An Explainable Machine Learning Framework for Green Maritime Logistics Using Dynamic Spatial Congestion Prediction and Relative Fuel Index Modeling

Abstract

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. Raw AIS data were pre-processed and processed into operational features at the vessel level to create two target variables of interest related to logistics, including Congestion Index (CI), and Relative Fuel Index (RFI). Three ensemble machine learning algorithms, namely Random Forest (RF), XGBoost, and LightGBM, were implemented and evaluated using the coefficient of determination (R2) and root mean square error (RMSE). The Random Forest model demonstrated the best performance in congestion prediction with an R² of 0.8328 and an RMSE of 2.3200, followed by XGBoost with an R² of 0.8151 and LightGBM with an R² of 0.8110. XGBoost showed the best performance in predicting fuel, with an R2 of 0.9984 and an RMSE of 2.8164, followed by LightGBM (R2 = 0.9971) and Random Forest (R2 = 0.9960). SHAP explainability revealed that historical traffic density was the most important factor contributing to congestion. At the same time, the speed of vessels was the most important factor affecting the fuel demand. The proposed framework supports optimized vessel scheduling, shorter port waiting times, fuel savings, and carbon emissions reduction, which help build intelligent and sustainable green maritime logistics.