Project Data for Estimation of Logistic Transportation System Performance under Extreme Weather Condition: A Data-driven Approach

This project develops a data-driven framework and archives the dataset to evaluate the resilience of multimodal freight transportation systems under both natural and infrastructure-induced disruptions. This collaborative effort between Texas A&M University (TAMU) and the University of Tennessee, Knoxville (UTK) seeks to address these vulnerabilities. The first study by TAMU, U.S. Port Disruption Analysis under Cyclones via Multi-Source Data, constructs the CyPort dataset to analyze 1,927 port–cyclone interactions using the Random Parameter Negative Binomial–Lindley model, revealing key resilience thresholds under tropical cyclones. The second study by UTK, Network Resilience Analysis in Response to Infrastructure Failures, examines freight performance during the 2021 Hernando de Soto Bridge closure, identifying how network topology and redundancy shape recovery. Together, the studies provide generalized insights into disruption dynamics and support data-informed strategies to enhance preparedness, infrastructure investment, and operational continuity under future extreme events.