Resilient intermodal freight networks are vital for sustaining supply chains amid increasing threats from natural hazards and cyberattacks. While transportation resilience has been widely studied, understanding how random and targeted disruptions affect both structural connectivity and functional performance remains a key challenge. To address this, our study evaluates the robustness of the U.S. intermodal freight network, comprising rail and water modes, using a simulation-based framework that integrates graph-theoretic metrics with flow-weighted centrality measures. We examine disruption scenarios including random failures as well as targeted node and edge removals based on static and dynamically updated degree and betweenness centrality. To reflect more realistic conditions, we also consider flow-weighted degree centralities and partial node degradation. Two resilience indicators are used: the size of the giant connected component (GCC) to measure structural connectivity, and flow-weighted network efficiency (NE) to assess freight mobility under disruption. Results show that progressively degrading nodes ranked by Weighted Degree Centrality to 60% of their original functionality causes a sharper decline in normalized NE, for up to approximately 45 affected nodes, than complete failure (100% loss of functionality) applied to nodes targeted by weighted betweenness centrality or selected at random. This highlights how partial degradation of high-tonnage hubs can produce disproportionately large functional losses. The findings emphasize the need for resilience strategies that go beyond network topology to incorporate freight flow dynamics.

翻译：面对自然灾害和网络攻击日益增多的威胁，具备韧性的联运货运网络对于维持供应链至关重要。尽管运输韧性已得到广泛研究，但理解随机性和针对性中断如何同时影响结构连通性与功能性能仍是一个关键挑战。为此，本研究采用基于仿真的框架，结合图论指标与流量加权中心性度量，评估了美国铁路和水路联运货运网络的鲁棒性。我们考察了多种中断场景，包括随机故障以及基于静态和动态更新的度中心性与介数中心性进行的针对性节点与边移除。为反映更现实的条件，我们还考虑了流量加权度中心性及部分节点功能退化。研究采用两个韧性指标：最大连通分量（GCC）的规模用于衡量结构连通性，流量加权网络效率（NE）用于评估中断下的货运流动性。结果表明，对于按加权度中心性排序的节点，将其功能逐步退化至原始功能的60%，在最多约45个受影响节点的情况下，相较于对加权介数中心性目标节点或随机选择节点施加完全故障（功能100%丧失），会导致归一化NE更急剧地下降。这突显了高吞吐量枢纽的部分功能退化可能产生不成比例的巨大功能损失。研究结果强调，韧性策略需要超越网络拓扑结构，纳入货运流动态特性。