Deep Learning for Vessel Trajectory Prediction Using Clustered AIS Data

Yang, Cheng‐Hong; Lin, Guan-Cheng; Wu, Chih-Hsien; Liu, Yen-Hsien; Wang, Yichuan; Chen, Kuo-Chang

doi:10.3390/math10162936

Cited by 11 publications

(4 citation statements)

References 44 publications

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“…The density-based clustering is a favorable approach for this task, as it can yield proficient clustering results by detecting clusters of diverse forms and identifying noise. Yang et al (2022) [18] integrated both the DBSCAN algorithm and long short-term memory (LSTM) models to enhance the accuracy of the trajectory prediction. Xu et al (2022) [19] introduced a novel location and the COG clustering algorithm, utilizing the DBSCAN algorithm, to identify critical points that represented the historical trajectory's distinguishing characteristics.…”

Section: Clustering Methodsmentioning

confidence: 99%

The Identification of Ship Trajectories Using Multi-Attribute Compression and Similarity Metrics

Liu,

Zhang,

Cao

et al. 2023

JMSE

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Automatic identification system (AIS) data record a ship’s position, speed over ground (SOG), course over ground (COG), and other behavioral attributes at specific time intervals during a ship’s voyage. At present, there are few studies in the literature on ship trajectory classification, especially the clustering of trajectory segments, to measure the multi-dimensional information of trajectories. Therefore, it is necessary to fully utilize the multi-dimensional information from AIS data when utilizing ship trajectory classification methods. Here, we propose a ship trajectory classification method based on multi-attribute trajectory similarity metrics which utilizes the following steps: (1) Improve the Douglas–Peucker (DP) algorithm by considering the SOG and COG; (2) use a multi-attribute symmetric segmentation path distance (MSSPD) for the similarity metric between trajectories; (3) cluster the segmented sub-trajectories based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm; (4) adaptively determinate the optimal input parameters based on the proposed comprehensive clustering performance metrics. The proposed method was tested on real AIS data from Bohai Sea waters, and the experimental results show that the algorithm can accurately cluster the ship trajectory groups and extract traffic distributions in key waters.

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Section: Clustering Methodsmentioning

confidence: 99%

The Identification of Ship Trajectories Using Multi-Attribute Compression and Similarity Metrics

Liu,

Zhang,

Cao

et al. 2023

JMSE

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show abstract

“…Instead of using the original features, several methods advocate the use of latent features derived from the neural network architecture. These methods leverage latent space representation using variational recurrent autoencoder (VRAE) [ 50 ] or LSTM [ 51 ]. These latent features can capture the spatial patterns present in the data.…”

Section: Literature Reviewmentioning

confidence: 99%

A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data

Syed

Ahmed

2023

Sensors

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In marine surveillance, distinguishing between normal and anomalous vessel movement patterns is critical for identifying potential threats in a timely manner. Once detected, it is important to monitor and track these vessels until a necessary intervention occurs. To achieve this, track association algorithms are used, which take sequential observations comprising the geological and motion parameters of the vessels and associate them with respective vessels. The spatial and temporal variations inherent in these sequential observations make the association task challenging for traditional multi-object tracking algorithms. Additionally, the presence of overlapping tracks and missing data can further complicate the trajectory tracking process. To address these challenges, in this study, we approach this tracking task as a multivariate time series problem and introduce a 1D CNN-LSTM architecture-based framework for track association. This special neural network architecture can capture the spatial patterns as well as the long-term temporal relations that exist among the sequential observations. During the training process, it learns and builds the trajectory for each of these underlying vessels. Once trained, the proposed framework takes the marine vessel’s location and motion data collected through the automatic identification system (AIS) as input and returns the most likely vessel track as output in real-time. To evaluate the performance of our approach, we utilize an AIS dataset containing observations from 327 vessels traveling in a specific geographic region. We measure the performance of our proposed framework using standard performance metrics such as accuracy, precision, recall, and F1 score. When compared with other competitive neural network architectures, our approach demonstrates a superior tracking performance.

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“…Subsequently, the trajectory associated with that pattern was predicted. Yang et al [4] combined Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Long Short-Term Memory (LSTM) models for trajectory prediction. In general, the research on ship trajectory prediction based on AIS data could be divided into methods based on statistics and methods based on Neural Networks and Deep Learning.…”

Section: Introductionmentioning

confidence: 99%

Ship Trajectory Prediction: An Integrated Approach Using ConvLSTM-Based Sequence-to-Sequence Model

Chen

et al. 2023

JMSE

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Maritime transportation is one of the major contributors to the development of the global economy. To ensure its safety and reduce the occurrence of a maritime accident, intelligent maritime monitoring and ship behavior identification have been drawing much attention from industry and academia, among which, the accurate prediction of ship trajectory is one of the key questions. This paper proposed a trajectory prediction model integrating the Convolutional LSTM (ConvLSTM) and Sequence to Sequence (Seq2Seq) models to facilitate simultaneous extraction of temporal and spatial features of ship trajectories, thereby enhancing the accuracy of prediction. Firstly, the trajectories are preprocessed using kinematic-based anomaly removal and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to improve the data quality for the training process of trajectory prediction. Secondly, the ConvLSTM-based Seq2seq model is designed to extract temporal and spatial features of the ship trajectory and improve the performance of long-time prediction. Finally, by using real AIS data, the proposed model is compared with the Seq2Seq and Bidirectional LSTM based on attention mechanism (Bi-Attention-LSTM) models to verify its effectiveness. The experimental results demonstrate that the proposed model achieves excellent performance in predicting turning trajectories, good predictive accuracy on straight line motions, and greater improvement in prediction accuracy compared to the other two benchmark models. Overall, the proposed model represents a promising contribution to improving ship trajectory prediction accuracy and may enhance the safety and quality of ship navigation in complex and volatile marine environments.

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Deep Learning for Vessel Trajectory Prediction Using Clustered AIS Data

Cited by 11 publications

References 44 publications

The Identification of Ship Trajectories Using Multi-Attribute Compression and Similarity Metrics

The Identification of Ship Trajectories Using Multi-Attribute Compression and Similarity Metrics

A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data

Ship Trajectory Prediction: An Integrated Approach Using ConvLSTM-Based Sequence-to-Sequence Model

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