基于改进CRITIC-VIKOR法的地铁车站运营韧性评价方法

邓勇亮; 高雨桐; 周琦; 李可为; 谷甜甜

doi:10.3963/j.jssn.1674-4861.2024.03.018

基于改进CRITIC-VIKOR法的地铁车站运营韧性评价方法

doi: 10.3963/j.jssn.1674-4861.2024.03.018

1.
苏州科技大学土木工程学院江苏苏州 215009
2.
中国矿业大学力学与土木工程学院江苏徐州 221116

基金项目:

国家自然科学基金项目 71801214

详细信息

通讯作者:
邓勇亮（1986—），博士，副教授. 研究方向：轨道交通安全管理. E-mail: hndyliang@163.com

中图分类号: X951
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- 文章访问数: 114
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- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2023-12-26
- 网络出版日期: 2024-10-21

An Operational Resilience Evaluation of Subway Station Based on Improved CRITIC-VIKOR Method

1.
Suzhou University of Science and Technology, School of Civil Engineering, Suzhou 215009, Jiangsu, China
2.
China University of Mining and Technology, School of Mechanics and Civil Engineering, Xuzhou 221116, Jiangsu, China

摘要

摘要: 地铁车站运营韧性具有多维性和复杂性。为了准确可靠地评价地铁车站运营韧性水平，提出了1种基于改进CRITIC-VIKOR法的评价方法。基于韧性理论和地铁车站运营特点，明确了地铁车站运营韧性的概念，并阐述了其压力、状态、响应3个维度的内涵。分析地铁车站运营中人、机、环、管这4个方面的关键要素，构建了8个一级指标。通过文献分析，建立了26个二级评价指标。通过采用标准差系数法衡量评价指标，并计算相关系数的绝对值，对CRITIC法进行改进，从而更加合理地给评价指标赋权，并结合VIKOR方法，提出了1种新的地铁车站运营韧性评价模型。对江苏省苏州市地铁4号线的同里站、流虹站、南门站进行实证分析，通过李克特量表收集相关数据，计算得到了26个评价指标的权重和3个车站的群体效用值、个体遗憾值、决策指标值，从而对地铁车站运营韧性进行量化分析和排序。与优序图法、层次分析法、熵值法等赋权方法相比，改进CRITIC法的偏差范围最小，为2%~40%，26个指标累计偏差最小，为553%。研究结果表明：在3个维度韧性中，压力韧性、状态韧性和响应韧性的权重分别为59.74%，21.48%，18.78%。在3个车站中，流虹站的运营韧性最高，其次是同里站和南门站。本文构建的评价模型能够为更加准确地评价地铁车站运营韧性提供理论支持。
- 轨道交通 /
- 车站运营韧性 /
- 评价指标体系 /
- 改进CRITIC /
- VIKOR
Abstract: The operational resilience of subway stations is characterized by its multidimensionality and complexity. To evaluate this resilience accurately and reliably, an improved A CRITIC-VIKOR based evaluation method is proposed. Based on resilience theory and the operational characteristics of subway stations, the concept of operational resilience for subway stations is defined, with a focus on its three core dimensions: pressure, state, and response. Key factors in four domains, including human, machine, environment, and management, are analyzed, leading to the development of 8 primary indexes. Through literature analysis, 26 secondary evaluation indexes are established. To assign more accurate weights to the indexes, the standard deviation coefficient method is applied, improving the CRITIC method through correlation coefficient analysis. By integrating the VIKOR method, a novel evaluation model for subway station operational resilience is proposed. An empirical analysis is conducted on Tongli station, Liuhong station, and Nanmen station of Suzhou subway Line 4. Data is collected using a Likert scale, and weights are calculated for the 26 evaluation indexes, as well as the group utility, individual regret, and decision-making index for each station. This allows for a quantitative ranking of the operational resilience of subway stations. Compared with superiority chart, analytic hierarchy process, and entropy method, the improved CRITIC method showed the smallest bias range, from 2% to 40%, with the cumulative bias of the 26 indexes being the smallest at 553%. The results indicate that the weights for pressure resilience, state resilience, and response resilience are 59.74%, 21.48%, and 18.78%, respectively. Among the three stations, Liuhong station demonstrates the highest operational resilience, followed by Tongli station and Nanmen station. The evaluation model provides theoretical foundation for more accurate evaluation of subway station operational resilience.
- rail transit /
- station operation resilience /
- evaluation index system /
- improved CRITIC /
- VIKOR

HTML全文

表 1 城市地铁车站运营韧性评价指标体系

Table 1. Evaluation index system of urban subway station operation resilience

韧性维度	一级指标	二级指标	序号
压力P	车站人员风险（A1）	（A11）乘客危险行为	1
		（A12）人为故意破坏	2
		（A13）突发大客流	3
	车站设备风险（A2）	（A14）工作人员违规操作	4
		（A21）设备故障或带病作业	5
		（A22）设备超强度运行	6
	车站环境风险（A3）	（A31）车站附近恶劣环境因素	7
		（A32）车站内不良微环境因素	8
		（A33）车站附近恶劣地质条件	9
状态S	车站监测预警能力（A4）	（A41）监测设备的配置状况	10
		（A42）监测预警能力	11
		（A43）工作人员的隐患识别能力	12
	车站安全管理能力（A5）	（A51）车站安全管理制度	13
		（A52）应急预案完备情况	14
		（A61）应急队伍救援能力	15
	车站应急保障能力（A6）	（A62）应急物资配备情况	16
		（A63）应急设备保障情况	17
		（A64）员工应急培训情况	18
响应R	车站应急响应能力（A7）	（A71）信息通报能力	19
		（A72）工作人员应急救援能力	20
		（A73）工作人员引导疏散能力	21
		（A74）组织协调能力	22
	车站应急恢复能力（A8）	（A81）事故损失评价	23
		（A82）车站恢复计划制定	24
		（A83）车站恢复计划落实	25
		（A84）经验总结及改进	26

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表 2 车站韧性评价指标权重

Table 2. Weight of station resilience evaluation index

韧性维度%	一级指标%	二级指标	改进CRITIC指标权重%	权重排序
压力P（59.74）	A1（26）	A11	6.5	5
		A12	6.93	3
		A13	6.15	9
	A2（14.22）	A14	6.36	7
		A21	7.11	1
		A22	7.11	2
	A3（19.58）	A31	6.32	8
		A32	6.44	6
		A33	6.82	4
状态S（21.48）	A4（6.94）	A41	2.41	14
		A42	2.2	23
		A43	2.33	20
	A5（5.45）	A51	2.56	11
		A52	2.89	10
		A61	2.25	22
	A6（9.09）	A62	2.18	24
		A63	2.48	13
		A64	2.18	25
响应R（18.78）	A7（9.5）	A71	2.38	16
		A72	2.34	19
		A73	2.38	17
		A74	2.4	15
	A8（9.28）	A81	2.11	26
		A82	2.36	18
		A83	2.54	12
		A84	2.27	21

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表 3 VIKOR分析结果

Table 3. VIKOR analysis results

车站	群体效用值S_i	个体遗憾值R_j	决策指标值Q_i	排名
同里站	0.756 1	0.068 2	0.968 7	2
流虹站	0.069 2	0.024 8	0.000 0	1
南门站	0.753 4	0.071 1	0.998 0	3

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表 4 指标权重偏差

Table 4. Index weight deviation

指标	平均值%	优序图法%	层次分析法%	熵值法%	改进CRITIC%
A11	6.078	58	37	85	10
A12	5.684	92	33	95	30
A13	5.828	61	35	88	8
A14	5.684	68	33	86	15
A21	5.516	83	32	80	35
A22	5.378	97	30	86	40
A31	5.676	68	33	85	15
A32	5.562	83	31	91	23
A33	5.722	76	34	86	24
A41	3.076	90	22	86	26
A42	3.254	108	15	86	37
A43	2.78	65	37	83	19
A51	2.824	61	35	83	13
A52	2.692	26	46	79	7
A61	2.77	66	38	83	21
A62	2.73	67	39	83	23
A63	2.446	16	60	78	2
A64	2.73	67	39	83	23
A71	3.066	91	22	86	27
A72	3.278	106	14	87	33
A73	3.066	91	22	86	27
A74	2.524	33	54	81	6
A81	3.234	110	16	8	39
A82	2.792	64	37	83	18
A83	2.56	31	52	81	2
A84	3.054	92	23	85	30
累计		1870	869	2202	553

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