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城市地铁网络拓扑结构脆弱性评价 被引量:16

Evaluation of Urban Metro Network Topological Structure Vulnerability
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摘要 为降低突发事件对城市地铁网络系统(UMNS)造成的影响,提出UMNS拓扑结构脆弱性评价方法。研究随机和蓄意移除地铁车站和区间对UMNS连通性造成的故障规模。实证分析表明:随机移除地铁车站造成的故障规模明显小于蓄意移除的;随机移除地铁区间造成的故障规模明显大于蓄意移除的;移除地铁车站造成的故障规模明显大于移除地铁区间所造成的。研究结果表明:UMNS对车站所受蓄意攻击具有脆弱性,对区间的蓄意攻击具有鲁棒性;在运营管理中,应重点保障大型换乘车站的正常运行,防止其遭恶意破坏而造成大规模故障。 A topological structure vulnerability assessment approach for UMNS was developed in order to minimize impact of incidents on the system. Failure scale of stations and sections random failure and target attacks was evaluated. The results show that UMNS is more vulnerable to target attacks on stations than random failure on stations, but UMNS is less vulnerable to target attacks on sections than random failure on sections, and UMNS is more vulnerable to station failure than sections. It could be concluded that more resources should be put on big transfer stations in UMNS operation management to avoid large scale impacts.
出处 《中国安全科学学报》 CAS CSCD 北大核心 2013年第8期114-119,共6页 China Safety Science Journal
基金 国家自然科学基金资助(51178116) 江苏省及东南大学优势学科建设项目(PAPD)
关键词 城市地铁网络系统(UMNS) 脆弱性 鲁棒性 蓄意攻击 随机故障 urban metro network system (UMNS) vulnerability robustness target attack random failure
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参考文献8

  • 1Angeloudis P, Fisk D. Large subway systems as complex networks[J]. Physica A: Statistical Mechanics and Its Applica- tions,2006, 367 : 553 -558.
  • 2HAN Chuan-feng, LIU Liang. Topological vulnerability of subway networks in China[C]. IEEE Management and Service Science, 2009 : 1 - 4.
  • 3李进,马军海.城市地铁网络复杂性研究[J].西安电子科技大学学报(社会科学版),2009,19(2):51-55. 被引量:14
  • 4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. 被引量:89
  • 5Derrible S, Kennedy C. The complexity and robustness of metro networks[J]. Physica A: Statistical Mechanics and Its Applications, 2010, 389(17): 3 678-3 691.
  • 6唐晋韬,王挺.复杂社会网络的介数性质近似计算方法研究[J].计算机工程与科学,2008,30(12):9-14. 被引量:14
  • 7De Nooy W, Mrvar A, Batagelj V. Exploratory Social Network Analysis with Pajek[M] American: Cambridge University Press, 2011: 31 -40.
  • 8Latora V, Marehiori M. Efficient behavior of small-world networks[J]. Physical Review I.etters, 2001, 87(19) : 198 -201.

二级参考文献40

  • 1Scot J. Social Network Analysis: A Handbook[M]. 2nd ed. Sage Publications, 1991.
  • 2Stolfo S J, Hershkoip S, WANG K, et al. Behavior Profiling of Email [C]//Proc of the 1st NSF/NIJ Syrup on Intelli gence, Security Informatics,2003.
  • 3Newman M E J. The Structure of Scientific Collaboration Networks[C]//Proc of the National Academy of Sciences of the United States of America, 2001,98(2): 404-409.
  • 4Ishida K. Extracting Latent Weblog Communities: A Parti tioning Algorithm for Bipartite Graphs[C]//Proc of the 2nd Annual Workshop on the Weblogging Ecosystem, 2005.
  • 5I.erman K, Jones L. Social Browsing on Flickr[C]//Proc of the Int' Conf on Weblogs and Social. Media, 2006.
  • 6Lerman K. Social Networks and Sccial Information Filtering on Digg[C]//Proc of the Int'l Conf on Weblogs and Social Media, 2006.
  • 7Milgram S. The Small World Problem[J]. Psychology Tody, 1967,2:60-67.
  • 8Contributors W. Six Degrees of Kevin Bacon.[EB/OL], [2006-01-15]. http://en. wikipedia. org/w/index. php? lille =Six_Degrees_of_Kevin_ Bacon &oldid= 48040027.
  • 9Watts D J, Strogalz S H. Collective Dynamics of ‘Small World' Networks[J]. Nature, 1998,393 : 140-442.
  • 10Barabdsi A-L, Albert R. Emergence of Scaling in Random Networks [J]. Science, 1999,286 : 509-512.

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