Abstract
Accurate vessel trajectory prediction is essential for maritime traffic control and management. In addition to collision avoidance, accurate vessel trajectory prediction can help in planning navigation routes, shortening the sailing distance, and increasing navigation efficiency. Vessel trajectory prediction with automatic identification system (AIS) data has thus attracted considerable attention in the maritime industry. Original AIS data may contain noise, which limits their application in real-world maritime traffic management. To overcome this problem, this study proposes a vessel trajectory prediction method that combines data denoising and a deep learning prediction model. In this method, data denoising is realized in three steps: trajectory separation, data denoising, and standardization. First, outliers from the original AIS data samples are removed, after which the moving average model is employed to further clean up the data; finally the denoised data are standardized into uniformly distributed time-series data. Bidirectional long short-term memory (Bi-LSTM) is then applied for vessel trajectory prediction. The performance of the proposed prediction model was verified using data on the trajectories of ten vessels and comparing the results obtained with those obtained using other prediction models (exponential smoothing, autoregressive integrated moving average, support vector regression, recurrent neural network, and LSTM models); the trajectory data were downloaded from a public AIS database. The experimental results revealed that model prediction accuracy increased after the data denoising process. Specifically, the Bi-LSTM model had the lowest mean absolute error, mean absolute percentage error, and root-mean-square error, demonstrating that the proposed method is highly efficient for trajectory prediction and can help vessel traffic controllers predict accurate vessel tracks; this would enable them to take early preventive measures to avoid collisions and thus improve the efficiency and safety of maritime traffic.
Highlights
71% of the Earth’s surface is covered by water, and only approximately 21% of the surface is land
Inspired by the successful use of deep learning methods in sequence prediction, we investigated whether an Recurrent neural network (RNN) model can be employed for vessel track prediction
2.(a) as an example, when we find that an abnormal speed over ground (SOG) value suddenly appears, MA processing is performed on two adjacent data, and the obtained value is used to replace the original abnormal value [47]
Summary
71% of the Earth’s surface is covered by water, and only approximately 21% of the surface is land. Because vessels frequently traverse the western Pacific Ocean international trade routes and because of frequent and prosperous fishing activities, the marine traffic flow around Taiwan is of medium to high complexity. According to relevant statistical data from the Ministry of Transportation and Communications R.O.C. and the Coast Guard Administration, Ocean Affairs Council, in recent years, the average number of VOLUME XX, 2020 marine vessel disasters that occur in the waters around Taiwan has reached 100 [2]. The safety of ships sailing at sea is a key problem in maritime areas or ports with high traffic density and complicated conditions. To improve the safety of ships sailing in an environment with complex and ever-changing sea conditions, it is necessary to provide trajectory prediction and danger warning functions to a ship’s intelligent navigation system.
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