摘要
为有效刻画多制式区域轨道交通(MRRT)网络,提高其网络韧性评估的准确性,依据MRRT评估系统内不同子网络间的协同工作关系以及运输服务特征,基于复杂网络理论构建MRRT相依网络模型;兼顾网络拓扑结构和服务质量,从抵抗能力、吸收能力、缓冲能力和恢复能力4个维度构建网络韧性评估指标,改进了基于引力模型识别其关键站点的方法;因MRRT网络复杂度高,其遭受突发事件扰动的潜在攻击信息具有灰色特征,故使用Matlab模拟不同信息精确度α下的灰色攻击策略,探究α对MRRT相依网络韧性的影响。结果表明:MRRT相依网络具有无标度网络特征;基于引力模型识别关键站点,可提高网络节点关键性评价的准确性;降低灰色攻击的信息精确度α,可提高网络韧性,且α对网络韧性的影响存在阈值,当α∈(02,08)时,网络遭受攻击后的性能损失会随α的降低而减小,当α≥08∪α≤02时,降低α并不会对网络韧性有显著影响。
In order to effectively describe the multi-modal regional rail transit network and improve the accuracy of its network resilience assessment,according to the coupling relationship between different sub-network layers and characteristics of transportation services,MRRT interdependent network model was constructed based on complex network theory.Considering the network topology and service quality,the network resilience measurement index was constructed from four dimensions:resistance ability,absorption ability,buffer ability and recovery ability,and the method of identifying its key stations based on gravity model was improved.Due to the high complexity of MRRT network,the potential attack information disturbed by emergencies had grey characteristics.Therefore,using Matlab to simulate the grey attack strategy under different information accuracy,the influence rule of the information accuracy of grey attack on the resilience of MRRT interdependent network was explored.The research shows that the interdependent network of MRRT has the characteristics of scale-free network.Identifying key stations evaluation index based on the gravity model can improve the accuracy of key evaluation of network nodes.Reducing the information accuracyαof grey attacks can improve network resilience,and there is a threshold for the impact ofαon network resilience.Whenα∈(02,08),the performance loss of network after being attack decreases as the increase of information accuracyα.Whenα≥08∪α≤02,reducing the information accuracyαhas no significant impact on network resilience.
作者
马飞
苟慧艳
杨梦楠
刘擎
委笑琳
敖誉芸
MA Fei;GOU Huiyan;YANG Mengnan;LIU Qing;WEI Xiaolin;AO Yuyun(School of Economics and Management,Chang'an University,Xi'an Shaanxi 710064,China;Xi'an Traffic Development Research Center,Xi'an Shaanxi 710082,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2023年第12期148-159,共12页
China Safety Science Journal
基金
国家自然科学基金资助(72104034)
陕西省自然科学基础研究计划项目(2022JM-423)
西安市社会科学规划基金重大项目资助(211423220227)。
