Data-Driven Stress Testing of Intermodal Freight Networks Using GAN-Generated Disruption Scenarios

Intermodal freight networks are increasingly exposed to correlated, multi-mode disruptions, yet resilience assessments often rely on historical or uncorrelated scenarios that understate systemic risk. This paper develops a data-driven stress-testing framework integrating generative adversarial networks (GANs) with an intermodal optimization model to evaluate performance under realistic compound disruptions. The case study examines weather-related disruptions in the Tennessee Valley corridor. Each GAN-generated scenario is used as a simulation input, and the resulting routing problem is solved to obtain system costs. Aggregating outcomes enables estimation of expected costs and identification of major risk drivers. Results show that historical disruptions increase total cost by about 3%, whereas GAN-generated scenarios raise costs by over 25%, producing an expected annual cost of $5.11 million. Risk is concentrated in correlated multi-node failures and critical nodes such as the Port of Knoxville. The framework helps identify vulnerabilities and prioritize resilience investments.