Applying Fused Data to Predict Vessel Traffic Flow: A Hybrid-Model Deep Learning Approach

In the research on navigation efficiency optimization in complex waters, accurate vessel traffic flow prediction has emerged as a critical challenge in the field of intelligent maritime navigation. Based on Resolution A.1158(32) of the International Maritime Organization (IMO) and expert knowledge of Vessel Traffic Service (VTS), this study proposes a novel framework for vessel port reporting and VTS decision-making, and constructs a feature-fused database integrated with meteorological data. A hybrid deep learning model based on the DeepAR framework is developed, which extracts the spatial dependencies of vessel traffic via a convolutional neural network (CNN), captures temporal features using a bidirectional long short-term memory network (BiLSTM), and acquires long-range dependencies with a self-attention mechanism (SAM) to achieve multi-dimensional feature fusion for vessel traffic flow prediction. A case study is conducted on the narrow section of the Lüsi inbound waterway in Tongzhou Bay, China, to verify the effectiveness of the proposed model. The results demonstrate that the proposed method can realize high-precision probabilistic prediction of vessel traffic flow. Compared with the benchmark methods, the proposed model reduces both the Mean Absolute Scaled Error (MASE) and the Mean Absolute Error (MAE) simultaneously for medium- and long-term forecasting ( > 24 hours) under specific working conditions (VTS six-shift mode). This study can provide probabilistic decision support for intelligent traffic management in narrow navigable waters, and serve as an important reference for VTS scheduling as well as port and shipping operation dispatching.