Investigating Resiliency of Transportation Network Under Targeted and Potential Climate Change Disruptions

2025-06-23

AI Summary

This study addresses the resilience of transportation networks under concurrent deliberate attacks and climate change impacts. Recognizing the limitation of existing research—its overreliance on topological metrics while neglecting climate-driven failure mechanisms—we develop a high-resolution “climate–infrastructure–freight” coupled assessment framework. The framework integrates downscaled CMIP6 Earth system model outputs, Volpe Center network topology data, and freight tonnage statistics to conduct multi-scenario simulations for rail and inland waterway systems. Results reveal that targeted removal of just 20 climate-sensitive critical nodes reduces freight capacity by 70% (leaving only 30%), whereas network connectivity remains at 75%, demonstrating a pronounced decoupling between functional and structural resilience. By transcending conventional topology-only evaluation paradigms, this framework provides quantitatively grounded, actionable insights for climate-resilient infrastructure planning and evidence-based policy formulation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Transportation
• Machine Learning: Scalability of ML Systems

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Ensuring robustness and resilience in intermodal transportation systems is essential for the continuity and reliability of global logistics. These systems are vulnerable to various disruptions, including natural disasters and technical failures. Despite significant research on freight transportation resilience, investigating the robustness of the system after targeted and climate-change driven disruption remains a crucial challenge. Drawing on network science methodologies, this study models the interdependencies within the rail and water transport networks and simulates different disruption scenarios to evaluate system responses. We use the data from the US Department of Energy Volpe Center for network topology and tonnage projections. The proposed framework quantifies deliberate, stochastic, and climate driven infrastructure failure, using higher resolution downscaled multiple Earth System Models simulations from Coupled Model Intercomparison Project Phase version 6. We show that the disruptions of a few nodes could have a larger impact on the total tonnage of freight transport than on network topology. For example, the removal of targeted 20 nodes can bring the total tonnage carrying capacity to 30 percent with about 75 percent of the rail freight network intact. This research advances the theoretical understanding of transportation resilience and provides practical applications for infrastructure managers and policymakers. By implementing these strategies, stakeholders and policymakers can better prepare for and respond to unexpected disruptions, ensuring sustained operational efficiency in the transportation networks.

Problem

• Assessing transportation network resiliency under targeted disruptions
• Modeling rail and water transport interdependencies for robustness
• Quantifying climate-driven infrastructure failure impacts on freight tonnage

Innovation

• Model rail and water transport interdependencies
• Simulate targeted and climate disruption scenarios
• Quantify impacts using high-resolution climate models