A Geospatial Grid Constrained Deep Learning Prediction Framework Based on AIS Data for Improving Vessel Traffic Services in Maritime Internet of Things

As a core component of the maritime Internet of Things (IoT), the automatic identification system (AIS) continuously collects dynamic vessel navigation data, providing a solid foundation for addressing complex maritime traffic prediction tasks that support intelligent vessel traffic services (VTSs), such as vessel trajectory prediction and vessel arrival time (VAT) estimation. However, existing methods typically focus on single prediction objectives, falling short of meeting practical multitask requirements. To address this gap, this study proposes a geospatial grid-constrained deep learning (DL) framework based on AIS data to simultaneously handle three key prediction tasks: vessel trajectory prediction, whether the vessel arrives within the specified time, and VAT. The framework incorporates a dynamic patch construction method and a graph soft evolution (GSE) module to capture temporal correlations among observations under spatial grid constraints. An encoder-decoder architecture is introduced, where the encoder employs a Squeeze-and-Excitation (SE) block to adaptively select feature channels, and the decoder models dependencies across both variable and temporal dimensions. In a case study of New York Harbor, the model achieved an R2 of 0.8386 and a root mean square error (RMSE) of 0.0329 for latitude increment prediction, and an R2 of 0.8432 with an RMSE of 0.0322 for longitude increment prediction. It also attained 99.83% accuracy in arrival status prediction and an R2 of 0.9350 with an RMSE of 0.0701 for VAT prediction. The framework demonstrated effectiveness in port scheduling and robust generalizability in cross-validation experiments at the Port of Los Angeles, thereby demonstrating its substantial potential to enhance the operational efficiency of VTS within maritime IoT systems.