基于SD-MEE模型的地铁拥挤踩踏事故韧性评估

doi:10.16265/j.cnki.issn1003-3033.2025.12.0798

摘要/Abstract

摘要：

为进一步避免地铁拥挤踩踏事故发生,提高地铁站安全管理水平,基于韧性理论提出地铁拥挤踩踏韧性理念。首先,以吸收能力、抵抗能力、恢复能力和适应能力为核心,重点分析地铁拥挤踩踏事故韧性发展阶段,围绕人群密度核心影响因素构建地铁拥挤踩踏韧性评估指标体系;然后,将系统动力学(SD)与物元可拓(MEE)模型相结合,构建地铁拥挤踩踏韧性评估模型;最后,以北京西直门地铁站为例验证该模型。结果表明:该模型突破传统静态评估模型难以刻画地铁系统韧性能力随时间动态演变过程的局限,实现韧性能力动态变化的定量化评估;西直门地铁站总体韧性等级为Ⅱ级(较高韧性),但由于实时监测滞后和应急响应不足,在早高峰期间降为Ⅲ级(一般韧性),适应性受限于智能系统覆盖率较低。

关键词: 系统动力学(SD), 物元可拓(MEE)模型, 地铁拥挤踩踏事故, 韧性评估, 人群密度

Abstract:

In order to further prevent subway crowd stampede accidents and improve the safety management of metro stations, a metro crowd stampede resilience concept was proposed based on resilience theory. Centered on absorption capacity, resistance capacity, recovery capacity, and adaptation capacity, the developmental stages of metro crowd stampede resilience were analyzed, and a resilience evaluation index system was constructed by identifying core influencing factors related to crowd density. By integrating SD with MEE model, a metro crowd stampede resilience evaluation model was developed. This model was then applied to analyze the Beijing Xizhimen Metro Station. Results show that the model overcomes the limitation of traditional static evaluation models in depicting the dynamic evolution of the metro system's resilience capacity over time, enabling a quantitative assessment of dynamic resilience changes. The overall resilience level of Xizhimen Station is Grade II (relatively high resilience). However, due to delays in real-time monitoring and insufficient emergency response, it drops to Grade III (moderate resilience) during morning peak hours, with adaptability constrained by the low coverage rate of intelligent systems.

Key words: system dynamics(SD), matter-element extension (MEE)model, subway crowd stampede accidents, resilience assessment, crowd density

中图分类号:

X928.9其他

王起全, 冯炜翔, 杨松立. 基于SD-MEE模型的地铁拥挤踩踏事故韧性评估[J]. 中国安全科学学报, 2025, 35(12): 187-195.

WANG Qiquan, FENG Weixiang, YANG Songli. Resilience assessment of subway crowd stampede accidents based on SD and MEE model[J]. China Safety Science Journal, 2025, 35(12): 187-195.

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一级指标	二级指标	定义
吸收能力	站内通道宽度C₁/m	指站台主要通道(含出入口、换乘通道最窄部门)的宽度
	实时密度监测响应时间C₂/s	从监测到人群密度超标到系统/人员作出响应的耗时
	疏散指标覆盖率C₃/%	疏散指示标志(含地面标识、电子屏等)覆盖关键区域的比例
	动态限流策略适配性C₄	地铁站是否根据人流量快速调整限流措施(如关闸机、分批进站)
	障碍物空间压缩系数C₅/m²	障碍物(如广告牌、垃圾桶设施)占用通道空间多少面积
抵抗能力	应急通道开放时间C₆	紧急情况下,所有逃生通道多久能全部打开
	广播覆盖率C₇/%	应急广播覆盖关键区域(站台、通道、出入口)的比例
	工作人员现场引导效率C₈/%	工作人员接到警报后,多久能抵达现场指挥,指挥的正确率
	多部门协同响应时间C₉/min	从事件发生到各部门(公安、消防、医疗)抵达现场的总耗时
恢复能力	疏散路径修复效率C₁₀/min	受损疏散路径(如故障电梯、封闭通道)恢复通行的耗时
	设备设施快速修复效能C₁₁/min	关键设备(如监控、闸机)故障后恢复运行的耗时
	运营重启与班次调整效能C₁₂/min	事故后恢复正常运营及调整班次的总耗时
适应能力	动态客流风险地图更新频率C₁₃	显示人流热点的地图多长时间更新一次
	限流策略动态调整周期C₁₄	根据人流量变化,多长时间调整一次限流措施
	智能密度调控系统覆盖率C₁₅/%	智能系统(如人工智能监控、自动报警)覆盖关键区域的比例

变量	变量方程
实时人群密度	${D}_{0}\left(t\right)=\frac{{P}_{i}+{P}_{o}+{P}_{t}}{{S}_{e}}$
人群密度变化量	$D\left(t\right)={D}_{0}\left(t\right)·(1-{\alpha }_{1}\times {R}_{1}-{\alpha }_{2}\times {R}_{2}-{\alpha }_{3}\times {R}_{3}-{\alpha }_{4}\times {R}_{4})$
吸收能力流量	0.119×C₁+0.059×C₂+0.053×C₃+0.203×C₄+0.056×C₅- ${\lambda }_{1}$×D(t)+ k₄·R₄
抵抗能力流量	0.061×C₆+0.034×C₇+0.045×C₈+0.020×C₉- ${\lambda }_{2}$×D(t)+ k₁·R₁
恢复能力流量	0.058×C₁₀+0.031×C₁₁+0.055×C₁₂- ${\lambda }_{3}$×D(t)+ k₂·R₂
适应能力流量	0.035× ${{C}_{1}}_{3}$+0.101×C₁₄+0.063×C₁₅+ ${\lambda }_{4}$×D(t)+ k₃·R₃

韧性等级	N1	N2	N3	N4	N5
K(C₁)	-0.331	0.017	-0.009	-0.339	-0.504
K(C₂)	-0.418	-0.137	0.283	-0.287	-0.491
K(C₃)	-0.194	0.317	-0.341	-0.561	-0.671
K(C₄)	-0.190	0.306	-0.347	-0.565	-0.674
K(C₅)	-0.137	0.189	-0.406	-0.604	-0.703
K(C₆)	-0.269	0.416	-0.208	-0.472	-0.604
K(C₇)	-0.364	-0.125	0.334	-0.222	-0.416
K(C₈)	-0.260	0.350	-0.159	-0.422	-0.560
K(C₉)	-0.349	-0.067	0.155	-0.282	-0.461
K(C₁₀)	-0.273	0.400	-0.200	-0.467	-0.600
K(C₁₁)	-0.291	0.306	-0.153	-0.435	-0.576
K(C₁₂)	-0.321	-0.109	0.350	-0.186	-0.367
K(C₁₃)	-0.444	-0.375	-0.286	-0.167	0.118
K(C₁₄)	-0.390	-0.220	0.217	-0.072	-0.304
K(C₁₅)	-0.455	-0.402	-0.337	-0.256	0.131