A sequential random forest for short-term vessel speed prediction

Abstract

Short-term vessel speed prediction is exceedingly crucial to various issues in maritime transport, notably in cases with high precision requirement, such as trajectory tracking and collision avoidance. The methods for speed prediction have successively experienced the evolution of empirical formulas, physics-based approaches and data-driven models. Compared to the other two pioneering methods, building the model from data-driven learning seems to be a more appropriate way for ever-larger vessels that entered service in recent years. However, owing to insufficient speed-related features that can be known in advance, such as the engine state and propeller propulsion, traditional data-driven models usually have poor generalization ability when facing new test data. In this paper, taking a case study of a 400,000 DWT ore carrier sailing between China and Brazil, we first provide comparisons of several widely-used data-driven models and select the framework of random forest as the basic. Then, by considering the hardship of directly utilizing real-time monitoring data, we creatively put forward a novel prediction model by introducing sequential data of last historical speed records to bring the potential information of engine state into model learning. Through comprehensive experiments, the superiority and robustness of the proposed model are demonstrated by comparing with other frequently-used time-series prediction methods.