An Explainable Machine Learning Framework for Green Maritime Logistics Using Dynamic Spatial Congestion Prediction and Relative Fuel Index Modeling
This study proposes an explainable AI approach for predicting localized congestion and vessel fuel demand to support sustainable port logistics, using Automatic Identification System (AIS) data and reveals that historical traffic density was the most important factor contributing to congestion.