Integrating Matrix Decomposition into Deep Learning for Vessel Traffic Flow Prediction in IoT Industries

Abstract

Accurate forecasting of vessel traffic flow (VTF) is essential for modern maritime and port management, as it improves route-planning efficiency, reduces congestion and collision risks, and optimizes port operations. This study proposes a novel deep learning framework, namely, the Bidimensional Empirical Mode Decomposition–Nocal Convolutional Neural Network–Transformer (BEMD–NocalCNN–Transformer), for high-precision VTF prediction. The proposed framework first applies the BEMD algorithm to decompose the original time-series data into high- and low-frequency components. The NocalCNN module is then employed to extract spatial features from each component, while the Transformer module captures temporal dependencies and predicts future traffic-flow trends. The final predictions are obtained by aggregating the outputs of the high- and low-frequency components. Sensitivity analyses are conducted on key parameters, including input sequence length, learning rate, number of iterations, and convolution kernel size, to optimize the model configuration. To comprehensively evaluate the proposed framework, SVM, BPNN, RNN, LSTM, GRU, Transformer, WVMA-LSTM, and NocalCNN–Transformer were implemented and evaluated using the same CFD and Wuhan datasets, data preprocessing procedures, training–testing partitions, prediction settings, and evaluation metrics. The experimental results demonstrate that the proposed model outperforms the benchmark models and achieves substantially lower prediction errors for both the Caofeidian Promontory (CFD) and Wuhan waterways. These findings demonstrate consistent prediction performance of the proposed framework and provide a robust technical foundation for intelligent maritime traffic management and port operation optimization.