• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.576-584
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• 2022

AIS is widely utilized in vessel traffic services for marine traffic safety. In 2021, Korea deployed the high-speed maritime wireless communication system (LTE-Maritime) on the sea following IMO's proposal for the introduction of e-Navigation. In this paper, vessel trajectory data from AIS and LTE-Maritime were used for vessel trajectory prediction to compare and analyze the two systems. The results show that the trajectory prediction error of LTE-Maritime was smaller than that of AIS due to the granular and uniform data provided by LTE-Maritime. Additionally, it was revealed that time interval is the most important factor influencing the errors in trajectory prediction, with the prediction error of LTE-Maritime growing at a slower rate of 17% than AIS. This research contributes to the literature by quantitatively comparing AIS and LTE-Maritime systems for the first time.

• Journal of IKEEE
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• v.24 no.4
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• pp.1176-1179
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• 2020

Vessel path prediction methods generally predict the latitude and longitude of a future location directly. However, in the case of direct prediction, errors could be large since the possible output range is too broad. In addition, error accumulation could occur since recurrent neural networks-based methods employ previous predicted data to forecast future data. In this paper, we propose a vessel path prediction method that does not directly predict the longitude and latitude. Instead, the proposed method predicts the acceleration of the vessel. Then the acceleration is employed to generate the velocity and direction, and the values decide the longitude and latitude of the future location. In the experiment, we show that the proposed method makes smaller errors than the direct prediction method, while both methods employ the same model.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.1-10
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• 2024

The research on predicting the routes of ships, which constitute the majority of maritime transportation, can detect potential hazards at sea in advance and prevent accidents. Unlike roads, there is no distinct signal system at sea, and traffic management is challenging, making ship route prediction essential for maritime safety. However, the time intervals of the ship route datasets are irregular due to communication disruptions. This study presents a method to adjust the time intervals of data using appropriate interpolation techniques for ship route prediction. Additionally, a deep learning model for predicting ship routes has been developed. This model is an LSTM model that predicts the future GPS coordinates of ships by understanding their movement patterns through real-time route information contained in AIS data. This paper presents a data preprocessing method using linear interpolation and a suitable deep learning model for ship route prediction. The experimental results demonstrate the effectiveness of the proposed method with an MSE of 0.0131 and an Accuracy of 0.9467.

• Journal of Navigation and Port Research
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• v.26 no.2
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• pp.221-226
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• 2002

To provide reliable data for drift prediction models, field experiments were carried out in the coastal region off Busan port. Four different size of vessels(10, 30, 50, 90G/T ton) were deployed for the experiment. Among them G/T 50ton class vessel was equipped with instruments measuring the currents, winds, headings and trajectory data. In the rest of vessels only the position data were recorded for the purpose of target divergence study. The trajectories of each vessel were measured by DGPS(Differential Global Positioning System) and collected by APRS(Automatic Position Reporting System). The experiment was done in wind of 2~10m/s and current of 0.5~1.5m/s. The leeway was derived by subtracting surface current velocity from target drifting velocity. The leeway rate of G/T 50ton vessel was found to be about 3.6% and the computed leeway speed equation was $U_L$=0.042 W - 0.034. The processed leeway angle data were deflected by $-30^{\circ}$~$40^{\circ}$ from the direction of ship drift.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.285-286
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• 2016

We aim to propose the prediction modeling method of ship's position with extracting ship's trajectory model through pattern recognition based on the data that are being collected in VTS centers at real time. Support Vector Machine algorithm was used for data modeling. The optimal parameters are calculated with k-fold cross validation and grid search. We expect that the proposed modeling method could support VTS operators' decision making in case of complex encountering traffic situations.