Graph Signal Variation Detection: A novel approach for identifying and reconstructing ship AIS tangled trajectories

Deng, Chuiyi; Wang, Shuangxin; Liu, Jingyi; Li, Hongrui; Chu, Boce; zhu, Jin

doi:10.1016/j.oceaneng.2023.115452

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…The advent of deep neural networks, computer vision, and natural language processing has spotlighted ship behavior mining as a key area within the intelligent maritime domain. Focused research endeavors in this area include ship trajectory detection (Deng et al, 2023;Zhang et al, 2023), intention prediction (Gao and Shi, 2020;Murray and Perera, 2021), and classification (Zhou et al, 2019), employing advanced computational models like convolutional neural networks (CNNs) (Chen et al, 2020(Chen et al, , 2024 and generative adversarial networks (GANs) (Jia et al, 2023). Wang et al (2023b) proposed a ship trajectory prediction model based on a sparse multi-graph convolutional hybrid network.…”

Section: Overview Of Related Work On Ship Behavior Miningmentioning

confidence: 99%

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

Li,

Liu,

Wang

et al. 2024

Front. Mar. Sci.

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In maritime logistics optimization, considerable research efforts are focused on the extraction of deep behavioral characteristics from comprehensive shipping data to discern patterns in maritime vessel behavior. The effective linkage of these characteristics with maritime infrastructure, such as berths, is critical for the enhancement of ship navigation systems. This endeavor is paramount not only as a research focus within maritime information science but also for the progression of intelligent maritime systems. Traditional methodologies have primarily emphasized the analysis of navigational paths of vessels without an extensive consideration of the geographical dynamics between ships and port infrastructure. However, the introduction of knowledge graphs has enabled the integration of disparate data sources, facilitating new insights that propel the development of intelligent maritime systems. This manuscript presents a novel framework using knowledge graph technology for profound analysis of maritime data. Utilizing automatic identification system (AIS) data alongside spatial information from port facilities, the framework forms semantic triplet connections among ships, anchorages, berths, and waterways. This enables the semantic modeling of maritime behaviors, offering precise identification of ships through their diverse semantic information. Moreover, by exploiting the semantic relations between ships and berths, a reverse semantic knowledge graph for berths is constructed, which is specifically tailored to ship type, size, and category. The manuscript critically evaluates a range of graph embedding techniques, dimensionality reduction methods, and classification strategies through experimental frameworks to determine the most efficacious methodologies. The findings reveal that the maritime knowledge graph significantly enhances the semantic understanding of unmanned maritime equipment, thereby improving decision-making capabilities. Additionally, it establishes a semantic foundation for the development of expansive maritime models, illustrating the potential of knowledge graph technology in advancing intelligent maritime systems.

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Section: Overview Of Related Work On Ship Behavior Miningmentioning

confidence: 99%

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

Li,

Liu,

Wang

et al. 2024

Front. Mar. Sci.

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“…Many research efforts have searched for effective methods to control AIS data quality. Such methods mainly reduce sparse and abnormal trajectories by restoring the completeness and accuracy of the trajectory data and also provide a high-quality database for subsequent tasks [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation

Ye,

Chen,

Liu

et al. 2024

Applied Sciences

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To address the issue of data integrity and reliability caused by sparse vessel trajectory data, this paper proposes a multi-step restoration method for sparse vessel trajectory based on feature correlation. First, we preserved the overall trend of the trajectory by detecting and marking the sparse and abnormal vessel trajectories points and using the cubic spline interpolation method for preliminary restoration. Then, we established a composite indicator of feature correlation for selecting highly correlated trajectory features as inputs to the model, reducing data redundancy while improving the key correlation between trajectory features. Finally, we developed a vessel trajectory restoration model based on the Seq2Seq network for secondary restoration of the trajectory to ensure the accurate restoration of the vessel trajectory. Through comparison and ablation experiments, we demonstrate that the method can efficiently extract highly correlated features from vessel trajectories and combines the advantages of the interpolation method and neural network model to effectively improve the accuracy of trajectory restoration and ensure the integrity and accuracy of trajectory data. The research results could provide crucial technical support for the subsequent mining of vessel behavior patterns and assisted decision-making, which holds significant application prospects and potential value.

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A ship trajectory prediction method based on GAT and LSTM

Zhao,

Yan,

Zhou

et al. 2023

Ocean Engineering

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Graph Signal Variation Detection: A novel approach for identifying and reconstructing ship AIS tangled trajectories

Cited by 4 publications

References 21 publications

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation

A ship trajectory prediction method based on GAT and LSTM

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