AIS-based operational phase identification using Progressive Ablation Feature Selection with machine learning for improving ship emission estimates

The work status of ships’ engines and boilers has a significant impact on emission estimates, which are closely related to ships’ operational phases. To improve the accuracy of emission estimates, this study proposed a machine learning-based classification model for identifying operational phases. We proposed 12 operational phase relevance features by analyzing motion behavior-related and geospatial characteristics-related features from the Automatic Identification System (AIS) data from the two bulk carriers. The random forest (RF) model showed the best performance in identifying one of the bulk carrier’s operational phases among the five machine models, with the accuracy, F1score and Area Under Curve (AUC) of 96.66%, 93.34% and 99.93%, respectively. By adopting the Progressive Ablation Feature Selection (PAFS) method with RF, the number of features was reduced from 12 to 8, and the accuracy (96.38%), F1score (92.70%), and AUC (98.81%) were almost same with that obtained from all 12 features. Additionally, the effectiveness of the RF model was validated on the other bulk carriers. Compared with the traditional algorithms, the RF model showed better performance in ship operational phase identification and improved the average accuracy of NOx emission estimation for the main engine and auxiliary engine by 57.83% and 93.89%, respectively, under different operational phases. These results provide the basis for port traffic management and ship emission control. Implications: A new ship operational phase identification approach was proposed in this study. If the proposed approach is adopted by International Maritime Organization, it will improve the accuracy of ship emission estimates and bring new insights into global shipping greenhouse gas (GHG) emissions and their impact on global change. The port authorities could benefit from the proposed approach, which can be extended to ship types with similar behavior to bulk carriers, such as containers and general cargoes. This can reveal patterns of ship behavior in specific areas, which helps to identify potential collision risks, channel blockages, and other safety issues and take appropriate management measures to ensure the safe operation of the port. The proposed approach can help shipping companies to accurately estimate the GHG emissions of their fleets and to accurately predict carbon tax costs. Base on that, carbon emissions and carbon tax burden can be reduced by adopting corresponding management control measures.