Port operation efficiency has grown in importance as container volumes and vessel sizes have increased. For improved port operations efficiency, the estimated time of arrival (ETA) of the upcomming vessels must be accurately predicted for optimal bunkering.
This estimation requires perfect route prediction of the vessels, knowing their real-time location.
We propose an AIS data-driven methodology for the estimation of vessel time of arrival at ports. For ETA prediction, we first introduce how to find possible vessel trajectories using AIS data mining methods. Waiting routes of the vessels for past events are detected by our effective trajectory detection algorithms. These trajectories are replaced with predicted cruise trajectories in our proposed method. Consequently, using the predicted vessel routes, a machine learning model is trained to estimate the earliest time of arrival of the vessels. Experimentation comparing the proposed methodology with an existing one was performed to verify the former's performance. We expect our proposed ETA and route prediction methodology to predict ETA for having an intelligent port bunkering system.
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