Vessel estimated time of arrival prediction system based on a path-finding algorithm
Kikun Park, Sunghyun Sim, Hyerim Bae
Abstract
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 sea-going vessels must be accurately predicted. In this paper, an AIS data-driven method- ology is proposed for the estimation of vessel ETA at ports. For ETA prediction, we first introduce how to find possible vessel trajectories using AIS data mining methods and reinforcement learning (RL); next, we introduce the Markov Chain property and Bayesian Sampling to estimate the speed over ground (SOG) of a vessel. Experimentation comparing the proposed methodology with an existing one was performed to verify the former's performance. We expect the proposed ETA prediction methodology to predict ETA to help build an intelligent port system.