In recent years, the maritime domain has experienced tremendous growth due to the exploitation of big traffic data. Particular emphasis has been placed on deep learning methodologies for decision-making. Accurate Vessel Traffic Flow Forecasting (VTFF) is essential for optimizing navigation efficiency and proactively managing maritime operations. In this work, we present a distributed Unified Approach for VTFF (dUA-VTFF), which employs Transformer models and leverages the Apache Spark big data distributed processing framework to learn from historical maritime data and predict future traffic flows over a time horizon of up to 30 min. Particularly, dUA-VTFF leverages vessel timestamped locations along with future vessel locations produced by a Vessel Route Forecasting model. These data are arranged into a spatiotemporal grid to formulate the traffic flows. Subsequently, through the Apache Spark, each grid cell is allocated to a computing node, where appropriately designed Transformer-based models forecast traffic flows in a distributed framework. Experimental evaluations conducted on real Automatic Identification System (AIS) datasets demonstrate the improved efficiency of the dUA-VTFF compared to state-of-the-art traffic flow forecasting methods.
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