培育发展现代化都市圈需要推动构建一体化交通网络体系，如何构建高效、稳定的综合交通网络推动现代化都市圈发展值得重点关注。该文以都市圈多模式交通网络(metropolitan area multi-modal transportation network, MA-MTN)为研究对象，基于复杂网络方法建立了不同类型交通网络构建流程，定义了MA-MTN运行中的负载重分配模型。然后基于集成化视角构建了MA-MTN集成韧性指标。最后，以中国西安市的都市圈为研究对象，通过情景模拟方法分析了MA-MTN中不同模式交通子网的敏感性水平；分别对MA-MTN受攻击时的网络性能损失阶段、实施恢复策略的恢复阶段以及网络进行动态恢复阶段进行模拟分析。研究结果表明，当MA-MTN遭受攻击时，在网络节点失效比例达到60%前，网络性能损失的变化程度最高；需对MA-MTN节点容量调节参数β进行动态调整，在网络日常运行中β宜设置为0.2，在面临极端天气或节假日期间β宜调整为0.7；在调整网络节点间的客流转移过程中重点考虑运输效率因素有助于全面提升MA-MTN的韧性水平；基于节点负载容量水平的恢复策略对MA-MTN具有优异的集成韧性水平提升效果。该研究通过多维集成化视角测度交通网络的韧性水平，对推动建设高效、稳定的都市圈综合交通网络具有重要意义。

Objective: The development of modern urban agglomerations requires cultivating integrated transportation networks, advancing transportation integration, and building resilient, comprehensive three-dimensional systems. Building a highly efficient and stable transportation network is fundamental to promoting the growth of modern metropolitan areas. Methods: This paper focuses on the metropolitan area multimodal transportation network (MA-MTN) and uses complex network methodologies to model different transportation network construction processes. By applying a load capacity model, it defines the load and capacity levels of the MA-MTN and develops a load redistribution model to facilitate network operations; then, different dimensions of network resilience evaluation indicators were developed, focusing on three levels: overall network, network structure, and network function. From an integrated perspective, a comprehensive resilience indicator for multimodal transportation networks in urban agglomerations was established. The Xi'an metropolitan area was selected as the research subject to conduct simulation analyses of the multimodal transportation network's integrated resilience. The sensitivity levels of different traffic subnetworks in the multimodal transportation network of the metropolitan area were assessed through scenario simulation methods. Simulations examined the performance loss stage, the recovery stage following the implementation of recovery strategies, and the dynamic recovery stage during network failures caused by attacks on the multimodal transportation network. The effectiveness of strategies aimed at improving the integrated resilience of the metropolitan multimodal transportation network was also compared and analyzed. Results: The research results indicated that different transportation subnets play distinct roles in the multimodal transportation network of urban agglomerations. When MA-MTN is attacked, the sharpest decline in network performance occurs before the failure rate of network nodes reaches 60%. The node capacity adjustment parameter β in MA-MTN needs to be dynamically calibrated, with a value of 0.2 recommended for daily operations and 0.7 during extreme weather events or holiday peaks. Enhancing the resilience of urban multimodal transportation networks requires prioritizing transportation efficiency factors in the transfer of passenger flow transfer between nodes; this approach improves the comprehensiveness of the network resilience. Recovery strategies based on node load capacity levels are the most effective in improving the integrated resilience level of these networks. Such strategies enhance resistance to attacks and significantly improve recovery capabilities during cascading failures, ensuring robust network performance even under dynamic recovery conditions. Conclusions: This paper measures the integrated resilience level of transportation networks against external disturbances using a multidimensional integrated perspective; moreover, it identifies effective strategies to improve the structural and functional resilience of multimodal transportation networks in urban areas. Based on the research results, this study emphasizes the importance of determining critical thresholds for network node failures. It recommends dynamically adjusting the node capacity parameter β according to the operational conditions of the MA-MTN and external factors. Furthermore, it advocates optimizing the transfer process of node passenger flow load by prioritizing transportation efficiency during network operations. The study also highlights the significance of prioritizing node load capacity and developing effective recovery strategies based on the comprehensive importance of nodes. These approaches are crucial for promoting the construction of efficient, stable, and multimodal transportation networks in urban areas.