多模式复合交通网脆弱性测度

构建了多模式复合交通网络拓扑模型；在传统测度指标的基础上，从各交通方式的差异性、资源公平性和网络可达性相结合的新视角提出了适用于多模式复合交通网的脆弱性测度，分别为子网敏感度、站点分布均衡度和可达指数；选取3种不同攻击策略进行Python仿真，以特点鲜明的东南沿海发达地区和西南边境山区的实际综合交通网为例，对比分析了网络结构脆弱性的差异性和共同点，多重验证了指标的有效性、稳定性和适用性。研究结果表明：浙江省和云南省多模式复合交通网络均符合小世界网络特性，能够使各交通方式间优势互补，降低网络脆弱性；在3组贡献度参数取值下，无论采取何种攻击策略，当失效节点数量相同时，浙江省子网敏感度从大到小总体趋势为公路网、水运网、铁路网，云南省子网敏感度从大到小总体趋势为航空网、公路网、铁路网；浙江省和云南省的公路网站点分布的基尼系数分别为0.196和0.086,均为分布绝对平均，铁路网站点分布的基尼系数分别为0.559和0.702,均为分布差距悬殊，云南省机场分布的基尼系数为0.363,分布相对合理，浙江省水运网港口分布的基尼系数为0.672,分布差距悬殊，说明需要进一步完善铁路网、水运网和航空网的布...

Vulnerability metrics of multimodal composite transportation network

A topological model for multimodal composite transportation networks was constructed. The vulnerability metrics applicable to multimodal composite transportation networks were proposed from a new perspective combining the differences in each transportation mode, resource equity, and network accessibility. The metrics were sub-network sensitivity, station distribution balance, and accessibility index. Three different attack strategies were selected for Python simulation. The actual comprehensive transportation networks in developed coastal areas of Southeast China and mountainous border areas of Southwest China were taken as examples to comprehensively analyze the differences and commonalities of network structure vulnerability. In this way, the validity, stability, and applicability of the metrics were verified in multiple cases. Research results show that multimodal composite transportation networks in Zhejiang Province and Yunnan Province are in agreement with the characteristics of small-world networks. They are capable of making the advantages of each transportation mode complement each other and reducing network vulnerability. Under the three sets of contribution parameter values, regardless of the attack strategy, when the number of failed nodes is the same, the overall trend of the sub-network sensitivities from large to small in Zhejiang Province is highway network, waterway network and railway network, and Yunnan Province is aviation network, highway network and railway network. The Gini coefficients of the distribution of highway network stations in Zhejiang Province and Yunnan Province are 0.196 and 0.086, respectively, indicating equal distribution. The Gini coefficients of the distribution of railway network stations are 0.559 and 0.702, respectively, indicating highly unequal distribution. In addition, the Gini coefficient of the distribution of airports in Yunnan Province is 0.363, denoting a relatively reasonable distribution. The Gini coefficient of the distribution of ports in Zhejiang Province is 0.672, denoting highly unequal distribution. It can be concluded that the layouts of railway networks, waterway networks, and aviation networks should be further improved. The multimodal composite transportation network should be separated into multiple connected subgraphs or isolated nodes when subjected to continuous attacks. As a result, network accessibility is suddenly degraded. Recovery measures should be taken as early as possible to avoid this phenomenon. Therefore, the proposed vulnerability metrics are able to effectively describe the comprehensive transportation network vulnerability and discover the differences and commonalities of vulnerability among networks.