Assessing operational variability for different ship types navigating the North and Baltic Seas using AIS data

Kurzfassung

Generalization of ship operations is essential for modeling fleet-wide emissions, and for developing effective maritime greenhouse gas (GHG) emission reduction strategies. This study introduces a structured methodology to define ship-type-and-size-specific operational traits and to assess their variability using Automatic Identification (AIS) data. Operational traits are defined in terms of 12 key performance metrics capturing ship’s movement patterns, speed, draught, engine load, and energy use. The metrics are defined by ship type and size, and a weighted standard deviation is used to evaluate the consistency of operational traits across different ship types and sizes. The results reveal significant differences in variability. Specifically, bulk carriers, container ships, and vehicle carriers show relatively uniform operational traits, whereas passenger ferries, offshore vessels, and service ships exhibit high dispersion. For individual ship types, the variability depends considerably on vessel size. These findings highlight the need for differentiated segmentation strategies in modeling and emission assessments – by ship type, by size, or through finer clustering. The proposed methodology provides a scalable, data-driven foundation for improving the accuracy of emission models, and informing the implementation of decarbonization measures.