Abstract: This study utilized automatic identification system (AIS) data to quantify vessel traffic patterns within a predominantly US port network from 1 January 2009 to 31 December 2020, with the methods validated using independent data sets collected between 1 January 2015 and 31 December 2019. The analysis focused on South Atlantic Division (SAD) ports. AIS-derived data characterized individual ports’ traffic and port-to-port connectivity for the network. With foreign vessel entrances and clearances (E&C) data, the AIS-reported vessel characteristics enabled calculation of ships’ physical volume, which was a reasonable proxy for tonnage at many SAD ports. The PageRank algorithm was then applied to port-to-port traffic, revealing how individual ports participate in cargo movement through the network. PageRank scores also provided insight into the maritime supply chain beyond traditional traffic metrics. For example, many East Coast SAD ports ranked higher by PageRank than by raw tonnage. Because of the supply chain implications of shared vessel traffic, PageRank scores can augment tonnage metrics when prioritizing channel and infrastructure maintenance. Vessel volume, port-to-port connectivity, and PageRank scores reveal maritime supply chain resilience by identifying alternative destinations for cargo bound for disrupted ports, robustness across supply chains, and the effects of seasonality and disruptions.