Multilayer network-based resilience evaluation for China-Europe container sea-rail intermodal transport systems
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摘要:
为了量化分析不同扰动情景和恢复策略下海铁联运系统韧性演化规律，提出了多层网络视角的韧性评估模型。基于真实班列与海运航线数据，构建了中欧集装箱海铁联运网络；将连通性、运输效率作为系统韧性评价指标，设计考虑了不同节点恢复时机、恢复优先级的韧性仿真模型，仿真了中欧海铁联运网络的韧性演化规律。研究结果表明：不同扰动情景对系统韧性影响具有异质性，不考虑恢复策略时，蓄意攻击下中欧海铁联运网络抗干扰能力较弱，少数关键枢纽节点的失效会导致整体效率的大幅下降，海铁联运网络韧性指标低于班列和海运子网络；而随机失效下，网络受攻击时的抗干扰能力较强，韧性指标均在0.8以上；考虑恢复策略时，海铁联运网络韧性的抗扰动性明显提升，但恢复时机存在临界点，当失效节点恢复时间超过阈值时，导致系统恢复成本指数上升，整体恢复时间增加；不同的恢复策略存在差异性，蓄意攻击下，海铁联运网络依据度中心性的恢复优先级对系统韧性提升成效最好，依据介数中心性恢复强度成本最低。研究结论揭示了中欧海铁联运网络的韧性演化规律，可为在面临突发事件和灾害情况下提升多式联运系统韧性提供理论依据。
Abstract:A resilience evaluation model from a multilayer network perspective was proposed to quantitatively analyze the resilience evolution law of the sea-rail intermodal transport system under different disturbance scenarios and recovery strategies. Based on real-world data of trains and shipping routes, a China-Europe sea-rail intermodal container transport network was constructed. Connectivity and transport efficiency were employed as evaluation indicators of system resilience, and a resilience simulation model considering different node recovery timings and recovery priorities was developed to simulate the resilience evolution law of the China-Europe sea-rail intermodal network. Heterogeneous influences of different disturbance scenarios on system resilience were revealed. Without recovery strategies, the network exhibits weak interference resistance under targeted attacks, and failures of a few critical hub nodes cause substantial decline in overall efficiency, with the network's resilience indicators lower than those of dedicated rail or sea networks. Under random failures, the network demonstrates strong interference resistance, with resilience indicators exceeding 0.8. Implementing recovery strategies significantly enhances the disturbance resistance of the network. However, a critical threshold exists for recovery timing. When the recovery timing of the failed node exceeds this threshold, system recovery costs increase exponentially and overall recovery time extends significantly. Distinct recovery strategies yield differential effects. Under targeted attacks, prioritizing node recovery based on degree centrality provides the greatest system resilience improvement, while prioritizing node recovery based on betweenness centrality achieves the lowest recovery cost intensity. The findings reveal the resilience evolution law of the China-Europe sea-rail intermodal transport network and provide a theoretical basis for enhancing the resilience of intermodal transport systems under emergencies and disasters.
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图 1 中欧集装箱海铁联运网络拓扑结构
Figure 1. Topological of China-Europe container sea-rail intermodal transport network
图 4 不考虑级联失效下的不同攻击顺序对网络韧性指标影响
Figure 4. Impact of different attack sequences on network resilience metrics without cascading failures
图 5 考虑级联失效下的不同攻击顺序对网络韧性指标影响
Figure 5. Impact of different attack sequences on network resilience metrics under cascading failures
图 6 蓄意攻击时不同节点恢复时机下的网络韧性指标变化曲线
Figure 6. Network resilience metric curves under targeted attacks with varying recovery timings
图 7 随机失效时不同节点恢复时机下的网络韧性指标变化曲线
Figure 7. Network resilience metric curves under random failures with varying recovery timings
图 8 蓄意攻击时不同节点恢复时机下的网络韧性指标恢复水平
Figure 8. Recovery levels of network resilience metrics under targeted attacks with varying node restoration timings
图 9 随机失效时不同节点恢复时机下的网络韧性指标恢复水平
Figure 9. Recovery levels of network resilience metrics under random failures with varying node restoration timings
图 10 蓄意攻击时不同节点恢复时机下的网络韧性指标恢复时间成本
Figure 10. Recovery time costs of network resilience metrics under targeted attacks with varying node restoration timings
图 11 随机失效时不同节点恢复时机下的网络韧性指标恢复时间成本
Figure 11. Recovery time costs of network resilience metrics under random failures with varying node restoration timings
图 12 蓄意攻击和随机失效时不同节点恢复优先级下的网络韧性评价指标演化曲线
Figure 12. Evolution curves of network resilience metrics under targeted attacks and random failures with varying node recovery priorities
图 13 蓄意攻击和随机失效时不同节点恢复优先级下的恢复水平
Figure 13. Recovery levels under targeted attacks and random failures with varying node recovery priorities
表 1 网络拓扑特性指标
Table 1. Network topological metrics
拓扑指标 定义 海铁联运网络 班列网络 海运网络 平均度值 网络中所有节点度值的平均值 4.836 3.852 5.246 平均距离 衡量网络中节点之间连接紧密程度和相互关联程度的重要指标 4.106 8 3.863 0 3.632 0 网络直径 网络中任意2个节点之间最短路径长度的最大值 13 10 12 平均集聚系数 衡量网络中节点之间连接紧密程度的指标 0.289 0.310 0.263 
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表 2 典型突发事件运输系统中断恢复时间
Table 2. Recovery time of transportation system disruptions under typical emergency events
事故发生日期 事故名称 恢复正常营运所需时间 2021-03-23 苏伊士运河货轮搁浅 约1周 2025-03-10 莫桑比克莫纳波铁路桥冲毁事故 约2周 2022-03-13 长程轮搁浅 约5周 2024-03-26 美国马里兰州撞桥事故 约10周 2020-04-06 韩国釜山港货轮碰撞事故 约20周 2022-09-18 中国台湾花东线铁路地震损坏事故 约30周 2024-08-21 苏伊士运河油轮搁浅 约40周 2023-08-10 瑞士圣哥达基线隧道脱轨 约50周
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表 3 蓄意攻击和随机失效时不同节点恢复优先级下的恢复强度成本
Table 3. Recovery cost by node prioritization under targeted attacks and random failures
TEU 失效情景恢复策略 度中心性 介数中心性 接近中心性 特征向量中心性 节点强度 蓄意攻击 2 676 781 2 055 629 2 575 837 2 573 046 3 070 871 随机失效 827 696.1 636 671.3 799 447.3 837 962.1 882 788.2
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表 4 蓄意攻击下不同运输方式子网络韧性指标数值
Table 4. Resilience metric values of sub-networks under targeted attacks for different transport modes
是否存在级联失效 不同恢复方式 韧性指标 G G1 G2 E E1 E2 不考虑级联失效 无恢复 0.004 5 0.008 2 0.008 8 0.000 0 0.000 0 0.000 0 时机T=10周 0.954 5 0.918 0 0.912 2 0.927 1 0.871 1 0.905 6 时机T=20周 0.909 1 0.836 0 0.824 5 0.851 4 0.741 4 0.784 7 时机T=30周 0.863 6 0.754 0 0.728 0 0.790 2 0.614 6 0.657 2 时机T=40周 0.818 2 0.672 1 0.640 3 0.724 3 0.518 8 0.535 9 时机T=50周 0.768 2 0.590 1 0.552 6 0.653 5 0.416 6 0.420 1 考虑级联失效 一次性攻击度值排名前20的节点 0.022 7 0.057 4 0.043 9 0.065 8 0.086 3 0.284 8 度中心性排名前5的节点 0.081 8 0.245 9 0.096 5 0.235 8 0.831 8 0.571 1 介数中心性排名前5的节点 0.081 8 0.213 1 0.122 8 0.213 2 0.602 7 0.678 7 接近中心性排名前5的节点 0.086 3 0.196 7 0.087 7 0.229 2 0.476 2 0.460 3 特征向量中心性排名前5的节点 0.059 0 0.204 9 0.122 8 0.171 6 0.500 0 0.683 8 节点强度排名前5的节点 0.063 6 0.106 6 0.078 9 0.187 1 0.223 2 0.511 3
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表 5 随机失效下不同运输方式子网络韧性指标数值
Table 5. Resilience metric values of sub-networks under random failures for different transport modes
是否存在级联失效 不同恢复方式 韧性指标 G G1 G2 E E1 E2 不考虑级联失效 无恢复 0.004 5 0.008 2 0.008 8 0.000 0 0.000 0 0.000 0 时机T=10周 0.946 8 0.918 0 0.885 1 0.891 2 0.776 5 0.780 2 时机T=20周 0.890 9 0.836 0 0.774 6 0.790 4 0.601 5 0.604 6 时机T=30周 0.837 3 0.754 0 0.658 8 0.697 9 0.459 4 0.443 5 时机T=40周 0.769 5 0.672 1 0.544 7 0.591 6 0.313 3 0.298 9 时机T=50周 0.740 0 0.590 1 0.443 0 0.513 6 0.219 6 0.230 2 考虑级联失效 一次性随机攻击20个节点 0.822 7 0.768 0 0.744 7 0.916 5 0.845 0 0.842 4 度中心性排名前5的节点 0.859 1 0.886 9 0.841 2 0.988 6 0.984 9 0.934 4 介数中心性排名前5的节点 0.868 6 0.893 4 0.864 9 0.997 9 0.995 9 0.927 0 接近中心性排名前5的节点 0.858 2 0.868 9 0.843 9 0.982 4 0.966 0 0.942 3 特征向量中心性排名前5的节点 0.853 6 0.862 3 0.829 8 0.965 7 0.954 4 0.914 9 节点强度排名前5的节点 0.853 6 0.859 8 0.849 1 0.965 1 0.927 3 0.943 6
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