A transformer-based method for vessel traffic flow forecasting

Abstract

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.