This study aims to improve vessel trajectory prediction in the inner harbor of Busan Port using Automatic Identification System (AIS) data and deep-learning techniques. The research addresses the challenge of irregular AIS data intervals through linear interpolation and focuses on enhancing the accuracy of predictions in complex port environments. Recurrent neural network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU models were developed, with LSTM delivering the highest performance. The primary scientific question of this study is how to reliably predict vessel trajectories under varying conditions in inner harbors. The results demonstrate that the proposed method not only improves the precision of predictions but also identifies critical areas where Vessel Traffic Service Operators (VTSOs) can better manage vessel movements. These findings contribute to safer and more efficient vessel traffic management in ports with high traffic density and complex navigational challenges.

In this study, basic research was conducted regarding the era of autonomous vessels and artificial intelligence (deep learning, big data, etc.). When a vessel is navigating autonomously, it must determine the optimal route by itself and accurately follow the designated route using route-following control technology. First, the optimal route should be generated in a manner that ensures safety and reduces fuel consumption by the vessel. To satisfy safety requirements, sea depth, under-keel clearance, and navigation charts are used; algorithms capable of determining and shortening the distance of travel and removing unnecessary waypoints are used to satisfy the requirements for reducing fuel consumption. In this study, a reinforcement-learning algorithm-based machine learning technique was used to generate an optimal route while satisfying these two sets of requirements. Second, when an optimal route is generated, the vessel must have a route-following controller that can accurately follow the set route without deviation. To accurately follow the route, a velocity-type fuzzy proportional–integral–derivative (PID) controller was established. This controller can prevent deviation from the route because overshoot rarely occurs, compared with a proportional derivative (PD) controller. Additionally, because the change in rudder angle is smooth, energy loss by the vessel can be reduced. Here, a method for determining the presence of environmental disturbance using the characteristics of the Kalman filter innovation process and estimating environmental disturbance with a fuzzy disturbance estimator is presented, which allows the route to be accurately maintained even under conditions involving environmental disturbance. The proposed approach can automatically set the vessel’s optimal route and accurately follow the route without human intervention, which is useful and can contribute to maritime safety and efficiency improvement.

In this study, a ship-route prediction model based on a long short-term memory network using port-to-port trajectory data is presented. Data from a traditional automatic identification system—often used for ship-route prediction—are limited by uneven sampling intervals and noise. To address these issues, equal-interval data collected every 10 s from a target ship, which is a liner container vessel, were employed. Our study focuses on predicting the entire trajectory between the Gunsan and Busan ports. The root mean square error (RMSE), mean absolute error (MAE), and average distance $\overline{d}$ between two trajectories were used as the key evaluation metrics. The analysis yielded excellent predictive performance, with the values RMSE = 0.000999, MAE = 0.000672, and $\overline{d}$ = 0.101 km. This study thus provides a foundation for predicting complete port-to-port routes and offers practical insights for managing vessel operations. Accurate route prediction contributes to reducing port congestion, improving fuel efficiency, and lowering carbon emissions.

Yang, H., 2018. A Cost-Effective Ship Safety Data Transfer in Coastal Area.. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 1206–1210. Coconut Creek (Florida), ISSN 0749-0208. Recent advances in information and communications technology (ICT) have improved ship safety management. Ships can be monitored and controlled remotely using ship-to-ship and ship-to-shore communications technologies. These methods entail the sharing of vessel status information to prevent ship-related accidents and increase safety. In coastal areas, most ships transmit and receive data via wireless communications networks based on radio frequency (RF), long-term evolution (LTE), satellite, and other technologies. RF-based and LTE-based networks can be utilized when the communications are within the range of dozens of kilometers. On the other hand, satellite-based networks cover the entire ocean, but the associated costs are very expensive. In this study, we propose a cost-effective ship data transfer scheme to guarantee reliable data transfer but also optimize communication costs via adaptive network selection. In the proposed scheme, RF-based and LTE-based networks are selected as the default modes to reduce costs. However, when a ship enters a radio shadow, it temporarily switches to satellite-based mode to guarantee data reliability. Therefore, it rarely transmits data over the satellite-based network, saving on communications costs. In an emergency, the data can be transmitted frequently without regard to costs to improve safety performance. To validate the performance of the proposed scheme, we performed several evaluations using the ns2 simulator. The results indicated that the proposed scheme guarantees reliable data transfer while also reducing communications costs.