U.S. Port Disruptions under Tropical Cyclones: Resilience Analysis by Harnessing Multiple-Source Dataset
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909810106040320 |
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| author | Kuai, Chenchen Li, Zihao Zhang, Yunlong Wang, Xiubin Bruce Lord, Dominique Zhou, Yang |
| author_facet | Kuai, Chenchen Li, Zihao Zhang, Yunlong Wang, Xiubin Bruce Lord, Dominique Zhou, Yang |
| contents | This study introduces the CyPort Dataset, recording disruptions to 145 U.S. principal ports and freight network from 90 tropical cyclones (2015-2023). It addresses limitations of event specific resilience studies and provides a comprehensive dataset for broader analysis. To account for excess zeros and unobserved heterogeneity in disruption outcomes, the Random Parameter Negative Binomial Lindley (RPNB Lindley) model is employed to produce more reliable resilience insights. The model demonstrates improved fit over traditional methods and uncovers variation in how features such as wind speed, storm surge height, rainfall, and distance to cyclone influence disruption outcomes across ports. This analysis reveals a tipping point at Saffir Simpson Hurricane Category 4, where disruptions escalate sharply, causing greater impacts and prolonged recovery. Regionally, ports along the Gulf of America show greatest vulnerability. Within the freight network, ports with high betweenness centrality are more resilient, while transshipment and local hubs are more fragile. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22656 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | U.S. Port Disruptions under Tropical Cyclones: Resilience Analysis by Harnessing Multiple-Source Dataset Kuai, Chenchen Li, Zihao Zhang, Yunlong Wang, Xiubin Bruce Lord, Dominique Zhou, Yang Applications This study introduces the CyPort Dataset, recording disruptions to 145 U.S. principal ports and freight network from 90 tropical cyclones (2015-2023). It addresses limitations of event specific resilience studies and provides a comprehensive dataset for broader analysis. To account for excess zeros and unobserved heterogeneity in disruption outcomes, the Random Parameter Negative Binomial Lindley (RPNB Lindley) model is employed to produce more reliable resilience insights. The model demonstrates improved fit over traditional methods and uncovers variation in how features such as wind speed, storm surge height, rainfall, and distance to cyclone influence disruption outcomes across ports. This analysis reveals a tipping point at Saffir Simpson Hurricane Category 4, where disruptions escalate sharply, causing greater impacts and prolonged recovery. Regionally, ports along the Gulf of America show greatest vulnerability. Within the freight network, ports with high betweenness centrality are more resilient, while transshipment and local hubs are more fragile. |
| title | U.S. Port Disruptions under Tropical Cyclones: Resilience Analysis by Harnessing Multiple-Source Dataset |
| topic | Applications |
| url | https://arxiv.org/abs/2509.22656 |