Open AIS Data to Assess Seaside Operations in Container Ports

Due to the rise of seaborne trade, maritime infrastructures have become critical nodes for the global market. This paper addresses maritime traffic using open-access data and develops approaches to evaluate vessel calls at the terminal. Historical data from Automatic Information Systems (AIS) are exploited for analyzing the operations at the container port of Los Angeles and Long Beach (CA), which serves as a hub between the United States and Asia, and the port of Oakland, located at the up to the San Francisco Bay (CA), whose represent a dynamic area with multiple infrastructures. Taking advantage of the availability of open raw data, this paper set up a framework that extracts navigation features for both underway and stopping phases, thus enhancing a step forward in extracting information from AIS and enabling the potentiality of using big data for freight mobility and allowing the analysis of vessel calls. Through a trajectory segmentation approach, the proposed methodology identifies anchoring and berth operations, thereby interpreting the most critical phases in the vessel turnaround time. The segmentation was reconstructed by analyzing changes in the navigation status. However, a rule-based method and the discrete events were classified through statistical analyses, thus extracting the patterns for both anchoring and berthing operations. The procedure was tested on different maritime infrastructures to assess the possibility of generalizing the framework, and the average dwell time obtained has been compared with the official statistics delivered by authoritative sources. The output assessed how the port's characteristics do not affect the model's capability in detecting events, and the results certify non-significant deviations from the official statistics.