型可根据实时采集数据计算后验参数,获取动态定量风险评估结果。利用所建的动态贝叶斯网络模型动态定量评估北京市某大型商业街区人群聚集风险。结果表明:该街区初始人群聚集拥堵概率为0.8×10^(-3),踩踏概率为7.6×10^(-6)。随着实时观测数据的引入,最终拥堵概率为2.4×10^(-3),踩踏概率为1.63×10^(-5),其中疏散不及时、疏散通道不畅、疏散标志不清等3个因素的相对重要度影响因子较大,是主要影响因素。实例中各基本事件的发生概率和相对影响因子动态变化,证明该模型有效。
In order to explore trigger factors of crowd stampede and evaluate the accident risk quantitatively,a static Bayesian network model was improved based on the Bayesian estimation theory. A dynamic Bayesian model,by which posterior parameters can be calculated according to the collected realtime data,was built for obtaining quantitative assessment results of dynamic risk. Finally,a certain commercial district with a large population in Beijing City was used as an example to verify the effectiveness of the proposed method. The results show that the risk of crowd jamming and that of stampede are 0. 8 × 10-3and 7. 6 × 10-6respectively,that with the introduction of real-time data,the risks are improved to 2. 4×10-3and 1. 63×10-5respectively,that slow evacuation,occupied evacuation routes,and the lack of safety logo are main causes of jam and stampede,that the probability of occurrence and weight of influence of each basic event in the instance are dynamic,which proves that the model is effective.
作者
赵鲁炎
马峻
ZHAO Luyan MA Jun(School of Safety and Environment Engineering, Capital University of Economics and Business, Beijing 100070,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2017年第7期157-162,共6页
China Safety Science Journal
基金
国家自然科学基金资助(71471121)
关键词
公共安全
风险分析
动态贝叶斯网络
参数更新
人群聚集
public safety
risk assessment
dynamic Bayesian network
probability updating
stampede
