Multimodal Intelligence Improves Maritime Congestion Forecasting and Port Optimization
DOI:
https://doi.org/10.62051/ajmse.v1n2.07Keywords:
Maritime Congestion Forecasting, Port Optimization, Multimodal Deep Learning, AIS Data Fusion, Berth Scheduling, Vessel Traffic Prediction, Intelligent Transportation SystemsAbstract
Maritime congestion at major global container ports represents one of the most pressing logistical bottlenecks of contemporary trade, with cascading consequences that permeate supply chains, freight economics, and carbon accounting simultaneously. This paper proposes a multimodal intelligence framework that integrates heterogeneous data streams — encompassing AIS positional records, meteorological observations, historical berth logs, and satellite-derived imagery — within a unified deep learning pipeline to forecast port congestion and optimize berth scheduling. A CNN-LSTM-GRU hybrid network with a cross-modal attention fusion layer is trained and evaluated on two-year real-world vessel traffic data from the Port of Shanghai and the Port of Singapore. Experimental results demonstrate that the proposed framework achieves a 23.7% reduction in MAE and a 19.4% reduction in RMSE relative to single-modality LSTM baselines. A downstream berth optimization module, driven by the congestion prediction output, reduces mean vessel waiting time by 17.6% under controlled simulation conditions. These findings confirm that multimodal data fusion constitutes a critical architectural principle for next-generation port intelligence systems.
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