基于D-S证据理论的地铁站安全风险评估方法

董升; 马云洁; 周继彪; 杜运潮; 李泽炜

doi:10.3963/j.jssn.1674-4861.2025.02.004

基于D-S证据理论的地铁站安全风险评估方法

doi: 10.3963/j.jssn.1674-4861.2025.02.004

董升^1,,
马云洁²,
周继彪^{3, 4, ,},
杜运潮⁵,
李泽炜⁶

1.
宁波工程学院建筑与交通工程学院浙江宁波 315211
2.
宁波工程学院经济与管理学院浙江宁波 315211
3.
宁波市高等级公路建设管理中心浙江宁波 315199
4.
同济大学交通学院上海 201804
5.
宁波工程学院国际交流学院浙江宁波 315211
6.
宁波大学海运学院浙江宁波 315832

基金项目:

国家自然科学基金项目 52002282

浙江省教育科学规划课题项目 2023SCG131

宁波市自然科学基金项目 2023J028

详细信息

作者简介:
董升（1980—），博士，副教授. 研究方向：交通行为. E-mail: dongsheng@nbut.edu.cn

通讯作者:
周继彪（1986—），博士，副教授. 研究方向：交通安全. E-mail: zhoujibiao@tongji.edu.cn

中图分类号: U491.2
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- 文章访问数: 33
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- 被引次数: 0
出版历程
- 收稿日期: 2024-11-02
- 网络出版日期: 2025-09-29

A Method of Risk Assessment for Subway Stations Based on D-S Evidence Theory

DONG Sheng^1
,,
MA Yunjie²,
ZHOU Jibiao^{3, 4
, ,},
DU Yunchao⁵,
LI Zewei⁶

1.
School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, Zhejiang, China
2.
School of Economics and Management, Ningbo University of Technology, Ningbo 315211, Zhejiang, China
3.
Department of Security, Ningbo Highway Construction & Management Center, Ningbo 315199, Zhejiang, China
4.
College of Transportation Engineering, Tongji University, Shanghai 201804, China
5.
International Exchange College, Ningbo University of Technology, Ningbo 315211, Zhejiang, China
6.
Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China

摘要

摘要: 高密度客流的安全风险评估对提升城市轨道交通系统应急响应能力具有重要意义。为解决传统评估方法存在的指标体系不健全、多源数据融合不充分、评估精准度低等问题，提出了改进的Dempster-Shafer（D-S）证据理论方法。该方法基于涵盖人员、设备、客流量、环境、管理的五维度指标体系，通过博弈论组合赋权确定综合权重，采用半梯形模糊隶属度函数量化各指标的安全等级隶属度，利用Jousselme距离公式构建证据相似度矩阵，引入校准系数和调节参数增强高冲突证据的识别与处理能力，运用线性加权得出风险等级。以宁波地铁鼓楼站为例，采集节假日晚高峰客流数据与专家评判信息构建多源证据集，并开展对比验证。结果显示：①与传统D-S方法、Yager方法相比，本文方法的平均冲突分别降低了34.4%和8.5%；②关键指标“客流量”隶属R3等级值达0.820 2，说明本文方法对高密度客流场景具备良好表征能力；③本文方法具有较强的适应性与稳定性，多场景对比验证中误差率低于5%。研究结果对高密度客流下轨道交通安全风险的识别与控制具有一定的借鉴价值。
- 交通工程 /
- 地铁站 /
- 安全风险评估 /
- D-S证据理论 /
- 客流量
Abstract: The safety risk assessment of high-density passenger flow holds significant importance for improving the emergency response capabilities of urban metro systems. To address t traditional methods'limitations, such as inadequate indicator systems, insufficient integration of multi-source data, and low assessment accuracy, an enhanced Dempster-Shafer (D-S) evidence theory method was proposed. This method is structured around a five-dimensional indicator system encompassing personnel, equipment, passenger flow, environmental conditions, and management protocols. Comprehensive weights were determined through Game Theory-Combinatorial Empowerment. The safety level membership degrees of each indicator were quantified using a fuzzy half-gradient affiliation function, while an evidence similarity matrix is constructed via Jousselme's evidential distance function. To address high-conflict evidence, a calibration factor α, and adjustment parameter μ, are introduced to refine the fusion process, where the final risk level derived through a linear weighted method. A case study was conducted at Ningbo Metro Gulou Station, utilizing holiday evening peak passenger flow data and expert evaluations to establish a multi-source evidence set for validation. The results demonstrated that: ①Compared to the traditional D-S method and Yager's method, the proposed approach reduces average evidence conflicts by 34.4% and 8.5%, respectively; ②The membership grade of the critical"passenger flow"indicator has an R3 rank value reaches 0.8202, confirming the method's effectiveness in characterizing scenarios of high-density passenger flow; ③The proposed method exhibits robust adaptability and stability, with an error rate below 5% in the comparison of multiple scenarios. These findings provided actionable insights for identifying and mitigating risks in metro systems under high-density passenger flow conditions.
- traffic engineering /
- subway station /
- security risk assessment /
- D-S evidence theory /
- passenger flow

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图 1 算法流程图

Figure 1. Algorithm flow chart

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图 2 评估指标敏感性分布图

Figure 2. Sensitivity maps for evaluation indicators

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图 3 高密度客流下地铁站安全风险评估指标体系

Figure 3. Safety risk evaluation indicator system for subway stations under high density passenger flow

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图 4 各设施设备客流量变化

Figure 4. Change of passenger flow of facilities and equipment

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图 5 疏散通道客流量变化

Figure 5. Change of passenger flow in each evacuation channel

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图 6 站台站厅客流密度变化

Figure 6. Changes in passenger density at platforms and halls

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表 1 三级指标安全风险等级MASS函数矩阵

Table 1. Matrix of MASS functions for the security risk level of the three-level indicator

三级指标	R₁	R₂	R₃	R₄	Θ	三级指标	R₁	R₂	R₃	R₄	Θ
A1	0.122 1	0.648 6	0.229 3	0.000 0	0.000 0	C4	0.000 0	0.080 4	0.186 5	0.733 1	0.000 0
A2	0.887 8	0.112 2	0.000 0	0.000 0	0.000 0	C5	0.931 4	0.068 6	0.000 0	0.000 0	0.000 0
A3	0.792 8	0.196 2	0.011 0	0.000 0	0.000 0	C6	0.000 0	0.0804	0.186 5	0.733 1	0.000 0
B1	0.000 0	0.205 7	0.794 3	0.000 0	0.000 0	D1	0.003 0	0.719 7	0.277 3	0.000 0	0.000 0
B2	0.000 0	0.000 0	0.141 0	0.857 1	0.000 0	D2	0.000 0	0.603 4	0.396 6	0.000 0	0.000 0
B3	0.000 0	0.621 2	0.378 8	0.000 0	0.000 0	D3	0.000 0	0.144 8	0.854 7	0.000 5	0.000 0
B4	0.947 4	0.052 6	0.000 0	0.000 0	0.000 0	D4	0.466 7	0.533 3	0.000 0	0.000 0	0.000 0
B5	0.875 0	0.125 0	0.000 0	0.000 0	0.000 0	D5	0.000 0	0.144 8	0.854 7	0.000 5	0.000 0
B6	0.886 9	0.113 1	0.000 0	0.000 0	0.000 0	E1	0.352 6	0.645 1	0.002 3	0.000 0	0.000 0
C1	0.000 0	0.080 4	0.186 5	0.733 1	0.000 0	E2	0.753 6	0.246 4	0.000 0	0.000 0	0.000 0
C2	0.000 0	0.321 0	0.679 0	0.000 0	0.000 0	E3	0.923 3	0.076 7	0.000 0	0.000 0	0.000 0
C3	0.000 0	0.019 7	0.178 6	0.801 7	0.000 0	E4	0.853 6	0.123 3	0.023 1	0.000 0	0.000 0

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表 2 二级指标安全等级隶属度矩阵

Table 2. Matrix of security level affiliations for level two indicators

评估指标	R₁	R2	R₃	R₄	Θ
A	0.857 5	0.142 5	0.000 0	0.000 0	0
B	0.2438	0.3151	0.441 1	0.000 0	0
C	0.000 0	0.061 0	0.820 2	0.118 8	0
D	0.000 0	0.699 4	0.300 6	0.000 0	0
E	0.823 6	0.176 1	0.000 3	0.000 0	0

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表 3 不同站点安全风险评估结果

Table 3. Safety risk assessment results of different sites

站点	指标	R₁	R₂	R₃	R₄	Θ
鼓楼站	A	0.857 5	0.142 5	0.000 1	0.000 0	0
	B	0.3426	0.5572	0.1002	0.000 0	0
	C	0.647 7	0.241 5	0.110 8	0.000 0	0
	D	0.000 0	0.699 4	0.300 6	0.000 0	0
	E	0.823 6	0.176 1	0.000 3	0.823 6	0
	T	0.693 9	0.251 1	0.055 0	0.000 0	-
城隍庙站	A	0.857 5	0.142 5	0.000 0	0.000 0	0
	B	0.307 7	0.652 2	0.040 1	0.000 0	0
	C	0.115 9	0.725 3	0.114 5	0.044 3	0
	D	0.133 2	0.658 5	0.208 3	0.000 0	0
	E	0.823 6	0.176 1	0.000 3	0.000 0	0
	T	0.215 6	0.724 4	0.060 0	0.000 0	-

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表 4 不同方法二级指标融合结果对比

Table 4. Comparison of secondary index synthesis results under different methods

方法	指标	R₁	R₂	R₃	R₄	Θ
传统D-S方法	A	0.993 3	0.006 6	0.000 0	0.000 0	0.000 1
	B	0.000 0	0.000 0	0.000 0	0.000 0	1.000 0
	C	0.000 0	0.262 3	0.000 0	0.000 0	0.737 7
	D	0.000 0	0.997 9	0.000 0	0.000 0	0.002 0
	E	0.994 6	0.004 9	0.000 0	0.000 0	0.000 3
Yager方法	A	0.491 0	0.0737	0.000 0	0.000 0	0.007 3
	B	0.000 0	0.478 8	0.000 0	0.000 0	0.033 5
	C	0.000 0	0.500 4	0.3789	0.000 0	0.378 9
	D	0.000 0	0.516 0	0.4833	0.000 0	0.000 7
	E	0.647 8	0.003 2	0.001 0	0.000 0	0.002 4
改进的D-S方法	A	0.857 5	0.142 5	0.000 0	0.000 0	0.000 0
	B	0.243 8	0.315 1	0.441 1	0.000 0	0.000 0
	C	0.000 0	0.061 0	0.820 2	0.118 8	0.000 0
	D	0.000 0	0.699 4	0.300 6	0.000 0	0.000 0
	E	0.823 6	0.176 1	0.000 3	0.000 0	0.000 0

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表 5 不同方法安全等级计算结果对比

Table 5. Comparison of safety level calculation results of different methods

方法	R₁	R₂	R₃	R₄
传统D-S方法	0.199 9	0.170 1	0.000 0	0.000 0
Yager方法	0.109 8	0.404 1	0.196 2	0.000 0
改进的D-S方法	0.242 1	0.186 4	0.517 6	0.053 9

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