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Group of Safety and Logistics of Maritime Transportation > 5.4 Maritime Safety and Human Factors > Publications
Ten, K.H., Kang, H-S., Siow, C-L., Goh, P.S. and Guedes Soares, C. (2023), Automatic identification system in accelerating decarbonization of maritime transportation: The state-of-the-art and opportunities, Ocean Engineering, Vol. 289, 116232
This paper presents a review of the state-of-the-art AIS data-driven studies and applications in effort to control the carbon footprint from maritime transportation, followed by the prospects of decarbonization measures that demands AIS complement. With the critical impact from global anthropogenic emissions, AIS data served as the primary source of information for the assessment of the ships emission inventories to evoke awareness from the maritime sector. Wide range of information types with greater transmission coverage and frequency made AIS data outshines other maritime information services for tedious studies of correlations between parameters. Owing to such properties, AIS data can be further exploited through machine learning approaches of predictive analytics for various interests, including estimated time of arrival , emission projections, and path generations. Ultimately, the outcomes of these studies will contribute piecewise to the fulfillment of the ideal concepts ‘green’ and ‘smart’ in maritime sector, by efficient processes with minimum waste of energy.
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