Dynamic Disruption Resilience in Intermodal Transport Networks: Integrating Flow Weighting and Centrality Measures

TL;DR

This study evaluates the robustness of the U.S. intermodal freight network against various disruptions using graph-theoretic and flow-weighted metrics, revealing that partial degradation of key hubs can cause significant functional losses.

Contribution

It introduces a simulation framework combining structural and flow-based centrality measures to assess intermodal network resilience under realistic disruption scenarios.

Findings

Degrading high-traffic hubs by 60% sharply reduces network efficiency.

Partial failure of key nodes causes larger functional losses than complete failure of less critical nodes.

Flow dynamics are crucial for understanding and improving network resilience.

Abstract

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.