Concurrent Processing Cluster Design to Empower Simultaneous Prediction for Hundreds of Vessels' Trajectories in Near Real-Time

The automatic identification system (AIS) plays a vital role in maritime traffic surveillance. AIS is designed for remotely tracking vessels but nowadays also becomes a useful data source to enable vessels' trajectory prediction so as to facilitate early alert of potential collision risks. Recent studies focus on improving prediction accuracy through the machine learning and knowledge-based technologies but the computational cost also greatly increases due to the model complexity of these methodologies. It becomes a practical challenge to realize near real-time (NRT) trajectory prediction of a large volume of ships. For risk alert application, forecasting timeliness is one of the key design considerations. In this paper, we propose a concurrent processing cluster solution to empower advanced trajectory forecasting for hundreds of vessels in NRT. The proposed solution relies on the properly determined frameworks by customizing the desired features for an integrated solution. Meanwhile, a novel task-based load balancing strategy with newly defined metrics are proposed, which aims to reduce the makespan of jobs and outperforms the existing load balancing algorithms. A practicable cluster system has been successfully implemented, serving as a step toward unlocking the power of advanced maritime traffic forecasting technologies and enabling the benefit from the latest progress on the methodological innovation.