ABSTRACT

This study utilized automatic identification system (AIS) data to quantify the vessel traffic patterns across a port network from January 2015 to June 2020, with a focus on Puerto Rican and the US Virgin Island ports. Vessel movement data was used to measure the port-to-port connectivity (“centrality”) for all ports in the network and the interconnectivity of traffic between the system’s ports. AIS data allows the identification of vessel type in addition to the standard metrics of total tonnage and commodity tonnage when ranking the utilization of waterways. Vessel type, in conjunction with dominant commodity type and tonnage, provides a more complete picture of waterway utilization. Along with port-to-port connectivity, this knowledge expands understanding of maritime supply chain resilience by revealing potential redundancies between ports, robustness across supply chains, and the impacts of seasonality.

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REFERENCES

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Go to Ports 2022
Ports 2022
Pages: 438 - 448

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Published online: Sep 15, 2022

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David L. Young, Ph.D. [email protected]
P.E.
1US Army Engineer Research and Development Center, Coastal and Hydraulics Lab. ORCID: https://orcid.org/0000-0003-4886-1304. Email: [email protected]
Marin M. Kress, Ph.D. [email protected]
2US Army Engineer Research and Development Center, Coastal and Hydraulics Lab. ORCID: https://orcid.org/0000-0002-5835-5686. Email: [email protected]
Katherine F. Chambers [email protected]
3US Army Engineer Research and Development Center, Coastal and Hydraulics Lab. ORCID: https://orcid.org/0000-0003-3558-386X. Email: [email protected]
Brandan M. Scully, Ph.D. [email protected]
P.E.
4US Army Engineer Research and Development Center, Coastal and Hydraulics Lab. ORCID: https://orcid.org/0000-0001-8641-2142. Email: [email protected]

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