Research on vulnerability of rail transit network under coordinated attack of associated stations

doi:10.16265/j.cnki.issn1003-3033.2023.03.1424

Abstract

Abstract:

In order to explore the vulnerability of the rail transit network after multiple associated stations were deliberately attacked and the differences in the impact of different attacks, the correlation between multiple stationswas measured and analyzed, and the vulnerability quantification method of the rail transit network under the coordinated attack of the associated stations was given. An attack model aiming at maximizing vulnerability was proposed, and the model was solved by immune algorithm to obtain the optimal attack strategy. Taking Shanghai metro network as an example, the vulnerability of rail transit networks under multi stations coordinated attack was studied. The results show that the vulnerability of rail transit networks under multi stations deliberate attack depends not only on the importance of each station, but also on the correlation between different stations, especially the spatial location correlation and passenger travel correlation. Multiple transfer stations located on different lines and with large passenger flow have the highest vulnerability to coordinated attacks. After multiple stations accounting for 3.56% of the total number of network stations are attacked at the same time, the rail transit network performance loss can reach up to 63.61%.

Key words: associated stations, coordinated attack, rail transit network, vulnerability, correlation, immune algorithm

CUI Xin, LU Qingchang, ZHANG Lei, XU Biao, QIN Han, LIU Peng. Research on vulnerability of rail transit network under coordinated attack of associated stations[J]. China Safety Science Journal, 2023, 33(3): 167-173.

Figures/Tables 8

Fig.1

Fig.2

Tab.1

Tab.2

Fig.3

Fig.4

Fig.5

Fig.6

References 11

[1]	BERDICA K. An introduction to road vulnerability:what has been done, is done and should be done[J]. Transport Policy, 2002, 9(2): 117-127. doi: 10.1016/S0967-070X(02)00011-2
[2]	LU Qingchang, ZHANG Lei, XU Pengcheng, et al. Modeling network vulnerability of urban rail transit under cascading failures: a coupled map lattices approach[J]. Reliability Engineering & System Safety, 2022, 221: DOI: 10.1016/j.ress.2022.108320. doi: 10.1016/j.ress.2022.108320
[3]	叶青. 基于复杂网络理论的轨道交通网络脆弱性分析[J]. 中国安全科学学报, 2012, 22(2):122-126.
	YE Qing. Vulnerability analysis of rail transit based on complex network theory[J]. China Safety Science Journal, 2012, 22(2): 122-126.
[4]	路庆昌, 崔欣, 徐标, 等. 公交接驳场景下轨道交通网络脆弱性研究[J]. 中国安全科学学报, 2021, 31(8): 141-146. doi: 10.16265/j.cnki.issn1003-3033.2021.08.020
	LU Qingchang, CUI Xin, XU Biao, et al. Vulnerability research of rail transit network under bus connection scenarios[J]. China Safety Science Journal, 2021, 31(8): 141-146. doi: 10.16265/j.cnki.issn1003-3033.2021.08.020
[5]	LÓPEZ F, PÁEZ A, CARRASCO J, et al. Vulnerability of nodes under controlled network topology and flow autocorrelation conditions[J]. Journal of Transport Geography, 2017, 59: 77-87. doi: 10.1016/j.jtrangeo.2017.02.002
[6]	MARTELLO M, WHITTLE A, KEENAN J, et al. Evaluation of climate change resilience for Boston's rail rapid transit network[J]. Transportation Research Part D: Transport and Environment, 2021, 97: DOI: 10.1016/j.trd.2021.102908. doi: 10.1016/j.trd.2021.102908
[7]	RODRÍGUEZ-NÚÑEZ E, GARCÍA-PALOMARES J. Measuring the vulnerability of public transport networks[J]. Journal Transport Geography, 2014, 35: 50-63. doi: 10.1016/j.jtrangeo.2014.01.008
[8]	薛锋, 何传磊, 黄倩. 成都地铁网络的关键节点识别方法及性能分析[J]. 中国安全科学学报, 2019, 29(1): 93-99. doi: 10.16265/j.cnki.issn1003-3033.2019.01.016
	XUE Feng, HE Chuanlei, HUANG Qian. Identification of key nodes in Chengdu metro network and analysis of network performance[J]. China Safety Science Journal, 2019, 29(1): 93-99. doi: 10.16265/j.cnki.issn1003-3033.2019.01.016
[9]	万丹, 郭倩, 邓良凯, 等. 城市轨道交通网络脆弱性对比研究[J]. 城市轨道交通研究, 2021, 24(1):60-64.
	WAN Dan, GUO Qian, DENG Liangkai, et al. Comparative study on vulnerability of urban rail transit network[J]. Urban Mass Transit, 2021, 24(1): 60-64.
[10]	肖明辉. 基于区域攻击的城市公共交通互补网络脆弱性分析[J]. 信息通信, 2018(8):1-5.
	XIAO Minghui. The vulnerability analysis of urban complementary transit network under spatially localized failures[J]. Information and Communications, 2018(8):1-5.
[11]	崔欣, 路庆昌, 李建宇. 基于网络冗余性的城市轨道交通关键站点识别[J]. 中国安全科学学报, 2022, 32(12): 158-164. doi: 10.16265/j.cnki.issn1003-3033.2022.12.0274
	CUI Xin, LU Qingchang, LI Jianyu, et al. Key station identification of urban rail transit based on network redundancy[J]. China Safety Science Journal, 2022, 32(12): 158-164. doi: 10.16265/j.cnki.issn1003-3033.2022.12.0274

受攻击的站点	单站点失效后网络性能损失程度	影响排序
上海火车站	0.104	1
人民广场	0.099	2
徐家汇	0.091	3
中山北路	0.075	4
延长路	0.072	5
上海南站	0.067	6
世纪大道	0.064	7
上海马戏城	0.063	8
南京东路	0.059	9
曹杨路	0.057	10

受攻击的多个站点	攻击场景I			攻击场景II			攻击场景III
受攻击的多个站点	攻击组合I-1	攻击组合I-2	差值占比/%	攻击组合II-1	攻击组合II-2	差值占比/%	攻击组合III-1	攻击组合III-2	差值占比/%
空间位置关联性	0.326	0.097	70.25	0.233	0.231	0.86	0.211	0.203	3.79
运行线路关联性	1.000	0.500	50.00	0.800	0.489	38.88	0.678	0.596	12.09
最短路径关联性	0.378	0.068	82.01	0.288	0.236	18.06	0.240	0.191	20.42
乘客出行关联性	0.399	0.114	71.43	0.310	0.147	52.58	0.232	0.177	23.71
不同攻击下网络脆弱性	0.185	0.191	3.24	0.322	0.483	50.00	0.532	0.636	19.55

[1]	YANG Jingfeng, ZHU Dapeng, ZHAO Ruilin. Evaluation of station importance and cascading failure resistance analysis of urban rail transit network [J]. China Safety Science Journal, 2022, 32(8): 161-167.
[2]	GUO Jiuxia, YANG Zongxin, XIA Zhenghong, TANG Weizhen. Invulnerability analysis for airport weighted networks before and after COVID-19 [J]. China Safety Science Journal, 2022, 32(8): 91-97.
[3]	ZHAO Luwei, WANG Qing'e. Study on vulnerability formation mechanism of metro system under storm disturbance [J]. China Safety Science Journal, 2022, 32(6): 193-199.
[4]	YUE Rentian, ZHANG Zhibo. Sub-safety state identification of ATC under multi-factor coupling of vulnerability [J]. China Safety Science Journal, 2022, 32(4): 8-14.
[5]	CHEN Liting, ZHENG Jing, GAO Jianqing, LIANG Juan. Generation of typhoon emergency response plan based on FastDTW case retrieval [J]. China Safety Science Journal, 2022, 32(4): 171-176.
[6]	MA Min, HU Dawei, LIU Jie, MA Zhuanglin. Invulnerability analysis of urban rail transit network based on weighted passenger flow [J]. China Safety Science Journal, 2022, 32(12): 141-149.
[7]	LU Jintao, JIA Xiaorong, GUO Xinyao. Analysis on sensitive indicators of gas outburst based on improved gray prediction method [J]. China Safety Science Journal, 2022, 32(11): 74-81.
[8]	ZHAO Luwei, WANG Qing'e. FCM-based vulnerability evolution analysis of metro systems under storm disturbances [J]. China Safety Science Journal, 2022, 32(10): 186-192.
[9]	WU Shengsheng, WANG Xiangyao. Vulnerability evaluation of wind farm operation based on multi-level extensible method [J]. China Safety Science Journal, 2021, 31(S1): 170-175.
[10]	ZHANG Zhaoning, CHEN Zichen, LU Fei. Correlation analysis of QAR parameters for paired approach lateral position error [J]. China Safety Science Journal, 2021, 31(8): 47-52.
[11]	ZHANG Xinsheng, CAI Baoquan. Corrosion prediction of submarine pipelines based on improved Random Forest model [J]. China Safety Science Journal, 2021, 31(8): 69-74.
[12]	LU Qingchang, CUI Xin, XU Biao, LIU Peng, WANG Yan. Vulnerability research of rail transit network under bus connection scenarios [J]. China Safety Science Journal, 2021, 31(8): 141-146.
[13]	WANG Haiyang, ZHAO Shulei, CHEN Xiang, WANG Jie, ZHOU Yanmin. Statistics and influencing factors analysis of tunnel gas accidents in China [J]. China Safety Science Journal, 2021, 31(4): 34-40.
[14]	WU Wei, LUO Xinran, WEI Ming. Vulnerability assessment of runway intrusion risk control network based on SD model [J]. China Safety Science Journal, 2021, 31(4): 41-48.
[15]	JIA Jinzhang, LI Bin, WANG Dongming. Study on influencing factors of cracking radius range caused by liquid CO₂ phase transition in coal seams [J]. China Safety Science Journal, 2021, 31(4): 57-63.