成都地铁网络的关键节点识别方法及性能分析
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摘要
为提高地铁网络性能分析的精度,基于复杂网络理论分析成都地铁网络的拓扑特性,构建节点重要度评价指标体系,应用灰色关联和逼近理想解法(TOPSIS)综合评价排序136个节点,完成关键节点的识别;采用网络效率等4个参数作为网络性能衡量指标,观察30个关键节点被蓄意攻击、106个普通节点被随机攻击后的网络性能变化趋势。结果表明:成都地铁网络在Space L模型下平均度为2.147,平均路径长度为13.146 5,网络介数、连通度、效率等指标较低,并识别出以成都东客站为首的30个关键节点;蓄意、随机攻击下网络效率与自然连通度下降趋势较慢,网络连通度与最大连通子图的下降趋势较快。
In order to improve the accuracy of metro network performance analysis, topological structures of Chengdu metro network were studied based on complex network theory, an evaluation index system of node importance was constructed, 136 nodes were evaluated by using grey correlation and Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) method to identify key nodes. Four parameters such as network efficiency were used as network performance indicators. Trends in network performance were observed after 30 key nodes were attacked maliciously and 106 common nodes randomly. The results show that the average degree of the Chengdu metro network is 2.147, the average path length is 13.146 5 under the Space L model, the network betweenness, connectivity and efficiency are low, and the East Passenger Station(56) ranks first in 30 key nodes, and that both malicious and random attacks will make the network efficiency and the natural connectivity decrease slowly, but will make both the network connectivity and the maximal connected subgraph decrease faster.
In order to improve the accuracy of metro network performance analysis, topological structures of Chengdu metro network were studied based on complex network theory, an evaluation index system of node importance was constructed, 136 nodes were evaluated by using grey correlation and Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) method to identify key nodes. Four parameters such as network efficiency were used as network performance indicators. Trends in network performance were observed after 30 key nodes were attacked maliciously and 106 common nodes randomly. The results show that the average degree of the Chengdu metro network is 2.147, the average path length is 13.146 5 under the Space L model, the network betweenness, connectivity and efficiency are low, and the East Passenger Station(56) ranks first in 30 key nodes, and that both malicious and random attacks will make the network efficiency and the natural connectivity decrease slowly, but will make both the network connectivity and the maximal connected subgraph decrease faster.
引文
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[3] 徐青刚.铁路运输系统的网络模型及其抗毁性研究[D].南昌:华东交通大学,2015.XU Qinggang. Network model and research on invulnerability for the railway transportation system [D]. Nanchang: East China Jiaotong University, 2015.
[4] 殷勇,刘杰.铁路网络的抗毁性分析研究[J].综合运输,2017(4):55-59.YIN Yong, LIU Jie. The survivability of railway network [J]. China Transportation Review, 2017(4): 55-59.
[5] YANG Yuhao, LIU Yongxue, ZHOU Minxi, et al. Robustness assessment of urban rail transit based on complex network theory: a case study of the Beijing subway[J]. Safety Science, 2015, 79: 149-162.
[6] 于宝,冯春,朱倩,等.中国高速铁路网络脆弱性分析[J].中国安全科学学报,2017,27(9):110-115.YU Bao, FENG Chun, ZHU Qian, et al. Vulnerability analysis of China's high speed railway network [J]. China Safety Science Journal, 2017, 27(9): 110-115.
[7] 吴建军.城市交通网络拓扑结构复杂性研究[D].北京: 北京交通大学,2008.WU Jianjun. Studies on the complexity of topology structure in the urban traffic network [D]. Beijing: Beijing Jiaotong University, 2008.
[8] 赵德.多方式公共交通资源耦合效能评价[D].南京: 东南大学,2016.ZHAO De. Evaluation on multimodal public transit resources integration [D]. Nanjing: Southeast University, 2016.
[9] 彭景新.南京市区公交、地铁加权复合网络建模与实证分析[D].南京:南京邮电大学,2017.PENG Jingxin. Modeling and empirical analysis of bus and subway weighted composite network in Nanjing [D]. Nanjing: Nanjing University of Posts and Telecommunications, 2017.
[10] 王班,马润年,王刚.基于自然连通度的复杂网络抗毁性研究[J].计算机仿真,2015,32(8):315-318.WANG Ban, MA Runnian, WANG Gang. Research on invulnerability of complex networks based on natural connectivity [J]. Computer Simulation, 2015, 32(8): 315-318.