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.本项目搭建了一套数据驱动框架，并归档了配套数据集，用于评估多模态货运系统在自然扰动与基础设施引发的中断场景下的韧性。本项目由德克萨斯农工大学（Texas A&M University, TAMU）与田纳西大学诺克斯维尔分校（University of Tennessee, Knoxville, UTK）联合开展，旨在破解货运系统的此类脆弱性难题。 德克萨斯农工大学主导的第一项研究《基于多源数据的飓风下美国港口中断分析》，构建了CyPort数据集，采用随机参数负二项-林德利（Random Parameter Negative Binomial–Lindley）模型对1927次港口-飓风交互事件进行分析，揭示了热带飓风场景下的关键韧性阈值。 田纳西大学诺克斯维尔分校主导的第二项研究《基础设施故障场景下的网络韧性分析》，以2021年埃尔南多·德·索托大桥关闭事件为案例分析货运系统运行表现，厘清了网络拓扑结构与冗余性如何影响系统恢复进程。 两项研究共同为中断动态机制提供了普适性研究洞见，可为未来极端事件下的防灾准备、基础设施投资与运营连续性保障提供数据驱动的决策支持策略。