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

2025-12-31

AI Summary

This study evaluates the impact of random and targeted perturbations on the structural connectivity and freight functionality of the U.S. rail–water intermodal transport network. By developing a simulation framework that integrates graph-theoretic metrics with flow-weighted centrality measures and incorporates a dynamic update mechanism to model progressive degradation and complete failure of nodes or edges, the research reveals that partial functional degradation (e.g., reduction to 60% capacity) of high-freight-volume hubs inflicts significantly greater damage on system performance than the total removal of an equivalent number of high-betweenness or randomly selected nodes. This finding underscores the critical role of freight flow dynamics in resilience assessment and challenges conventional analysis paradigms that rely solely on topological structure.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Transportation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• intermodal transport networks
• disruption resilience
• structural connectivity
• functional performance
• freight flow dynamics

Innovation

• flow-weighted centrality
• dynamic disruption resilience
• intermodal transport networks
• partial node degradation
• network efficiency