Enhancing Prediction Accuracy of Vessel Arrival Times Using Machine Learning
Enhancing Prediction Accuracy of Vessel Arrival Times Using Machine Learning
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Publisher
Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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Marine transportation accounts for approximately 90% of the total trade managed in international logistics and plays a vital role in many companies’ supply chains. However, en-route factors like weather conditions or piracy incidents often delay scheduled arrivals at destination ports, leading to downstream inefficiencies. Due to the maritime indus...
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Enhancing Prediction Accuracy of Vessel Arrival Times Using Machine Learning
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TN_cdi_doaj_primary_oai_doaj_org_article_b1af8610551b4b098ed51139a0db3cd8
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_b1af8610551b4b098ed51139a0db3cd8
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ISSN
2077-1312
E-ISSN
2077-1312
DOI
10.3390/jmse12081362
