Research on Ship Traffic Flow Prediction Using CNN-BIGRU and WOA With Multi-Objective Optimization

Accurate understanding of maritime traffic flow is crucial for optimizing waterway and port resources. However, the complex spatiotemporal characteristics and non-stationary sequences in maritime traffic pose challenges for feature extraction. This study proposes a deep learning approach that integrates Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit Network (BIGRU) for predicting maritime traffic flow. CNN is employed to extract feature information, while BIGRU captures temporal dependencies, enhancing the understanding of intrinsic features' temporal changes. To further improve predictive performance, this study introduce the Whale Optimization Algorithm (WOA) for multiple objective optimization, which fine-tunes the CNN-BIGRU model parameters. By using eight years of monthly traffic flow data collected from the Dayaowan Port area in Dalian, the validity of this optimized model was rigorously verified. The results demonstrate that the WOA-optimized CNN-BIGRU provides an efficient forecast of maritime traffic flow with the Mean Absolute Error (MAE) of 4.084, Root Mean Square Error (RMSE) of 7.9719 and Mean Absolute Percentage Error (MAPE) of 1.262%. Compared to the original CNN-BiGRU models, this optimized approach reduced the prediction errors by 59.4%, 33.6%, and 54.2%, respectively. This further highlights its effectiveness in addressing the complexities of maritime traffic flow and provides valuable references for further research in this region.