English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Vessel navigation is influenced by various factors, such as dynamicenvironmental factors that change over time or static features such as vesseltype or depth of the ocean. These dynamic and static navigational factorsimpose limitations on vessels, such as long waiting times in regions outsidethe actual ports, and we call these waiting regions gateway ports. Identifyinggateway ports and their associated features such as congestion and availableutilities can enhance vessel navigation by planning on fuel optimization orsaving time in cargo operation. In this paper, we propose a novel temporalgraph neural network (TGNN) based port classification method to enable vesselsto discover gateway ports efficiently, thus optimizing their operations. Theproposed method processes vessel trajectory data to build dynamic graphscapturing spatio-temporal dependencies between a set of static and dynamicnavigational features in the data, and it is evaluated in terms of portclassification accuracy on a real-world data set collected from ten vesselsoperating in Halifax, NS, Canada. The experimental results indicate that ourTGNN-based port classification method provides an f-score of 95% in classifyingports.

Discovering Gateway Ports in Maritime Using Temporal Graph Neural Network Port Classification