Automatic identification system data to assess container port performances

This paper, in the context of a rise in global seaborne trade, addresses the challenges faced by ports which represents critical nodes in the supply chain. This study evaluates the opportunities enabled by the information achieved from Automatic Identification System (AIS) with the aim to suggest a set of valuable indicators capable of describing port operations and terminal performances. Thus, focusing on a wide dataset of ship calls for a container port, proposes a set of spatial-temporal indicators such as spatial density, average service time ratio and berth use ratio as discriminants for the investigation of operations taking place in container ports. The algorithms were tested over the maritime traffic for the Port of Melbourne, Australia, over the year 2023. Historical data from AIS database enabled the opportunity to extracting ship stopping for both mooring and anchoring. The contemporary use of spatial density and service time ratio prove to be a useful synthesis indicators taking in those scenarios where multiple activities that take place in a busy terminal such as in Melbourne. Moreover, the information stored let to evaluate berths activity, in terms of percentage of occupancy, and average service time for handling operations it emerged that berths operations results constant over the same quarter of the year, and no criticalities emerged. Finally, the information achieved on AIS devices let to organize a synthetic pattern for the space-time movement classification achieved reveals insights into ship operations, contributing to a nuanced understanding of port performance.