Advanced Prediction of Container Vessels Arrival Time at Hong Kong Port in 2023: A Comparative Analysis

In this study, we predicate the estimated arrival time of container vessels at Hong Kong Port in 2023 by leveraging a comprehensive dataset that integrates vessel arrival data, physical characteristic information, and real-time in-port vessels flow records. The dataset, comprising over 640,000 samples with 24 features, is meticulously constructed through rigorous data preprocessing, including duplicate removal, cross-source matching using unique identifiers, and extensive feature engineering with various encoding techniques. To mitigate data leakage and enhance model generalizability, we employ two partitioning strategies: a time-based split, which organizes data chronologically, and a trip-based split, which groups records by complete voyages. Based on these datasets, we implement four predictive models—eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and the deep learning-based Mamba tabular model (Mambular)—optimizing the first three’s hyperparameters via five-fold cross-validation and grid search. The models are evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), with performance assessed by comparing the predicted remaining voyage time to the actual arrival time. Our results indicate that XGBoost achieve the best performance on the time-series dataset, while CatBoost and LightGBM excel on the voyage-based dataset. Although the deep learning model do not yield superior results in this instance, its application to tabular data represents a promising avenue for future research. These findings underscore the potential of advanced machine learning algorithms, combined with tailored data partitioning strategies, to enhance predictive accuracy in vessel arrival time forecasting and offer valuable insights for optimizing port operations and logistics planning.