Use of Artificial Intelligence as a Problem Solution for Maritime Transport

Jurdana, Irena; Krylov, Artem V.; Yamnenko, Julia

doi:10.3390/jmse8030201

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…In addition, many methods, theories, and technologies involving the use of massive data, data exploration, and machine learning have been proposed. Developing an approach for efficiently using the information obtained to increase maritime transportation intelligence has become critical [11,12]. However, the AIS onboard a vessel usually adds one piece of information every 2 s to 3 min, which results in an extremely large AIS dataset; this also engenders the data redundancy problem, which makes it difficult to use the data for research and practical applications.…”

Section: Introductionmentioning

confidence: 99%

AIS-Based Intelligent Vessel Trajectory Prediction Using Bi-LSTM

Yang

Shao

et al. 2022

IEEE Access

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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.

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Section: Introductionmentioning

confidence: 99%

AIS-Based Intelligent Vessel Trajectory Prediction Using Bi-LSTM

Yang

Shao

et al. 2022

IEEE Access

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“…Numerous researchers have proposed various machine learning methods, theories, and technologies for big data processing and exploration. Methods of fully exploiting AIS data and promoting the intellectualization of maritime transportation have thus become a key research priority [5,6].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Vessel Trajectory Prediction Using Clustered AIS Data

Yang

Lin

et al. 2022

Mathematics

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Accurate vessel track prediction is key for maritime traffic control and management. Accurate prediction results can enable collision avoidance, in addition to being suitable for planning routes in advance, shortening the sailing distance, and improving navigation efficiency. Vessel track prediction using automatic identification system (AIS) data has attracted extensive attention in the maritime traffic community. In this study, a combining density-based spatial clustering of applications with noise (DBSCAN)-based long short-term memory (LSTM) model (denoted as DLSTM) was developed for vessel prediction. DBSCAN was used to cluster vessel tracks, and LSTM was then used for training and prediction. The performance of the DLSTM model was compared with that of support vector regression, recurrent neural network, and conventional LSTM models. The results revealed that the proposed DLSTM model outperformed these models by approximately 2–8%. The proposed model is able to provide a better prediction performance of vessel tracks, which can subsequently improve the efficiency and safety of maritime traffic control.

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“…Over the last century, there has been steady advancement in the power of computers; simultaneously, data science and artificial intelligence have undergone rapid developments [1,2]. The effective combination of machine learning and artificial neural network (ANN) has been the focus of ship motion prediction research.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network

Lin

Zhao

et al. 2020

JMSE

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In marine environments, ships are bound to be disturbed by several external factors, which can cause stochastic fluctuations and strong nonlinearity in the ship motion. Predicting ship motion is pivotal to ensuring ship safety and providing early warning of risks. This report proposes a real-time ship vertical acceleration prediction algorithm based on the long short-term memory (LSTM) and gated recurrent units (GRU) models of a recurrent neural network. The vertical acceleration time history data at the bow, middle, and stern of a large-scale ship model were obtained by performing a self-propulsion test at sea, and the original data were pre-processed by resampling and normalisation via Python. The prediction results revealed that the proposed algorithm could accurately predict the acceleration time history data of the large-scale ship model, and the root mean square error between the predicted and real values was no greater than 0.1. The optimised multivariate time series prediction program could reduce the calculation time by approximately 55% compared to that of a univariate time series prediction program, and the run time of the GRU model was better than that of the LSTM model.

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Use of Artificial Intelligence as a Problem Solution for Maritime Transport

Cited by 7 publications

References 3 publications

AIS-Based Intelligent Vessel Trajectory Prediction Using Bi-LSTM

AIS-Based Intelligent Vessel Trajectory Prediction Using Bi-LSTM

Deep Learning for Vessel Trajectory Prediction Using Clustered AIS Data

Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network

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