Towards the Evaluation of Date Time Features in a Ship Route Prediction Model

Abstract

Ship Route Prediction (SRP) is an algorithm that allows assessing the future position of a ship using historical data, extracted from AIS messages. In an SRP task, it is very important to select the set of input features, used to train the model. In this paper, we try to evaluate if time-dependent features are relevant in an SRP model, based on a K-Nearest Neighbor classifier, through a practical experiment. In practice, we build two models, with and without the Date Time features, and for both models, we calculate some performance metrics and the SHAP value. Tests show that although the model with the Date Time features outperforms the other model in terms of evaluation metrics, it does not in the practical experiments.

Keywords

• machine learning
• Ship Route Prediction
• feature engineering
• marine data science